Attachment E Overview of CPS Sample Design and Methodology

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Attachment E Overview of CPS Sample Design and Methodology

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Design and Methodology
Current Population Survey

Issued October 2006

TP66

Technical Paper 66

This updated version can be
found at .

U.S. Department Labor

U.S. Department of Commerce

U.S. BUREAU OF LABOR STATISTICS

Economics and Statistics Administration
U.S. CENSUS BUREAU

ACKNOWLEDGMENTS
This Current Population Survey Technical Paper (CPS TP66) is an updated version of TP63RV. Previous
chapters and appendices have been brought up-to-date to reflect the 2000 Current Surveys’ Sample
Redesign and current U.S. Census Bureau confidentiality concerns. This included the deletion of five
appendices. However, most of the design and methodology of the 2000 CPS design and methodology
remains the same as that of January 1994, which is documented in CPS TP63RV. As updates in the
design and methodology occur, they will be posted on the Internet version.
There were many individuals who contributed to the publication of the CPS TP63 and TP63RV. Their
valued expertise and efforts laid the basis for TP66. However, only those involved with this current
version are listed in these Acknowledgments. Some authored sections/chapters/appendices, some
reviewed the text for technical, procedural, and grammatical accuracy, and others did both. Some are
still with their agencies and some have since left.
This technical paper was written under the coordination of Andrew Zbikowski and Antoinette
Lubich of the U.S. Census Bureau. Tamara Sue Zimmerman served as the coordinator/contact for the
Bureau of Labor Statistics.
It has been produced through the combined efforts of many individuals. Contributing from the U.S.
Census Bureau were Samson A. Adeshiyan, Adelle Berlinger, Sam T. Davis, Karen D. Deaver,
John Godenick, Carol Gunlicks, Jeffrey Hayes, David V. Hornick, Phawn M. Letourneau, Zijian
Liu, Antoinette Lubich, Khandaker A. Mansur, Alexander Massey, Thomas F. Moore, Richard C.
Ning, Jeffrey M. Pearson, Benjamin Martin Reist, Harland Shoemaker, Jr., Bonnie S. Tarsia, Bac
Tran, Alan R. Tupek, Cynthia L. Wellons-Hazer, Gregory D. Weyland, and Andrew Zbikowski.
Lawrence S. Cahoon and Marjorie Hanson served as editorial reviewers of the entire document.
Contributing from the Bureau of Labor Statistics were Sharon Brown, Shail Butani, Sharon R.
Cohany, Samantha Cruz, John Dixon, James L. Esposito, Thomas Evans, Howard V. Hayghe,
Kathy Herring, Diane Herz, Vernon Irby, Sandi Mason, Brian Meekins, Stephen M. Miller,
Thomas Nardone, Kenneth W. Robertson, Ed Robison, John F. Stinson, Jr., Richard Tiller,
Clyde Tucker, Stephanie White, and Tamara Sue Zimmerman.
Catherine M. Raymond, Corey T. Beasley, Theodora S. Forgione, and Susan M. Kelly of the
Administrative and Customer Services Division, Walter C. Odom, Chief, provided publications and
printing management, graphics design and composition, and editorial review for print and electronic
media. General direction and production management were provided by James R. Clark, Assistant
Division Chief, Wanda K. Cevis, Chief, Publications Services Branch, and Everett L. Dove, Chief,
Printing Section.
We are grateful for the assistance of these individuals, and all others who are not specifically mentioned,
for their help with the preparation and publication of this document.

Design and Methodology
Current Population Survey

U.S. Department of Labor
Elaine L. Chao,
Secretary
U.S. Bureau of Labor Statistics
Philip L. Rones
Acting Commissioner

U.S. Department of Commerce
Carlos M. Gutierrez,
Secretary
David A. Sampson,
Deputy Secretary
Economics and Statistics Administration
Cynthia A. Glassman,
Under Secretary
for Economic Affairs
U.S. CENSUS BUREAU
Charles Louis Kincannon,
Director

Issued October 2006

TP66

SUGGESTED CITATION
U.S. CENSUS BUREAU
Current Population Survey
Design and Methodology
Technical Paper 66
October 2006

ECONOMICS
AND STATISTICS
ADMINISTRATION

Economics
and Statistics
Administration
Cynthia A. Glassman,
Under Secretary
for Economic Affairs

U.S. CENSUS BUREAU

U.S. BUREAU OF LABOR STATISTICS

Charles Louis Kincannon,
Director
Hermann Habermann,
Deputy Director and
Chief Operating Officer

Philip L. Rones,
Acting Commissioner
Philip L. Rones,
Deputy Commissioner

Howard Hogan,
Associate Director
for Demographic Programs

John Eltinge,
Associate Commissioner for Survey
Methods Research
John M. Galvin,
Associate Commissioner for Employment
and Unemployment Statistics
Thomas J. Nardone, Jr.,
Assistant Commissioner for Current
Employment Analysis

Foreword
The Current Population Survey (CPS) is one of the oldest, largest, and most well-recognized surveys in the United States. It is immensely important, providing information on many of the things
that define us as individuals and as a society—our work, our earnings, our education. It is also
immensely complex. Staff of the Census Bureau and the Bureau of Labor Statistics have attempted,
in this publication, to provide data users with a thorough description of the design and methodology used in the CPS. The preparation of this technical paper was a major undertaking, spanning
several years and involving dozens of statisticians, economists, and others from the two agencies.
While the basic approach to collecting labor force and other data through the CPS has remained
intact over the intervening years, much has changed. In particular, a redesigned CPS was introduced in January 1994, centered around the survey’s first use of a computerized survey instrument by field interviewers. The questionnaire itself was rewritten to better communicate CPS concepts to the respondent, and to take advantage of computerization. In January 2003, the CPS
adopted the 2002 census industry and occupation classification systems, derived from the 2002
North American Industry Classification System and the 2000 Standard Occupational Classification
System.
Users of CPS data should have access to up-to-date information about the survey’s methodology.
The advent of the Internet allows us to provide updates to the material contained in this report on
a more timely basis. Please visit our CPS Web site at , where
updated survey information will be made available. Also, we welcome comments from users
about the value of this document and ways that it could be improved.

Charles Louis Kincannon
Director
U.S. Census Bureau

Kathleen P. Utgoff
Commissioner
U.S. Bureau of Labor Statistics

May 2006

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Foreword

iii

CONTENTS

Chapter 1. Background
Background

1–1

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 2. History of the Current Population Survey
Introduction . . . . . . . . . . . . . . . . . .
Major Changes in the Survey: A Chronology
References . . . . . . . . . . . . . . . . . . .

2–1
2–1
2–7

. . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . .

Chapter 3. Design of the Current Population Survey Sample
.
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3–1
3–1
3–2
3–6
3–13
3–13
3–15

Introduction . . . . . . . . . . . . . . . . . . . .
Listing Activities . . . . . . . . . . . . . . . . .
Third Stage of the Sample Design (Subsampling)
Interviewer Assignments . . . . . . . . . . . . .

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.

4–1
4–3
4–5
4–5

Introduction . . . . . . . . . . . . .
Survey Requirements and Design .
First Stage of the Sample Design . .
Second Stage of the Sample Design
Third Stage of the Sample Design .
Rotation of the Sample . . . . . . .
References . . . . . . . . . . . . . .

.
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.
.

Chapter 4. Preparation of the Sample

Chapter 5. Questionnaire Concepts and Definitions for the Current Population
Survey
.
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.

.
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5–1
5–1
5–1
5–6

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Motivation for Redesigning the Questionnaire Collecting Labor Force Data
Objectives of the Redesign . . . . . . . . . . . . . . . . . . . . . . . . . .
Highlights of the Questionnaire Revision . . . . . . . . . . . . . . . . . .
Continuous Testing and Improvements of the Current Population Survey
and its Supplements . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

.
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6–1
6–1
6–1
6–3

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6–8
6–8

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7–1
7–1
7–3
7–4

. . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . .

8–1
8–1

Introduction . . . . . . . . . . . . .
Structure of the Survey Instrument .
Concepts and Definitions . . . . . .
References . . . . . . . . . . . . . .

.
.
.
.

Chapter 6. Design of the Current Population Survey Instrument

Chapter 7. Conducting the Interviews
Introduction . . . . . . . . . . . . . . . .
Noninterviews and Household Eligibility .
Initial Interview . . . . . . . . . . . . . .
Subsequent Months’ Interviews . . . . .

.
.
.
.

Chapter 8. Transmitting the Interview Results
Introduction . . . . . . . . . . .
Transmission of Interview Data .

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Contents

CONTENTS

Chapter 9. Data Preparation
Introduction . . . . . . . . . . . . . . .
Daily Processing . . . . . . . . . . . . .
Industry and Occupation (I&O) Coding .
Edits and Imputations . . . . . . . . . .

.
.
.
.

9–1
9–1
9–1
9–1

Chapter 10. Weighting and Seasonal Adjustment for Labor Force Data
Introduction . . . . . . . . . . . . . . .
Unbiased Estimation Procedure . . . . .
Adjustment for Nonresponse . . . . . .
Ratio Estimation . . . . . . . . . . . . .
First-Stage Ratio Adjustment . . . . . .
National Coverage Adjustment . . . . .
State Coverage Adjustment . . . . . . .
Second-Stage Ratio Adjustment . . . .
Composite Estimator . . . . . . . . . .
Producing Other Labor Force Estimates
Seasonal Adjustment . . . . . . . . . .
References . . . . . . . . . . . . . . . .

.
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.
.
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.
.
.
.

10–1
10–1
10–2
10–3
10–3
10−4
10−6
10−7
10–10
10–13
10–15
10–16

.
.
.
.

11–1
11–1
11–2
11–10

. . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . .

12–1
12–1
12–1

.
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.
.
.
.
.
.
.

Chapter 11. Current Population Survey Supplemental Inquiries
Introduction . . . . . . . . . . . . .
Criteria for Supplemental Inquiries .
Recent Supplemental Inquiries . . .
Summary . . . . . . . . . . . . . .

.
.
.
.

Chapter 12. Data Products From the Current Population Survey
Introduction . . . . . . . . . . . . .
Bureau of Labor Statistics Products .
Census Bureau Products . . . . . .

Chapter 13. Overview of Data Quality Concepts
.
.
.
.
.

.
.
.
.
.

13–1
13–1
13–2
13–3
13–3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . .
Variance Estimates by the Replication Method . . . . . . . .
Method for Estimating Variance for 1990 and 2000 Designs
Variances for State and Local Area Estimates . . . . . . . .
Generalizing Variances . . . . . . . . . . . . . . . . . . . .
Variance Estimates to Determine Optimum Survey Design .
Total Variances as Affected by Estimation . . . . . . . . . .
Design Effects . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . .

.
.
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.

14–1
14–1
14–2
14–3
14–3
14–5
14–6
14–7
14–9

.
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.
.
.
.
.
.

15–1
15–1
15–2
15–4
15–5
15–5
15–6
15–8

Introduction . . . . . . . . . . . . . . . . . . . . .
Quality Measures in Statistical Science . . . . . . .
Quality Measures in Statistical Process Monitoring
Summary . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . .

.
.
.
.
.

Chapter 14. Estimation of Variance

Chapter 15. Sources and Controls on Nonsampling Error
Introduction . . . . . . . . . . .
Sources of Coverage Error . . .
Controlling Coverage Error . . .
Sources of Nonresponse Error .
Controlling Nonresponse Error .
Sources of Response Error . . .
Controlling Response Error . . .
Sources of Miscellaneous Errors

Contents

.
.
.
.
.
.
.
.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

CONTENTS

Chapter 15.—Con.
Controlling Miscellaneous Errors .
References . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . .

15–8
15–9

Chapter 16. Quality Indicators of Nonsampling Errors
Introduction . . . .
Coverage Errors . .
Nonresponse . . .
Response Variance
Mode of Interview .
Time in Sample . .
Proxy Reporting . .
Summary . . . . .
References . . . . .

.
.
.
.
.
.
.
.
.

16–1
16–1
16–2
16–5
16–6
16–7
16–9
16–9
16–10

.
.
.
.
.

A–1
A–1
A–1
A–1
A–1

. . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . .

B–1
B–1

.
.
.
.
.
.
.
.
.

Appendix A. Sample Preparation Materials
Introduction . . . . . . . . . . .
Unit Frame Materials . . . . . .
Area Frame Materials . . . . . .
Group Quarters Frame Materials
Permit Frame Materials . . . . .

.
.
.
.
.

Appendix B. Maintaining the Desired Sample Size
Introduction . . . . . . .
Maintenance Reductions

Appendix C. Derivation of Independent Population Controls
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
Population Universe for CPS Controls . . . . . . . . . . .
Calculation of Population Projections for the CPS Universe
Procedural Revisions . . . . . . . . . . . . . . . . . . . .
Summary List of Sources for CPS Population Controls . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . .

.
.
.
.
.
.

C–1
C–3
C–4
C–11
C–11
C–12

Appendix D. Organization and Training of the Data Collection Staff
Introduction . . . . . . . . . . . . . . . . . . . .
Organization of Regional Offices/CATI Facilities .
Training Field Representatives . . . . . . . . . .
Field Representative Training Procedures . . . .
Field Representative Performance Guidelines . .
Evaluating Field Representative Performance . .

.
.
.
.
.
.

D–1
D–1
D–1
D–1
D–3
D–4

.
.
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.
.

E−1
E−1
E−1
E–2
E–2
E–2

Appendix E. Reinterview: Design and Methodology
Introduction . . . . . . .
Response Error Sample .
Quality Control Sample .
Reinterview Procedures .
Summary . . . . . . . .
References . . . . . . . .
Acronyms
Index

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.
.
.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Acronyms−1
. . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index–1
.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Contents

vii

CONTENTS

Figures
3–1
5–1
7–1
7–2
7–3
7–4
7–5
7–6
7–7
7–8
7–9
7–10
8–1
11–1
16–1
16–2
16−3
16−4
D−1

CPS Rotation Chart: January 2006−April 2008 . . . . . . . . . . . .
Questions for Employed and Unemployed . . . . . . . . . . . . . .
Introductory Letter . . . . . . . . . . . . . . . . . . . . . . . . . .
Noninterviews: Types A, B, and C . . . . . . . . . . . . . . . . . . .
Noninterviews: Main Items of Housing Unit Information Asked for
Types A, B, and C . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviews: Main Housing Unit Items Asked in MIS 1 and
Replacement Households . . . . . . . . . . . . . . . . . . . . . .
Summary Table for Determining Who is to be Included as a Member
of the Household . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviews: Main Demographic Items Asked in MIS 1 and
Replacement Households . . . . . . . . . . . . . . . . . . . . . .
Demographic Edits in the CPS Instrument . . . . . . . . . . . . . .
Interviews: Main Items (Housing Unit and Demographic) Asked in
MIS 5 Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Interviewing Results (September 2004). . . . . . . . . . . . . . . .
Telephone Interview Rates (September 2004) . . . . . . . . . . . .
Overview of CPS Monthly Operations . . . . . . . . . . . . . . . . .
Diagram of the ASEC Weighting Scheme . . . . . . . . . . . . . . .
CPS Total Coverage Ratios: September 2001−September 2004,
National Estimates . . . . . . . . . . . . . . . . . . . . . . . . . .
Average Yearly Type A Noninterview and Refusal Rates for the CPS
1964−2003, National Estimates . . . . . . . . . . . . . . . . . . .
CPS Nonresponse Rates: September 2003−September 2004,
National Estimates . . . . . . . . . . . . . . . . . . . . . . . . . .
Basic CPS Household Nonresponse by Month in Sample,
July 2004−September 2004, National Estimates . . . . . . . . . .
2000 Decennial and Survey Boundaries . . . . . . . . . . . . . . .

3–14
5–6
7–2
7–3

7–4

7–5

7–6

7–7
7–8

.
.
.

7–9
7–10
7–10
8–3
11–9

16–2

16–3

16−3

16−9
D−2

.
.
.
.

Illustrations
1
2
3
4
5
6
7

Segment Folder, BC-1669 (CPS) . . . . . . . . . . . . . . . .
Multi-Unit Listing Aid, Form 11-12 . . . . . . . . . . . . . .
Unit/Permit Listing Sheet, 11-3 (Blank) . . . . . . . . . . . .
Incomplete Address Locator Actions Form, NPC 1138 . . . .
Unit/Permit Listing Sheet, 11-3 (Single unit in Permit Frame)
Unit/Permit Listing Sheet, 11-3 (Multi-unit in Permit Frame) .
Permit Sketch Map, Form 11-187 . . . . . . . . . . . . . . .

. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .
. . . . .

A–3
A–4
A–5
A–6
A–7
A−8
A−9

Tables
3–1
3–2
3–3

Estimated Population in Sample Areas for 824-PSU Design by State.
Summary of Sampling Frames . . . . . . . . . . . . . . . . . . . .
Index Numbers Selected During Sampling for Code Assignment
Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3–4

Example of Post-Sampling Survey Design Code Assignments Within
a PSU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Example of Post-Sampling Operational Code Assignments Within a
PSU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Proportion of Sample in Common for 4-8-4 Rotation System . . . .
National Coverage Adjustment Cell Definitions . . . . . . . . . . .
State Coverage Adjustment Cell Definitions . . . . . . . . . . . . .
Second-Stage Adjustment Cell by Ethnicity, Age, and Sex . . . . . .
Second-Stage Adjustment Cell by Race, Age, and Sex . . . . . . . .

3–5
3–6
10–1
10−2
10–3
10–4

viii

Contents

3–5
3–10

3–12

3–13
3–14
10–5
10−6
10–7
10–8

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.
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.
.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

CONTENTS

Tables—Con.
10–5 Composite National Ethnicity Cell Definition . . . . . . . . . . . . . . 10–12
10–6 Composite National Race Cell Definition . . . . . . . . . . . . . . . . 10–12
11–1 Current Population Survey Supplements September 1994−December
2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11–3
11–2 MIS Groups Included in the ASEC Sample for Years 2001, 2002,
2003, and 2004 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11–6
11–3 Summary of 2004 ASEC Interview Months . . . . . . . . . . . . . . . 11–7
11–4 Summary of ASEC SCHIP Adjustment Factor for 2004 . . . . . . . . . 11–10
12–1 Bureau of Labor Statistics Data Products From the Current
Population Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . 12–3
14–1 Parameters for Computation of Standard Errors for Estimates of
Monthly Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14–5
14–2 Components of Variance for SS Monthly Estimates . . . . . . . . . . . 14–6
14–3 Effects of Weighting Stages on Monthly Relative Variance Factors . . . 14–7
14–4 Effect of Compositing on Monthly Variance and Relative Variance
Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14–8
14–5 Design Effects for Total and Within-PSU Monthly Variances . . . . . . 14–8
16–1 Components of Type A Nonresponse Rates, Annual Averages for
1993−1996 and 2003, National Estimates . . . . . . . . . . . . . . 16–4
16–2 Percentage of Households by Number of Completed Interviews
During the 8 Months in the Sample, National Estimates . . . . . . . 16–4
16–3 Labor Force Status by Interview/Noninterview Status in Previous and
Current Month, National Estimates . . . . . . . . . . . . . . . . . . 16–5
16–4 CPS Items With Missing Data (Allocation Rates, %), National
Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16–5
16–5 Comparison of Responses to the Original Interview and the
Reinterview, National Estimates . . . . . . . . . . . . . . . . . . . . 16–6
16–6 Index of Inconsistency for Selected Labor Force Characteristics in
2003, National Estimates . . . . . . . . . . . . . . . . . . . . . . . 16–6
16–7 Percentage of Households With Completed Interviews With Data
Collected by Telephone (CAPI Cases Only), National Estimates . . . 16–7
16–8 Month-in-Sample Bias Indexes (and Standard Errors) in the CPS for
Selected Labor Force Characteristics . . . . . . . . . . . . . . . . . 16–8
16–9 Month-in-Sample Indexes in the CPS for Type A Noninterview Rates
January−December 2004 . . . . . . . . . . . . . . . . . . . . . . . 16–9
16–10 Percentage of CPS Labor Force Reports Provided by Proxy Reporters . 16–9
D–1 Average Monthly Workload by Regional Office: 2004 . . . . . . . . . D–3

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Contents

Chapter 1.
Background
The Current Population Survey (CPS), sponsored jointly by
the U.S. Census Bureau and the U.S. Bureau of Labor Statistics (BLS), is the primary source of labor force statistics for
the population of the United States. The CPS is the source
of numerous high-profile economic statistics, including
the national unemployment rate, and provides data on a
wide range of issues relating to employment and earnings.
The CPS also collects extensive demographic data that
complement and enhance our understanding of labor market conditions in the nation overall, among many different
population groups, in the states and in substate areas.
The labor force concepts and definitions used in the CPS
have undergone only slight modification since the survey’s
inception in 1940. Those concepts and definitions are discussed in Chapter 5. Although labor market information is
central to the CPS, the survey provides a wealth of other
social and economic data that are widely used in both the
public and private sectors. In addition, because of its long
history and the quality of its data, the CPS has been a
model for other household surveys, both in the United
States and in other countries.
The CPS is a source of information not only for economic
and social science research, but also for the study of survey methodology. This report provides all users of the CPS
with a comprehensive guide to the survey. The report
focuses on labor force data because the timely and accurate collection of those data remains the principal purpose
of the survey.
The CPS is administered by the Census Bureau using a
probability selected sample of about 60,000 occupied
households.1 The fieldwork is conducted during the calendar week that includes the 19th of the month. The questions refer to activities during the prior week; that is, the
week that includes the 12th of the month.2 Households
from all 50 states and the District of Columbia are in the
survey for 4 consecutive months, out for 8, and then
return for another 4 months before leaving the sample
permanently. This design ensures a high degree of continuity from one month to the next (as well as over the
year). The 4-8-4 sampling scheme has the added benefit
of allowing the constant replenishment of the sample
without excessive burden to respondents.

The CPS questionnaire is a completely computerized document that is administered by Census Bureau field representatives across the country through both personal and
telephone interviews. Additional telephone interviewing is
conducted from the Census Bureau’s three centralized collection facilities in Hagerstown, Maryland; Jeffersonville,
Indiana; and Tucson, Arizona.
To be eligible to participate in the CPS, individuals must be
15 years of age or over and not in the Armed Forces.
People in institutions, such as prisons, long-term care hospitals, and nursing homes are ineligible to be interviewed
in the CPS. In general, the BLS publishes labor force data
only for people aged 16 and over, since those under 16
are limited in their labor market activities by compulsory
schooling and child labor laws. No upper age limit is used,
and full-time students are treated the same as nonstudents. One person generally responds for all eligible members of the household. The person who responds is called
the ‘‘reference person’’ and usually is the person who
either owns or rents the housing unit. If the reference person is not knowledgeable about the employment status of
the others in the household, attempts are made to contact
those individuals directly.
Within 2 weeks of the completion of these interviews, the
BLS releases the major results of the survey. Also included
in BLS’s analysis of labor market conditions are data from
a survey of nearly 400,000 employers (the Current
Employment Statistics [CES] survey, conducted concurrently with the CPS). These two surveys are complementary in many ways. The CPS focuses on the labor force status (employed, unemployed, not-in-labor force) of the
working-age population and the demographic characteristics of workers and nonworkers. The CES focuses on
aggregate estimates of employment, hours, and earnings
for several hundred industries that would be impossible to
obtain with the same precision through a household survey. The CPS reports on individuals not covered in the CES,
such as the self employed, agricultural workers, and
unpaid workers in a family business. Information also is
collected in the CPS about people who are not working.

1
The sample size was increased from 50,000 occupied households in July 2001. (See Chapter 2 for details.)
2
In the month of December, the survey is often conducted 1
week earlier to avoid conflicting with the holiday season.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

Background

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In addition to the regular labor force questions, the CPS
often includes supplemental questions on subjects of
interest to labor market analysts. These include annual
work activity and income, veteran status, school enrollment, contingent employment, worker displacement, and
job tenure, among other topics. Because of the survey’s
large sample size and broad population coverage, a wide
range of sponsors use the CPS supplements to collect data
on topics as diverse as expectation of family size, tobacco
use, computer use, and voting patterns. The supplements
are described in greater detail in Chapter 11.

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Background

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Chapter 2.
History of the Current Population Survey
INTRODUCTION
The Current Population Survey (CPS) has its origin in a program established to provide direct measurement of unemployment each month on a sample basis. Several earlier
efforts attempted to estimate the number of unemployed
using various devices ranging from guesses to enumerative counts. The problem of measuring unemployment
became especially acute during the economic depression
of the 1930s.
The Enumerative Check Census, taken as part of the 1937
unemployment registration, was the first attempt to estimate unemployment on a nationwide basis using probability sampling. During the latter half of the 1930s, the Work
Projects Administration (WPA) developed techniques for
measuring unemployment, first on a local area basis and
later on a national basis. This research combined with the
experience from the Enumerative Check Census led to the
Sample Survey of Unemployment, which was started in
March 1940 as a monthly activity by the WPA.
MAJOR CHANGES IN THE SURVEY:
A CHRONOLOGY
In August 1942, responsibility for the Sample Survey of
Unemployment was transferred to the Bureau of the Census, and in October 1943, the sample was thoroughly
revised. At that time, the use of probability sampling was
expanded to cover the entire sample, and new sampling
theory and principles were developed and applied to
increase the efficiency of the design. The households in
the revised sample were in 68 Primary Sampling Units
(PSUs) (see Chapter 3), comprising 125 counties and independent cities. By 1945, about 25,000 housing units were
designated for the sample, of which about 21,000 contained interviewed households.
One of the most important changes in the CPS sample
design took place in 1954 when, for the same total budget, the number of PSUs was expanded from 68 to 230,
without any change in the number of sample households.
The redesign resulted in a more efficient system of field
organization and supervision, and it provided more information per unit of cost. Thus the accuracy of published
statistics improved as did the reliability of some regional
as well as national estimates.
Since the mid-1950s, the CPS sample has undergone major
revision on a regular basis. The following list chronicles
the important modifications to the CPS starting in the mid1940s:
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

• July 1945. The CPS questionnaire was revised. The
revision consisted of the introduction of four basic
employment status questions. Methodological studies
showed that the previous questionnaire produced
results that misclassified large numbers of part-time and
intermittent workers, particularly unpaid family workers. These groups were erroneously reported as not
active in the labor force.
• August 1947. The selection method was revised. The
method of selecting sample units within a sample area
was changed so that each unit selected would have the
same chance of selection. This change simplified tabulations and estimation procedures.
• July 1949. Previously excluded dwelling places were
now covered. The sample was extended to cover special
dwelling places—hotels, motels, trailer camps, etc. This
led to improvements in the statistics, (i.e., reduced bias)
since residents of these places often have characteristics that are different from the rest of the population.
• February 1952. Document-sensing procedures were
introduced into the survey process. The CPS questionnaire was printed on a document-sensing card. In this
procedure, responses were recorded by drawing a line
through the oval representing the correct answer using
an electrographic lead pencil. Punch cards were automatically prepared from the questionnaire by documentsensing equipment.
• January 1953. Ratio estimates now used data from the
1950 population census. Starting in January 1953,
population data from the 1950 census were introduced
into the CPS estimation procedure. Prior to that date,
the ratio estimates had been based on 1940 census
relationships for the first-stage ratio estimate, and 1940
population data were used to adjust for births, deaths,
etc., for the second-stage ratio estimate. In September
1953, a question on ‘‘color’’ was added and the question
on ‘‘veteran status’’ was deleted in the second-stage
ratio estimate. This change made it feasible to publish
separate, absolute numbers for individuals by race
whereas only the percentage of distributions had previously been published.
• July 1953. The 4-8-4 rotation system was introduced.
This sample rotation system was adopted to improve
measurement over time. In this system, households are
interviewed for 4 consecutive months during 1 year,
leave the sample for 8 months, and return for the same
History of the Current Population Survey

2–1

period of 4 months the following year. In the previous
system, households were interviewed for 6 months and
then replaced. The 4-8-4 system provides some year-toyear overlap, thus improving estimates of change on
both a month-to-month and year-to-year basis.
• September 1953. High speed electronic equipment
was introduced for tabulations. The introduction of electronic calculation greatly increased timeliness and led to
other improvements in estimation methods. Other benefits included the substantial expansion of the scope
and content of the tabulations and the computation of
sampling variability. The shift to modern computers was
made in 1959. Keeping abreast of modern computing is
a continuous process, and the Census Bureau regularly
updates its computer environment.
• February 1954. The number of PSUs was expanded to
230. The number of PSUs was increased from 68 to 230
while retaining the overall sample size of 25,000 designated housing units. The 230 PSUs consisted of 453
counties and independent cities. At the same time, a
substantially improved estimation procedure (see Chapter 10, Composite Estimation) was introduced. Composite estimation took advantage of the large overlap in the
sample from month-to-month.
These two changes improved the reliability of most of
the major statistics by a magnitude that could otherwise be achieved only by doubling the sample size.
• May 1955. Monthly questions on part-time workers
were added. Monthly questions exploring the reasons
for part-time work were added to the standard set of
employment status items. In the past, this information
had been collected quarterly or less frequently and was
found to be valuable in studying labor market trends.
• July 1955. Survey week was moved. The CPS survey
week was moved to the calendar week containing the
12th day of the month to align the CPS time reference
with that of other employment statistics. Previously, the
survey week had been the calendar week containing the
8th day of the month.
• May 1956. The number of PSUs was expanded to 330.
The number of PSUs was expanded from 230 to 330.
The overall sample size also increased by roughly twothirds to a total of about 40,000 households units
(about 35,000 occupied units). The expanded sample
covered 638 counties and independent cities.
All of the former 230 PSUs were also included in the
expanded sample.
The expansion increased the reliability of the major statistics by around 20 percent and made it possible to
publish more detailed statistics.
• January 1957. The definition of employment status
was changed. Two relatively small groups of people,
both formerly classified as employed ‘‘with a job but not
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History of the Current Population Survey

at work,’’ were assigned to new classifications. The
reassigned groups were (1) people on layoff with definite instructions to return to work within 30 days of the
layoff date and (2) people waiting to start new wage
and salary jobs within 30 days of the interview. Most of
the people in these two groups were shifted to the
unemployed classification. The only exception was the
small subgroup in school during the survey week who
were waiting to start new jobs; these were transferred
to ‘‘not-in-labor force.’’ This change in definition did not
affect the basic question or the enumeration procedures.
• June 1957. Seasonal adjustment was introduced. Some
seasonally adjusted unemployment data were introduced early in 1955. An extension of the data—using
more refined seasonal adjustment methods programmed on electronic computers—was introduced in
July 1957. The new data included a seasonally adjusted
rate of unemployment and trends of seasonally adjusted
total employment and unemployment. Significant
improvements in methodology emerged from research
conducted at the Bureau of Labor Statistics (BLS) and the
Census Bureau in the following years.
• July 1959. Responsibility for CPS was moved between
agencies. Responsibility for the planning, analysis, and
publication of the labor force statistics from the CPS
was transferred to the BLS as part of a large exchange
of statistical functions between the Commerce and
Labor Departments. The Census Bureau continued to
have (and still has) responsibility for the collection and
computer processing of these statistics, for maintenance of the CPS sample, and for related methodological research. Interagency review of CPS policy and technical issues continues to be the responsibility of the
Statistical Policy Division, Office of Management and
Budget.
• January 1960. Alaska and Hawaii were added to the
population estimates and the CPS sample. Upon achieving statehood, Alaska and Hawaii were included in the
independent population estimates and in the sample
survey. This increased the number of sample PSUs from
330 to 333. The addition of these two states affected
the comparability of population and labor force data
with previous years. Another result was in an increase
of about 500,000 in the noninstitutionalized population
of working age and about 300,000 in the labor force,
four-fifths of this in nonagricultural employment. The
levels of other labor force categories were not appreciably changed.
• October 1961. Conversion to the Film Optical Sensing
Device for Input to the Computer (FOSDIC) system. The
CPS questionnaire was converted to the FOSDIC type
used by the 1960 census. Entries were made by filling
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

in small circles with an ordinary lead pencil. The questionnaires were photographed to microfilm. The microfilms were then scanned by a reading device which
transferred the information directly to computer tape.
This system permitted a larger form and a more flexible
arrangement of items than the previous documentsensing procedure and did not require the preparation
of punch cards. This data entry system was used
through December 1993.
• January 1963. In response to recommendations of a
review committee, two new items were added to the
monthly questionnaire. The first was an item, formerly
carried out only intermittently, on whether the unemployed were seeking full- or part-time work. The second
was an expanded item on household relationships, formerly included only annually, to provide greater detail
on the marital status and household relationship of
unemployed people.
• March 1963. The sample and population data used in
ratio estimates were revised. From December 1961 to
March 1963, the CPS sample was gradually revised. This
revision reflected the changes in both population size
and distribution as established by the 1960 census.
Other demographic changes, such as the industrial mix
between areas, were also taken into account. The overall sample size remained the same, but the number of
PSUs increased slightly to 357 to provide greater coverage of the fast growing portions of the country. For
most of the sample, census lists replaced the traditional
area sampling. These lists were developed in the 1960
census. These changes resulted in further gains in reliability of about 5 percent for most statistics. The
census-based updated population information was used
in April 1962 for first- and second-stage ratio estimates.
• January 1967. The CPS sample was expanded from
357 to 449 PSUs. An increase in total budget allowed
the overall sample size to increase by roughly 50 percent to a total of about 60,000 housing units (52,500
occupied units). The expanded sample had households
in 863 counties and independent cities with at least
some coverage in every state.
This expansion increased the reliability of the major statistics by about 20 percent and made it possible to publish more detailed statistics.
The concepts of employment and unemployment were
modified. In line with the basic recommendations of the
President’s Committee to Appraise Employment and
Unemployment Statistics (U.S. Department of Commerce, 1976), a several-year study was conducted to
develop and test proposed changes in the labor force
concepts. The principal research results were implemented in January 1967. The changes included a
revised age cutoff in defining the labor force and new
questions to improve the information on hours of work,
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

the duration of unemployment, and the self-employed.
The definition of unemployment was also revised
slightly. The revised definition of unemployment led to
small differences in the estimates of level and month-tomonth change.
• March 1968. Separate age/sex ratio estimation cells
were introduced for Negro1 and Other races. Previously,
the second-stage ratio estimation used non-White and
White race categories by age groups and sex. The
revised procedures allowed separate ratio estimates for
Negro and Other2 race categories.
This change amounted essentially to an increase in the
number of ratio estimation cells from 68 to 116.
• January 1971 and January 1972. The 1970 census
occupational classification was introduced. The questions on occupation were made more comparable to
those used in the 1970 census by adding a question on
major activities or duties of current job. The new classification was introduced into the CPS coding procedures
in January 1971. Tabulated data were produced in the
revised version beginning in January 1972.
• December 1971−March 1973. The sample was
expanded to 461 PSUs and the data used in ratio estimation were updated. From December 1971 to March
1973, the CPS sample was revised gradually to reflect
the changes in population size and distribution
described by the 1970 census. As part of an overall
sample optimization, the sample size was reduced
slightly (from 60,000 to 58,000 housing units), but the
number of PSUs increased to 461. Also, the cluster
design was changed from six nearby (but not contiguous) to four usually contiguous households. This
change was undertaken after research found that
smaller cluster sizes would increase sample efficiency.
Even with the reduction in sample size, this change led
to a small gain in reliability for most characteristics. The
noninterview adjustment and first stage ratio estimate
adjustment were also modified to improve the reliability
of estimates for central cities and the rest of the standard metropolitan statistical areas (SMSAs).
In January 1972, the population estimates used in the
second-stage ratio estimation were updated to the 1970
census base.
• January 1974. The inflation-deflation method was
introduced for deriving independent estimates of the
population. The derivation of independent estimates of
the civilian noninstitutionalized population by age, race,

Negro was the race terminology used at that time.
Other includes American Indian, Eskimo, Aleut, Asian, and
Pacific Islander.
2

History of the Current Population Survey

2–3

and sex used in second-stage ratio estimation in preparing the monthly labor force estimates now used the
inflation-deflation method (see Chapter 10).
• September 1975. State supplementary samples were
introduced. An additional sample, consisting of about
14,000 interviews each month, was introduced in July
1975 to supplement the national sample in 26 states
and the District of Columbia. In all, 165 new PSUs were
involved. The supplemental sample was added to meet
a specific reliability standard for estimates of the annual
average number of unemployed people for each state.
In August 1976, an improved estimation procedure and
modified reliability requirements led to the supplement
PSUs being dropped from three states.
Thus, the size of the supplemental sample was reduced
to about 11,000 households in 155 PSUs.
• October 1978. Procedures for determining demographic characteristics were modified. At this time,
changes were made in the collection methods for
household relationship, race, and ethnicity data. From
now on, race was determined by the respondent rather
than by the interviewer.
Other modifications included the introduction of earnings questions for the two outgoing rotations. New
items focused on usual hours worked, hourly wage
rate, and usual weekly earnings. Earnings items were
asked of currently employed wage and salary workers.
• January 1979. A new two-level, first-stage ratio estimation procedure was introduced. This procedure was
designed to improve the reliability of metropolitan/
nonmetropolitan estimates.
Other newly introduced items were the monthly tabulation of children’s demographic data, including relationship, age, sex, race, and origin.
• September/October 1979. The final report of the
National Commission on Employment and Unemployment Statistics (NCEUS; ‘‘Levitan’’ Commission) (Executive Office of the President, 1976) was issued. This
report shaped many of the future changes to the CPS.
• January 1980. To improve coverage, about 450 households were added to the sample, increasing the number
of total PSUs to 629.
• May 1981. The sample was reduced by approximately
6,000 assigned households, bringing the total sample
size to approximately 72,000 assigned households.
• January 1982. The race categories in the second-stage
ratio estimation adjustment were changed from
White/Non-White to Black/Non-Black. These changes
were made to eliminate classification differences in race
that existed between the 1980 census and the CPS. The
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History of the Current Population Survey

change did not result in notable differences in published
household data. Nevertheless, it did result in more variability for certain ‘‘White,’’ ‘‘Black,’’ and ‘‘Other’’ characteristics.
As is customary, the CPS uses ratio estimates from the
most recent decennial census. Beginning in January
1982, these ratio estimates were based on findings
from the 1980 census. The use of the 1980 censusbased population estimates, in conjunction with the
revised second-stage adjustment, resulted in about a 2
percent increase in the estimates for total civilian noninstitutionalized population 16 years and over, civilian
labor force, and unemployed people. The magnitude of
the differences between 1970 and 1980 census-based
ratio estimates affected the historical comparability and
continuity of major labor force series; therefore, the BLS
revised approximately 30,000 series going back to
1970.
• November 1982. The question series on earnings was
extended to include items on union membership and
union coverage.
• January 1983. The occupational and industrial data
were coded using the 1980 classification systems. While
the effect on industry-related data was minor, the conversion was viewed as a major break in occupationrelated data series. The census developed a ‘‘list of conversion factors’’ to translate occupation descriptions
based on the 1970 census-coding classification system
to their 1980 equivalents.
Most of the data historically published for the ‘‘Black
and Other’’ population group were replaced by data that
relate only to the ‘‘Black’’ population.
• October 1984. School enrollment items were added for
people 16−24 years of age.
• April 1984. The 1970 census-based sample was
phased out through a series of changes that were completed by July 1985. The redesigned sample used data
from the 1980 census to update the sampling frame,
took advantage of recent research findings to improve
the efficiency and quality of the survey, and used a
state-based design to improve the estimates for the
states without any change in sample size.
• September 1984. Collection of veterans’ data for
females was started.
• January 1985. Estimation procedures were changed to
use data from the 1980 census and the new sample.
The major changes were to the second-stage adjustment, which replaced population estimates for ‘‘Black’’
and ‘‘Non-Black’’ (by sex and age groups) with population estimates for ‘‘White,’’ ‘‘Black,’’ and ‘‘Other’’ population groups. In addition, a separate, intermediate step
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

was added as a control to the Hispanic3 population. The
combined effect of these changes on labor force estimates and aggregates for most population groups was
negligible; however, the Hispanic population and associated labor force estimates were dramatically affected
and revisions were made back to January 1980 to the
extent possible.
• June 1985. The CPS computer-assisted telephone interviewing (CATI) facility was opened at Hagerstown, Maryland. A series of tests over the next few years were conducted to identify and resolve the operational issues
associated with the use of CATI. Later tests focused on
CATI-related issues, such as data quality, costs, and
mode effects on labor force estimates. Samples used in
these tests were not used in the CPS.
• April 1987. First CATI cases were used in CPS monthly
estimates. Initially, CATI started with 300 cases a
month. As operational issues were resolved and new
telephone centers were opened—Tucson, Arizona, (May
1992) and Jeffersonville, Indiana, (September
1994)—the CATI workload was gradually increased to
about 9,200 cases a month (January 1995).
• June 1990. The first of a series of experiments to test
alternative labor force questionnaires was started at the
Hagerstown Telephone Center. These tests used random
digit dialing and were conducted in 1990 and 1991.
• January 1992. Industry and occupation codes from the
1990 census were introduced. Population estimates
were converted to the 1990 census base for use in ratio
estimation procedures.
• July 1992. The CATI and computer-assisted personal
interviewing (CAPI) Overlap (CCO) experiments began.
CATI and automated laptop versions of the revised CPS
questionnaire were used in a sample of about 12,000
households selected from the National Crime Victimization Survey sample. The experiment continued through
December 1993.
The CCO ran parallel to the official CPS. The CCO’s main
purpose was to gauge the combined effect of the new
questionnaire and computer-assisted data collection. It
is estimated that the redesign had no statistically significant effect on the total unemployment rate, but it
did affect statistics related to unemployment, such as
the reasons for unemployment, the duration of unemployment, and the industry and occupational distribution of the unemployed with previous work experience.
It also is estimated that the redesign significantly
increased the employment-to-population ratio and the
labor force participation rate for women, but significantly decreased the employment-to-population ratio
for men. Along with the changes in employment data,
3

Hispanics may be any race.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

the redesign significantly influenced the measurement
of characteristics related to employment, such as the
proportion of the employed working part-time, the proportion working part-time for economic reasons, the
number of individuals classified as self-employed, and
industry and occupational distribution of the employed.
• January 1994. A new questionnaire designed solely
for use in computer-assisted interviewing was introduced in the official CPS. Computerization allowed the
use of a very complex questionnaire without increasing
respondent burden, increased consistency by reducing
interviewer error, permitted editing at time of interviewing, and allowed the use of dependent interviewing
where information reported in one month
(industry/occupation, retired/disabled statuses, and
duration of unemployment) was confirmed or updated
in subsequent months.
CPS data used by the BLS were adjusted to reflect an
undercount in the 1990 decennial census. Quantitative
measures of this undercount are derived from a postenumeration survey. Because of reliability issues associated with the post-enumeration survey for small areas
of geography (i.e., places with populations of less than
one million), the undercount adjustment was made only
to state and national level estimates. While the undercount varied by geography and demographic group, the
overall undercount was estimated to be slightly more
than 2 percent for the total 16 and over civilian noninstitutionalized population.
• April 1994. The 16-month phase-in of the redesigned
sample based on the 1990 census began. The primary
purpose of this sample redesign was to maintain the
efficiency of the sampling frames. Once phased in, this
resulted in a monthly sample of 56,000 eligible housing
units in 792 sample areas. The details of the 1990
sample redesign are described in TP63RV.
• December 1994. Starting in December 1994, a new set
of response categories was phased in for the relationship to reference person question. This modification
was directed at individuals not formally related to the
reference person to identify whether there were unmarried partners in a household. The old partner/roommate
category was deleted and replaced with the following
categories: unmarried partner, housemate/roommate,
and roomer/boarder. This modification was phased in
for two rotation groups at a time and was fully in place
by March 1995. This change had no effect on the family
statistics produced by CPS.
• January 1996. The 1990 CPS design was changed
because of a funding reduction. The original reliability
requirements of the sample were relaxed, allowing a
reduction in the national sample size from roughly
56,000 eligible housing units to 50,000 eligible housing
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units. The reduced CPS national sample contained 754
PSUs. The details of the sample design changes as of
January 1996 are described in Appendix H, TP63RV.
• January 1998. A new two-step composite estimation
method for the CPS was implemented (See Appendix I).
The first step involved computation of composite estimates for the main labor force categories, classified by
key demographic characteristics. The second adjusted
person-weights, through a series of ratio adjustments,
to agree with the composite estimates, thus incorporating the effect of composite estimation into the personweights. This new technique provided increased operational simplicity for microdata users and improved the
accuracy of labor force estimates by using different
compositing coefficients for different labor force categories. The weighting adjustment method assured additivity while allowing this variation in compositing coefficients.
• July 2001. Effective with the release of July 2001 data,
official labor force estimates from the CPS and the Local
Area Unemployment Statistics (LAUS) program reflect
the expansion of the monthly CPS sample from about
50,000 to about 60,000 eligible households. This
expansion of the monthly CPS sample was one part of
the Census Bureau’s plan to meet the requirements of
the State Children’s Health Insurance Program (SCHIP)
legislation. The SCHIP legislation requires the Census
Bureau to improve state estimates of the number of children who live in low-income families and lack health
insurance. These estimates are obtained from the
Annual Demographic Supplement to the CPS. In September 2000, the Census Bureau began expanding the
monthly CPS sample in 31 states and the District of
Columbia. States were identified for sample supplementation based on the standard error of their March estimate of low-income children without health insurance.
The additional 10,000 households were added to the
sample over a 3-month period. The BLS chose not to
include the additional households in the official labor
force estimates, however, until it had sufficient time to
evaluate the estimates from the 60,000 household
sample. See Appendix J, Changes to the Current Population Survey Sample in July 2001, for details.
• January 2003. The 2002 Census Bureau occupational
and industrial classification systems, which are derived
from the 2000 Standard Occupational Classification
(SOC) and the 2002 North American Industry Classification System (NAICS), were introduced into the CPS. The
composition of detailed occupational and industrial classifications in the new systems was substantially
changed from the previous systems, as was the structure for aggregating them into broad groups. This created breaks in existing data series at all levels of aggregation.
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Questions on race and ethnicity were modified to comply with new federal standards. Beginning in January
2003, individuals are asked whether they are of Hispanic ethnicity before being asked about their race.
Individuals are now asked directly if they are Spanish,
Hispanic, or Latino. With respect to race, the response
category of Asian and Pacific Islanders was split into
two categories: a) Asian and b) Native Hawaiian or
Other Pacific Islanders. The questions on race were
reworded to indicate that individuals could select more
than one race and to convey more clearly that individuals should report their own perception of what their
race is. These changes had little or no impact on the
overall civilian noninstitutionalized population and civilian labor force but did reduce the population and labor
force levels of Whites, Blacks or African Americans, and
Asians beginning in January 2003. There was little or no
impact on the unemployment rates of these groups. The
changes did not affect the size of the Hispanic or Latino
population and had no significant impact on the size of
their labor force, but did cause an increase of about half
a percentage point in their unemployment rate.
New population controls reflecting the results of Census
2000 substantially increased the size of the civilian
noninstitutionalized population and the civilian labor
force. As a result, data from January 2000 through
December 2002 were revised. In addition, the Census
Bureau introduced another large upward adjustment to
the population controls as part of its annual update of
population estimates for 2003. The entire amount of
this adjustment was added to the labor force data in
January 2003. The unemployment rate and other ratios
were not substantially affected by either of these population control adjustments.
The CPS program began using the X-12 ARIMA software
for seasonal adjustment of time series data with release
of the data for January 2003. Because of the other revisions being introduced with the January data, the
annual revision of 5 years of seasonally adjusted data
that typically occurs with the release of data for December was delayed until the release of data for January. As
part of the annual revision process, the seasonal adjustment of CPS series was reviewed to determine if additional series could be adjusted and if the series currently adjusted would pass a technical review. As a
result of this review, some series that were seasonally
adjusted in the past are no longer adjusted.
Improvements were introduced to both the secondstage and composite weighting procedures. These
changes adapted the weighting procedures to the new
race/ethnic classification system and enhanced the stability over time of national and state/substate labor
force estimates for demographic groups.
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

More detailed information on these changes and an
indication of their effect on national labor force estimates appear in ‘‘Revisions to the Current Population
Survey Effective in January 2003’’ in the February 2003
issue of Employment and Earnings, available on the
Internet at .
• January 2004. Population controls were updated to
reflect revised estimates of net international migration
for 2000 through 2003. The updated controls resulted
in a decrease of 560,000 in the estimated size of the
civilian noninstitutionalized population for December
2003. The civilian labor force and employment levels
decreased by 437,000 and 409,000 respectively. The
Hispanic or Latino population and labor force estimates
declined by 583,000 and 446,000 respectively and Hispanic or Latino employment was lowered by 421,000.
The updated controls had little or no effect on overall
and subgroup unemployment rates and other measures
of labor market participation.
More detailed information on the effect of the updated
controls on national labor force estimates appears in
‘‘Adjustments to Household Survey Population Estimates
in January 2004’’ in the February 2004 issue of Employment and Earnings, available on the Internet at
.
Beginning with the publication of December 2003 estimates in Janaury 2004, the practice of concurrent seasonal adjustment was adopted. Under this practice, the
current month’s seasonally adjusted estimate is computed using all relevant original data up to and including those for the current month. Revisions to estimates
for previous months, however, are postponed until the
end of the year. Previously, seasonal factors for the CPS
labor force data were projected twice a year. With the
introduction of concurrent seasonal adjustment, BLS will
no longer publish projected seasonal factors for CPS
data. More detailed information on concurrent seasonal
adjustment is available in the January 2004 issue of
Employment and Earnings in ‘‘Revision of Seasonally
Adjusted Labor Force Series,’’ available on the Internet
at .
In addition to introducing population controls that
reflected revised estimates of net international migration for 2000 through 2003, in January 2004, the LAUS
program introduced a linear wedge adjustment to CPS
16+ statewide estimates of the population, labor force,
employment, unemployment, and unemployment rate.

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This adjustment linked the 1990 decennial censusbased CPS estimates, adjusted for the undercount (see
January 1994), to the 2000 decennial census-based CPS
estimates. This adjustment provided consistent estimates of statewide labor force characteristics from the
1990s to the 2000s. It also provided consistent CPS
series for use in the LAUS program’s econometric models that are used to produce the official labor force estimates for states and selected sub-state areas, which use
CPS employment and unemployment estimates as
dependent variables.
• April 2004. The 16-month phase-in of the redesigned
sample based on the 2000 census began. This is the
sample design documented in this technical paper.
• September 2005. Hurricane Katrina made landfall on
the Gulf Coast after the August 2005 survey reference
period. The data produced for the September reference
period were the first from the CPS to reflect any impacts
of the storm. The Census Bureau attempted to contact
all sample households in the disaster areas except those
areas under mandatory evacuation at the time of the
survey. Starting in October, all areas were surveyed. In
accordance with standard procedures, uninhabitable
households, and those for which the condition was
unknown, were taken out of the CPS sample universe.
People in households that were successfully interviewed
were given a higher weight to account for those missed.
Also starting in October, BLS and the Census Bureau
added several questions to identify persons who were
evacuated from their homes, even temporarily, due to
Hurricane Katrina. Beginning in November 2005, state
population controls used for CPS estimation were
adjusted to account for interstate moves by evacuees.
This had a negligible effect on estimates for the total
United States. The CPS will continue to identify Katrina
evacuees monthly, possibly through December 2006.
REFERENCES
Executive Office of the President, Office of Management
and Budget, Statistical Policy Division (1976), Federal Statistics: Coordination, Standards, Guidelines: 1976,
Washington, DC: Government Printing Office.
U.S. Department of Commerce, Bureau of the Census, and
U.S. Department of Labor, Bureau of Labor Statistics. ‘‘Concepts and Methods Used in Labor Force Statistics Derived
from the Current Population Survey,’’ Current Population
Reports. Special Studies Ser. P23, No. 62. Washington,
DC: Government Printing Office, 1976.

History of the Current Population Survey

2–7

Chapter 3.
Design of the Current Population Survey Sample
INTRODUCTION
For more than six decades, the Current Population Survey
(CPS) has been one of the major sources of up-to-date
information on the labor force and demographic characteristics of the U.S. population. Because of the CPS’s importance and high profile, the reliability of the estimates has
been evaluated periodically. The design has often been
under close scrutiny in response to demand for new data
and to improve the reliability of the estimates by applying
research findings and new types of information (especially
census results). All changes are implemented with concern
for minimizing cost and maximizing comparability of estimates across time. The methods used to select the sample
households for the survey are evaluated after each decennial census. Based on these evaluations, the design of the
survey is modified and systems are put in place to provide
sample for the following decode. The most recent decennial revision incorporated new information from Census
2000 and was complete as of July 2005.
This chapter describes the CPS sample design as of July
2005. It is directed to a general audience and presents
many topics with varying degrees of detail. The following
section provides a broad overview of the CPS design and
is recommended for all readers. Later sections of this
chapter provide a more in-depth description of the CPS
design and are recommended for readers who require
greater detail.
SURVEY REQUIREMENTS AND DESIGN
Survey Requirements
The following list briefly describes the major characteristics of the CPS sample as of July 2005:
1. The CPS sample is a probability sample.
2. The sample is designed primarily to produce national
and state estimates of labor force characteristics of
the civilian noninstitutionalized population 16 years of
age and older (CNP16+).
3. The CPS sample consists of independent samples in
each state and the District of Columbia. Each state
sample is specifically tailored to the demographic and
labor market conditions that prevail in that particular
state. California and New York State are further
divided into two substate areas that also have independent designs: Los Angeles County and the rest of
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California; New York City and the rest of New York
State.1 Since the CPS design consists of independent
designs for the states and substate areas, it is said to
be state-based.
4. Sample sizes are determined by reliability requirements that are expressed in terms of the coefficient of
variation or CV. The CV is a relative measure of the
sampling error and is calculated as sampling error
divided by the expected value of the given characteristic. The specified CV requirement for the monthly
unemployment level for the nation, given a 6 percent
unemployment rate, is 1.9 percent. The 1.9 percent
CV is based on the requirement that a difference of
0.2 percentage points in the unemployment rate for
two consecutive months be statistically significant at
the 0.10 level.
5. The required CV on the annual average unemployment
level for each state, substate area, and the District of
Columbia, given a 6 percent unemployment rate, is 8
percent.
Overview of Survey Design
The CPS sample is a multistage stratified sample of
approximately 72,000 assigned housing units from 824
sample areas designed to measure demographic and labor
force characteristics of the civilian noninstitutionalized
population 16 years of age and older. Approximately
12,000 of the assigned housing units are sampled under
the State Children’s Health Insurance Program (SCHIP)
expansion that has been part of the official CPS sample
since July 2001. The CPS samples housing units from lists
of addresses obtained from the 2000 Decennial Census of
Population and Housing. The sample is updated continuously for new housing built after Census 2000. The first
stage of sampling involves dividing the United States into
primary sampling units (PSUs)—most of which comprise a
metropolitan area, a large county, or a group of smaller
counties. Every PSU falls within the boundary of a state.
The PSUs are then grouped into strata on the basis of independent information that is obtained from the decennial
census or other sources.
The strata are constructed so that they are as homogeneous as possible with respect to labor force and other

1
New York City consists of Bronx, Kings, New York, Queens,
and Richmond Counties.

Design of the Current Population Survey Sample

3–1

social and economic characteristics that are highly correlated with unemployment. One PSU is sampled in each
stratum. The probability of selection for each PSU in the
stratum is proportional to its population as of Census
2000.

overall probabilities of selection. The system of statebased designs ensures that both the state and national
reliability requirements are met.

In the second stage of sampling, a sample of housing
units within the sample PSUs is drawn. Ultimate sampling
units (USUs) are small groups of housing units. The bulk of
the USUs sampled in the second stage consist of sets of
addresses that are systematically drawn from sorted lists
of blocks prepared as part of Census 2000. Housing units
from blocks with similar demographic composition and
geographic proximity are grouped together in the list. In
parts of the United States where addresses are not recognizable on the ground, USUs are identified using area sampling techniques. The CPS sample is usually described as a
two-stage sample, but occasionally, a third stage of sampling is necessary when actual USU size is extremely large.
In addition, a sample of building permits is selected to
provide coverage of construction since 2000. The sample
of building permits is based on listings of new construction obtained from local jurisdictions in sample PSUs.

The first stage of the CPS sample design is the selection of
counties. The purpose of selecting a subset of counties
instead of having all counties in the sample is to minimize
the cost of the survey. This is done mainly by minimizing
the number of field representatives needed to conduct the
survey, and reducing the travel cost incurred in visiting
the sample housing units. Two features of the first-stage
sampling are: (1) to ensure that sample counties represent
other counties with similar labor force characteristics that
are not selected and (2) to ensure that each field representative is allotted a manageable workload in his or her
sample area.

Each month, interviewers collect data from the sample
housing units. A housing unit is interviewed for 4 consecutive months, dropped out of the sample for the next 8
months, and interviewed again in the following 4 months.
In all, a sample housing unit is interviewed eight times.
Households are rotated in and out of the sample in a way
that improves the accuracy of the month-to-month and
year-to-year change estimates. The rotation scheme
ensures that in any single month, one-eighth of the housing units are interviewed for the first time, another eighth
is interviewed for the second time, and so on. That is,
after the first month, 6 of the 8 rotation groups will have
been in the survey for the previous month—there will
always be a 75 percent month-to-month overlap. When the
system has been in full operation for 1 year, 4 of the 8
rotation groups in any month will have been in the survey
for the same month, 1 year ago; there will always be a 50
percent year-to-year overlap. This rotation scheme
upholds the scientific tenets of probability sampling and
each month’s sample produces a true representation of
the target population. The rotation system makes it possible to reduce sampling error by using a composite estimation procedure2 and, at slight additional cost, by
increasing the representation in the sample of USUs with
unusually large numbers of housing units.
Each state’s sample design ensures that most housing
units within a state have the same overall probability of
selection. Because of the state-based nature of the design,
sample housing units in different states have different

See Chapter 10: Estimation Procedures for Labor Force Data
for more information on the composite estimation procedure.

3–2

Design of the Current Population Survey Sample

FIRST STAGE OF THE SAMPLE DESIGN

The first-stage sample selection is carried out in three
major steps:
1. Definition of the PSUs.
2. Stratification of the PSUs within each state.
3. Selection of the sample PSUs in each state.
Definition of the Primary Sampling Units
PSUs are delineated so that they encompass the entire
United States. The land area covered by each PSU is made
reasonably compact so it can be traversed by an interviewer without incurring unreasonable costs. The population is as heterogeneous with regard to labor force characteristics as can be made consistent with the other
constraints. Strata are constructed that are homogenous in
terms of labor force characteristics to minimize betweenPSU variance. Between-PSU variance is a component of
total variance that arises from selecting a sample of PSUs
rather than selecting all PSUs. In each stratum, a PSU is
selected that is representative of the other PSUs in the
same stratum. When revisions are made in the sample
each decade, a procedure used for reselection of PSUs
maximizes the overlap in the sample PSUs with the previous CPS sample.
Most PSUs are groups of contiguous counties rather than
single counties. A group of counties is more likely than a
single county to have diverse labor force characteristics.
Limits are placed on the geographic size of a PSU to contain the distance a field representative must travel.
Rules for Defining PSUs
1. Each PSU is contained within the boundary of a single
state.
2. Metropolitan statistical areas (MSAs) are defined as
separate PSUs using projected 2003 Core-Based Statistical Area (CBSA) definitions. CBSAs are defined as
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

metropolitan or micropolitan areas and include at least
one county. Micropolitan areas and areas outside of
CBSAs are considered nonmetropolitan areas. If any
metropolitan area crosses state boundaries, each
state-metropolitan area intersection is a separate PSU.3
3. For most states, PSUs are either one county or two or
more contiguous counties. In some states, county
equivalents are used: cities, independent of any
county organization, in Maryland, Missouri, Nevada,
and Virginia; parishes in Louisiana; and boroughs and
census divisions in Alaska.
4. The area of the PSU should not exceed 3,000 square
miles except in cases where a single county exceeds
the maximum area.
5. The population of the PSU is at least 7,500 except
where this would require exceeding the maximum
area specified in number 4.
6. In addition to meeting the limitation on total area,
PSUs are formed to limit extreme length in any direction and to avoid natural barriers within the PSU.
The PSU definitions are revised each time the CPS sample
design is revised. Revised PSU definitions reflect changes
in metropolitan area definitions and an attempt to have
PSU definitions consistent with other U.S. Census Bureau
demographic surveys. The following are steps for combining counties, county equivalents, and independent cities
into PSUs for the 2000 design:
1. The 1990 PSUs are evaluated by incorporating into the
PSU definitions those counties comprising metropolitan areas that are new or have been redefined.
2. Any single county is classified as a separate PSU,
regardless of its 2000 population, if it exceeds the
maximum area limitation deemed practical for interviewer travel.
3. Other counties within the same state are examined to
determine whether they might advantageously be
combined with contiguous counties without violating
the population and area limitations.
4. Contiguous counties with natural geographic barriers
between them are placed in separate PSUs to reduce
the cost of travel within PSUs.
These steps created 2,025 PSUs in the United States from
which to draw the sample for the CPS when it was redesigned after the 2000 decennial census.

3
Final metropolitan area definitions were not available from
the Office of Management and Budget when PSUs were defined.
Fringe counties having a good chance of being in final CBSA definitions are separate PSUs. Most projected CBSA definitions are the
same as final CBSA definitions (Executive Office of the President,
2003).

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Stratification of Primary Sampling Units
The CPS sample design calls for combining PSUs into
strata within each state and selecting one PSU from each
stratum. For this type of sample design, sampling theory
and cost considerations suggest forming strata with
approximately equal population sizes. When the design is
self-weighting (same sampling fraction in all strata) and
one field representative is assigned to each sample PSU,
equal stratum sizes have the advantage of providing equal
field representative workloads (at least during the early
years of each decade, before population growth and
migration significantly affect the PSU population sizes).
The objective of the stratification, therefore, is to group
PSUs with similar characteristics into strata having
approximately equal populations in 2000.
Sampling theory and costs dictate that highly populated
PSUs should be selected for sample with certainty. The
rationale is that some PSUs exceed or come close to the
population size needed for equalizing stratum sizes.
These PSUs are designated as self-representing (SR); that
is, each of the SR PSUs is treated as a separate stratum
and is included in the sample.
The following describes the steps for stratifying PSUs for
the 2000 redesign:
1.

A PSU which consists of at least one county that is
likely to be part of the 151 most populous metropolitan areas based on projected definitions and Census
2000 population is required to be SR.

The remaining PSUs are grouped into non-selfrepresenting (NSR) strata within state boundaries. In
each NSR stratum, one PSU will be selected to represent all of the PSUs in the stratum. They are formed by
adhering to the following criteria:
a. Roughly equal-sized NSR strata are formed within a
state.
b. NSR strata are formed so as to yield reasonable
field representative workloads in an NSR PSU of
roughly 35 to 55 housing units. The number of NSR
strata in a state is a function of the 2000 population, civilian labor force, state CV, and between-PSU
variance4 on the unemployment level. (Workloads
in NSR PSUs are constrained because one field representative must canvass the entire PSU. No such
constraints are placed on SR PSUs.) In Alaska, the
strata are also a function of expected interview
cost.
c. NSR strata are formed with PSUs homogeneous with
respect to labor force and other social and economic characteristics that are highly correlated with
unemployment. This helps to minimize the
between-PSU variance.

4
Between-PSU variance is the component of total variance arising from selecting a sample of PSUs from all possible PSUs.

Design of the Current Population Survey Sample

3–3

d. Stratification is performed independently of previous
CPS sample designs.
Key variables used for stratification are:
• Number of males unemployed.
• Number of females unemployed.
• Number of families with female head of household.
• Ratio of occupied housing units with three or
more people, of all ages, to total occupied housing units.
In addition to these, a number of other variables,
such as industry and wage variables obtained from
the Bureau of Labor Statistics, are used for some
states. The number of stratification variables in a
state ranges from 3 to 7.
e. In states with SCHIP sample, the self-representing
PSUs are the same for both CPS and SCHIP. In most
states, the same non-self-representing sample PSUs
are in sample for both surveys. However, to improve
the reliability of the SCHIP estimates in Maine, Maryland, and Nevada, the SCHIP non- self-representing
PSUs are selected independent of the CPS sample
PSUs, with replacement. The methodology for the
stratification of PSUs for SCHIP in these states is similar to the other stratifications, except that the stratification variable used is the number of people under
age 18 with a household income below 200 percent
of poverty.
Table 3−1 summarizes the percentage of the targeted
population in SR and sampled NSR areas by state.
Several current surveys, including CPS, use the Stratification Search Program (SSP) created by the Demographic Statistical Methods Division of the Census Bureau to perform
the PSU stratification. CPS strata in all states except Alaska
are formed by the SSP. (A separate program performs the
stratification for Alaska.) The SSP classifies certain PSUs as
SR, using the criteria mentioned previously, and creates
NSR strata. First, initial parameter sets for each stratification area (i.e., state or substate area) are formed by creating unique combinations of the number of NSR PSUs, the
number of SR PSUs, the number of strata that must be
formed, and the average monthly workload for NSR PSUs
and SR PSUs. Non-self-representing PSUs are reclassified as
SR if additional SR PSUs are needed to provide adequate
samples and if they are among the most populous PSUs in
the stratification area. Some NSR PSUs are reclassified SR if
they are not similar enough to other NSR PSUs to produce
a favorable stratification. Some of the created parameter
sets are eliminated because of unsatisfactory PSU workloads or lack of a self-weighting design.

3–4

Design of the Current Population Survey Sample

Next, random stratifications for each parameter set are
formed. NSR PSUs are moved from one stratum to another
to even out the size of the strata. Stratifications are evaluated based on the criteria in the previous section. A
national stratification is then chosen by selecting one
stratification from each state. The national stratification is
refined through a series of moves and swaps to minimize
the difference in workloads among NSR PSUs and the CV
for unemployment level for each stratification area.
After the strata are defined, some state sample sizes are
increased to bring the national CV for unemployment level
down to 1.9 percent assuming a 6 percent unemployment
rate.
A consequence of the above stratification criteria is that
states that are geographically small, mostly urban, or
demographically homogeneous are entirely SR. These
states are Connecticut, Delaware, Hawaii, Massachusetts,
New Hampshire, New Jersey, Rhode Island, Vermont, and
the District of Columbia.
Selection of Sample Primary Sampling Units
Each SR PSU is in the sample by definition. There are currently 446 SR PSUs. In each of the remaining 378 NSR
strata, one PSU is selected for the sample following the
guidelines described next. Four of the NSR strata only contain SCHIP sample.
At each sample redesign of the CPS, it is important to
minimize the cost of introducing a new set of PSUs. Substantial investment has been made in hiring and training
field representatives in the existing sample PSUs. For each
PSU dropped from the sample and replaced by another in
the new sample, the expense of hiring and training a new
field representative must be accepted. Furthermore, there
is a temporary loss in accuracy of the results produced by
new and relatively inexperienced field representatives.
Concern for these factors is reflected in the procedure
used for selecting PSUs.
Objectives of the selection procedure. The selection
of the sample of NSR PSUs is carried out within the strata
using the Census 2000 population. The selection procedure accomplishes the following objectives:
1. Select one sample PSU from each stratum with probability proportional to the 2000 population.
2. Retain in the new sample the maximum number of
sample PSUs from the 1990 design sample.
Using a procedure designed to maximize overlap, one PSU
is selected per stratum with probability proportional to its
2000 population. This procedure uses mathematical programming techniques to maximize the probability of
selecting PSUs that are already in sample while maintaining the correct overall probabilities of selection.

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Table 3–1. Estimated Population in Sample Areas for 824-PSU Design by State
Self-representing
(SR) areas

Non-self-representing
(NSR) areas

State

Total sample areas

Percentage

Population1
in NSR sample
areas

Percentage

Population1
in sample areas

Percentage

161,282,405
1,556,994
336,349
3,076,477
876,805
23,690,332
7,040,665
16,649,667
2,444,539
2,588,699
595,030

76.1
46.2
77.2
80.5
43.4
94.6
100.0
92.5
75.4
100.0
100.0

18,957,760
746,019
28,541
421,667
388,413
587,004
0
587,004
401,785
0
0

8.9
22.1
6.5
11.0
19.2
2.3
0.0
3.3
12.4
0.0
0.0

180,240,165
2,303,013
364,890
3,498,144
1,265,218
24,277,336
7,040,665
17,236,671
2,846,324
2,588,699
595,030

85.0
68.3
83.7
91.5
62.6
96.9
100.0
95.8
87.8
100.0
100.0

District of Columbia. . . . . . . . . . . .
Florida . . . . . . . . . . . . . . . . . . . . . . .
Georgia . . . . . . . . . . . . . . . . . . . . . .
Hawaii . . . . . . . . . . . . . . . . . . . . . . .
Idaho . . . . . . . . . . . . . . . . . . . . . . . .
Illinois. . . . . . . . . . . . . . . . . . . . . . . .
Indiana. . . . . . . . . . . . . . . . . . . . . . .
Iowa . . . . . . . . . . . . . . . . . . . . . . . . .
Kansas. . . . . . . . . . . . . . . . . . . . . . .
Kentucky . . . . . . . . . . . . . . . . . . . . .

457,495
11,247,999
3,921,187
902,559
582,187
7,459,357
2,504,432
718,299
1,028,154
1,414,196

100.0
90.5
64.7
100.0
61.5
79.9
54.6
32.2
51.5
45.9

0
702,456
730,347
0
128,137
875,368
705,866
590,359
294,193
525,654

0.0
5.7
12.1
0.0
13.5
9.4
15.4
26.5
14.7
17.1

457,495
11,950,455
4,651,534
902,559
710,324
8,334,725
3,210,298
1,308,658
1,322,347
1,939,850

100.0
96.1
76.8
100.0
75.0
89.3
69.9
58.7
66.2
63.0

Louisiana. . . . . . . . . . . . . . . . . . . . .
Maine . . . . . . . . . . . . . . . . . . . . . . . .
Maryland . . . . . . . . . . . . . . . . . . . . .
Massachusetts . . . . . . . . . . . . . . . .
Michigan . . . . . . . . . . . . . . . . . . . . .
Minnesota . . . . . . . . . . . . . . . . . . . .
Mississippi. . . . . . . . . . . . . . . . . . . .
Missouri . . . . . . . . . . . . . . . . . . . . . .
Montana . . . . . . . . . . . . . . . . . . . . .
Nebraska. . . . . . . . . . . . . . . . . . . . .

1,779,331
831,273
3,635,366
4,915,261
5,708,265
2,534,229
658,381
2,592,889
449,818
669,967

54.1
83.7
91.2
100.0
76.1
68.2
31.4
61.3
65.6
52.3

570,528
104,708
249,759
0
788,772
293,420
518,634
532,452
60,735
166,160

17.4
10.5
6.3
0.0
10.5
7.9
24.8
12.6
8.9
13.0

2,349,859
935,981
3,885,125
4,915,261
6,497,037
2,827,649
1,177,015
3,125,341
510,553
836,127

71.5
94.2
97.5
100.0
86.6
76.1
56.2
73.9
74.4
65.2

Nevada . . . . . . . . . . . . . . . . . . . . . .
New Hampshire . . . . . . . . . . . . . . .
New Jersey. . . . . . . . . . . . . . . . . . .
New Mexico . . . . . . . . . . . . . . . . . .
New York. . . . . . . . . . . . . . . . . . . . .
New York City. . . . . . . . . . . . .
Remainder of New York . . . .
North Carolina . . . . . . . . . . . . . . . .
North Dakota . . . . . . . . . . . . . . . . .
Ohio . . . . . . . . . . . . . . . . . . . . . . . . .

1,361,183
946,316
6,424,830
869,341
12,976,943
6,197,673
6,779,270
3,227,560
258,088
6,512,613

90.3
100.0
100.0
64.9
89.4
100.0
81.5
53.0
53.2
75.7

73,785
0
0
195,258
604,945
0
604,945
1,112,224
97,567
597,297

4.9
0.0
0.0
14.6
4.2
0.0
7.3
18.2
20.1
6.9

1,434,968
946,316
6,424,830
1,064,599
13,581,888
6,197,673
7,384,215
4,339,784
355,655
7,109,910

95.2
100.0
100.0
79.4
93.6
100.0
88.8
71.2
73.3
82.6

Oklahoma . . . . . . . . . . . . . . . . . . . .
Oregon. . . . . . . . . . . . . . . . . . . . . . .
Pennsylvania . . . . . . . . . . . . . . . . .
Rhode Island . . . . . . . . . . . . . . . . .
South Carolina . . . . . . . . . . . . . . . .
South Dakota . . . . . . . . . . . . . . . . .
Tennessee. . . . . . . . . . . . . . . . . . . .
Texas . . . . . . . . . . . . . . . . . . . . . . . .
Utah . . . . . . . . . . . . . . . . . . . . . . . . .
Vermont . . . . . . . . . . . . . . . . . . . . . .

1,453,949
1,803,289
7,456,685
810,041
1,923,917
320,381
2,449,113
11,598,894
1,228,567
472,874

56.4
68.5
78.7
100.0
63.7
57.2
56.4
76.6
78.1
100.0

288,633
255,641
877,672
0
330,931
105,941
761,816
947,146
157,145
0

11.2
9.7
9.3
0.0
11.0
18.9
17.5
6.3
10.0
0.0

1,742,582
2,058,930
8,334,357
810,041
2,254,848
426,322
3,210,929
12,546,040
1,385,712
472,874

67.6
78.2
88.0
100.0
74.7
76.1
73.9
82.9
88.0
100.0

Virginia. . . . . . . . . . . . . . . . . . . . . . .
Washington . . . . . . . . . . . . . . . . . . .
West Virginia . . . . . . . . . . . . . . . . .
Wisconsin . . . . . . . . . . . . . . . . . . . .
Wyoming . . . . . . . . . . . . . . . . . . . . .

3,750,284
3,226,608
778,715
2,021,672
234,672

70.9
72.5
54.5
49.6
63.3

460.699
505,437
256,355
877,326
40,965

8.7
11.4
17.9
21.5
11.0

4,210,983
3,732,045
1,035,070
2,898,998
275,637

79.6
83.9
72.4
71.1
74.3

Population1
in SR areas
Total. . . . . . . . . . . . . . . . .
Alabama . . . . . . . . . . . . . . . . . . . . .
Alaska . . . . . . . . . . . . . . . . . . . . . . .
Arizona . . . . . . . . . . . . . . . . . . . . . .
Arkansas . . . . . . . . . . . . . . . . . . . . .
California . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . .
Remainder of California . . . .
Colorado . . . . . . . . . . . . . . . . . . . . .
Connecticut. . . . . . . . . . . . . . . . . . .
Delaware . . . . . . . . . . . . . . . . . . . . .

Civilian noninstitutionalized population 16 years of age and over based on Census 2000.

Current Population Survey TP66
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Design of the Current Population Survey Sample

3–5

Calculation of overall state sampling interval. After
stratifying the PSUs within the states, the overall sampling
interval in each state is computed. The overall state sampling interval is the inverse of the probability of selection
of each housing unit in a state for a self-weighting
design. By design, the overall state sampling interval is
fixed, but the state sample size is not fixed, allowing
growth of the CPS sample because of housing units built
after Census 2000. (See Appendix B for details on how the
desired sample size is maintained.)
The state sampling interval is designed to meet the
requirements for the variance on an estimate of the unemployment level. This variance can be thought of as a sum
of variances from the first stage and the second stage of
sample selection.5 The first-stage variance is called the
between-PSU variance and the second-stage variance is
called the within-PSU variance. The square of the state CV,
or the relative variance, on the unemployment level is
expressed as
2

σb + σw
CV =
[E(x)]2
2

where
2

σb

(3.1)

= between-PSU variance contribution to the
variance of the state unemployment level
estimator.

σw

= within-PSU variance contribution to the
variance of the state unemployment level
estimator.
= the expected value of the unemployment
level for the state.

E(x)
2

The term, σw, can be written as the variance assuming a
binomial distribution from a simple random sample multiplied by a design effect
2

σw =

N2 p q (deff)
n

Substituting.
q = 1 – p.
This formula can be rewritten as
2

σw = SI (x q) (deff)

(3.2)

where
SI

N
= the state sampling interval, or n .

Substituting (3.2) into (3.1) and rewriting in terms of the
state sampling interval gives
2

SI =

CV2x2 − σb
x q (deff)

Generally, this overall state sampling interval is used for
all strata in a state yielding a self-weighting state design.
(In some states, the sampling interval is adjusted in certain strata to equalize field representative workloads.)
When computing the sampling interval for the current CPS
sample, a 6 percent state unemployment rate is assumed
for 2005. Table 3-1 provides information on the proportion of the population in sample areas for each state.
The SCHIP sample is allocated among the states after the
CPS sample is allocated. A sampling interval accounting
for both the CPS and SCHIP samples can be computed as:
⫺1
⫺1
SICOMB ⫽ 共SICPS
⫹ SISCHIP
兲⫺1

The between-PSU variance component for the combined
sample in the three states which were restratified for
SCHIP can be estimated using a weighted average of the
individual CPS and SCHIP between-PSU variance. The
weight is the proportion of the total state sample
accounted for by each individual survey:
2
2
2
␴B,COMB
⫽ (SICOMB 冫SICPS)␴B,CPS
⫹ (1 ⫺ SICOMB 冫SICPS)␴B,SCHIP

where
SECOND STAGE OF THE SAMPLE DESIGN
N

p
n
deff

= the civilian noninstitutionalized population, 16 years of age and older (CNP16+),
for the state.
= proportion of unemployed in the
x
CNP16+ for the state, or N.
= the state sample size.
= the state within-PSU design effect. This is
a factor accounting for the difference
between the variance calculated from a
multistage stratified sample and that
from a simple random sample.

The variance of an estimator, u, based on a two-stage sample
has the general form:
Var共u兲 ⫽ VarIEII共u兩 set of sample PSUs兲 ⫹ EIVarII共u兩 set of sample PSUs兲
where I and II represent the first and second stage designs,
respectively. The left term represents the between-PSU variance,
2

σb. The right term represents the within-PSU variance, σw.

3–6

Design of the Current Population Survey Sample

The second stage of the CPS sample design is the selection of sample housing units within PSUs. The objectives
of within-PSU sampling are to:
1. Select a probability sample that is representative of
the civilian noninstitutionalized population.
2. Give each housing unit in the population one and only
one chance of selection, with virtually all housing
units in a state or substate area having the same overall chance of selection.
3. For the sample size used, keep the within-PSU variance on labor force statistics (in particular, unemployment) at as low a level as possible, subject to respondent burden, cost, and other constraints.
4. Select within-PSU sample units for additional samples
that will be needed before the next decennial census.
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

5. Put particular emphasis on providing reliable estimates of monthly levels and change over time of labor
force items.
USUs are the sample units selected during the second
stage of the CPS sample design. As discussed earlier in
this chapter, most USUs consist of a geographically compact cluster of approximately four addresses, corresponding to four housing units at the time of the census. Use of
housing unit clusters lowers travel costs for field representatives. Clustering slightly increases within-PSU variance
of estimates for some labor force characteristics since
respondents within a compact cluster tend to have similar
labor force characteristics.
Overview of Sampling Sources
To accomplish the objectives of within-PSU sampling,
extensive use is made of data from the 2000 Decennial
Census of Population and Housing and the Building Permit
Survey. Census 2000 collected information on all living
quarters existing as of April 1, 2000, including characteristics of living quarters as well as the demographic composition of people residing in these living quarters. Data
on the economic well-being and labor force status of individuals were solicited for about 1 in 6 housing units. However, since the census does not cover housing units constructed since April 1, 2000, a sample of building permits
issued in 2000 and later is used to supplement the census
data. These data are collected via the Building Permit Survey, which is an ongoing survey conducted by the Census
Bureau. Therefore, a list sample of census addresses,
supplemented by a sample of building permits, is used in
most of the United States. However, where city-type street
addresses from Census 2000 do not exist, or where residential construction does not need or require building permits, area samples are sometimes necessary. (See the next
section for more detail on the development of the sampling frames.)
These sources provide sampling information for numerous
demographic surveys conducted by the Census Bureau.6 In
consideration of respondents, sampling methodologies are
coordinated among these surveys to ensure a sampled
housing unit is selected for one survey only. Consistent
definition of sampling frames allows the development of
separate, optimal sampling schemes for each survey. The
general strategy for each survey is to sort and stratify all
the elements in the sampling frame (eligible and not eligible) to satisfy individual survey requirements, select a
6

CPS sample selection is coordinated with the following
demographic surveys in the 2000 redesign: the American Housing Survey—Metropolitan sample, the American Housing
Survey—National sample, the Consumer Expenditure Survey—
Diary sample, the Consumer Expenditure Survey—Quarterly
sample, the Current Point of Purchase Survey, the National Crime
Victimization Survey, the National Health Interview Survey, the
Rent and Property Tax Survey, and the Survey of Income and
Program Participation.

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systematic sample, and remove the selected sample from
the frame. Sample is selected for the next survey from
what remains. Procedures are developed to determine eligibility of sample cases at the time of interview for each
survey. This coordinated sampling approach is computer
intensive and was not possible in previous redesigns.7
Development of Sampling Frames
Results from Census 2000 and the Building Permit Survey,
and the relationship between these two sources, are used
to develop sampling frames. Four frames are created: the
unit frame, the area frame, the group quarters frame, and
the permit frame. The unit, area, and group quarters
frames are collectively called old construction. To describe
frame development methodology, several terms must be
defined.
Two types of living quarters were defined for the census.
The first type is a housing unit. A housing unit is a group
of rooms or a single room occupied as a separate living
quarter or intended for occupancy as a separate living
quarter. A separate living quarter is one in which the occupants live and eat separately from all other people on the
property and have direct access to their living quarter
from the outside or through a common hall or lobby as
found in apartment buildings. A housing unit may be
occupied by a family or one person, as well as by two or
more unrelated people who share the living quarter. About
97 percent of the population counted in Census 2000
resided in housing units.
The second type of living quarter is a group quarters. A
group quarter is a living quarter where residents share
common facilities or receive formally authorized care.
Examples include college dormitories, retirement homes,
and communes. For some group quarters, such as fraternity and sorority houses and certain types of group
houses, a group quarter is distinguished from a housing
unit if it houses ten or more unrelated people. The group
quarters population is classified as institutional or noninstitutionalized and as military or civilian. CPS targets only
the civilian noninstitutionalized population residing in
group quarters. Military and institutional group quarters
are included in the group quarters frame and given a
chance of selection in case of conversion to civilian noninstitutionalized housing by the time it is scheduled for
interview. Less than 3 percent of the population counted
in Census 2000 resided in group quarters.
Old Construction Frames
Old construction consists of three sampling frames: unit,
area, and group quarters. The primary objectives in constructing the three sampling frames are maximizing the
7
This sampling strategy is unbiased because if a random
selection is removed from a frame, the part of the frame that
remains is a random subset. Also, the sample elements selected
and removed from each frame for a particular survey have similar
characteristics as the elements remaining in the frame.

Design of the Current Population Survey Sample

3–7

use of census information to reduce variance of estimates,
ensure adequate coverage, and minimize cost. The sampling frames used in a particular geographic area take into
account three major address features:
1. Type of living quarters—housing units or group quarters.

block measure of size (MOS) and is calculated as follows:
H
area block MOS = 4 + [GQ block MOS]
where
H

= the number of housing units enumerated in the block for Census 2000.

GQ block MOS

= the integer number of group quarters
measures in a block (see equation 3.4).

2. Completeness of addresses—complete or incomplete.
3. Building permit office coverage of the area—covered
or not covered.
An address is considered complete if it describes a specific
location; otherwise, the address is considered incomplete.
(When Census 2000 addresses cannot be used to locate
sample units, area listings must be performed before
sample units can be selected for interview. See Chapter 4
for more detail.) Examples of a complete address are city
delivery types of mailing addresses composed of a house
number, street name, and possibly a unit designation,
such as ‘‘1599 Main Street’’ or ‘‘234 Elm Street, Apartment
601.’’ Examples of incomplete addresses are addresses
composed of postal delivery information without indicating specific locations, such as ‘‘PO Box 123’’ or ‘‘Box 4’’ on
a rural route. Housing units in complete blocks covered by
building permit offices are assigned to the unit frame.
Group quarters in complete blocks covered by building
permit offices are assigned to the group quarters frame.
Other blocks are assigned to the area frame.
Unit frame. The unit frame consists of housing units in
census blocks that contain a very high proportion of complete addresses and are essentially covered by building
permit offices. The unit frame covers most of the population. A USU in the unit frame consists of a geographically
compact cluster of four addresses, which are identified
during sample selection. The addresses, in most cases, are
those for separate housing units. However, over time
some buildings may be demolished or converted to nonresidential use, and others may be split up into several
housing units. These addresses remain sample units,
resulting in a small variability in cluster size.
Area frame. The area frame consists of housing units
and group quarters in census blocks that contain a high
proportion of incomplete addresses, or are not covered by
building permit offices. A CPS USU in the area frame also
consists of about four housing unit equivalents, except in
some areas of Alaska that are difficult to access where a
USU is eight housing unit equivalents. The area frame is
converted into groups of four housing unit equivalents,
called ‘‘measures,’’ because the census addresses of individual housing units or people within a group quarter are
not used in the sampling.
An integer number of area measures is calculated at the
census block level. The number is referred to as the area
3–8

Design of the Current Population Survey Sample

(3.3)

The first term of equation (3.3) is rounded to the nearest
nonzero integer. When the fractional part is 0.5 and the
term is greater than 1, it is rounded to the nearest even
integer.
Sometimes census blocks are combined with geographically nearby blocks before the area block MOS is calculated. This is done to ensure that newly constructed units
have a chance of selection in blocks with no housing units
or group quarters at the time of the census and that are
not covered by a building permit office. This also reduces
the sampling variability caused when USU size differs from
four housing unit equivalents for small blocks with fewer
than four housing units.
Depending on whether or not a block is covered by a
building permit office, area frame blocks are classified as
area permit or area nonpermit. No distinction is made
between area permit and area nonpermit blocks during
sampling. Field procedures are developed to ensure
proper coverage of housing units built after Census 2000
in the area blocks to (1) prevent these housing units from
having a chance of selection in area permit blocks and (2)
give these housing units a chance of selection in area nonpermit blocks. These field procedures have the added benefit of assisting in keeping USU size constant as the number of housing units in the block increases because of new
construction.
Group quarters frame. The group quarters frame consists of group quarters in census blocks that contain a sufficient proportion of complete addresses and are essentially covered by building permit offices. Although nearly
all blocks are covered by building permit offices, some are
not, which may result in minor undercoverage. The group
quarters frame covers a small proportion of the population. A CPS USU in the group quarters frame consists of
four housing unit equivalents. The group quarters frame,
like the area frame, is converted into housing unit equivalents because Census 2000 addresses of individual group
quarters or people within a group quarter are not used in
the sampling. The number of housing unit equivalents is
computed by dividing the Census 2000 group quarters
population by the average number of people per household (calculated from Census 2000 as 2.59).
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An integer number of group quarters measures is calculated at the census block level. The number of group quarters measures is referred to as the GQ block MOS and is
calculated as follows:
NIGQPOP
GQ block MOS = (4)(2.59) + MIL + IGQ

(3.4)

where
NIGQPOP

MIL
IGQ

= the noninstitutionalized group quarters
population in the block from Census
2000.
= the number of military barracks in the
block from Census 2000.
= 1 if one or more institutional group quarters are in the block or 0 if no institutional group quarters are in the block
from Census 2000.

The first term of equation (3.4) is rounded to the nearest
nonzero integer. When the fractional part is 0.5 and the
term is greater than 1, it is rounded to the nearest even
integer.
Only the civilian noninstitutionalized population is interviewed for CPS. Military barracks and institutional group
quarters are given a chance of selection in case group
quarters convert status over the decade. A military barrack or institutional group quarters is equivalent to one
measure, regardless of the number of people counted
there in Census 2000.
Special situations in old construction. During development of the old construction frames, several situations
are given special treatment. National park blocks are
treated as if covered by a building permit office to
increase the likelihood of being in the unit or group quarters frames to minimize costs. Blocks in American Indian
Reservations are treated as if not covered by a building
permit office and are put in the area frame to improve coverage. To improve coverage of newly constructed college
housing, special procedures are used so blocks with existing college housing and small neighboring blocks are in
the area frame. Blocks in Ohio which are covered by building permit offices that issue permits for only certain types
of structures are treated as area nonpermit blocks. Two
examples of blocks excluded from sampling frames are
blocks consisting entirely of docked maritime vessels
where crews reside and street locations where only homeless people were enumerated in Census 2000.
Permit Frame
Permit frame sampling ensures coverage of housing units
built since Census 2000. The permit frame grows as building permits are issued during the decade. Data collected
by the Building Permit Survey are used to update the permit frame monthly. About 92 percent of the population
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lives in areas covered by building permit offices. Housing
units built since Census 2000 in areas of the United States
not covered by building permit offices have a chance of
selection in the nonpermit portion of the area frame.
Group quarters built since Census 2000 are generally not
covered in the permit frame, although the area frame does
pick up new group quarters. (This minor undercoverage is
discussed in Chapter 16.)
A permit measure, which is equivalent to a CPS USU, is
formed within a permit date and a building permit office,
resulting in a cluster containing an expected four newly
built housing units. The integer number of permit measures is referred to as the BPOMOS and is calculated as follows:
HPt
BPOMOSt = 4

(3.5)

where
HPt = the total number of housing units for which the
building permit office issues permits for a time
period, t, normally a month; for example, a building
permit office issued 2 permits for a total 24 housing
units to be built in month t.
BPOMOS for time period t is rounded to the nearest integer
except when nonzero and less than 1, then it is rounded
to 1. Permit cluster size varies according to the number of
housing units for which permits are actually issued. Also,
the number of housing units for which permits are issued
may differ from the number of housing units that actually
get built.
When developing the permit frame, an attempt is made to
ensure inclusion of all new housing units constructed after
Census 2000. To do this, housing units for which building
permits had been issued but which had not yet been constructed by the time of the census should be included in
the permit frame. However, by including permits issued
prior to Census 2000 in the permit frame, there is a risk
that some of these units will have been built by the time
of the census and, thus, included in the old construction
frame. These units will then have two chances of selection
in the CPS: one in the permit frame and one in the old construction frames.
For this reason, permits issued too long before the census
should not be included in the permit frame. However,
excluding permits issued long before the census brings
the risk of excluding units for which permits were issued
but which had not yet been constructed by the time of the
census. Such units will have no chance of selection in the
CPS, since they are not included in either the permit or old
construction frames. In developing the permit frame, an
attempt is made to strike a reasonable balance between
these two problems.
Design of the Current Population Survey Sample

3–9

Summary of Sampling Frames
Table 3–2 summarizes the features of the sampling frames
and CPS USU size discussed above. Roughly 80 percent of
the CPS sample is from the unit frame, 12 percent is from
the area frame, and less than 1 percent is from the group
quarters frame. In addition, about 6 percent of the sample
is from the permit frame initially. The permit frame has
grown, historically, about 1 percent a year. Optimal cluster
size or USU composition differs for the demographic surveys. The unit frame allows each survey a choice of cluster size. For the area, group quarters, and permit frames,
MOS must be defined consistently for all demographic surveys.
Table 3–2. Summary of Sampling Frames
Frame

Typical characteristics
of frame

CPS USU

Unit frame

High percentage of
Cluster of four
complete addresses in addresses
areas covered by a
building permit office

Group quarters frame

High percentage of
complete addresses in
areas covered by a
building permit office

Area frame
Area permit . . . . . . Many incomplete
addresses in areas
covered by a building
permit office
Area nonpermit . . Not covered by a
building permit office
Permit frame. . . . . . . . . Housing units built
since 2000 census in
areas covered by a
building permit office

Measure containing
group quarters of four
expected housing unit
equivalents
Measure containing
housing units and
group quarters of four
expected housing unit
equivalents
Cluster of four
expected housing units

Selection of Sample Units
The CPS sample is designed to be self-weighting by state
or substate area. A systematic sample is selected from
each PSU at a sampling rate of 1 in k, where k is the
within-PSU sampling interval which is equal to the product
of the PSU probability of selection and the stratum sampling interval. The stratum sampling interval is usually the
overall state sampling interval. (See the earlier section in
this chapter, ‘‘Calculation of overall state sampling interval.’’)
The first stage of selection is conducted independently for
each demographic survey involved in the 2000 redesign.
Sample PSUs overlap across surveys and have different
sampling intervals. To make sure housing units get
selected for only one survey, the largest common geographic areas obtained when intersecting each survey’s
sample PSUs are identified. These intersecting areas, as
well as the residual areas of those PSUs, are called basic
PSU components (BPCs). A CPS stratification PSU consists
of one or more BPCs. For each survey, a within-PSU sample
is selected from each frame within BPCs. However, sampling by BPCs is not an additional stage of selection. After
3–10

Design of the Current Population Survey Sample

combining sample from all frames for all BPCs in a PSU,
the resulting within-PSU sample is representative of the
entire civilian, noninstitutionalized population of the PSU.
When CPS is not the first survey to select a sample in a
BPC, the CPS within-PSU sampling interval is decreased to
maintain the expected CPS sample size after other surveys
have removed sampled USUs. When a BPC does not
include enough sample to support all surveys present in
the BPC for the decade, each survey proportionally
reduces its expected sample size for the BPC. This makes
a state no longer self-weighting, but this adjustment is
rare.
CPS sample is selected separately within each sampling
frame. Since sample is selected at a constant overall rate,
the percentage of sample selected from each frame is proportional to population size. Although the procedure is the
same for all sampling frames, old construction sample
selection is performed once for the decade while permit
frame sample selection is an ongoing process each month
throughout the decade.
Within-PSU Sort
Units or measures are arranged within sampling frames
based on characteristics of Census 2000 and geography.
Sorting minimizes within-PSU variance of estimates by
grouping together units or measures with similar characteristics. The Census 2000 data and geography are used
to sort blocks and units. (Sorting is done within BPCs since
sampling is performed within BPCs.) The unit frame is
sorted on block level characteristics, keeping housing
units in each block together, and then by a housing unit
identification to sort the housing units geographically.
General Sampling Procedure
The CPS sampling in the unit frame and GQ frame is a onetime operation that involves selecting enough sample for
the decade. In the area and permit frames, sampling is a
continuous operation. To accommodate the CPS rotation
system and the phasing in of new sample designs, 21
samples are selected. A systematic sample of USUs is
selected and 20 adjacent sample USUs identified. The
group of 21 sample USUs is known as a hit string. Due to
the sorting variables, persons residing in USUs within a hit
string are likely to have similar labor force characteristics.
The within-PSU sample selection is performed independently by BPC and frame. Four dependent random numbers (one per frame) between 0 and 1 are calculated for
each BPC within a PSU.8 Random numbers are used to calculate random starts. Random starts determine the first
sampled USU in a BPC for each frame.

8
Random numbers are evenly distributed by frame within BPC
and by BPC within PSU to minimize variability of sample size.

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The method used to select systematic samples of hit
strings of USUs within each BPC and sampling frame follows:
1. Units or measures within the census blocks are sorted
using within-PSU sort criteria.
2. Each successive USU not selected by another survey is
assigned an index number 1 through N.
3. A random start (RS) for the BPC/frame is calculated. RS
is the product of the dependent random number and
the adjusted within-PSU sampling interval (SIw).
4. Sampling sequence numbers are calculated. Given N
USUs, sequence numbers are:
RS, RS + (1(SIw)), RS + (2(SIw)), ..., RS + (n(SIw))
where n is the largest integer such that RS +
(n(SIw)) ≤ N. Sequence numbers are rounded up to the
next integer. Each rounded sequence number represents the first unit or measure designating the beginning of a hit string.
5. Sequence numbers are compared to the index numbers assigned to USUs. Hit strings are assigned to
sequence numbers. The USU with the index number
matching the sequence number is selected as the first
sample. The 20 USUs that follow the sequence number
are selected as the next 20 samples. This method may
yield hit strings with fewer than 21 samples (called
incomplete hit strings) at the beginning or end of
BPCs.9 Allowing incomplete hit strings ensures that
each USU has the same probability of selection.
6. A sample designation uniquely identifying 1 of the 21
samples is assigned to each USU in a hit string. The 21
samples are designated A77 through A97 for the CPS,
and B77 through B97 for the SCHIP.
Assignment of Post-Sampling Codes
Two types of post-sampling codes are assigned to the
sampled units. First, there are the CPS technical codes
used to weight the data, estimate the variance of characteristics, and identify representative subsamples of the
CPS sample units. The technical codes include final hit
number, rotation group, and random group codes. Second,
there are operational codes common to the demographic
household surveys used to identify and track the sample
units through data collection and processing. The operational codes include field PSU, segment number and segment number suffix.
Final hit number. The final hit number identifies the
original within-PSU order of selection. All USUs in a hit
string are assigned the same final hit number. For each
9
When RS + I > SIw, an incomplete hit string occurs at the
beginning of a BPC. When (RS + I) + (n( SIw)) > N, an incomplete hit
string occurs at the end of a BPC (I = 1 to 20).

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PSU, this code is assigned sequentially, starting with 1 for
both the old construction and the permit frames. The final
hit number is used in the application of the CPS variance
estimation method discussed in Chapter 15.
Rotation group. The sample is partitioned into eight representative subsamples, called rotation groups, used in
the CPS rotation scheme. All USUs in a hit string are
assigned to the same rotation group. Assignment is performed separately for old construction and the permit
frame. Rotation groups are assigned after sorting hits by
state, MSA/non-MSA status (old construction only), SR/NSR
status, stratification PSU, and final hit number. Because of
this sorting, the eight subsamples are balanced across
stratification PSUs, states, and the nation. Rotation group
is used in conjunction with sample designation to determine units in sample for particular months during the
decade.
Random group. The sample is partitioned into ten representative subsamples called random groups. All USUs in
the hit string are assigned to the same random group.
Assignment is performed separately for old construction
and the permit frame. Since random groups are assigned
after sorting hits by state, stratification PSU, rotation
group, and final hit number, the ten subsamples are balanced across stratification PSUs, states, and the nation.
Random groups can be used to partition the sample into
test and control panels for survey research.
Field PSU. A field PSU is a single county within a stratification PSU. Field PSU definitions are consistent across all
demographic surveys and are more useful than stratification PSUs for coordinating field representative assignments among demographic surveys.
Segment number. A segment number is assigned to each
USU within a hit string. If a hit string consists of USUs
from only one field PSU, then the segment number applies
to the entire hit string. If a hit string consists of USUs in
different field PSUs, then each portion of the hit
string/field PSU combination gets a unique segment number. The segment number is a four-digit code. The first
digit corresponds to the rotation group of the hit. The
remaining three digits are sequence numbers. In any 1
month, a segment within a field PSU identifies one USU or
an expected four housing units that the field representative is scheduled to visit. A field representative’s workload
usually consists of a set of segments within one or more
adjacent field PSUs.
Segment number suffix. Adjacent USUs with the same
segment number may be in different blocks for area and
group quarters sample or in different building permit
office dates or ZIP Codes for permit sample, but in the
same field PSU. If so, an alphabetic suffix appended to the
segment number indicates that a hit string has crossed
one of these boundaries. Segment number suffixes are not
assigned to the unit sample.
Design of the Current Population Survey Sample

3–11

Examples of Post-Sampling Code Assignments
Two examples are provided to illustrate assignment of
codes. To simplify the examples, only two samples are
selected, and sample designations A1 and A2 are
assigned. The examples illustrate a stratification PSU consisting of all sampling frames (which often does not
occur). Assume the index numbers (shown in Table 3–3)
are selected in two BPCs.
These sample USUs are sorted and survey design codes
assigned as shown in Table 3–4.
The example in Table 3–4 illustrates that assignment of
rotation group and final hit number is done separately for
old construction and the permit frame. Consecutive numbers are assigned across BPCs within frames. Although not
shown in the example, assignment of consecutive rotation
group numbers carries across stratification PSUs. For
example, the first old construction hit in the next stratification PSU is assigned to rotation group 1. However,

assignment of final hit numbers is performed within stratification PSUs. A final hit number of 1 is assigned to the
first old construction hit and the first permit hit of each
stratification PSU. Operational codes are assigned as
shown in Table 3–5.
After sample USUs are selected and post-sampling codes
assigned, addresses are needed in order to interview
sampled units. The procedure for obtaining addresses differs by sampling frame. For operational purposes, identifiers are used in the unit frame during sampling instead of
actual addresses. The procedure for obtaining unit frame
addresses by matching identifiers to census files is
described in Chapter 4. Field procedures, usually involving
a listing operation, are used to identify addresses in other
frames. A description of listing procedures is also given in
Chapter 4. Illustrations of the materials used in the listing
phase are shown in Appendix B.

Table 3–3. Index Numbers Selected During Sampling for Code Assignment Examples
BPC number
1
2

Unit frame

Group quarters frame

Area frame

Permit frame

3−4, 27−28, 51−52
10−11, 34−35

10−11
none

1 (incomplete), 32−33
6−7

7−8, 45−46
14−15

Table 3–4. Example of Post-Sampling Survey Design Code Assignments Within a PSU
Frame

Index

Sample
designation

Final hit number

Rotation group

1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2

Unit
Unit
Unit
Unit
Unit
Unit
Group quarters
Group quarters
Area
Area
Area
Unit
Unit
Unit
Unit
Area
Area

3
4
27
28
51
52
10
11
1
32
33
10
11
34
35
6
7

A1
A2
A1
A2
A1
A2
A1
A2
A2
A1
A2
A1
A2
A1
A2
A1
A2

1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
9

8
8
1
1
2
2
3
3
4
5
5
6
6
7
7
8
8

1
1
1
1
2
2

Permit
Permit
Permit
Permit
Permit
Permit

7
8
45
46
14
15

A1
A2
A1
A2
A1
A2

1
1
2
2
3
3

3
3
4
4
5
5

BPC number

3–12

Design of the Current Population Survey Sample

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

Table 3–5. Examples of Post-Sampling Operational Code Assignments Within a PSU
County

Frame

Block

Index

Sample
designation

Final hit
number

1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2

1
1
1
2
2
2
1
1
1
1
1
3
3
3
3
3
3

Unit
Unit
Unit
Unit
Unit
Unit
Group quarters
Group quarters
Area
Area
Area
Unit
Unit
Unit
Unit
Area
Area

1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
10

3
4
27
28
51
52
10
11
1
32
33
10
11
34
35
6
7

A1
A2
A1
A2
A1
A2
A1
A2
A2
A1
A2
A1
A2
A1
A2
A1
A2

1
1
2
2
3
3
4
4
5
6
6
7
7
8
8
9
9

1
1
1
2
2
2
1
1
1
1
1
3
3
3
3
3
3

8999
8999
1999
1999
2999
2999
3599
3599
4699
5699
5699
6999
6999
7999
7999
8699
8699A

1
1
1
1
2
2

1
1
2
2
3
3

Permit
Permit
Permit
Permit
Permit
Permit

7
8
45
46
14
15

A1
A2
A1
A2
A1
A2

1
1
2
2
3
3

3001
3001
4001
4001
5001
5001

BPC number

THIRD STAGE OF THE SAMPLE DESIGN
The actual USU size in the field can deviate from what is
expected from the computer sampling. Occasionally, the
deviation is large enough to jeopardize the successful
completion of a field representative’s assignment. When
these situations occur, a third stage of selection is conducted to maintain a manageable field representative
workload. This third stage is called field subsampling.
Field subsampling occurs when a USU consists of more
than 15 sample housing units identified for interview. Usually, this USU is identified after a listing operation. (See
Chapter 4 for a description of field listing.) The regional
office staff selects a systematic subsample of the USU to
reduce the number of sample housing units to a more
manageable number, from 8 to 15 housing units. To facilitate the subsampling, an integer take-every (TE) and startwith (SW) are used. An appropriate value of the TE reduces
the USU size to the desired range. For example, if the USU
consists of 16 to 30 housing units, a TE of 2 reduces USU
size to 8 to 15 housing units. The SW is a randomly
selected integer between 1 and the TE.
Field subsampling changes the probability of selection for
the housing units in the USU. An appropriate adjustment
to the probability of selection is made by applying a special weighting factor in the weighting procedure. See ‘‘Special Weighting Adjustments’’ in Chapter 10 for more on
this procedure.
ROTATION OF THE SAMPLE
The CPS sample rotation scheme is a compromise between
a permanent sample (from which a high response rate
would be difficult to maintain) and a completely new
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Field Segment/
PSU
suffix

sample each month (which results in more variable estimates of change). The CPS sample rotation scheme represents an attempt to strike a balance in the minimization of
the following:
1. Variance of estimates of month-to-month change:
three-fourths of the sample is the same in consecutive
months.
2. Variance of estimates of year-to-year change: one-half
of the sample is the same in the same month of consecutive years.
3. Variance of other estimates of change: outgoing
sample is replaced by sample likely to have similar
characteristics.
4. Response burden: eight interviews are dispersed
across 16 months.
The rotation scheme follows a 4-8-4 pattern. A housing
unit or group quarters is interviewed 4 consecutive
months, not in sample for the next 8 months, interviewed
the next 4 months, and then retired from sample. The
rotation scheme is designed so outgoing housing units are
replaced by housing units from the same hit string which
have similar characteristics.
The following summarizes the main characteristics (in
addition to the sample overlap described above) of the
CPS rotation scheme:
1. In any single month, one-eighth of the sample housing
units are interviewed for the first time; another eighth
is interviewed for the second time; and so on.
Design of the Current Population Survey Sample

3–13

2. The sample for 1 month is composed of units from
two or three consecutive samples.
3. One new sample designation-rotation group is activated each month. The new rotation group replaces
the rotation group retiring permanently from sample.
4. One rotation group is reactivated each month after its
8-month resting period. The returning rotation group
replaces the rotation group beginning its 8-month
resting period.
5. Rotation groups are introduced in order of sample
designation and rotation group:
A77(1), A77(2), ..., A77(8), A78(1), A78(2), ..., A78(8),
..., A97(1), A97(2), ..., A97(8).
This rotation scheme has been used since 1953. The most
recent research into alternate rotation patterns was prior
to the 1980 redesign when state-based designs were introduced (Tegels, 1982).
Rotation Chart
The CPS rotation chart illustrates the rotation pattern of
CPS sample over time. Figure 3–1 presents the rotation
chart beginning in January 2006. The following statements
provide guidance in interpreting the chart:
1. Numbers in the chart refer to rotation groups. Sample
designations appear in column headings. In January
2006, rotation groups 3, 4, 5, and 6 of A79; 7 and 8
of A80; and 1 and 2 of A81 are designated for interview.
2. Consecutive monthly samples have six rotation
groups in common. The sample housing units in
A79(4−6), A80(8), and A81(1−2), for example, are
interviewed in January and February of 2006.
3. Monthly samples 1 year apart have four rotation
groups in common. For example, the sample housing
units in A80(7−8) and A81(1−2) are interviewed in
January 2006 and January 2007.
4. Of the two rotation groups replaced from month-tomonth, one is in sample for the first time and one
returns after being excluded for 8 months. For
example, in October 2006, the sample housing units
in A82(3) are interviewed for the first time and the
sample housing units in A80(7) are interviewed for the
fifth time after last being in sample in January.

3–14

Design of the Current Population Survey Sample

Figure 3–1. CPS Rotation Chart:
January 2006−April 2008
Sample designation and rotation groups
Year/month

A/B79

2006 Jan
Feb
Mar
Apr

3456
4567
5678
678 1

May
June
July
Aug
Sept
Oct
Nov
Dec

A/B80

A/B81

A/B82

A/B83

A/B84

78 12
8 123
1234
2345

3456
4567
5678
678 1

2007 Jan
Feb
Mar
Apr

78 12
8 123
1234
2345

May
June
July
Aug

3456
4567
5678
678 1

Sept
Oct
Nov
Dec

3456
4567
5678
678 1
78 12
8 123
1234
2345

78 12
8 123
1234
2345

2008 Jan
Feb
Mar
Apr

3456
4567
5678
678 1

3456
4567
5678
678 1
78 12
8 123
1234
2345

Overlap of the Sample
Table 3–6 shows the proportion of overlap between any 2
months of sample depending on the time lag between
them. The proportion of sample in common has a strong
effect on correlation between estimates from different
months and, therefore, on variances of estimates of
change.
Table 3-6. Proportion of Sample in Common for 4-8-4
Rotation System

Percent of sample in common
between the 2 months

Interval (in months)
1 ............................
2 ............................
3 ............................
4-8 . . . . . . . . . . . . . . . . . . . . . . . . . .
9 ............................
10 . . . . . . . . . . . . . . . . . . . . . . . . . . .
11 . . . . . . . . . . . . . . . . . . . . . . . . . . .
12 . . . . . . . . . . . . . . . . . . . . . . . . . . .
13 . . . . . . . . . . . . . . . . . . . . . . . . . . .
14 . . . . . . . . . . . . . . . . . . . . . . . . . . .
15 . . . . . . . . . . . . . . . . . . . . . . . . . . .
16 and greater . . . . . . . . . . . . . . . .

75
50
25
0
12.5
25
37.5
50
37.5
25
12.5
0

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Phase-In of a New Design
When a newly redesigned sample is introduced into the
ongoing CPS rotation scheme, there are a number of reasons not to discard the old CPS sample one month and
replace it with a completely redesigned sample the next
month. Since redesigned sample contains different sample
areas, new field representatives must be hired. Modifications in survey procedures are usually made for a redesigned sample. These factors can cause discontinuity in
estimates if the transition is made at one time.
Instead, a gradual transition from the old sample design to
the new sample design is undertaken. Beginning in April
2004, the 2000 census-based design was phased in
through a series of changes completed in July 2005 (U.S.
Department of Labor, 2004).
REFERENCES
Cahoon, L. (2002), ‘‘Specifications for Creating a Stratification Search Program for the 2000 Sample Redesign (3.1-S1),’’ Internal Memorandum, Demographic Statistical Methods Division, U.S. Census Bureau.
Executive Office of the President, Office of Management
and Budget (1993), Metropolitan Area Changes Effective With the Office of Management and Budget’s
Bulletin 93−17, June 30, 1993.
Hansen, Morris H., William N. Hurwitz, and William G.
Madow, (1953), Survey Sample Methods and Theory,
Vol. I, Methods and Applications. New York: John Wiley
and Sons.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Hanson, Robert H. (1978), The Current Population Survey: Design and Methodology, Technical Paper 40,
Washington, DC: Government Printing Office.
Kostanich, Donna, David Judkins, Rajendra Singh, and
Mindi Schautz, (1981), ‘‘Modification of Friedman-Rubin’s
Clustering Algorithm for Use in Stratified PPS Sampling,’’
paper presented at the 1981 Joint Statistical Meetings,
American Statistical Association.
Ludington, Paul W. (1992), ‘‘Stratification of Primary Sampling Units for the Current Population Survey Using Computer Intensive Methods,’’ paper presented at the 1992
Joint Statistical Meetings, American Statistical Association.
Statt, Ronald, E. Ann Vacca, Charles Wolters, and Rosa Hernandez, (1981), ‘‘Problems Associated With Using Building
Permits as a Frame of Post-Census Construction: Permit
Lag and ED Identification,’’ paper presented at the 1981
Joint Statistical Meetings, American Statistical Association.
Tegels, Robert and Lawrence Cahoon, (1982), ‘‘The Redesign of the Current Population Survey: The Investigation
Into Alternate Rotation Plans,’’ paper presented at the
1982 Joint Statistical Meetings, American Statistical Association.
U.S. Department of Labor, Bureau of Labor Statistics
(1994), ‘‘Redesign of the Sample for the Current Population
Survey,’’ Employment and Earnings, Washington, DC:
Government Printing Office, December 2004.

Design of the Current Population Survey Sample

3–15

Chapter 4.
Preparation of the Sample
INTRODUCTION
The Current Population Survey (CPS) sample preparation
operations have been developed to fulfill the following
goals:
1. Implement the sampling procedures described in
Chapter 3.
2. Produce virtually complete coverage of the eligible
population.
3. Ensure that only a trivial number of households will
appear in the CPS sample more than once over the
course of a decade, or in more than one of the household surveys conducted by the U. S. Census Bureau.
4. Provide cost-efficient data collection by producing
most of the sampling materials needed for both the
CPS and other household surveys in a single, integrated operation.
The CPS is one of many household surveys conducted on
a regular basis by the Census Bureau. Insofar as possible,
Census Bureau programs have been designed so that survey materials, survey procedures, personnel, and facilities
can be used by as many surveys as possible. Sharing personnel and sampling material among a number of programs yields a number of benefits. For example, training
costs are reduced when CPS field representatives are
employed on non-CPS activities because the sampling
materials, listing and coverage instructions, and, to a
lesser extent, questionnaire content are similar for a number of different programs. In addition, sharing sampling
materials helps ensure that respondents will be in only
one sample.
The postsampling codes described in Chapter 3 identify,
among other information, the sample cases that are
scheduled to be interviewed for the first time in each
month of the decade and indicate the types of materials
(maps, listing of addresses, etc.) needed by the census
field representative to locate the sample addresses. This
chapter describes how these materials are put together.
The next section is an overview, while subsequent sections provide a more in-depth description of the CPS
sample preparation.
The successful completion of the CPS data collection rests
on the combined efforts of headquarters and regional
staff. Census Bureau headquarters are located in the
Washington, DC, area. Staff at headquarters coordinate
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CPS functions ranging from sample design, sample selection, and resolution of subject matter issues to administration of the interviewing staffs maintained under the 12
regional offices, and data processing. Census Bureau staff
located in Jeffersonville, IN, also participate in CPS planning and administration. Their responsibilities include
preparation and dissemination of interviewing materials,
such as maps and segment folders. The regional offices
coordinate the interview activities of the interviewing
staff.
Monthly sample preparation of the CPS has three major
components:
1. Identifying addresses.
2. Listing living quarters.
3. Assigning sample to field representatives.
The within-PSU sample described in Chapter 3 is selected
from four distinct sampling frames, not all of which consist of specific addresses. Since the field representatives
need to know the exact location of the households or
group quarters they are going to interview, much of
sample preparation involves the conversion of selected
sample information (e.g., maps, lists of building permits)
to a set of addresses. This conversion is described below.
Address Identification in the Unit Frame
About 80 percent of the CPS sample is selected from the
unit frame. The unit frame sample is selected from a 2000
census file that contains the information necessary for
within-PSU sample selection, but does not contain address
information. The address information from the 2000 census is stored in a separate file. The addresses of unit segments are obtained by matching the file of 2000 census
information to the file containing the associated 2000 census addresses. This is a one-time operation for the entire
unit sample and is performed at headquarters. If the
addresses are thought to be incomplete (missing a house
number or street name), the 2000 census information is
reviewed in an attempt to complete the address before
sending it to the field representative for interview. In sampling operations, this facilitates the formation of clusters
of about 4 housing units, called Ultimate Sampling Units
(USUs), as described in Chapter 3.
Address Identification in the Area Frame
About 12 percent of the CPS sample is selected from the
area frame. Measures of expected four housing units are
selected during within-PSU sampling instead of individual
Preparation of the Sample

4–1

housing units. This is because many of the addresses in
the area frame are not city-style or there is no building
permit office coverage. This essentially means that no particular housing units are as yet associated with the
selected measure. The only information available is an
automated map of the blocks that contain the area segment, the addresses within the block(s) that are in the
2000 census file, the number of measures the block contains, and which measure is associated with the area segment. Before the individual housing units in the area segment can be identified, additional procedures are used to
ensure that field representatives can locate the housing
units and that all newly built housing units have a probability of selection. A field representative will be sent to
canvass the block to create a complete list of the housing
units located in the block. This activity is called a listing
operation, which is described more thoroughly in the next
section. A systematic sampling pattern is applied to this
listing to identify the housing units in the area segment
that will be designated for each month’s sample.
Address Identification in the Group Quarters
Frame
About 1 percent of the CPS sample is selected from the
group quarters frame. The decennial census files did not
have information on the characteristics of the group quarters. The files contain information about the residents as
of April 1, 2000, but there is insufficient information
about their living arrangements within the group quarters
to provide a tangible sampling unit for the CPS. Measures
are selected during within-PSU sampling since there is no
way to associate the selected sample cases with people to
interview at a group quarters. A two-step process is used
to identify the group quarters segment. First, the group
quarters addresses are obtained by matching to the file of
2000 census addresses, similar to the process for the unit
frame. This is a one-time operation done at headquarters.
Before the individuals living at the group quarters associated with the group quarters segment can be identified,
an interviewer visits the group quarters and creates a
complete list of eligible sample units (consisting of
people, rooms, or beds) or obtains a count of eligible
sample units from a usable register. This is referred to as a
listing operation. Then a systematic sampling pattern is
applied to the listing to identify the individuals to be interviewed at the group quarters facilities.
Address Identification in the Permit Frame
The proportion of the CPS sample selected from the permit
frame increases over the decade as new housing units are
constructed. The CPS sample is redesigned about 4 or 5
years after each decennial census, and at this time the permit sample makes up about 6 percent of the CPS sample;
this proportion has historically increased about 1 percent
a year. Hypothetical measures are selected during withinPSU sampling in anticipation of the construction of new
4–2

Preparation of the Sample

housing units. Identifying the addresses for these new
units involves a listing operation at the building permit
office, clustering addresses to form measures, and associating these addresses with the hypothetical measures (or
USUs) in the sample.
The Census Bureau conducts the Building Permit Survey,
which collects information on a monthly basis from each
building permit office (BPO) nationwide about the number
of housing units authorized to be built. The Building Permit Survey results are converted to measures of expected
four housing units. These measures are continuously accumulated and linked with the frame of hypothetical measures used to select the CPS sample. This matching identifies which BPO contains the measure that is in sample.
Using an automated instrument, a field representative visits the BPO to list addresses of units that were authorized
to be built; this is the Permit Address Listing (PAL) operation. This list of addresses is transmitted to headquarters,
where clusters are formed that correspond one-to-one
with the measures. Using this link between addresses and
measures, the clusters of four addresses to be interviewed
in each permit segment are identified.
Forming clusters. To ensure some geographic clustering
of addresses within permit measures and to make PAL listing more efficient, information collected by the Survey of
Construction (SOC)1 is used to identify many of the
addresses in the permit frame. The Census Bureau collects
information on the characteristics of units to be built for
each permit issued by BPOs that are in the SOC. This information is used to form measures in SOC building permit
offices. This data is not collected for non-SOC building
permit offices.
1. SOC PALs are listings from BPOs that are in the SOC. If
a BPO is in the SOC, then the actual permits issued by
the BPO and the number of units authorized by each
permit (though not the addresses) are known in
advance of the match to the skeleton universe. Therefore, the measures for sampling are identified directly
from the actual permits. The sample permits can then
be identified. These sample permits are the only ones
for which addresses are collected.
Because measures for SOC permits were constrained
to be within permits, once the listed addresses are
complete, the formation of clusters follows easily. The
measures formed at the time of sampling are, in
effect, the clusters. The sample measures within permits are fixed at the time of sampling; that is, there
cannot be any later rearrangement of these units into
more geographically compact clusters without voiding
the original sampling results.
1
The Survey of Construction (SOC) is conducted by the Census
Bureau in conjunction with the U.S. Department of Housing and
Urban Development. It provides current regional statistics on
starts and completions of new single-family and multifamily units
and sales of new one-family homes.

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Even without geographic clustering, there is some
degree of compactness inherent in the manner in
which measures are formed for sampling. The units
drawn from the SOC permits are assigned to measures
in the same order in which they were listed in the
SOC; thus units within apartment buildings will normally be in the same clusters, and permits listed on
adjacent lines on the SOC listing often represent
neighboring structures.
2. Non-SOC PALs are listings from BPOS not in the SOC.
At the time of sampling, the only data known for nonSOC BPOs is a cumulative count of the units authorized on all permits from a particular office for a given
month (or year). Therefore, all addresses for a
BPO/date are collected, together with the number of
apartments in multiunit buildings. The addresses are
clustered using all units on the PAL.
The purpose of clustering is to group units together
geographically, thus enabling a reduction in field
travel costs. For multiunit addresses, as many whole
clusters as possible are created from the units within
each address. The remaining units on the PAL are clustered within ZIP Code and permit day of issue.
LISTING ACTIVITIES
When address information from the census is not available
or the address information from the census no longer corresponds to the current address situation, then a listing of
all eligible units without addresses must be created. Creating this list of basic addresses is referred to as listing. Listing can occur in all four frames: units within multiunit
structures, living quarters in blocks, units or residents
within group quarters, and addresses for building permits
issued.
The living quarters to be listed are usually housing units.
In group quarters such as transient hotels, rooming
houses, dormitories, trailer camps, etc., where the occupants have special living arrangements, the living quarters
listed may be rooms, beds, etc. In this discussion of listing, all of these living quarters are included in the term
‘‘unit’’ when it is used in context of listing or interviewing.
Completed listings are sampled at headquarters. Performing the listing and sampling in two separate steps allows
each step to be verified and allows more complete control
of sampling procedures to avoid bias in designating the
units to be interviewed.
In order to ensure accurate and complete coverage of the
area and group quarters segments, the initial listing is
updated periodically throughout the decade. The updating
ensures that changes such as units missed in the initial
listing, demolished units, residential/commercial conversions, and new construction are accounted for.
Listing in the Unit Frame
Listing in the unit frame is usually not necessary. The only
time it is done is when the field representative discovers
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that the address information from the 2000 census is no
longer accurate for a multiunit structure and the field representative cannot adequately correct the information.
For multiunit addresses (addresses where the expected
number of unit designations is two or more), the field representative receives a segment folder containing a preprinted (computer-generated) Multi-Unit Listing Aid
(MULA), Form 11-12, showing unit designations for the
segment as recorded in the 2000 census. The MULA displays the unit designations of all units in the structure,
even if some of the units are not in sample. Other important information on the MULA includes: the address, the
expected number of units at the address, the sample designations, and serial numbers for the selected sample
units. If the address is incomplete (missing house number,
street name), the field representative receives an Incomplete Address Locator Action Form, which provides additional information to locate the address.
The first time a multiunit address enters the sample, the
field representative does one or more of the following:
• For addresses with 2−4 units, verifies that the 2000
census information on the MULA is accurate and corrects the listing sheet when it does not agree with what
he/she finds at the address.
• For larger addresses of 5 or more units, resolves missing and duplicate unit designations only for addresses
with missing or duplicate unit designations that are in
any sample.
• If the changes are so extensive that a MULA cannot
handle the corrections, relists the address on a blank
Unit/Permit Listing Sheet.
After the field representative has an accurate MULA or listing sheet, he/she conducts an interview for each unit that
has a current sample designation. If an address is relisted,
the field representative provides information to the
regional office on the relisting. The regional office staff
will resample the listing sheet (using the sampling pattern
on the MULA) and provide the line numbers for the specific lines on the listing sheet that identify the units that
should be interviewed.
A regular system of updating the listing in unit segments
is not necessary. The field representative may correct an
in-sample listing during any visit if a change is noticed.
For single-unit addresses, a preprinted listing sheet is not
provided to the field representative since only one unit is
expected, based on the 2000 census information. If the
address is incomplete (missing house number, no street
name), the field representative receives an Incomplete
Address Locator Form, which provides additional information to locate the address. If a field representative discovers other units at the address at the time of interview,
he/she prepares a Unit/Permit Listing Sheet and lists the
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extra units. These additional units, up to and including 15,
are interviewed. If the field representative discovers more
than 15 units, he/she must contact the regional office for
subsampling instructions. For an example of a segment
folder, MULA, Unit/Permit Listing Sheet, and Incomplete
Address Locator Actions Form, see Appendix A.

for CPS must be identified from a listing that has been
updated within the last 24 months. The field representative updates the area block by verifying the existence of
each unit and map feature, accounting for units or features no longer in existence, and adding any new units or
features.

Listing in the Area Frame

Listing in the Group Quarters Frame

All blocks that contain area frame sample cases must be
listed. Several months before the first area segment in a
block is to be interviewed, a field representative visits the
block to establish an accurate list of living quarters.

The listing procedure is applied to group quarters
addresses in the CPS sample that are in the group quarters
or area frames. Before the first interviews at a group quarters address can be conducted, a field representative visits
the group quarters to establish a list of eligible units
(rooms, beds, persons, etc.) at the group quarters. The
same procedures apply for group quarters found in the
area frame.

This is a dependent operation conducted via a laptop computer using the Automated Listing and Mapping Instrument (ALMI) software. The field representative starts with
a list of the addresses within the block and a digitized
map of the block with some addresses mapspotted onto
it. The addresses come from the Master Address File
(MAF), which is a list of 2000 census addresses, updated
periodically through other operations. The digitized map
is downloaded from the Census Bureau Topological Integrated Geographic Encoding Reference (TIGER®) system.
The field representative updates the block information by
matching the living quarters and block features found to
the list of addresses and map on the laptop, and makes
additions, deletions, or other corrections as necessary.
The field representative collects additional data for any
group quarters in the block using the Group Quarters
Automated Instrument for Listing (GAIL) software.
If the area segment is within a jurisdiction where building
permits are issued, housing units constructed since April
1, 2000, are eliminated from the area segment through
the year-built procedure to avoid giving an address more
than one chance of selection. This is required because
housing units constructed since April 1, 2000, Census
Day, are already represented by segments in the permit
frame. The field representative determines the year each
unit was built except for: units in the 2000 census, mobile
homes and trailers, group quarters, and nonstructures
(buses, boats, tents). To determine ‘‘year built,’’ the field
representative inquires at each appropriate unit and enters
the appropriate information on the computer.
If an area segment is not in a building-permit-issuing jurisdiction, then housing units constructed after the 2000
census do not have a chance of being selected for interview in the permit frame. The field representative does not
determine ‘‘year built’’ for units in such blocks.
After the listing of living quarters in the area segment has
been completed, the files are transmitted to headquarters
where staff then apply the sampling pattern and identify
the units to be interviewed.
Periodic updates of the listing are done to reflect change
in the housing inventory in the listed block. The following
rule is used: The USU being interviewed for the first time
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Preparation of the Sample

Group quarters listing is an independent listing operation
conducted via a laptop computer. The instrument, referred
to as the Group Quarters Automated Instrument for Listing
(GAIL), is used to record the group quarters name, group
quarters type, address, the name and telephone number
of a contact person, and to list the eligible units within the
group quarters, or to obtain a count of the number of eligible units from a register (card file, computer printout, or
file) located at the group quarters. Field representatives do
not list institutional and military groups quarters; however, they verify that the status has not changed from
institutional or military. If it has changed, the field representative will list the addresses of noninstitutional units.
After listing group quarters units or obtaining a count of
eligible units from a register, the files are transmitted to
headquarters, where staff then apply the sampling pattern
to identify the group quarters unit(s) (or units corresponding to sample line(s) within a register) to be interviewed.
The rule for the frequency of updating group quarters listings is the same as for area segments.
Listing in the Permit Frame
There are two phases of listing in the permit frame. The
first is the PAL operation, which establishes a list of
addresses authorized to be built by a BPO. This is done
shortly after the permit has been issued by the BPO and is
associated with a sample hypothetical measure. The second listing is required when the field representative visits
the unit to conduct an interview.
PAL operation. For each BPO containing a sample measure, a field representative visits the BPO and lists the necessary permit and address information using a laptop
computer. If an address given on a permit is missing a
house number or street name (or number), then the
address is considered incomplete. In this case, a field representative visits the new construction site and draws a
Permit Sketch Map showing the location of the structure
and, if possible, completes the address.
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Permit listing. Listing in the permit frame is necessary
for all permit segments. Prior to interviewing, the field
representative will receive a segment folder containing a
Unit/Permit Listing Sheet (Form 11−3) and, possibly, a Permit Sketch Map to help locate the address. For an example
of a segment folder, Unit/Permit Listing Sheets, and a Permit Sketch Map, see Appendix A.
At the time of the interview, the field representative verifies or corrects the basic address. Since the PAL operation
does not capture unit designations at a multiunit address,
the field representative will list the unit designations prior
to interviewing. At both single and multiunit addresses,
the field representative will also note any relevant information about the address that would affect sampling or
interviewing, such as conversions, abandoned permits,
construction-not-started situations, and more units found
than expected for the permit address.
After the field representative has an accurate listing sheet,
he/she conducts an interview for each unit that has a current (preprinted) sample designation. If more than 15 units
are in sample, the field representative must contact their
regional office for subsampling instructions.
The listing of permit segments is not updated systematically; however, the field representative may correct an
in-sample listing during any visit if a change is noticed.
The change may result in additional units being added or
removed from sample.
THIRD STAGE OF THE SAMPLE DESIGN
(SUBSAMPLING)
Although the CPS sample is often characterized as a twostage sample, chapter 3 describes a third stage of the
sample design, referred to as subsampling. The need for
subsampling depends on the results of the listing operations and the results of the clerical sampling. Subsampling
is required when the number of housing units in a segment for a given sample is greater than 15 or when 1 of
the 4 units in the USU yields more than 4 total units. For
unit segments and permit segments, this can happen
when more units than expected are found at the address
at the time of the first interview. For more information on
subsampling, see Chapter 3.
INTERVIEWER ASSIGNMENTS
The final stage of sample preparation includes the operations that break the sample down into manageable interviewer workloads and transmit the resulting assignments
to the field representatives for interview. At this point, all
the sample cases for the month have been identified and
all the necessary information about these sample cases is
available in a central database at headquarters. The listings have been completed, sampling patterns have been
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applied, and the addresses are available. The central database also includes additional information, such as telephone numbers for cases which have been interviewed in
previous months. The rotation of sample used in the CPS
is such that seven-eighths of the sample cases each month
have been in the sample in previous months (see Chapter
3). This central database is part of an integrated system
described briefly below. This integrated system also
affects the management of the sample and the way the
interview results are transmitted to central headquarters,
as described in Chapter 8.
Overview of the Integrated System
Technological advances have changed data preparation
and collection activities at the Census Bureau. Since the
mid-1980s, the Census Bureau has been developing
computer-based methods for survey data collection, communications, management, and analysis. Within the Census Bureau, this integrated data collection system is called
the computer-assisted interviewing system. This system
has two principal components: computer-assisted personal
interviewing (CAPI) and centralized computer-assisted telephone interviewing (CATI). Chapter 7 explains the data
collection aspects of this system. The integrated system is
designed to manage decentralized data collection using
laptop computers, a centralized telephone collection, and
a central database for data management and accounting.
The integrated system is made up of three main parts:
1. Headquarters operations in the central database. The headquarters operations include loading
the monthly sample into the central database, transmission of CATI cases to the telephone centers, and
database maintenance.
2. Regional office operations in the central database. The regional office operations include preparation of assignments, transmission of cases to field
representatives, determination of CATI assignments,
reinterview selection, and review and reassignment of
cases.
3. Field representative case management operations on the laptop computer. The field representative operations include receipt of assignments,
completion of interview assignments, and transmittal
of completed work to the central database for processing (see Chapter 8).
The central database resides at headquarters, where the
file of sample cases is maintained. The database stores
field representative data (name, phone number, address,
etc.), information for making assignments (field PSU, segment, address, etc.), and all the data for cases that are in
the sample.
Regional Office (RO) Operations
Once the monthly sample has been loaded into the central
database, the ROs can begin the assignment preparation
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phase. This includes making assignments to field representatives and selecting cases for the three telephone
facilities. Regional offices access the central database to
break down the assignment areas geographically and key
in information that is used to aid the monthly field representative assignment operations. The RO supervisor considers such characteristics as the size of the field PSU, the
workload in that field PSU, and the number of field representatives working in that field PSU when deciding the
best geographic method for dividing the workload in Field
PSUs among field representatives.
The CATI assignments are also made at this time. Each RO
selects at least 10 percent of the sample for centralized
telephone interviewing. The selection of cases for CATI
involves several steps. Cases may be assigned to CATI if
several criteria are met, pertaining to the household and
the time in sample. In general terms, the criteria are as
follows:
1. The household must have a telephone and be willing
to accept a telephone interview.
2. The field representative may recommend that the case
be sent to CATI.
3. First and fifth month cases are generally not eligible
for a telephone or CATI.
The ROs may temporarily assign cases to CATI in order to
cover their workloads in certain situations, primarily to fill
in for field representatives who are ill or on vacation.
When interviewing for the month is completed, these
cases will automatically be reassigned for CAPI.
The majority of the cases sent to CATI are successfully
completed as telephone interviews. Those that cannot be
completed from the telephone centers are returned to the
field prior to the end of the interview period. These cases
are called ‘‘CATI recycles.’’ See Chapter 8 for further
details.

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Preparation of the Sample

The final step is the releasing of assignments. After all
changes to the interview and CATI assignments have been
made, and all assignments have been reviewed for geographic efficiency and the proper balance among field representatives, the ROs release the assignments. The release
of assignments by all ROs signals the Master Control in
the central data to create the CATI workload files.
After assignments are made and released, the ROs transmit the assignments to the central database, which places
the assignments on the telecommunications server for the
field representatives. Prior to the interview period, field
representatives receive their assignments by initiating a
transmission to the telecommunications server at headquarters. Assignments include the instrument (questionnaire and/or supplements) and the cases they must interview that month. These files are copied to the laptop
during the transmission from the server. The files include
the household demographic information and labor force
data collected in previous interviews. All data sent and
received from the field representatives pass through the
central communications system maintained at headquarters. See Chapter 8 for more information on the transmission of interview results.
Finally, the ROs prepare the remaining paper materials
needed by the field representatives to complete their
assignments. The materials include:
1. Field Representative Assignment Listing (CAPI−35).
2. Segment folders for cases to be interviewed (including
maps, listing sheets, and other pertinent information
that will aid the interviewer in locating specific cases).
3. Blank listing sheets, respondent letters, and other supplies requested by the field representative.
Once the field representative has received the above materials and has successfully completed a transmission to
retrieve his/her assignments, the sample preparation
operations are complete and the field representative is
ready to conduct the interviews.

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Chapter 5.
Questionnaire Concepts and Definitions for the Current
Population Survey
INTRODUCTION
An important component of the Current Population Survey
(CPS) is the questionnaire, also called the survey instrument. The survey instrument utilizes automated data collection methods; that is, computer-assisted personal interviewing (CAPI) and computer-assisted telephone
interviewing (CATI). This chapter describes and discusses
its concepts, definitions, and data collection procedures
and protocols.
STRUCTURE OF THE SURVEY INSTRUMENT
The CPS interview is divided into three basic parts: (1)
household and demographic information, (2) labor force
information, and (3) supplement information. Supplemental questions are added to the CPS in most months and
cover a number of different topics. The order in which
interviewers attempt to collect information is: (1) housing
unit data, (2) demographic data, (3) labor force data, (4)
more demographic data, (5) supplement data, and finally
(6) more housing unit data.
The concepts and definitions of the household, demographic, and labor force data are discussed below. (For
more information about supplements to the CPS, see
Chapter 11.)
CONCEPTS AND DEFINITIONS
Household and Demographic Information
Upon contacting a household, interviewers proceed with
the interview unless the case is a definite noninterview.
(Chapter 7 discusses the interview process and explains
refusals and other types of noninterviews.) When interviewing a household for the first time, interviewers collect
information about the housing unit and all individuals who
usually live at the address.
Housing unit information. Upon first contact with a
housing unit, interviewers collect information on the housing unit’s physical address, its mailing address, the year it
was constructed, the type of structure (single or multiple
family), whether it is renter- or owner-occupied, whether
the housing unit has a telephone and, if so, the telephone
number.
Household roster. After collecting or updating the housing unit data, the interviewer either creates or updates a
list of all individuals living in the unit and determines
whether or not they are members of the household. This
list is referred to as the household roster.
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Household respondent. One person may provide all of
the CPS data for the entire sample unit, provided that the
person is a household member 15 years of age or older
who is knowledgeable about the household. The person
who responds for the household is called the household
respondent. Information collected from the household
respondent for other members of the household is
referred to as proxy response.
Reference person. To create the household roster, the
interviewer asks the household respondent to give ‘‘the
names of all persons living or staying’’ in the housing unit,
and to ‘‘start with the name of the person or one of the
persons who owns or rents’’ the unit. The person whose
name the interviewer enters on line 1 (presumably one of
the individuals who owns or rents the unit) becomes the
reference person. The household respondent and the reference person are not necessarily the same. For example, if
you are the household respondent and you give your
name ‘‘first’’ when asked to report the household roster,
then you are also the reference person. If, on the other
hand, you are the household respondent and you give
your spouse’s name first when asked to report the household roster, then your spouse is the reference person.
Household. A household is defined as all individuals
(related family members and all unrelated individuals)
whose usual place of residence at the time of the interview is the sample unit. Individuals who are temporarily
absent and who have no other usual address are still classified as household members even though they are not
present in the household during the survey week. College
students compose the bulk of such absent household
members, but people away on business or vacation are
also included. (Not included are individuals in institutions
or the military.) Once household/nonhousehold membership has been established for all people on the roster, the
interviewer proceeds to collect all other demographic data
for household members only.
Relationship to reference person. The interviewer will
show a flash card with relationship categories (e.g.,
spouse, child, grandchild, parent, brother/sister) to the
household respondent and ask him/her to report each
household member’s relationship to the reference person
(the person listed on line one). Relationship data also are
used to define families, subfamilies, and individuals
whose usual place of residence is elsewhere. A family is
defined as a group of two or more individuals residing

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together who are related by birth, marriage, or adoption;
all such individuals are considered members of one family.
Families are further classified either as married-couple
families or as families maintained by women or men without spouses present. Subfamilies are defined as families
that live in housing units where none of the members of
the family are related to the reference person. A household may contain unrelated individuals; that is, people
who are not living with any relatives. An unrelated individual may be part of a household containing one or more
families or other unrelated individuals, may live alone, or
may reside in group quarters, such as a rooming house.
Additional demographic information. In addition to
asking for relationship data, the interviewer asks for other
demographic data for each household member, including:
birth date, marital status, Armed Forces status, level of
education, race, ethnicity, nativity, and social security
number (for those 15 years of age or older in selected
months). Total household income is also collected. The
following terms are used to define an individual’s marital
status at the time of the interview: married spouse
present, married spouse absent, widowed, divorced, separated, or never married. The term ‘‘married spouse
present’’ applies to a husband and wife who both live at
the same address, even though one may be temporarily
absent due to business, vacation, a visit away from home,
a hospital stay, etc. The term ‘‘married spouse absent’’
applies to individuals who live apart for reasons such as
marital problems, as well as husbands and wives who are
living apart because one or the other is employed elsewhere, on duty with the Armed Forces, or any other reason. The information collected during the interview is
used to create three marital status categories: single never
married, married spouse present, and other marital status.
The latter category includes those who were classified as
widowed; divorced; separated; or married, spouse absent.
Educational attainment for each person in the household
15 or older is obtained through a question asking about
the highest grade or degree completed. Additional questions are asked for several educational attainment categories to ascertain the total number of years of school or
credit years completed.
Questions on race and Hispanic origin comply with federal
standards. Respondents are asked a question to determine
if they are Hispanic, which is considered an ethnicity
rather than a race. The question asks if the individual is
Spanish, Hispanic, or Latino, and is placed before the
question on race. Next, all respondents, including those
who identify themselves as Hispanic, are asked to choose
which of the following races they consider themselves to
be: White, Black or African American, American Indian or
Alaska Native, Asian, or Native Hawaiian or Other Pacific
Islander. Responses of ‘‘other’’ are accepted and allocated
among the race categories. Respondents may choose
more than one race.
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Labor Force Information
To avoid any chance of misunderstanding, it is emphasized here that the CPS provides a measure of monthly
employment—not jobs.
Labor force information is obtained after the household
and demographic information has been collected. One of
the primary purposes of the labor force information is to
classify individuals as employed, unemployed, or not in
the labor force. Other information collected includes hours
worked, occupation, industry and related aspects of the
working population. The major labor force categories are
defined hierarchically and, thus, are mutually exclusive.
Employed supersedes unemployed which supersedes not
in the labor force. For example, individuals who are classified as employed, even if they worked less than full-time
during the reference week (defined below), are not asked
the questions about having looked for work, and cannot
be classified as unemployed. Similarly, an individual who
is classified as unemployed is not asked the questions
used to determine one’s primary nonlabor-market activity.
For instance, retired people who are currently working are
classified as employed even though they have retired from
previous jobs. Consequently, they are not asked the questions about their previous employment nor can they be
classified as not in the labor force. The current concepts
and definitions underlying the collection and estimate of
the labor force data are presented below.
Reference week. The CPS labor force questions ask
about labor market activities for 1 week each month. This
week is referred to as the ‘‘reference week.’’ The reference
week is defined as the 7-day period, Sunday through Saturday, that includes the 12th of the month.
Civilian noninstitutionalized population. In the CPS,
labor force data are restricted to people 16 years of age
and older, who currently reside in 1 of the 50 states or the
District of Columbia, who do not reside in institutions
(e.g., penal and mental facilities, homes for the aged), and
who are not on active duty in the Armed Forces.
Employed people. Employed people are those who, during the reference week (a) did any work at all (for at least
1 hour) as paid employees; worked in their own businesses, professions, or on their own farms; or worked 15
hours or more as unpaid workers in an enterprise operated by a family member or (b) were not working, but who
had a job or business from which they were temporarily
absent because of vacation, illness, bad weather, childcare
problems, maternity or paternity leave, labor-management
dispute, job training, or other family or personal reasons
whether or not they were paid for the time off or were
seeking other jobs. Each employed person is counted only
once, even if he or she holds more than one job. (See the
discussion of multiple jobholders below.)

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Employed citizens of foreign countries who are temporarily in the United States but not living on the premises of
an embassy are included. Excluded are people whose only
activity consisted of work around their own house (painting, repairing, cleaning, or other home-related housework)
or volunteer work for religious, charitable, or other organizations.
The initial survey question, asked only once for each
household, inquires whether anyone in the household has
a business or a farm. Subsequent questions are asked for
each household member to determine whether any of
them did any work for pay (or for profit if there is a household business) during the reference week. If no work for
pay or profit was performed and a family business exists,
respondents are asked whether they did any unpaid work
in the family business or farm.
Multiple jobholders. These are employed people who,
during the reference week, had either two or more jobs as
wage and salary workers; were self-employed and also
held one or more wage and salary jobs; or worked as
unpaid family workers and also held one or more wage
and salary jobs. A person employed only in private households (cleaner, gardener, babysitter, etc.) who worked for
two or more employers during the reference week is not
counted as a multiple jobholder since working for several
employers is considered an inherent characteristic of private household work. Also excluded are self-employed
people with multiple unincorporated businesses and
people with multiple jobs as unpaid family workers.
CPS respondents are asked questions each month to identify multiple jobholders. First, all employed people are
asked ‘‘Last week, did you have more than one job (or
business, if one exists), including part-time, evening, or
weekend work?’’ Those who answer ‘‘yes’’ are then asked,
‘‘Altogether, how many jobs (or businesses) did you have?’’
Hours of work. Information on both actual and usual
hours of work have been collected. Published data on
hours of work relate to the actual number of hours spent
‘‘at work’’ during the reference week. For example, people
who normally work 40 hours a week but were off on the
Memorial Day holiday, would be reported as working 32
hours, even though they were paid for the holiday. For
people working in more than one job, the published figures relate to the number of hours worked at all jobs during the week.
Data on people ‘‘at work’’ exclude employed people who
were absent from their jobs during the entire reference
week for reasons such as vacation, illness, or industrial
dispute. Data also are available on usual hours worked by
all employed people, including those who were absent
from their jobs during the reference week.
At work part-time for economic reasons. Sometimes
referred to as involuntary part-time, this category refers to
individuals who gave an economic reason for working 1 to
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34 hours during the reference week. Economic reasons
include slack work or unfavorable business conditions,
inability to find full-time work, and seasonal declines in
demand. Those who usually work part-time also must indicate that they want and are available to work full-time to
be classified as being part-time for economic reasons.
At work part-time for noneconomic reasons. This
group includes people who usually work part-time and
were at work 1 to 34 hours during the reference week for
a noneconomic reason. Noneconomic reasons include illness or other medical limitation, childcare problems or
other family or personal obligations, school or training,
retirement or social security limits on earnings, and being
in a job where full-time work is less than 35 hours. The
group also includes those who gave an economic reason
for usually working 1 to 34 hours but said they do not
want to work full-time or were unavailable for such work.
Usual full- or part-time status. In order to differentiate
a person’s normal schedule from his/her activity during
the reference week, people also are classified according to
their usual full- or part-time status. In this context, fulltime workers are those who usually work 35 hours or
more (at all jobs combined). This group includes some
individuals who worked fewer than 35 hours in the reference week—for either economic or noneconomic
reasons—as well as those who are temporarily absent
from work. Similarly, part-time workers are those who usually work fewer than 35 hours per week (at all jobs),
regardless of the number of hours worked in the reference
week. This may include some individuals who actually
worked more than 34 hours in the reference week, as well
as those who were temporarily absent from work. The fulltime labor force includes all employed people who usually
work full-time and unemployed people who are either
looking for full-time work or are on layoff from full-time
jobs. The part-time labor force consists of employed
people who usually work part-time and unemployed
people who are seeking or are on layoff from part-time
jobs.
Occupation, industry, and class-of-worker. For the
employed, this information applies to the job held in the
reference week. A person with two or more jobs is classified according to the job at which he or she worked the
largest number of hours. The unemployed are classified
according to their last jobs. The occupational and industrial classification of CPS data is based on the coding systems used in Census 2000. A list of these codes can be
found in the Alphabetical Index of Industries and
Occupations at . The class-of-worker classification
assigns workers to one of the following categories: wage
and salary workers, self-employed workers, and unpaid
family workers. Wage and salary workers are those who
receive wages, salary, commissions, tips, or pay in kind
from a private employer or from a government unit.

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The class-of-worker question also includes separate
response categories for ‘‘private for-profit company’’ and
‘‘nonprofit organization’’ to further classify private wage
and salary workers. The self-employed are those who
work for profit or fees in their own businesses, professions, trades, or farms. Only the unincorporated selfemployed are included in the self-employed category since
those whose businesses are incorporated technically are
wage and salary workers because they are paid employees
of a corporation. Unpaid family workers are individuals
working without pay for 15 hours a week or more on a
farm or in a business operated by a member of the household to whom they are related by birth, marriage, or adoption.
Occupation, industry, and class-of-worker on second
job. The occupation, industry, and class-of-worker information for individuals’ second jobs is collected in order to
obtain a more accurate measure of multiple jobholders, to
obtain more detailed information about their employment
characteristics, and to provide information necessary for
comparing estimates of number of employees in the CPS
and in BLS’s establishment survey (the Current Employment Statistics; for an explanation of this survey see BLS
Handbook of Methods at ). For the majority of multiple jobholders,
occupation, industry, and class-of-worker data for their
second jobs are collected only from one-fourth of the
sample—those in their fourth or eighth monthly interview.
However, for those classified as ‘‘self-employed unincorporated’’ on their main jobs, class-of-worker of the second
job is collected each month. This is done because, according to the official definition, individuals who are ‘‘selfemployed unincorporated’’ on both of their jobs are not
considered multiple jobholders.
The questions used to determine whether an individual is employed or not, along with the questions an
employed person typically will receive, are presented in
Figure 5–1 at the end of this chapter.
Earnings. Information on what people earn at their main
job is collected only for those who are receiving their
fourth or eighth monthly interviews. This means that earnings questions are asked of only one-fourth of the survey
respondents each month. Respondents are asked to report
their usual earnings before taxes and other deductions
and to include any overtime pay, commissions, or tips
usually received. The term ‘‘usual’’ means as perceived by
the respondent. If the respondent asks for a definition of
usual, interviewers are instructed to define the term as
more than half the weeks worked during the past 4 or 5
months. Respondents may report earnings in the time
period they prefer—for example, hourly, weekly, biweekly,
monthly, or annually. (Allowing respondents to report in a
periodicity with which they were most comfortable was a
feature added in the 1994 redesign.) Based on additional
5–4

information collected during the interview, earnings
reported on a basis other than weekly are converted to a
weekly amount in later processing. Data are collected for
wage and salary workers, and for self-employed people
whose businesses are incorporated; earnings data are not
collected for self-employed people whose businesses are
unincorporated. (Earnings data are not edited and are not
released to the public for the ‘‘self-employed incorporated.’’) These earnings data are used to construct estimates of the distribution of usual weekly earnings and
median earnings. Individuals who do not report their earnings on an hourly basis are asked if they are, in fact, paid
at an hourly rate and if so, what the hourly rate is. The
earnings of those who reported hourly and those who are
paid at an hourly rate is used to analyze the characteristics of hourly workers, for example, those who are paid
the minimum wage.
Unemployed people. All people who were not employed
during the reference week but were available for work
(excluding temporary illness) and had made specific
efforts to find employment some time during the 4-week
period ending with the reference week are classified as
unemployed. Individuals who were waiting to be recalled
to a job from which they had been laid off need not have
been looking for work to be classified as unemployed.
People waiting to start a new job must have actively
looked for a job within the last 4 weeks in order to be
counted as unemployed. Otherwise, they are classified as
not in the labor force.
As the definition indicates, there are two ways people may
be classified as unemployed. They are either looking for
work (job seekers) or they have been temporarily separated from a job (people on layoff). Job seekers must have
engaged in an active job search during the above mentioned 4-week period in order to be classified as unemployed. (Active methods are defined as job search methods that have the potential to result in a job offer without
any further action on the part of the job seeker.) Examples
of active job search methods include going to an employer
directly or to a public or private employment agency, seeking assistance from friends or relatives, placing or answering ads, or using some other active method. Examples of
the ‘‘other active’’ category include being on a union or
professional register, obtaining assistance from a community organization, or waiting at a designated labor pickup
point. Passive methods, which do not qualify as job
search, include reading ‘‘help wanted’’ ads and taking a job
training course, as opposed to actually answering ‘‘help
wanted’’ ads or placing ‘‘employment wanted’’ ads. The
response categories for active and passive methods are
clearly delineated in separately labeled columns on the
interviewers’ computer screens. Job search methods are
identified by the following questions: ‘‘Have you been
doing anything to find work during the last 4 weeks?’’ and

Questionnaire Concepts and Definitions for the Current Population Survey

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‘‘What are all of the things you have done to find work during the last 4 weeks?’’ To ensure that respondents report
all of the methods of job search used, interviewers ask
‘‘Anything else?’’ after the initial or a subsequent job
search method is reported.
Persons ‘‘on layoff’’ are defined as those who have been
separated from a job to which they are waiting to be
recalled (i.e., their layoff status is temporary). In order to
measure layoffs accurately, the questionnaire determines
whether people reported to be on layoff did in fact have
an expectation of recall; that is, whether they had been
given a specific date to return to work or, at least, had
been given an indication that they would be recalled
within the next 6 months. As previously mentioned,
people on layoff need not be actively seeking work to be
classified as unemployed.
Reason for unemployment. Unemployed individuals are
categorized according to their status at the time they
became unemployed. The categories are: (1) Job losers: a
group composed of (a) people on temporary layoff from a
job to which they expect to be recalled and (b) permanent
job losers, whose employment ended involuntarily and
who began looking for work; (2)Job leavers: people who
quit or otherwise terminated their employment voluntarily
and began looking for work; (3)People who completed temporary jobs: individuals who began looking for work after
their jobs ended; (4)Reentrants: people who previously
worked but were out of the labor force prior to beginning
their job search; (5)New entrants: individuals who never
worked before and who are entering the labor force for
the first time. Each of these five categories of unemployed
can be expressed as a proportion of the entire civilian
labor force or as a proportion of the total unemployed.
Duration of unemployment. The duration of unemployment is expressed in weeks. For individuals who are classified as unemployed because they are looking for work,
the duration of unemployment is the length of time
(through the current reference week) that they have been
looking for work. For people on layoff, the duration of
unemployment is the number of full weeks (through the
reference week) they have been on layoff.
The questions used to classify an individual as unemployed can be found in Figure 5–1.
Not in the labor force. Included in this group are all
members of the civilian noninstitutionalized population
who are neither employed nor unemployed. Information is
collected on their desire for and availability to take a job
at the time of the CPS interview, job search activity in the
prior year, and reason for not looking in the 4-week period

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prior to the survey week. This group includes discouraged
workers, defined as those not in the labor force who want
and are available for a job and who have looked for work
sometime in the past 12 months (or since the end of their
last job if they held one within the past 12 months), but
are not currently looking, because they believe there are
no jobs available or there are none for which they would
qualify. (Specifically, the main reason identified by discouraged workers for not recently looking for work is one of
the following: believes no work available in line of work or
area; could not find any work; lacks necessary schooling,
training, skills, or experience; employers think too young
or too old; or other types of discrimination.)
Data on a larger group of people outside the labor force,
one that includes discouraged workers as well as those
who desire work but give other reasons for not searching
(such as childcare problems, school, family responsibilities, or transportation problems) are also published regularly. This group is made up of people who want a job, are
available for work, and have looked for work within the
past year. This group is generally described as having
some marginal attachment to the labor force.
Questions about the desire for work among those who are
not in the labor force are asked of the full CPS sample.
Consequently, estimates of the number of discouraged
workers as well as those with a marginal attachment to
the labor force are published monthly rather than quarterly.
Additional questions relating to individuals’ job histories
and whether they intend to seek work continue to be
asked only of people not in the labor force who are in the
sample for either their fourth or eighth month. Data based
on these questions are tabulated only on a quarterly basis.
Estimates of the number of employed and unemployed are
used to construct a variety of measures. These measures
include:
• Labor force. The labor force consists of all people 16
years of age or older classified as employed or unemployed in accordance with the criteria described above.
• Unemployment rate. The unemployment rate represents the number of unemployed as a percentage of the
labor force.
• Labor force participation rate. The labor force participation rate is the proportion of the age-eligible population that is in the labor force.
• Employment-population ratio. The employmentpopulation ratio represents the proportion of the ageeligible population that is employed.

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5–5

Figure 5–1. Questions for Employed and
Unemployed
1. Does anyone in this household have a business or a
farm?
2. LAST WEEK, did you do ANY work for (either) pay (or
profit)?
Parenthetical filled in if there is a business or farm in
the household. If 1 is ‘‘yes’’ and 2 is ‘‘no,’’ ask 3. If 1 is
‘‘no’’ and 2 is ‘‘no,’’ ask 4.
3. LAST WEEK, did you do any unpaid work in the family
business or farm?
If 2 and 3 are both ‘‘no,’’ ask 4.
4. LAST WEEK, (in addition to the business) did you have
a job, either full-or part-time? Include any job from
which you were temporarily absent.
Parenthetical filled in if there is a business or farm in
the household.
If 4 is ‘‘no,’’ ask 5.
5. LAST WEEK, were you on layoff from a job?
If 5 is ‘‘yes,’’ ask 6. If 5 is ‘‘no,’’ ask 8.
6. Has your employer given you a date to return to work?
If ‘‘no,’’ ask 7.

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7. Have you been given any indication that you will be
recalled to work within the next 6 months?
If ‘‘no,’’ ask 8.
8. Have you been doing anything to find work during the
last 4 weeks?
If ‘‘yes,’’ ask 9.
9. What are all of the things you have done to find work
during the last 4 weeks?
Individuals are classified as employed if they say ‘‘yes’’ to
questions 2, 3 (and work 15 hours or more in the reference week or receive profits from the business/farm), or 4.
Individuals who are available to work are classified as
unemployed if they say ‘‘yes’’ to 5 and either 6 or 7, or if
they say ‘‘yes’’ to 8 and provide a job search method that
could have brought them into contact with a potential
employer in 9.
REFERENCES
U.S. Department of Commerce, U.S. Census Bureau (1992),
Alphabetical Index of Industries and Occupations,
from .
U.S. Department of Labor, U.S. Bureau of Labor Statistics,
BLS Handbook of Methods, from .

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Chapter 6.
Design of the Current Population Survey Instrument
INTRODUCTION
Chapter 5 describes the current concepts and definitions
underpinning the Current Population Survey (CPS) data collection instrument. The current survey instrument is the
result of an 8-year research and development effort to
redesign the data collection process and to implement previously recommended changes in the underlying labor
force concepts. The changes described here were introduced in January 1994. For virtually every labor force concept, the current questionnaire wording is different from
what was used previously. Data collection was redesigned
so that the instrument is fully automated and is administered either on a laptop computer or from a centralized
telephone facility. This chapter describes the work on the
data collection instrument and changes that were made as
a result of that work.
MOTIVATION FOR REDESIGNING THE
QUESTIONNAIRE COLLECTING LABOR FORCE DATA
The CPS produces some of the most important data used
to develop economic and social policy in the United States.
Although the U.S. economy and society have undergone
major shifts in recent decades, the survey questionnaire
remained unchanged from 1967 to 1994. The growth in
the number of service-sector jobs and the decline in the
number of factory jobs were two key developments. Other
changes include the more prominent role of women in the
workforce and the growing popularity of alternative work
schedules. The 1994 revisions were designed to accommodate these changes. At the same time, the redesign
took advantage of major advances in survey research
methods and data collection technology. Recommendations for changes in the CPS had been proposed in the late
1970s and 1980s, primarily by the Presidentiallyappointed National Commission on Employment and
Unemployment Statistics (commonly referred to as the
Levitan Commission). No changes were implemented at
that time, however, due to the lack of funding for a large
overlap sample necessary to assess the effect of the redesign. In the mid-1980s, funding for an overlap sample
became available. Spurred by all of these developments,
the decision was made to redesign the CPS questionnaire.
OBJECTIVES OF THE REDESIGN
There were five main objectives in redesigning the CPS
questionnaire: (1) to better operationalize existing definitions and reduce reliance on volunteered responses; (2) to
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

reduce the potential for response error in the
questionnaire-respondent-interviewer interaction and,
hence, improve measurement of CPS concepts; (3) to
implement minor definitional changes within the labor
force classifications; (4) to expand the labor force data
available and improve longitudinal measures; and (5) to
exploit the capabilities of computer-assisted interviewing
for improving data quality and reducing respondent burden (see Copeland and Rothgeb (1990) for a fuller discussion).
Enhanced Accuracy
In redesigning the CPS questionnaire, the U.S. Bureau of
Labor Statistics (BLS) and U.S. Census Bureau developed
questions that would lessen the potential for response
error. Among the approaches used were: (1) shorter,
clearer question wording; (2) splitting complex questions
into two or more separate questions; (3) building concept
definitions into question wording; (4) reducing reliance on
volunteered information; (5) explicit and implicit strategies for the respondent to provide numeric data on hours,
earnings, etc.; and (6) the use of revised precoded
response categories for open-ended questions (Copeland
and Rothgeb, 1990).
Definitional Changes
The labor force definitions used in the CPS have undergone only minor modifications since the survey’s inception in 1940, and with only one exception, the definitional
changes and refinements made in 1994 were small. The
one major definitional change dealt with the concept of
discouraged workers; that is, people outside the labor
force who are not looking for work because they believe
that there are no jobs available for them. As noted in
Chapter 5, discouraged workers are similar to the unemployed in that they are not working and want a job. Since
they are not conducting an active job search, however,
they do not satisfy a key element necessary to be classified as unemployed. The former measurement of discouraged workers was criticized by the Levitan Commission as
too arbitrary and subjective. It was deemed arbitrary
because assumptions about a person’s availability for
work were made from responses to a question on why the
respondent was not currently looking for work. It was considered too subjective because the measurement was
based on a person’s stated desire for a job regardless of
whether the individual had ever looked for work. A new,
more precise measurement of discouraged workers was
Design of the Current Population Survey Instrument

6–1

introduced that specifically asked if a person had searched
for a job during the prior 12 months and was available for
work. The new questions also enable estimation of the
number of people outside the labor force who, although
they cannot be precisely defined as discouraged, satisfy
many of the same criteria as discouraged workers and
thus show some marginal attachment to the labor force.
Other minor changes were made to fine-tune the definitions of unemployment, categories of unemployed people,
and people who were employed part-time for economic
reasons.
New Labor Force Information Introduced
With the revised questionnaire, several types of labor force
data became available regularly for the first time. For
example, information is now available each month on
employed people who have more than one job. Also, by
separately collecting information on the number of hours
multiple jobholders work on their main job and secondary
jobs, estimates of the number of workers who combined
two or more part-time jobs into a full-time work week, and
the number of full- and part-time jobs in the economy can
be made. The inclusion of the multiple job question also
improves the accuracy of answers to the questions on
hours worked and facilitates comparisons of employment
estimates from the CPS with those from the Current
Employment Statistics program, the survey of nonfarm
business establishments (for a discussion of the CES survey, see BLS Handbook of Methods, Bureau of Labor
Statistics, April 1997). In addition, beginning in 1994,
monthly data on the number of hours usually worked per
week and data on the number of discouraged workers are
collected from the entire CPS sample rather than from the
one-quarter of respondents who are in their fourth or
eighth monthly interviews.
Computer Technology
A key feature of the redesigned CPS is that the new questionnaire was designed for a computer-assisted interview.
Prior to the redesign, CPS data were primarily collected
using a paper-and-pencil form. In an automated environment, most interviewers now use laptop computers on
which the questionnaire has been programmed. This
mode of data collection is known as computer-assisted
personal interviewing (CAPI). Interviewers ask the survey
questions as they appear on the screen of the laptop and
then type the responses directly into the computer. A portion of sample households—currently about 18 percent—is
interviewed via computer-assisted telephone interviewing
(CATI) from three centralized telephone centers located in
Hagerstown, MD; Tucson, AZ; and Jeffersonville, IN.
Automated data collection methods allow greater flexibility in questionnaire design than paper-and-pencil data collection methods. Complicated skips, respondent-specific
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Design of the Current Population Survey Instrument

question wording, and carry-over of data from one interview to the next are all possible in an automated environment. For example, automated data collection allows
capabilities such as (1) the use of dependent interviewing,
that is carrying over information from the previous
month—for industry, occupation, and duration of unemployment data, and (2) the use of respondent-specific
question wording based on the person’s name, age, and
sex, answers to prior questions, household characteristics,
etc. By automatically bringing up the next question on the
interviewer’s screen, computerization reduces the probability that an interview will ask the wrong set of questions. The computerized questionnaire also permits the
inclusion of several built-in editing features, including
automatic checks for internal consistency and unlikely
responses, and verification of answers. With these built-in
editing features, errors can be caught and corrected during the interview itself.
Evaluation and Selection of Revised Questions
Planning for the revised CPS questionnaire began in 1986,
when BLS and the Census Bureau convened a task force to
identify areas for improvement. Studies employing methods from the cognitive sciences were conducted to test
possible solutions to the problems identified. These studies included interviewer focus groups, respondent focus
groups, respondent debriefings, a test of interviewers’
knowledge of concepts, in-depth cognitive laboratory
interviews, response categorization research, and a study
of respondents’ comprehension of alternative versions of
labor force questions (Campanelli, Martin, and Rothgeb,
1991; Edwards, Levine, and Cohany, 1989; Fracasso,
1989; Gaertner, Cantor, and Gay,1989; Martin, 1987;
Palmisano, 1989).
In addition to qualitative research, the revised questionnaire, developed jointly by Census Bureau and BLS staff,
used information collected in a large two-phase test of
question wording. During Phase I, two alternative questionnaires were tested using the then official questionnaire
as the control. During Phase II, one alternative questionnaire was tested with the control. The questionnaires were
tested using computer-assisted telephone interviewing
and a random digit dialing sample (CATI/RDD). During
these tests, interviews were conducted from the centralized telephone interviewing facilities of the Census
Bureau.
Both quantitative and qualitative information was used in
the two phases to select questions, identify problems, and
suggest solutions. Analyses were based on information
from item response distributions, respondent and interviewer debriefing data, and behavior coding of
interviewer/ respondent interactions. For more on the
evaluation methods used for redesigning the questions,
see Esposito and Rothgeb (1997).
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Item Response Analysis
The primary use of item response analysis was to determine whether different questionnaires produce different
response patterns, which may, in turn, affect the labor
force estimates. Unedited data were used for this analysis.
Statistical tests were conducted to ascertain whether diferences among the response patterns of different questionnaire versions were statistically significant. The statistical
tests were adjusted to take into consideration the use of a
nonrandom clustered sample, repeated measures over
time, and multiple persons in a household.
Response distributions were analyzed for all items on the
questionnaires. The response distribution analysis indicated the degree to which new measurement processes
produced different patterns of responses. Data gathered
using the other methods outlined above also aided interpretation of the response differences observed. (Response
distributions were calculated on the basis of people who
responded to the item, excluding those whose response
was ‘‘don’t know’’ or ‘‘refused.’’)
Respondent Debriefings
At the end of the test interview, respondent debriefing
questions were administered to a sample of respondents
to measure respondent comprehension and response formulation. From these data, indicators of how respondents
interpret and answer the questions and some measures of
response accuracy were obtained.
The debriefing questions were tailored to the respondent
and depended on the path the interview had taken. Two
forms of respondent debriefing questions were
administered— probing questions and vignette classification. Question-specific probes were used to ascertain
whether certain words, phrases, or concepts were understood by respondents in the manner intended (Esposito et
al., 1992). For example, those who did not indicate in the
main survey that they had done any work were asked the
direct probe ‘‘LAST WEEK did you do any work at all, even
for as little as 1 hour?’’ An example of the vignettes
respondents received is ‘‘Last week, Amy spent 20 hours
at home doing the accounting for her husband’s business.
She did not receive a paycheck.’’ Individuals were asked to
classify the person in the vignette as working or not working based on the wording of the question they received in
the main survey (e.g., ‘‘Would you report her as working
last week not counting work around the house?’’ if the
respondent received the unrevised questionnaire, or
‘‘Would you report her as working for pay or profit last
week?’’ if the respondent received the current, revised
questionnaire (Martin and Polivka, 1995).

wording, probing behavior, inadequate answers, requests
for clarification). During the early stages of testing, behavior coding data were useful in identifying problems with
proposed questions. For example, if interviewers frequently reword a question, this may indicate that the
question was too difficult to ask as worded; respondents’
requests for clarification may indicate that they were
experiencing comprehension difficulties; and interruptions
by respondents may indicate that a question was too
lengthy (Esposito et al., 1992).
During later stages of testing, the objective of behavior
coding was to determine whether the revised questionnaire improved the quality of interviewer/respondent
interactions as measured by accurate reading of the questions and adequate responses by respondents. Additionally, results from behavior coding helped identify areas of
the questionnaire that would benefit from enhancements
to interviewer training.
Interviewer Debriefings
The primary objective of interviewer debriefing was to
identify areas of the revised questionnaire or interviewer
procedures that were problematic for interviewers or
respondents. The information collected was used to identify questions that needed revision, and to modify initial
interviewer training and the interviewer manual. A secondary objective was to obtain information about the questionnaire, interviewer behavior, or respondent behavior
that would help explain differences observed in the labor
force estimates from the different measurement processes.
Two different techniques were used to debrief interviewers. The first was the use of focus groups at the centralized telephone interviewing facilities and in geographically dispersed regional offices. The focus groups were
conducted after interviewers had at least 3 to 4 months
experience using the revised CPS instrument. Approximately 8 to 10 interviewers were selected for each focus
group. Interviewers were selected to represent different
levels of experience and ability.
The second technique was the use of a self-administered
standardized interviewer debriefing questionnaire. Once
problematic areas of the revised questionnaire were identified through the focus groups, a standardized debriefing
questionnaire was developed and administered to all interviewers. See Esposito and Hess (1992) for more information on interviewer debriefing.

Behavior Coding

HIGHLIGHTS OF THE QUESTIONNAIRE REVISION

Behavior coding entails monitoring or audiotaping interviews and recording significant interviewer and respondent behaviors (e.g., minor/major changes in question

A copy of the questionnaire can be obtained from the
Internet at .

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Design of the Current Population Survey Instrument

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General
Definition of reference week. In the interviewer
debriefings that were conducted in 13 different geographic areas during 1988, interviewers reported that the
current question 19 (Q19, major activity question) ‘‘What
were you doing most of LAST WEEK, working or something
else?’’ was unwieldy and sometimes misunderstood by
respondents. In addition to not always understanding the
intent of the question, respondents were unsure what was
meant by the time period ‘‘last week’’ (BLS, 1988). A
respondent debriefing conducted in 1988 found that only
17 percent of respondents had definitions of ‘‘last week’’
that matched the CPS definition of Sunday through Saturday of the reference week. The majority (54 percent) of
respondents defined ‘‘last week’’ as Monday through Friday (Campanelli et al., 1991).
In the revised questionnaire, an introductory statement
was added with the reference period clearly stated. The
new introductory statement reads, ‘‘I am going to ask a
few questions about work-related activities LAST WEEK. By
last week I mean the week beginning on Sunday, August 9
and ending Saturday, August 15.’’ This statement makes
the reference period more explicit to respondents. Additionally, the former Q19 has been deleted from the questionnaire. In the past, Q19 had served as a preamble to
the labor force questions, but in the revised questionnaire
the survey content is defined in the introductory statement, which also defines the reference week.
Direct question on presence of business. The definition of employed persons includes those who work without pay for at least 15 hours per week in a family business. In the former questionnaire, there was no direct
question on the presence of a business in the household.
Such a question is included in the revised questionnaire.
This question is asked only once for the entire household
prior to the labor force questions. The question reads,
‘‘Does anyone in this household have a business or a
farm?’’ This question determines whether a business exists
and who in the household owns the business. The primary
purpose of this question is to screen for households that
may have unpaid family workers, not to obtain an estimate of household businesses. (See Rothgeb et al. [1992],
Copeland and Rothgeb [1990], and Martin [1987] for a
fuller discussion of the need for a direct question on presence of a business.)
For households that have a family business, direct questions are asked about unpaid work in the family business
by all people who were not reported as working last week.
BLS produces monthly estimates of unpaid family workers
who work 15 or more hours per week.
Employment Related Revisions
Revised ‘‘at work’’ question. Having a direct question
on the presence of a family business not only improved
the estimates of unpaid family workers, but also permitted
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Design of the Current Population Survey Instrument

a revision of the ‘‘at work’’ question. In the former questionnaire, the ‘‘at work’’ question read: ‘‘LAST WEEK, did
you do any work at all, not counting work around the
house?’’ In the revised questionnaire, the wording reads,
‘‘LAST WEEK did you do ANY work for (either) pay (or
profit)?’’ (The parentheticals in the question are read only
when a business or farm is in the household.) The revised
wording ‘‘work for pay (or profit)’’ better captures the concept of work that BLS is attempting to measure. (See Martin (1987) or Martin and Polivka (1995) for a fuller discussion of problems with the concept of ‘‘work.’’)
Direct question on multiple jobholding. In the former
questionnaire, the actual hours question read: ‘‘How many
hours did you work last week at all jobs?’’ During the interviewer debriefings conducted in 1988, it was reported
that respondents do not always hear the last phrase ‘‘at all
jobs.’’ Some respondents who work at two jobs may have
only reported hours for one job (BLS, 1988). In the revised
questionnaire, a question is included at the beginning of
the hours series to determine whether or not the person
holds multiple jobs. A follow-up question also asks for the
number of jobs the multiple jobholder has. Multiple jobholders are asked about their hours on their main job and
other job(s) separately to avoid the problem of multiple
jobholders not hearing the phrase ‘‘at all jobs.’’ These new
questions also allow monthly estimates of multiple jobholders to be produced.
Hours series. The old question on ‘‘hours worked’’ read:
‘‘How many hours did you work last week at all jobs?’’ If a
person reported 35−48 hours worked, additional
follow-up probes were asked to determine whether the
person worked any extra hours or took any time off. Interviewers were instructed to correct the original report of
actual hours, if necessary, based on responses to the
probes. The hours data are important because they are
used to determine the sizes of the full-time and part-time
labor forces. It is unknown whether respondents reported
exact actual hours, usual hours, or some approximation of
actual hours.
In the revised questionnaire, a revised hours series was
adopted. An anchor-recall estimation strategy was used to
obtain a better measure of actual hours and to address the
issue of work schedules more completely. For multiple
jobholders, it also provides separate data on hours
worked at a main job and other jobs. The revised questionnaire first asks about the number of hours a person
usually works at the job. Then, separate questions are
asked to determine whether a person worked extra hours,
or fewer hours, and finally a question is asked on the
number of actual hours worked last week. The new hours
series allows monthly estimates of usual hours worked to
be produced for all employed people. In the former questionnaire, usual hours were obtained only in the outgoing
rotation for employed private wage and salary workers
and were available only on a quarterly basis.
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

Industry and occupation—Dependent interviewing.
Prior to the revision, CPS industry and occupation (I&O)
data were not always consistent from month-to-month for
the same person in the same job. These inconsistencies
arose, in part, because the household respondent frequently varies from one month to the next. Furthermore, it
is sometimes difficult for a respondent to describe an
occupation consistently from month-to-month. Moreover,
distinctions at the three-digit occupation and industry
level, that is, at the most detailed classification level, can
be very subtle. To obtain more consistent data and make
full use of the automated interviewing environment,
dependent interviewing for the I&O question which uses
information collected during the previous month’s interview in the current month’s interview was implemented in
the revised questionnaire for month-in-sample 2−4 households and month-in-sample 6−8 households. (Different
variations of dependent interviewing were evaluated during testing. See Rothgeb et al. [1991] for more detail.)
In the revised CPS, respondents are provided the name of
their employer as of the previous month and asked if they
still work for that employer. If they answer ‘‘no,’’ respondents are asked the independent questions on industry
and occupation.
If they answer ‘‘yes,’’ respondents are asked ‘‘Have the
usual activities and duties of your job changed since last
month?’’ If individuals say ‘‘yes,’’ their duties have
changed, these individuals are then asked the independent questions on occupation, activities or duties, and
class-of-worker. If their duties have not changed, individuals are asked to verify the previous month’s description
through the question ‘‘Last month, you were reported as
(previous month’s occupation or kind of work performed)
and your usual activities were (previous month’s duties). Is
this an accurate description of your current job?’’
If they answer ‘‘yes,’’ the previous month’s occupation and
class-of-worker are brought forward and no coding is
required. If they answer ‘‘no,’’ they are asked the independent questions on occupation activities and duties and
class-of-worker. This redesign permits a direct inquiry
about job change before the previous month’s information
is provided to the respondent.
Earnings. The earnings series in the revised questionnaire is considerably different from that in the former
questionnaire. In the former questionnaire, persons were
asked whether they were paid by the hour, and if so, what
the hourly wage was. All wage and salary workers were
then asked for their usual weekly earnings. In the former
version, earnings could be reported as weekly figures
only, even though that may not have been the easiest way
for the respondent to recall and report earnings. Data from
early tests indicated that a small proportion (14 percent)
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

(n = 853) of nonhourly wage workers were paid at a
weekly rate, and less than 25 percent (n = 1623) of nonhourly wage workers found it easiest to report earnings as
a weekly amount.
In the revised questionnaire, the earnings series is
designed to first request the periodicity for which the
respondent finds it easiest to report earnings and then
request an earnings amount in the specified periodicity, as
displayed below. The wording of questions requesting an
earnings amount is tailored to the periodicity identified
earlier by the respondent. (Because data on weekly earnings are published quarterly by BLS, earnings data provided by respondents in periodicities other than weekly
are converted to a weekly earnings estimate later during
processing operations.)
Revised Earnings Series (Selected items)
1. For your (MAIN) job, what is the easiest way for you to
report your total earnings BEFORE taxes or other
deductions: hourly, weekly, annually, or on some other
basis?
2. Do you usually receive overtime pay, tips, or commissions (at your MAIN job)?
3. (Including overtime pay, tips and commissions,) What
are your usual (weekly, monthly, annual, etc.) earnings
on this job, before taxes or other deductions?
As can be seen from the revised questions presented
above, other revisions to the earnings series include a specific question to determine whether a person usually
receives overtime pay, tips, or commissions. If so, a preamble precedes the earnings questions that reminds
respondents to include overtime pay, tips, and commissions when reporting earnings. If a respondent reports
that it is easiest to report earnings on an hourly basis,
then a separate question is asked regarding the amount of
overtime pay, tips and commissions usually received, if
applicable.
An additional question is asked of people who do not
report that it is easiest to report their earnings hourly. The
question determines whether they are paid at an hourly
rate and is displayed below. This information, which
allows studies of the effect of the minimum wage, is used
to identify hourly wage workers.
‘‘Even though you told me it is easier to report your
earnings annually, are you PAID AT AN HOURLY RATE
on this job?’’
Unemployment Related Revisions
Persons on layoff—direct question. Previous research
(Rothgeb, 1982; Palmisano, 1989) demonstrated that the
former question on layoff status—‘‘Did you have a job or
business from which you were temporarily absent or on
layoff LAST WEEK?’’—was long, awkwardly worded, and
Design of the Current Population Survey Instrument

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frequently misunderstood by respondents. Some respondents heard only part of the question, while others
thought that they were being asked whether they had a
business.
In an effort to reduce response error, the revised questionnaire includes two separate direct questions about layoff
and temporary absences. The layoff question is: ‘‘LAST
WEEK, were you on layoff from a job?’’ Questions asked
later screen out those who do not meet the criteria for layoff status.
People on layoff—expectation of recall. The official
definition of layoff includes the criterion of an expectation
of being recalled to the job. In the former questionnaire,
people reported being on layoff were never directly asked
whether they expected to be recalled. In an effort to better
capture the existing definition, people reported being on
layoff in the revised questionnaire are asked ‘‘Has your
employer given you a date to return to work?’’ People who
respond that their employers have not given them a date
to return are asked ‘‘Have you been given any indication
that you will be recalled to work within the next 6
months?’’ If the response is positive, their availability is
determined by the question, ‘‘Could you have returned to
work LAST WEEK if you had been recalled?’’ People who do
not meet the criteria for layoff are asked the job search
questions so they still have an opportunity to be classified
as unemployed.
Job search methods. The concept of unemployment
requires, among other criteria, an active job search during
the previous 4 weeks. In the former questionnaire, the following question was asked to determine whether a person
conducted an active job search. ‘‘What has ... been doing
in the last 4 weeks to find work?’’ Responses that could be
checked included:
• public employment agency
• private employment agency
• employer directly
• friends and relatives
• placed or answered ads
• nothing
• other
Interviewers were instructed to code all passive job search
methods into the ‘‘nothing’’ category. This included such
activities as looking at newspaper ads, attending job training courses, and practicing typing. Only active job search
methods for which no appropriate response category
exists were to be coded as ‘‘other.’’
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Design of the Current Population Survey Instrument

In the revised questionnaire, several additional response
categories were added and the response options were reordered and reformatted to more clearly represent the distinction between active job search methods and passive
methods. The revisions to the job search methods question grew out of concern that interviewers were confused
by the precoded response categories. This was evident
even before the analysis of the CATI/RDD test. Martin
(1987) conducted an examination of verbatim entries for
the ‘‘other’’ category and found that many of the ‘‘other’’
responses should have been included in the ‘‘nothing’’ category. The analysis also revealed responses coded as
‘‘other’’ that were too vague to determine whether or not
an active job search method had been undertaken. Fracasso (1989) also concluded that the current set of
response categories was not adequate for accurate classification of active and passive job search methods.
During development of the revised questionnaire, two
additional passive categories were included: (1)‘‘looked at
ads’’ and (2) ‘‘attended job training programs/courses.’’
Two additional active categories were included: (1)‘‘contacted school/university employment center’’ and
(2)‘‘checked union/ professional registers.’’ Later research
also demonstrated that interviewers had difficulty coding
relatively common responses such as ‘‘sent out resumes’’
and ‘‘went on interviews’’; thus, the response categories
were further expanded to reflect these common job search
methods.
Duration of job search and layoff. The duration of
unemployment is an important labor market indicator
published monthly by BLS. In the former questionnaire,
this information was collected by the question ‘‘How many
weeks have you been looking for work?’’ This wording
forced people to report in a periodicity that may not have
been meaningful to them, especially for the longer-term
unemployed. Also, asking for the number of weeks (rather
than months) may have led respondents to underestimate
the duration. In the revised questionnaire, the question
reads: ‘‘As of the end of LAST WEEK, how long had you
been looking for work?’’ Respondents can select the periodicity themselves and interviewers are able to record the
duration in weeks, months, or years.
To avoid clustering of answers around whole months, the
revised questionnaire also asks those who report duration
in whole months (between 1 and 4 months) a follow-up
question to obtain an estimated duration in weeks: ‘‘We
would like to have that in weeks, if possible. Exactly how
many weeks had you been looking for work?’’ The purpose
of this is to lead people to report the exact number of
weeks instead of multiplying their monthly estimates by
four as was done in an earlier test and may have been
done in the former questionnaire.
As mentioned earlier, the CATI/CAPI technology makes it
possible to automatically update duration of job search
and layoff for people who are unemployed in consecutive
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

months. For people reported to be looking for work for 2
consecutive months or longer, the previous month’s duration is updated without re-asking the duration questions.
For those on layoff for at least 2 consecutive months, the
duration of layoff is also automatically updated. This revision was made to reduce respondent burden and enhance
the longitudinal capability of the CPS. This revision also
will produce more consistent month-to-month estimates of
duration. Previous research indicates that about 25 percent of those unemployed in consecutive months who
received the former questionnaire (where duration was
collected independently each month) increased their
reported durations by 4 weeks plus or minus a week.
(Polivka and Rothgeb, 1993; Polivka and Miller, 1998). A
very small bias is introduced when a person has a brief
(less than 3 or 4 weeks) period of employment in between
surveys. However, testing revealed that only 3.2 percent
of those who had been looking for work in consecutive
months said that they had worked in the interlude
between the surveys. Furthermore, of those who had
worked, none indicated that they had worked for 2 weeks
or more.
Revisions to ‘‘Not-in-the-Labor-Force’’ Questions
Response options of retired, disabled, and unable
to work. In the former questionnaire, when individuals
reported they were retired in response to any of the labor
force items, the interviewer was required to continue asking whether they worked last week, were absent from a
job, were looking for work, and, in the outgoing rotation,
when they last worked and their job histories. Interviewers commented that elderly respondents frequently complained that they had to respond to questions that seemed
to have no relevance to their own situation.
In an attempt to reduce respondent burden, a response
category of ‘‘retired’’ was added to each of the key labor
force status questions in the revised questionnaire. If individuals 50 years of age or older volunteer that they are
retired, they are immediately asked a question inquiring
whether they want a job. If they indicate that they want to
work, they are then asked questions about looking for
work and the interview proceeds as usual. If they do not
want to work, the interview is concluded and they are
classified as not in the labor force—retired. (If they are in
the outgoing rotation, an additional question is asked to
determine whether they worked within the last 12
months. If so, the industry and occupation questions are
asked about the last job held.)
A similar change has been made in the revised questionnaire to reduce the burden for individuals reported to be
‘‘unable to work’’ or ‘‘disabled.’’ (Individuals who may be
‘‘unable to work’’ for a temporary period of time may not
consider themselves as ‘‘disabled’’ so both response
options are provided.) If a person is reported to be ‘‘disabled’’ or ‘‘unable to work’’ at any of the key labor force
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

classification items, a follow-up question is asked to determine whether he/she can do any gainful work during the
next 6 months. Different versions of the follow-up probe
are used depending on whether the person is disabled or
unable to work.
Dependent interviewing for people reported to be
retired, disabled, or unable to work. The revised
questionnaire also is designed to use dependent interviewing for individuals reported to be retired, disabled, or
unable to work. An automated questionnaire increases the
ease with which information from the previous month’s
interview can be used during the current month’s interview.
Once it is reported that the person did not work during
the current month’s reference week, the previous month’s
status of retired (if a person is 50 years of age or older),
disabled, or unable to work is verified, and the regular
series of labor force questions is not asked. This revision
reduces respondent and interviewer burden.
Discouraged workers. The implementation of the Levitan Commission’s recommendations on discouraged workers resulted in one of the major definitional changes in the
1994 redesign. The Levitan Commission criticized the
former definition because it was based on a subjective
desire for work and questionable inferences about an individual’s availability to take a job. As a result of the redesign, two requirements were added: For persons to qualify
as discouraged, they must have engaged in some job
search within the past year (or since they last worked if
they worked within the past year), and they must currently
be available to take a job. (Formerly, availability was
inferred from responses to other questions; now there is a
direct question.)
Data on a larger group of people outside the labor force
(one that includes discouraged workers as well as those
who desire work but give other reasons for not searching,
such as child care problems, family responsibilities,
school, or transportation problems) also are published
regularly. This group is made up of people who want a
job, are available for work, and have looked for work
within the past year. This group is generally described as
having some marginal attachment to the labor force. Also
beginning in 1994, questions on this subject are asked of
the full CPS sample rather than a quarter of the sample,
permitting estimates of the number of discouraged workers to be published monthly rather than quarterly.
Tests of the revised questionnaire showed that the quality
of labor force data improved as a result of the redesign of
the CPS questionnaire, and in general, measurement error
diminished. Data from respondent debriefings, interviewer
debriefings, and response analysis demonstrated that the
revised questions are more clearly understood by respondents and the potential for labor force misclassification is
Design of the Current Population Survey Instrument

6–7

reduced. Results from these tests formed the basis for the
design of the final revised version of the questionnaire.
This revised version was tested in a separate year-and-ahalf parallel survey prior to implementation as the official
survey in January 1994. In addition, from January 1994
through May 1994, the unrevised procedures were used
with the parallel survey sample. These parallel surveys
were conducted to assess the effect of the redesign on
national labor force estimates. Estimates derived from the
initial year-and-a-half of the parallel survey indicated that
the redesign might increase the unemployment rate by 0.5
percentage points. However, subsequent analysis using
the entire parallel survey indicates that the redesign did
not have a statistically significant effect on the unemployment rate. (Analysis of the effect of the redesign on the
unemployment rate and other labor force estimates can be
found in Cohany, Polivka, and Rothgeb [1994].) Analysis of
the redesign on the unemployment rate along with a wide
variety of other labor force estimates using data from the
entire parallel survey can be found in Polivka and Miller
(1995).
CONTINUOUS TESTING AND IMPROVEMENTS OF
THE CURRENT POPULATION SURVEY AND ITS
SUPPLEMENTS
Experience gained during the redesign of the CPS has
demonstrated the importance of testing questions and
monitoring data quality. The experience, along with contemporaneous advances in research on questionnaire
design, also has helped inform the development of methods for testing new or improved questions for the basic
CPS and its periodic supplements (Martin [1987]; Oksenberg; Bischoping, K.; Cannell and Kalton [1991]; Campanelli, Martin, and Rothgeb [1991]; Esposito et al.[1992]; and
Forsyth and Lessler [1991] ). Methods to continuously test
questions and assess data quality are discussed in Chapter
15. Despite the benefits of adding new questions and
improving existing ones, changes to the CPS should be
approached cautiously and the effects measured and
evaluated. When possible, methods to bridge differences
caused by changes and techniques to avoid the disruption
of historical series should be included in the testing of
new or revised questions.

Cohany, S. R., A. E. Polivka, and J. M. Rothgeb (1994), Revisions in the Current Population Survey Effective January
1994, Employment and Earnings, February 1994 vol.
41 no. 2 pp. 1337.
Copeland, K. and J. M. Rothgeb (1990), Testing Alternative
Questionnaires for the Current Population Survey, Proceedings of the Section on Survey Research Methods, American Statistical Association, pp. 63−71.
Edwards, S. W., R. Levine, and S. R. Cohany (1989), Procedures for Validating Reports of Hours Worked and for Classifying Discrepancies Between Reports and Validation
Totals, Proceedings of the Section on Survey
Research Methods, American Statistical Association.
Esposito, J. L., and J. Hess (1992), ‘‘The Use of Inteviewer
Debriefings to Identify Problematic Questions on Alternate
Questionnaires,″ Paper presented at the Annual Meeting of
the American Association for Public Opinion Research, St.
Petersburg, FL.
Esposito, J. L., J. M. Rothgeb, A. E. Polivka, J. Hess, and P.
C. Campanelli (1992), Methodologies for Evaluating Survey
Questions: Some Lessons From the Redesign of the Current Population Survey, Paper presented at the International Conference on Social Science Methodology, Trento,
Italy, June, 1992.
Esposito, J. L. and J. M. Rothgeb (1997), ‘‘Evaluating Survey
Data: Making the Transition From Pretesting to Quality
Assessment,’’ in Survey Measurement and Process Quality,
New York: Wiley, pp.541−571.
Forsyth, B. H. and J. T. Lessler (1991), Cognitive Laboratory
Methods: A Taxonomy, in P. P. Biemer, R. M. Groves, L. E.
Lyberg, N. A. Mathiowetz, and S. Sudman (eds.), Measurement Errors in Surveys, New York: Wiley, pp. 393−418.
Fracasso, M. P. (1989), Categorization of Responses to the
Open-Ended Labor Force Questions in the Current Population Survey, Proceedings of the Section on Survey
Research Methods, American Statistical Association, pp.
481−485.

REFERENCES

Gaertner, G., D. Cantor, and N. Gay (1989), Tests of Alternative Questions for Measuring Industry and Occupation
in the CPS, Proceedings of the Section on Survey
Research Methods, American Statistical Association.

Bischoping, K. (1989), An Evaluation of Interviewer
Debriefings in Survey Pretests, In C. Cannell et al. (eds.),
New Techniques for Pretesting Survey Questions,
Chapter 2.

Martin, E. A. (1987), Some Conceptual Problems in the Current Population Survey, Proceedings of the Section on
Survey Research Methods, American Statistical Association, pp. 420−424.

Campanelli, P. C., E. A. Martin, and J. M. Rothgeb (1991),
The Use of Interviewer Debriefing Studies as a Way to
Study Response Error in Survey Data, The Statistician,
vol. 40, pp. 253−264.

Martin, E. A. and A. E. Polivka (1995), Diagnostics for
Redesigning Survey Questionnaires: Measuring Work in the
Current Population Survey, Public Opinion Quarterly,
Winter 1995 vol. 59, no.4, pp. 547−67.

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Design of the Current Population Survey Instrument

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

Oksenberg, L., C. Cannell, and G. Kalton (1991), New Strategies for Pretesting Survey Questions, Journal of Official Statistics, vol. 7, no. 3, pp. 349−365.

Rothgeb, J. M. (1982), Summary Report of July Followup of
the Unemployed, Internal memorandum, December 20,
1982.

Palmisano, M. (1989), Respondents Understanding of Key
Labor Force Concepts Used in the CPS, Proceedings of
the Section on Survey Research Methods, American
Statistical Association.

Rothgeb, J. M., A. E. Polivka, K. P. Creighton, and S. R.
Cohany (1992), Development of the Proposed Revised Current Population Survey Questionnaire, Proceedings of
the Section on Survey Research Methods, American
Statistical Association, pp. 56−65.

Polivka, A. E. and S. M. Miller (1998),The CPS After the
Redesign Refocusing the Economic Lens, Labor Statistics
Measurement Issues, edited by J. Haltiwanger et al., pp.
249−86.
Polivka, A. E. and J. M. Rothgeb (1993), Overhauling the
Current Population Survey: Redesigning the Questionnaire,
Monthly Labor Review, September 1993 vol. 116, no. 9,
pp. 10−28.

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

U.S. Department of Labor, Bureau of Labor Statistics
(1988), Response Errors on Labor Force Questions Based
on Consultations With Current Population Survey Interviewers in the United States. Paper prepared for the OECD
Working Party on Employment and Unemployment Statistics.
U.S. Department of Labor, Bureau of Labor Statistics
(1997), BLS Handbook of Methods, Bulletin 2490, April
1997.

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Chapter 7.
Conducting the Interviews
INTRODUCTION
Each month during interview week, field representatives
(FRs) and computer-assisted telephone interviewers
attempt to contact and interview a responsible person living in each sample unit selected to complete a Current
Population Survey (CPS) interview. Typically, the week containing the 19th of the month is the interview week. The
week containing the 12th is the reference week (i.e., the
week about which the labor force questions are asked). In
December, the week containing the 12th is used as interview week, provided the reference week (in this case the
week containing the 5th) falls entirely within the month of
December. As outlined in Chapter 3, households are in
sample for 8 months. Each month, one-eighth of the
households are in sample for the first time (month-insample 1 [MIS 1]), one-eighth for the second time, etc.
Because of this schedule, different types of interviews
(due to differing MIS) are conducted by each FR within
his/her weekly assignment. An introductory letter is sent
to each sample household prior to its first and fifth month
interviews. The letter describes the CPS, announces the
forthcoming visit, and provides respondents with information regarding their rights under the Privacy Act, the voluntary nature of the survey, and the guarantees of confidentiality for the information they provide. Figure 7–1
shows the introductory letter sent to sample units in the
area administered by the Atlanta Regional Office. A
personal-visit interview is required for all first month-insample households because the CPS sample is strictly a
sample of addresses. The U.S. Census Bureau has no way
of knowing who the occupants of the sample household
are, or even whether the household is occupied or eligible
for interview. (Note: For some MIS 1 households, telephone interviews are conducted if, during the initial personal contact, the respondent requests a telephone interview.)
NONINTERVIEWS AND HOUSEHOLD ELIGIBILITY
The FR’s first task is to establish the eligibility of the
sample address for the CPS. There are many reasons an
address may not be eligible for interview. For example, the
address may have been converted to a permanent business, condemned or demolished, or it may be outside the
boundaries of the area for which it was selected. Regardless of the reason, such sample addresses are classified as
Type C noninterviews. The Type C units have no chance of
becoming eligible for the CPS interview in future months

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

because the condition is considered permanent. These
addresses are stricken from the roster of sample
addresses and are never visited again with regard to CPS.
All households classified as Type C undergo a full supervisory review of the circumstances surrounding the case
before the determination is made final.
Type B ineligibility includes units that are intended for
occupancy but are not occupied by any eligible individuals. Reasons for such ineligibility include a vacant housing
unit (either for sale or rent), units occupied entirely by
individuals who are not eligible for a CPS labor force interview (individuals with a usual residence elsewhere (URE),
or in the Armed Forces). Such units are classified as Type B
noninterviews. Type B noninterview units have a chance of
becoming eligible for interview in future months, because
the condition is considered temporary (e.g., a vacant unit
could become occupied). Therefore, Type B units are reassigned to FRs in subsequent months. These sample
addresses remain in sample for the entire 8 months that
households are eligible for interviews. Each succeeding
month, an FR visits the unit to determine whether the unit
has changed status and either continues the Type B classification, revises the noninterview classification, or conducts an interview as applicable. Some of these Type B
households are found to be eligible for the Housing
Vacancy Survey (HVS), described in Chapter 11.
Additionally, one final set of households not interviewed
for CPS are Type A households. These are households that
the FR has determined are eligible for a CPS interview but
for which no useable data were collected. To be eligible,
the unit has to be occupied by at least one person eligible
for an interview (an individual who is a civilian, at least 15
years old, and does not have a usual residence elsewhere).
Even though such households are eligible, they are not
interviewed because the household members refuse, are
absent during the interviewing period, or are unavailable
for other reasons. All Type A cases are subject to full
supervisory review before the determination is made final.
Every effort is made to keep such noninterviews to a minimum. All Type A cases remain in the sample and are
assigned for interview in all succeeding months. Even in
cases of confirmed refusals (cases that still refuse to be
interviewed despite supervisory attempts to convert the
case), the FR must verify that the same household still
resides at that address before submitting a Type A noninterview.

Conducting the Interviews

7–1

Figure 7−1. Introductory Letter

7–2

Conducting the Interviews

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Figure 7–2 shows how the three types of noninterviews
are classified and the various reasons that define each category. Even if a unit is designated as a noninterview, FRs
are responsible for collecting information about the unit.
Figure 7–3 lists the main housing unit items that are collected for noninterviews and summarizes each item
briefly.
Figure 7–2. Noninterviews: Types A, B, and C
Note: See the CPS Interviewing Manual for more details
regarding the answer categories under each type of noninterview. Figure 7–3 shows the main housing unit information gathered for each noninterview category and a brief
description of what each item covers.

TYPE A
1
2
3
4

No one home
Temporarily absent
Refusal
Other occupied

TYPE B
1
2
3
4
5
6
7
8
9
10
11

Vacant regular
Temporarily occupied by persons with usual residence elsewhere
Vacant—storage of household furniture
Unfit or to be demolished
Under construction, not ready
Converted to temporary business or storage
Unoccupied tent site or trailer site
Permit granted, construction not started
Entire household in the Armed Forces
Entire household under age 15
Other Type B—specify

TYPE C
1
2
3
4
5
6
7
8
9
10

Demolished
House or trailer moved
Outside segment
Converted to permanent business or storage
Merged
Condemned
Removed during subsampling
Unit already had a chance of selection
Unused line of listing sheet
Other—specify

INITIAL INTERVIEW
If the unit is not classified as a noninterview, the FR initiates the CPS interview. The FR attempts to interview a
knowledgeable adult household member (known as the
household respondent). The FRs are trained to ask the
questions worded exactly as they appear on the computer
screen. The interview begins with the verification of the
unit’s address and confirmation of its eligibility for a CPS
interview. Part 1 of Figure 7–4 shows the household items
asked at the beginning of the interview. Once this is established, the interview moves into the demographic portion
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

of the instrument. The primary task of this portion of the
interview is to establish the household’s roster (the listing
of all household residents at the time of the interview). At
this point in the interview, the main concern is to establish
an individual’s usual place of residence. (These rules are
summarized in Figure 7–5.) For all individuals residing in
the household without a usual residence elsewhere, a
number of personal and family demographic characteristics are collected. Part 1 of Figure 7–6 shows the demographic information collected from MIS 1 households.
These characteristics are the relationship to the reference
person (the person who owns or rents the home), parent
or spouse pointers (if applicable), age, sex, marital status,
educational attainment, veteran’s status, current Armed
Forces status, and race and ethnic origin. As discussed in
Figure 7–7, these characteristics are collected in an interactive format that includes a number of consistency edits
embedded in the interview itself. The goal is to collect as
consistent a set of demographic characteristics as possible. The final steps in this portion of the interview are to
verify the accuracy of the roster. To this end, a series of
questions is asked to ensure that all household members
have been accounted for. Before moving on to the labor
force portion of the interview, the FR is prompted to
review the roster and all data collected up to this point.
The FR has an opportunity to correct any incorrect or
inconsistent information at this time. The instrument then
begins the labor force portion of the interview.
In a household’s initial interview, information about a few
additional characteristics are collected after completion of
the labor force portion of the interview. This information
includes questions on family income and on all household
members’ countries of birth (and the country of birth of
the member’s father and mother) and, for the foreign born,
on year of entry into the United States and citizenship status. See Part 2 of Figure 7–6 for a list of these items.
After completing the household roster, the FR collects the
labor force data described in Chapter 6. The labor force
data are collected from all civilian adult individuals (age
15 and older) who do not have a usual residence elsewhere. To the extent possible, the FR attempts to collect
this information from each eligible individual him/herself.
In the interest of timeliness and efficiency, however, a
household respondent (any knowledgeable adult household member) often provides the data. Just over one-half
of the CPS labor force data are collected by self-response.
The bulk of the remainder is collected by proxy from the
household respondent. Additionally, in certain limited situations, collection of the data from a nonhousehold member is allowed. All such cases receive direct supervisory
review before the data are accepted into the CPS processing system.
Conducting the Interviews

7–3

Figure 7–3. Noninterviews: Main Items of Housing Unit Information Asked for Types A, B, and C
Note: This list of items is not inclusive. The list covers only the main data items and does not include related items used to arrive at the
final categories (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the actual instrument screens for all
CPS items.
Housing Unit Items for Type A Cases
Item name

Item asks

2
3
4
5

TYPE A
ABMAIL
PROPER
ACCES-scr
LIVQRT

INOTES-1

Which specific kind of Type A is the case.
What is the property’s mailing address.
If there is any other building on the property (occupied or vacant).
If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.
What is the type of housing unit (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based
on observation.
If the interviewer wants to make any notes about the case that might help with the next interview.

Housing Unit Items for Type B Cases
Item name

Item asks

2
3
4
5
6
7

TYPE B
ABMAIL
BUILD
FLOOR
PROPER
ACCES-scr
LIVQRT

8
9

SEASON
BCINFO

INOTES-1

Which specific kind of Type B is the case.
What is the property’s mailing address.
If there are any other units (occupied or vacant) in the unit.
If there are any occupied or vacant living quarters besides this one on this floor.
If there is any other building on the property (occupied or vacant).
If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.
What is the type of housing unit (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based
on observation.
If the unit is intended for occupancy year round, by migratory workers, or seasonally.
What are the name, title, and phone number of contact who provided Type B or C information; or if the information was
obtained by interviewer observation.
If the interviewer wants to make any notes about the case that might help with the next interview.

Housing Unit Items for Type C Cases
Item name

Item asks

2
3
4

TYPE C
PROPER
ACCES-scr
LIVQRT

BCINFO

INOTES-1

Which specific kind of Type C is the case.
If there is any other building on the property (occupied or vacant).
If access to the household is direct or through another unit; this item is answered by the interviewer based on observation.
What is the type of housing unit (house/apt., mobile home or trailer, etc.); this item is answered by the interviewer based
on observation.
What are the name, title, and phone number of contact who provided Type B or C information; or if the information was
obtained by interviewer observation.
If the interviewer wants to make any notes about the case that might help with the next interview.

SUBSEQUENT MONTHS’ INTERVIEWS
For households in sample for the second, third, and fourth
months, the FR has the option of conducting the interview
over the telephone. Use of this interviewing mode must be
approved by the respondent. Such approval is obtained at
the end of the first month’s interview upon completion of
the labor force and any supplemental questions. Telephone interviewing is the preferred method for collecting
the data; it is much more time and cost efficient. We
obtain approximately 85 percent of interviews in these 3
months-in-samples (MIS) via the telephone. See Part 2 of
Figure 7–4 for the questions asked to determine household eligibility and obtain consent for the telephone interview. FRs must attempt to conduct a personal-visit interview for the fifth-month interview. After one attempt, a
telephone interview may be conducted provided the original household still occupies the sample unit. This fifthmonth interview follows a sample unit’s 8-month dormant
7–4

Conducting the Interviews

period and is used to reestablish rapport with the household. Fifth-month households are more likely than any
other MIS household to be a replacement household, that
is, a replacement household in which all the previous
month’s residents have moved out and been replaced by
an entirely different group of residents. This can and does
occur in any MIS except for MIS 1 households. As with
their MIS 2, 3, and 4 counterparts, households in their
sixth, seventh, and eighth MIS are eligible for telephone
interviewing. Once again, we collect about 85 percent of
these cases via the telephone.
The first thing the FR does in subsequent interviews is
update the household roster. The instrument presents a
screen (or a series of screens for MIS 5 interviews) that
prompts the FR to verify the accuracy of the roster. Since
households in MIS 5 are returning to sample after an
8-month hiatus, additional probing questions are asked to
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Figure 7–4. Interviews: Main Housing Unit Items Asked in MIS 1 and Replacement Households
Note: This list of items is not inclusive. The list covers only the main data items and does not include related items used to identify the
final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the actual instrument screens for all
CPS items.
Part 1. Items Asked at the Beginning of the Interview
Item name

Item asks

1
2
3
4
5
6
7
8
9
10

INTRO-b
NONTYP
VERADD
MAILAD
STRBLT
BUILD
FLOOR
PROPER
TENUR-scrn
ACCES-scr

11
12

MERGUA
LIVQRT

13
14

LIVEAT
HHLIV

If interviewer wants to classify case as a noninterview.
What type of noninterview the case is (A, B, or C); asked depending on answer to INTRO-b.
What is the street address (as verification).
What is the mailing address (as verification).
If the structure was originally built before or after 4/1/00.
If there are any other units (occupied or vacant) in the building.
If there are any occupied or vacant living quarters besides the sample unit on the same floor.
If there is any other building on the property (occupied or vacant).
If unit is owned, rented, or occupied without paid rent.
If access to household is direct or through another unit; this item is answered by the interviewer (not read to the
respondent).
If the sample unit has merged with another unit.
What is the type of housing unit (house/apt., mobile home or trailer, etc.); this item is answered by the
interviewer (not read to the respondent).
If all persons in the household live or eat together.
If any other household on the property lives or eats with the interviewed household.

Part 2. Items Asked at the End of the Interview
Item name

Item asks

15
16

TELHH-scrn
TELAV-scrn

17
18
19
20
21
22
23

TELWHR-scr
TELIN-scrn
TELPHN
BSTTM-scrn
NOSUN-scrn
THANKYOU
INOTES-1

If there is a telephone in the unit.
If there is a telephone elsewhere on which people in this household can be contacted; asked depending on
answer to TELHH-scrn.
If there is a telephone elsewhere, where is the phone located; asked depending on answer to TELAV-scrn.
If a telephone interview is acceptable.
What is the phone number and whether it is a home or office phone.
When is the best time to contact the respondent.
If a Sunday interview is acceptable.
If there is any reason why the interviewer will not be able to interview the household next month.
If the interviewer wants to make any notes about the case that might help with the next interview; also asks for
a list of names/ages of ALL additional persons if there are more than 16 household members.

establish the household’s current roster and update some
characteristics. See Figure 7−8 for a list of major items asked
in MIS 5 interviews. If there are any changes, the instrument
goes through the steps necessary to add or delete an
individual(s). Once all the additions/deletions are completed,
the instrument then prompts the FR/interviewer to correct
or update any relationship items (e.g., relationship to reference person, marital status, and parent and spouse pointers) that may be subject to change. After making the appropriate corrections, the instrument will take the interviewer
to any items, such as educational attainment, that require
periodic updating. The labor force interview in MIS 2, 3, 5, 6,
and 7 collects the same information as the MIS 1 interview.
MIS 4 and 8 interviews are different in several respects.
Additional information collected in these interviews includes
a battery of questions for employed wage and salary workers on their usual weekly earnings at their only or main job.
For all individuals who are multiple jobholders, information
is collected on the industry and occupation of their second
job. For individuals who are not in the labor force, we obtain
additional information on their previous labor force attachment.

Current Population Survey TP66
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Dependent interviewing is another enhancement made
possible by the computerization of the labor force interview. Information collected in the previous month’s interview, like the household roster and demographic data, is
imported into the current interview to ease response burden and improve the quality of the labor force data. This
change is most noticeable in the collection of main job
industry and occupation data. Importing the previous
month’s job description into the current month’s interview
shows whether an individual has the same job as he/she
had the preceding month. Not only does this enhance
analysis of month-to-month job mobility, it also frees the
FR/interviewer from re-entering the detailed industry and
occupation descriptions. This speeds the labor force interview and reduces respondent burden. Other information
collected using dependent interviewing is the duration of
unemployment (either job search or layoff duration), and
data on the not-in-labor-force subgroups of retired and disabled. Dependent interviewing is not used in the MIS 5
interviews or for any of the data collected solely in MIS 4
and 8 interviews.

Conducting the Interviews

7–5

Figure 7–5. Summary Table for Determining Who is to be Included as a Member of the Household
Include as memberofhousehold
A. PERSONS STAYING IN SAMPLE UNIT AT TIME OF INTERVIEW
Person is member of family, lodger, servant, visitor, etc.
1. Ordinarily stays here all the time (sleeps here). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Here temporarily–no living quarters held for person elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Here temporarily–living quarters held for person elsewhere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Yes
Yes

Person is in Armed Forces
1. Stationed in this locality, usually sleeps here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Temporarily here on leave–stationed elsewhere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Yes

Person is a student–Here temporarily attending school−living quarters held for person elsewhere
1. Not married or not living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Married and living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Student nurse living at school . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

No
No
Yes
Yes

B. ABSENT PERSON WHO USUALLY LIVES HERE IN SAMPLE UNIT
Person is inmate of institutional special place–absent because inmate in a specified institution regardless of
whether or not living quarters held for person here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Person is temporarily absent on vacation, in general hospital, etc. (including veterans’ facilities that are general
hospitals)–Living quarters held here for person. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Person is absent in connection with job
1. Living quarters held here for person–temporarily absent while ‘‘on the road’’ in connection with job (e.g., traveling
salesperson, railroad conductor, bus driver) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Living quarters held here and elsewhere for person but comes here infrequently (e.g., construction engineer) . . . . . .
3. Living quarters held here at home for unmarried college student working away from home during summer school
vacation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Yes

Yes
No
Yes

Person is in Armed Forces–was member of this household at time of induction but currently stationed elsewhere . .
Person is a student in school–away temporarily attending school−living quarters held for person here
1. Not married or not living with immediate family. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Married and living with immediate family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3. Attending school overseas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4. Student nurse living at school . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

No
Yes
No
No
No

C. EXCEPTIONS AND DOUBTFUL CASES
Person with two concurrent residences–determine length of time person has maintained two concurrent residences
1. Has slept greater part of that time in another locality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Has slept greater part of that time in sample unit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

No
Yes

Citizen of foreign country temporarily in the United States
1. Living on premises of an Embassy, Ministry, Legation, Chancellery, or Consulate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2. Not living on premises of an Embassy, Ministry, etc.
a. Living here and no usual place of residence elsewhere in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
b. Visiting or traveling in the United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Another milestone in the computerization of the CPS is the
use of centralized facilities for computer- assisted telephone interviewing (CATI). The CPS has been experimenting with the use of CATI since 1983. The first use of CATI
in production was the Tri-Cities Test, which started in April
1987. Since that time, more and more cases have been
sent to CATI facilities for interviewing, currently numbering about 7,000 cases each month. The facilities generally
interview about 80 percent of the cases assigned to them.
The net result is that about 12 percent of all CPS interviews are completed at a CATI facility.
Three CATI facilities are in use: Hagerstown, MD; Tucson,
AZ; and Jeffersonville, IN. During the time of the initial
phase-in of CATI data, use of a controlled selection criteria
(see Chapter 4) allowed the analysis of the effects of the
CATI collection methodology. Chapter 16 provides a discussion of CATI effects on the labor force data. One of the
7–6

Conducting the Interviews

No
Yes
No

main reasons for using CATI is to ease the recruiting and
hiring effort in hard to enumerate areas. It is much easier
to hire an individual to work in the CATI facilities than it is
to hire individuals to work as FRs in most major metropolitan areas, particularly most large cities. Most of the cases
sent to CATI are from major metropolitan areas. CATI is
not used in most rural areas because the small sample
sizes in these areas do not cause the FRs undo hardship. A
concerted effort is made to hire some Spanish speaking
interviewers in the Tucson Telephone Center, enabling
Spanish interviews to be conducted from this facility. No
MIS 1 or 5 cases are sent to the facilities, as explained
above.
The facilities complete all but 20 percent of the cases sent
to them. These uncompleted cases are recycled back to
the field for follow-up and final determination. For this
Current Population Survey TP66
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Figure 7–6. Interviews: Main Demographic Items Asked in MIS 1 and Replacement Households
Note: This list of items in not all inclusive. The list covers only the main data items and does not include related items used to arrive at
the final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the actual instrument screens for
all CPS items.
Part 1. Items Asked at Beginning of Interview
Item name

Item asks

HHRESP
RPNAME

3
4
5

NEXTNM
VERURE
HHMEM-scrn

6
7
8

SEX-scrn
MCHILD
MAWAY

9
10
11

MLODGE
MELSE
RRP-nscr

12
13

VR-NONREL
SBFAMILY

14
15
16
17
18
19
20
21

PAREN-scrn
BMON-scrn
BDAY-scrn
BYEAR-scrn
AGEVR
MARIT-scrn
SPOUS-scrn
AFEVE-scrn
AFWHE-scrn
AFNOW-scrn

EDUCA-scrn

HISPNON-R

RACE*-R

SSN-scrn

CHANGE

What is the line number of the household respondent.
What is the name of the reference person (i.e., person who owns/rents home, whose name should appear on line
number 1 of the household roster).
What is the name of the next person in the household (lines number 2 through a maximum of 16).
If the sample unit is the person’s usual place of residence.
If the person has his/her usual place of residence elsewhere; asked only when the sample unit is not the person’s
usual place of residence.
What is the person’s sex; this item is answered by the interviewer (not read to the respondent).
If the household roster (displayed on the screen) is missing any babies or small children.
If the household roster (displayed on the screen) is missing usual residents temporarily away from the unit (e.g.,
traveling, at school, in a hospital).
If the household roster (displayed on the screen) is missing any lodgers, boarders, or live-in employees.
If the household roster (displayed on the screen) is missing anyone else staying in the unit.
How is the person related to the reference person; the interviewer shows the respondent a flashcard from which
he/she chooses the appropriate relationship category.
If the person is related to anyone else in the household; asked only when the person is not related to the
reference person.
Who on the household roster (displayed on the screen) is the person related to; asked depending on answer to
VR-NONREL.
What is the parent’s line number.
What is the month of birth.
What is the day of birth.
What is the year of birth.
How many years old is the person (as verification).
What is the person’s marital status; asked only of persons 15+ years old.
What is the spouse’s line number; asked only of persons 15+ years old.
If the person ever served on active duty in the U.S. Armed Forces; asked only of persons 17+ years old.
When did the person serve; asked only of persons 17+ years old who have served in the U.S. Armed Forces.
If the person is now in the U.S. Armed Forces; asked only of persons 17+ years old who have served in the U.S.
Armed Forces. Interviewers will continue to ask this item each month as long as the answer is ‘‘yes.’’
What is the highest level of school completed or highest degree received; asked only of persons 15+ years old.
This item is asked for the first time in MIS 1, and then verified in MIS 5 and in specific months (i.e., February,
July, and October).
What is the person’s origin; the interviewer shows the respondent a flashcard from which he/she chooses the
appropriate origin categories.
What is the person’s race; the interviewer shows the respondent a flashcard from which he/she chooses the
appropriate race category.
What is the person’s social security number; asked only of persons 15+ years old. This item is asked only from
December through March, regardless of month in sample.
If there has been any change in the household roster (displayed with full demographics) since last month,
particularly in the marital status.

Part 2. Items Asked at the End of the Interview
Item name

Item asks

27
28
29
30

NAT1
MNAT1
FNAT1
CITZN-scr

31
32
33

CITYA-scr
CITYB-scr
INUSY-scr

FAMIN-scrn

What is the person’s country of birth.
What is his/her mother’s country of birth.
What is his/her father’s country of birth.
If the person is a citizen of the U.S.; asked only when neither the person nor both of his/her parents were born
in the U.S. or U.S. territory.
If the person was born a citizen of the U.S.; asked when the answer to CITZN-scr is yes.
If the person became a citizen of the U.S. through naturalization; asked when the answer to CITYA-scr is no.
When did the person come to live in the U.S.; asked of U.S. citizens born outside of the 50 states (e.g., Puerto
Ricans, U.S. Virgin Islanders, etc.) and of non-U.S. citizens.
What is the household’s total combined income during the past 12 months; the interviewer shows the
respondent a flashcard from which he/she chooses the appropriate income category.

Current Population Survey TP66
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Conducting the Interviews

7–7

reason, the CATI facilities generally cease conducting the
labor force portions of the interview on Wednesday of
interview week, so the field staff has 3 to 4 days to check
on the cases and complete any required interviewing or
classify the cases as noninterviews. The field staff is
highly successful in completing these cases as interviews,
generally interviewing about 85−90 percent of them. The

cases that are sent to the CATI facilities are selected by the
supervisors in each of the regional offices, based on the
FR’s analysis of a household’s probable acceptance of a
CATI interview, and the need to balance workloads and
meet specific goals on the number of cases sent to the
facilities.

Figure 7–7. Demographic Edits in the CPS Instrument
Note: The following list of edits is not inclusive; only the major edits are described. The demographic edits in the CPS instrument take
place while the interviewer is creating or updating the roster. After the roster is in place, the interviewer may still make changes to the
roster (e.g., add/delete persons, change variables) at the Change screen. However, the instrument does not include demographic edits
past the Change screen.
Education Edits
1.

The instrument will force interviewers to probe if the education level is inconsistent with the person’s age; interviewers will probe for
the correct response if the education entry fails any of the following range checks:
• If 19 years old, the person should have an education level below the level of a master’s degree (EDUCA-scrn < 44).
• If 16-18 years old, the person should have an education level below the level of a bachelor’s degree (EDUCA-scrn 43).
• If younger than 15 years old, the person should have an education below college level (EDUCA-scrn < 40).

The instrument will force the interviewer to probe before it allows him/her to lower an education level reported in a previous month
in sample.
Veterans’ Edits

The instrument will display only the answer categories that apply (i.e., periods of service in the Armed Forces), based on the person’s
age. For example, the instrument will not display certain answer categories for a 40-year-old veteran (e.g., World War I, World War II,
Korean War), but it will display them for a 99-year-old veteran.
Nativity Edits

The instrument will force the interviewer to probe if the person’s year of entry into the U.S. is earlier than his/her year of birth.
Spouse Line Number Edits

If the household roster does not include a spouse for the reference person, the instrument will set the reference person’s SPOUSE line
number equal to zero. It will also omit the first answer category (i.e., married spouse present) when it asks for the marital status of
the reference person).

The instrument will not ask SPOUSE line number for both spouses in a married couple. Once it obtains the SPOUSE line number for the
first spouse on the roster, it will fill the second spouse’s SPOUSE line number with the line number of the first spouse. Likewise, the
instrument will not ask marital status for both spouses. Once it obtains the marital status for the first spouse on the roster, it will set
the second spouse’s marital status equal to that of his/her spouse.

Before assigning SPOUSE line numbers, the instrument will verify that there are opposing sex entries for each spouse. If both spouses
are of the same sex, the interviewer will be prompted to fix whichever one is incorrect.

For each household member with a spouse, the instrument will ensure that his/her SPOUSE line number is not equal to his/her own
line number, nor to his/her own PARENT line number (if any). In both cases, the instrument will not allow the interviewer to make the
wrong entry and will display a message telling the interviewer to ‘‘TRY AGAIN.’’
Parent Line Number Edits

The instrument will never ask for the reference person’s PARENT line number. It will set the reference person’s PARENT line number
equal to the line number of whomever on the roster was reported as the reference person’s parent (i.e., an entry of 24 at RRP-nscr),
or equal to zero if no one on the roster fits that criteria.

Likewise, for each individual reported as the reference person’s child (an entry of 22 at RRP-nscr), the instrument will set his/her
PARENT line number equal to the reference person’s line number, without asking for each individual’s PARENT line number.

The instrument will not allow more than two parents for the reference person.

If the individual is the reference person’s brother or sister (i.e., an entry of 25 at RRP-nscr), the instrument will set his/her PARENT line
number equal to the reference person’s PARENT line number. However, the instrument will not do so without first verifying that the
parent that both siblings have in common is indeed the one whose line number appears in the reference person’s PARENT line number
(since not all siblings have both parents in common).

For each household member, the instrument will ensure that his/her PARENT line number is not equal to his/her own line number. In
such a case, the instrument will not allow the interviewer to make the wrong entry and will display a message telling the interviewer
to ‘‘TRY AGAIN.’’

7–8

Conducting the Interviews

Current Population Survey TP66
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Figure 7–8. Interviews: Main Items (Housing Unit and Demographic) Asked in MIS 5 Cases
Note: This list of items is not inclusive. The list covers only the main data items and does not include related items used to arrive at the
final response (e.g., probes and verification screens). See CPS Interviewing Manual for illustrations of the actual instrument screens for all
CPS items.

Housing Unit Items

1
2
3
4
5
6
7
8
9
10
11
12

Item name

Item asks

HHNUM-vr
VERADD
CHNGPH
MAILAD
TENUR-scrn
TELHH-scrn
TELIN-scrn
TELPHN
BSTTM-scrn
NOSUN-scrn
THANKYOU
INOTES-1

If household is a replacement household.
What is the street address (as verification).
If current phone number needs updating.
What is the mailing address (as verification).
If unit is owned, rented, or occupied without paid rent.
If there is a telephone in the unit.
If a telephone interview is acceptable.
What is the phone number and whether it is a home or office phone.
When is the best time to contact the respondent.
If a Sunday interview is acceptable.
If there is any reason why the interviewer will not be able to interview the household next month.
If the interviewer wants to make any notes about the case that might help with the next interview; also asks for
a list of names/ages of ALL additional persons if there are more than 16 household members.

Demographic Items
Item name

Item asks

13
14
15
16
17

RESP1
STLLIV
NEWLIV
MCHILD
MAWAY

18
19
20

MLODGE
MELSE
EDUCA-scrn

CHANGE

If respondent is different from the previous interview.
If all persons listed are still living in the unit.
If anyone else is staying in the unit now.
If the household roster (displayed on the screen) is missing any babies or small children.
If the household roster (displayed on the screen) is missing usual residents temporarily away from the unit (e.g.,
traveling, at school, in hospital).
If the household roster (displayed on the screen) is missing any lodgers, boarders, or live-in employees.
If the household roster (displayed on the screen) is missing anyone else staying in the unit.
What is the highest level of school completed or highest degree received; asked for the first time in MIS 1, and
then verified in MIS 5 and in specific months (i.e., February, July, and October).
If, since last month, there has been any change in the household roster (displayed with full demographics),
particularly in the marital status.

Figures 7–9 and 7–10 show the results of a typical
month’s (September 2004) CPS interviewing. Figure 7–9
lists the outcomes of all the households in the CPS
sample. The expectations for normal monthly interviewing
are a Type A rate around 7.5 percent with an overall noninterview rate in the 21−23 percent range. In September
2004, the Type A rate was 7.56 percent. For the April
2003−March 2003 period, the CPS Type A rate was 7.25
percent. The overall noninterview rate for September 2004
was 22.98 percent, compared to the 12-month average of
23.19 percent.

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Conducting the Interviews

7–9

Figure 7–9. Interviewing Results (September 2004)
Description
Total HHLD
Eligible HHLD
Interviewed HHLD
Response rate
Noninterviews
Rate
Type A
Rate
No one home
Temporarily absent
Refused
Other—specify
Callback needed—no progress
Type B
Rate
Entire HH Armed Forces
Entire HH under 15
Temp. occupied with persons with URE
Vacant regular (REG)
Vacant HHLD furniture storage
Unfit, to be demolished
Under construction, not ready
Converted to temp. business or storage
Unoccupied tent or trailer site
Permit granted, construction not started
Other Type B
Type C
Rate
Demolished
House or trailer moved
Outside segment
Converted to permanent business or storage
Merged
Condemned
Built after April 1, 2000
Unused serial no./listing Sheet Llne
Removed during subsampling
Unit already had a chance of selection
Other Type C

7–10

Conducting the Interviews

Result
71,575
59,641
55,130
92.44%
16,445
22.98%
4,511
7.56%
1,322
432
2,409
348
0
11,522
16.19%
130
3
1,423
7,660
514
428
429
167
354
50
364
412
0.58%
82
45
7
48
26
9
14
54
0
4
123

Figure 7–10. Telephone Interview Rates
(September 2004)
Note: Figure 7−10 shows the rates of personal and telephone
interviewing in September 2004. It is highly consistent with the
usual monthly results for personal and telephone interviews.

Telephone

Personal

Characteristic
Total Number
Total. . . . . . . 55,130 35,566
MIS 1&5 . . . . . . . . 13,515
2,784
MIS 2−4, 6−8 . . . 41,615 32,782

Percent Number Percent
64.5 19,564
20.6 10,731
78.8
8,833

35.5
79.4
21.2

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Chapter 8.
Transmitting the Interview Results
INTRODUCTION
With the advent of completely electronic interviewing,
transmission of the interview results took on a heightened
importance to the field representative (FR). The data transmissions between headquarters, the regional field office,
and the FRs is all done electronically. This chapter provides a summary of these procedures and how the data
are prepared for production processing.
The system for transmission of data is centralized at U.S.
Census Bureau headquarters. All data transfers must pass
through headquarters even if that is not the final destination of the information. The system was designed this way
for ease of management and to ensure uniformity of procedures within a given survey and between different surveys. The transmission system was designed to satisfy the
following requirements:
• Provide minimal user intervention.
• Upload and/or download in one transmission.
• Transmit all surveys in one transmission.
• Transmit software upgrades with data.
• Maintain integrity of the software and the assignment.
• Prevent unauthorized access.
• Handle mail messages.
The central database system at headquarters cannot initiate transmissions. Either the FR or the regional offices
(ROs) must initiate any transmissions. Computers in the
field are not continuously connected to the headquarters
computers. Instead, the field computers contact the headquarters computers to exchange data using a toll free 800
number. The central database system contains a group of
servers that store messages and case information required
by the FRs or the ROs. When an interviewer calls in, the
transfer of data from the FR’s computer to headquarters
computers is completed first and then any outgoing data
are transferred to the FR’s computer.
A major concern with the use of an electronic method of
transmitting interview data is the need for complete security. Both the Census Bureau and the Bureau of Labor Statistics (BLS) are required to honor the pledge of confidentiality given to all Current Population Survey (CPS)
respondents. The system was designed to safeguard this
pledge. All transmissions between the headquarters central database and the FRs’ computers are compacted and
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

encrypted. All transmissions between headquarters, the
ROs, the Centralized Telephone Facilities, and the National
Processing Center (NPC) are over secure telecommunications lines that are leased by the Census Bureau and are
accessible only by Census Bureau employees through their
computers.
TRANSMISSION OF INTERVIEW DATA
Telecommunications exchanges between an FR and the
central database usually take place once per day during
the survey interview period. Additional transmissions may
be made at any time as needed. Each transmission is a
batch process in which all relevant files are automatically
transmitted and received.
Each FR is expected to make a telecommunications transmission at the end of every work day during the interview
period. This is usually accomplished by a preset transmission; that is, each evening the FR sets up his/her laptop
computer to transmit the completed work. During the
night, at a preset time, the computer modem automatically dials into the headquarters central database and
transmits the completed cases. At the same time, the central database returns any messages and other data to complete the FR’s assignment. It is also possible for an FR to
make an immediate transmission at any time of the day.
The results of such a transmission are identical to a preset
transmission, both in the types and directions of various
data transfers, but the FR has instant access to the central
database as necessary. This type of procedure is used primarily around the time of closeout when an FR might have
one or two straggler cases that need to be received by
headquarters before the field staff can close out the
month’s workload and production processing can begin.
The RO staff may also perform a daily data transmission,
sending in cases that require supervisory review or were
completed at the RO.
Centralized Telephone Facility Transmission
Most of the cases sent to the Census Bureau’s Centralized
Telephone Facilities are successfully completed as
computer-assisted telephone interviews (CATI). Those that
cannot be completed from the telephone center are transferred to an FR prior to the end of the interview period.
These cases are called ‘‘CATI recycles.’’ Each telephone
facility daily transmits both completed cases and recycles
to the headquarters database. All the completed cases are
batched for further processing. Each recycled case is
Transmitting the Interview Results

8–1

transmitted directly to the computer of the FR who has
been assigned the case. Case notes that include the reason for recycle are also transmitted to the FR to assist in
follow-up. Daily transmissions are performed automatically for each region every hour during the CPS interview
week, sending reassigned or recycled cases to the FRs.
The RO staff also monitor the progress of the CATI
recycled cases with the Recycle Report. All cases that are
sent to a CATI facility are also assigned to an FR by the RO
staff. The RO staff keep a close eye on recycled cases to
ensure that they are completed on time, to monitor the
reasons for recycling so that future recycling can be minimized, and to ensure that recycled cases are properly
handled by the CATI facility and correctly identified as
CATI-eligible by the FR.
Transmission of Interviewed Data From the
Centralized Database
Each day during the production cycle (see Figure 8-1 for
an overview of the daily processing cycle), the field staff
send to the production processing system at headquarters
four files containing the results of the previous day’s interviewing. A separate file is received from each of the CATI
facilities, and all data received from the FRs are batched
together and sent as a single file. At this time, cases

8–2

Transmitting the Interview Results

requiring industry and occupation coding (I&O) are identified, and a file of such cases is created. This file is then
used by NPC coders to assign the appropriate I&O codes.
This cycle repeats itself until all data are received by headquarters, usually on Tuesday or Wednesday of the week
after interviewing begins. By the middle of the interview
week, the CATI facilities close down, usually Wednesday,
and only one file is received daily by the headquarters production processing system. This continues until field
closeout day when multiple files may be sent to expedite
the preprocessing.
Data Transmissions for I&O Coding
The I&O data are not actually transmitted to Jeffersonville.
Rather, the coding staff directly access the data on headquarters computers through the use of remote monitors in
the NPC. When a batch of data has been completely coded,
that file is returned to headquarters, and the appropriate
data are loaded into the headquarters database. See Chapter 9 for a complete overview of the I&O coding and processing system.
Once these transmission operations have been completed,
final production processing begins. Chapter 9 provides a
description of the processing operation.

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Current Population Survey TP66

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Transmitting the Interview Results

8–3

Fri

Sat

Sun

Mon

Tue

Wed

Thu

Fri

Week 2
(week containing the 12th)

Sat

* CATI interviewing is extended in March and certain other months.

Field reps and CATI interField reps pick up viewers complete practice Field reps pick up
computerized ques- interviews and home
assignments via
tionnaireviamodem. studies.
modem.

Thu

Week 1
(week containing
the 5th)

Mon

Tue

Thu

Fri

Sun

All assignments completedexcept
fortelephone
holds.

Sat

Allinterviews
completed.

Mon

ROs
complete
final
field
closeout.

Tue

NPC sends back completed I&O cases.

Thu

Fri

Sat

Headquartersperforms
final computer processing
● Edits
● Recodes Census
● Weightdeliversdata
ing
to BLS.

Initial
process
closeout.
NPC
closeout.

Wed

Week 4
(week containing the 26th)

Processing begins:
● Files received overnight are checked in by ROs and
headquarters daily.
● Headquarters performs initial processing and sends eligible cases to NPC for industry and occupation coding.

CAPI interviewing:
● FRs transmit completed work to headquarters nightly.
● ROs monitor interviewing assignments.

CATI
interviewing
ends*.

Wed

Week 3
(week containing the 19th)

CATI interviewing
CATI facilities transmit completed work
to headquarters
nightly.

Sun

Figure 8–1. Overview of CPS Monthly Operations

Fri

Results
released
to
public
at
BLS
8:30
ana- a.m.
lyzes by
data. BLS.

Sun
thru
Thu

Week 5

Chapter 9.
Data Preparation
INTRODUCTION

INDUSTRY AND OCCUPATION (I&O) CODING

For the Current Population Survey (CPS), post datacollection activities transform a raw data file, as collected
by interviewers, into a microdata file that can be used to
produce estimates. Several processes are needed for this
transformation. The raw data files must be read and processed. Textual industry and occupation responses must
be coded. Even though some editing takes place in the
instrument at the time of the interview (see Chapter 7),
further editing is required once all the data are received.
Editing and imputations, explained below, are performed
to improve the consistency and completeness of the
microdata. New data items are created based upon
responses to multiple questions. These activities prepare
the data for weighting and estimation procedures,
described in Chapter 10.

The operation to assign the I&O codes for a typical month
requires ten coders for a period of just over 1 week to
code data from 30,000 individuals. Sometimes the coders
are available for similar activities on other surveys, where
their skills can be maintained. The volume of codes has
decreased significantly with the introduction of dependent
interviewing for I&O codes (see Chapter 6). Only new
monthly CPS cases and those where a person’s industry or
occupation has changed since the previous month of interviewing, are sent to Jeffersonville to be coded. For those
whose industry and occupation have not changed, the
four-digit codes are brought forward from the previous
month of interviewing and require no further coding.

DAILY PROCESSING
For a typical month, computer-assisted telephone interviewing (CATI) starts on Sunday of the week containing
the 19th of the month and continues through Wednesday
of the same week. The answer files from these interviews
are sent to headquarters on a daily basis from Monday
through Thursday of this interview week. One file is
received for all of the three CATI facilities: Hagerstown,
MD; Tucson, AZ; and Jeffersonville, IN. Computer-assisted
personal interviewing (CAPI) also begins on the same Sunday and continues through Monday of the following week.
The CAPI answer files are again sent to headquarters daily
until all the interviewers and regional offices have transmitted the workload for the month. This phase is generally completed by Wednesday of the following week.
The answer files are read, and various computer checks
are performed to ensure the data can be accepted into the
CPS processing system. These checks include, but are not
limited to, ensuring the successful transmission and
receipt of the files, confirming the item range checks, and
rejecting invalid cases. Files containing records needing
four-digit industry and occupation (I&O) codes are electronically sent to Jeffersonville for assignment of these
codes. Once the Jeffersonville staff has completed the I&O
coding, the files are electronically transferred back to
headquarters, where the codes are placed on the CPS production file. When all of the expected data for the month
are accounted for and all of Jeffersonville’s I&O coding
files have been returned and placed on the appropriate
records on the data file, editing and imputation are performed.
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A computer-assisted industry and occupation coding system is used by the Jeffersonville I&O coders. Files of all
eligible I&O cases are sent to this system each day. Each
coder works at a computer terminal where the computer
screen displays the industry and occupation descriptions
that were captured by the field representatives at the time
of the interview. The coder then enters the four-digit
numeric industry and occupation codes used in the 2000
census that represent the industry and occupation descriptions.
A substantial effort is directed at supervision and control
of the quality of this operation. The supervisor is able to
turn the dependent verification setting ‘‘on’’ or ‘‘off’’ at any
time during the coding operation. The ‘‘on’’ mode means
that a particular coder’s work is verified by a second
coder. In addition, a 10-percent sample of each month’s
cases is selected to go through a quality assurance system
to evaluate the work of each coder. The selected cases are
verified by another coder after the current monthly processing has been completed.
After this operation, the batch of records is electronically
returned to headquarters for the next stage of monthly
production processing.
EDITS AND IMPUTATIONS
The CPS is subject to two sources of nonresponse. The
largest is noninterview households. To compensate for
this data loss, the weights of noninterviewed households
are distributed among interviewed households, as
explained in Chapter 10. The second source of data loss is
from item nonresponse, which occurs when a respondent
Data Preparation

9–1

either does not know the answer to a question or refuses
to provide the answer. Item nonresponse in the CPS is
modest (see Chapter 16, Table 16−4).
One of three imputation methods are used to compensate
for item nonresponse in the CPS. Before the edits are
applied, the daily data files are merged and the combined
file is sorted by state and PSU within state. This sort
ensures that allocated values are from geographically
related records; that is, missing values for records in Maryland will not receive values from records in California. This
is an important distinction since many labor force and
industry and occupation characteristics are geographically
clustered.
The edits effectively blank all entries in inappropriate
questions (e.g., followed incorrect path of questions) and
ensure that all appropriate questions have valid entries.
For the most part, illogical entries or out-of-range entries
have been eliminated with the use of electronic instruments; however, the edits still address these possibilities,
which may arise from data transmission problems and
occasional instrument malfunctions. The main purpose of
the edits, however, is to assign values to questions where
the response was ‘‘Don’t know’’ or ‘‘Refused.’’ This is
accomplished by using 1 of the 3 imputation techniques
described below.
The edits are run in a deliberate and logical sequence.
Demographic variables are edited first because several of
those variables are used to allocate missing values in the
other modules. The labor force module is edited next
since labor force status and related items are used to
impute missing values for industry and occupation codes
and so forth.
The three imputation methods used by the CPS edits are
described below:
1. Relational imputation infers the missing value from
other characteristics on the person’s record or within
the household. For instance, if race is missing, it is
assigned based on the race of another household
member, or failing that, taken from the previous
record on the file. Similarly, if relationship data is
missing, it is assigned by looking at the age and sex
of the person in conjunction with the known relationship of other household members. Missing occupation
codes are sometimes assigned by analyzing the industry codes and vice versa. This technique is used as
appropriate across all edits. If missing values cannot
be assigned using this technique, they are assigned
using one of the two following methods.
2. Longitudinal edits are used in most of the labor force
edits, as appropriate. If a question is blank and the
individual is in the second or later month’s interview,
the edit procedure looks at last month’s data to determine whether there was an entry for that item. If so,
9–2

Data Preparation

last month’s entry is assigned; otherwise, the item is
assigned a value using the appropriate hot deck, as
described next.
3. The third imputation method is commonly referred to
as ‘‘hot deck’’ allocation. This method assigns a missing value from a record with similar characteristics,
which is the hot deck. Hot decks are defined by variables such as age, race, and sex. Other characteristics
used in hot decks vary depending on the nature of the
unanswered question. For instance, most labor force
questions use age, race, sex, and occasionally another
correlated labor force item such as full- or part-time
status. This means the number of cells in labor force
hot decks are relatively small, perhaps fewer than
100. On the other hand, the weekly earnings hot deck
is defined by age, race, sex, usual hours, occupation,
and educational attainment. This hot deck has several
thousand cells.
All CPS items that require imputation for missing values
have an associated hot deck . The initial values for the hot
decks are the ending values from the preceding month. As
a record passes through the editing procedures, it will
either donate a value to each hot deck in its path or
receive a value from the hot deck. For instance, in a hypothetical case, the hot deck for question X is defined by the
characteristics Black/non-Black, male/female, and age
16−25/25+. Further assume a record has the value of
White, male, and age 64. When this record reaches question X, the edits determine whether it has a valid entry. If
so, that record’s value for question X replaces the value in
the hot deck reserved for non-Black, male, and age 25+.
Comparably, if the record was missing a value for item X,
it would be assigned the value in the hot deck designated
for non-Black, male, and age 25+.
As stated above, the various edits are logically sequenced,
in accordance with the needs of subsequent edits. The
edits and codes, in order of sequence, are:
1. Household edits and codes. This processing step
performs edits and creates recodes for items pertaining to the household. It classifies households as interviews or noninterviews and edits items appropriately.
Hot deck allocations defined by geography and other
related variables are used in this edit.
2. Demographic edits and codes. This processing
step ensures consistency among all demographic variables for all individuals within a household. It ensures
all interviewed households have one and only one reference person and that entries stating marital status,
spouse, and parents are all consistent. It also creates
families based upon these characteristics. It uses longitudinal editing, hot deck allocation defined by
related demographic characteristics, and relational
imputation.
Current Population Survey TP66
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Demographic-related recodes are created for both
individual and family characteristics.
3. Labor force edits and codes. This processing step
first establishes an edited Major Labor Force Recode
(MLR), which classifies adults as either employed,
unemployed, or not in the labor force.
Based upon MLR, the labor force items related to each
series of classification are edited. This edit uses longitudinal editing and hot deck allocation matrices. The
hot decks are defined by age, race, and/or sex and,
possibly, by a related labor force characteristic.
4. I&O edits and codes. This processing step assigns
four-digit industry and occupation codes to those I&O
eligible individuals for whom the I&O coders were
unable to assign a code. It also ensures consistency,
wherever feasible, between industry, occupation, and
class of worker. I&O related recode guidelines are also
created. This edit uses longitudinal editing, relational

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

allocation, and hot deck allocation. The hot decks are
defined by such variables as age, sex, race, and educational attainment.
5. Earnings edits and codes. This processing step
edits the earnings series of items for earnings-eligible
individuals. A usual weekly earnings recode is created
to allow earnings amounts to be in a comparable form
for all eligible individuals. There is no longitudinal
editing because this series of questions is asked only
of MIS 4 and 8 households. Hot deck allocation is used
here. The hot deck for weekly earnings is defined by
age, race, sex, major occupation recode, educational
attainment, and usual hours worked. Additional earnings recodes are created.
6. School enrollment edits and codes. School enrollment items are edited for individuals 16−24 years old.
Hot deck allocation based on age and other related
variables is used.

Data Preparation

9–3

Chapter 10.
Weighting and Seasonal Adjustment for Labor Force Data
INTRODUCTION
The Current Population Survey (CPS) is a multistage probability sample of housing units in the United States. It produces monthly labor force and related estimates for the
total U.S. civilian noninstitutionalized population and provides details by age, sex, race, and Hispanic origin. In
addition, estimates for a number of other population subdomains (e.g., families, veterans, people with earnings,
households) are produced on either a monthly or quarterly
basis. Each month a sample of eight panels (called rotation
groups) is interviewed, with demographic data collected
for all occupants of the sample housing units. Labor force
data are collected for people 15 years and older. Each rotation group is itself a representative sample of the U.S.
population. The labor force estimates are derived through
a number of weighting steps in the estimation procedure.1
In addition, the weighting at each step is replicated in
order to derive variances for the labor force estimates.
(See Chapter 14 for details.)
The weighting procedures of the CPS supplements are discussed in Chapter 11. Many of the supplements apply to
specific demographic subpopulations and differ in coverage from the basic CPS universe. The supplements tend to
have higher nonresponse rates.
In order to produce national and state estimates from survey data, a statistical weight for each person in the sample
is developed through the following steps, each of which is
explained below:
• Preparation of simple unbiased estimates from baseweights and special weights derived from CPS sampling
probabilities.
• Adjustment for nonresponse.
• First-stage ratio adjustment to reduce variances due to
the sampling of primary sampling units (PSUs).
• National and state coverage adjustments to improve
CPS coverage.
• Second-stage ratio adjustment to reduce variances by
controlling CPS estimates of the population to independent estimates of the current population.

Weights are needed when the sampled elements are selected
by unequal probability sampling. They are also used in poststratification and in making adjustments for nonresponse.

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• Composite estimation using estimates from previous
months to reduce the variances.
• Seasonally-adjusted estimates for key labor force statistics.
In addition to estimates of basic labor force characteristics, several other types of estimates are also produced,
either on a monthly or a quarterly basis. Each of these
involve additional weighting steps to produce the final
estimate. The types of characteristics include:
• Household-level estimates and estimates of married
couples living in the same household using household
and family weights.
• Estimates of earnings, union affiliation, and industry
and occupation of second jobs collected from respondents in the quarter sample using the outgoing rotation
group’s weights.
• Estimates of labor force status by age for veterans and
nonveterans using veterans’ weights.
• Estimates of monthly gross flows using longitudinal
weights.
The additional estimation procedures provide highly accurate estimates for particular subdomains of the civilian
noninstitutionalized population. Although the processes
described in this chapter have remained essentially
unchanged since January 1978, and seasonal adjustment
has been part of the estimation process since June 1975,
modifications have been made in some of the procedures
from time-to-time. For example, in January 1998, a new
compositing procedure was introduced; in January 2003,
new race cells for the first-stage, second-stage, national,
and state coverage steps were added; in January 2005, the
number of cells used in the national coverage adjustment
and in the second-stage ratio adjustment was expanded to
improve the estimates of children.
UNBIASED ESTIMATION PROCEDURE
A probability sample is defined as a sample that has a
known nonzero probability of selection for each sample
unit. With probability samples, unbiased estimators can be
obtained. These are estimates that on average, over
repeated samples, yield the population’s values.
An unbiased estimator of the population total for any characteristic investigated in the survey may be obtained by
multiplying the value of that characteristic for each
Weighting and Seasonal Adjustment for Labor Force Data

10–1

sample unit (person or household) by the reciprocal of the
probability with which that unit was selected and summing the products over all units in the sample (Hansen,
Hurwitz, and Madow, 1953). By starting with unbiased
estimates from a probability sample, various kinds of estimation and adjustment procedures (such as for noninterview) can be applied with reasonable assurance that the
overall accuracy of the estimates will be improved.
In the CPS sample for any given month, not all units
respond, and this nonresponse is a potential source of
bias. This nonresponse averages between 7 and 8 percent.
Other factors, such as occasional errors caused by the
sample selection procedure or the omission of households
or individuals missed by interviewers, can also introduce
bias. These omitted households or people can be considered as having zero probability of selection. These two
exceptions notwithstanding, the probability of selecting
each unit in the CPS is known, and every attempt is made
to keep departures from true probability sampling to a
minimum.
If all units in a sample have the same probability of selection, the sample is called self-weighting, and unbiased
estimators can be computed by multiplying sample totals
by the reciprocal of this probability. Most of the state
samples in the CPS come close to being self-weighting.
Basic Weighting
The sample designated for the current design used in the
CPS was selected with probabilities equal to the inverse of
the required state sampling intervals. These sampling
intervals are called the basic weights (or baseweights).
Almost all sample persons within the same state have the
same probability of selection. As the first step in the estimation procedure, raw counts from the sample housing
units are multiplied by the baseweights. Every person in
the same housing unit receives the same baseweight.
Effect of Sample Reductions on Basic Weights
As time goes on, the number of households and the population as a whole increases. New sample is continually
selected and added to the CPS to provide coverage for
newly constructed housing units. This results in a larger
sample size with an associated increase in costs. Small
maintenance sample reductions are implemented on a
periodic basis to offset the increasing sample size. (See
Appendix B.)
Special Weighting Adjustments
As discussed in Chapter 3, some ultimate sampling units
(USUs) are subsampled in the field because their observed
size is much larger than expected. During the estimation
procedure, housing units in these USUs must receive special weighting factors (also called weighting control factors) to account for the change in their probability of
10–2

Weighting and Seasonal Adjustment for Labor Force Data

selection. For example, an area sample USU expected to
have 4 housing units (HUs) but found at the time of interview to contain 36 HUs, could be subsampled at the rate
of 1 in 3 to reduce the interviewer’s workload. Each of the
12 designated housing units in this case would be given a
special weighting factor of 3. In order to limit the effect of
this adjustment on the variance of sample estimates,
these special weighting factors are limited to a maximum
value of 4. At this stage of CPS estimation process, the
special weighting factors are multiplied by the baseweights. The resulting weights are then used to produce
‘‘unbiased’’ estimates. Although this estimate is commonly
called ‘‘unbiased,’’ it does still include some negligible bias
because the size of the special weighting factor is limited
to 4. The purpose of this limitation is to achieve a compromise between a reduction in the bias and an increase in
the variance.
ADJUSTMENT FOR NONRESPONSE
Nonresponse arises when households or other units of
observation that have been selected for inclusion in a survey fail to provide all or some of the data that were to be
collected. This failure to obtain complete results from all
the units selected can arise from several different sources,
depending upon the survey situation. There are two major
types of nonresponse: item nonresponse and complete (or
unit) nonresponse. Item nonresponse occurs when a cooperating HU fails or refuses to provide some specific items
of information. Procedures for dealing with this type of
nonresponse are discussed in Chapter 9. Unit nonresponse
refers to the failure to collect any survey data from an
occupied sample HU. For example, data may not be
obtained from an eligible household in the survey because
of impassable roads, a respondent’s absence or refusal to
participate in the interview, or unavailability of the respondent for other reasons. This type of nonresponse in the
CPS is called a Type A noninterview. Recently, the Type A
rate has averaged to between 7 and 8 percent (see Chapter 16).
In the CPS estimation process, the weights for all interviewed households are adjusted to account for occupied
sample households for which no information was obtained
because of unit nonresponse (Type A noninterviews). This
noninterview adjustment is made separately for similar
sample areas that are usually, but not necessarily, contained within the same state. Increasing the weights of
interviewed sample units to account for eligible sample
units that are not interviewed is valid if the interviewed
units are similar to the noninterviewed units with regard
to their demographic and socioeconomic characteristics.
This may or may not be true. Nonresponse bias is present
in CPS estimates when the nonresponding units differ in
relevant respects from those that respond to the survey or
to the particular items.
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Noninterview Clusters and Noninterview
Adjustment Cells
To reduce the size of the estimate’s bias, the noninterview
adjustment is performed based on sample PSUs that are
similar in metropolitan status and population size. These
PSUs are grouped together to form noninterview clusters.
In general, PSUs with a metropolitan status of the same (or
similar) size in the same state belong to the same noninterview cluster. PSUs classified as metropolitan are
assigned to metropolitan clusters. Nonmetropolitan PSUs
are assigned to nonmetropolitan clusters. Within each metropolitan cluster, there is a further breakdown into two
noninterview adjustment cells (also called residence cells).
Each is split into ‘‘central city’’ and ‘‘not central city’’ cells.
The nonmetropolitan clusters are not divided further, making a total of 214 adjustment cells from 127 noninterview
clusters.
Computing Noninterview Adjustment Factors
Weighted counts of interviewed and noninterviewed
households are tabulated separately for each noninterview
adjustment cell. The basic weight multiplied by any special weighting factor is used as the weight for this purpose. The noninterview factor Fij is computed as:
Fij =

Zij+ Nij
Zij

where
Zij = the weighted count of interviewed households in
cell j of cluster i, and
Nij = the weighted count of Type A noninterviewed
households in cell j of cluster i.
These factors are applied to data for each interviewed person except in cells where either of the following situations
occurs:
• The computed factor is greater than or equal to 2.0.
• There are fewer than 50 unweighted interviewed households in the cell.
• The cell contains only Type A noninterviewed households and no interviewed households.
If any one of these situations occurs, the weighted counts
are combined for the residence cells within the noninterview cluster. A common adjustment factor is computed
and applied to weights for interviewed people within the
cluster. If after collapsing, any of the cells still meet any of
the situations above, the cell is output to an ‘‘extreme cell
file’’ that is created for review each month.

(baseweight) x (special weighting factor)
x (noninterview adjustment factor)
At this point, records for all individuals in the same household have the same weight, since the adjustments discussed so far depend only on household characteristics.
RATIO ESTIMATION
Distributions of the demographic characteristics derived
from the CPS sample in any month will be somewhat different from the true distributions, even for such basic
characteristics as age, race, sex, and Hispanic origin.2
These particular population characteristics are closely correlated with labor force status and other characteristics
estimated from the sample. Therefore, the variance of
sample estimates based on these characteristics can be
reduced when, by the use of appropriate weighting adjustments, the sample population distribution is brought as
closely into agreement as possible with the known distribution of the entire population with respect to these characteristics. This is accomplished by means of ratio adjustments. There are five ratio adjustments in the CPS
estimation process: the first-stage ratio adjustment, the
national coverage adjustment, the state coverage adjustment, the second-stage ratio adjustment, and the composite ratio adjustment leading to the composite estimator.
In the first-stage ratio adjustment, weights are adjusted so
that the distribution of the single-race Black population
and the population that is not single-race Black (based on
the census) in the sample PSUs in a state corresponds to
the same population groups’ census distribution in all
PSUs in the state. In the national-coverage ratio adjustment, weights are adjusted so that the distribution of agesex-race-ethnicity3 groups match independent estimates
of the national population. In the state-coverage ratio
adjustment, weights are adjusted so that the distribution
of age-sex-race groups match independent estimates of
the state population. In the second-stage ratio adjustment,
weights are adjusted so that aggregated CPS sample estimates match independent estimates of population in various age/sex/race and age/sex/ethnicity cells at the
national level. Adjustments are also made so that the estimated state populations from the CPS match independent
state population estimates by age and sex.
FIRST-STAGE RATIO ADJUSTMENT
Purpose of the First-Stage Ratio Adjustment
The purpose of the first-stage ratio adjustment is to
reduce the variance of sample state-level estimates caused
by the sampling of PSUs, that is, the variance that would

Weights After the Noninterview Adjustment
2

At the completion of the noninterview adjustment procedure, the weight for each interviewed person is:
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Hispanics may be any race.
Although ethnicity, in this chapter, only means Hispanic or
non-Hispanic origin, the word ethnicity will be used.
3

Weighting and Seasonal Adjustment for Labor Force Data

10–3

still be associated with the state-level estimates even if
the survey included all households in every sample PSU.
This is called the between-PSU variance. For some states,
the between-PSU variance makes up a relatively large proportion of the total variance. The relative contribution of
the between-PSU variance at the national level is generally
quite small.
There are several factors to be considered in determining
what information to use in applying the first-stage adjustment. The information must be available for each PSU, correlated with as many of the statistics of importance published from the CPS as possible, and reasonably stable
over time so that the accuracy gained from the ratio
adjustment procedure does not deteriorate. The basic
labor force categories (unemployed, nonagricultural
employed, etc.) could be considered. However, this information could badly fail the stability criterion. The distribution of the population by race (Black alone4/non-Black
alone5) by age groups 0−15 and 16+ satisfies all three criteria.
By using the Black alone/non-Black alone categories, the
first-stage ratio adjustment compensates for the fact that
the racial composition of an NSR (non-self-representing)
sample PSU could differ substantially from the racial composition of the stratum it is representing. This adjustment
is not necessary for SR (self-representing) PSUs since they
represent only themselves.

= 2000 probability of selection for sample PSU k in
state s

total number of NSR PSUs (sample and nonsample) in state s

number of sample NSR PSUs in state s

The estimate in the denominator of each of the ratios is
obtained by multiplying the Census 2000 civilian noninstitutionalized population in the appropriate age/race cell for
each NSR sample PSU by the inverse of the probability of
selection for that PSU and summing over all NSR sample
PSUs in the state.
The Black alone and non-Black alone cells are collapsed
within a state when a cell meets one of the following criteria:
• The factor (FSsj) is greater than 1.3.
• The factor is less than 1/1.3=.769230.
• There are fewer than 4 NSR sample PSUs in the state.
• There are fewer than ten expected interviews in an
age/race cell in the state.
Weights After First-Stage Ratio Adjustment
At the completion of the first-stage ratio adjustment, the
weight for each responding person is the product of:

Computing First-Stage Ratio Adjustment Factors
The first-stage adjustment factors are based on Census
2000 data and are applied only to sample data for the NSR
PSUs. Factors are computed for the two race categories
(Black alone/non-Black alone) for each state containing
NSR PSUs. The following formula is used to compute the
first-stage adjustment factors for each state:

(baseweight) x (special weighting factor)
x (noninterview adjustment factor)
x (first-stage ratio adjustment factor)
The weight after the first-stage adjustment is called the
first-stage weight.
NATIONAL COVERAGE ADJUSTMENT

兺 Csij

FSsj ⫽

i⫽1

冋 册

兺
k⫽1

␲sk

共Cskj兲

where
FSsj = the first-stage factor for state s and age/race cell
j (j=1, 2, 3, 4)
Csij

= the Census 2000 civilian noninstitutional population for NSR PSU i (sample or nonsample) in state
s, age/race cell j

Cskj

= the Census 2000 civilian noninstitutional population for NSR sample PSU k in state s, age/race cell
j

Alone is defined as single race.
Non-Black alone can be defined as everyone in the population who is not of the single race Black.
5

10–4

Weighting and Seasonal Adjustment for Labor Force Data

Purpose of the National Coverage Adjustment
The purpose of the national coverage adjustment is to correct for interactions between race and ethnicity that are
not addressed in the second-stage weighting. For
example, research has shown that the undercoverage of
certain combinations (e.g., non-Black Hispanic) cannot be
corrected with the second-stage adjustment alone. The
national coverage adjustment also helps to speed the convergence of the second-stage adjustment (Robison, Duff,
Schneider, and Shoemaker, 2002).
Computing the National Coverage Adjustment
The national coverage adjustment factors are based on
independently derived estimates of the population. (See
Appendix C.) Person records are grouped into four pairs
based on months-in-sample (MIS) (MIS 1 and 5, MIS 2 and
6, MIS 3 and 7, and MIS 4 and 8). This increases cell size
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For example, in October 2005, 80 percent of the respondents who were adjusted by a national coverage adjustment factor, received a Fjkvalue between 0.9868 and
1.2780. The factor Fjk is computed for each of the cells
listed in Table 10−1.

and preserves the structure needed for composite weighting. Each MIS pair is then adjusted to age/sex/race/ethnicity population controls (see Table 10−1) using the following formula:
Fjk =

The independent population controls used for the national
coverage adjustment are from the same source as those
for the second-stage ratio adjustment.

Ejk

where
Fjk =

Ejk

Extreme cells are identified for any of the following criteria:

national coverage adjustment factor for cell j and
month-in-sample pair k

• Cell contains less than 20 persons.
• National coverage adjustment factor is greater than or
equal to 2.0.

national coverage adjustment control for cell j;
national current population estimate for cell j

• National coverage adjustment factor is less than or
equal to 0.6.

weighted tally for the cell j and month-in-sample
pair k (using weights after the first-stage adjustment)

No collapsing is performed because the cells were created
in order to minimize the number of extreme cells.

Table 10−1: National Coverage Adjustment Cell Definitions
Black alone
non-Hispanic

White alone
non-Hispanic

Age Male Female

White alone
Hispanic

Age Male Female

Non-White alone
Hispanic

Age Male Female

0−1
2−4
5−7
8−9
10−11
12−13
14
15

0
1
2
3
4
5
6
7

0−15

16−19
20−24
25−29
30−34
35−39

8
9
10−11
12−13
14

16+

40−44
45−49

15
16−19

50−54
55−64
65+

20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−59
60−62
63−64
65−69
70−74
75+

20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−64
65+

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Asian alone
non-Hispanic
Age Male Female

Residual race
non-Hispanic
Age Male

0−4

5−9

10−15

16−24

25−34

35−44

45−54

55−64
65+

Weighting and Seasonal Adjustment for Labor Force Data

Female

10–5

Weights After National Coverage Adjustment
After the completion of the national coverage adjustment,
the weight for each person is the product of:
(baseweight) x (special weighting factor)
x (noninterview adjustment factor)
x (first-stage ratio adjustment factor)
x (national coverage adjustment factor)
This weight will usually vary within households due to different household members having different demographic
characteristics.
STATE COVERAGE ADJUSTMENT
Purpose of the State Coverage Adjustment
The purpose of the state coverage adjustment is to adjust
for state-level differences in sex, age, and race coverage.
Research has shown that estimates of characteristics of
certain racial groups (e.g., Blacks) can be far from the
population controls if a state coverage step is not used.
Computing the State Coverage Adjustment
The state coverage adjustment factors are based on independently derived estimates of the population. Except for
the District of Columbia (DC), person records for the nonBlack-alone population are grouped into four pairs based
on MIS (MIS 1 and 5, MIS 2 and 6, MIS 3 and 7, and MIS 4
and 8). This increases cell size and preserves the structure
needed for composite weighting. Person records for the
Black-alone population for all states and the non-Blackalone population for DC are formed at the state level with
all months-in-sample combined. For the Black-alone component of the adjustment, states were assigned to different tables (Tables 10−2A, 10−2B and 10−2C) based on the
expected number of sample records in each age/sex cell.
For the non-Black-alone component, all states except DC
were assigned to Table 10−2D. (DC was assigned to Table
10−2C.) Each cell is then adjusted to age/sex/race population controls in each state6 using the following formula:
Fjk =

Cj
Ejk

where
Fjk =

state coverage adjustment factor for cell j and MIS
pair k.

Cj =

state coverage adjustment control for cell j; state
current population estimate for cell j.

Ejk =

weighted tally for the cell j and MIS pair k.

Extreme cells are identified for any of the following criteria:
• Cell contains less than 20 persons.
• State coverage adjustment factor is greater than or
equal to 2.0.
• State coverage adjustment factor is less than or equal to
0.6.
No collapsing is performed because the cells were created
in order to minimize the number of extreme cells.

Table 10−2: State Coverage Adjustment Cell
Definitions
Table 10−2A — Black Alone
Age

In the state coverage adjustment, California is split into two
parts and each part is treated like a state—Los Angeles County
and the balance of California. Similarly, New York is split into two
parts with each part being treated as a separate state—New York
City (New York, Queens, Bronx, Kings and Richmond Counties)
and the balance of New York.

Weighting and Seasonal Adjustment for Labor Force Data

Male and female combined

0+
States assigned to Table 10−2A: HI, ID, IA, ME, MT, ND, NH, NM,
OR, SD, UT, VT, WY
(all rotation groups combined)

Table 10−2B — Black Alone
Age

Male

Female

0+
States assigned to Table 10−2B: AK, AZ, CO, IN, KS, KY, MN, NE,
NV, OK, RI, WA, WI, WV
(all rotation groups combined)

Table 10−2C — Black Alone and Non-Black Alone for DC
Age

Male

Female

0−15
16−44
45+
States assigned to Table 10−2C: Black Alone: AL, AR, CT, DC, DE,
FL, GA, IL, LA, MA, MD, MI, MO, MS, NC, NJ, OH, PA, SC, TN, TX,
VA, LA county, bal CA, NYC, bal NY; Non-Black Alone: DC
(all rotation groups combined)
Table 10−2D — Non-Black Alone
Age

10–6

For example, in October 2005, 80 percent of the respondents who were adjusted by a state coverage adjustment
factor, received a Fjk value between 0.8451 and 1.1517.
The independent population controls used for the state
coverage adjustment are from the same source as those
for the second-stage ratio adjustment. (See next section
for details on the second-stage adjustment.)

Male

Female

0−15
16−44
45+
States assigned to Table 10−2D: Non-Black Alone: All states + LA
county + NYC + bal CA + bal NY (DC is excluded)
(by rotation group pair)

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Weights After State Coverage Adjustment
After the completion of the state coverage adjustment, the
weight for each person is the product of:
(baseweight) x (special weighting factor)
x (noninterview adjustment factor)
x (first-stage ratio adjustment factor)
x (national coverage adjustment factor)
x (state coverage adjustment factor)
This weight will usually vary within households due to different household members having different demographic
characteristics.
SECOND-STAGE RATIO ADJUSTMENT
The second-stage ratio adjustment decreases the error in
the great majority of sample estimates. Chapter 14 illustrates the amount of reduction in variance for key labor
force estimates. The procedure is also believed to reduce
the bias due to coverage errors (see Chapter 15). The procedure adjusts the weights for sample records within each
month-in-sample pair to control the sample estimates for a
number of geographic and demographic subgroups of the
population to ensure that these sample-based estimates of
population match independent population controls in each
of these categories. These independent population controls are updated each month. Three sets of controls are
used:
• The civilian noninstitutionalized population for the
50 states and the District of Columbia by sex and age
(0−15, 16−44, 45 and older).
• Total national civilian noninstitutionalized population
for 26 Hispanic and 26 non-Hispanic age-sex categories
(see Table 10–3).

• Total national civilian noninstitutionalized population
for 56 White, 36 Black, and 34 ‘‘Residual Race’’ age-sex
categories (see Table 10–4).
The adjustment is done separately for each MIS pair (MIS 1
and 5, MIS 2 and 6, MIS 3 and 7, and MIS 4 and 8). Adjusting the weights to match one set of controls can cause differences in other controls, so an iterative process is used
to simultaneously control all variables. Successive iterations begin with the weights as adjusted by all previous
iterations. A total of ten iterations is performed, which
results in virtual consistency between the sample estimates and population controls. The three-way (state,
Hispanic/sex/age, race/sex/age) raking is also known as
iterative proportional fitting or raking ratio estimation.
In addition to reducing the error in many CPS estimates
and converging to the population controls within ten iterations for most items, the raking ratio estimator has
another desirable property. When it converges, this estimator minimizes the statistic

兺i W2iIn共W2i ⲐW1i)
where
W2i = the weight for the ith sample record after the
second-stage adjustment, and
W1i = the weight for the ith record after the first-stage
adjustment.
Thus, the raking adjusts the weights of the records so that
the sample estimates converge to the population controls
while minimally affecting the weights after the state coverage adjustment. The article by Ireland and Kullback (1968)
provides more details on the properties of raking ratio
estimation.

Table 10−3: Second-Stage Adjustment Cell by Ethnicity, Age, and Sex
Hispanic
Age

Male

0−4
5−9
10−15
16−19
20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−64
65+

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Non-Hispanic
Female

Age

Male

Female

0−4
5−9
10−15
16−19
20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−64
65+

Weighting and Seasonal Adjustment for Labor Force Data

10–7

Table 10−4: Second-Stage Adjustment Cell by Race, Age, and Sex
Black alone
Age

Male

White alone
Female

Age

Male

Residual race
Female

Age

0−1
2−4
5−7
8−9
10−11
12−13
14
15
16−19

0
1
2
3
4
5
6
7
8

0−1
2−4
5−7
8−9
10−11
12−13
14−15
16−19
20−24

20−24
25−29

9
10−11

25−29
30−34

30−34
35−39

12−13
14

35−39
40−44

40−44

45−49

45−49
50−54
55−64
65+

16−19
20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−59
60−62

50−54
55−64
65+

Male

Female

63−64
65−69
70−74
75+

Sources of Independent Controls

• Hispanic/sex/age

The independent population controls used in the secondstage ratio adjustment and in the coverage adjustment
steps are prepared by projecting forward the population
figures derived from Census 2000 using information from
a variety of other sources that account for births, deaths,
and net migration. Subtracting estimated numbers of resident Armed Forces personnel and institutionalized people
from the resident population gives the civilian noninstitutionalized population. Prepared in this manner, the controls are themselves estimates. However, they are derived
independently of the CPS and provide useful information
for adjusting the sample estimates. See Appendix C for
more details on sources and derivation of the independent
controls.

• Race/sex/age

Computing Initial Second-Stage Ratio Adjustment
Factors
As mentioned before, the second-stage adjustment
involves a three-way rake:
• State/sex/age
10–8

Weighting and Seasonal Adjustment for Labor Force Data

There is no collapsing done for the second-stage adjustment. The cells are designed to avoid having small cells.
Instead, a small or extreme cell is identified as follows:
• It contains fewer than 20 people.
• It has an adjustment factor greater than or equal to 2.0.
• It has an adjustment factor less than or equal to 0.6.
Raking
For each iteration of each rake an adjustment factor is
computed for each cell and applied to the estimate of that
cell. The factor is the population control divided by the
estimate of the current iteration for the particular cell.
These three steps are repeated through ten iterations. The
following simplified example begins after one state rake.
The example shows the raking for two cells in an ethnicity
rake and two cells in a race rake. Age/sex cells and one
race cell (see Tables 10–2 and 10–3) have been collapsed
here for simplification.
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Iteration 1:
State rake
Hispanic rake
Race rake
Example of Raking Ratio Adjustment
Raking Estimates by Ethnicity and Race
Es = Estimate from CPS sample after state rake
Ee = Estimate from CPS sample after ethnicity rake
Er = Estimate from CPS sample after race rake
Fe = Ratio adjustment factor for ethnicity
Fr = Ratio adjustment factor for race
Iteration 1 of the Ethnicity Rake
Non-Hispanic

Es = 650
Non-Black

Black

Fe =

Hispanic

Es =150

1050

= 1.265

650+180

Fe =

250

= 1.471

150+20

Ee = EsFe = 822

Ee = EsFe= 221

Es = 180

Es = 20

Fe =

1050

= 1.265

650+180

Ee = EsFe = 228
Population
controls

Population controls

Fe =

250

= 1.471

150+20

Ee = EsFe = 29

1050

1000

300

250

1300

Hispanic

Population controls

Iteration 1 of the Race Rake
Non-Hispanic
Ee = 822
Non-Black

Fr =

Ee=221

1000
822+221

= 0.959

Er = EeFr = 788
Ee = 228
Black

Fr =

300
228+29

1000
822+221

=0.959

Er = EeFr = 212

1000

Ee = 29
= 1.167

Er = EeFr = 266
Population
controls

Fr =

1050

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Fr =

300
228+29

= 1.167

Er = EeFr = 34
250

300
1300

Weighting and Seasonal Adjustment for Labor Force Data

10–9

Iteration 2: (Repeat steps above beginning with sample
cell estimates at the end of iteration 1.)
•
•
•
Iteration 10:
Note that the matching of estimates to controls for the
race rake causes the cells to differ slightly from the controls for the ethnicity rake or previous rake. With each
rake, these differences decrease when cells are matched to
the controls for the most recent rake. For the most part,
after ten iterations, the estimates for each cell have converged to the population controls for each cell. Thus, the
weight for each record after the second-stage ratio adjustment procedure can be thought of as the weight for the
record after the first-stage ratio adjustment multiplied by
a series of 30 adjustment factors (ten iterations of three
rakes). The product of these 30 adjustment factors is
called the second-stage ratio adjustment factor.
Weight After the Second-Stage Ratio Adjustment
At the completion of the second-stage ratio adjustment,
the record for each person has a weight reflecting the
product of:
(baseweight) x (special weighting factor)
x (noninterview adjustment factor)
x (first-stage ratio adjustment factor)
x (national coverage adjustment factor)
x (state coverage adjustment factor)
x (second-stage ratio adjustment factor)
COMPOSITE ESTIMATOR
Once each record has a second-stage weight, an estimate
of level for any given set of characteristics identifiable in
the CPS can be computed by summing the second-stage
weights for all the sample cases that have that set of characteristics. The process for producing this type of estimate
has been variously referred to as a Horvitz-Thompson estimator, a two-stage ratio estimator, or a simple weighted
estimator. But the estimator actually used for the derivation of most official CPS labor force estimates that are
based upon information collected every month from the
full sample (in contrast to information collected in periodic
supplements or from partial samples) is a composite estimator.
In general, a composite estimate is a weighted average of
several estimates. The composite estimate from the CPS
has historically combined two estimates. The first of these
is the estimate at the completion of the second-stage ratio
adjustment which is described above. The second consists
of the composite estimate for the preceding month and an
estimate of the change from the preceding to the current
month. The estimate of the change is based upon data
from the part of the sample that is common to the two
10–10

months (about 75 percent). The higher month-to-month
correlation between estimates from the same sample units
tends to reduce the variance of the estimate of month-tomonth change. Although the average improvements in
variance from the use of the composite estimator are
greatest for estimates of month-to-month change,
improvements are also realized for estimates of change
over other intervals of time and for estimates of levels in a
given month (Breau and Ernst, 1983).
Prior to 1985, the two estimators described in the preceding paragraph were the only terms in the CPS composite
estimator and were given equal weight. Since 1985, the
weights for the two estimators have been unequal and a
third term has been included, an estimate of the net difference between the incoming and continuing parts of the
current month’s sample.
Effective with the release of January 1998 data, the Bureau
of Labor Statistics (BLS) implemented a new composite
estimation method for the CPS. The new technique provides increased operational simplicity for microdata users
and allows optimization of compositing coefficients for
different labor force categories.
Under the procedure, weights are derived for each record
that, when aggregated, produce estimates consistent with
those produced by the composite estimator. Under the
previous procedure, composite estimation was performed
at the macro level. The composite estimator for each tabulated cell was a function of aggregated weights for sample
persons contributing to that cell in current and prior
months. The different months of data were combined
together using compositing coefficients. Thus, microdata
users needed several months of CPS data to compute composite estimates. To ensure consistency, the same coefficients had to be used for all estimates. The values of the
coefficients selected were much closer to optimal for
unemployment than for employment or labor force totals.
The new composite weighting method involves two steps:
(1) the computation of composite estimates for the main
labor force categories, classified by important demographic characteristics and (2) the adjustment of the
microdata weights, through a series of ratio adjustments,
to agree with these composite estimates, thus incorporating the effect of composite estimation into the microdata
weights. Under this procedure, the sum of the composite
weights of all sample persons in a particular labor force
category equals the composite estimate of the level for
that category. To produce a composite estimate for a particular month, a data user may simply access the microdata file for that month and compute a weighted sum. The
new composite weighting approach also improves the
accuracy of labor force estimates by using different compositing coefficients for different labor force categories.
The weighting adjustment method assures additivity while
allowing this variation in compositing coefficients.

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weight of ␤ˆ t an adjustment term that reduces both the
variance of the composite estimator and the bias associated with time in sample. (See Mansur and Shoemaker,
1999, Breau and Ernst, 1983, Bailar, 1975.) Also, see section ‘‘Time in Sample’’ of Chapter 16. ‘‘Time in Sample’’ is
concerned with the effects on labor force estimates from
the CPS as the result of interviewing the same CPS respondents several times.

Composite Estimation in CPS
Eight panels or rotation groups, approximately equal in
size, make up each monthly CPS sample. Due to the 4-8-4
rotation pattern, six of these panels (about 75 percent of
the sample) continue in the sample the following month
and one-half of the households in a given month’s sample
will be back in the sample for the same calendar month
one year later. The sample overlap improves estimates of
change over time. Through composite estimation, the
positive correlation among CPS estimators for different
months is increased. This increase in correlation improves
the accuracy of monthly labor force estimates.

Before January 1998, a single pair of values for K and A
was used to produce all CPS composite estimates. Optimal
values of the coefficients, however, depend on the correlation structure of the characteristic to be estimated.
Research has shown, for example, higher values of K and
A result in more reliable estimates for employment levels
because the ratio estimators for employment are more
strongly correlated across time than those for unemployment. The new composite weighting approach allows use
of different compositing coefficients, thus improving the
accuracy of labor force estimates, while ensuring the additivity of estimates. For a more detailed description of the
selection of compositing parameters, see Lent et al.(1999).

The CPS AK composite estimator for a labor force total
(e.g., the number of people unemployed) in month t is
given by
Ⲑ
YtⲐ ⫽ 共1⫺K兲Yˆt ⫹ K共Yt⫺1
⫹ 䉭t兲 ⫹ A␤ˆ t

where
8

Yˆt ⫽

兺 xt,i
i⫽1

Computing Composite Weights
䉭t ⫽

兺 共xt,i ⫺ xt⫺1, i⫺1兲 and
3i僆s

␤ˆ t ⫽
i

兺 xt,i ⫺ 3i僆s
兺 xt,i
i僆s

1,2,...,8 month in sample

xt,i =

sum of weights after second-stage ratio adjustment
of respondents in month t, and month-in-sample i
with characteristic of interest

{2,3,4,6,7,8} sample continuing from previous
month

Composite weights are produced only for sample people
aged 16 or older. As described in previous sections, the
CPS estimation process begins with the computation of a
‘‘baseweight’’ for each adult in the survey. The
baseweight—the inverse of the probability of selection—is
adjusted for nonresponse, and four successive stages of
ratio adjustments to population controls are applied. The
second-stage raking procedure ensures that sample
weights add to independent population controls for states
by sex and age, as well as for age/sex/ethnicity groups
and age/sex/race groups, specified at the national level.

0.4 for unemployed
0.7 for employed

= 0.3 for unemployed
0.4 for employed

The values given above for the constant coefficients A and
K are close to optimal (with respect to variance) for
month-to-month change estimates of unemployment level
and employment level. The coefficient K determines the
weight, in the weighted average, of each of two estimators for the current month: (1) the current month’s ratio
ˆt and (2) the sum of the previous month’s comestimator Y
posite estimator Y/t-1 and an estimator 䉭t of the change
since the previous month. The estimate of change is based
on data from sample households in the six panels common to months t and t-1. The coefficient A determines the

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The post-January 1998 method of computing composite
weights for the CPS imitates the second-stage ratio adjustment. Sample person weights are raked to force their
sums to equal the control totals. Composite labor force
estimates are used as controls in place of independent
population estimates. The composite raking process is
performed separately within each of the three major labor
force categories: employed, unemployed, and those not in
the labor force.
Adjustment of microdata weights to the composite estimates for each labor force category proceeds as follows.
For simplicity, we describe the method for estimating the
number of people unemployed (UE); analogous procedures
are used to estimate the number of people employed and
the number not in the labor force. Data from all eight rotation groups are combined for the purpose of computing
composite weights.

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10–11

1. For each state7 and the District of Columbia (53 cells),
j, the direct (optimal) composite estimate of UE, comp
(UEj), is computed as described above. Similarly, direct
composite estimates of UE are computed for 20
national age/sex/ethnicity cells and 46 national age/
sex/race cells. These computations use cell definitions
specified in Tables 10−5 and 10−6. Coefficients K =
0.4 and A = 0.3 are used for all UE estimates in all categories.
2. Sample records are classified by state. Within each
state j, a simple estimate of UE, simp (UEj), is computed by adding the weights of all unemployed
sample persons in the state.
3. Within each state j, the weight of each unemployed
sample person in the state is multiplied by the following ratio: comp (UEj)/simp (UEj).
4. Sample records are cross-classified by age, sex, and
ethnicity. Within each cross-classification cell, a simple
estimate of UE is computed by adding the weights (as
adjusted in step 3) of all unemployed sample persons
in the cell.
5. Weights are adjusted within each age/sex/ethnicity
cell in a manner analogous to step 3.
6. Steps 4 and 5 are repeated for age/sex/race cells.

Table 10−5: Composite National Ethnicity Cell
Definition
Hispanic
Age

Non-Hispanic
Age

For people 16 years and older, the composite weights are
the final weights. Since the procedure does not apply to
persons under age 16, their final weights are the secondstage weights.

California is split into two parts and each part is treated like
a state—Los Angeles County and the balance of California. Similarly, New York is split into two parts with each part being treated
as a separate state—New York City (New York, Queens, Bronx,
Kings and Richmond Counties) and the balance of New York.

10–12

Female

Table 10−6: Composite National Race Cell
Definition
Black alone
Age

Male

Female

16−19
20−24
25−29
30−34
35−39
40−44
45+
White alone
Male

Female

16−19
20−24
25−29
30−34
35−39
40−44
45−49
50−54
55−59
60−64
65+

Extreme cells are identified if a cell size is less than 10, or
an adjustment factor is greater than 1.3 or less than 0.7.
Final Weights

Male

16−19
20−24
25−34
35−44
45+

Age

For the not-in-labor force category (NILF), the same raking
steps are performed, but the controls are obtained as the
residuals from the population controls and the direct composite estimates for employed (E) and unemployed (UE).
The formula is NILF = Population − (E + UE).

Female

16−19
20−24
25−34
35−44
45+

7. Steps 2−6 are repeated nine more times for a total of
10 iterations.
An analogous procedure is done for estimating the number of people employed using coefficients K = 0.7 and A =
0.4.

Male

Residual race
Age

Male

Female

16−19
20−24
25−34
35−44
45+

Since data for all eight rotation groups are combined for
the purpose of computing composite weights, summations of final weights within rotation group will not match
independent population controls for people 16 years and
older. Summations of final weights for the entire sample
will be consistent with these second-stage controls, but
will only match a selected number of those controls. If the

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composite ethnicity x gender x age detail or race x gender
x age detail is the same as the second-stage detail, then
there will be a match. The composite-age detail is coarser
than the second-stage age detail. For example, the composite procedure controls for White alone, male, ages
60−64; the second stage has more age detail (60−62 and
63−64). Summations of final (composite) weights for all
White alone males by age will not match the corresponding second-stage population control for either the 60−62
or the 63−64 age group. However, the summed final
weights for White alone, males, ages 60−64 will match the
sum of the two second-stage population controls.

wife’s weight is usually used as the family weight, since
CPS coverage ratios for women tend to be higher and subject to less month-to-month variability than those for men.

PRODUCING OTHER LABOR FORCE ESTIMATES

Some items in the CPS questionnaire are asked only in
households due to rotate out of the sample temporarily or
permanently after the current month. These are the households in the rotation groups in their fourth or eighth
month- in-sample, sometimes referred to as the ‘‘outgoing’’ rotation groups. Items asked in the outgoing rotations include those on discouraged workers (through
1993), earnings (since 1979), union affiliation (since
1983), and industry and occupation of second jobs of multiple jobholders (beginning in 1994). Since the data are
collected from only one-fourth of the sample each month,
these estimates are averaged over 3 months to improve
their reliability, and they are published quarterly.

In addition to basic weighting to produce estimates for
individuals, several ‘‘special-purpose’’ weighting procedures are performed each month. These include:
• Weighting to produce estimates for households and
families.
• Weighting to produce estimates from data based on
only 2 of 8 rotation groups (outgoing rotation weighting
for the quarter sample data).
• Weighting to produce labor force estimates for veterans
and nonveterans (veterans’ weighting).

Most of these special weights are based on the weight
after the second-stage ratio adjustment. Some also make
use of composited estimates. In addition, consecutive
monthly estimates are often averaged to produce quarterly or annual average estimates. Each of these procedures is described in more detail below.
Family Weight
Family weights are used to produce estimates related to
families and family composition. They also provide the
basis for household weights. The family weight is derived
from the second-stage weight of the reference person in
each household. In most households, it is exactly the reference person’s weight. However, when the reference person is a married man, for purposes of family weights, he
is given the same weight as his wife. This is done so that
weighted tabulations of CPS data by sex and marital status
show an equal number of married women and married
men with their spouses present. If the second-stage
weights were used for this tabulation (without any further
adjustment), the estimated numbers of married women
and married men would not be equal, since the secondstage ratio adjustment tends to increase the weights of
males more than the weights of females. This is because
there is better coverage of females than for males. The

U.S. Bureau of Labor Statistics and U.S. Census Bureau

The same household weight is assigned to every person in
the same household and is equal to the family weight of
the household reference person. The household weight
can be used to produce estimates at the household level,
such as the number of households headed by a woman.
Outgoing Rotation Weights (Quarter-Sample Data)

Since 1979, most CPS files have included separate weights
for the outgoing rotations. These weights were generally
referred to as ‘‘earnings weights’’ on files through 1993,
and are generally called ‘‘outgoing rotation weights’’ on
files for 1994 and subsequent years. In addition to ratio
adjustment to independent population controls (in the second stage), these weights also reflect additional constraints that force them to sum to the composited estimates of employment, unemployment, and not-in-labor
force each month. An individual’s outgoing rotation
weight will be approximately four times his or her final
weight.

• Weighting to produce estimates from longitudinallylinked files (longitudinal weighting).

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Household Weight

To compute the outgoing rotation adjustment factors, the
second-stage weights of the appropriate records in the
two outgoing rotation groups are tallied. CPS composited
estimates from the full sample for the labor force categories of employed wage and salary workers, other
employed, unemployed, and not-in-labor force by age,
race and sex are used as the controls. The adjustment factor for a particular cell is the ratio of the control total to
the weighted tally from the outgoing rotation groups.
The outgoing rotation weights are obtained by multiplying
the outgoing ratio adjustment factors by the second-stage
weights. For consistency, an outgoing rotation group
weight equal to four times the basic CPS family weight is
assigned to all people in the two outgoing rotation groups
who were not eligible for this special weighting (mainly
military personnel and those aged 15 and younger).

Weighting and Seasonal Adjustment for Labor Force Data

10–13

Production of monthly, quarterly, and annual estimates
using the quarter-sample data and the associated weights
is completely parallel to production of uncomposited,
simple weighted estimates from the full sample—the
weights are summed and divided by the number of
months used. The composite estimator is not applicable
for these estimates because there is no overlap between
the quarter samples in consecutive months. Because the
outgoing rotations are all independent samples within any
consecutive 12-month period, averaging of these estimates on a quarterly and annual basis realizes relative
reductions in variance greater than those achieved by
averaging full-sample estimates.
Family Outgoing Rotation Weight
The family outgoing rotation weight is analogous to the
family weight computed for the full sample, except that
outgoing rotation weights are used, rather than the
weights from the second-stage ratio adjustment.
Veterans’ Weights
Since 1986, CPS interviewers have collected data on veteran status from all respondents. Veterans’ weights are
calculated for all CPS respondents based on their veteran
status. This information is used to produce tabulations of
employment status for veterans and nonveterans.
The process begins with the composite weights. Each
respondent is classified as a veteran or a nonveteran. Veterans’ records are classified into cells based on veteran
status (Vietnam-era, Not Vietnam-era or Peactime), age and
sex.
The composite weights for CPS veterans are tallied into
type-of-veteran/sex/age cells using the classifications
described above. Separate ratio adjustment factors are
computed for each cell, using independently established
monthly counts of veterans provided by the Department
of Veterans Affairs. The ratio adjustment factor is the ratio
of the independent control total to the sample estimate.
The composite weight for each veteran is multiplied by
the appropriate adjustment factor to produce the veteran’s
weight.
To compute veterans’ weights for nonveterans, a table of
composited estimates is produced from the CPS data by
sex, race (White alone/non-White alone), labor force status
(unemployed, employed, and not-in-labor force), and age.
The veterans’ weights produced in the previous step are
tallied into the same cells. The estimated number of veterans is then subtracted from the corresponding cell entry
for the composited table to produce nonveteran control
totals. The composite weights for CPS nonveterans are tallied into the same sex/race/labor force status/age cells.
Separate ratio adjustment factors are computed for each
cell, using the nonveteran controls derived above. The factor is the ratio of the nonveteran control total to the
10–14

sample estimate. The composite weight for each nonveteran is multiplied by the appropriate factor to produce the
nonveteran weight.
Longitudinal Weights
For many years, the month-to-month overlap of 75 percent of the sample households has been used as the basis
for estimating monthly ‘‘gross flow’’ statistics. The difference or change between consecutive months for any given
level or ‘‘stock’’ estimate is an estimate of net change that
reflects a combination of underlying flows in and out of
the group represented. For example, the month-to-month
change in the employment level is the number of people
who went from not being employed in the first month to
being employed in the second month minus the number
who made the opposite transition. The gross flow statistics provide estimates of these underlying flows and can
provide useful insights beyond those available in the stock
data.
The estimation of monthly gross flows, and any other longitudinal use of the CPS data, begins with a longitudinal
matching of the microdata (or person-level) records within
the rotation groups common to the months of interest.
Each matched record brings together all the information
collected in those months for a particular individual. The
CPS matching procedure uses the household identifier and
person line number as the keys for matching. Prior to
1994, it was also necessary to check other information
and characteristics, such as age and sex, for consistency
to verify that the match based on the keys was almost certainly a valid match. Beginning with 1994 data, the simple
match on the keys provides an essentially certain match.
Because the CPS does not follow movers (rather, the
sample addresses remain in the sample according to the
rotation pattern), and because not all households are successfully interviewed every month they are in sample, it is
not possible to match interview information for all people
in the common rotation groups across the months of interest. The highest percentage of matching success is generally achieved in the matching of consecutive months,
where between 90 and 95 percent of the potentially
matchable records (or about 67 to 71 percent of the full
sample) can usually be matched. The use of CATI and CAPI
since 1994 has also introduced dependent interviewing,
which eliminated much of the erratic differences in
response between pairs of months.
On most CPS files from 1994 forward, a longitudinal
weight allows users to estimate gross labor force flows by
summing up the longitudinal weights after matching.
These longitudinal weights reflect the technique that had
been used prior to 1994 to inflate the gross flow estimates to appropriate population levels. That technique
inflates all estimates or final weights by the ratio of the
current month’s population controls to the sum of the
second-stage weights for the current month in the

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1993). That study showed that characteristics for which
the month-to-month correlation is low, such as unemployment, are helped considerably by such averaging, while
characteristics for which the correlation is high, such as
employment, benefit less from averaging. For unemployment, variances of national estimates were reduced by
about one-half for quarterly averages and about one-fifth
for annual averages.

matched cases by sex. Although the technique provides
estimates consistent with the population levels for the
stock data in the current month, it does not force consistency with labor force stock levels in either the current or
the previous month, nor does it control for the effects of
the bias and sample variation associated with the exclusion of movers, differential noninterview in the matched
months, the potential for the compounding of classification errors in flow data, and the particular rotations that
are common to the matched months.

SEASONAL ADJUSTMENT

There have been a number of proposals for improving the
estimation of gross labor force flows, but none has yet
been adopted in official practice. See Proceedings of the
Conference on Gross Flows in Labor Force Statistics (U.S.
Department of Commerce and U.S. Department of Labor,
1985) for information on some of these proposals and for
more complete information on gross labor force flow data
and longitudinal uses of the CPS. For information about
more recent work on gross flows estimation methodology,
refer to two articles in the Monthly Labor Review, accessible online through . (See Frazis, Robison, Evans and Duff,
2005; Ilg, 2005.) Test tables produced by the BLS using
the methodology described in the two articles cannot be
reproduced using the longitudinal weights specified in this
section.
Averaging Monthly Estimates
CPS estimates are frequently averaged over a number of
months. The most commonly computed averages are (1)
quarterly, which provide four estimates per year by grouping the months of the calendar year in nonoverlapping
intervals of three, and (2) annual, combining all 12 months
of the calendar year. Quarterly and annual averages can be
computed by summing the weights for all of the months
contributing to each average and dividing by the number
of months involved. Averages for calculated cells, such as
rates, percents, means, and medians, are computed from
the averages for the component levels, not by averaging
the monthly values (e.g., a quarterly average unemployment rate is computed by taking the quarterly average
unemployment level as a percentage of the quarterly average labor force level, not by averaging the three monthly
unemployment rates together).
Although such averaging multiplies the number of interviews contributing to the resulting estimates by a factor
approximately equal to the number of months involved in
the average, the sampling variance for the average estimate is actually reduced by a factor substantially less than
that number of months. This is primarily because the CPS
rotation pattern and resulting month-to-month overlap in
sample units ensure that estimates from the individual
months are not independent. The reduction in sampling
error associated with the averaging of CPS estimates over
adjacent months was studied using 12 months of data collected beginning January 1987 (Fisher and McGuinness,
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Short-run movements in labor-force time series are
strongly influenced by seasonality, which refers to periodic fluctuations that are associated with recurring
calendar-related events such as weather, holidays, and the
opening and closing of schools. Seasonal adjustment is
the process of estimating and removing these fluctuations
to yield a seasonally-adjusted series. The reason for doing
so is to make it easier for data users to observe fundamental changes in the level of the series, particularly
those associated with general economic expansions and
contractions.
For example, the unadjusted CPS levels of employment
and unemployment in June are consistently higher than
those for May because of the influx of students into the
labor force. If the only change that occurs in the unadjusted estimates between May and June approximates the
normal seasonal change, then the seasonally-adjusted estimates for the two months should be about the same, indicating that essentially no change occurred in the underlying business cycle and trend even though there may have
been a large change in the unadjusted data. Changes that
do occur in the seasonally-adjusted series reflect changes
not associated with normal seasonal change and should
provide information about the direction and magnitude of
changes in the behavior of trend and business cycle
effects. They may, however, also reflect the effects of sampling error and other irregularities, which are not removed
by the seasonal adjustment process. Change in the
seasonally-adjusted series can and often will be in a direction opposite to the movement in the unadjusted series.
Refinements of the methods used for seasonal adjustment
have been under development for decades. The current
procedure used for the seasonal adjustment of national
CPS series is the X-12-ARIMA program from the U.S.
Census Bureau. This program is an enhanced version of
earlier programs utilizing the widely used X-11 method,
first developed by the Census Bureau and later modified
by Statistics Canada. The X-11 approach to seasonal
adjustment is univariate and nonparametric and involves
the iterative application of a set of moving averages that
can be summarized as one lengthy weighted average
(Dagum, 1983). Nonlinearity is introduced by a set of rules
and procedures for identifying and reducing the effect of
outliers. In most uses of the X-11 method, including that

Weighting and Seasonal Adjustment for Labor Force Data

10–15

for national CPS labor force series, the seasonality is estimated as evolving rather than fixed over time. A detailed
description of the X-12-ARIMA program is given in the U.
S. Census Bureau (2002) reference.
The current official practice for the seasonal adjustment of
CPS national labor force data is to run the X-12-ARIMA program monthly as new data become available, which is
referred to as concurrent seasonal adjustment.8 The season factors for the most recent month are produced by
applying a set of moving averages to the entire data set,
including data for the current month. While all previousmonth seasonally-adjusted data are revised in this process, no revisions are made during the year. Revisions are
applied at the end of each year for the most recent five
years of data.
Seasonally-adjusted estimates of many national labor force
series, including the levels of the civilian labor force, total
employment and total unemployment, and all unemployment rates, are derived indirectly by arithmetically combining the series directly adjusted with X-12-ARIMA. For
example, the overall national unemployment rate is computed using eight directly-adjusted series: females aged
16−19, males aged 16−19, females aged 20+, and males
aged 20+ for both employment and unemployment. The
principal reason for doing such indirect adjustment is that
it ensures that the major seasonally-adjusted totals will be
arithmetically consistent with at least one set of components. If the totals are directly adjusted along with the
components, such consistency would generally not occur,
since X-11 is not a sum- or ratio-preserving procedure. It is
not generally appropriate to apply factors computed for an
aggregate series to its components because various components tend to have statistically significant different patterns of seasonal variation.
For up-to-date information and a more thorough discussion on seasonal adjustment of national labor force series,
see any monthly issue of Employment and Earnings
(U.S. Department of Labor).
REFERENCES
Bailar, B. (1975), ‘‘The Effects of Rotation Group Bias on
Estimates from Panel Surveys,’’ Journal of the American
Statistical Association, Vol. 70, pp. 23−30.
Bell, W. R., and S. C. Hillmer (1990), ‘‘The Time Series
Approach to Estimation for Repeated Surveys,’’ Survey
Methodology, 16, pp. 195−215.
Breau, P. and L. Ernst (1983), ‘‘Alternative Estimators to the
Current Composite Estimator,’’ Proceedings of the Section on Survey Research Methods, American Statistical
Association, pp. 397−402.

8
Seasonal adjustment of the data is performed by the Bureau
of Labor Statistics, U.S. Department of Labor.

10–16

Copeland, K. R., F. K. Peitzmeier, and C. E. Hoy (1986), ‘‘An
Alternative Method of Controlling Current Population Survey Estimates to Population Counts,’’ Proceedings of the
Survey Research Methods Section, American Statistical
Association, pp.332−339.
Dagum, E. B. (1983), The X-11 ARIMA Seasonal Adjustment Method, Statistics Canada, Catalog No. 12−564E.
Denton, F. T. (1971), ‘‘Adjustment of Monthly or Quarterly
Series to Annual Totals: An Approach Based on Quadratic
Minimization,’’ Journal of the American Statistical
Association, 64, pp. 99−102.
Evans, T. D., R. B. Tiller, and T. S. Zimmerman (1993),
‘‘Time Series Models for State Labor Force Estimates,’’ Proceedings of the Survey Research Methods Section,
American Statistical Association, pp. 358−363.
Fisher, R. and R. McGuinness (1993), ‘‘Correlations and
Adjustment Factors for CPS (VAR80 1),’’ Internal Memorandum for Documentation, January 6th, Demographic Statistical Methods Division, U.S. Census Bureau.
Frazis, H.J., E.L. Robison, T.D. Evans, and M.A. Duff (2005),
‘‘Estimating Gross Flows Consistent With Stocks in the
CPS,’’ Monthly Labor Review, September, Vol. 128, No.
9, pp. 3−9.
Hansen, M. H., W. N. Hurwitz, and W. G. Madow (1953),
Sample Survey Methods and Theory, Vol. II, New York:
John Wiley and Sons.
Harvey, A. C. (1989), Forecasting, Structural Time
Series Models and the Kalman Filter, Cambridge:
Cambridge University Press.
Ilg, R. (2005), ‘‘Analyzing CPS Data Using Gross Flows,’’
Monthly Labor Review, September, Vol. 128, No. 9, pp.
10−18.
Ireland, C. T., and S. Kullback (1968), ‘‘Contingency Tables
With Given Marginals,’’ Biometrika, 55, pp. 179−187.
Kostanich, D. and P. Bettin (1986), ‘‘Choosing a Composite
Estimator for CPS,’’ presented at the International Symposium on Panel Surveys, Washington, DC.
Lent, J., S. Miller, and P. Cantwell (1994), ‘‘Composite
Weights for the Current Population Survey,’’ Proceedings
of the Survey Research Methods Section, American
Statistical Association, pp. 867−872.
Lent, J., S. Miller, P. Cantwell, and M. Duff (1999), ‘‘Effect of
Composite Weights on Some Estimates From the Current
Population Survey,’’ Journal of Official Statistics, Vol.
15, No. 3, pp. 431−438.
Mansur, K.A. and H. Shoemaker (1999), ‘‘The Impact of
Changes in the Current Population Survey on Time-inSample Bias and Correlations Between Rotation Groups,’’
Proceedings of the Survey Research Methods Section, American Statistical Association, pp. 180−183.

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Oh, H. L. and F. Scheuren (1978), ‘‘Some Unresolved Application Issues in Raking Estimation,’’ Proceedings of the
Section on Survey Research Methods, American Statistical Association, pp. 723−728.

Tiller, R. B. (1989), ‘‘A Kalman Filter Approach to Labor
Force Estimation Using Survey Data,’’ Proceedings of the
Survey Research Methods Section, American Statistical
Association, pp. 16−25.
Tiller, R. B. (1992), ‘‘Time Series Modeling of Sample Survey Data From the U.S. Current Population Survey,’’ Journal of Official Statistics, 8, pp, 149−166.

Proceedings of the Conference on Gross Flows in
Labor Force Statistics (1985), U.S. Department of Commerce and U.S. Department of Labor.

U.S. Census Bureau (2002), X-12-ARIMA Reference Manual
(Version 0.2.10), Washington, DC.

Robison, E., M. Duff, B. Schneider, and H. Shoemaker
(2002), ‘‘Redesign of Current Population Survey Raking to
Control Totals,’’ Proceedings of the Survey Research
Methods Section, American Statistical Assocation.

U.S. Department of Labor, Bureau of Labor Statistics (Published monthly), Employment and Earnings, Washington, DC: Government Printing Office.

Scott, J. J., and T. M. F. Smith (1974), ‘‘Analysis of Repeated
Surveys Using Time Series Methods,’’ Journal of the
American Statistical Association, 69, pp. 674−678.

Young, A. H. (1968), ‘‘Linear Approximations to the Census
and BLS Seasonal Adjustment Methods,’’ Journal of the
American Statistical Association, 63, pp. 445−471.

Thompson, J. H. (1981), ‘‘Convergence Properties of the
Iterative 1980 Census Estimator,’’ Proceedings of the
American Statistical Association on Survey Research
Methods, American Statistical Association, pp. 182−185.

Zimmerman, T. S., T. D. Evans, and R. B. Tiller (1994),
‘‘State Unemployment Rate Time Series,’’ Proceedings of
the Survey Research Methods Section, American Statistical Association, pp. 1077−1082.

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Weighting and Seasonal Adjustment for Labor Force Data

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Chapter 11.
Current Population Survey Supplemental Inquiries
INTRODUCTION
In addition to providing data on the labor force status of
the population, the Current Population Survey (CPS) is
used to collect data for a variety of studies on the entire
U.S. population and specific population subsets. These
studies keep the nation informed of the economic and
social well-being of its people and are used by federal and
state agencies, private foundations, and other organizations. Supplemental inquiries take advantage of several
special features of the CPS: large sample size and general
purpose design; highly skilled, experienced interviewing
and field staff; and generalized processing systems that
can easily accommodate the inclusion of additional questions.
Some CPS supplemental inquiries are conducted annually,
others every other year, and still others on a one-time
basis. The frequency and recurrence of a supplement
depend on what best meets the needs of the supplement’s
sponsor. In addition, any supplemental inquiry must meet
strict criteria discussed in the next section.
Producing supplemental data from the CPS involves more
than just including additional questions. Separate data
processing is required to edit responses for consistency
and to impute missing values. An additional weighting
method is often necessary because the supplement targets
a different universe from that of the basic CPS. A supplement can also engender a different level of response or
cooperation from respondents.
CRITERIA FOR SUPPLEMENTAL INQUIRIES
A number of criteria to determine the acceptability of
undertaking supplements for federal agencies or other
sponsors have been developed and refined over the years
by the U.S. Census Bureau, in consultation with the U.S.
Bureau of Labor Statistics (BLS).
The staff of the Census Bureau, working with the sponsoring agency, develops the survey design, including the
methodology, questionnaires, pretesting options, interviewer instructions and processing requirements. The Census Bureau provides a written description of the statistical
properties associated with each supplement. The same
standards of quality that apply to the basic CPS apply to
the supplements.
The following criteria are considered before undertaking a
supplement:
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1. The subject matter of the inquiry must be in the public interest.
2. The inquiry must not have an adverse effect on the
CPS or other Census Bureau programs. The questions
must not cause respondents to question the importance of the survey and result in losses of response or
quality. It is essential that the image of the Census
Bureau as the objective fact finder for the nation is not
damaged. Other important functions of the Census
Bureau, such as the decennial censuses or the economic censuses, must not be affected in terms of
quality or response rates or in congressional acceptance and approval of these programs.
3. The subject matter must be compatible with the basic
CPS survey and not introduce a concept that could
affect the accuracy of responses to the basic CPS information. For example, a series of questions incorporating a revised labor force concept that could inadvertently affect responses to the standard labor force
items would not be allowed.
4. The inquiry must not slow down the work of the basic
survey or impose a response burden that may affect
future participation in the basic CPS. In general, the
supplemental inquiry must not add more than 10 minutes of interview time per respondent or 25 minutes
per household. Competing requirements for the use of
Census Bureau staff or facilities that arise in dealing
with a supplemental inquiry are resolved by giving the
basic CPS first priority. The Census Bureau will not
jeopardize the schedule for completing the CPS or
other Census Bureau work to favor completing a
supplemental inquiry within a specified time frame.
5. The subject matter must not be sensitive. This criterion is imprecise, and its interpretation has changed
over time. For example, the subject of birth expectations, once considered sensitive, has been included as
a CPS supplemental inquiry.
6. It must be possible to meet the objectives of the
inquiry through the survey method. That is, it must be
possible to translate the supplemental survey’s objectives into meaningful questions, and the respondent
must be able to supply the information required to
answer the questions.
7. If the supplemental information is to be collected during the CPS interview, the inquiry must be suitable for
the personal visit/telephone procedures used in the
CPS.
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8. All data must abide by the Census Bureau’s enabling
legislation, which, in part, ensures that no information
will be released that can identify an individual.
Requests for a person’s name, address, social security
number, or other information that can directly identify
an individual will not be included. In addition, information that could be used to indirectly identify an
individual with a high probability of success (e.g.,
small geographic areas in conjunction with income or
age) will be suppressed.

different from the basic CPS universe. Thus, some supplements require weighting procedures that are different
from those of the basic CPS. These variations are
described for three of the major supplements—the Housing Vacancy Survey (HVS), the American Time Use Survey
(ATUS), and the Annual Social and Economic (ASEC)
supplement—in the following sections.

9. The cost of supplements must be borne by the sponsor, regardless of the nature of the request or the relationship of the sponsor to the ongoing CPS.

The Housing Vacancy Survey (HVS) is a monthly supplement to the CPS sponsored by the Census Bureau. The
supplement is administered when the CPS encounters a
unit in sample that is intended for year-round or seasonal
occupancy and is currently vacant or occupied by people
with a usual residence elsewhere. The interviewer asks a
reliable respondent (e.g., the owner, a rental agent, or a
knowledgeable neighbor) questions on year built; number
of rooms, bedrooms, and bathrooms; how long the housing unit has been vacant; the vacancy status (for rent, for
sale, etc); and when applicable, the selling price or rent
amount.

The questionnaires developed for the supplement are subject to the Census Bureau’s pretesting policy. This policy
was established in conjunction with other sponsoring
agencies to encourage questionnaire research aimed at
improving data quality.
The Census Bureau does not make the final decision
regarding the appropriateness or utility of the supplemental survey. The Office of Management and Budget (OMB),
through its Statistical Policy Division, reviews the proposal
to make certain it meets government-wide standards
regarding the need for the data and the appropriateness of
the design and ensures that the survey instruments, strategy, and response burden are acceptable.
RECENT SUPPLEMENTAL INQUIRIES
The scope and type of CPS supplemental inquiries vary
considerably from month to month and from year to year.
Generally, in any given month, a respondent who is
selected for a supplement is asked the additional questions that are included in the supplemental inquiry after
completing the regular part of the CPS. Table 11−1 summarizes CPS supplemental inquiries that were conducted
between September 1994 and December 2004.
The Housing Vacancy Supplement (HVS) and American
Time Use Surveys (ATUS) are unusual in that they are separate survey operations that base their sample on the
results of the basic CPS interview. The HVS supplement
collects additional information (e.g., number of rooms,
plumbing, and rental/sales price) on housing units identified as vacant in the basic CPS and the ATUS collects information about how people spend their time. The HVS is collected at the time of the basic CPS interview, while the
ATUS is collected after the household’s last CPS interview.
Probably the most widely used supplement is the Annual
Social and Economic (ASEC) Supplement, which is conducted every March. This supplement collects data on
work experience, several sources of income, migration,
household composition, health insurance coverage, and
receipt of noncash benefits.
The basic CPS weighting is not always appropriate for
supplements, since supplements tend to have higher nonresponse rates. In addition, supplement universes may be
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Current Population Survey Supplemental Inquiries

Housing Vacancy Survey (HVS) Supplement
Description of supplement

The purpose of the HVS is to provide current information
on the rental and homeowner vacancy rates, home ownership rates, and characteristics of units available for occupancy in the United States as a whole, geographic regions,
and inside and outside metropolitan areas. The rental
vacancy rate is a component of the index of leading economic indicators, which is used to gauge the current economic climate. Although the survey is performed monthly,
data for the nation and for the Northeast, South, Midwest,
and West regions are released quarterly and annually. The
data released annually include information for states and
large metropolitan areas.
Calculation of vacancy rates
The HVS collects data on year-round and seasonal vacant
units. Vacant year-round units are those intended for occupancy at any time of the year, even though they may not
be in use year-round. In resort areas, a housing unit that is
intended for occupancy on a year-round basis is considered a year-round unit; those intended for occupancy only
during certain seasons of the year are considered seasonal. Also, vacant housing units held for occupancy by
migratory workers employed in farm work during the crop
season are classified as seasonal.
The rental and homeowner vacancy rates are the most
prominent HVS statistics. The vacancy rates are determined using information collected by the HVS and CPS,
since the statistical formulas use both vacant and occupied housing units.
The rental vacancy rate is calculated as the ratio of vacant
year-round units for rent to the sum of renter- occupied
units, vacant year-round units rented but awaiting occupancy, and vacant year-round units for rent.
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Table 11–1. Current Population Survey Supplements September 1994−December 2004
Title

Month

Purpose

Sponsor

Housing Vacancy

Monthly

Provide quarterly data on vacancy rates and characteristics of vacant units.

Census

Health/Pension

September 1994

Provide information on health/pension coverage for persons 40 years of age and PWBA
older. Information includes benefit coverage by former as well as current
employer and reasons for noncoverage, as appropriate. Amount, cost, employer
contribution, and duration of benefits are also measured. Periodicity: As requested.

Lead Paint Hazards
Awareness

December 1994,
Provide information on the current awareness of the health hazards associated HUD
June 1997, December 1999 with lead-based paint. Periodicity: As requested.

Contingent Workers

February 1995, 1997, 1999, Provide information on the type of employment arrangement workers have on BLS
2001
their current job and other characteristics of the current job such as earnings,
benefits, longevity, etc., along with their satisfaction with and expectations for
their current jobs. Periodicity: Biennial.

Annual Social and
Economic Supplement
(formally known as the
AnnualDemographic
Supplement)

March 1995−2004

Food Security

April 1995, September 1996, Provide data that will measure hunger and food security. It will provide data on FNS
April 1997, August 1998, April food expenditure, access to food, and food quality and safety.
1999, September 2000, April
2001, December 2001, 2002,
2003, 2004

Race and Ethnicity

May 1995, July 2000, May Test alternative measurement methods to evaluate how best to collect these BLS/Census
2002
types of data.

Provide data concerning family characteristics, household composition, marital Census/BLS
status, education attainment, health insurance coverage, foreign-born population, previous year’s income from all sources, work experience, receipt of
noncash benefit, poverty, program participation, and geographic mobility. Periodicity: Annual

Collect information from ″ever-married″ persons on marital history.

Marital History

June 1995

Fertility

June 1998, 2000, 2002, 2004 Provide data on the number of children that women aged 15-44 have ever had Census/BLS
and the children’s characteristics. Periodicity: Biennial.

Educational Attainment

July 1995

Veterans

August 1995, September 1997, Provide data for veterans of the United States on Vietnam-theater and Persian BLS
1999, August 2001, 2003
Gulf-theater status, service-connected income, effect of a service-connected
disability on current labor force participation and participation in veterans’
programs. Periodicity: Biennial.

School Enrollment

October 1994−2004

Tobacco Use

September 1995, January 1996, Provide data for population 15 years and older on current and former use of NCI
May 1996, September 1998, tobacco products; restrictions of smoking in workplace for employed persons;
January 1999, May 1999, Janu- and personal attitudes toward smoking. Periodicity: As requested.
ary 2000, May 2000, June
2001, November 2001, February 2002, February 2003,
June 2003, November 2003

Displaced Workers

February 1996, 1998, 2000, Provide data on workers who lost a job in the last 5 years due to plant closing, BLS
January 2002, 2004
shift elimination, or other work-related reason. Periodicity: Biennial.

Job Tenure/
Occupational Mobility

February 1996, 1998, 2000, Provide data that will measure an individual’s tenure with his/her current BLS
January 2002, 2004
employer and in his/her current occupation. Periodicity: As requested.

Child Support

April 1996, 1998, 2000, 2002, Identify households with absent parents and provide data on child support OCSE
2004
arrangements, visitation rights of absent parent, amount and frequency of actual
versus awarded child support, and health insurance coverage. Data are also
provided on why child support was not received or awarded. April data will be
matched to March data.

Voting and Registration

November 1994, 1996, 1998, Provide demographic information on persons who did and did not register to Census
2000, 2002, 2004
vote. Also measures number of persons who voted and reasons for not
registering. Periodicity: Biennial.

Work Schedule/
Home-Based Work

May 1997, 2001, 2004

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Census/BLS

Test several methods of collecting these data. Test both the current method BLS/Census
(highest grade completed or degree received) and the old method (highest grade
attended and grade completed).

Provide information on population 3 years old and older on school enrollment, BLS/Census/
junior or regular college attendance, and high school graduation. Periodicity: NCES
Annual.

Provide information about multiple job holdings and work schedules and BLS
telecommuters who work at a specific remote site.

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Table 11–1. Current Population Survey Supplements September 1994−December 2004—Con.
Title
Computer Use/Internet
Use
Participation in the Arts

Month

Purpose

Sponsor

November 1994, October 1997, Provide information about household access to computers and the use of the NTIA
December 1998, August 2000, Internet or World Wide Web.
September 2001, October 2003
August 2002
Provide data on the type and frequency of adult participation in the arts; training NEA
and exposure (particularly while young); and their musical artistic activity
preferences.

Volunteers

September 2002, 2003, 2004 Provide a measurement of participation in volunteer service, specifically about USA
frequency of volunteer activity, the kinds of organizations volunteered with, and Freedom
types of activities chosen. Among nonvolunteers, questions identify what barriers Corps
were experienced in volunteering, or what encouragement is needed to increase
participation.

Cell Phone Use

February 2004

Provide data about household use of regular landline telephones and household BLS/Census
use of cell phones. If both were used in the household, it asked about the amount
of cell phone usage.

The homeowner vacancy rate is calculated as the ratio of
vacant year-round units for sale to the sum of owneroccupied units, vacant year-round units sold but awaiting
occupancy, and vacant year-round units for sale.
Weighting procedure
Since the HVS universe differs from the CPS universe, the
HVS records require a different weighting procedure from
the CPS records. The HVS records are weighted by the CPS
basic weight, the CPS special weighting factor, two HVS
adjustments and a regional housing unit (HU) adjustment.
(Refer to Chapter 10 for a description of the two CPS
weighting adjustments.) The two HVS adjustments are
referred to as the HVS first-stage ratio adjustment and the
HVS second-stage ratio adjustment.
The HVS first-stage ratio adjustment is comparable to the
CPS first-stage ratio adjustment in that it reduces the contribution to variance from the sampling of PSUs. The
adjustment factors are based on 2000 census data. There
are separate first-stage factors for year-round and seasonal housing units. For each state, they are calculated as
the ratio of the state-level census count of vacant yearround or seasonal housing units in all NSR PSUs to the corresponding state-level estimate of vacant year-round or
seasonal housing units from the NSR PSUs in sample.
The appropriate first-stage adjustment factor is applied to
every vacant year-round and seasonal housing units in the
NSR PSUs.
The HVS second-stage ratio adjustment, which applies to
vacant year-round and seasonal housing units in SR and
NSR PSUs, is calculated as the ratio of the weighted CPS
interviewed housing units after CPS second-stage ratio
adjustment to the weighted CPS interviewed housing units
after CPS first-stage ratio adjustment.
The cells for the HVS second-stage adjustment are calculated within each month-in-sample by census region and
type of area (metropolitan/nonmetropolitan, central
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city/balance of MSA, and urban/rural). This adjustment is
made to all eligible HVS records.
The regional HU adjustment is the final stage in the HVS
weighting procedure. The factor is calculated as the ratio
of the HU control estimates by the four major geographic
regions of the United States (Northeast, South, Midwest,
and West) supplied by the Population Division, to the sum
of estimated occupied (from the CPS) plus vacant HUs,
through the HVS second stage adjustment.1 This factor is
applied to both occupied and vacant housing units.
The final weight for each HVS record is determined by calculating the product of the CPS basic weight, the CPS special weighting factor, the HVS first-stage ratio adjustment,
and the HVS second-stage ratio adjustment. The occupied
units in the denominator of the vacancy rate formulas use
a different final weight since the data come from the CPS.
The final weight applied to the renter- and owner-occupied
units is the CPS household weight. (Refer to Chapter 10
for a description of the CPS household weight.)
American Time Use Survey (ATUS)
Description
The American Time Use Survey (ATUS) collects data each
month on how people spend their time. Data collection for
the ATUS began in January 2003. There are seventeen
major categories for activities such as work, sleep, eating
and drinking, leisure and sports. Data are tabulated by
variables such as sex, race/ethnicity, age, and education.
Sample
Approximately 2,200 households are selected for ATUS
each month, and each household is interviewed just once.
The ATUS sample comes from CPS sample households that
1
The estimate of occupied housing units from the CPS is
obtained by aggregating the family weight of each interviewed
CPS household.

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have completed their eighth and final CPS interview (i.e.,
CPS MIS 8). There is a two-month lag from the last CPS
interview, to initial contact with the household for an ATUS
interview. For example, households selected from the
January 2005 CPS sample would be part of the March
2005 ATUS sample.
The CPS households from which the ATUS sample is
selected are stratified by race/ethnicity and presence of
children. Non-White households are sampled at a higher
rate to insure that valid comparisons can be made across
major race/ethnicity groups. Half of the households are
assigned to report on Saturdays and Sundays, and half are
assigned to report on weekdays. One person 15 years or
older is selected from each ATUS sample household for
participation in the ATUS.
Weighting procedure
The basic weight for each ATUS record is the product of
the CPS first-stage weight, an adjustment for the CPS
state-based design, the within-stratum sampling interval
and a household size factor. The ATUS basic weight is then
adjusted by an ATUS noninterview factor, plus a set of
population control adjustments; the population control
adjustments are based on sex, age, race/ethnicity, education, labor force status, and presence of children in the
household. A day adjustment factor is computed separately for weekdays, Saturdays, and Sundays. This factor
accounts for increased numbers of weekend interviews,
and for varying frequencies of each day of the week for a
particular month.
Annual Social and Economic Supplement (ASEC)
Description of supplement
The ASEC is sponsored by the Census Bureau and the BLS.
The Census Bureau has collected data in the ASEC since
1947. From 1947 to 1955, the ASEC took place in April,
and from 1956 to 2001 the ASEC took place in March.
Prior to 2003, the ASEC was known as the Annual Demographic Supplement or the March Supplement. In 20012, a
sample increase was implemented that required more time
for data collection. Thus, additional ASEC interviews are
now taking place in February and April. Even with this
sample increase, most of the data collection still occurs in
March.
The supplement collects data on family characteristics,
household composition, marital status, education attainment, health insurance coverage, the foreign-born population, work experience, income from all sources, receipt of
2
The expanded sample was first used in 2001 for testing and
was not included in the official ADS statistics for 2001. The statistics from 2002 are the first official set of statistics published
using the expanded sample. The 2001 expanded sample statistics
were released and are used for comparing the 2001 data to the
official 2002 statistics.

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noncash benefit, poverty, program participation, and geographic mobility. A major reason for conducting the ASEC
in the month of March is to obtain better income data. It
was thought that since March is the month before the
deadline for filing federal income tax returns, respondents
were likely to have recently prepared tax returns or be in
the midst of preparing such returns and could report their
income more accurately than at any other time of the year.
The universe for the ASEC is slightly different from that for
the basic CPS. It includes certain members of the armed
forces in the estimates. This requires some minor changes
to the sampling procedures and to the weighting methodology.
The ASEC sample consists of the March CPS sample, plus
additional CPS households identified in prior CPS samples
and the following April CPS sample. Table 11−2 shows the
months when the eligible sample is identified for years
2001 through 2004. Starting in 2004, the eligible ASEC
sample households are:
1. The entire March CPS sample.
2. Hispanic households—identified in November (from all
month-in-sample (MIS) groups) and in April (from MIS
1 and 5 groups).
3. Non-Hispanic non-White households—identified in
August (MIS 8), September (MIS 8), October (MIS 8),
November (MIS 1 and 5), and April (MIS 1 and 5).
4. Non-Hispanic White households with children 18 years
or younger—identified in August (MIS 8), September
(MIS 8), October (MIS 8), November (MIS 1 and 5), and
April (MIS 1 and 5).
Prior to 1976, no additional sample households were
added. From 1976 to 2001, only the November CPS
households containing at least one person of Hispanic origin were added to the ASEC. The households added in
2001, along with a general sample increase in selected
states, are collectively known as the State Children’s
Health Insurance Program (SCHIP) sample expansion. The
added households improve the reliability of the ASEC estimates for the Hispanic households, non-Hispanic nonWhite households, and non-Hispanic White households
with children 18 years or younger.
Because of the characteristics of CPS sample rotation (see
Chapter 3), the additional cases from the August, September, October, November and April CPS are completely different from those in the March CPS. The additional sample
cases increase the effective sample size of the ASEC compared with the March CPS sample alone. The ASEC sample
includes 18 MIS groups for Hispanic households, 15 MIS
groups for non-Hispanic non-White households, 15 MIS
groups for non-Hispanic White households with children
18 years or younger, and 8 MIS groups for all other households.

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Table 11−2. MIS Groups Included in the ASEC Sample for Years 2001, 2002, 2003, and 2004
Month in sample
CPS month/Hispanic status
1
August
September
October

Hispanic1
NonHispanic3
Hispanic1
NonHispanic3
Hispanic1
NonHispanic3
Hispanic

NonHispanic3

April

1
2
3

NonHispanic1

2004

NI2

2004

NI2
2003
2004

NI2
2001
2002
2003
2004
2001
2002
2003
2004

2001
2002
2003
2004

NI2

2001
2002

2001
2002
2003

2001
2002
2003
2004

NonHispanic3
Hispanic1

NI2

Hispanic1
March

NI2

November

2001
2002
2003
2004

NI2

Hispanics may be any race.
NI - Not interviewed for the ASEC.
The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children 18 years old or younger.

The March and April ASEC eligible cases are administered
the ASEC questionnaire in those respective months (see
Table 11−3). The April cases are classified as ‘‘split path’’
cases because some households receive the ASEC supplement questionnaire, while other households receive the
supplement scheduled for April. The November eligible
Hispanic households are administered the ASEC questionnaire in February for MIS groups 1 and 5, during their
regular CPS interviewing time, and the remaining MIS
groups (MIS 2−4 and 6−8) receive the ASEC interview in
March. (November MIS 6−8 households have already completed all 8 months of interviewing for the CPS before
March, and the November MIS 2−4 households have an
extra contact scheduled for the ASEC before the 5th interview of the CPS later in the year.)
The August, September, October, and November eligible
non-Hispanic households are administered the ASEC questionnaire in either February or April. November ASEC eligible cases in MIS 1 and 5 are interviewed for the CPS in
February (in MIS 4 and 8, respectively), so the ASEC questionnaire is administered in February. (These are also split
path cases, since households in other rotation groups get
the regular supplement scheduled for February). The
August, September, and October MIS 8 eligible cases are
split between the February and April CPS interviewing
months.
Mover households are defined at the time of the ASEC
interview as households with a different reference person
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Current Population Survey Supplemental Inquiries

when compared to the previous CPS interview, or the person causing the household to be eligible has moved out
(i.e., the Hispanic person or other race minority moved
out, or a single child aged the household out of eligibility.)
Mover households identified from the August, September,
October, and November eligible sample are removed from
the ASEC sample. Mover households identified in the
March and April eligible samples receive the ASEC questionnaire.
The ASEC sample universe is slightly different from that in
the CPS. The CPS completely excludes military personnel
while the ASEC includes military personnel who live in
households with at least one other civilian adult. These
differences require the ASEC to have a different weighting
procedure from the regular CPS.
Weighting procedure
Prior to weighting, missing supplement items are assigned
values based on hot deck imputation, a system that uses a
statistical matching process. Values are imputed even
when all the supplement data are missing. Thus, there is
no separate adjustment for households that respond to
the basic survey but not to the supplement. The ASEC
records are weighted by the CPS basic weight, the CPS
special weighting factor, the CPS noninterview adjustment,
the CPS first-stage ratio adjustment, and the CPS secondstage ratio adjustment procedure. (Chapter 10 contains a

Current Population Survey TP66
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Table 11−3. Summary of 2004 ASEC Interview Months

CPS month/Hispanic status
1
Hispanic

Non-Hispanic

Month in sample

October
Non-Hispanic
Hispanic

Month in sample

September
Hispanic

Nonmover

August
Hispanic

Mover

Non-Hispanic

4
NI

NI2

NI2
Feb

NI2

November

March
2

Feb.

Apr.
Feb

March

Feb.

NI2

Hispanic1
March

March

Non-Hispanic3

March

Hispanic1
April

Apr.

NI2

Apr.

NI2

Apr.

NI2

Non-Hispanic3
1
2
3

Hispanics may be any race.
NI - Not interviewed for the ASEC.
The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children 18 years old or younger.

description of these and the following adjustments.) The
ASEC also receives an additional noninterview adjustment
for the August, September, October, and November ASEC
sample, a SCHIP Adjustment Factor, a family equalization
adjustment, and weights applied to Armed Forces members.
The August, September, October, and November eligible
samples are weighted individually through the CPS noninterview adjustment and then combined. A noninterview
adjustment for the combined samples and the CPS firststage ratio adjustments are applied before the SCHIP
adjustment is applied.
The March eligible sample, and the April eligible sample
are also weighted separately before the second-stage
weighting adjustment. All the samples are then combined
so that one second-stage adjustment procedure is performed. The flowchart in Figure 11−1 illustrates the
weighting process for the ASEC sample.
Households from August, September, October, and
November eligible samples: The households from the
August, September, October, and November eligible samples start with their basic CPS weight as calculated in the
appropriate month, modified by the appropriate CPS special weighting factor and appropriate CPS noninterview
adjustment. At this point, a second noninterview adjustment is made for eligible households that are still occupied, but for which an interview could not be obtained in
the February, March, or April CPS. Then, the ASEC sample
weights for the prior sample are adjusted by the CPS firststage adjustment ratio and the SCHIP Adjustment Factor.
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The ASEC noninterview adjustment for the August, September, October, and November eligible sample. The second noninterview adjustment is applied to the August,
September, October, and November eligible sample households to reflect noninterviews of occupied housing units
that occur in the February, March, or April CPS. If a noninterviewed household is actually a mover household, it
would not be eligible for interview. Since the mover status
of noninterviewed households is not known, we assume
that the proportion of mover households is the same for
interviewed and noninterviewed households. This is
reflected in the noninterview adjustment. With this exception, the noninterview adjustment procedure is the same
as described in Chapter 10. The weights of the interviewed households are adjusted by the noninterview factor as described below. At this point, the noninterviews
and those mover households receive no further ASEC
weighting. The noninterview adjustment factor, Fij, is computed as follows:
Fij =

Zij + Nij + Bij
Zij + Bij

where:

Zij =

the weighted number of August, September, October, and November eligible
sample households interviewed in the
February, March, or April CPS in cell j of
cluster i.

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Nij =

the weighted number of August, September, October, and November eligible
sample occupied, noninterviewed housing
units in the February, March, or April CPS
in cell j of cluster i.

non-White households and non-Hispanic White households with children 18 years or younger receive a SCHIP
adjustment factor of 8/15. Table 11−4 summarizes
these weight adjustments.

Bij =

the weighted number of August, September, October, and November eligible
sample Mover households identified in the
February, March, or April CPS in cell j of
cluster i.

Eligible households from the March sample: The
March eligible sample households start with their basic
CPS weight, modified by their CPS special weighting
factor, the March CPS noninterview adjustment, the
March CPS first-stage ratio adjustment (as described in
Chapter 10), and the SCHIP adjustment factor.

The weighted counts used in this formula are those
after the CPS noninterview adjustment is applied. The
clusters refer to the variously defined regions that compose the United States. These include clusters for the
Northeast, Midwest, South, and West, as well as clusters
for particular cities or smaller areas. Within each of
these clusters is a pair of residence cells. These could
be (1) Central City and Balance of MSA, (2) MSA and
Non-MSA, or (3) Urban and Rural, depending on the
type of cluster.
SCHIP adjustment factor for the August, September,
October, and November eligible sample. The SCHIP
adjustment factor is applied to nonmover eligible
households that contain residents who are Hispanic,
non-Hispanic non-White, and non-Hispanic Whites with
children 18 years or younger to compensate for the
increased sample in these demographic categories. Hispanic households receive a SCHIP adjustment factor of
8/18 and non-Hispanic non-White households and nonHispanic White households with children 18 years or
younger receive a SCHIP adjustment factor of 8/15. (See
Table 11−4.) After this adjustment is applied, the
August, September, October, and November eligible
sample households are ready to be combined with the
March and April eligible samples for the application of
the second-stage ratio adjustment.
Eligible households from the April sample: The
households in the April eligible sample start with their
basic CPS weight as calculated in April, modified by
their April CPS special weighting factor, the April CPS
noninterview adjustment, and the SCHIP adjustment
factor. After the SCHIP adjustment factor is applied, the
April eligible sample is ready to be combined with the
November and March eligible samples for the application of the second-stage ratio adjustment.
SCHIP adjustment factor for the April eligible sample.
The SCHIP adjustment factor is applied to April eligible
households that contain residents who are Hispanic,
non-Hispanic non-Whites, or non-Hispanic Whites with
children 18 years or younger to compensate for the
increased sample size in these demographic categories
regardless of Mover status. Hispanic households receive
a SCHIP adjustment factor of 8/18 and non-Hispanic

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Current Population Survey Supplemental Inquiries

SCHIP adjustment factor for the March eligible sample.
The SCHIP adjustment factor is applied to the March eligible nonmover households that contain residents who
are Hispanic, non-Hispanic non-White, and non-Hispanic
Whites with children 18 years or younger to compensate for the increased sample size in these demographic categories. Hispanic households receive a
SCHIP adjustment factor of 8/18 and non-Hispanic nonWhite households and non-Hispanic White resident
households with children 18 years or younger receive a
SCHIP adjustment factor of 8/15. Mover households
and all other households receive a SCHIP adjustment of
1. Table 11-4 summarizes these weight adjustments.
Combined sample of eligible households from
the August, September, October, November,
March, and April CPS: At this point, the eligible
samples from August, September, October, November,
March, and April are combined. The remaining adjustments are applied to this combined sample file.
ASEC second-stage ratio adjustment: The secondstage ratio adjustment adjusts the ASEC estimates so
that they agree with independent age, sex, race, and
Hispanic-origin population controls as described in
Chapter 10. The same procedure used for CPS is used
for the ASEC.
Additional ASEC weighting: After the ASEC weight
through the second-stage procedure is determined, the
next step is to determine the final ASEC weight. There
are two more weighting adjustments applied to the
ASEC sample cases. The first is applied to the Armed
Forces members. The Armed Forces adjustment assigns
weights to the eligible Armed Forces members so they
are included in the ASEC estimates. The second adjustment is for family equalization. Without this adjustment, there would be more married men than married
women. Weights, mostly of males, are adjusted to give
a husband and wife the same weight, while maintaining
the overall age/race/sex/Hispanic control totals.
Armed Forces. Male and female members of the Armed
Forces living off post or living with their families on
post are included in the ASEC as long as at least one
civilian adult lives in the same household, whereas the

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Current Population Survey Supplemental Inquiries

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Table 11−4. Summary of ASEC SCHIP Adjustment Factor for 2004
CPS month/Hispanic status
1
Hispanic1
August

NonHispanic3

Hispanic
October

Nonmover
Month in sample
3
4
5
6
02

NonHispanic3
Hispanic

September

Mover
Month in sample
3
4
5
6
02

NonHispanic3

1
2
3

8/15

NonHispanic3

8/15

8/18
1

NonHispanic3
Hispanic

April

8/15
8/18

8/15

NonHispanic3
Hispanic

March

8/15

Hispanic1
November

8/15
8/18

8/18
02

8/15

8/18
02

8/15

8/18
02

8/15

02
8/15

Hispanics may be any race.
Zero weight indicates the cases are ineligible for the ASEC.
The non-Hispanic group includes both non-Hispanic non-Whites and non-Hispanic Whites with children 18 years old or younger.

CPS excludes all Armed Forces members. Households
with no civilian adults in the household, i.e., households with only Armed Forces members, are excluded
from the ASEC. The weights assigned to the Armed
Forces members included in the ASEC are the same
weights civilians receive through the SCHIP adjustment.
Control totals, used in the second-stage factor, do not
include Armed Forces members, so Armed Forces members do not go through the second-stage ratio adjustment. During family equalization, the weight of a male
Armed Forces member with a spouse or partner is reassigned to the weight of his spouse/partner.
Family equalization. The family equalization procedure
categorizes adults (at least 15 years old) into seven
groups based on sex and household composition:
1. Female partners in female/female unmarried partner households
2. All other civilian females
3. Married males, spouse present
4. Male partners in male/female unmarried partner
households
5. Other civilian male heads of households
6. Male partners in male/male unmarried partner
households
7. All other civilian males

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Current Population Survey Supplemental Inquiries

Three different methods, depending on the household
composition, are used to assign the ASEC weight to other
members of the household. The methods are 1) assigning
the weight of the householder to the spouse or partner, 2)
averaging the weights of the householder and partner, or
3) computing a ratio adjustment factor and multiplying
the factor by the ASEC weight.

SUMMARY
Although this discussion focuses on only three CPS
supplements, the HVS, the ATUS, and the ASEC Supplement, every supplement has its own unique objectives.
The particular questions, edits, and imputations are tailored to each supplement’s data needs. For many supplements this also means altering the weighting procedure to
reflect a different universe, account for a modified sample,
or adjust for a higher rate of nonresponse. The weighting
revisions discussed here for HVS, ATUS, and ASEC indicate
only the types of modifications that might be used for a
supplement.

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Chapter 12.
Data Products From the Current Population Survey
INTRODUCTION
Information collected in the Current Population Survey
(CPS) is made available by both the U.S. Bureau of Labor
Statistics and the U.S. Census Bureau through broad publication programs that include news releases, periodicals,
and reports. CPS-based information is also available on
magnetic tapes, CD-ROM, and computer diskettes and can
be obtained online through the Internet. This chapter lists
many of the different types of products currently available
from the survey, describes the forms in which they are
available, and indicates how they can be obtained. This
chapter is not intended to be an exhaustive reference for
all information available from the CPS. Furthermore, given
the rapid ongoing improvements occurring in computer
technology, more CPS-based products will be electronically
accessible in the future.
BUREAU OF LABOR STATISTICS PRODUCTS
Each month, employment and unemployment data are
published initially in The Employment Situation news
release about 2 weeks after data collection is completed.
The release includes a narrative summary and analysis of
the major employment and unemployment developments
together with tables containing statistics for the principal
data series. The news release also is available electronically on the Internet and can be accessed at
.
Subsequently, more detailed statistics are published in
Employment and Earnings, a monthly periodical. The
detailed tables provide information on the labor force,
employment, and unemployment by a number of characteristics, such as age, sex, race, marital status, industry,
and occupation. Estimates of the labor force status and
detailed characteristics of selected population groups not
published on a monthly basis, such as Asians and Hispanics,1 are published every quarter. Data also are published
quarterly on usual median weekly earnings classified by a
variety of characteristics. In addition, the January issue of
Employment and Earnings provides annual averages on
employment and earnings by detailed occupational categories, union affiliation, and employee absences.
About 10,000 of the monthly labor force data series plus
quarterly and annual averages are maintained in LABSTAT,
the BLS public database, on the Internet. They can be
1

Hispanics may be any race.

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accessed from . In most
cases, these data are available from the inception of the
series through the current month. Approximately 250 of
the most important estimates from the CPS are presented
monthly and quarterly on a seasonally adjusted basis.
The CPS also is used to collect detailed information on particular segments or particular characteristics of the population and labor force. About four such special surveys are
made each year. The inquiries are repeated annually in the
same month for some topics, including the extent of work
experience of the population during the calendar year; the
marital and family characteristics of workers; and the
employment of school-age youth, high school graduates
and dropouts, and recent college graduates. Surveys are
also made periodically on subjects such as contingent
workers, job tenure, displaced workers, disabled veterans,
and volunteers. The results of these special surveys are
first published as news releases and subsequently in the
Monthly Labor Review or BLS reports.
In addition to the regularly tabulated statistics described
above, special data can be generated through the use of
the CPS individual (micro) record files. These files contain
records of the responses to the survey questionnaire for
all individuals in the survey and can be used to create
additional cross-sectional detail. The actual identities of
the individuals are protected on all versions of the files
made available to noncensus staff. Microdata files are
available for all months since January 1976 and for various months in prior years. These data are made available
on magnetic tape, CD-ROM, or diskette.
Annual averages from the CPS for the four census regions
and nine census divisions, the 50 states and the District of
Columbia, 50 large metropolitan areas, and 17 central cities are published annually in Geographic Profile of Employment and Unemployment. Data are provided on the
employed and unemployed by selected demographic and
economic characteristics. The publication is available electronically on the Internet and can be accessed at
.
Table 12–1 provides a summary of the CPS data products
available from BLS.
CENSUS BUREAU PRODUCTS
The Census Bureau has been analyzing data from the Current Population Survey and reporting the results to the
public for over five decades. The reports provide information on a recurring basis about a wide variety of social,
Data Products From the Current Population Survey

12–1

demographic, and economic topics. In addition, special
reports on many subjects also have been produced. Most
of these reports have appeared in 1 of 3 series issued by
the Census Bureau: P−20, Population Characteristics;
P−23, Special Studies; and P−60, Consumer Income. Many
of the reports are based on data collected as part of the
March demographic supplement to the CPS. However,
other reports use data from supplements collected in
other months (as noted in the listing below). A full inventory of these reports as well as other related products is
documented in Subject Index to Current Population
Reports and Other Population Report Series, CPR P23−192,
which is available from the Government Printing Office, or
the Census Bureau. Most reports have been issued in
paper form; more recently, some have been made available on the Internet . Generally,
reports are announced by news release and are released to
the public via the Census Bureau Public Information Office.

P−60, Consumer Income. Regularly recurring reports in
this series include information concerning families, individuals, and households at various income and poverty
levels, shown by a variety of demographic characteristics.
Other reports focus on health insurance coverage and
other noncash benefits.
In addition to the population data routinely reported from
the CPS, Housing Vacancy Survey (HVS) data are collected
from a sample of vacant housing units in the CPS sample.
Using these data, quarterly and annual statistics are produced on rental vacancy rates and home ownership rates
for the United States, the four census regions, locations
inside and outside metropolitan areas, the 50 states and
the District of Columbia, and the 75 largest metropolitan
areas. Information is also made available on national home
ownership rates by age of householder, family type, race,
and Hispanic ethnicity. A quarterly news release and quarterly and annual data tables are released on the Internet.
Supplemental Data Files

Census Bureau Report Series
P−20, Population Characteristics. Regularly recurring
reports in this series include topics such as geographic
mobility, educational attainment, school enrollment (October supplement), marital status, households and families,
Hispanic origin, the Black population, fertility (June supplement), voter registration and participation (November
supplement), and the foreign-born population.
P−23, Special Studies. Information pertaining to special
topics, including one-time data collections, as well as
research on methods and concepts are produced in this
series. Examples of topics include computer ownership
and usage, child support and alimony, ancestry, language,
and marriage and divorce trends.

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Data Products From the Current Population Survey

Public-use microdata files containing supplement data are
available from the Census Bureau. These files contain the
full battery of basic labor force and demographic data
along with the supplement data. A standard documentation package containing a record layout, source and accuracy statement, and other relevant information is included
with each file. (The actual identities of the individuals surveyed are protected on all versions of the files made available to non-census staff.) These files can be purchased
through the Customer Services Branch of the Census
Bureau and are available in either tape or CD-ROM format.
The CPS homepage is the other source for obtaining these
files . The Census
Bureau plans to add most historical files to the site along
with all current and future files.

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Table 12–1. Bureau of Labor Statistics Data Products From the Current Population Survey
Product

Description

Periodicity

Source

Cost

News Releases
College Enrollment and Work
Activity of High School
Graduates

An analysis of the college enrollment and work activity of
the prior year’s high school graduates by a variety of characteristics

Annual

October CPS
supplement

Free (1)

Contingent and Alternative
Employment Arrangements

An analysis of workers with ‘‘contingent’’ employment arrangements (lasting less than 1 year) and alternative arrangements including temporary and contract employment by a
variety of characteristics

Biennial

January CPS
supplement

Free (1)

Displaced Workers

An analysis of workers who lost jobs in the prior 3 years due
to plant or business closings, position abolishment, or other
reasons by a variety of characteristics

Biennial

February CPS
supplement

Free (1)

Employment and Unemployment An analysis of the labor force, employment, and unemployAmong Youth in the Summer
ment characteristics of 16- to 24-year-olds between April
and July with the focus on July

Annual

Monthly CPS

Free (1)

Employment Characteristics
of Families

An analysis of employment and unemployment by family
relationship and the presence and age of children

Annual

Monthly CPS

Free (1)

Employment Situation
of Veterans

An analysis of the work activity and disability status of
persons who served in the Armed Forces

Biennial

August CPS
supplement

Free (1)

Job Tenure of American
Workers

An analysis of employee tenure by industry and a variety of
demographic characteristics

Biennial

January CPS
supplement

Free (1)

Labor Force Characteristics of
Foreign-Born Workers

An analysis of the labor force characteristics of foreigh-born
workers and a comparison with the labor force characteristics of their native-born counterparts

Annual

Monthly CPS

Free (1)

The Employment Situation

Seasonally adjusted and unadjusted data on the Nation’s
employed and unemployed workers by a variety of characteristics

Monthly (2)

Monthly CPS

Free (1)

Union Membership

An analysis of the union affiliation and earnings of the
Nation’s employed workers by a variety of characteristics

Annual

Monthly CPS;
outgoing
rotation groups

Free (1)

Usual Weekly Earnings of
Wage and Salary Workers

Median usual weekly earnings of full- and part-time wage Quarterly (3)
and salary workers by a variety of characteristics

Monthly CPS;
outgoing
rotation groups

Free (1)

Volunteering in the United States An analysis of the incidence of volunteering and the characteristics of volunteers in the United States

Annual

September CPS
supplement

Free (1)

Work Experience of the
Population

Annual

Annual Social
and Economic
Supplement
to the CPS
(February−April)

Free (1)

An examination of the employment and unemployment
experience of the population during the entire preceding
calendar year by a variety of characteristics

Periodicals
Employment and Earnings

A monthly periodical providing data on employment, unemployment, hours, and earnings for the Nation, states, and
metropolitan areas

Monthly (3)

CPS; other $53.00 domestic;
$74.20 foreign;
surveys and
per year
programs

Monthly Labor Review

A monthly periodical containing analytical articles on employment, unemployment, and other economic indicators, book
reviews, and numerous tables of current labor statistics

Monthly

CPS; other $49.00 domestic;
$68.60 foreign;
surveys and
per year
programs

Other Publications
A Profile of the Working Poor An annual report on workers whose families are in poverty
by work experience and various characteristics

Annual

Geographic Profile of Employ- An annual publication of employment and unemployment
ment and Unemployment
data for regions, states, and metropolitan areas by a variety
of characteristics

Annual

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U.S. Bureau of Labor Statistics and U.S. Census Bureau

Annual Social
and Economic
Supplement
to the CPS
(February−April)

Free

CPS annual $25.00 domestic;
$35.00 foreign
averages

Data Products From the Current Population Survey

12–3

Table 12–1. Bureau of Labor Statistics Data Products From the Current Population Survey—Con.
Product

Description

Highlights of Women’s Earnings An analysis of the hourly and weekly earnings of women by
a variety of characteristics
Issues in Labor Statistics

Brief analysis of important and timely labor market issues

Periodicity

Source

Annual

Monthly CPS;
outgoing
rotation groups
Occasional
CPS; other
surveys and
programs

Cost
Free
Free

Microdata Files
Annual

Annual Social
and Economic
Supplement
to the CPS
(February−April)

(4)

Contingent Work

Biennial

January CPS
supplement

(4)

Displaced Workers

Biennial

February CPS
supplement

(4)

Job Tenure and
Occupational Mobility

Biennial

January CPS
supplement

(4)

School Enrollment

Annual

October CPS
supplement

(4)

Usual Weekly Earnings
(outgoing rotation groups)

Annual

Monthly CPS;
outgoing
rotation groups

(4)

Biennial

August CPS
supplement

(4)

Occasional

May CPS
supplement

(4)

Monthly

Labstat(5)

(1)

Monthly

Electronic files

Free

Annual Demographic Survey

Veterans
Work Schedules/
Home-Based Work
Time Series (Macro) Files
National Labor Force Data
Unpublished Tabulations
National Labor Force Data
1
2
3
4
5

Accessible from the Internet .
About 3 weeks following period of reference.
About 5 weeks after period of reference.
Diskettes ($80); cartridges ($165-$195); tapes ($215-$265); and CD-ROMs ($150).
Electronic access via the Internet .

Note: Prices noted above are subject to change.

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Current Population Survey TP66
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Chapter 13.
Overview of Data Quality Concepts
INTRODUCTION
It is far easier to put out a figure than to accompany
it with a wise and reasoned account of its liability to
systematic and fluctuating errors. Yet if the figure is
… to serve as the basis of an important decision, the
accompanying account may be more important than
the figure itself. John W. Tukey (1949, p. 9)
The quality of any estimate based on sample survey data
should be examined from two perspectives. The first is
based on the mathematics of statistical science, and the
second stems from the fact that survey measurement is a
production process conducted by human beings. From
both perspectives, survey estimates are subject to error,
and to avoid misusing or reading too much into the data,
we should use them only after their potential for error
from both sources has been examined relative to the particular use at hand.
This chapter provides an overview of how these two
sources of potential error can affect data quality, discusses
their relationship to each other from a conceptual viewpoint, and defines a number of technical terms. The definitions and discussion are applicable to all sample surveys,
not just the Current Population Survey (CPS). Succeeding
chapters go into greater detail about the specifics as they
relate to the CPS.
QUALITY MEASURES IN STATISTICAL SCIENCE
The statistical theory of finite population sampling is
based on the concept of repeated sampling under fixed
conditions. First, a particular method of selecting a sample
and aggregating the data from the sample units into an
estimate of the population parameter is specified. The
method for sample selection is referred to as the sample
design (or just the design). The procedure for producing
the estimate is characterized by a mathematical function
known as an estimator. After the design and estimator
have been determined, a sample is selected and an estimate of the parameter is computed. The difference
between the value of the estimate and the population
parameter is referred to here as the sampling error, and it
will vary from sample to sample (Särndal, Swensson, and
Wretman, 1992, p. 16).
Properties of the sample design-estimator methodology
are determined by looking at the distribution of estimates
that would result from taking all possible samples that
could be selected using the specified methodology. The
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U.S. Bureau of Labor Statistics and U.S. Census Bureau

mean value of the individual estimates is referred as the
expected value of the estimator. The difference between
the expected value of a particular estimator and the value
of the population parameter is known as bias. When the
bias of the estimator is zero, the estimator is said to be
unbiased. The mean value of the squared difference of the
values of the individual estimates and the expected value
of the estimator is known as the sampling variance of the
estimator. The variance measures the magnitude of the
variation of the individual estimates about their expected
value while the mean squared error measures the magnitude of the variation of the estimates about the value of
the population parameter of interest. The mean squared
error is the sum of the variance and the square of the bias.
Thus, for an unbiased estimator, the variance and the
mean squared error are equal.
Quality measures of a design-estimator methodology
expressed in this way, that is, based on mathematical
expectation assuming repeated sampling, are inherently
grounded on the assumption that the process is correct
and constant across sample repetitions. Unless the measurement process is uniform across sample repetitions,
the mean squared error is not by itself a full measure of
the quality of the survey results.
The assumptions associated with being able to compute
any mathematical expectation are extremely rigorous and
rarely practical in the context of most surveys. For
example, the basic formulation for computing the true
mean squared error requires that there be a perfect list of
all units in the universe population of interest, that all
units selected for a sample provide all the requested data,
that every interviewer be a clone of an ideal interviewer
who follows a predefined script exactly and interacts with
all varieties of respondents in precisely the same way, and
that all respondents comprehend the questions in the
same way and have the same ability to recall from
memory the specifics needed to answer the questions.
Recognizing the practical limitations of these assumptions, sampling theorists continue to explore the implications of alternative assumptions that can be expressed in
terms of mathematical models. Thus, the mathematical
expression for variance has been decomposed in various
ways to yield expressions for statistical properties that
include not only sampling variance but also simple
response variance (a measure of the variability among the
possible responses of a particular respondent over
repeated administrations of the same question) (Hansen,
Overview of Data Quality Concepts

13–1

Hurwitz, and Bershad, 1961) and correlated response variance, one form of which is interviewer variance (a measure of the variability among responses obtained by different interviewers over repeated administrations). Similarly,
when a particular design-estimator fails over repeated
sampling to include a particular set of population units in
the sampling frame or to ensure that all units provide the
required data, bias can be viewed as having components
such as coverage bias, unit nonresponse bias, or item nonresponse bias (Groves, 1989). For example, a survey
administered solely by telephone could result in coverage
bias for estimates relating to the total population if the
nontelephone households were different from the telephone households with respect to the characteristic being
measured (which almost always occurs).
One common theme of these types of models is the
decomposition of total mean squared error into two sets
of components, one resulting from the fact that estimates
are based on a sample of units rather than the entire
population (sampling error) and the other due to alternative specifications of procedures for conducting the
sample survey (nonsampling error). (Since nonsampling
error is defined negatively, it ends up being a catch-all
term for all errors other than sampling error, and can
include issues such as individual behavior.) Conceptually,
nonsampling error in the context of statistical science has
both variance and bias components. However, when total
mean squared error is decomposed mathematically to
include a sampling error term and one or more other nonsampling error terms, it is often difficult to categorize
such terms as either variance or bias. The term nonsampling error is used rather loosely in the survey literature to
denote mean squared error, variance, or bias in the precise
mathematical sense and to imply error in the more general
sense of process mistakes (see next section).
Some nonsampling error components which are conceptually known to exist have yet to be expressed in practical
mathematical models. Two examples are the bias associated with the use of a particular set of interviewers and
the variance associated with the selection of one of the
numerous possible sets of questions. In addition, the estimation of many nonsampling errors—and sampling
bias—is extremely expensive and difficult or even impossible in practice. The estimation of bias, for example,
requires knowledge of the truth, which may be sometimes
verifiable from records (e.g., number of hours paid for by
employer) but often is not verifiable (e.g., number of
hours actually worked). As a consequence, survey organizations typically concentrate on estimating the one component of total mean squared error for which practical
methods have been developed—variance.
It is frequently possible to construct an unbiased estimator of variance. In the case of complex surveys like the
CPS, estimators have been developed that typically rely on
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Overview of Data Quality Concepts

the proposition— usually well-grounded—that the variability among estimates based on various subsamples of the
one actual sample is a good proxy for the variability
among all the possible samples like the one at hand. In
the case of the CPS, 160 subsamples or replicates are used
in variance estimation for the 2000 design. (For more specifics, see Chapter 14.) It is important to note that the estimates of variance resulting from the use of this and similar methods are not merely estimates of sampling
variance. The variance estimates include the effects of
some nonsampling errors, such as response variance and
intra-interviewer correlation. On the other hand, users
should be aware of the fact that for some statistics these
estimates of standard error might be statistically significant underestimates of total error, an important consideration when making inferences based on survey data.
To draw conclusions from survey data, samplers rely on
the theory of finite population sampling from a repeated
sampling perspective: If the specified sample designestimator methodology were implemented repeatedly and
the sample size sufficiently large, the probability distribution of the estimates would be very close to a normal distribution.
Thus, one could safely expect 90 percent of the estimates
to be within two standard errors of the mean of all possible sample estimates (standard error is the square root
of the estimate of variance) (Gonzalez et al., 1975; Moore,
1997). However, one cannot claim that the probability is
.90 that the true population value falls in a particular interval. In the case of a biased estimator due to nonresponse,
undercoverage, or other types of nonsampling error, confidence intervals may not cover the population parameter at
the desired 90-percent rate. In such cases, a standard
error estimator may indirectly account for some elements
of nonsampling error in addition to sampling error and
lead to confidence intervals having greater than the nominal 90-percent coverage. On the other hand, if the bias is
substantial, confidence intervals can have less than the
desired coverage.
QUALITY MEASURES IN STATISTICAL PROCESS
MONITORING
The process of conducting a survey includes numerous
steps or components, such as defining concepts, translating concepts into questions, selecting a sample of units
from what may be an imperfect list of population units,
hiring and training interviewers to ask people in the
sample unit the questions, coding responses into predefined categories, and creating estimates that take into
account the fact that not everyone in the population of
interest had a chance to be in the sample and not all of
those in the sample elected to provide responses. It is a
process where the possibility exists at each step of making a mistake in process specification and deviating during
implementation from the predefined specifications.
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For example, we now recognize that the initial labor force
question used in the CPS for many years (‘‘What were you
doing most of last week. . .’’) was problematic to many
respondents (see Chapter 6). Moreover, many interviewers
tailored their presentation of the question to particular
respondents, for example, saying ‘‘What were you doing
most of last week—working, going to school, etc.?’’ if the
respondent was of school age. Having a problematic question is a mistake in process specification; varying question
wording in a way not prespecified is a mistake in process
implementation.
Errors or mistakes in process contribute to nonsampling
error in that they would contaminate results even if the
whole population were surveyed. Parts of the overall survey process that are known to be prone to deviations from
the prescribed process specifications and thus could be
potential sources of nonsampling error in the CPS are discussed in Chapter 15, along with the procedures put in
place to limit their occurrence.
A variety of quality measures have been developed to
describe what happens during the survey process. These
measures are vital to help managers and staff working on
a survey understand the process is quality. They can also
aid users of the various products of the survey process
(both individual responses and their aggregations into statistics) in determining a particular product’s potential limitations and whether it is appropriate for the task at hand.
Chapter 16 contains a discussion of quality indicators and,
in a few cases, their potential relationship to nonsampling
errors.
SUMMARY
The quality of estimates made from any survey, including
the CPS, is a function of decisions made by designers and
implementers. As a general rule of thumb, designers make
decisions aimed at minimizing mean squared error within
given cost constraints. Practically speaking, statisticians

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are often compelled to make decisions on sample designs
and estimators based on variance alone. In the case of the
CPS, the availability of external population estimates and
data on rotation group bias makes it possible to do more
than that. Designers of questions and data collection procedures tend to focus on limiting bias, assuming that the
specification of exact question wording and ordering will
naturally limit the introduction of variance. Whatever the
theoretical focus of the designers, the accomplishment of
the goal is heavily dependent upon those responsible for
implementing the design.
Implementers of specified survey procedures, like interviewers and respondents, are concentrating on doing the
best job possible. Process monitoring through quality indicators, such as coverage and response rates, can determine when additional training or revisions in process
specification are needed. Continuing process improvement
is a vital component for achieving the survey’s quality
goals.
REFERENCES
Gonzalez, M. E., J. L. Ogus, G. Shapiro, and B. J. Tepping
(1975), ‘‘Standards for Discussion and Presentation of
Errors in Survey and Census Data,’’ Journal of the
American Statistical Association, 70, No. 351, Part II,
5−23.
Groves, R. M. (1989), Survey Errors and Survey Costs,
New York: John Wiley & Sons.
Hansen, M. H., W. N. Hurwitz, and M. A. Bershad (1961),
‘‘Measurement Errors in Censuses and Surveys,’’ Bulletin
of the International Statistical Institute, 38(2), pp.
359−374.
Moore, D. S. (1997), Statistics Concepts and Controversies, 4th Edition, New York: W. H. Freeman.
Särndal, C., B. Swensson, and J. Wretman (1992), Model
Assisted Survey Sampling, New York: Springer-Verlag.
Tukey, J. W. (1949), ‘‘Memorandum on Statistics in the Federal Government,’’ American Statistician, 3, No. 1, pp.
6−17; No. 2, pp. 12−16.

Overview of Data Quality Concepts

13–3

Chapter 14.
Estimation of Variance
INTRODUCTION
The following two objectives are considered in estimating
variances of the major statistics of interest for the Current
Population Survey (CPS):
1. Estimate the variance of the survey estimates for use
in various statistical analyses.
2. Analyze the survey design by evaluating the effect of
each of the stages of sampling and estimation on the
overall precision of the survey estimates.
CPS variance estimates take into account the magnitude of
the sampling error as well as the effects of some nonsampling errors, such as response variance and intrainterviewer correlation. Chapter 13 provides additional
information on these topics. Certain aspects of the CPS
sample design, such as the use of one sample PSU per
non-self-representing stratum and the use of systematic
sampling within PSUs, make it impossible to obtain a completely unbiased estimate of the total variance. The use of
ratio adjustments in the estimation procedure also contributes to this problem. Although imperfect, the current variance estimation procedure is accurate enough for all practical uses of the data, and captures the effects of sample
selection and estimation on the total variance. Variance
estimates of selected characteristics and tables, which
show the effects of estimation steps on variances, are presented at the end of this chapter.
VARIANCE ESTIMATES BY THE REPLICATION
METHOD
Replication methods are able to provide satisfactory estimates of variance for a wide variety of designs using
probability sampling, even when complex estimation procedures are used. This method requires that the sample
selection, the collection of data, and the estimation procedures be independently carried out (replicated) several
times. The dispersion of the resulting estimates can be
used to measure the variance of the full sample.
Method
One would not likely repeat the entire CPS several times
each month simply to obtain variance estimates. A practical alternative is to draw a set of random subsamples from
the full sample surveyed each month, using the same principles of selection as those used for the full sample, and
to apply the regular CPS estimation procedures to these
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subsamples, which are called replicates. Determining the
number of replicates to use involves balancing the cost
and the reliability of the variance estimator; because
increasing the number of replicates decreases the variance
of the variance estimator.
Prior to the design introduced after the 1970 census, variance estimates were computed using 40 replicates. The
replicates were subjected to only the second-stage ratio
adjustment for the same age-sex-race categories used for
the full sample at the time. The noninterview and firststage ratio adjustments were not replicated. Even with
these simplifications, limited computer capacity allowed
the computation of variances for only 14 characteristics.
For the 1970 design, an adaptation of the Keyfitz method
of calculating variances was used. These variance estimates were derived using the Taylor approximation, dropping terms with derivatives higher than the first. By 1980,
improvements in computer memory capacity allowed the
calculation of variance estimates for many characteristics
with replication of all stages of the weighting through
compositing. The seasonal adjustment has not been replicated. A study from an earlier design indicated that the
seasonal adjustment of CPS estimates had relatively little
impact on the variances; however, it is not known what
impact this adjustment would have on the current design
variances.
Starting with the 1980 design, variances were computed
using a modified balanced half-sample approach. The
sample was divided to form 48 replicates that retained all
the features of the sample design, for example, the stratification and the within-PSU sample selection. For total variance, a pseudo first-stage design was imposed on the CPS
by dividing large self-representing (SR) PSUs into smaller
areas called Standard Error Computation Units (SECUs) and
combining small non-self-representing (NSR) PSUs into
paired strata or pseudostrata. One NSR PSU was selected
randomly from each pseudostratum for each replicate.
Forming these pseudostrata was necessary since the first
stage of the sample design has only one NSR PSU per stratum in the sample. However, pairing the original strata for
variance estimation purposes creates an upward bias in
the variance estimator. For self-representing PSUs each
SECU was divided into two panels, and one panel was
selected for each replicate. One column of a 48-by-48 Hadamard orthogonal matrix was assigned to each SECU or
pseudostratum. The unbiased weights were multiplied by
replicate factors of 1.5 for the selected panel and 0.5 for
Estimation of Variance

14–1

the other panel in the SR SECU or NSR pseudostratum (Fay,
Dippo, and Morganstein, 1984). Thus the full sample was
included in each replicate, but the matrix determined differing weights for the half samples. These 48 replicates
were processed through all stages of the CPS weighting
through compositing. The estimated variance for the characteristic of interest was computed by summing a squared
ˆr) and the full
difference between each replicate estimate (Y
ˆ0) The complete formula1 is
sample estimate (Y
Var(Yˆ0) =

4
48

兺
r=

(Yˆr ⳮ Yˆ0)2.

Due to costs and computer limitations, variance estimates
were calculated for only 13 months (January 1987 through
January 1988) and for about 600 estimates at the national
level. Replication estimates of variances at the subnational
level were not reliable because of the small number of
SECUs available (Lent, 1991). Based on the 13 months of
variance estimates, generalized sampling errors (explained
below) were calculated. (See Wolter 1985; or Fay 1984,
1989 for more details on half-sample replication for variance estimation.)
METHOD FOR ESTIMATING VARIANCE FOR 1990
AND 2000 DESIGNS
The general goal of the current variance estimation methodology, the method in use since July 1995, is to produce
consistent variances and covariances for each month over
the entire life of the design. Periodic maintenance reductions in the sample size and the continuous addition of
new construction to the sample complicated the strategy
needed to achieve this goal. However, research has shown
that variance estimates are not adversely affected as long
as the cumulative effect of the reductions is less than 20
percent of the original sample size (Kostanich, 1996).
Assigning all future new construction sample to replicates
when the variance subsamples are originally defined provides the basis for consistency over time in the variance
estimates.
The current approach to estimating the 1990 and 2000
design variances is called successive difference replication. The theoretical basis for the successive difference
method was discussed by Wolter (1984) and extended by
Fay and Train (1995) to produce the successive difference
replication method used for the CPS. The following is a
description of the application of this method. Successive
1

Usually balanced half-sample replication uses replicate factors of 2 and 0 with the formula,
1 k
Var(Yˆ0) = 兺 (Yˆr ⳮ Yˆ0)2
k r=1
where k is the number of replicates. The factor of 4 in our variance estimator is the result of using replicate factors of 1.5 and
0.5.

14–2

Estimation of Variance

USUs2 (ultimate sampling units) formed from adjacent hit
strings (see Chapter 3) are paired in the order of their
selection to take advantage of the systematic nature of the
CPS within-PSU sampling scheme. Each USU usually occurs
in two consecutive pairs: for example, (USU1, USU2),
(USU2, USU3), (USU3, USU4), etc. A pair then is similar to a
SECU in the 1980 design variance methodology. For each
USU within a PSU, two pairs (or SECUs) of neighboring
USUs are defined based on the order of selection—one
with the USU selected before and one with the USU
selected after it. This procedure allows USUs adjacent in
the sort order to be assigned to the same SECU, thus better reflecting the systematic sampling in the variance estimator. Also, the large increase in the number of SECUs and
in the number of replicates (160 vs. 48) over the 1980
design increases the precision of the variance estimator.
Replicate Factors for Total Variance
Total variance is composed of two types of variance, the
variance due to sampling of housing units within PSUs
(within-PSU variance) and the variance due to the selection
of a subset of all NSR PSUs (between-PSU variance). Replicate factors are calculated using a 160-by-1603 Hadamard
orthogonal matrix. To produce estimates of total variance,
replicates are formed differently for SR and NSR samples.
Between-PSU variance cannot be estimated directly using
this methodology; it is the difference between the estimates of total variance and within-PSU variance. NSR
strata are combined into pseudo- strata within each state,
and one NSR PSU from the pseudostratum is randomly
assigned to each panel of the replicate as in the 1980
design variance methodology. Replicate factors of 1.5 or
0.5 adjust the weights for the NSR panels. These factors
are assigned based on a single row from the Hadamard
matrix and are further adjusted to account for the unequal
sizes of the original strata within the pseudostratum
(Wolter, 1985). In most cases these pseudostrata consist of
a pair of strata except where an odd number of strata
within a state requires that a triplet be formed. In this
case, for the 1990 design, two rows from the Hadamard
matrix are assigned to the pseudostratum resulting in replicate factors of about 0.5, 1.7, and 0.8; or 1.5, 0.3, and
1.2 for the three PSUs assuming roughly equal sizes of the
original strata. However, for the 2000 design, these factors were further adjusted to account for the unequal
sizes of the original strata within the pseudostratum. All
USUs in a pseudostratum are assigned the same row number(s).
For an SR sample, two rows of the Hadamard matrix are
assigned to each pair of USUs creating replicate factors,
fr for r = 1,...,160

2
An ultimate sampling unit is usually a group of four neighboring housing units.
3
Rows 1 and 81 have been dropped from the matrix.

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ⳮ3

fir ⫽ 1 ⫹ 共2兲

ⳮ3

ai⫹1,r ⫺ 共2兲

Complicating Factors for State Variances
ai⫹2,r

where ai,r equals a number in the Hadamard matrix (+1 or
−1) for the ith USU in the systematic sample. This formula
yields replicate factors of approximately 1.7, 1.0, or 0.3.
As in the 1980 methodology, the unbiased weights (baseweight x special weighting factor) are multiplied by the
replicate factors to produce unbiased replicate weights.
These unbiased replicate weights are further adjusted
through the noninterview adjustment, the first-stage ratio
adjustment, national and state coverage adjustments, the
second-stage ratio adjustments, and compositing just as
the full sample is weighted. A variance estimator for the
characteristic of interest is a sum of squared differences
ˆr) and the full sample
between each replicate estimate (Y
ˆ0). The formula is
estimate (Y
Var(Yˆ0) =

160

r=1

兺 (Yˆr ⳮ Yˆ0)2.

The replicate factors 1.7, 1.0, and 0.3 for the selfrepresenting portion of the sample were specifically constructed to yield ‘‘4’’ in the above formula in order that the
formula remain consistent between SR and NSR areas (Fay
and Train, 1995).
Replicate Factors for Within-PSU Variance
The above variance estimator can also be used for withinPSU variance. The same replicate factors used for total
variance are applied to an SR sample. For an NSR sample,
alternate row assignments are made for USUs to form
pairs of USUs in the same manner that was used for the SR
assignments. Thus for within-PSU variance all USUs (both
SR and NSR) have replicate factors of approximately 1.7,
1.0, or 0.3.
The successive difference replication method is used to
calculate total national variances and within-PSU variances
for some states and metropolitan areas. For more detailed
information regarding the formation of replicates, see the
internal Census Bureau memoranda (Gunlicks, 1996, and
Adeshiyan, 2005).
VARIANCES FOR STATE AND LOCAL AREA
ESTIMATES
For estimates at the national level, total variances are estimated from the sample data by the successive difference
replication method previously described. For local areas
that are coextensive with one or more sample PSUs, a variance estimator can be derived using the methods of variance estimation used for the SR portion of the national
sample. However, estimates for states and areas that have
substantial contributions from NSR sample areas have
variance estimation problems that are more difficult to
resolve.
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Most states contain a small number of NSR sample PSUs,
so variances at the state level are based on fairly small
sample sizes. Pairing these PSUs into pseudostrata further
reduces the number of NSR SECUs and increases reliability
problems. Also, the component of variance resulting from
sampling PSUs can be more important for state estimates
than for national estimates in states where the proportion
of the population in NSR strata is larger than the national
average. Further, creating pseudostrata for variance estimation purposes introduces a between-stratum variance
component that is not in the sample design, causing overestimation of the true variance. The between-PSU variance,
which includes the between-stratum component, is relatively small at the national level for most characteristics,
but it can be much larger at the state level (Gunlicks,
1993; Corteville, 1996). Thus, this additional component
should be accounted for when estimating state variances.
Some research has been done to produce improved state
and local variances obtained directly from successive difference replication and the modified balanced half-sample
methods. Griffiths and Mansur (2000a, 2000b, 2001a and
2001b) and Mansur and Griffiths (2001) examine methods
based on times series and generalized linear modeling
techniques to address the small sample size problem and
the bias induced by collapsing NSR strata to estimate
between-PSU variances.
GENERALIZING VARIANCES
With some exceptions, the standard errors provided with
published reports and public data files are based on generalized variance functions (GVFs). The GVF is a simple
model that expresses the variance as a function of the
expected value of the survey estimate. The parameters of
the model are estimated using the direct replicate variances discussed above. These models provide a relatively
easy way to obtain an approximate standard error on
numerous characteristics.
Why Generalized Standard Errors Are Used
It would be possible to compute and show an estimate of
the standard error based on the survey data for each estimate in a report, but there are a number of reasons why
this is not done. A presentation of the individual standard
errors would be of limited use, since one could not possibly predict all of the combinations of results that may be
of interest to data users. Also, for estimates of differences
and ratios that users may compute, the published standard errors would not account for the correlation between
the estimates.
Most importantly, variance estimates are based on sample
data and have variances of their own. The variance estimate for a survey estimate for a particular month generally has less precision than the survey estimate itself. This
Estimation of Variance

14–3

means that the estimates of variance for the same characteristic may vary considerably from month-to-month or for
related characteristics (that might actually have nearly the
same level of precision) in a given month. Therefore, some
method of stabilizing these estimates of variance, for
example, by generalization or by averaging over time, is
needed to improve their reliability.
Experience has shown that certain groups of CPS estimates have a similar relationship between their variance
and expected value. Modeling or generalization provides
more stable variance estimates by taking advantage of
these similarities.
Generalization Method
The GVF that is used to estimate the variance of an estiˆ, is of the form
mated population total, X
Var(Xˆ) ⫽ aXˆ2 ⫹ bXˆ

共14.1兲

where a and b are two parameters estimated using least
squares regression. The rationale for this form of the GVF
ˆ can be
model is the assumption that the variance of X
expressed as the product of the variance from a simple
random sample for a binomial random variable and a
‘‘design effect.’’ The design effect (deff) accounts for the
effect of a complex sample design relative to a simple random sample. Defining P = X/N as the proportion of the
population having the characteristic X, where N is the
population size, and Q = 1-P, the variance of the estimated
ˆ, based on a sample of n individuals from the poputotal X
lation, is

Var共Xˆ兲 ⫽

N2PQ共deff兲
n

共14.2兲

For many subpopulations of interest, N is a control total
used in the second-stage ratio adjustment. In these subˆ approaches N, the variance of X
ˆ
populations, as X
approaches zero, since the second-stage ratio adjustment
guarantees that these sample population estimates match
independent population controls (Chapter 10).5 The GVF
model satisfies this condition. This generalized variance
model has been used since 1947 for the CPS and its
supplements, although alternatives have been suggested
and investigated from time to time (Valliant, 1987). The
model has been used to estimate standard errors of means
or totals. Variances of estimates based on continuous variables (e.g., aggregate expenditures, amount of income,
etc.) would likely fit a different functional form better.
The parameters, a and b, are estimated by use of the
model for relative variance

This can be written as
Var(Xˆ) ⫽ ⫺ 共deff兲

grouped together. This should give us estimates in the
same group that have similar design effects. These design
effects incorporate the effect of the estimation procedures, particularly the second stage, as well as the effect
of the sample design. In practice, the characteristics
should be clustered similarly by PSU, by USU, and among
individuals within housing units. For example, estimates
of total people classified by a characteristic of the housing
unit or of the household, such as the total urban population, number of recent migrants, or people of Hispanic4
origin, would tend to have fairly large design effects. The
reason is that these characteristics usually appear among
all people in the sample household and often among all
households in the USU as well. On the other hand, lower
design effects would result for estimates of labor force
status, education, marital status, or detailed age categories, since these characteristics tend to vary among members of the same household and among households within
a USU.

()

N Xˆ2
n N

N
⫹ 共deff兲 Xˆ .
n

Vx2 ⫽ a ⫹

Letting
a⫽⫺

b
N

and
b⫽

共deff兲N

gives the functional form
Var共Xˆ兲 ⫽ aXˆ2 ⫹ bXˆ.
We choose
a⫽ ⫺

N
where N is a control total so that the variance will be zero
ˆ = N.
when X
In generalizing variances, all estimates that follow a common model such as 14.1 (usually the same characteristics
for selected demographic or geographic subgroups) are
14–4

Estimation of Variance

b
X

where the relative variance (Vx2) is the variance divided
by the square of the expected value of the estimate. The a
and b parameters are estimated by fitting a model to a
group of related estimates and their estimated relative
variances. The relative variances are calculated using the
successive difference replication method.
The model fitting technique is an iterative weighted least
squares procedure, where the weight is the inverse of the
square of the predicted relative variance. The use of these
weights prevents items with large relative variances from
unduly influencing the estimates of the a and b parameters.
4

Hispanics may be any race.
The variance estimator assumes no variance on control
totals, even though they are estimates.
5

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Usually at least a year’s worth of data is used in this
model fitting process and each group of items should
comprise at least 20 characteristics with their relative variances, although occasionally fewer characteristics are
used.

ˆ = 9,000,000). From Table 14–1
(X

Direct estimates of relative variances are required for estimates covering a wide range, so that observations are
available to ensure a good fit of the model at high, low,
and intermediate levels of the estimates. Using a model to
estimate the relative variance of an estimate in this way
introduces some error, since the model may substantially
and erroneously modify some legitimately extreme values.
Generalized variances are computed for estimates of
month-to-month change as well as for estimates of
monthly levels. Periodically, the a and b parameters are
updated to reflect changes in the levels of the population
totals or changes in the ratio (N/n) which result from
sample reductions. This can be done without recomputing
direct estimates of variances as long as the sample design
and estimation procedures are essentially unchanged (Kostanich, 1996).

An approximate 90-percent confidence interval for the
monthly estimate of unemployed men is between
8,739,200 and 9,260,800 [or 9,000,000 ± 1.6(163,000)].

How the Relative Variance Function is Used
After the parameters a and b of expression (14.1) are
determined, it is a simple matter to construct a table of
standard errors of estimates for publication with a report.
In practice, such tables show the standard errors that are
appropriate for specific estimates, and the user is
instructed to interpolate for estimates not explicitly shown
in the table. However, many reports present a list of the
parameters, enabling data users to compute generalized
variance estimates directly. A good example is a recent
monthly issue of Employment and Earnings (U.S.
Department of Labor) from which the following table was
taken.

Table 14–1.Parameters for Computation of
Standard Errors for Estimates of
Monthly Levels
Characteristic
Unemployed:
Total or White . . . . . . . . . . . . . . .
Black . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . .
1

−0.000016350
0.000151396
−0.000141225

3095.55
3454.72
3454.72

Hispanics may be any race.

Example:
The approximate standard error, sˆX , of an estimated
ˆ can be obtained with a and b from the
monthly level X
above table and the formula
sXˆ ⫽

公aXˆ2 ⫹ bXˆ

Assume that in a given month there are an estimated
9 million unemployed men in the civilian labor force
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a = -0.000016350 and b = 3095.55 , so
sx ⫽

公共⫺0.000016350兲共9,000,000兲2 ⫹ 共3095.55兲共9,000,000兲 ⬇ 163,000

VARIANCE ESTIMATES TO DETERMINE OPTIMUM
SURVEY DESIGN
The following tables show variance estimates computed
using replication methods by type (total and within-PSU),
and by stage of estimation. The estimates presented are
based on the 1990 sample design and new weighting procedures introduced in January 2003. Updated estimates
based on the 2000 design will be provided when they are
available.
Averages over 13 months have been used to improve the
reliability of the estimated monthly variances. The
13-month period, March 2003−March 2004, was used for
estimation because the sample design was essentially
unchanged throughout this period (there was a maintenance reduction for CPS in April 2003). Data from January
and February 2003 were not used in order to allow new
weighting procedures to be fully reflected in the composite estimation step.
Variance Components Due to Stages of Sampling
Table 14–2 indicates, for the estimate after the secondstage (SS) adjustment, how the several stages of sampling
affect the total variance of each of the given characteristics. The SS estimate is the estimate after the first-stage,
national coverage, state coverage and second-stage ratio
adjustments are applied. Within-PSU variance and total
variance are computed as described earlier in this chapter.
Between-PSU variance is estimated by subtracting the
within-PSU variance estimate from the total variance estimate. Due to variation of the variance estimates, the
between-PSU variance is sometimes negative. The far right
two columns of Table 14–2 show the percentage withinPSU variance and the percentage between-PSU variance in
the total variance estimate.
For all characteristics shown in Table 14–2, the proportion
of the total variance due to sampling housing units within
PSUs (within-PSU variance) is larger than that due to sampling a subset of NSR PSUs (between-PSU variance). In fact,
for most of the characteristics shown, the within-PSU component accounts for over 90 percent of the total variance.
For civilian labor force and not-in-labor force characteristics, almost all of the variance is due to sampling housing
units within PSUs. For the total population and White-alone
population employed in agriculture, the within-PSU component still accounts for 70 to 80 percent of the total variance, while the between-PSU component accounts for the
remaining 20 to 30 percent.
Estimation of Variance

14–5

Table 14–2. Components of Variance for SS Monthly Estimates
[Monthly averages: March 2003−March 2004]
SS1 estimate
(x 106)

Standard error
(x 105)

Coefficient of
variation
(percent)

Unemployed, total. . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . .
Black alone. . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . .

8.81
6.34
1.80
1.46
1.24

1.70
1.43
0.76
0.75
0.56

Employed—agriculture,
total. . . . . . . . . . . . . . . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . .
Black alone. . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . .

2.26
2.12
0.06
0.45
0.12

Employed—nonagriculture,
total. . . . . . . . . . . . . . . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . .
Black alone. . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . .

Civilian noninstitutionalized
population
16 years old and over

Percent of total variance
Within

Between

1.93
2.25
4.20
5.16
4.49

98.7
99.0
100.6
101.0
99.5

1.3
1.0
−0.6
−1.0
0.5

1.12
1.10
0.15
0.59
0.19

4.98
5.18
23.04
13.06
16.67

75.4
74.7
94.8
86.2
88.3

24.6
25.3
5.2
13.8
11.7

135.76
112.34
14.66
17.01
5.84

3.84
3.29
1.41
1.53
1.04

0.28
0.29
0.96
0.90
1.77

97.4
102.2
94.9
92.7
97.6

2.6
−2.2
5.1
7.3
2.4

Civilian labor force, total . . .
White alone . . . . . . . . . . . . . . . . . . .
Black alone. . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . .

146.83
120.80
16.52
18.92
7.19

3.56
3.07
1.31
1.32
1.09

0.24
0.25
0.79
0.70
1.52

92.3
95.9
93.7
91.3
93.7

7.7
4.1
6.3
8.7
6.3

Not-in-labor force, total . . . . .
White alone . . . . . . . . . . . . . . . . . . .
Black alone. . . . . . . . . . . . . . . . . . . .
Hispanic origin2 . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . .

74.79
60.77
9.24
8.74
8.93

3.56
3.07
1.31
1.32
1.09

0.48
0.50
1.42
1.51
1.22

92.3
95.9
93.7
91.3
93.7

7.7
4.1
6.3
8.7
6.3

1
2

Estimates after the second-stage ratio adjustments (SS).
Hispanics may be any race.

Because the SS estimate of the total civilian labor force
and total not-in-labor-force populations must add to the
independent population controls (which are assumed to
have no variance), the standard errors and variance components for these estimated totals are the same.
TOTAL VARIANCES AS AFFECTED BY ESTIMATION
Table 14–3 shows how the separate estimation steps
affect the variance of estimated levels by presenting ratios
of relative variances. It is more instructive to compare
ratios of relative variances than the variances themselves,
since the various stages of estimation can affect both the
level of an estimate and its variance (Hanson, 1978; Train,
Cahoon, and Makens, 1978). The unbiased estimate uses
the baseweight with weighting control factors applied.
The noninterview estimate includes the baseweights, the
weighting control adjustment, and the noninterview
adjustment. The SS estimate includes baseweights, the
weighting control adjustment, the noninterview adjustment, the first-stage, national and state coverage adjustments, and second-stage ratio adjustments.
In Table 14–3, the figures for unemployed show, for
example, that the relative variance of the SS estimate of
level is 3.741 x 10−4 (equal to the square of the coefficient
of variation in Table 14–2). The relative variance of the
14–6

Estimation of Variance

unbiased estimate for this characteristic would be 1.11
times as large. If the noninterview stage of estimation is
also included, the relative variance is 1.10 times the size
of the relative variance for the SS estimate of level. Including the first stage of estimation maintains the relative variance factor at 1.11. The relative variance for total unemployed after applying the second-stage adjustment
without the first-stage adjustment is about the same as
the relative variance that results from applying the firstand second-stage adjustments.
The relative variance as shown in the last column of this
table illustrates that the first-stage ratio adjustment has
little effect on the variance of national level characteristics
in the context of the overall estimation process. However,
as illustrated in Rigby (2000), removing the first-stage
adjustment increased the variances of some state-level
estimates.
The second-stage adjustment, however, appears to greatly
reduce the total variance, as intended. This is especially
true for characteristics that belong to high proportions of
age, sex, or race/ethnicity subclasses, such as White
alone, Black alone, or Hispanic people in the civilian labor
force or employed in nonagricultural industries. Without
the second-stage adjustment, the relative variances of
these characteristics would be 5−10 times as large. For
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Table 14–3. Effects of Weighting Stages on Monthly Relative Variance Factors
[Monthly averages: March 2003−March 2004]
Relative variance factor1
Civilian noninstitutionalized
population
16 years old and over

Relative variance
of SS estimate of
level (x 10–4)

Unbiased
estimator

NI2

NI & FS3

NI & SS4

Unemployed, total . . . . . . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

3.741
5.065
17.654
26.600
20.186

1.11
1.12
1.27
1.28
1.11

1.10
1.11
1.26
1.27
1.11

1.10
1.11
1.25
1.27
1.11

1.00
1.00
1.00
1.00
1.00

Employed—agriculture, total . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

24.847
26.829
530.935
170.521
277.985

0.99
0.99
1.07
1.08
1.03

0.99
0.99
1.07
1.09
1.05

1.00
0.99
1.04
1.10
1.05

0.99
1.00
1.00
1.00
1.00

Employed—nonagriculture,
total . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

0.080
0.086
0.920
0.813
3.146

6.53
8.16
6.67
6.48
1.79

6.34
8.00
6.57
6.43
1.78

6.14
7.70
5.89
6.42
1.77

1.00
1.00
1.00
1.00
1.00

Civilian labor force, total. . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

0.059
0.064
0.631
0.487
2.310

8.28
10.38
9.02
10.45
2.04

8.01
10.15
8.87
10.35
2.03

7.75
9.76
7.86
10.34
2.01

1.00
1.00
1.00
1.00
1.00

Not-in-labor force, total. . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin5 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

0.226
0.254
2.015
2.282
1.499

2.67
3.01
3.84
3.58
2.64

2.55
2.91
3.73
3.54
2.60

2.50
2.80
3.10
3.54
2.60

1.00
1.00
1.00
1.00
1.00

1
2
3
4
5

Unbiased estimator with—

Relative variance factor is the ratio of the relative variance of the specified level to the relative variance of the SS level.
NI = Noninterview.
FS = First-stage.
SS = Estimate after Second-stage when the First-stage adjustment is skipped.
Hispanics may be any race.

smaller groups, such as the unemployed and those
employed in agriculture, the effect of second-stage adjustment is not as dramatic.
After the second-stage ratio adjustment, a composite estimator is used to improve estimates of month-to-month
change by taking advantage of the 75 percent of the total
sample that continues from the previous month (see Chapter 10). Table 14–4 compares the variance and relative
variance of the composited estimates of level to those of
the SS estimates. For example, the estimated variance of
the composited estimate of unemployed Hispanics is
5.445 x 109. The variance factor for this characteristic is
0.96, implying that the variance of the composited estimate is 96 percent of the variance of the estimate after
the second-stage adjustments. The relative variance of the
composite estimate of unemployed Hispanics, which takes
into account the estimate of the number of people with
this characteristic, is about the same as the size of the
relative variance of the SS estimate (hence, the relative
variance factor of 0.99). The two factors are similar for
most characteristics, indicating that compositing tends to
have a small effect on the level of most estimates.
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

DESIGN EFFECTS
Table 14−5 shows the design effects for the total and
within-PSU variances for selected labor force characteristics. A design effect (deff) is the ratio of the variance from
complex sample design or a sophisticated estimation
method to the variance of a simple random sample (SRS)
design. The design effects in this table were computed by
solving the equation (14.2) for deff and replacing N/n in
the formula with an estimate of the national sampling
interval. Estimates of P and Q were obtained from the 13
months of data.
For the unemployed, the design effect for total variance is
1.377 for the uncomposited (SS) estimate and 1.328 for
the composited estimate. This means that, for the same
number of sample cases, the design of the CPS (including
the sample selection, weighting, and compositing)
increases the total variance by about 32.8 percentage
points over the variance of an unbiased estimate based on
a simple random sample. On the other hand, for the civilian labor force the design of the CPS decreases the total
variance by about 20.6 percentage points. The design
effects for composited estimates are generally lower than
those for the SS estimates, indicating again the tendency
Estimation of Variance

14–7

Table 14–4. Effect of Compositing on Monthly Variance and Relative Variance Factors
[Monthly averages: March 2003−March 2004]
Civilian noninstitutionalized
population, 16 years old and over

Variance of composited
estimate of level (x 109)

Variance factor1

Relative variance factor2

Unemployed, total
White alone
Black alone . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . . . . .

27.729
19.462
5.163
5.445
3.067

0.96
0.96
0.90
0.96
0.98

0.97
0.97
0.93
0.99
1.01

Employed—agriculture, Total . .
White alone. . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . . . . .

12.446
11.876
0.213
3.425
0.354

0.98
0.98
1.01
0.99
0.95

0.99
1.00
1.00
1.00
0.99

Employed—nonagriculture,
total . . . . . . . . . . . . . . . . . . . . . . . . . . .
White alone. . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . . . . .

115.860
87.363
15.624
20.019
9.604

0.79
0.81
0.79
0.85
0.90

0.79
0.81
0.79
0.86
0.92

Civilian labor force, total . . . . . .
White alone. . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . . . . .

98.354
74.009
14.172
14.277
10.830

0.78
0.79
0.82
0.82
0.91

0.78
0.79
0.82
0.82
0.93

Not-in-labor force, total . . . . . . . .
White alone. . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin3 . . . . . . . . . . . . . . . . . .
Teenage, 16−19 . . . . . . . . . . . . . . . . . .

98.354
74.009
14.172
14.277
10.830

0.78
0.79
0.82
0.82
0.91

0.77
0.78
0.83
0.80
0.88

1
2
3

Variance factor is the ratio of the variance of a composited estimate to the variance of an SS estimate.
Relative variance factor is the ratio of the relative variance of a composited estimate to the relative variance of an SS estimate.
Hispanics may be any race.

Table 14–5. Design Effects for Total and Within-PSU Monthly Variances
[Monthly averages: March 2003−March 2004]
Civilian noninstitutionalized
population, 16 years old and over

Design effects for total variance

Design effects for within-PSU variance

After second stage

After compositing

After second stage

After compositing

Unemployed, total . . . . . . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

1.377
1.325
1.283
1.567
1.012

1.328
1.277
1.180
1.530
1.008

1.359
1.312
1.291
1.583
1.007

1.259
1.220
1.195
1.533
1.000

Employed—agriculture, Total. . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

2.271
2.304
1.344
3.089
1.292

2.248
2.281
1.351
3.071
1.255

1.712
1.722
1.274
2.662
1.141

1.703
1.715
1.279
2.658
1.112

Employed—nonagriculture,
total . . . . . . . . . . . . . . . . . . . . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

1.124
0.782
0.579
0.601
0.756

0.883
0.632
0.456
0.513
0.688

1.094
0.799
0.550
0.557
0.738

0.804
0.590
0.403
0.478
0.655

Civilian labor force, total. . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

1.024
0.685
0.452
0.404
0.689

0.794
0.540
0.371
0.332
0.633

0.946
0.657
0.423
0.369
0.645

0.693
0.471
0.323
0.319
0.589

Not-in-labor force, total. . . . . . . . . .
White alone . . . . . . . . . . . . . . . . . . . . . . . .
Black alone . . . . . . . . . . . . . . . . . . . . . . . .
Hispanic origin1 . . . . . . . . . . . . . . . . . . . .
Teenage, 16−19. . . . . . . . . . . . . . . . . . . .

1.025
0.854
0.780
0.833
0.560

0.795
0.671
0.643
0.676
0.501

0.946
0.819
0.730
0.761
0.524

0.693
0.586
0.559
0.650
0.466

Hispanics may be any race.

14–8

Estimation of Variance

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

of the compositing to reduce the variance of most estimates. Since the same denominator (SRS variance) is used
in computing deff for both the total and within-PSU variances, deff is directly proportional to the respective variances. As a result, the total variance design effects tend to
be higher than the within-PSU variances. This is consistent
with results from Table 14−2 where total variance estimates are expected to be higher than within-PSU variance
estimates.
REFERENCES
Adeshiyan, S. (2005), ‘‘2000 Replicate Variance System
(VAR2000−1),’’ Internal Memorandum for Documentation,
Demographic Statistical Methods Division, U.S. Census
Bureau.
Corteville, J. (1996), ‘‘State Between-PSU Variances and
Other Useful Information for the CPS 1990 Sample Design
(VAR90−16),’’ Memorandum for Documentation, March
6th, Demographic Statistical Methods Division, U. S.
Census Bureau.
Fay, R.E. (1984) ‘‘Some Properties of Estimates of Variance
Based on Replication Methods,’’ Proceedings of the Section on Survey Research Methods, American Statistical
Association, pp. 495−500.
Fay, R. E. (1989) ‘‘Theory and Application of Replicate
Weighting for Variance Calculations,’’ Proceedings of the
Section on Survey Research Methods, American Statistical Association, pp. 212−217.
Fay, R., C. Dippo, and D. Morganstein, (1984), ‘‘Computing
Variances From Complex Samples With Replicate Weights,’’
Proceedings of the Section on Survey Research
Methods, American Statistical Association, pp. 489−494.
Fay, R. and G. Train, (1995), ‘‘Aspects of Survey and ModelBased Postcensal Estimation of Income and Poverty Characteristics for States and Counties,’’ Proceedings of the
Section on Government Statistics, American Statistical
Association, pp. 154−159.
Griffiths, R. and K. Mansur, (2000a), ‘‘Preliminary Analysis
of State Variance Data: Sampling Error Autocorrelations
(VAR90−36),’’ Internal Memorandum for Documentation,
May 31st, Demographic Statistical Methods Division, U.S.
Census Bureau.
Griffiths, R. and K. Mansur, (2000b), ‘‘Preliminary Analysis
of State Variance Data: Autoregressive Integrated Moving
Average Model Fitting and Grouping States (VAR90−37),’’
Internal Memorandum for Documentation, June 15th,
Demographic Statistical Methods Division, U.S. Census
Bureau.
Griffiths, R. and K. Mansur, (2001a), ‘‘Current Population
Survey State-Level Variance Estimation,’’ paper prepared
for presentation at the Federal Committee on Statistical
Methodology Conference, Arlington, VA, November 14−16,
2001.
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Griffiths, R. and K. Mansur, (2001b), ‘‘The Current Population Survey State Variance Estimation Story (VAR90−38),’’
Internal Memorandum for Documentation, October 29th,
Demographic Statistical Methods Division, U.S. Census
Bureau.
Gunlicks, C. (1993), ‘‘Overview of 1990 CPS PSU Stratification (S−S90−DC−11),’’ Internal Memorandum for Documentation, February 24th, Demographic Statistical Methods
Division, U.S. Census Bureau.
Gunlicks, C. (1996), ‘‘1990 Replicate Variance System
(VAR90−20),’’ Internal Memorandum for Documentation,
June 4th, Demographic Statistical Methods Division, U.S.
Census Bureau.
Hanson, R. H. (1978), The Current Population Survey:
Design and Methodology, Technical Paper 40, Washington, D.C.: Government Printing Office.
Kostanich, D. (1996), ‘‘Proposal for Assigning Variance
Codes for the 1990 CPS Design (VAR90−22),’’ Internal
Memorandum to BLS/Census Variance Estimation Subcommittee, June 17th, Demographic Statistical Methods Division, U.S. Census Bureau.
Kostanich, D. (1996), ‘‘Revised Standard Error Parameters
and Tables for Labor Force Estimates: 1994−1995
(VAR80−6),’’ Internal Memorandum for Documentation,
February 23rd, Demographic Statistical Methods Division,
U.S. Census Bureau.
Lent, J. (1991), ‘‘Variance Estimation for Current Population
Survey Small Area Estimates,’’ Proceedings of the Section on Survey Research Methods, American Statistical
Association, pp. 11−20.
Mansur, K. and R. Griffiths, (2001), ‘‘Analysis of the Current Population Survey State Variance Estimates,’’ paper
presented at the 2001 Joint Statistical Meetings, American
Statistical Association, Section on Survey Research Methods.
Rigby, B. (2000), ‘‘The Effect of the First Stage Ratio Adjustment in the Current Population Survey,’’ paper presented
at the 2000 Joint Statistical Meetings, American Statistical
Association, Proceedings of the Section on Survey
Research Methods.
Train, G., L. Cahoon, and P. Makens, (1978), ‘‘The Current
Population Survey Variances, Inter-Relationships, and
Design Effects,’’ Proceedings of the Section on Survey
Research Methods, American Statistical Association, pp.
443−448.
U.S. Dept. of Labor, Bureau of Labor Statistics, Employment and Earnings, 42, p.152, Washington, D.C.: Government Printing Office.
Valliant, R. (1987), ‘‘Generalized Variance Functions in
Stratified Two-Stage Sampling,’’ Journal of the American
Statistical Association, 82, pp. 499−508.
Estimation of Variance

14–9

Wolter, K. (1984), ‘‘An Investigation of Some Estimators of
Variance for Systematic Sampling,’’ Journal of the
American Statistical Association, 79, pp. 781−790.

14–10

Estimation of Variance

Wolter, K. (1985), Introduction to Variance Estimation,
New York: Springer-Verlag.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Chapter 15.
Sources and Controls on Nonsampling Error
INTRODUCTION
For a given estimator, the difference between the estimate
that would result if the sample were to include the entire
population and the true population value being estimated
is known as nonsampling error. Nonsampling error can
enter the survey process at any point or stage, and many
of these errors are not readily identifiable. Nevertheless,
the presence of these errors can affect both the bias and
variance components of the total survey error. The effect
of nonsampling error on the estimates is difficult to measure accurately. For this reason, the most appropriate
strategy is to examine the potential sources of nonsampling error and to take steps to prevent these errors from
entering the survey process. This chapter discusses the
various sources of nonsampling error and the measures
taken to control their presence in the Current Population
Survey (CPS).
Sources of nonsampling error can include the following:
1. Inability to obtain information about all sample cases
(unit nonreponse).
2. Definitional difficulties.
3. Differences in the interpretation of questions.
4. Respondent inability or unwillingness to provide correct information.
5. Respondent inability to recall information.
6. Errors made in data collection, such as recording and
coding data.
7. Errors made in processing the data.
8. Errors made in estimating values for missing data.
9. Failure to represent all units with the sample (i.e.,
undercoverage).
It is clear that there are two main types of nonsampling
error in the CPS. The first type is error imported from
other frames or sources of information, such as decennial
census omissions, errors from the Master Address File or
its extracts, and errors in other sources of information
used to keep the sample current, such as building permits.
All nonsampling errors that are not of the first type are
considered preventable, such as when the sample is not
completely representative of the intended population,
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within-household omissions, respondents not providing
true answers to a questionnaire item, proxy response, or
errors produced during the processing of the survey data.
Chapter 16 discusses the presence of CPS nonsampling
error but not the effect of the error on the estimates. The
present chapter focuses on the sources and operational
efforts used to control the occurrence of error in the survey processes. Each section discusses a procedure aimed
at reducing coverage, nonresponse, response, or data processing errors. Despite the effort to treat each control as a
separate entity, they nonetheless affect the survey in a
general way. For example, training, even if focused on a
specific problem, can have broad-reaching effects on the
total survey.
This chapter deals with many sources of and controls on
nonsampling error, but it is not exhaustive. It includes
coverage error, error from nonresponse, response error,
and processing errors. Many of these types of errors interact, and they can occur anywhere in the survey process.
Although the full effects of nonsampling error on the survey estimates are unknown, research in this area is being
conducted (e.g., latent class analysis; Tran and Mansur,
2004). Ultimately, the CPS attempts to prevent such errors
from entering the survey process and tries to keep these
that occur as small as possible.
SOURCES OF COVERAGE ERROR
Coverage error exists when a survey does not completely
represent the population of interest. When conducting a
sample survey, a primary goal is to give every unit (e.g.,
person or housing unit) in the target universe a known
probability of being selected into the sample. When this
occurs, the survey is said to have 100-percent coverage.
On the other hand, a bias in the survey estimates results if
characteristics of units erroneously included or excluded
from the survey differ from those correctly included in the
survey. Historically in the CPS, the net effect of coverage
errors has been an undercount of population (resulting
from undercoverage).
The primary sources of CPS coverage error are:
1. Frame omission. Frame omission occurs when the
list of addresses used to select the sample is incomplete. This can occur in any of the four sampling
frames (i.e., unit, permit, area, and group quarters;
see Chapter 3). Since these erroneously omitted units
cannot be sampled, undercoverage of the target population results. Reasons for frame omissions are:
Sources and Controls on Nonsampling Error

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• Master Address File (MAF): The MAF is a list of
every living quarters nationwide and their geographic locations. The MAF is updated throughout
the decade to provide addresses for delivery of
Census 2000 questionnaires, to serve as the sampling frame for the Census Bureau’s demographic
surveys, and to support other Census Bureau statistical programs. The MAF may be incomplete or contain some units that are not locatable. This occurs
when the decennial census lister fails to canvass an
area thoroughly or misses units in multiunit structures.
• New construction: This is a sampling problem.
Some areas of the country are not covered by building permit offices and are not surveyed (unit frame)
to pick up new housing. New housing units in these
areas have zero probability of selection. (Based on
initial results, the level of errors from this source of
frame omission is expected to be quite small in the
2000 CPS Sample Redesign.)
• Mobile homes: This is also a sampling problem.
Mobile homes that move into areas that are not surveyed (unit frame) also have a zero probability of
selection.
• Group quarters: The information used to identify
group quarters may be incomplete, or new group
quarters may not be identified.
2. Erroneous frame inclusion. Erroneous frame inclusion of housing units occurs when any of the four
frames or the MAF (extracts) contains units that do not
exist on the ground; for example, a housing unit is
demolished, but still exists in the frame. Other erroneous inclusions occur when a single unit is recorded as
two units through faulty application of the housing
unit definition.
Since erroneously included housing units can be
sampled, overcoverage of the target population
results if the errors are not detected and corrected.
3. Misclassification errors. Misclassification errors
occur when out-of-scope units are misclassified as
in-scope or vice versa. For example, a commercial unit
is misclassified as a residential unit, an institutional
group quarter is misclassified as a noninstitutional
group quarter, or an occupied housing unit with no
one at home is misclassified as vacant. Errors can also
occur when units outside an appropriate area boundary are misclassified as inside the boundary or vice
versa. These errors can cause undercoverage or overcoverage of the target population.
4. Within-housing unit omissions. Undercoverage of
individuals can arise from failure to list all usual residents of a housing unit on the household roster or
from misclassifying a household member as a nonmember.
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5. Within-housing unit inclusions. Overcoverage can
occur because of the erroneous inclusion of people on
the roster for a household, for instance, when people
with a usual residence elsewhere are incorrectly
treated as members of the sample housing unit.
Other sources of coverage error are omission of homeless
people from the frame, unlocatable addresses from building permit lists, and missed housing units due to the start
dates for the sampling of permits.1 For example, housing
units whose permits were issued before the start month
and not built by the time of the census may be missed.
Newly constructed group quarters are another possible
source of undercoverage. If they are noticed on the permit
lists, the general rule is purposely to remove these group
quarters; if they are not noticed and the field representative (FR) discovers a newly constructed group quarters,
the case is stricken from the roster of sample addresses
and never visited again for the CPS until possibly the next
sample redesign.
Through various studies, it has been determined that the
number of housing units missed is small because of the
way the sampling frames are designed and because of the
routines in place to ensure that the listing of addresses is
performed correctly. A measure of potential undercoverage is the coverage ratio that attempts to quantify the
overall coverage of the survey, despite its coverage errors.
For example, the coverage ratio for the total population
that is at least 16 years old has been approximately 88
percent since the CPS started phasing in the new 2000based sample in April 2004. See Chapter 16 for further
discussion of coverage error.
CONTROLLING COVERAGE ERROR
This section focuses on those processes during the sample
selection and sample preparation stages that control housing unit omissions. Other sources of undercoverage can
be viewed as response error and are described in a later
section of this chapter.
Sample Selection
The CPS sample selection is coordinated with and does not
duplicate the samples of at least five other demographic
surveys. These surveys basically undergo the same verification processes, so the discussion in the rest of this section is not unique to the CPS.
Sampling verification consists of testing and production.
The intent of the testing phase (unit testing and system
testing) is to design the processes better in order to

For the 2000 Sample Redesign, sampling of permits began
with those issued in 1999; the month varied depending on the
size of the structure and region. Housing units whose permits
were issued before the start month in 1999 and not built by the
time of the census may be missed.

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improve quality, and the intent of the production phase is
to verify or inspect the quality of the final product in order
to improve the processes. Both phases are coordinated
across the various surveys. Sampling intervals, measures
of expected sample sizes, and the premise that a housing
unit is selected for only one survey for 10 years are all
part of the sample selection process. Verification in each
of the testing and production phases is done independently by two verifiers.
The testing phase tests and verifies the various sampling
programs before any sample is selected, ensuring that the
programs work individually and collectively. Smaller data
sets with unusual observations are used to test the performance of the system in extreme situations.
The production phase of sampling verification consists of
verification of output using the actual sample, focusing on
whether the system ran successfully. The following are
illustrations and do not represent any particular priority or
chronology:
1. PSU probabilities of selection are verified; for example,
their sum within a stratum should be one.
2. Edits are done on file contents, checking for blank
fields and out-of-range data.
3. When applicable, the files are coded to check for logic
and consistency.
4. Information is centralized; for example, the sampling
rates for all states and substate areas for all surveys
are located in one parameter file.
5. As a form of overall consistency verification, the output at several stages of sampling is compared to that
of the former CPS design.
Sample Preparation2
Sample preparation activities, in contrast to those of initial
sample selection, are ongoing. The listing review3 and
check-in of sample are monthly production processes, and
the listing check is an ongoing field process.
Listing review. The listing review is a check on each
month’s listings to keep the nonsampling error as low as
possible. Its aim is to ensure that the interviews will be
conducted at the correct units and that all units have one
and only one chance of selection. This review also plays a

2
See Chapter 4, Preparation of the Sample, specifically its section Listing Activities, for more details. Also see Appendix A,
Sample Preparation Materials.
3
Because of the automated instrument, it is appropriate to use
the generic term ‘‘listing review” for the area, unit, and group
quarters frames. ‘‘Listing sheet” and ‘‘listing sheet review” are
appropriate for the permit frame. Regardless, these sections will
use the generic terms.

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role in the verification of the sampling, subsampling, and
relisting. Automation of the review is a major advancement in improving the timing of the process and, thus, a
more accurate CPS frame.
Various listing reviews are performed by both the regional
offices (ROs) and the National Processing Center (NPC) in
Jeffersonville, IN. Performing these reviews in different
parts of the Census Bureau is, in itself, a means of controlling nonsampling error.
After an FR makes an initial visit either to a multiunit
address from the unit frame or to a sample address from
the permit frame, the RO staff reviews the Multiunit Listing
Aid or the Permit Listing Sheet, respectively. (Refer to
Appendix A for a description of these listing forms.) This
review occurs the first time the address is in the sample
for any survey. However, if major changes appear at an
address on a subsequent visit, the revised listing is
reviewed. If there is evidence that the FR encountered a
special or unusual situation, the materials are compared to
the instructions in the manual ‘‘Listing and Coverage: A
Survival Guide for the Field Representative’’ (see Chapter
4) to ensure that the situation was handled correctly.
Depending on whether it is a permit-frame sample address
or a multiunit address from the unit frame, the following
are verified:
1. Were correct entries made on the listing when either
fewer or more units were listed than expected?
2. Did the FR relist the address if major structural
changes were found?
3. Were no units listed (for the permit frame)?
4. Was an extra unit discovered (for the permit frame)?
5. Were additional units interviewed if listed on a line
with the current CPS sample designation?
6. Did the unit designation change?
7. Was a sample unit demolished or condemned?
Errors and omissions are corrected and brought to the
attention of the FR. This may involve contacting the FR for
more information.
The Automated Listing and Mapping Instrument (ALMI)
features an edit that prevents duplication of units in area
frame blocks4. The RO review of area-frame block updates
is performed for new FRs or those with limited listing
experience. This review focuses on deletions, moved
units, added units, and house number changes. There is

4
Other features of the ALMI are: pick lists that minimize the
amount of data entry and, therefore, keying errors; edit checks
that check the logic of the data entry and identify missing/critical
data.

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no RO review of group quarters listings in the area and
group quarters frames; however, basic edits are included
with the Group Quarters Automated Instrument for Listing
(GAIL).
As stated previously, listing reviews are also performed by
the NPC. The following are activities involved in the
reviews for the unit-frame sample:
1. Verify that the FR resolved all unit designations that
were missing or duplicated in Census 2000; i.e., if any
sample units contained missing or duplicate unit designations.
2. Review for accuracy any large multiunit addresses that
were relisted.
3. Check whether additional units (found during listing)
already had a chance of selection. If so, the RO is contacted for instructions for correcting the Multiunit Listing Aid.
In terms of the review of listing sheets for permit frame
samples, the NPC checks to see whether the number of
units is more than expected and whether there are any
changes to the address. If there are more units than
expected, the NPC determines whether the additional
units already had a chance of selection. If so, the RO is
contacted with instructions for updating the listing sheet.
The majority of listings are found to be correct during the
RO and NPC reviews. When errors or omissions are
detected, they usually occur in batches, signifying a misinterpretation of instructions by an RO or a newly hired FR.
Check-in of sample. Depending on the sampling frame
and mode of interview, the monthly process of check-in of
the sample occurs as the sample cases progress through
the ROs, the NPC, and headquarters. Check-in of sample
describes the processes by which the ROs verify that the
FRs receive all their assigned sample cases and that all are
completed and returned. Since the CPS is now conducted
entirely by computer-assisted personal or telephone interview, it is easier than ever before to track a sample case
through the various systems. Control systems are in place
to control and verify the sample count. The ROs monitor
these systems to control the sample during the period
when it is active, and all discrepancies must be resolved
before the office is able to certify that the workload in the
database is correct. A check-in also occurs when the cases
are transmitted to headquarters.
Listing check. Listing check is a quality assurance program for the Demographic Area Address Listing (DAAL). It
uses the same instrument as the area-frame listing, i.e.,
the ALMI. Each month, a random sample of FRs who have
completed sufficient listing work is selected for a listing
check. The goal of this sampling process is to check each
FR’s work at least once a year. If the FR is selected for a
listing check, then a sample of the work performed by this
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Sources and Controls on Nonsampling Error

FR is selected and is checked by a senior staff member.
Various types of errors, including coverage errors, content
errors, and mapping errors, are recorded during the listing
check. Classification of errors is completely automated
within the ALMI instrument. The results are then compared
against the 3-month aggregate average error rate for each
RO to determine a ‘‘pass” or ‘‘fail” status for the FR. This
allows ROs to monitor the performance of FRs and provides information about the overall quality of the DAAL
that allows continual improvements of the listing process.
Depending on the severity and type of errors, the ROs
must give feedback to FRs who failed the listing check and
retrain them as necessary. The automated system will
warn the supervisors if they try to assign work to an FR
who failed a listing check but has not been retrained.
SOURCES OF NONRESPONSE ERROR
There are three main sources of nonresponse error in the
CPS: unit nonresponse, person nonresponse, and item
nonresponse. Unit nonresponse error occurs when households that are eligible for interview are not interviewed for
some reason: a respondent refuses to participate in the
survey, is incapable of completing the interview, or is not
available or not contacted by the interviewer during the
survey period, perhaps due to work schedules or vacation.
These household noninterviews are called Type A noninterviews. The weights of eligible households who do
respond to the survey are increased to account for those
who do not, but nonresponse error can be introduced if
the characteristics of the interviewed households differ
from those that are not interviewed.
Individuals within the household may refuse to be interviewed, resulting in person nonresponse. Person nonresponse has not been much of a problem in the CPS
because any responsible adult in the household is able to
report for others in the household as a proxy reporter.
Panel nonresponse exists when those who live in the same
household during the entire time they are in the CPS
sample do not agree to be interviewed in any of the 8
months. Thus, panel nonresponse can be important if the
CPS data are used longitudinally. Finally, some respondents who complete the CPS interview may be unable or
unwilling to answer specific questions, resulting in item
nonresponse. Imputation procedures (explained in Chapter 9) are implemented for item nonresponse. However,
because there is no way of ensuring that the errors of item
imputation will balance out, even on an expected basis,
item nonresponse also introduces potential bias into the
estimates.
One example of the magnitude of the error due to nonresponse is the national Type A household noninterview

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rate, which was 7.70 percent in July 20045. For item nonresponse, a few of the average allocation rates in July
2004, by topical module were: 0.52 percent for household, 1.99 percent for demographic, 2.35 percent for
labor force, 9.98 percent for industry and occupation, and
18.46 percent for earnings. (See Chapter 16 for discussion
of various quality indicators of nonresponse error.)
CONTROLLING NONRESPONSE ERROR
Field Representative Guidelines6
Response/nonresponse rate guidelines have been developed for FRs to help ensure the quality of the data collected. Maintaining high response rates is of primary
importance, and response/nonresponse guidelines have
been developed with this in mind. These guidelines, when
used in conjunction with other sources of information, are
intended to assist supervisors in identifying FRs needing
performance improvement. An FR whose response rate,
household noninterview rate (Type A), or minutes-per-case
falls below the fully acceptable range based on one quarter’s work is considered in need of additional training and
development. The CPS supervisor then takes appropriate
remedial action. National and regional response performance data are also provided to permit the RO staff to
judge whether their activities are in need of additional
attention.
Summary Reports
Another way to monitor and control nonresponse error is
the production and review of summary reports. Produced
by headquarters after the release of the monthly data
products, they are used to detect changes in historical
response patterns. Since they are distributed throughout
headquarters and the ROs, other indications of data quality and consistency can be focused upon. The contents of
some of the summary report tables are: noninterview rates
by RO for both the basic CPS and its supplements,
monthly comparisons to prior year, noninterview-tointerview conversion rates, resolution status of computerassisted telephone interview cases, interview status by
month-in-sample, daily transmittals, percent of personalvisit cases actually conducted in person, allocation rates
by topical module, and coverage ratios.
Headquarters and Regional Offices Working as a
Team
As detailed in a Methods and Performance Evaluation
Memorandum (Reeder, 1997), the Census Bureau and the
Bureau of Labor Statistics formed an interagency work
group to examine CPS nonresponse in detail. One goal
5
In April 2004, CPS started phasing in the new 2000-based
sample.
6
See Appendix D for a detailed discussion, especially in terms
of the performance evaluation system.

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was to share possible reasons and solutions for the declining CPS response rates. A list of 31 questions was prepared to help the ROs understand CPS field operations, to
solicit and share the ROs’ views on the causes of the
increasing nonresponse rates, and to evaluate methods to
decrease these rates. All of the answers provide insight
into the CPS operations that may affect nonresponse and
follow-up procedures for household noninterviews. A few
are:
1. The majority of ROs responded that there is written
documentation of the follow-up process for CPS
household noninterviews.
2. The standard process is that an FR must let the RO
know about a possible household noninterview as
soon as possible.
3. Most regions attempt to convert confirmed refusals to
interviews under certain circumstances.
4. All regions provide monthly feedback to their FRs on
their household noninterview rates.
5. About half of the regions responded that they provide
specific region-based training/activities for FRs on
converting or avoiding household noninterviews.
Much research has been distributed regarding whether
and how to convince someone who refused to be
interviewed to change their mind.
Most offices use letters in a consistent manner to
follow-up with noninterviews. Most ROs also include informational brochures about the survey with the letters, and
they are tailored to the respondent.
SOURCES OF RESPONSE ERROR
The survey interviewer asks a question and collects a
response from the respondent. Response error exists if the
response is not the true answer. Reasons for response
error can include:
1. The respondent misinterprets the question, does not
know the true answer and guesses (e.g., recall
effects), exaggerates, has a tendency to give an
answer that appears more ‘socially desireable,’ or
chooses a response randomly.
2. The interviewer reads the question incorrectly, does
not follow the appropriate skip pattern, misunderstands or misapplies the questionnaire, or records the
wrong answer.
3. A proxy responder (i.e., a person who answers on
someone else’s behalf) provides an incorrect response.
4. The data collection modes (e.g., personal visit and
telephone) elicit different responses.
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5. The questionnaire does not elicit correct responses
due to a format that is not easy to understand, or has
complicated or incorrect skip patterns or difficult coding procedures.
Thus, response error can arise from many sources. The
survey instrument, the mode of data collection, the interviewer, and the respondent are the focus of this section,7
while their interactions are discussed in the section The
Reinterview Program—Quality Control and Response
Error.8
In terms of magnitude, measures of response error are
obtainable through the reinterview program, specifically,
the index of inconsistency. This index is a ratio of the estimated simple response variance to the estimated total
variance arising from sampling and simple response variance. When identical responses are obtained from interview to interview, both the simple response variance and
the index of inconsistency are zero. Theoretically, the
index has a range of 0 to 100. For example, the index of
inconsistency for the labor force characteristic of unemployed for 2003 is considered moderate at 37.9. Other
statistical techniques being used to measure response
error include latent class analysis (Tran and Mansur, 2004),
which is being used to look at how responses vary across
time-in-sample.
CONTROLLING RESPONSE ERROR
Survey Instrument9
The survey instrument involves the CPS questionnaire, the
computer software that runs the questionnaire, and the
mode by which the data are collected. The modes are personal visits or telephone calls made by FRs and telephone
calls made by interviewers at centralized telephone centers. Regardless of the interview mode, the questionnaire
and the software are basically the same (see Chapter 7).
Software. Computer-assisted interviewing technology in
the CPS allows very complex skip patterns and other procedures that combine data collection, data input, and a
degree of in-interview consistency editing into a single
operation.
This technology provides an automatic selection of questions for each interview. The screens display response
options, if applicable, and information about what to do
next. The interviewer does not have to worry about skip
patterns, with the possibility of error. Appropriate proper

7
Most discussion in this section is applicable whether the
interview is conducted via computer-assisted personal interview
or computer-assisted telephone interview by the FR or in a centralized telephone facility.
8
Appendix E provides an overview of the design and methodology of the entire reinterview program.
9
Many of the topics in this section are presented in more
detail in Chapter 6.

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Sources and Controls on Nonsampling Error

names, pronouns, verbs, and reference dates are automatically filled into the text of the questions. If there is a
refusal to answer a demographic item, that item is not
asked again in later interviews; rather, it is longitudinally
allocated. This balances nonsampling error existing for
the item with the possibility of a total noninterview. The
instrument provides opportunities for the FR to review and
correct any incorrect/inconsistent information before the
next series of questions is asked, especially in terms of
the household roster. In later months, the instrument
passes industry and occupation information to the FR to
be verified and corrected. In addition to reducing response
and interviewer burden, the instrument avoids erratic
variations in industry and occupation codes among pairs
of months for people who have not changed jobs but who
describe their industry and occupation differently in the 2
months.
Questionnaire. Two objectives of the design of the CPS
questionnaire are to reduce the potential for response
error in the questionnaire-respondent-interviewer interaction and to improve measurement of CPS concepts. The
approaches used to lessen the potential for response error
(i.e., enhanced accuracy) are: short and clear question
wording, splitting complex questions into two or more
questions, building concept definitions into question
wording, reducing reliance on volunteered information,
using explicit and implicit strategies for the respondent to
provide numeric data, and using precoded response categories for open-ended questions. Interviewer notes
recorded at the end of the interview are critical to obtaining reliable and accurate responses.
Modes of data collection. As stated in Chapters 7 and
16, the first and fifth months’ interviews are done in person whenever possible, while the remaining interviews
may be conducted via telephone either by the FR or by an
interviewer from a centralized telephone facility. (In July
2004, nationally, 81 percent and 62.8 percent of personal
visit cases were actually conducted in person in monthsin-sample 1 and 5, respectively.) Although each mode has
its own set of performance guidelines that must be
adhered to, similarities do exist. The controls detailed in
The Interviewer and Error Due to Nonresponse sections
are mainly directed at personal visits but are also basically
valid for the calls made from the centralized facility via
the supervisor’s listening in.
Continuous testing and improvements. Insights
gained from past research on questionnaire design have
assisted in the development of methods for testing new or
revised questions for the CPS. In addition to reviewing
new questions to ensure that they will not jeopardize the
collection of basic labor force information and to determine whether the questions are appropriate additions to a
household survey about the labor force, the wording of
new questions is tested to gauge whether respondents are
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correctly interpreting the questions. Chapter 6 provides an
extensive list of the various methods of testing. The Census Bureau has also developed a set of protocols for pretesting demographic surveys.
To improve existing questions, the ‘‘don’t know’’ and
refusal rates for specific questions are monitored, inconsistencies caused by instrument-directed paths through
the survey or instrument-assigned classifications are
looked for during the estimation process, and interviewer
notes recorded at the conclusion of the interview are
reviewed. Also, focus groups with the CPS interviewers
and supervisors are conducted periodically.
Despite the benefits from adding new questions and
improving existing ones, changes to the CPS are
approached cautiously until the effects are measured and
evaluated. In order to avoid the distruption of historical
series, methods to bridge differences caused by changes
or techniques are included in the testing whenever possible. In the past, for example, parallel surveys have been
conducted using the revised and unrevised procedures.
Results from the parallel survey have been used to anticipate the effect the changes would have on the survey’s
estimates and nonresponse rates (Kostanich and Cahoon,
1994; Polivka, 1994; and Thompson, 1994).
Interviewer
Interviewer training, observation, monitoring, and evaluation are all methods used to control nonsampling error,
arising from inaccuracies in both the frame and data collection activities. For further discussion, see this chapter’s
section Field Representative Guidelines and Appendix D.
Group training and home study are continuing efforts in
each RO to control various nonsampling errors, and they
are tailored to the types of duties and length of service of
the interviewer.
Field observation is an extension of classroom training
and provides on-the-job training and on-the-job evaluation. It is one of the methods used by the supervisor to
check and improve the performance of the FR. It provides
a uniform method for assessing the FR’s attitudes toward
the job and use of the computer, and evaluates the FR’s
ability to apply CPS concepts and procedures during actual
work situations. There are three types of observations: initial, general performance review, and special needs.
Across all types, the observer stresses good interviewing
techniques such as the following:
1. Asking questions as worded and in the order presented on the questionnaire.
2. Adhering to instructions on the instrument and in the
manuals.
3. Knowing how to probe.
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4. Recording answers in the correct manner and in
adequate detail.
5. Developing and maintaining good rapport with the
respondent conducive to an exchange of information.
6. Avoiding questions or probes that suggest a desired
answer to the respondent.
7. Determining the most appropriate time and place for
the interview.
The emphasis is on correcting habits that interfere with
the collection of reliable statistics.
Respondent: Self Versus Proxy
The CPS Interviewing Manual states that any household
member 15 years of age or older is technically eligible to
act as a respondent for the household. The FR attempts to
collect the labor force data from each eligible individual;
however, in the interests of timeliness and efficiency, any
knowledgeable adult household member is allowed to provide the information. Also, the survey instrument is structured so that every effort is made to interview the same
respondent every month. The majority of the CPS labor
force data is collected by self-response, and most of the
remainder is collected by proxy from a household respondent. The use of a nonhousehold member as a household
respondent is only allowed in certain limited situations;
for example, the household may consist of a single person
whose physical or mental health does not permit a personal interview.
There has been a substantial amount of research into selfversus-proxy reporting, including research involving CPS
respondents (Kojetin and Mullin, 1995; Tanur, 1994). Much
of the research indicates that self-reporting is more reliable than proxy reporting, particularly when there are
motivational reasons for proxy and self-respondents to
report differently. For example, parents may intentionally
‘‘paint a more favorable picture’’ of their children than fact
supports. However, there are some circumstances in which
proxy reporting is more accurate, such as in responses to
certain sensitive questions.
Interviewer/Respondent Interaction
Rapport with the respondent is a means of improving data
quality. This is especially true for personal visits, which
are required for months-in-sample 1 and 5 whenever possible. By showing a sincere understanding of and interest
in the respondent, a friendly atmosphere is created in
which the respondent can talk honestly and openly. Interviewers are trained to ask questions exactly as worded
and to ask every question. If the respondent misunderstands or misinterprets a question, the question is
repeated as worded and the respondent is given another
Sources and Controls on Nonsampling Error

15–7

chance to answer; probing techniques are used if a relevant response is still not obtained. The respondent
should be left with a friendly feeling towards the interviewer and the Census Bureau, clearing the way for future
contacts.
Reinterview Program—Quality Control and
Response Error10
The reinterview program has two components: the Quality
Control Reinterview Program and the Response Error Reinterview Program. One of the objectives of the Quality Control Reinterview Program is to evaluate individual FR’s performance. It checks a sample of the work of an FR and
identifies and measures aspects of the field procedures
that may need improvement. It is also critical in the detection and prevention of data falsification.
The Response Error Reinterview Program provides a measure of response error. Responses from first and second
interviews at selected households are compared and differences are identified and analyzed. This helps to evaluate the accuracy of the survey’s original results; as a
by-product, instructions, training, and procedures are also
evaluated.
SOURCES OF MISCELLANEOUS ERRORS
Data processing errors are one focus of this final section.
Their sources can include data entry, industry and occupation coding, and methodologies for edits, imputations,
and weighting. Also, the CPS population controls are not
error-free; a number of approximations or assumptions are
used in their derivations. Other potential sources are composite estimation and modeling errors, which may arise
from, for example, seasonally adjusted series for selected
labor force data, and monthly model-based state labor
force estimates.
CONTROLLING MISCELLANEOUS ERRORS
Industry and Occupation Coding Verification
To be accepted into the CPS processing system, files containing records needing three-digit industry and occupation codes are electronically sent to the NPC for the
assignment of these codes (see Chapter 9). Once completed and transmitted back to headquarters, the remainder of the production processing, (including edits, weighting, microdata file creation, and tabulations) can begin.
Using online industry and occupation reference materials,
the NPC coder enters three-digit numeric industry and
occupation codes that represent the description for each
case on the file. If the coder cannot determine the proper
code, the case is assigned a referral code and will later be

Appendix E provides an overview of the design and methodology of the entire reinterview program.

15–8

Sources and Controls on Nonsampling Error

coded by a referral coder. A substantial effort is directed at
the supervision and control of the quality of this operation. The supervisor is able to turn the dependent verification setting on or off at any time during the coding operation. In the on mode, a particular coder’s work is to be
verified by a second coder. Additionally, a 10-percent
sample of each month’s cases is selected to go through a
quality assurance system to evaluate each coder’s work.
The selected cases are verified by another coder after the
current monthly processing has been completed. Upon
completion of the coding and possible dependent verification of a particular batch, all cases for which a coder
assigned at least one referral code must be reviewed and
coded by a referral coder.
Edits, Imputation, and Weighting
As detailed in Chapter 9, there are six edit modules:
household, demographic, industry and occupation, labor
force, earnings, and school enrollment. Each module
establishes consistency between logically-related items;
assigns missing values using relational imputation, longitudinal editing, or cross-sectional imputation; and deletes
inappropriate entries. Each module also sets a flag for
each edit step that can potentially affect the unedited
data.
Consistency editing is one of the checks used to control
nonsampling error. Are the data logically correct, and are
the data consistent within the month? For example, if a
respondent says that he/she is a doctor, is he/she old
enough to have achieved this occupation? Are the data
consistent with that from the previous month and across
the last 12 months? The imputation rates should normally
stay about the same as the previous month’s, taking seasonal patterns into account. Are the universes verified,
and how consistent are the interview rates? In terms of the
weighting, a check is made for zero weighting or very
large weights. If such outliers are detected, verification
and possible correction follow. Another method to validate
the weighting is to look at coverage ratios, which should
fall within certain historical bounds. A key working document used by headquarters for all of these checks is a
four-page tabulation of monthly summary statistics that
highlights the six edit modules and the two main weighting programs for the current month and the preceding 12
months. This is called the ‘‘CPS Monthly Check-in Sheet.”
Also, as part of the routine production processing at headquarters, and as part of the routine check-in of the data by
the Bureau of Labor Statistics, both agencies compute
numerous tables in composited and uncomposited modes.
These tables are then checked cell-by-cell to ensure that
the cell entries across the two agencies are identical. If so,
the data are deemed to have been computed correctly.
Extensive verification is done every time any change is
made to any part of the CPS estimation process and its
impact is evaluated. Examples are weighting changes,
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changes in replication methodology, and the introduction
of new noninterview cluster codes and transitional baseweights during the phasing-out of one sample design and
the phasing-in of another design. In fact, there are several
guidelines and specifications that deal solely with CPS
verification roles and responsibilities.
CPS Population Controls
National and state-level CPS population controls are developed by the Census Bureau independently from the collection and processing of the CPS data. These monthly and
independent projections of the population are used for the
iterative, second-stage and composite weighting of the
CPS data. All of the estimates start with data from the last
census (currently Census 2000), and use administrative
records and projection techniques to provide updates. (See
Appendix C for a detailed discussion of the methodologies.)
As a means of controlling nonsampling error throughout
the processes, numerous internal consistency checks in
the programming are performed. For example, input files
containing age and sex details are compared to independent files that give age and sex totals. Second, internal
redundancy is intentionally built into the programs that
process the files, as are files that contain overlapping/
redundant data. Third, a two-person clerical review of all
data with comments/notes is performed. An important
means of assuring that quality data are used as input into
the CPS population controls is continuous research into
improvements in methods of making population estimates
and projections.
Modeling Errors
This section focuses on a few of the methods to reduce
nonsampling error that are applied to the seasonal adjustment programs and monthly model-based state labor
force estimates. (See Chapter 10 for a discussion of these
procedures.)
Changes that occur in a seasonally adjusted series reflect
changes other than those arising from normal seasonal
change. They are believed to provide information about
the direction and magnitude of changes in the behavior of
trend and business cycle effects. They may, however, also
reflect the effects of sampling and nonsampling errors,

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which are not removed by the seasonal adjustment process. Research into the sources of these irregularities, specifically nonsampling error, can then lead to controlling
their effects and even removal. The seasonal adjustment
programs contain built-in checks as verification that the
data are well-fit and that the modeling assumptions are
reasonable. These diagnostic measures are a routine part
of the output.
The processes for controlling nonsampling error during
the production of monthly model-based state labor force
estimates are very similar to those used for the seasonal
adjustment programs. Built-in checks exist in the programs and, again, a wide range of diagnostics are produced that indicate the degree of deviation from the
assumptions.
REFERENCES
Kojetin, B.A. and P. Mullin (1995), ‘‘The Quality of Proxy
Reports on the Current Population Survey (CPS),’’ Proceedings of the Section on Survey Research Methods,
American Statistical Association, pp. 1110−1115.
Kostanich, D.L. and L.S. Cahoon, (1994), Effect of
Design Differences Between the Parallel Survey and
the New CPS, CPS Bridge Team Technical Report 3, dated
March 4.
Polivka, A.E. (1994), Comparisons of Labor Force Estimates From the Parallel Survey and the CPS During
1993: Major Labor Force Estimates, CPS Overlap
Analysis Team Technical Report 1, dated March 18.
Reeder, J.E. (1997), Regional Response to Questions
on CPS Type A Rates, Bureau of the Census, CPS Office
Memorandum No. 97-07, Methods and Performance Evaluation Memorandum No. 97-03, January 31.
Tanur, J.M. (1994), ‘‘Conceptualizations of Job Search: Further Evidence From Verbatim Responses,’’ Proceedings
of the Section on Survey Research Methods, American Statistical Association, pp. 512−516.
Thompson, J. (1994), Mode Effects Analysis of Major
Labor Force Estimates, CPS Overlap Analysis Team
Technical Report 3, April 14.
Tran, B. and K. Mansur, (2004), ‘‘Analysis of the Unemployment Rate in the Current Population Survey—A Latent
Class Approach,” Journal of the American Statistical
Association, August 12.

Sources and Controls on Nonsampling Error

15–9

Chapter 16.
Quality Indicators of Nonsampling Errors
(Updated coverage ratios, nonresponse rates, and other measures of quality can be found by clicking on ‘‘Quality Measures’’ at
.)

INTRODUCTION
Chapter 15 contains a description of the different sources
of nonsampling error in the CPS and the procedures
intended to limit those errors. In the present chapter, several important indicators of potential nonsampling error
are described. Specifically, coverage ratios, response variance, nonresponse rates, mode of interview, time-insample biases, and proxy reporting rates are discussed. It
is important to emphasize that, unlike sampling error,
these indicators show only the presence of potential nonsampling error, not an actual degree of nonsampling error
present.
Nonetheless, these indicators of nonsampling error are
regularly used to monitor and evaluate data quality. For
example, surveys with high nonresponse rates are judged
to be of low quality, but the actual nonsampling error of
concern is not the nonresponse rate itself, but rather nonresponse bias, that is, how the respondents differ from the
nonrespondents on the variables of interest. Although it is
possible for a survey with a lower nonresponse rate to
have a larger nonresponse bias than a survey that has a
higher nonresponse rate (if the difference between respondents and nonrespondents is larger in the survey with the
lower nonresponse rate than it is in the survey with the
higher nonresponse rate), one would generally expect that
larger nonresponse indicates a greater potential for bias.
While it is relatively easy to measure nonresponse rates, it
is extremely difficult to measure or even estimate nonresponse bias. Thus, these indicators are simply a measurement of the potential presence of nonsampling errors. We
are not able to quantify the effect the nonsampling error
has on the estimates, and we do not know the combined
effect of all sources of nonsampling error.
COVERAGE ERRORS
When conducting a sample survey, the primary goal is to
give every person in the target universe a known probability of being selected for the sample. When this occurs, the
survey is said to have 100 percent coverage. This is rarely
the case, however. Errors can enter the system during
almost any phase of the survey process, from frame creation to interviewing. A bias in the survey estimates
results when characteristics of people erroneously
included or excluded from the survey differ from those of
individuals correctly included in the survey. Historically in
the CPS, the net effect of coverage errors has been an

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underestimate of the size of the total population for most
major demographic population subgroups before the
population controls are applied (known as undercoverage).
Coverage Ratios
One way to estimate the coverage error present in a survey is to compute a coverage ratio. A coverage ratio is the
outcome of dividing the estimated number of people in a
specific demographic group from the survey by an independent population total for that group. The CPS coverage
ratios are computed by dividing a CPS estimate using the
weights after the first-stage ratio adjustment by the independent population controls used to perform the national
and state coverage adjustments and the second-stage
ratio adjustment. See Chapter 10 for more information on
computation of weights. Population controls are not error
free. A number of approximations or assumptions are
required in deriving them. See Appendix C for details on
how the controls are computed. Chapter 15 highlighted
potential error sources in the population controls. Undercoverage exists when the coverage ratio is less than 1.0
and overcoverage exists when the ratio is greater than
1.0. Figure 16−1 shows the average monthly coverage
ratios for September 2001 through September 2004.
In terms of race, Whites have the highest coverage ratio
(90.7 percent), while Blacks have the lowest (82.2 percent). Females across all races have higher coverage ratios
than males. Hispanics1 also have relatively low coverage
rates. Historically, Hispanics and Blacks have lower coverage rates than Whites for each age group, particularly the
20−29 age group. This is by no fault of the interviewers or
the CPS process. These lower coverage rates for minorities
affect labor force estimates because people who are
missed by the CPS are on the average likely to be different
from those who are included. People who are missed are
accounted for in the CPS, but they are given the same
labor force characteristics as those of the people who are
included. This produces bias in the CPS estimates.
This graph, as well as two other graphs of coverage ratios
by race and gender, can be found at . (Their
updates, with more current data, will be posted on this
site as they are made available.) The three graphs provide

Hispanics may be any race.

Quality Indicators of Nonsampling Errors

16–1

Figure 16−1.
CPS Total Coverage Ratios: September 2001−September 20041, National Estimates

Coverage Ratio

Total

1.00

White

Black

Other

Hispanic

0.95

0.90

0.85

0.80

0.75

0.70
Sep-01

Dec-01

Mar-02

Jun-02

Sep-02

Dec-02

Mar-03

Jun-03

Sep-03

Dec-03

Mar-04

Jun-04

Sep-04

1
There is a drop in January 2003. This is when the new definitions for race and ethnicity were introduced, as well as some adjusted population
controls based on Census 2000.

a picture of coverage for the population at least 16 years
old in the CPS from September 2001 through September
2004. The first one gives the coverage ratios for racial
groups. Traditionally, Blacks and Hispanics have been the
most underrepresented groups in the CPS. The other two
graphs show the coverage ratios by race and gender. Coverage ratios are lowest for Black and Hispanic males.
NONRESPONSE
As noted in Chapter 15, there are a variety of sources of
nonresponse in the CPS, such as unit or household nonresponse, panel nonresponse, and item nonresponse. Unit
nonresponse, referred to as Type A noninterviews, represents households that are eligible for interview but were
not interviewed for some reason. Type A noninterviews
occur because a respondent refuses to participate in the
survey, is too ill, or is incapable of completing the interview, or is not available or not contacted by the interviewer, perhaps because of work schedules or vacation
during the survey period. Because the CPS is a panel survey, households that respond in one month may not
respond during a following month. Thus, there is also
panel nonresponse in the CPS, which can become particularly important if CPS data are used longitudinally. Finally,
some respondents who complete the CPS interview may
be unable or unwilling to answer specific questions in the
CPS, resulting in some level of item nonresponse.
Type A Nonresponse
Type A noninterview rate. The Type A noninterview
rate is calculated by dividing the total number of Type A
16–2

Quality Indicators of Nonsampling Errors

households (refusals, temporarily absent, noncontacts,
and other noninterviews) by the total number of eligible
households (which includes Type As and interviewed
households).
As seen in Figure 16−2, the noninterview rate for the CPS
remained relatively stable at around 4 to 5 percent for
most of 1964 through 1993; however, there have been
some changes since 1993. Figure 16−2 shows that there
was a major change in the CPS nonresponse rate in January 1994, which reflects the launching of the redesigned
survey using computer-assisted survey collection procedures. This rise is discussed below.
The end of 1995 and the beginning of 1996 also show a
jump in Type A noninterview rates that was chiefly
because of disruptions in data collection due to shutdowns of the federal government (see Butani, Kojetin, and
Cahoon, 1996). After 1994, refusals, noninterviews, and
noncontacts increased. The relative stability of the overall
noninterview rate from 1960 to 1994 masked some underlying changes that occurred. Specifically, the refusal portion of the noninterview rate increased over this period
with the bulk of the increase in refusals taking place from
the early 1960s to the mid-1970s. In the late 1970s, there
was a leveling off so that refusal rates were fairly constant
until 1994. A corresponding decrease in the rate of noncontacts and other noninterviews compensated for this
increase in refusals.
Seasonal variation also appears in both the overall noninterview rates and the refusal rates (see Figure 16−3). During the year, the noninterview and refusal rates have
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Figure 16−2.
Average Yearly Type A Noninterview and Refusal Rates for the CPS 1964−2003, National Estimates

Rate (Percent)
8
7
6
Type A Noninterview Rate

5
4
3

Refusal Rate

2
1
1964

1967

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

Figure 16−3.
CPS Nonresponse Rates: September 2003−September 2004, National Estimates

Rate (Percent)
Overall Type A Rate

6
Refusal Rate

4
Noncontact Rate

Sep-03 Oct-03 Nov-03 Dec-03

Jan-04

Feb-04 Mar-04

tended to increase after January until they reach a peak in
March or April, at the time of the Annual Social and Economic Supplement. At this point, there is a drop in noninterview and refusal rates that extends below the starting
point in January until they bottom out in July or August.
The rates then increase and approach the initial level. This
pattern has been evident most years in the recent past
and appears to be similar for 2003−2004. The noncontact
rates are higher during the winter holidays and the summer, when some household members are either away from
home or difficult to contact.
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Apr-04 May-04 Jun-04

July-04 Aug-04

Sep-04

Effect on noninterview rates of the transition to a
redesigned survey with computer-assisted data collection. With the transition to the redesigned CPS questionnaire using computerized data collection in January
1994, Type A nonresponse rates increased as seen in Figure 16−2. This transition included several procedural
changes in the collection of the data, and the adjustment
to these new procedures may account for this increase.
For example, the computer-assisted personal interviewing
(CAPI) instrument requires the interviewers to go through
the entire interview, while previously some interviewers
may have conducted shortened interviews with reluctant
Quality Indicators of Nonsampling Errors

16–3

respondents, obtaining answers to only a couple of critical
questions. Another change in the data collection procedures was an increased reliance on using centralized telephone interviewing. Households not interviewed by the
computer-assisted telephone interviewing (CATI) centers
are recycled back to the field representatives continuously
during the survey week. However, cases recycled late in
the survey week (some reach the field as late as Friday
morning) can present difficulties for the field representatives because there are only a few days left to make contact before the end of the interviewing period.
As depicted in Figure 16-2, there has been greater variability in the monthly Type A nonresponse rates in CPS since
the transition in January 1994. The annual overall Type A
rate, the refusal rate, and noncontact rate (which includes
temporarily absent households and other noncontacts) are
shown in Table 16−1 for the period 1993−1996 and 2003.

Table 16−1. Components of Type A Nonresponse Rates,
Annual Averages for 1993−1996 and 2003,
National Estimates
[Percent distribution]
Nonresponse rate
Overall Type A . . . . . . . . .
Noncontact . . . . . . . . . . . .
Refusal . . . . . . . . . . . . . . . .
Other . . . . . . . . . . . . . . . . . .

1993
4.69
1.77
2.85
.13

1994
6.19
2.30
3.54
.32

1995
6.86
2.41
3.89
.34

1996
6.63
2.28
4.09
.25

2003
7.25
2.58
4.10
.57

Panel nonresponse. Households are selected into the
CPS sample for a total of 8 months in a 4-8-4 pattern as
described in Chapter 3. Many families in these households
may not be in the CPS the entire 8 months because of
moving (movers are not followed, but the new household
members are interviewed). Those who live in the same
household during the entire time they are in the CPS
sample may not agree to be interviewed each month.
Table 16−2 shows the percentage of households who were
interviewed 0, 1, 2, …, 8 times during the 8 months that
they were eligible for interview during the period January
1994 to October 1995. These households represent seven
rotation groups (see Chapter 3) that completed all of their
rotations in the sample during this period. The vast majority of households, about 82 percent, completed interviews
each month, and only 2 percent never participated (for further information, see Harris-Kojetin and Tucker, 1997).
Dixon (2000) compared those who moved out to those
who moved in. Out-movers were more likely to be unemployed but more likely to respond compared with
in-movers. Unemployment may be slightly underestimated
due to the combination of these two effects.
Effect of Type A Noninterviews on Labor Force
Classification.
Although the CPS has monthly measures of Type A nonresponse, the total effect of nonresponse on labor force estimates produced from the CPS cannot be calculated from
16–4

Quality Indicators of Nonsampling Errors

Table 16–2. Percentage of Households by Number of
Completed Interviews During the 8 Months
in the Sample, National Estimates1
[January 1994−October 1995]
Number of
completed interviews
0
1
2
3
4
5
6
7
8

..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................
..............................................

Percent
1994−1995
2.0
0.5
0.5
0.6
2.0
1.2
2.5
8.9
82.0

1
Includes only households in the sample all 8 months with only
interviewed and Type A nonresponse interview status for all 8 months,
i.e., households that were out of scope (e.g., vacant) for any month
they were in the sample were not included in these tabulations. Movers
were not included in this tabulation.

CPS data alone. It is the nature of nonresponse that we do
not know what we would like to know from the nonrespondents, and therefore, the actual degree of bias
because of nonresponse is unknown. Nonetheless,
because the CPS is a panel survey, information is often
available at some point in time from households that were
nonrespondents at another point. Some assessment can
be made of the effect of nonresponse on labor force classification by using data from adjacent months and examining the month-to-month flows of people from labor force
categories to nonresponse as well as from nonresponse to
labor force categories. Comparisons can then be made for
labor force status between households that responded
both months and households that responded one month
but failed to respond in the other month. However, the
labor force status of people in households that were nonrespondents for both months is unknown.
Monthly labor force data were used for each consecutive
pair of months for January through June 1997, for households whose members responded for each consecutive
pair of months and separately for households whose
respondents responded only one month and were nonrespondents the other month (see Tucker and Harris-Kojetin,
1997). The top half of Table 16−3 shows the labor force
classification in the first month for people in households
who were respondents the second month compared with
people who were in households that were noninterviews
the second month. People from households that became
nonrespondents had higher rates of participation in the
labor force, employment, and unemployment than those
from households that responded in both months. The bottom half of Table 16−3 shows the labor force classification
for the second month for people in households that were
respondents in the previous month compared with people
who were in households that were noninterviews the previous month. The pattern of differences is similar, but the
magnitude of the differences is less. Because the overall
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Table 16–3. Labor Force Status by Interview/Noninterview Status in Previous and Current Month,
National Estimates
[Average January–June 19971 percent distribution]
First month labor force status

Interview in second month Nonresponse in second month

Civilian labor force . . . . . . . . . . . . . . . . . . . .
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . . .
Second month labor force status

65.98
62.45
5.35

Difference

68.51
63.80
6.87

**2.53
**1.35
**1.52

Interview in first month Nonresponse in first month

Difference

Civilian labor force . . . . . . . . . . . . . . . . . . . .
Employed. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unemployment rate . . . . . . . . . . . . . . . . . . .

65.79
62.39
5.18

67.41
63.74
5.48

**1.62
**1.35
*.30

** p < .01 * <. 05
1
From Tucker and Harris-Kojetin (1997).

Type A noninterview rate is quite small, the effect of the
nonresponding households on the overall unemployment
rate is also relatively small. However, the labor force characteristics of the people in households who never
responded are not measured or included here. In addition,
other nonsampling errors are also likely present, such as
those due to repeated interviewing or month-in-sample
effects (described later in this chapter).
Dixon (2001) found similar effects in data from 1996−
1999, and further found that the bias came from noncontacts more than refusals. These results were supported by
an analysis of data from the CPS matched to the long form
of Census 2000 (Dixon, 2004), where nonresponders (and
their census employment status) in the CPS could be identified. This addressed the problem of those who never
responded to the CPS. The estimates were in the same
direction as found by Tucker and Kojetin, although the
bias was higher for the 2000 data.
Item Nonresponse
Another component of nonresponse is item nonresponse.
Respondents may refuse or may be unable to answer certain items but still respond to most of the CPS questions
with only a few items missing. To examine the prevalence
of item nonresponse in the CPS, allocation rates of all
interviewed cases were examined from both January 1997
and January 2004. The average levels of item-missing data
are represented by the allocation rates in Table 16−4. It
is likely that the bias due to imputation or allocation for
the demographic and labor force items is quite small, but
there may be a concern for industry/occupation and earnings data. Allocation rates have increased over the years,
as indicated by differences between 1997 and 2004.

Dixon (2002) found that item refusals predicted unit refusals and were negatively related to unemployment. Respondents who refused to answer particular items (especially
items related to income data) were more likely to refuse to
participate in subsequent surveys and were less likely to
be unemployed.
RESPONSE VARIANCE
Estimates of the Simple Response Variance
Component
To obtain an unbiased estimate of the simple response
variance, it is necessary to have at least two independent
measurements of the characteristic for each person in a
subsample of the entire sample using the identical measurement procedure on each trial. It is also necessary that
responses to a second interview not be affected by the
response obtained in the first interview.
Two difficulties occur in every attempt to measure the
simple response variance by a reinterview. The first is lack
of independence between the original interview and the
reinterview: a person visited twice within a short period
and asked the same questions may tend to remember
his/her original responses and repeat them. A second difficulty is that the data collection methods used in the original interview and in the reinterview are seldom the same.
Both of these difficulties exist in the CPS reinterview program data used to measure simple response variance. For
example, the interviews in the response variance reinterview sample are conducted by telephone by senior interviewers or supervisory personnel. Also, some characteristics of the reinterview survey process itself introduce
unknown biases in the estimates (e.g., a high noninterview rate.)

Table 16–4. CPS Items With Missing Data (Allocation Rates, %), National Estimates
Household

Demographic

Labor force

I&O

Earnings

Month/year
Range
Jan. 1997. . . . . . 0.16−5.84
Jan. 2004. . . . . . 0.01−3.42

Mean

Range

0.41
0.08−9.2
0.56 0.12−19.0

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Mean

Range

1.35 0.15−12.0
2.41 0.21−15.0

Mean

Range

1.57
2.12−5.0
2.18 5.43−10.0

Mean

Range

Mean

4.09 0.99−32.0
8.74 0.01−36.0

11.02
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Quality Indicators of Nonsampling Errors

16–5

It is useful to consider a schematic representation of the
results of the original CPS interview and the reinterview in
estimating, say, the number of people reported as unemployed (see Table 16−5). In this schematic representation,
the number of identical people giving a different response

Table 16–5. Comparison of Responses to the
Original Interview and the Reinterview,
National Estimates
Original interview
Unemployed

Not
unemployed

Total

a
c
a+c

b
d
b+d

a+b
c+d
n=a+b+c+d

Reinterview

Unemployed . . . . . .
Not unemployed . .
Total . . . . . . . . . . . . . .

in the original interview and the reinterview for the characteristic unemployed is given by (b + c). If these
responses are two independent measurements for identical people using identical measurement procedures, then
an estimate of the simple response variance component

(␴R2 ) is given by (b + c)/2n and
E

b⫹c

( )
2n

= ␴R

and as in formula 14.3 (Hansen, Hurwitz, and Pritzker,
1964)2. We know that (b + c)/2n, using CPS reinterview
2

data, underestimates ␴R, chiefly because conditioning
sometimes takes place between the two responses for a
specific person resulting in correlated responses, so that
fewer differences (i.e., entries in the b and c cells) occur
than would be expected if the responses were in fact independent.
Besides its effect on the total variance, the simple
response variance is potentially useful to evaluate the precision of the survey measurement methods. It can be used
to evaluate the underlying difficulty in assigning individuals to some category of a distribution on the basis of
responses to the survey instrument. As such, it is an aid to
determine whether a concept is sufficiently measurable by
a household survey technique and whether the resulting
survey data fulfill their intended purpose. For characteristics having ordered categories (e.g., number of hours
worked last week), the simple response variance is helpful
in determining whether the detail of the classification is
too fine. To provide a basis for this evaluation, the estimated simple response variance is expressed, not in absolute terms, but as a proportion of the estimated total
2
The expression (b+c)/n is referred to as the gross difference
rate; thus, the simple response variance is estimated as one-half
the gross difference rate.

16–6

Quality Indicators of Nonsampling Errors

population variance. This ratio is called the index of inconsistency and has a theoretical range of 0.0 to 100.0 when
expressed as a percentage (Hansen, Hurwitz, and Pritzker,
1964). The denominator of this ratio, the population vari2
ance, is the sum of the simple response variance, ␴R, and
2
the population sampling variance, ␴S. When identical
responses are obtained from trial to trial, the simple
response variance is zero and the index of inconsistency
has a value of zero. As the variability in the classification
of an individual over repeated trials increases (and the
measurement procedures become less reliable), the value
of the index increases until, at the limit, the responses are
so variable that the simple response variance equals the
total population variance. At the limit, if a single response
from N individuals is required, equivalent information is
obtained if one individual is randomly selected and interviewed N times, independently.
Two important inferences can be made from the index of
inconsistency for a characteristic. One is to compare it to
the value of the index that could be obtained for the characteristic by the best (or preferred) set of measurement
procedures that could be devised. The second is to consider whether the precision of the measurement procedures indicated by the level of the index is still adequate
to serve the purposes for which the survey is intended. In
the CPS, the index is more commonly used for the latter
purpose. As a result, the index is used primarily to monitor the measurement procedures over time. Substantial
changes in the indices that persist for several months will
trigger a review of field procedures to determine and remedy the cause.
Table 16−6 provides estimates of the index of inconsistency shown as percentages for selected labor force characteristics for 2003.
Table 16–6. Index of Inconsistency for Selected Labor
Force Characteristics in 2003,
National Estimates
Labor force characteristic
Employed at work. . . . . . . . .
Employed absent . . . . . . . . .
Unemployed layoff . . . . . . . .
Unemployed looking . . . . . .
Not-in-labor force retired . .
Not-in-labor force disabled.
Not-in-labor force other . . .

Estimate of index
of inconsistency

90-percent
confidence limits

14.0
50.4
60.8
38.5
7.8
21.1
31.1

19.0−20.8
45.7−55.0
49.8−71.8
34.8−42.1
6.8−8.6
18.4−24.0
29.4−32.8

MODE OF INTERVIEW
Incidence of Telephone Interviewing
As described in Chapter 7, the first and fifth months’ interviews are typically done in person while the remaining
interviews may be done over the telephone, either by the
field interviewer or an interviewer from a centralized telephone facility. Although the first and fifth interviews are
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

supposed to be done in person, the entire interview may
not be completed in person, and the field interviewer may
call the respondent back to obtain missing information.
The CPS CAPI instrument records whether the last contact
with the household was by telephone or personal visit.
The percentage of CAPI cases from each month-in-sample
that were completed by telephone is shown in Table 16−7.
Overall, about 66 percent of the cases are done by
telephone, with almost 80 percent of the cases in monthin-samples (MIS) 2−4 and 6−8 done by telephone. Furthermore, a number of cases in months 1 and 5 are completed
by telephone, despite standing instructions to the field
interviewers to conduct personal visit interviews.
Because the indicator variable in the CPS instrument
reflects only the last contact with the household, it may
not be the best indicator of how most of the data were
gathered from a household. For example, an interviewer
may obtain information for several members of the household during the first month’s personal visit but may make
a telephone call back to obtain the labor force data for the
last household member, causing the interview to be
recorded as a telephone interview. In June 1996, an additional item was added to the CPS instrument that asked
interviewers whether the majority of the data for each
completed case was obtained by telephone or personal
visit. The results using this indicator are presented in the
second column of Table 16−7. It was expected that in MIS
Table 16–7. Percentage of Households With Completed
Interviews With Data Collected by
Telephone (CAPI Cases Only),
National Estimates
[Percent]
Month in sample

Last contact with houseMajority of data
hold was telephone collected by telephone
(average, Jan.–Dec. 2004) (average, Jan.−Dec. 2004)

1...............
2...............
3...............
4...............
5...............
6...............
7...............
8...............

13.5
73.0
76.0
76.3
28.3
76.2
77.8
77.7

20.5
75.6
78.5
79.0
37.9
78.8
80.0
80.5

1 and 5, interviewers would have reported that the majority of the data was collected though personal visit more
often than was revealed by data on the last contact with
the household. This would seem likely because, as noted
above, the last contact with a household may be a telephone call to obtain missing information not collected at
the initial personal visit. However, the percentage of cases
where the majority of the data was reported to have been
collected by telephone was larger than the percentage of
cases where the last contact with the household was by
telephone for MIS 1 and 5. The percentage increased in
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

2004 compared with 1995. The explanation for this pattern of results is not clear at the present time. Some reasons hypothesized are that some respondents may be
more comfortable over the telephone, or using CATI may
solve a difficult scheduling problem.
TIME IN SAMPLE
The rotation pattern of the CPS sample was described in
detail in Chapter 3, and the use of composite estimation in
the CPS was discussed briefly in Chapter 9. The effects of
interviewing the same respondents for CPS several times
has been discussed for a long time (e.g., Bailar, 1975;
Brooks and Bailar, 1978; Hansen, Hurwitz, Nisselson, and
Steinberg, 1955; McCarthy, 1978; Williams and Mallows,
1970). It is possible to measure the effect of the time
spent in the sample on labor force estimates from the CPS
by creating a month-in-sample index that shows the relationships of all the month-in-sample groups. This index is
the ratio of the estimate based on a particular month-insample group to the average estimate from all eight
month-in-sample groups combined, multiplied by 100. If
an equal percentage of people with the characteristic are
present in each month-in-sample group, then the index for
each group would be 100. Table 16−8 shows indices for
each group by the number of months they have been in
the sample. The indices are based on CPS labor force data
from March 2003 to February 2004. For the percentage of
the total population that is unemployed, the index of
106.26 for the first-month households indicates that the
estimate from households in the sample in the first month
is about 1.0626 times the average overall eight month-insample groups; the index of 99.29 for unemployed for the
fourth-month households indicates that it is about the
same as the average overall month-in-sample groups.
Estimates from one of the month-in-sample groups should
not be taken as the standard, in the sense that they would
provide unbiased estimates while the estimates for the
other seven would be biased. It is far more likely that the
expected value of each group is biased—but to varying
degrees. Total CPS estimates, which are the combined data
from all month-in-sample groups (see Chapter 9), are subject to biases that are functions of the biases of the individual groups. Since the expected values vary appreciably
among some of the groups, it follows that the CPS survey
conditions must be different in one or more significant
ways when applied separately to these subgroups.
One way in which the survey conditions differ among rotation groups is reflected in the noninterview rates. The
interviewers, being unfamiliar with households that are in
the sample for the first time, are likely to be less successful at calling when a responsible household member is
available. Thus, noninterview rates generally start above
average with first-month households, decrease with more
time in the sample, go up a little for households in the
fifth month of the sample (after 8 months, the household
Quality Indicators of Nonsampling Errors

16–7

Table 16–8. Month-in-Sample Bias Indexes (and Standard Errors) in the CPS for Selected Labor Force
Characteristics
[Average March 2003−February 2004]
Month-in-sample
Employment status and sex
1

101.00
0.34
106.26
1.33

100.97
0.33
103.07
1.41

100.64
0.36
100.56
1.32

100.50
0.36
99.29
1.58

99.74
0.37
101.37
1.04

98.95
0.40
99.14
1.40

99.15
0.38
95.64
1.29

99.06
0.36
94.67
1.40

100.73
0.39
105.69
1.65

101.04
0.40
102.44
1.63

100.66
0.40
102.43
1.64

100.37
0.41
101.90
1.92

99.86
0.43
99.66
1.49

98.92
0.43
97.42
1.74

99.36
0.40
96.07
1.89

99.07
0.38
94.39
1.95

101.32
0.39
106.98
1.77

100.89
0.35
103.87
1.98

100.61
0.43
98.22
2.09

100.64
0.44
96.03
2.30

99.60
0.40
103.49
1.43

98.99
0.45
101.29
1.72

98.90
0.44
95.10
1.31

99.04
0.43
95.02
2.06

101.87
1.08
112.74
3.41

100.75
1.15
105.44
3.44

101.37
1.18
103.25
3.04

100.76
1.19
98.69
3.21

99.17
1.18
96.63
3.14

98.62
1.29
99.99
3.39

98.12
1.28
93.23
2.94

99.35
1.34
90.03
3.23

101.69
1.23
109.55
4.45

100.70
1.42
105.16
4.78

101.14
1.42
103.97
4.50

100.05
1.50
99.15
4.56

98.76
1.51
97.13
4.30

98.55
1.60
97.03
4.55

99.42
1.63
98.56
4.20

99.68
1.61
89.46
4.44

102.03
1.29
115.86
4.10

100.79
1.30
105.72
4.09

101.57
1.34
102.55
4.43

101.38
1.33
98.25
4.53

99.52
1.25
96.13
3.55

98.68
1.39
102.89
4.32

96.99
1.40
88.01
3.21

99.05
1.52
90.59
4.46

TOTAL POPULATION,
16 YEARS AND OLDER
Civilian labor force . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
MALES,
16 YEARS AND OLDER
Civilian labor force . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
FEMALES,
16 YEARS AND OLDER
Civilian labor force . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
BLACK POPULATION,
16 YEARS AND OLDER
Civilian labor force . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
BLACK MALES,
16 YEARS AND OLDER
Civilian labor force . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
BLACK FEMALES,
16 YEARS AND OLDER
Civilian labor force . . . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .
Unemployment level . . . . . . . . . . . .
Standard error . . . . . . . . . . . . . . . .

was not in sample), and then decrease again in the final
months. (This pattern may also reflect the prevalence of
personal visit interviews conducted for each of the
months-in-sample as noted above.) Figure 16-4, showing
the Type A noninterview rate by month-in-sample for
July−September 2004, illustrates this pattern. An index of
the noninterview rate constructed in a similar manner to
the month-in-sample group index can be seen in Table
16−9. This noninterview rate index shows that the greatest proportion of noninterviews occurs for cases that are
in the sample for the first month, and the second-largest
proportion of noninterviews are due to cases returning to
the sample in the fifth month. These noninterview

16–8

Quality Indicators of Nonsampling Errors

changes are unlikely to be distributed proportionately
among the various labor force categories (see Table 16−3).
Consequently, it is reasonable to assume that the representation of labor force categories will differ somewhat
among the month-in-sample groups and that these differences can affect the expected values of these estimates,
which would imply that at least some of the month-insample bias can be attributed to actual differences in
response probabilities among the month-in-sample groups
(Williams and Mallows, 1970). Although the individual
probabilities are not known, they can be estimated by
nonresponse rates. However, other factors are also likely
to affect the month-in-sample patterns.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Figure 16−4.

Basic CPS Household Nonresponse by Month in Sample, July 2004−September 2004,
National Estimates

Nonresponse Rate in Percent

Jul-04
Aug-04
Sep-04

10
8
6
4
2
0
1

Month in Sample

Table 16–9. Month-In-Sample Indexes in the CPS for
Type A Noninterview Rates
January−December 2004
Month in sample
1.....................................
2.....................................
3.....................................
4.....................................
5.....................................
6.....................................
7.....................................
8.....................................

Noninterview rate index
129.1
93.7
89.1
88.6
119.8
98.2
94.2
87.1

PROXY REPORTING
Like many household surveys, the CPS seeks information
about all people in the household, whether they are available for an interview or not. CPS field representatives
accept reports from responsible adults in the household
(see Chapter 6 for a discussion of respondent rules) to
provide information about all household members.
Respondents who provide labor force information about
other household members are called proxy reporters.
Because some household members may not know or be
able to provide accurate information about the labor force
status and activities of other household members, nonsampling error may occur because of the use of proxy
reporters.
The level of proxy reporting in the CPS was generally
around 50 percent in the past and continues to be so in
the revised CPS. As can be seen in Table 16−10, the
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month-in-sample has very little effect on the level of proxy
reporting and the levels have remained the same over
time. Thus, whether the interview is more likely to be a
personal visit (for MIS 1 and 5) or a telephone interview
(MIS 2−4 and 6−8) has very little effect on proxy use.
Table 16–10. Percentage of CPS Labor Force Reports
Provided by Proxy Reporters
Percent reporting

1995

2004

All Interviews
Proxy reports . . . . . . . . . . .
Both self and proxy . . . . .

49.68
0.28

48.84
1.14

MIS 1 and 5
Proxy reports . . . . . . . . . . .
Both self and proxy . . . . .

50.13
0.11

48.98
0.78

MIS 2−4 and 6−8
Proxy reports . . . . . . . . . . .
Both self and proxy . . . . .

49.53
0.35

48.79
1.25

Although one can make overall comparisons of the data
given by self-reporters and proxy reporters, there is an
inherent bias in the comparisons because proxy reporters
were the people more likely to be found at home when the
field representative called or visited. For this reason,
household members with self- and proxy reporters, tend
to differ systematically on important labor force and
demographic characteristics. In order to compare the data
given by self- and proxy reporters systematic studies must
be conducted that control assignment of proxy reporting
status. Such studies have not been carried out.
SUMMARY
This chapter contains a description of several quality indicators in the CPS, namely, coverage, noninterview rates,
telephone interview rates, and proxy reporting rates.
Quality Indicators of Nonsampling Errors

16–9

These rates can be used to monitor the processes of conducting the survey, and they indicate the potential for
some nonsampling error to enter the process. This chapter
also includes information on the potential effects on the
CPS estimates due to nonresponse, centralized telephone
interviewing, and the duration that groups are in the CPS
sample. This research comes close to identifying the presence and effects of nonsampling error in the CPS, but the
results are not conclusive.

Dixon, J. (2002), ‘‘The Effects of Item and Unit Nonresponse on Estimates of Labor Force Participation.’’ Paper
presented at the Joint Statistical Meetings of the American
Statistical Association, New York City, New York.

The full extent of nonsampling error in the CPS cannot be
known without an external source for comparison. Recent
work cited here indicates undercoverage has increased
since 2000, and nonresponse continues to be a concern.
However, the effect of nonresponse bias is considered to
be small. The month-in-sample bias persists, and item
nonresponse has increased over the last ten years. On the
other hand, there is evidence that, while some inconsistency in reporting exists, the measurement of employment
status is on a sounder conceptual footing than ever
before. (See Biemer (2004), Miller and Polivka (2004),
Tucker (2004), and Vermunt (2004).)

Hansen, M. H., W. N. Hurwitz, H. Nisselson, and J. Steinberg (1955), ‘‘The Redesign of the Current Population Survey,’’ Journal of the American Statistical Association,
50, 701−719.

REFERENCES
Bailar, B. A. (1975), ‘‘The Effects of Rotation Group Bias on
Estimates from Panel Surveys,’’ Journal of the American
Statistical Association, 70, 23−30.
Biemer, P. P. (2004), ‘‘An Analysis of Classification Error for
the Revised Current Population Survey Employment Questions,’’ Survey Methodology, Vol. 30, 2, 127−140.
Brooks, C. A. and B. A. Bailar (1978), Statistical Policy
Working Paper 3 An Error Profile: Employment as
Measured by the Current Population Survey, Subcommittee on Nonsampling Errors, Federal Committee on Statistical Methodology, U.S. Department of Commerce, Washington, DC, 16−10.
Butani, S., B. A. Kojetin, and L. Cahoon (1996), ‘‘Federal
Government Shutdown: Options for Current Population
Survey (CPS) Data Collection.’’ Paper presented at the Joint
Statistical Meetings of the American Statistical Association,
Chicago, Illinois.
Dixon, J. (2000), ‘‘The Relationship Between Household
Moving, Nonresponse, and Unemployment Rate in the Current Population Survey.’’ Paper presented at the Joint Statistical Meetings of the American Statistical Association,
Indianapolis, Indiana.
Dixon, J. (2001), ‘‘Relationship Between Household Nonresponse, Demographics, and Unemployment Rate in the
Current Population Survey.’’ Paper presented at the Joint
Statistical Meetings of the American Statistical Association,
Atlanta, Georgia.

16–10

Quality Indicators of Nonsampling Errors

Dixon, J. (2004), ‘‘Using Census Match Data to Evaluate
Models of Survey Nonresponse.’’ Paper presented at the
Joint Statistical Meetings of the American Statistical Association, Toronto, Canada.

Hansen, M. H., W. N. Hurwitz, and L. Pritzker (1964),
‘‘Response Variance and Its Estimation,’’ Journal of the
American Statistical Association, 59, 1016−1041.
Hanson, R. H. (1978), The Current Population Survey:
Design and Methodology, Technical Paper 40, U.S. Census Bureau, Washington, DC.
Harris-Kojetin, B. A. and C. Tucker (1997), ‘‘Longitudinal
Nonresponse in the Current Population Survey.’’ Paper presented at the 8th International Workshop on Household
Survey Nonresponse, Mannheim, Germany.
McCarthy, P. J. (1978), Some Sources of Error in Labor
Force Estimates From the Current Population Survey, Background Paper No. 15, National Commission on
Employment and Unemployment Statistics, Washington,
DC.
Miller, S. H. and A. E. Polivka (2004), ‘‘Comment,’’ Survey
Methodology, Vol. 30, 2, 145−150.
Thompson, J. (1994). ‘‘Mode Effects Analysis of Labor
Force Estimates,’’ CPS Overlap Analysis Team Technical Report 3, Bureau of Labor Statistics and U.S. Census
Bureau, Washington, DC.
Tucker, C. and B. A. Harris-Kojetin (1997), ‘‘The Impact of
Nonresponse on the Unemployment Rate in the Current
Population Survey.’’ Paper presented at the 8th International Workshop on Household Survey Nonresponse,
Mannheim, Germany.
Tucker, C. (2004), ‘‘Comment,’’ Survey Methodology. Vol.
30, 2, 151−153.
Vermunt, J. K. (2004), ‘‘Comment,’’ Survey Methodology,
Vol. 30, 2, 141−144.
Williams, W. H. and C. L. Mallows (1970), ‘‘Systematic
Biases in Panel Surveys Due to Differential Nonresponse,’’
Journal of the American Statistical Association, 65,
1338−1349, 16−11.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Appendix A
Sample Preparation Materials
INTRODUCTION
Despite the conversion of CPS to an automated listing and
interviewing environment, some paper materials are still
in use. These materials include segment folders, listing
sheets, etc. Regional office staff and field representatives
use these materials as aids in identifying and locating
selected sample housing units. This appendix provides
illustrations and explanations of these materials by frame
(unit, area, group quarters, and permit). The information
provided here should be used in conjunction with the
information in Chapter 4 of this document.
UNIT FRAME MATERIALS
Segment Folder, BC−1669 (CPS)
Illustration 1
A field representative receives a segment folder for each
unit segment with incomplete addresses or multi-unit
addresses.

The segment folder cover provides the following information about a segment:

1. Identifying information about the segment such as the
regional office code, the field primary sampling unit
(PSU), place name, sample designation for the segment, and basic geography.
2. Helpful information about the segment entered by the
regional office or the field representative in the
Remarks section.

A segment folder for a unit segment may contain some or
all of the following: Multi-UnitListing Aids (MULA) (Form
11-12), Unit/Permit Listing Sheets (Form 11-3), and
Incomplete Address Locator materials.
Multi-Unit Listing Aids (Form 11−12)
Illustration 2

MULAs are provided for all unit segments with multi-unit
addresses. For each multi-unit,the field representative
receives a preprinted listing that shows addresses and unit
information as recorded from Census 2000.
Unit/Permit Listing Sheets (Form 11−3)
Illustration 3
Unit/Permit Listing Sheets are used only when the entire
address needs to be relisted and the field representative
prefers not to relist directly on the MULA. The field representative has a supply of blank Unit/Permit Listing Sheets
for this use.
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Incomplete Address Locator Action Form (Form
NPC−1138)
Illustration 4
The Incomplete Address Locator Action Form is used when
the information obtained from Census 2000 is in some
way incomplete (i.e. missing a house number, unit designation, etc.). The form provides the field representative
the additional information that can be used to locate the
incomplete address. The information on the form includes:
1. The address as it appeared in Census 2000 and
possibly a suggested complete address resulting from
research conducted by Census Bureau staff,

2. A list of additional materials provided to the field representative to aid in locating the address, and
3. An outline of the actions that the field representative
should take to locate the address.

After locating the address, the field representative completes or corrects the basic address in the laptop case
management (for single and multi-units)and on the MULA
(for multi-units).
AREA FRAME MATERIALS
There are no longer paper sample preparation materials
for the area frame. Sample preparation has been fully
automated.
GROUP QUARTERS FRAME MATERIALS
There are no longer paper sample preparation materials
for the group quarters frame. Sample preparation has
been fully automated.
PERMIT FRAME MATERIALS
Segment Folder, BC−1669 (CPS)
Illustration 1
See the description and illustration in the Unit Frame Materials above. A field representative receives a segment
folder for every permit segment. The folder will contain a
Unit/Permit Listing Sheet (Form 11-3) and possibly a Permit Sketch Map.
Unit/Permit Listing Sheets (Form 11−3)
Illustration 3, 5, and 6
For each permit address in sample for the segment, the
field representative receives a Unit/Permit Listing Sheet
with heading information, sample designations, and serial
Sample Preparation Materials

A–1

numbers preprinted on it. The field representative also
receives blank Unit/Permit Listing Sheets in case there are
not enough lines on the preprinted listing sheets(s) to list
all units at a multi-unitaddress.
Permit Sketch Map (Form 11−187)
Illustration 7

field representative attempts to obtain a complete
address. If a complete address cannot be obtained, the
field representative visits the new construction site and
draws a Permit Sketch Map. The map shows the location
of the construction site and streets in the vicinity of the
site.

When completing the permit address list (PAL) operation, if
the address given on a building permit is incomplete, the

A–2

Sample Preparation Materials

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Current Population Survey TP66

U.S. Bureau of Labor Statistics and U. S. Census Bureau

Sample Preparation Materials A-3

ZIP

SEG #

PSU

(fold is here). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PLACE NAME or MCD NAME

SEG#
FRAME (Unit or Permit)

PSU

SPECIAL INSTRUCTIONS

MO/YR

REMARKS

MO/YR

(BAR CODE INFORMATION)

COUNTY

MO/YR

DATE

(SURVEY CPS)

REDUCED
REINSTATED
REASSIGNED

MO/YR

SEGMENT FOLDER

FORM BC-1669 (CPS)

Illustration 1. Segment Folder, BC-1669 (CPS)

Illustration 2. Multi-Unit Listing Aid, Form 11-12
FORM 11-12

U.S. DEPARTMENT OF COMMERCE
U.S. CENSUS BUREAU

MULTI-UNIT LISTING AID (MULA)
Address
* 4827 Labor Force Ave.
North Hollywood, CA 91601
Line
No.

RO
32
Tract:
004893

State
CA

PSU
06037

Segment
1999
Block
3005

Survey:
CPS
Expected Number of Units
16

County
Los Angeles

Unit Designation
(or apartment number)

Sample
Desig.

Serial
Number

Line
No.

Unit Designation
(or apartment number)

Sample
Desig.

21
A79

Serial
Number

A78

# 101

A78

A77

# 102

A77

A79

# 103

A78

Apt. 103

# 104

A77

# 105

A79

# 201

# 202

A78

A79

# 204

A79

A78

# 205

A77

#301

#302

A79

#303

A78

#304

A77

A78

A79

A77

17
18

A79

A78

A77

A79

Footnotes:

Contact Person:
Title:
Telephone:

Resolve all Missing Unit Designations.
Resolve all Duplicate Unit Designations.

FR Code
FR Initials
Month/Year

BSAID: 0002
ESD within segment:

A-4 Sample Preparation Materials

200408

Date Printed: 05/09/04
Page: 1 of 1

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Illustration 3. Unit/Permit Listing Sheet, 11-3 (Blank)
FORM 11-3
U.S. DEPARTMENT OF COMMERCE
(6-11-2001) Economics and Statistics Administration
U.S. CENSUS BUREAU

UNIT/PERMIT LISTING SHEET

PSU

Survey name

Segment

Type of segment

Address

Post office

Expected no. of units

Permit office name

State

ZIP Code

Urban/Rural

Permit date of issue
-----------------------Year
* Month
* Day

PAL sequence/line no.

Permit number (or BSAID orSPID/GQID)

County

Combined
address

PAL keyed remarks

Line
No.

Unit Designation
(or apartment number)

Sample
Desig.

Serial
Number

Remarks
(Reason and date for changes)

(1)
1
2

10
11
12
13
14
15
19. Multi-Units

20. Listed and Updated

Name of Complex:

FR Code

Contact Person:

FR Initials

Title:
Month/Year
Phone No.
Total No. of
Units

Footnotes
ESD: 200411

Current Population Survey TP66

Date Printed: 08/13/04

Sheet

Sheets

Sample Preparation Materials A-5

Illustration 4 Incomplete Address Locator Actions Form, NPC 1138

U.S. Bureau of Labor Statistics and U.S. Census Bureau

Illustration 4. Incomplete Address Locator Actions Form, NPC 1138
NPC-1138 (March 19, 2004)

U.S. Department of Commerce
Economics and Statistics Administration
U.S. Census Bureau
2000 SAMPLE REDESIGN

INCOMPLETE ADDRESS LOCATOR ACTIONS
SECTION I – IDENTIFICATION
Survey

Sample

County

BSAID

Block

MAFID

Sample Date

Tract/BNA

PSU

Segment

Serial#

SECTION II - NPC LOCATOR REVIEW/RESULTS
1. Address AFTER NPC Review:
Name:
Address:
Post Office:

Expected
# units:
ST:

ZIP:

2. Research
[ ] Future File:
[ ] MAF Browser:
[ ] Power Finder 2000
[ ] Internet
[ ] Zip +4:
[ ] Directory Assistance
[ ] Post Office:
[ ] Outcome Code:
(NPC use only)
SECTION III - ACTIONS THE RO/FR MUST PERFORM
To locate the sample unit:
[ ] Use Locator Materials Enclosed
[ ] Refer to attached list..
[ ] Use Multi-Unit Listing Sheet Enclosed.
[ ] Use Multi-Unit Listing Sheet, previously sent to you for:
Sample:

Sample Date:

[ ] Other – Specify:
REMARKS/COMMENTS

A-6 Sample Preparation Materials

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Illustration 5. Unit/Permit Listing Sheet, 11-3 (Single unit in Permit Frame)
FORM 11-3
U.S. DEPARTMENT OF COMMERCE
(6-11-2001) Economics and Statistics Administration
U.S. CENSUS BUREAU

RO
30

UNIT/PERMIT LISTING SHEET

PSU
48155

Survey name
CPS

Segment
2001

Expected no. of units
1

Type of segment
PERMIT

Address
350 POPULATION PLACE
Post office
WALKER

County

State
TX

Permit office name
WALKER
ZIP Code
77484

Urban/Rural
RURAL

Permit date of issue
-----------------------Year 2000 * Month 10* Day 01

FOARD COUNTY

PAL sequence/line no.
70035215/11

Permit number (or BSAID orSPID/GQID)
0256

Combined
address
NO

PAL keyed remarks

Line
No.

Unit Designation
(or apartment number)

Sample
Desig.

Serial
Number

Remarks
(Reason and date for changes)

(1)
1

A79

19. Multi-Units

20. Listed and Updated

Name of Complex:

FR Code

Contact Person:

FR Initials

Title:
Month/Year
Phone No.
Total No. of
Units

Footnotes
ESD: 200409

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Date Printed: 06/10/04

Sheet

Sheets

Sample Preparation Materials A-7

Illustration 6. Unit/Permit Listing Sheet, 11-3 (Multi-unit in Permit Frame)
FORM 11-3
U.S. DEPARTMENT OF COMMERCE
(6-11-2001) Economics and Statistics Administration
U.S. CENSUS BUREAU

RO
23

UNIT/PERMIT LISTING SHEET

PSU
42101

Survey name
CPS

Segment
5001

Expected no. of units
71

Type of segment
PERMIT

Address
2100 SURVEY ST

Permit office name
PHILADELPHIA
State
PA

Post office
PHILADELPHIA

ZIP Code
19001

Urban/Rural
URBAN

Permit date of issue
-----------------------Year 2002 * Month 07* Day 21

PHILADELPHIA COUNTY

County

PAL sequence/line no.
45639851/0001

Permit number (or BSAID orSPID/GQID)

Combined
address
NO

78126
PAL keyed remarks

Unit Designation
(or apartment number)

Sample
Desig.

Serial
Number

A78

A79

A78

A79

A78

Line
No.

Remarks
(Reason and date for changes)

(1)

19. Multi-Units

20. Listed and Updated

Name of Complex:

FR Code

Contact Person:

FR Initials

Title:
Month/Year
Phone No.
Total No. of
Units

Footnotes
ESD: 200404

A-8 Sample Preparation Materials

Date Printed: 01/09/04

Sheet

Sheets

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Illustration 7. Permit Sketch Map, Form
11-187
FORM 11-187
U.S. DEPARTMENT OF COMMERCE
(10-15-02) Economics and Statistics Administration
U.S. CENSUS BUREAU

2. PSU
06703

3. Permit month/year
07/2005

4. Sequence number
69003100

5. Permit Office name
Alameda County

PERMIT SKETCH MAP

6. Locality (Post Office)
Oakland City

PERMIT ADDRESS
LIST OPERATION
ATTENTION
REGIONAL OFFICE

1. RO
27

Keep both copies in the
Regional Office files

7. County
Alameda

8. State
CA
12. U/R

9. ZIP Code
10. Permit number
20709
47325
13. Field Representative name

Rural

Sue Hood

11. PAL line
32
Code
M5

Quimby Street
X

1 mile

Hwy 64

Notes

Red brick house, garage on left side.

U.S.GOVERNMENT PRINTING OFFICE 1992-750-111/40529

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Sample Preparation Materials A-9

Appendix B.
Maintaining the Desired Sample Size
INTRODUCTION
The Current Population Survey (CPS) sample is continually
updated to include housing units built after the most
recent census. If the same sampling rates were used
throughout the decade, the growth of the U.S. housing
inventory would lead to increases in the CPS sample size
and, consequently, to increases in cost. To avoid exceeding the budget, the sampling rate is periodically reduced
to maintain the desired sample size. Referred to as maintenance reductions, these changes in the sampling rate
are implemented in a way that retains the desired set of
reliability requirements. The most recent sampling maintenance reductions were implemented in 1999 and 2003.
These maintenance reductions are different from changes
to the base CPS sample size resulting from modifications
to the CPS funding levels. The methodology for designing
and implementing this type of sample-size change is generally dictated by new requirements specified by the
Bureau of Labor Statistics (BLS). For example, the sample
reduction implemented in January 1996 was due to a
reduction in CPS funding and new design requirements
were specified.

frames. The original sample of USUs is partitioned into
101 subsamples called reduction groups; each is representative of the overall sample. The decision to use 101 subsamples is somewhat arbitrary. A useful attribute of the
number used is that it is prime to the number of rotation
groups (eight) so that reductions have a uniform effect
across rotations. A number larger than 101 would allow
greater flexibility in pinpointing proportions of the sample
to reduce. However, a large number of reduction groups
can lead to imbalances in the distribution of sample cuts
across PSUs, since small PSUs may not have enough
sample to have all reduction groups represented.
All USUs in a hit string have the same reduction group
number (see Chapter 3). For the unit, area, and group
quarters (GQ) frames, hit strings are sorted and then
sequentially assigned a reduction group code from
1 through 101. The sort sequence is:
1. State or substate.
2. Metropolitan statistical area/nonmetropolitan statistical area status.
3. Self-representing/non-self-representing status.

MAINTENANCE REDUCTIONS

4. Stratification PSU.

Developing the Reduction Plan

5. Final hit number, which defines the original order of
selection.

The CPS sample size for the United States is projected forward for about a year using linear regression based on
previous CPS monthly sample sizes. The future CPS
sample size must be predicted because CPS maintenance
reductions are gradually introduced over 16 months and
operational lead time is needed so that dropped cases will
not be interviewed.
Housing growth is examined in all states and major substate areas to determine whether it is uniform or not. The
states with faster growth are candidates for maintenance
reduction. The post-reduction sample must be sufficient to
maintain the individual state and national reliability
requirements. Generally, the sample in a state is reduced
by the same proportion in all frames in all primary sampling units (PSUs) to maintain the self-weighting nature of
the state-level design.
Implementing the Reduction Plan
Reduction groups. The CPS sample size is reduced by
deleting one or more subsamples of ultimate sampling
units (USUs) from both the old construction and permit
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

For the permit frame, a random start is generated for each
stratification PSU and permit frame hits are assigned a
reduction group code from 1 through 101 following a specific, nonsequential pattern. This method of assigning
reduction group code is used to improve balancing of
reduction groups because of small permit sample sizes in
some PSUs and the uncertainty about which PSUs will actually have permit samples over the life of the design. The
sort sequence is:
1. Stratification PSU.
2. Basic PSU Component (BPC)
3. Original hit number.
The state or national sample can be reduced by deleting
USUs from both the old construction and permit frames in
one or more reduction groups. If there are k reduction
groups in the sample, the sample may be reduced by 1/k
by deleting one of k reduction groups. For the first reduction applied to redesigned samples, each reduction group
represents roughly 1 percent of the sample. Reduction
Maintaining the Desired Sample Size

B−1

group numbers are chosen for deletion in a specific
sequence designed to maintain the nature of the systematic sample to the extent possible.
For example, suppose a state has an overall state sampling interval of 500 at the start of the 2000 design. Suppose the original selection probability of 1 in 500 is modified by deleting 5 of 101 reduction groups. The resulting
overall state sampling interval (SI) is
101

= 526.0417
101 − 5
This makes the resulting overall selection probability in
the state 1 in 526.0417. In the subsequent maintenance
reduction, the state has 96 reduction groups remaining. A
further reduction of 1 in 96 can be accomplished by deleting 1 of the remaining 96 reduction groups.
SI = 500 x

The resulting overall state sampling interval is the new
basic weight for the remaining uncut sample.

B−2

Maintaining the Desired Sample Size

Introducing the reduction. A maintenance reduction is
implemented only when a new sample designation is
introduced, and it is gradually phased in with each incoming rotation group to minimize the effect on survey estimates and reliability and to prevent sudden changes to
the interviewer workloads. The basic weight applied to
each incoming rotation group reflects the reduction. Once
this basic weight is assigned, it does not change until
future sample changes are made. In all, it takes 16 months
for a maintenance sample reduction and new basic
weights to be fully reflected in all eight rotation groups
interviewed for a particular month. During the phase-in
period, rotation groups have different basic weights; consequently, the average weight over all eight rotation
groups changes each month. After the phase-in period, all
eight rotation groups have the same basic weight.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Appendix C.
Derivation of Independent Population Controls
INTRODUCTION
Each month, for the purpose of the iterative, second-stage
weighting of the Current Population Survey (CPS) data,
independent projections of the eligible population are produced by the Census Bureau’s Population Division. In addition to the CPS, other survey-based programs sponsored
by the Bureau of the Census, the Bureau of Labor Statistics, the National Center for Health Statistics, and other
agencies use these independent projections to calibrate
surveys. These projections consist of the civilian noninstitutionalized population of the United States and the 50
states (including the District of Columbia). The projections
for the country are distributed by demographic characteristics in two ways: (1) age, sex, and race, and (2) age, sex,
and Hispanic origin.1 The projections for the states are
distributed by race (Black only and all other race groups
combined), age (0−15, 16−44, and 45 and over), and sex.
They are produced in association with the Population Division’s population estimates and projections programs,
which provide published estimates and long-term projections of the population of the United States, the 50 states
and the District of Columbia, the counties, and subcounty
jurisdictions.
Organization of This Appendix
The CPS population controls, like other population estimates and projections produced by the Census Bureau, are
based on a demographic framework of population
accounting. Under this framework, time series of population estimates and projections are anchored by decennial
census enumerations, with measurements of populations
for dates between two previous censuses, since the last
census, or in the future derived by the estimation or projection of population change. The method by which population change is estimated depends on the defining demographic and geographic characteristics of the population.
This appendix seeks first to present this framework systematically, defining terminology and concepts, then to
describe data sources and their application within the
framework. The first subsection is operational and is
devoted to the organization of the chapter and a glossary
of terms. The second and third subsections deal with two
broad conceptual definitions. The second subsection distinguishes population estimates from population projections and describes how monthly population controls for
1

Hispanics may be any race.

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

surveys fit into this distinction. The third subsection
defines the CPS control universe, the set of inclusion and
classification rules specifying the population to be projected for the purpose of weighting CPS data. The fourth
subsection, ‘‘Calculation of Population Projections for the
CPS Control Universe,’’ comprises the bulk of the appendix. It provides the mathematical framework and data
sources for updating the total population from a census
date via the demographic components of change and
explains how each of the requisite inputs to the mathematical framework is measured. This analysis is presented separately for the total population of the United
States and its distribution by demographic characteristics
and by state of residence by demographic characteristics.
The final two sections are, again, operational. A subsection, ‘‘The Monthly and Annual Revision Process for Independent Population Controls,’’ describes the protocols for
incorporating new information in the series through revision. The final section, ‘‘Procedural Revisions,’’ contains an
overview of various technical problems for which solutions are being sought.
Terminology Used in This Appendix
The following is an alphabetical list of terms used in this
appendix, which are essential to understanding the derivation of census-based population controls for surveys but
not necessarily prevalent in the literature on survey methodology.
The population base (or base population) for a population
estimate or projection is the population count or estimate
to which some measurement or assumption of population
change is added to yield the population estimate or projection.
A census-level population estimate or projection is an estimate or projection of population change that can be linked
to a census count.
The civilian population is the portion of the resident population not in the active-duty military. Active-duty military,
used in this context, refers to people defined to be on
active duty by one of the five branches of the Armed
Forces, as well as people in the National Guard or the
reserves actively participating in certain training programs.
Components of population change are any subdivisions of
the numerical population change over a time interval. The
Derivation of Independent Population Controls

C–1

demographic components often cited in the literature consist of births, deaths, and net migration. This appendix
also refers to changes in the institutional and active-duty
Armed Forces populations, which affect the CPS control
universe.
The CPS control universe is characterized by two
attributes: (1) restriction to the civilian noninstitutionalized population, and (2) modification of census data by
race. Each of these defining concepts appears separately
in this glossary.
Emigration is the departure of a person from a country of
residence, in this case the United States. In the present
context, it refers to people legally resident in the United
States but is not confined to legally permanent residents
(immigrants) who make their usual place of residence outside of the United States.
Population estimates are population figures that do not
arise directly from a census or count but can be determined from available data (e.g., administrative data).
Population estimates discussed in this appendix stipulate
an enumerated base population, coupled with estimates of
population change from the enumeration date of the base
population to the date of the estimate.
The institutional population refers to a population universe consisting of inmates or residents of CPS-defined
institutions, such as prisons, nursing homes, juvenile
detention facilities, or residential mental hospitals.
Internal consistency of a population time series occurs
when the difference between any two population estimates in the series can be construed as population change
between the reference dates of the two population estimates.
International migration is generally the change of a person’s residence from one country to another. In the
present context, this concept includes change of residence
into the United States by non-U.S. citizens and by previous
residents of Puerto Rico or the U.S. outlying areas, as well
as the change of residence out of the United States by
people intending to live or make their usual residence
abroad, in Puerto Rico, or the outlying areas.
Legal permanent residents are people whose right to
reside in the United States is legally defined either by
immigration or by U.S. citizenship. In the present context,
the term generally refers to noncitizen immigrants.
Modified race describes the census population after the
definition of race has been aligned with definitions from
other administrative sources.
The natural increase of a population over a time interval
is the number of births minus the number of deaths during the interval.
C–2

Derivation of Independent Population Controls

Population projections are population figures relying on
modeled or assumed values for some or all of their components, and therefore not entirely calculated from actual
data. Projections discussed in this appendix stipulate a
base population that may be an estimate or count, and
components of population change from the reference date
of the base population to the reference date of the projection.
The reference date of an estimate or projection is the date
to which the population figure applies. The CPS control
reference date, in particular, is the first day of the month
in which the CPS data are collected.
The resident population of the United States is the population usually resident in the 50 states and District of
Columbia. For the census date, this population matches
the total population in census decennial publications,
although applications to the CPS series beginning in 2001
include some count resolution corrections to Census 2000
not included in original census publications.
A population universe is the population represented in a
data collection, in this case, the CPS. It is determined by a
set of rules or criteria defining inclusion and exclusion of
people from a population, as well as rules of classification
for specified geographic or demographic characteristics
within the population. Examples include the resident
population universe and the civilian population universe,
both of which are defined elsewhere in this glossary. Frequent reference is made to the ‘‘CPS control universe,’’
defined separately in this glossary as the civilian noninstitutional population residing in the United States.
CPS Population Controls: Estimates or Projections?
Throughout this appendix, the independent population
controls for the CPS will be cited as population projections. Throughout much of the scientific literature on
population, demographers take care to distinguish the
concepts of ‘‘estimate’’ and ‘‘projection’’; yet the CPS population controls lie close to the line of distinction. Generally,
population estimates relate to past dates, and in order to
be called ‘‘estimates,’’ must be supported by a reasonably
complete data series. If the estimating procedure involves
the computation of population change from a base date to
a reference date, the inputs to the computation must have
a solid basis in data. Projections, on the other hand, allow
the replacement of unavailable data with assumptions.
The reference date for CPS population controls is in the
past, relative to their date of production. However, the
past is so recent—3 to 4 weeks prior to production—that
for all intents and purposes, CPS could be projections. No
data relating to population change are available for the
month prior to the reference date; very little data are available for 3 to 4 months prior to the reference date; no data
for state-level geography are available past July 1 of the
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

year prior to the current year. Hence, we refer to CPS controls as projections, despite their frequent description as
estimates in the literature.
CPS controls are founded primarily on a population census
and on administrative data. Analysis of coverage rates has
shown that even without adjustment for underenumeration in the controls, the lowest coverage rates of the CPS
often coincide with those age-sex-race-Hispanic segments
of the population having the lowest coverage in the census. Therefore, the contribution of independent population
controls to the weighting of CPS data is useful.
POPULATION UNIVERSE FOR CPS CONTROLS
In the concept of ‘‘population universe,’’ as used in this
appendix, are found not only the rules specifying who is
included in the population under consideration, but also
the rules specifying their geographic locations and their
relevant characteristics, such as age, sex, race, and Hispanic origin. This section considers three population universes: the resident population universe defined by Census 2000, the resident population universe used for
official population estimates, and the CPS control universe
that relates directly to the calculation of CPS population
controls. These three universes are distinct from one
another; each one is derived from the one that precedes it;
hence, the necessity of considering all three.
The primacy of the decennial census in the definition of
these three universes is a consequence of its importance
within the federal statistical system, the extensive detail
that it provides on the geographic distribution and characteristics of the U.S. population and the legitimacy
accorded it by the U.S. Constitution.
The universe defined by Census 2000 is the U.S. resident
population, including people resident in the 50 states and
the District of Columbia. This definition excludes residents
of the Commonwealth of Puerto Rico and residents of the
outlying areas under U.S. sovereignty or jurisdiction (principally American Samoa, Guam, the Virgin Islands of the
United States, and the Commonwealth of the Northern
Mariana Islands). The definition of residence conforms to
the criterion used in the census, which defines a resident
of a specified area as a person ‘‘usually resident’’ in the
area. For the most part, ‘‘usual residence’’ is defined subjectively by the census respondent; it is not defined by de
facto presence in a particular house or dwelling, nor is it
defined by any de jure (legal) basis for a respondent’s
presence. There are two exceptions to the subjective character of the residence definition.
1. People living in military barracks, prisons, and some
other types of residential group-quarters facilities are
generally reported by the administration of their facility, and their residence is generally the location of the
facility. Naval personnel aboard ships reside at the
home port of the ship, unless the ship is deployed to
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

the overseas fleets, in which case they are not
counted as U.S. residents. Exceptions may occur in the
case of Armed Forces personnel who have an official
duty location different from the location of their barracks, in which case their place of residence is their
duty location.
2. Students residing in dormitories report their own residences, but are instructed on the census form to
report their dormitories, rather than their parental
homes, as their residences.
The universe of interest for the official estimates of population that the Census Bureau produces is the resident
population of the United States, as it would be counted by
the latest census (2000) if this census had been held on
the reference date of the estimates. However, subgroups
within this universe are distinct from those in the census
universe due to race recoding, which does not affect the
total population.
The estimates differ from the census in their definition of
race, with categories redefined to be consistent with other
administrative-data sources. Specifically, people enumerated in Census 2000 who gave responses to the census
race question not codable to White, Black, American
Indian, Eskimo, Aleut, or any of several Asian and Pacific
Islander categories were assigned a race within one of
these categories. Most of these people (18.5 million
nationally) were Hispanics who gave a Hispanic-origin
response to the race question. Publications from Census
2000 do not incorporate this modification, while all official population estimates do incorporate it.
The universe underlying the CPS sample is confined to the
civilian noninstitutionalized population. Thus, independent population controls are sought for this population to
define the CPS control universe. The previously mentioned
alternative definitions of race, associated with the universe for official estimates, is carried over to the CPS control universe. Three additional changes are incorporated,
which distinguish the CPS control universe (civilian noninstitutional population) from the universe for official Census Bureau estimates (resident population).
1. The CPS control universe excludes active-duty Armed
Forces personnel, including those stationed within the
United States, while these individuals are included in
the resident population. ‘‘Active duty’’ is taken to refer
to personnel reported in military strength statistics of
the Departments of Defense (Army, Navy, Marines, and
Air Force) and Transportation (Coast Guard), including
reserve forces on 3- and 6-months active duty for
training, National Guard reserve forces on active duty
for 4 months or more, and students at military academies. Reserve forces not active by this definition are
included in the CPS control universe, if they meet the
other inclusion criteria.
Derivation of Independent Population Controls

C–3

2. The CPS control universe excludes people residing in
institutions, such as nursing homes, correctional facilities, juvenile detention facilities, and long-term mental
health care facilities. The resident population, on the
other hand, includes the institutionalized population.
3. The CPS control universe, like the resident population
base for population estimates, includes students residing in dormitories. Unlike the population estimates
base, however, it accepts as state of residence a family home address within the United States, in preference to the address of the dormitory. This difference
affects estimation procedures at the state level, but
not at the national level.

net migration to the United States (migration
into the United States minus migration out of
the United States) from time t0 to time t1.
It is essential that the components of change in a balancing equation represent the same population universe as
the base population. If we derive the current population
controls for the CPS independently from a base population
in the CPS universe, deaths in the civilian noninstitutional
population would replace resident deaths; ‘‘net migration’’
would be confined to civilian migrants but would incorporate net recruits to the Armed Forces and net admissions
to the institutional population. The equation would thus
appear as follows:
PCNt1 = PCNt0 + B − CND +(IM − NRAF − NEI)

CALCULATION OF POPULATION PROJECTIONS FOR
THE CPS UNIVERSE
This section explains the methodology for computing
population projections for the CPS control universe used
in the actual calibration of the survey. Three subsections
are devoted to the calculation of (i) the total population,
(ii) its distribution by age, sex, race, and Hispanic origin,
and (iii) its distribution by state of residence, age, sex, and
race.
Total Population
Projections of the population to the CPS control reference
date (the first of each month) are determined by a base
population (either estimated or enumerated) and a projection of population change. The latter is generally an amalgam of components measured from administrative data
and components determined by projection. The ‘‘balancing
equation of population change’’ used to produce estimates
and projections of the U.S. population states that the
population at some reference date is the sum of the base
population and the net of various components of population change. The components of change are the sources of
increase or decrease in the population from the reference
date of the base population to the date of the estimate.
The exact specification of the balancing equation depends
on the population universe and the extent to which components of population change are disaggregated. For the
resident population universe, the equation in its simplest
form is given by
PRESt1 = PRESt0 + B − D + NM
where:
PRESt1
PRESt0
B
D

C–4

Resident population at time t1
(the reference date),
= Resident population at time t0
(the base date),
= births to U.S. resident women,
from time t0 to time t1,
= deaths of U.S. residents,
from time t0 to time t1,

Derivation of Independent Population Controls

(1)

where:
P C Nt1

(2)

civilian noninstitutionalized population,
time t1,
P C Nt 0
= civilian noninstitutionalized population,
time t0,
B
= births to the U.S. resident population,
time t0 to time t1,
CND
= deaths of the civilian noninstitutionalized
population, time t0 to time t1,
IM
= net civilian migration, time t0 to time t1,
NRAF
= net recruits (inductions minus discharges) to
the U.S. Armed Forces from the domestic civilian population, from time t0 to time t1,
NEI
= net admissions (enrollments minus discharges)
to institutions, from time t0 to time t1.
In the actual estimation process, it is not necessary to
directly measure the variables CND, NRAF, and NEI in the
above equation. Rather, they are derived from the following equations:
CND = D − DRAF − DI

(3)

where D = deaths of the resident population, DRAF =
deaths of the resident Armed Forces, and DI = deaths of
inmates of institutions (the institutionalized population).
NRAF = WWAFt1 − WWAFt0 + DRAF + DAFO − NRO

(4)

where WWAFt1 and WWAFt0 represent the U.S. Armed
Forces worldwide at times t1 and t0, DRAF represents
deaths of the Armed Forces residing in the United States
during the interval, DAFO represents deaths of the Armed
Forces residing overseas, and NRO represents net recruits
(inductions minus discharges) to the Armed Forces from
the overseas civilian population.
NEI = PCIt1 − PCIt0 + DI

(5)

where PCIt1 and PCIt0 represent the civilian institutionalized population at times t1 and time t0, and DI represents
deaths of inmates of institutions during the interval. The
appearance of DI in both equations (3) and (5) with opposite sign, and DRAF in equations (3) and (4) with opposite
sign, ensures that they will cancel each other when
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applied to equation (2). This fact obviates measuring institutionalized inmate deaths or deaths of the Armed Forces
members residing in the United States.
where IM = net international migration in the time interval
(t0,t1).
At this point, we restate equation (2) to incorporate equations (3), (4), and (5), and simplify. The resulting equation,
which follows, is the operational equation for population
change in the civilian noninstitutionalized population universe.
PCNt1 = PCNt0 + B − D + IM −
(WWAFt1 − WWAFt0 + DAFO − NRO) − (PCIt1 − PCIt0)

(6)

Equation (6) identifies all the components that must be
separately measured or projected to update the total civilian noninstitutionalized population from a base date to a
later reference date.
Aside from forming the procedural basis for all estimates
and projections of total population (in whatever universe),
balancing equations (1), (2), and (6) also define a recursive
concept of internal consistency of a population series
within a population universe. We consider a time series of
population estimates or projections to be internally consistent if any population figure in the series can be derived
from any earlier population figure as the sum of the earlier
population and the components of population change for
the interval between the two reference dates.
Measuring the Components of the Balancing
Equation
Balancing equations such as equation (6) requires a base
population, whether estimated or enumerated, and information on the various components of population change.
Because a principal objective of the population estimating
procedure is to uphold the integrity of the population universe, the various inputs to the balancing equation need
to agree as much as possible with respect to geographic
scope and residential definition, and this requirement is a
major aspect of the way they are measured. A description
of the individual inputs to equation (6), which applies to
the CPS control universe, and their data sources follows.
The census base population (PCNt0). While any base
population (PCNt0, in equation [6]), whether a count or an
estimate, can be the basis of estimates and projections for
later dates, the original base population for all unadjusted
postcensal estimates and projections is the enumerated
population from the last census. For the survey controls in
this decade, Census 2000 results were not adjusted for
undercount. The census base population includes minor
revisions arising from count resolution corrections. These
corrections arise from the tabulation of data from the enumerated population. As of May 21, 2004, count resolution
corrections incorporated in the estimates base population

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amounted to a gain of 2,697 people from the originally
published count. In order to apply equation (6), which is
derived from the balancing equation for the civilian noninstitutionalized population, the resident base population
enumerated by the census must be transformed into a
base population consistent with this universe. The transformation is given as follows:
PCN0 = R0 − RAF0 − PCI0

(7)

where R0 = the enumerated resident population, RAF0 =
the Armed Forces resident in the United States on the census date, and PCI0 = the civilian institutionalized population residing in the United States on the census date. This
is consistent with previous notation, but with the stipulation that t0 = 0, since the base date is the census date, the
starting point of the series. The transformation can also
be used to adapt any postcensal estimate of resident
population to the CPS universe for use as a population
base in equation (6). In practice, this is what occurs when
the monthly CPS population controls are produced.
Births and deaths (B, D). In estimating total births and
deaths of the resident population (B and D, in equation
[6]), we assume the population universe for vital statistics
matches the population universe for the census. If we
define the vital statistics universe to be the population
subject to the natural risk of giving birth or dying and
having the event recorded by vital registration systems,
the assumption implies the match of this universe with the
census-level resident population universe. We relax this
assumption in the estimation of some characteristic detail,
and this will be discussed later in this appendix. The
numbers of births and deaths of U.S. residents are supplied by the National Center for Health Statistics (NCHS).
These are based on reports to NCHS from individual state
and local registries; the fundamental unit of reporting is
the individual birth or death certificate. For years in which
reporting is considered final by NCHS, the birth and death
statistics are considered final; these generally cover the
period from the census until the end of the calendar year
3 years prior to the year of the CPS series. For example,
the last available final birth and death statistics available
in time for the 2004 CPS control series were for calendar
year 2001. Final birth and death data are summarized in
NCHS publications (see Martin et al., 2002, and Arias et
al., 2003). Monthly births and deaths for calendar year(s)
up to 2 years before the CPS reference dates (e.g., 2002
for 2004 controls) are based on provisional estimates by
NCHS (see Hamilton et al. and Kochanek et al., 2004). For
the year before the CPS reference date through the last
month before the CPS reference date, births and deaths
are projected based on the population by age according to
current estimates and age-specific rates of fertility and
mortality and seasonal distributions of births and deaths
observed in the preceding year.

Derivation of Independent Population Controls

C–5

A concern exists relative to the consistency of the NCHS
vital statistics universe with the census resident universe
and the CPS control universe derived from it.
Birth and death statistics obtained from NCHS exclude
events occurring to U.S. residents while outside the United
States. The resulting slight downward bias in the numbers
of births and deaths would be partially compensatory.
This source of bias, which is assumed to be minor, affects
the accounting of total population. Other comparability
problems exist in the consistency of NCHS vital statistics
with the CPS control universe with respect to race, Hispanic origin, and place of residence within the United
States. These will be discussed in later sections of this
appendix.
Net International Movement. The net international
component combines three parts: (1) net migration of the
foreign-born, (2) emigration of natives, and (3) net movement from Puerto Rico to the United States. Data from the
American Community Survey (ACS) is the basis for estimating the level of net migration of the foreign-born
between 2000 to 2001 and 2001 to 2002 because it provided annually updated data based on a larger sample
than the CPS. After determining the level of migration for
the foreign-born (the net difference between two time
periods for the foreign-born population), we accounted for
deaths to the entire foreign-born population during the
periods of interest to arrive at the final estimate of net
migration of the foreign-born. We applied the Census
2000 age-sex-race-Hispanic-origin county-level distribution of the non-citizen foreign-born population who
entered in 1995 or later to the national-level estimate of
net migration of the foreign-born. The remaining two parts
of the net international migration component, the net
movement from Puerto Rico to the United States and the
emigration of natives, were produced in similar ways. For
both parts, we do not have current annually updated information. Therefore, we used the levels of movement produced from change in the decennial censuses.2 For both
parts, we applied the age-sex-race-Hispanic origin-county
distributions from Census 2000 that were most similar to
the population of interest. For the net movement from
Puerto Rico, the underlying distribution was based on
those who indicated that their place of birth was Puerto
Rico and who had entered the United States in 1995 or
later. We assumed that natives who emigrated were likely
to have the same distributions as natives who currently
reside in the United States. Therefore, the characteristics
of natives who emigrated were assumed to be the same
age-sex-race-Hispanic-origin county-level distribution as
natives residing in the 50 states and the District of Columbia in Census 2000.
2
For more information on the estimate of 11,133 for the net
movement from Puerto Rico, see Christenson. For information on
estimates of native emigration, see Gibbs, et al.

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Derivation of Independent Population Controls

Once the net migration of the foreign-born, net movement
from Puerto Rico, and emigration of natives were estimated, all three parts were combined to estimate a final
net international migration component. Adding up the
parts of net international migration, the net migration of
the foreign-born (1,300,000) plus the net migration from
Puerto Rico (+11,133) minus the net emigration of natives
(−18,012) yielded 1,293,121 as the annual estimate for
net international migration for the 2000−2001 year and
the 2001−2002 year.
Net recruits to the Armed Forces from the civilian
population (NRAF). The net recruits to the worldwide
U.S. Armed Forces from the U.S. resident civilian population is given by the expression
(WWAFt1 − WWAFt0 + DRAF + DAFO − NRO)
in equation (4). The first two terms represent the change
in the number of Armed Forces personnel worldwide. The
third and fourth represent deaths of the Armed Forces in
the U.S. and overseas, respectively. The fifth term represents net recruits to the Armed Forces from the overseas
civilian population. While this procedure is indirect, it
allows us to rely on data sources that are consistent with
our estimates of the Armed Forces population on the base
date.
Most of the information required to estimate the components of this expression is supplied directly by the Department of Defense, generally through a date one month
prior to the CPS control reference date; the last month is
projected, for various detailed subcomponents of military
strength. The military personnel strength of the worldwide
Armed Forces, by branch of service, is supplied by personnel offices in the Army, Navy, Marines and Air Force, and
the Defense Manpower Data Center supplies total strength
figures for the Coast Guard. Participants in various reserve
forces training programs (all reserve forces on 3- and
6-month active duty for training, National Guard reserve
forces on active duty for 4 months or more, and students
at military academies) are treated as active-duty military
for purpose of estimating this component, and all other
applications related to the CPS control universe, although
the Department of Defense would not consider them to be
on active duty.
The last three components of net recruits to the Armed
Forces from the U.S. civilian population, deaths in the resident Armed Forces (DRAF), deaths in the Armed Forces
overseas (DAFO), and net recruits to the Armed Forces
from overseas (NRO) are usually very small and require
indirect inference. Four of the five branches of the Armed
Forces (those in DOD) supply monthly statistics on the
number of deaths within each service. Normally, deaths
are apportioned to the domestic and overseas component
of the Armed Forces relative to the number of the domestic and overseas military personnel. If a major fatal incident occurs for which an account of the number of deaths
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is available, these are assigned to domestic or overseas
populations, as appropriate, before application of the pro
rata assignment. Lastly, the number of net recruits to the
Armed Forces from the overseas population (NRO) is computed annually, for years from July 1 to July 1, as the difference between successive numbers of Armed Forces personnel giving a ‘‘home of record’’ outside the 50 states
and District of Columbia. To complete the monthly series,
the ratio of individuals with home of record outside the
U.S. to the worldwide military is interpolated linearly, or
extrapolated as a constant from the last July 1 to the CPS
reference date. These monthly ratios are applied to
monthly worldwide Armed Forces strengths; successive
differences of the resulting estimates of people with home
of record outside the U.S. yield the series for net recruits
to the Armed Forces from overseas.
Change in the institutionalized population (CIPt1 CIPt0). The change in the civilian population residing in
institutions is currently held constant, equal to the civilian
institutionalized group quarters population obtained from
Census 2000.
Population by Age, Sex, Race, and Hispanic Origin
The CPS second-stage weighting process requires the independent population controls to be disaggregated by age,
sex, race, and Hispanic origin. The weighting process, as
currently constituted, requires cross-categories of age
group by sex and by race, and age group by sex and by
Hispanic origin, with the number of age groups varying by
race and Hispanic origin. Thirty-one categories of race and
two classes of ethnic origin (Hispanic, non-Hispanic) are
required, with no cross-classifications of race and Hispanic
origin. The population projection program now uses the
full cross-classification of age by sex and by race and by
Hispanic origin in its monthly series, with 101 single-year
age categories (single years from 0 to 99, and 100 and
over), two sex categories (male, female), 31 race categories (White; Black; American Indian, Eskimo, and Aleut;
Asian; Native Hawaiian and Pacific Islander; and all
multiple-race groupings), and two Hispanic-origin categories (not Hispanic, Hispanic).3 The resulting matrix has
12,524 cells (101 x 2 x 31 x 2), which are then aggregated to the distributions used as controls for the CPS.
In discussing the distribution of the projected population
by characteristic, we will stipulate the existence of a base
population and components of change, each having a distribution by age, sex, race, and Hispanic origin. In the case
of the components of change, ‘‘age’’ is understood to be
age at last birthday as of the estimate date. The present
section on the method of updating the base population

3
Throughout this appendix, ‘‘American Indian’’ refers to the
aggregate of American Indian, Eskimo, and Aleut; ‘‘API’’ refers to
Asian and Pacific Islander.

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with components of change is followed by a section on
how the base population and component distributions are
measured.
Update of the Population by Sex, Race, and
Hispanic Origin
The full cross-classification of all variables except age
amounts to 124 cells, defined by variables not naturally
changing over time (2 values of sex by 31 of race by 2 of
Hispanic origin). The logic for projecting the population of
each of these cells follows the same logic as the projection
of the total population and involves the same balancing
equations. The procedure, therefore, requires a distribution by sex, race, and Hispanic origin to be available for
each component on the right side of equation (6). Similarly, the derivation of the civilian noninstitutionalized
base population from the census resident population follows equation (7).
Distribution of Census Base and Components of
Change by Age, Sex, Race, and Hispanic Origin
The discussion until now has addressed the method of
estimating population detail in the CPS control universe
and has assumed the existence of various data inputs. The
current section is concerned with the origin of the inputs.
Census Base Population by Age, Sex, Race and
Hispanic Origin: Modification of the Census Race
Distribution
The race modification conforms to the Office of Management and Budget’s (OMB) 1997 revised standards for collecting and presenting data on race and ethnicity. The
revised OMB standards identified five race categories:
White, Black or African American, American Indian and
Alaska Native, Asian, and Native Hawaiian and Other
Pacific Islander. Additionally, the OMB recommended that
respondents be given the option of marking or selecting
one or more races to indicate their racial identity. For
respondents unable to identify with any of the five race
categories, the OMB approved including a sixth category,
‘‘Some other race,’’ on the Census 2000 questionnaire.
No modification was necessary for responses indicating
only an OMB race alone or in combination with another
race. However, about 18.5 million people checked ‘‘Some
other race’’ alone or in combination with another race.
These people were primarily Hispanic and many wrote in
their place of origin or Hispanic-origin type (such as Mexican or Puerto Rican) as their race. For purposes of estimates production, responses of ‘‘Some other race’’ alone
were modified by blanking the ‘‘Some other race’’ response
and imputing an OMB race alone or in combination with
another race response. The responses were imputed from
a donor who matched on response to the question on Hispanic origin. Responses of both ‘‘Some other race’’ and an
OMB race were modified by blanking the ‘‘Some other race’’response and keeping the OMB race response.
Derivation of Independent Population Controls

C–7

The resulting race categories (White, Black, American
Indian and Alaska Native, Asian, and Native Hawaiian and
Other Pacific Islander) conform with OMB’s 1997 revised
standards for the collection of data on race and ethnicity
and are more consistent with the race categories in other
administrative sources, such as vital statistics.
Distribution of births by sex, race, and Hispanic
origin. Data on registered births to U.S. residents by birth
month, sex of child, and race and Hispanic origin of
mother and father were supplied by the National Center
for Health Statistics (NCHS). For the 2004 CPS controls,
final data were available through 2001 and preliminary
data were available for 2002.
Registered births to U.S. resident women were estimated
from data supplied by NCHS. At present, NCHS continues
to collect birth certificate data using the 1977 race standards of White, Black, American Indian, Eskimo or Aleut,
and Asian or Pacific Islander, under the ‘‘mark one race’’
scenario. To produce post-2000 population estimates, it
was necessary to develop birth data that coincided with
the new race categories: White, Black, American Indian and
Alaska Native, Asian, and Native Hawaiian and other
Pacific Islander, under the ‘‘mark one or more races’’ scenario. Because of this inconsistency in data on race, it was
necessry to model the full 31 possible single- and
multiple-race combinations. For a detailed description of
this modeling process, see Smith and Jones, 2003.
Data on births by birth month, sex, and race and Hispanic
origin of the mother and father are based on final microdata files for calendar year 2001 from the NCHS registration system. The model was based on information from
Census 2000 on race and Hispanic origin reporting within
households for the age zero (under 1 year of age) population and their parent(s). First, the NCHS births were tabulated for each of the combinations of parents’ race and
Hispanic origin. These births by parents’ race and Hispanic
origin were then distributed according to the Census 2000
race and Hispanic origin distribution for the age zero
population for the matching combination of parents’ race
and Hispanic origin. Race and Hispanic origin modeling
was done separately for mother-only and two-parent
households.
To estimate the distribution of births for calendar year
2002, data on preliminary 2002 births received from
NCHS from their 90-percent sample of final births were
distributed according to the 2001 births by birth month,
sex and modeled race and Hispanic origin.
To estimate the distribution of births by race and Hispanic
origin of the mother for the first half of 2003, age-specific
birth rates centered on July 1, 2002, for women in each
race group and for women of Hispanic origin were applied
to preliminary estimates of the number of resident women
in the specified age groups.
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Derivation of Independent Population Controls

Births through the remaining period through the end of
2004 were then projected from the births estimated
above.
Deaths by age, sex, race, and Hispanic origin. Data
on registered deaths of U.S. residents by death month,
age, sex, race, and Hispanic origin were supplied by
NCHS. Final data were available through 2001 and preliminary data were available for 2002.
It was again necessary to model the race distribution
because death certificates ask for the race of the deceased
using only four race categories (1977 race categories for
White, Black, American Indian, Eskimo or Aleut, Asian or
Pacific Islander). Separate death rates were calculated for
the 1977 race categories by age, sex, and Hispanic origin.
Rates were constructed using the 1998 mortality4 and
1998 population estimates.5 These rates were then
applied to the July 2001 population in the 31 modified
race categories from the Vintage 2002 estimates. Death
rates for the White, the Black, the American Indian, Eskimo
or Aleut, and the Asian and Pacific Islander groups were
applied to the corresponding White alone, Black alone,
American Indian and Alaska Native alone, Asian alone, and
Native Hawaiian and Other Pacific Islander alone populations. The Asian and Pacific Islander death rate was
applied to both the Asian alone population and the Native
Hawaiian and Other Pacific Islander alone population.
Multiple-race deaths were estimated as the difference
between total 2001 deaths as reported by NCHS and the
sum of deaths estimated for the single-race groups. Consequently, a constant death rate was applied to each of
the 26 multiple- race groups.
The 2002 deaths were distributed using 2001 deaths by
modeled race, death month, age, and sex and were controlled to the 2002 preliminary deaths from NCHS by Hispanic origin.
To estimate the distribution of deaths by race and Hispanic origin for the first half of 2003, projected agespecific mortality rates for July 1, 2002, were applied to
preliminary 2003 estimates of the population by single
year of age, sex, race, and Hispanic origin.
Deaths by race and Hispanic origin for the period beyond
the first half of 2003 through the end of 2004 are projected from the deaths produced above.

4
The race distribution for the 1998 deaths as set in the processing of the national estimate is used here because it adjusts
for NCHS/Census race inconsistencies. In the production of
national population estimates in the 1990s, preliminary deaths to
the American Indian and Asian and Pacific Islander populations by
sex were projected using life tables, with proportional adjustment
to sum to the other races total. Hispanic origin deaths by sex and
race were estimated for all years using life tables applied to a distribution of the Hispanic population by age, sex, and race.
5
The 1998 population estimate from the vintage 2000 population estimates.

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International migration by age, sex, race, and
Hispanic origin. For the purposes of the estimates program, the net international migration component consists
of three parts: (1) net migration of the foreign-born, (2)
emigration of natives, and (3) net movement from Puerto
Rico to the United States.
Net migration of the foreign-born component.
Although a component basis was used throughout most of
the 1990s, the findings from an extensive evaluation
showed that the use of administrative sources for developing migration components by migrant status for the
foreign-born was problematic.6 Therefore, we assumed
that the net difference between two time periods for the
foreign-born population using the same data source would
be attributable to net migration. To maximize the use of
available data, the American Community Survey (ACS) data
for 2000, 2001, and 2002 was the basis for the net migration between 2000 to 2001, and 2001 to 2002.7 The calculation of the change in the size of the foreign-born
population, must be adjusted for deaths in the population
during the period of interest.
Characteristics for net migrants were imputed by taking
the estimated net migration of the foreign-born and then
redistributing that total to the Census 2000 distribution
by single year of age (0−115+), sex-race (31 groups), Hispanic origin and county distribution of the noncitizen,
foreign-born who reported that they entered in 1995 or
later. The rationale was that the most recent immigrants in
Census 2000 would most closely parallel the distribution
of the foreign-born in the ACS, by age-sex-race-Hispanic
origin and county.
Net migration from Puerto Rico component. Since no
data set would easily provide updated information on
movement from Puerto Rico, there was no way to update
the estimate that was based on the average movement of
the 1990s.8
Characteristics were imputed for the net migration from
Puerto Rico using the age-sex-race-Hispanic origin-county
distribution of those who indicated that their place of
birth was Puerto Rico and who had entered the United
States in 1995 or later. The underlying assumption was
that the characteristics of those who indicated that they
were born in Puerto Rico and had migrated since 1995
would parallel the distribution of recent migrants from
Puerto Rico.

6
An overview of the Demographic Analysis-Population Estimates (DAPE) project along with a summary of findings is in Deardorff, K. E. and L. Blumerman. Mulder, T., et.al., gives an overview
of the methods used throughout the 1990s’ estimates series.
7
Throughout, ‘‘ACS’’ will refer to the ACS combined with the
Supplementary Surveys for the corresponding years.
8
See Christenson, M., for an explanation of how the annual
level of 11,133 was derived.

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Net native emigration component. Since no current
data set would easily provide updated information on
native emigration, there was no systematic way to update
the estimate.9 We assumed that natives who emigrated
were likely to have the same distributions as natives who
currently reside in the United States. Therefore, the level
was distributed to the same age-sex-race-Hispanic origincounty proportions as those of the native population (born
in the 50 states and DC) living in the 50 states and the
District of Columbia in Census 2000.
Combined net international migration component.
The estimates of net migration of the foreign-born, net
movement from Puerto Rico, and net emigration of natives
were combined to create the estimate of the total net
international migration component.
The net international migration component was kept constant for years 2000 to 2003. The change in the foreignborn population from the 2000 to 2001 period and from
the 2001 to 2002 period was not statistically significant.
This finding, in conjunction with limited time for additional research, led to the decision to keep the estimate
constant for the projected years 2004 and 2005. The additional parts of the net international migration component
are kept constant because they are currently measured
only once per decade.
Migration of Armed Forces by age, sex, race, and
Hispanic origin. Estimation of demographic details for
the remaining components of change requires estimation
of the details for the Armed Forces residing overseas and
in the United States. These data are required to assign
detail to the effects of Armed Forces recruitment on the
civilian population.
Distributions of the Armed Forces by branch of service,
age, sex, race/Hispanic origin, and location inside or outside the United States are provided by the Department of
Defense, Defense Manpower Data Center. The location and
service-specific totals closely resemble those provided by
the individual branches of the services for all services
except the Navy. For the Navy, it is necessary to adapt the
location distribution of people residing on board ships to
conform to census definitions, which is accomplished
through a special tabulation (also provided by Defense
Manpower Data Center) of people assigned to sea duty.
These data are prorated to overseas and U.S. residence,
based on the distribution of the total population afloat by
physical location, supplied by the Navy.
In order to incorporate the resulting Armed Forces distributions in estimates, the race-Hispanic-origin distribution
must also be adapted. The Armed Forces ‘‘race-ethnic’’ categories supplied by the Defense Manpower Data Center
9
See Gibbs, et.al., for an explanation of how the estimated
annual level of 18.000 native emigrants was derived. Because of
rounding, the sum of the detailed age, sex, race, and Hispanic origin estimates used in the actual estimates process is 18,012.

Derivation of Independent Population Controls

C–9

treat race and Hispanic origin as a single variable, with the
Hispanic component of Blacks and Whites included under
‘‘Hispanic,’’ and the Hispanic components of the American
Indian and the API populations assumed to be nonexistent. A residual ‘‘other race’’ category is also caculated. The
method of converting this distribution to be consistent
with Census-modified race categories employs, for each
age-sex category, the Census 2000 race distribution of the
total population (military and civilian) to supply all race
information missing in the Armed Forces race-ethnic categories. Hispanic origin is thus prorated to the White and
Black populations according to the total race-modified
population of each age-sex category in 2000; American
Indian and API are similarly prorated to Hispanic and nonHispanic categories. As a final step, the small residual category is distributed as a simple prorata of the resulting
Armed Forces distribution for each age-sex group. This
adaptation is robust; it requires imputing the distributions
of only small race and Hispanic-origin categories.
The two small components of deaths of the Armed Forces
overseas and net recruits to the Armed Forces from the
overseas population, previously described, are also
assigned the same level of demographic detail. Deaths of
the overseas Armed Forces are assigned the same demographic characteristics as the overseas Armed Forces. Net
recruits from overseas are assigned race and Hispanic origin based on data from an aggregate of overseas censuses, in which Puerto Rico dominates numerically. The
age and sex distribution follows the worldwide Armed
Forces.
The civilian institutionalized population by age,
sex, race, and Hispanic origin. The fundamental source
for the distribution of the civilian institutionalized population by age, sex, race, and Hispanic origin is a Census
2000 tabulation of institutional population by age, sex,
race (modified), Hispanic origin, and type of institution.
The last variable has four categories: nursing home, correctional facility, juvenile facility, and a small residual.
Data for the resident armed forces are also obtained from
Census 2000 and updated with data from the Department
of Defense. The size of the civilian population is obtained
by subtracting the resident armed forces from the resident
population. The civilian noninstitutionalized population is
obtained by subtracting the institutionalized population
from the civilian population.
Population Controls for States
State coverage adjustment for the CPS requires a distribution of the national civilian noninstitutionalized population
by age, sex, and race to the states. The second-stage
weighting procedure requires a disaggregation only by
age and sex by state. This distribution is determined by a
linear extrapolation through two population estimates for
each characteristic group for each state and the District of
Columbia. The reference dates are July 1 of the years that
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Derivation of Independent Population Controls

are 2 years and 1 year prior to the reference date for the
CPS population controls. The extrapolated state distribution is forced to sum to the national total population for
each age, sex, race and Hispanic-origin group by proportional adjustment. For example, the state distribution for
June 1, 2003, was determined by extrapolation along a
straight line determined by two data points (July 1, 2001,
and July 1, 2002) for each state. The resulting distribution
for June 1, 2003, was then proportionately adjusted to the
national total for each age, sex and race group, computed
for the same date. This procedure does not allow for any
difference among states in the seasonality of population
change, since all seasonal variation is attributable to the
proportional adjustment to the national population.
The procedure for state estimates (e.g., through July 1 of
the year prior to the CPS control reference date), which
forms the basis for the extrapolation, differs from the
national-level procedure in a number of ways. The primary
reason for the differences is the importance of interstate
migration for state estimates, and the need to impute it by
indirect means. Like the national procedure, the state-level
procedure depends on fundamental demographic principles embodied in the balancing equation for population
change; however, they are not applied to the total resident
or civilian noninstitutionalized population but to the
household population under 65 years of age and 65 years
of age and over. Estimates procedures for the population
under 65 and the population 65 and older are similar, but
differ in that they employ a different data source for developing mea- sures of internal migration. Furthermore, while
national-level population estimates are produced for the
first of each month, state estimates are produced at oneyear intervals, with July 1 reference dates.
The household population under 65 years of age.
The procedure for estimating the nongroup quarters population under 65 is analogous to the national-level procedures for the total population, with the following major
differences.
1. Because the population is restricted to individuals
under 65 years of age, the number of people advancing to age 65 must be subtracted, along with deaths
of those under 65. This component is composed of
the 64-year-old population at the beginning of each
year, and is measured by projecting census-based
ratios of the 64-year-old to the 65-year-old population
by the national-level change, along with proration of
other components of change to age 64.
2. All national-level components of population change
must be distributed to state-level geography, with
separation of each state total into the number of
events to people under 65 and people 65 and over.
Births and deaths are available from NCHS by state of
residence and age (for deaths). Similar data can be
obtained—often on a more timely basis—from FSCPE
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state representatives. These data, after appropriate
review, are adopted in place of NCHS data for those
states. The immigration total and the emigration of
native-born residents are distributed on the basis of
the various segments of the population enumerated in
Census 2000.
3. The population updates include a component of
change for net internal migration. This is obtained
through the computation for each county of rates of
net migration based on the number of exemptions
under 65 years of age claimed on year-to-year
matched pairs of IRS tax returns. Because the IRS
codes tax returns by ZIP Code, the ZIP-CRS program
(Sater, 1994) is used to identify county and state of
residence on the matched returns. The matching of
returns allows the identification for any pair of states
or counties of the number of ‘‘stayers’’ who remain
within the state or county of origin and the number of
‘‘movers’’ who change address between the two. This
identification is based on the number of exemptions
on the returns and the existence or nonexistence of a
change of address.
A detailed explanation of the use of IRS data and other facets of the subnational estimates can be found at .
The population 65 years of age and over. For the
population ages 65 and over, the approach is similar to
that used for the population under age 65. The population
ages 65 and over in Census 2000, is added to the population estimated to be turning age 65. This quantity, as discussed in the previous section, is subtracted from the
base under-age-65 population. The Census Bureau subtracts deaths and adds net immigration, occurring for this
same age group. Migration is estimated using data on
Medicare enrollees, which are available by county of residence from the U.S. Centers for Medicare and Medicaid
Services. Because there is a direct incentive for eligible
individuals to enroll in the Medicare program, the coverage rate for this source is high. To adapt this estimate to
the Census 2000 level, the Medicare enrollment is
adjusted to reflect the difference in coverage of the population 65 years of age and over.
Exclusion of the Armed Forces population and
inmates of civilian institutions. Once the resident
population by the required age, sex, and race groups has
been estimated for each state, the population must be
restricted to the civilian noninstitutionalized universe.
Armed Forces residing in each state are excluded, based
on reports of location of duty assignment from the
branches of the Armed Forces (including the homeport
and projected deployment status of ships for the Navy).
Some of the Armed Forces data (especially information on
deployment of naval vessels) must be projected from the
last available date to the later annual estimate dates.
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PROCEDURAL REVISIONS
The process of producing estimates and projections, like
most research activity, is in a constant state of flux. Various technical issues are addressed with each cycle of estimates for possible implementation in the population estimating procedures, which carry over to the CPS controls.
These procedural revisions tend to be concentrated early
in the decade, reflecting the introduction of data from a
new decennial census, which can be associated with new
policies regarding population universe or estimating methods. However, revisions may occur at any time, depending
on new information obtained regarding the components of
population change, or the availability of resources to complete methodological research.
SUMMARY LIST OF SOURCES FOR CPS POPULATION
CONTROLS
The following is a summary list of agencies providing data
used to calculate independent population controls for the
CPS. Under each agency are listed the major data inputs
obtained.
1. The U.S. Census Bureau (Department of Commerce):
The Census 2000 population by age, sex, race, Hispanic origin, state of residence, and household or type
of group quarters residence
Sample data on distribution of the foreign-born population by sex, race, Hispanic origin, and country of
birth
The population of April 1, 1990, estimated by the
Method of Demographic Analysis
Decennial census data for Puerto Rico
2. National Center for Health Statistics (Department of
Health and Human Services):
Live births by age of mother, sex, race, and Hispanic
origin
Deaths by age, sex, race, and Hispanic origin
3. The U.S. Department of Defense:
Active-duty Armed Forces personnel by branch of service
Personnel enrolled in various active-duty training programs
Distribution of active-duty Armed Forces Personnel by
age, sex, race, Hispanic origin, and duty location (by
state and outside the United States)
Deaths to active-duty Armed Forces personnel
Dependents of Armed Forces personnel overseas
Births occurring in overseas military hospitals
Derivation of Independent Population Controls

C–11

REFERENCES
Ahmed, B. and J. G. Robinson, (1994), Estimates of Emigration of the Foreign-Born Population: 1980−1990,
U.S. Census Bureau, Population Division Technical Working
Paper No. 9, December, 1994, .

Martin, Joyce A., Brady E. Hamilton, Stephanie Ventura, Fay
Menacker, Melissa M. Park, and Paul D. Sutton (2002),
‘‘Births: Final Data for 2001,’’ National Vital Statistics
Reports, Volume 51, Number 2, Division of Vital Statistics, National Center for Health Statistics, .

Arias, Elizabeth, Robert Anderson, Hsiang-Ching Kung,
Sherry Murphy, and Kenneth Kochaneck,‘‘Deaths: Final
Data for 2001,’’ National Vital Statistics Reports, Volume 52, Number 3, Division of Vital Statistics, National
Center for Health Statistics, .

Mulder, T., Frederick Hollmann, Lisa Lollock, Rachel
Cassidy, Joseph Costanzo, and Josephine Baker, ‘‘U.S. Census Bureau Measurement of Net International Migration to
the United States: 1990 to 2000,’’ Population Division
Technical Working Paper No. 51, February, 2002, .

Christenson, M., ‘‘Evaluating Components of International
Migration: Migration Between Puerto Rico and the United
States,’’ Population Division Technical Working Paper No.
64, January 2002, .

Robinson, J. G. (1994), ‘‘Clarification and Documentation
of Estimates of Emigration and Undocumented Immigration,’’ unpublished U.S. Census Bureau memorandum.

Deardoff, K.E. and L. Blumerman, ‘‘Evaluating Components
of International Migration: Estimates of the Foreign- Born
Population by Migrant Status in 2000,’’ Population Division
Technical Working Paper No. 58, December, 2001, .
Gibbs, J. G. Harper, M. Rubin, and H. Shin, ‘‘Evaluating
Components of International Migration: Native-Born Emigrants,’’ Population Division Technical Working Paper No.
63, January, 2003, .
Hamilton, Brady E., Joyce A. Martin, and Paul D. Sutton
(2003), ‘‘Births: Preliminary Data for 2002,’’ National Vital
Statistics Reports, Volume 51, Number 11, Division of
Vital Statistics, National Center for Health Statistics,
.
Kochaneck, Kenneth; and Betty Smith (2004), ‘‘Deaths: Preliminary Data for 2002,’’ National Vital Statistics
Reports, Volume 52, Number 13, Division of Vital Statistics, National Center for Health Statistics, .

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Derivation of Independent Population Controls

Sater, D. K. (1994), Geographic Coding of Administrative Records— Current Research in the ZIP/SECTORto-County Coding Process, U.S. Census Bureau, Population Division Technical Working Paper No. 7, June, 1994,
.
Smith, Amy Symens, and Nicholas A. Jones (2003), Dealing With the Changing U.S. Racial Definitions: Producing Population Estimates Using Data With Limited Race Detail. Paper presented at the 2003 meetings
of the Population Association of America, Minneapolis,
Minnesota, May, 2003.
U.S. Census Bureau (1991), Age, Sex, Race, and Hispanic Origin Information From the 1990 Census: A
Comparison of Census Results With Results Where
Age and Race Have Been Modified, 1990 Census of
Population and Housing, Publication CPH-L-74.
U.S. Immigration and Naturalization Service (1997), Statistical Yearbook of the Immigration and Naturalization Service, 1996, Washington, DC: U.S. Government
Printing Office.

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Appendix D.
Organization and Training of the Data Collection Staff
INTRODUCTION
The data collection staff for all U.S. Census Bureau programs is directed through 12 regional offices (ROs) and 3
telephone centers. The ROs collect data using two modes:
CAPI (computer-assisted personal interviewing) and CATI
(computer-assisted telephone interviewing). The 12 ROs
report to the Chief of the Field Division whose headquarters is located in Washington, DC. The three CATI facility
managers report to the Chief of the National Processing
Center (NPC), located in Jeffersonville, Indiana.
ORGANIZATION OF REGIONAL OFFICES/CATI
FACILITIES
The staffs of the ROs and CATI facilities carry out the Census Bureau’s field data collection programs for both
sample surveys and censuses. Currently, the ROs supervise over 6,500 part-time and intermittent field representatives (FRs) who work on continuing current programs
and one-time surveys. Approximately 1,900 of these FRs
work on the Current Population Survey (CPS). When a census is being taken, the field staff increases dramatically.
The location of the ROs and the boundaries of their
responsibilities are displayed in Figure D–1. RO areas were
originally defined to evenly distribute the office workloads
for all programs. Table D–1 shows the average number of
CPS units assigned for interview per month in each RO.
A regional director is in charge of each RO. Program coordinators report to the director through an assistant
regional director. The CPS is the responsibility of the
demographic program coordinator, who has two or three
CPS program supervisors on staff. The program supervisors have a staff of two to four office clerks working
essentially full-time. Most of the clerks are full-time federal
employees who work in the RO. The typical RO employs
about 100 to 250 FRs who are assigned to the CPS. Most
FRs also work on other surveys. The RO usually has 20−35
senior field representatives (SFRs) who act as team leaders
to FRs. Each team leader is assigned 6 to 10 FRs. The primary function of the team leader is to assist the program
supervisors with training and supervising the field interviewing staff. In addition, the SFRs conduct nonresponse
and quality assurance reinterview follow-up with eligible
households. Like other FRs, the SFR is a part-time or intermittent employee who works out of his or her home.
Despite the geographic dispersion of the sample areas,
there is a considerable amount of personal contact
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between the supervisory staff and the FRs, accomplished
mainly through the training programs and various aspects
of the quality control program. For some of the outlying
PSUs, it is necessary to use the telephone and written
communication to keep in continual touch with all FRs.
With the introduction of CAPI, the ROs also communicate
with the FRs using e-mail. Assigning new functions, such
as team leadership responsibilities, also improves communications between the ROs and the interviewing staff. In
addition to communications relating to the work content,
there is a regular system for reporting progress and costs.
The CATI centers are staffed with one facility manager
who directs the work of two to three supervisory survey
statisticians. Each supervisory survey statistician is in
charge of about 15 supervisors and between 100-200
interviewers.
A substantial portion of the budget for field activities is
allocated to monitoring and improving the quality of the
FRs’ work. This includes FRs’ group training, monthly
home studies, personal observation, and reinterview.
Approximately 25 percent of the CPS budget (including
travel for training) is allocated to quality enhancement.
The remaining 75 percent of the budget went to FR and
SFR salaries, all other travel, clerical work in the ROs,
recruitment, and the supervision of these activities.
TRAINING FIELD REPRESENTATIVES
Approximately 20 to 25 percent of the CPS FRs leave the
staff each year. As a result, the recruitment and training of
new FRs is a continuing task in each RO. To be selected as
a CPS FR, a candidate must pass the Field Employee Selection Aid test on reading, arithmetic, and map reading. The
FR is required to live in or near the Primary Sampling Unit
(PSU) in which the work is to be performed and have a
residence telephone and, in most situations, an automobile. As a part-time or intermittent employee, the FR works
40 or fewer hours per week or month. In most cases, new
FRs are paid at the GS−3 level and are eligible for payment
at the GS−4 scale after 1 year of fully successful or better
work. FRs are paid mileage for the use of their own cars
while interviewing and for commuting to classroom training sites. They also receive pay for completing their home
study training packages.
FIELD REPRESENTATIVE TRAINING PROCEDURES
Initial training for new field representatives. Each
FR, when appointed, undergoes an initial training program
prior
Organization and Training of the Data Collection Staff

D–1

Figure D–1. Map

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Organization and Training of the Data Collection Staff

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Table D–1. Average Monthly Workload by Regional Office: 2004
Regional office
Total . . . . . . . . . . . . . . . . .
Boston. . . . . . . . . . . . . . . . . . . . . . . . . .
New York . . . . . . . . . . . . . . . . . . . . . . .
Philadelphia . . . . . . . . . . . . . . . . . . . .
Detroit . . . . . . . . . . . . . . . . . . . . . . . . .
Chicago . . . . . . . . . . . . . . . . . . . . . . . .
Kansas City . . . . . . . . . . . . . . . . . . . . .
Seattle. . . . . . . . . . . . . . . . . . . . . . . . . .
Charlotte . . . . . . . . . . . . . . . . . . . . . . .
Atlanta . . . . . . . . . . . . . . . . . . . . . . . . .
Dallas . . . . . . . . . . . . . . . . . . . . . . . . . .
Denver . . . . . . . . . . . . . . . . . . . . . . . . .
Los Angeles . . . . . . . . . . . . . . . . . . . .

Base workload

CATI workload

Total workload

Percent

67,383
8,813
2,934
6,274
4,848
4,486
6,519
5,251
5,401
4,927
4,418
9,763
3,749

6,839
844
275
620
495
507
657
676
541
455
391
1,029
349

74,222
9,657
3,209
6,894
5,343
4,993
7,176
5,927
5,942
5,382
4,809
10,792
4,098

100.00
13.01
4.32
9.29
7.20
6.73
9.67
7.99
8.01
7.25
6.48
14.54
5.52

to starting his/her assignment. The initial training program consists of up to 24 hours of pre-classroom home
study, 3.5 to 4.5 days of classroom training (dependent
upon the trainee’s interviewing experience) conducted by
the program supervisor or coordinator, and as a minimum,
an on-the-job field observation by the program supervisor
or SFR during the FR’s first 2 days of interviewing. The
classroom training includes comprehensive instruction on
the completion of the survey using the laptop computer. In
classroom training, special emphasis is placed on the
labor force concepts to ensure that the new FRs fully
grasp these concepts before conducting interviews. In
addition, a large part of the classroom training is devoted
to practice interviews that reinforce the correct interpretation and classification of the respondents’ answers.
Trainees receive extensive training on interviewing skills,
such as how to handle noninterview situations, how to
probe for information, ask questions as worded, and
implement face-to-face and telephone techniques. Each FR
completes a home study exercise before the second
month’s assignment and, during the second month’s interview assignment, is observed for at least 1 full day by the
program supervisor or the SFR, who gives supplementary
training, as needed. The FR also completes a home study
exercise and a final review test prior to the third month’s
assignment.
Training for all field representatives. As part of each
monthly assignment, FRs are required to complete a home
study exercise, which usually consists of questions concerning labor force concepts and survey coverage procedures. Once a year, the FRs are gathered in groups of
about 12 to 15 for 1 or 2 days of refresher training. These
sessions are usually conducted by program supervisors
with the aid of SFRs. These group sessions cover regular
CPS and supplemental survey procedures.

not cover subject matter material. While in training, new
CATI interviewers are monitored for a minimum of 5 percent of their time on the computer, compared with 2.5 percent monitoring time for experienced staff. In addition,
once a CATI interviewer has been assigned to conduct
interviews on the CPS, she/he receives an additional 3 1/2
days of classroom training.
FIELD REPRESENTATIVE PERFORMANCE
GUIDELINES
Performance guidelines have been developed for CPS CAPI
FRs for response/nonresponse and production.
Response/nonresponse rate guidelines have been developed to ensure the quality of the data collected. Production guidelines have been developed to assist in holding
costs within budget and to maintain an acceptable level of
efficiency in the program. Both sets of guidelines are
intended to help supervisors analyze activities of individual FRs and to assist supervisors in identifying FRs who
need to improve their performance.
Each CPS supervisor is responsible for developing each
employee to his/her fullest potential. Employee development requires meaningful feedback on a continuous basis.
By acknowledging strong points and highlighting areas for
improvement, the CPS supervisor can monitor an employee’s progress and take appropriate steps to improve weak
areas.
FR performance is measured by a combination of the following: response rates, production rates, supplement
response, reinterview results, observation results, accurate payrolls submitted on time, deadlines met, reports to
team leaders, training sessions, and administrative
responsibilities.

Training for interviewers at the CATI centers. Candidates selected to be CATI interviewers receive 3 days of
training on using the computer. This initial training does

The most useful tool to help supervisors evaluate the FRs
performance is the CPS 11−39 form. This report is generated monthly and is produced using data from the
Regional Office Survey Control System (ROSCO) and Cost
and Response Management Network (CARMN) systems.

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D–3

This report provides the supervisor information on:
Workload and number of interviews
Response rate and adjective rating
Noninterview results (counts)
Production rate, adjective rating, and mileage
Observation and reinterview results
Meeting transmission goals
Rate of personal visit telephone interviews
Supplement response rate
Refusal/Don’t Know counts and rates
Industry and Occupation (I&O) entries that NPC could
not code.
EVALUATING FIELD REPRESENTATIVE
PERFORMANCE
Census Bureau headquarters, located in Suitland, Maryland, provides guidelines to the ROs for developing performance standards for FRs’ response and production rates.
The ROs have the option of using the guidelines, modifying them, or establishing a completely different set of
standards for their FRs. If ROs establish their own standards, they must notify the FRs of the standards.
Maintaining high response rates is of primary importance
to the Census Bureau. The response rate is defined as the
proportion of all sample households eligible for interview
that is actually interviewed. It is calculated by dividing the
total number of interviewed households by the sum of the
number of interviewed households, the number of refusals, noncontacts, and noninterviewed households for
other reasons, including temporary refusals. (All of these
noninterviews are referred to as Type A noninterviews.)
Type A cases do not include vacant units, those that are
used for nonresidential purposes, or other addresses that
are not eligible for interview.
Production guidelines. The production guidelines used
in the CPS CAPI program are designed to measure the efficiency of individual FRs and the RO field functions. Efficiency is measured by total minutes per case which
include interview time and travel time. The measure is calculated by dividing total time reported on payroll documents by total workload. The standard acceptable
minutes-per-case rate for FRs varies with the characteristics of the PSU. When looking at an FR’s production, a program supervisor must consider extenuating circumstances, such as:
• Unusual weather conditions, such as floods, hurricanes,
or blizzards.
• Extreme distances between sample units, or assignments that cover multiple PSUs.
• Large number of inherited or confirmed refusals.
• Working part of another FR’s assignment.
• Inordinate number of temporarily absent cases.
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Organization and Training of the Data Collection Staff

• High percentage of Type B/C noninterviews that
decrease the base for nonresponse rate.
• Other substantial changes in normal assignment conditions.
• Personal visits/phones.
Supplement response rate. The supplement response
rate is another measure that CPS program supervisors
must use in measuring the performance of their FR staff.
Transmittal rates. The ROSCO system allows the supervisor to monitor transmittal rates of each CPS FR. A daily
receipts report is printed each day showing the progress
of each case on CPS.
Observation of field work. Field observation is one of
the methods used by the supervisor to check and improve
performance of the FR staff. It provides a uniform method
for assessing the FR’s attitude toward the job, use of the
computer, and the extent to which FRs apply CPS concepts
and procedures during actual work situations. There are
three types of observations:
1. Initial observations (N1, N2, N3)
2. General performance review (GPR)
3. Special needs (SN)
Initial observations are an extension of the initial classroom training for new hires and provide on-the-job training for FRs new to the survey. They also allow the survey
supervisor to assess the extent to which a new CPS-CAPI
FR grasps the concepts covered in initial training, and are
an integral part of the initial training given to all FRs. A
2-day initial observation (N1) is scheduled during the FR’s
first CPS CAPI assignment. A second 1-day initial observation (N2) is scheduled during the FR’s second CPS CAPI
assignment. A third 1-day initial observation (N3) is scheduled during the FR’s fourth through sixth CPS CAPI assignment.
General performance review observations are conducted
at least annually and allow the supervisor to provide continuing developmental feedback to all FRs. Each FR is regularly observed at least once a year.
Special-needs observations are made when an FR has
problems or poor performance. The need for a specialneeds observation is usually detected by other checks on
the FR’s work. For example, special-needs observations
are conducted if an FR has a high Type A noninterview
rate, a high minutes-per-case rate, a failure on reinterview,
an unsatisfactory evaluation on a previous observation,
made a request for help, or for other reasons related to
the FR’s performance.
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An observer accompanies the FR for a minimum of 6
hours during an actual work assignment. The observer
notes the FR’s performance, including how the interview is
conducted and how the computer is used. The observer
stresses good interviewing techniques: asking questions
as worded and in the order presented on the CAPI screen,
adhering to instructions on the instrument and in the
manuals, knowing how to probe, recording answers correctly and in adequate detail, developing and maintaining
good rapport with the respondent conducive to an
exchange of information, avoiding questions or probes
that suggest a desired answer to the respondent, and
determining the most appropriate time and place for the
interview. The observer also stresses vehicular and personal safety practices.

observer to clarify survey procedures not fully understood
and to seek the observer’s advice on solving other problems encountered.

The observer reviews the FR’s performance and discusses
the FR’s strong and weak points, with an emphasis on correcting habits that interfere with the collection of reliable
statistics. In addition, the FR is encouraged to ask the

Unsatisfactory performance. When the performance of
an FR is at the unsatisfactory level over any period (usually 90 days), he/she may be placed in a trial period for 30
to 90 days. Depending on the circumstances, and with
guidance from the Human Resources Division, the FR will
be issued a letter stating that he/she is being placed in a
Performance Opportunity Period (POP) or a Performance
Improvement Period (PIP). These administrative actions
warn the FR that his/her work is substandard, provide
specific suggestions on ways to improve performance,
alert the FR to actions that will be taken by the survey
supervisor to assist the FR to improve his/her performance, and notify the FR that he/she is subject to separation if the work does not show improvement in the allotted time. Once placed on a PIP, the regional office provides
performance feedback during a specified time period (usually 30 days, 60 days, and 90 days).

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D–5

Appendix E.
Reinterview: Design and Methodology
INTRODUCTION
A continuing program of reinterviews on subsamples of
Current Population Survey (CPS) households is carried out
every month. The reinterview is a second, independent
interview of a household conducted by a different interviewer. The CPS reinterview program has been in place
since 1954. Reinterviewing for CPS serves two main quality control (QC) purposes: to monitor the work of the field
representatives (FRs) and to evaluate data quality via the
measurement of response error (RE) where all the labor
force questions are repeated. The reinterview program is a
major tool for limiting the occurrence of nonsampling
error and is a critical part of the CPS program.
Prior to the automation of CPS in January 1994, the reinterview consisted of one sample selected in two stages.
The FRs were primary sampling units and the households
within the FRs’ assignments were secondary sampling
units. In 75 percent of the reinterview sample, differences
between original and reinterview responses were reconciled, and the results were used both to monitor the FRs
and to estimate response bias, that is, the accuracy of the
original survey responses. In the remaining 25 percent of
the samples, the differences were not reconciled and were
used only to estimate simple response variance, that is,
the consistency in response between the original interview
and the reinterview. Because the one-sample approach did
not provide a monthly reinterview sample that was fully
representative of the original survey sample for estimating
response error, the decision was made to separate the RE
and QC reinterview samples beginning with the introduction of the automated system in January 1994.
As a QC tool, reinterviewing is used to deter and detect
falsification. It provides a means of limiting nonsampling
error, as described in Chapter 15. The RE reinterview currently estimates simple response variance, a measure of
reliability.
The measurement of simple response variance in the reinterview assumes an independent replication of the interview. However, this assumption does not always hold,
since the respondent may remember his or her previous
interview response and repeat it in the reinterview (conditioning). Also, the fact that the reinterview is done by telephone for personal visit interviews may violate this
assumption. In terms of the measurement of RE, errors or
variations in response may affect the accuracy and reliability of the results of the survey. Responses from interviews
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and reinterviews are compared, identified and analyzed.
(See Chapter 16 for a more detailed description of
response variance.)
Sample cases for QC and RE reinterviews are selected by
different methods and have somewhat different field procedures. To minimize response burden, a household is
only reinterviewed one time (or not at all) during its life in
the sample. This rule applies to both the RE and QC
samples. Any household contacted for reinterview is ineligible for reinterview selection during its remaining
months in the sample. An analysis of respondent participation in later months of the CPS showed that the reinterview had no significant effect on the respondent’s willingness to respond (Bushery, Dewey, and Weller, 1995).
Sample selection for reinterview is done immediately after
the monthly assignments are certified (see Chapter 4).
RESPONSE ERROR SAMPLE
The regular RE sample is selected first. It is a systematic
random sample across all households eligible for interview
each month. It includes households assigned to both the
telephone centers and the regional offices (ROs). Only
households that can be reached by telephone and for
which a completed or partial interview is obtained are eligible for RE reinterview. This restriction introduces a small
bias into the RE reinterview results because households
without ‘‘good’’ telephone numbers are made ineligible for
the RE reinterview. About 1 percent of CPS households are
assigned for RE reinterview each month.
QUALITY CONTROL SAMPLE
The QC sample is selected next. The QC sample uses the
FRs in the field as its first stage of selection. The QC
sample does not include interviewers at the telephone
centers. It is felt that the monitoring operation at the telephone centers sufficiently serves the QC purpose. The QC
sample uses a 15-month cycle. The FRs are randomly
assigned to 15 different groups. Both the frequency of
selection and the number of households within assignments are based upon the length of tenure of the FRs. A
falsification study determined that a relationship exists
between tenure and both frequency of falsification and the
percentage of assignments falsified (Waite, 1993 and
1997). Experienced FRs (those with at least 5 years of service) were found less likely to falsify. Also, experienced
FRs who did falsify were more circumspect, falsifying
fewer cases within their assignments than inexperienced
FRs (those with under 5 years of service).
Reinterview: Design and Methodology

E–1

Because inexperienced FRs are more likely to falsify, more
of them are selected for reinterview each month: three
groups of inexperienced FRs to two groups of experienced
FRs. Since inexperienced FRs falsify a greater percentage
of cases within their assignments, fewer of their cases are
needed to detect falsification. For inexperienced FRs, five
households are selected for reinterview. For experienced
FRs, eight households are selected. The selection system
is set up so that an FR is in reinterview at least once but
no more than four times within a 15-month cycle.
A sample of households assigned to the telephone centers
is selected for QC reinterview, but these households
become eligible for reinterview only if recycled to the field
and assigned to FRs already selected for reinterview.
Recycled cases are included in reinterview because
recycles are more difficult to interview and may be more
subject to falsification.
All cases, except noninterviews for occupied households,
are eligible for QC reinterview: completed and partial
interviews and all other types of noninterviews (vacant,
demolished, etc.). FRs are evaluated on their rate of noninterviews for occupied households. Therefore, FRs have no
incentive to misclassify a case as a noninterview for an
occupied household.
Approximately 2 percent of CPS households are assigned
for QC reinterview each month.
REINTERVIEW PROCEDURES
QC reinterviews are conducted out of the regional offices
by telephone, if possible, but in some cases by personal
visit. They are conducted on a flow basis extending
through the week following the interview week. They are
conducted mostly by senior FRs and sometimes by program supervisors. For QC reinterviews, the reinterviewers
are instructed to try to reinterview the original household
respondent, but are allowed to conduct the reinterview
with another eligible household respondent.
The QC reinterviews are computer assisted and are a brief
check to verify the original interview outcome. The reinterview asks questions to determine if the FR conducted the
original interview and followed interviewing procedures.
The labor force questions are not asked.1 The QC reinterview instrument also has a special set of outcome codes
to indicate whether or not any noninterview misclassifications occurred or any falsification is suspected.
RE reinterviews are conducted only by telephone from
both the telephone centers and the reional offices (ROs).
Cases interviewed at the telephone centers are reinterviewed from the telephone centers, and cases interviewed
in the field are reinterviewed in the field. At the telephone
1
Prior to October 2001, QC reinterviews asked the full set of
labor force questions for all eligible household members.

E–2

Reinterview: Design and Methodology

centers reinterviews are conducted by the telephone center interviewing staff on a flow basis during interview
week. In the field they are conducted on a flow basis and
by the same staff as the QC reinterviews. The reinterviewers are instructed to try to reinterview the original household respondent, but are allowed to conduct the reinterview with another knowledgeable adult household
member.2 All RE reinterviews are computer assisted and
consist of the full set of labor force questions for all eligible household members. The RE reinterview instrument
dependently verifies household membership and asks the
industry and occupation questions exactly as they were
asked in the original interview.3 Currently, no reconciliation is conducted.4
SUMMARY
Periodic reports on the QC reinterview program are issued
showing the number of FRs determined to have falsified
data. Since FRs are made aware of the QC reinterview program and are also informed of the results following reinterview, falsification is not a major problem in the CPS.
Only about 0.5 percent of CPS FRs are found to falsify
data. These FRs resign or are terminated.
Results from the RE reinterview are also issued on a periodic basis. They contain response variance results for the
basic labor force categories and for certain selected other
questions.
REFERENCES
Bushery, J. M., J. A. Dewey, and G. Weller, (1995), ‘‘Reinterview’s Effect on Survey Response,’’ Proceedings of the
1995 Annual Research Conference, U.S. Census
Bureau, pp. 475−485.
U.S. Census Bureau (1996), Current Population Survey
Office Manual, Form CPS−256, Washington, DC: Government Printing Office, Ch. 6.
U.S. Census Bureau (1996), Current Population Survey
SFR Manual, Form CPS−251, Washington, DC: Government Printing Office, Ch. 4.
U.S. Census Bureau (1993), ‘‘Falsification by Field Representatives 1982−1992,’’ memorandum from Preston Jay
Waite to Paula Schneider, May 10, 1993.
U.S. Census Bureau (1997), ‘‘Falsification Study Results for
1990−1997,’’ memorandum from Preston Jay Waite to
Richard L. Bitzer, May 8, 1997.

2
Beginning in February 1998, the RE reinterview respondent
rule was made the same as the QC reinterview respondent rule.
3
Prior to October 2001, RE reinterviews asked the industry
and occupation questions independently.
4
Reconciled reinterview, which was used to estimate reponse
bias and provide feedback for FR performance, was discontinued
in January 1994.

Current Populuation Survey TP66
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Acronyms
ADS
API
ARMA
ASEC
BLS
BPC
CAPE
CAPI
CATI
CCM
CCO
CES
CESEM
CNP
CPS
CPSEP
CPSRT
CV
DA
DAFO
deff
DoD
DRAF
FNS
FOSDIC
FR
FSC
FSCPE
GQ
GVF
HUD
HVS
I&O
IM
INS
IRCA
LAUS
MARS
MIS
MLR
MOS
MSA

Annual Demographic Supplement
Asian and Pacific Islander
Autoregressing Moving Average
Annual Social and Economic Supplement
Bureau of Labor Statistics
Basic PSU Components
Committee on Adjustment of Postcensal
Estimates
Computer-Assisted Personal Interviewing
Computer-Assisted Telephone Interviewing
Civilian U.S. Citizens Migration
CATI and CAPI overlap
Current Employment Statistics
Current Employment Statistics Survey
Employment
Civilian Noninstitutional Population
Current Population Survey
CPS Employment-to-Population Ratio
CPS Unemployment Rate
Coefficient of Variation
Demographic Analysis
Deaths of the Armed Forces Overseas
Design Effect
Department of Defense
Deaths of the Resident Armed Forces
Food and Nutrition Service
Film Optical Sensing Device for Input to the
Computer
Field Representative
First- and Second-Stage Combined
Federal-State Cooperative Program for
Population Estimates
Group Quarters
Generalized Variance Function
Housing and Urban Development
Housing Vacancy Survey
Industry and Occupation
International Migration
Immigration and Naturalization Service
Immigration Reform and Control Act
Local Area Unemployment Statistics
Modified Age, Race, and Sex
Month-in-Sample
Major Labor Force Recode
Measure of Size
Metropolitan Statistical Area

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NCES
NCEUS
NCHS
NCI
NEA
NHIS
NPC
NRAF
NRO
NSR
NTIA
OB
OCSE
OD
OMB
ORR
PAL
PES
PIP
POP
PSU
PWBA
QC
RDD
RE
RO
SCHIP
SECU
SFR
SMSA
SOC
SR
SS
STARS
SW
TE
UE
UI
URE
USU
WPA

National Center for Education Statistics
National Commission on Employment and
Unemployment Statistics
National Center for Health Statistics
National Cancer Institute
National Endowment for the Arts
National Health Interview Survey
National Processing Center
Net Recruits to the Armed Forces
Net Recruits to the Armed Forces From
Overseas
Non-Self-Representing
National Telecommunications and
Information Administration
Number of Births
Office of Child Support Enforcement
Number of Deaths
Office of Management and Budget
Office of Refugee Resettlement
Permit Address List
Post-Enumeration Survey
Performance Improvement Period
Performance Opportunity Period
Primary Sampling Unit
Pension and Welfare Benefits
Administration
Quality Control
Random Digit Dialing
Response Error
Regional Office
State Children’s Health Insurance Program
Standard Error Computation Unit
Senior Field Representative
Standard Metropolitan Statistical Area
Survey of Construction
Self-Representing
Second-Stage
State Time Series Analysis and Review
System
Start-With
Take-Every
Number of People Unemployed
Unemployment Insurance
Usual Residence Elsewhere
Ultimate Sampling Unit
Work Projects Administration

Acronyms–1

Index
4-8-4 rotation system

2-1−2-2, 3-13−3-15
A

Active duty
components of population change C-2
net recruits to the armed forces from the civilian
population (NRAF) C-6−C-7
population universe for CPS controls C-3
summary list of sources for CPS population controls
C-12
terminology used in the appendix: civilian population
C-1
Address identification 4-1−4-2
Adjusted
census base population C-5
population 65 years of age and over C-11
population controls for states C-10
Age
age definition C-7
births and deaths C-5
census base population C-7
civilian institutional population C-10
death by C-8
distribution of census base and components of change
C-7
exclusion of armed forces population and inmates of
civilian institutions C-11
household population under 65 years of age
C-10−C-11
international migration C-6, C-9
introduction, demographic characteristics C-1
migration of armed forces C-9−C-10
net migration movement C-6, C-9
population 65 years of age and over C-11
population by age, sex, race, and Hispanic origin C-7
population controls C-10
population universe for CPS controls C-3
Alaska addition to population estimates 2-2
American Indian Reservations 3-9
American Time Use Survey (ATUS) 11-4−11-5
Annual Demographic Supplement 11-5−11-10
Annual Social and Economic Supplement (ASEC)
11-5−11-10
Area frame
address identification 4-1−4-2
description 3-8
listing in 4-3−4-5

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Armed Forces/Military Personnel
adjustment 11-8
census base population C-5
civilian institutional population by age, sex, race, and
Hispanic origin C-10
components of population change C-1−C-2
exclusion of armed forces population and inmates of
civilian institutions C-11
members 11-7−11-8, 11-10
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
net recruits to the armed forces from the civilian
population C-6−C-7
population universe for CPS controls C-3
summary list of sources for CPS population controls
C-12
terminology used in the appendix, civilian population
C-1
total population C-4−C-5
B
Base population
census base population C-5
census base population by age, sex, race, and Hispanic
origin: modification of the census race distribution
C-7
definition C-1
measuring the components of the balancing equation
C-5
population estimation C-2
population projection C-2
total population C-4−C-5
update of the population by sex, race, and Hispanic
origin C-7
Baseweights (see also basic weights) 10-2
Basic primary sampling unit components (BPCs) 3-9−3-10
Behavior coding 6-3
Between primary sampling unit (PSU) variances 10-3−10-4
Births
births and deaths C-5−C-6
components of population change C-1−C-2
distribution of births by sex, race, and Hispanic origin
C-8
household population under 65 years of age
C-10−C-11
natural increase C-2
summary list of sources for CPS population controls
C-12
Index−1

Births—Con.
total population C-4−C-5
Building permits
offices 3-8−3-9
permit frame 3-9
sampling 3-2
survey 3-7, 3-9, 4-2
Business presence 6-4
C
California substate areas 3-1
CATI recycles 4-6, 8-1−8-2
Census base population
census base population C-5
census base population by age, sex, race, and Hispanic
origin: modification of the census race distribution
C-7
Census Bureau report series 12-2
Census-level population, terminology used in the
appendix C-1
Check-in of sample 15-4
Citizens/Citizenship/Non-Citizen
international migration C-2
permanent residents C-2
Civilian institutional population by age, sex, race, and
Hispanic origin C-10
Civilian noninstitutional population (CNP)
census base population C-5
general 3-1, 3-6−3-7, 3-9−3-10, 5-2−5-3, 10-4,
10-7−10-8
population universe C-2−C-3
total population C-4
Civilian noninstitutional housing 3-7
Civilian population
change in the institutional population C-7
civilian institutional population by age, sex, race, and
Hispanic origin C-10
migration of armed forces C-9
net recruits to the armed forces from the civilian
population C-6−C-7
terminology used in the appendix: civilian population
C-1
terminology used in the appendix: universe C-2
total population C-4
Class-of-worker classification 5-4
Cluster design, changes in 2-3
Clustering, geographic 4-2−4-3
Coding data 9-1
Coefficients of variation (CV), calculating 3-1
Collapsing cells 10-3−10-6, 10-8
Complete nonresponse 10-2
Components of population change
definition C-1−C-2
household population under 65 years of age C-10
measuring the components of the balancing equation
C-5
Index−2

Components of population change—Con.
population projection C-2
procedural revisions C-11
total population C-4−C-5
Composite estimation
estimators 10-9−10-11
introduction of 2-6
Computer-Assisted Interviewing System 2-5, 4-5
Computer-Assisted Personal Interviewing (CAPI)
daily processing 9-1
description of 4-1, 4-5, 6-2
effects on labor force characteristics 16-7
implementation of 2-5
nonsampling errors 16-3−16-4, 16-7
Computer-Assisted Telephone Interviewing (CATI)
daily processing 9-1
description of 4-5, 6-2, 7-6−7-9
effects on labor force characteristics 16-7
implementation of 2-4−2-5
nonsampling errors 16-3−16-4, 16-7
training of staff D-1−D-3
transmission of 8-1−8-3
Computer-Assisted Telephone Interviewing and ComputerAssisted Personal Interviewing (CATI/CAPI) Overlap 2-5
Computerization
electronic calculation system 2-2
questionnaire development 2-5
Consumer income report 12-2
Control card information 5-1
Control universe
introduction C-1−C-2
population controls for states C-10
population universe for CPS controls C-3−C-4
total population C-4
Core-Based Statistical Area (CBSA) 3-2
Counties
combining into primary sampling units 3-2−3-3
selection of 3-2
Coverage errors
controlling 15-2−15-3
coverage ratios 16-1−16-2
sources of 15-1−15-2
CPS control universe
births and deaths C-5−C-6
calculation of population projections for the CPS
universe C-4
components of population change C-1−C-2
CPS control universe C-2
distribution of census base and components of change
by age, sex, race and Hispanic origin C-7
measuring the components of the balancing equation
C-5
net recruits to the armed forces from the civilian
population C-6−C-7
organization of this appendix C-1
population universe for CPS controls C-3
Current Population Survey TP66
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CPS control universe—Con.
universe C-2
CPS reports 12-2−12-4
Current Employment Statistics 1-1
Current Population Survey (CPS)
data products from 12-1−12-4
decennial and survey boundaries D-2
design overview 3-1−3-2
history of revisions 2-1−2-5
overview 1-1
participation eligibility 1-1
reference person 1-1, 2-5
requirements 3-1
sample design 3-2−3-15
sample rotation 3-13−3-15
supplemental inquiries 11-1−11-8
D
Data collection staff, organization and training D-1−D-5
Data preparation 9-1−9-3
Data quality 13-1−13-3
Deaths
births and deaths C-5−C-6
components of population change C-1−C-2
deaths by age, sex, race, and Hispanic origin C-8
household population under 65 years of age
C-10−C-11
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
natural increase C-2
net international movement C-6
net migration of the foreign-born component C-9
net recruits to the armed forces from the civilian
population C-6−C-7
population 65 years of age and over C-11
summary list of sources for CPS population controls
C-12
total population C-4−C-5
Demographic components of population change C1−C2
introduction C-1
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
organization of this appendix C-1
population controls for states C-10
population universe C-2
summary list of sources for CPS population controls
C-12
Demographic data
edits 7-8
edits and codes 9-2−9-3
for children 2-4
interview information 7-3, 7-7, 7-9
procedures for determining characteristics 2-4
questionnaire information 5-1−5-2

Current Population Survey TP66
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Department of Defense
civilian institutional population by age, sex, race, and
Hispanic origin C-10
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
net recruits to the armed forces from the civilian
population C-6−C-7
summary list of sources for CPS population controls
C-12
Dependent interviewing 7-5−7-6
Design effects 14-7
Disabled persons, questionnaire information 6-7
Discouraged Workers
definition 5-5, 6-1−6-2
questionnaire information 6-7−6-8
Document sensing procedures 2-1
Dormitory, population universe for CPS controls C-3−C-4
Dwelling places 2-1
E
Earnings
edits and codes 9-3
information 5-4, 6-5
weights 10-13−10-14
Edits 9-1−9-3, 15-8−15-9
Educational attainment categories 5-2
Electronic calculation system 2-2
Emigration
combined net international migration component C-9
definition 5-3, C-2
household population under 65 years of age C-9−C-10
international migration by age, sex, race, and Hispanic
origin C-9
net international movement C-6
net native emigration component C-9
Employment and Earnings (E & E) 12-1, 12-3, 14-5
Employment status (see also Unemployment)
full-time workers 5-3
layoffs 2-2, 5-5, 6-5−6-7
monthly estimates 10-15
not in the labor force 5-2, 5-5−5-6
occupational classification additions 2-3
part-time workers 2-2, 5-3
seasonal adjustment 2-2, 10-15−10-16
Employment-population ratio definition 5-5
Estimates, population
after the second-stage ratio adjustment 14-5−14-7
census base population C-5
CPS population controls: estimates or projections
C-2−C-3
deaths by age, sex, race, and Hispanic origin C-8
definition C-2
distribution of births by sex, race, and Hispanic origin
C-8
introduction C-1
organization of this appendix C-1
Index−3

Estimates, population—Con.
population for states C-10
population universe for CPS controls C-3−C-4
total population C-4−C-5
Estimators 10-1−10-2, 10-10−10-11, 13-1
Expected values 13-1
F
Families, definition 5-1−5-2
Family businesses, presence of 6-4
Family equalization 11-10
Family weights 10-13
Federal State Cooperative Program for Population
Estimates (FSCPE), household population under 65 years
of age C-10−C-11
Field primary sampling units (PSUs) 3-11, 3-13
Field representatives (FRs) (see also Interviewers)
conducting interviews 7-1, 7-3−7-6, 7-10
controlling response error 15-7
evaluating performance D-1− D-5
interaction with respondents 15-7−15-8
performance guidelines D-3−D-4
training procedures D-1−D-3
transmitting interview results 8-1−8-3
Field subsampling 3-13
Film Optical Sensing Device for Input to the Computer
(FOSDIC) 2-2
Final hit numbers 3-11−3-15
Final weight 10-12−10-13
First-stage ratio adjustment
Annual Social and Economic Supplement (ASEC) 11-6
Housing Vacancy Survey (HVS) 11-4
First-stage weights 10-4
Full-time workers, definition 5-3
G
Generalized variance functions (GVF) 14-3−14-5, B-2
Geographic clustering 4-2−4-3
Geographic Profile of Employment and Unemployment
12-1, 12-3
Group Quarters (GQ) frame
address identification 4-2, 4-4
change in the institutional population C-7
description 3-8−3-9
general 4-1−4-4
group quarters (GQ) listing 4-4
household population under 65 years of age
C-10−C-11
listing in 4-5
population universe for CPS controls C-3
summary list of sources for CPS population controls
C-12
H
Hadamard matrix 14-1−14-3
Hawaii addition to population estimates
Index−4

2-2

Hispanic origin
births and deaths C-5−C-6
calculation of population projections for the CPS
universe C-3−C-4
census base population by age, sex, race, and Hispanic
origin: modification of the census race distribution
C-7−C-8
deaths by age, sex, race, and Hispanic origin C-8
distribution of births by sex, race, and Hispanic origin
C-8
international migration by age, sex, race, and Hispanic
origin C-9
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
mover and nonmover households 11-5−11-10
net international movement C-6
net migration from Puerto Rico component C-9
net migration of the foreign-born component C-9
net native emigration component C-9
population by age, sex, race, and Hispanic origin C-7
population controls for states C-10
population universe for CPS controls C-4
summary list of sources for CPS population controls
C-12
update of the population by sex, race, and Hispanic
origin C-7
Hit strings 3-10−3-11
Home Ownership Rates 11-2
Homeowner Vacancy Rates 11-2
Horvitz-Thompson estimators 10-10
Hot deck
allocations 9-2−9-3
imputation 11-6
Hours of work
definition 5-3
questionnaire information 6-4
Households
definition 5-1
distribution of births by sex, race, and Hispanic origin
C-8
edits and codes 9-2
eligibility 7-1, 7-3−7-4
household population under 65 years of age
C-10−C-11
mover and nonmover households size factor
11-5−11-10
population controls for states C-10
questionnaire information 5-1−5-2
respondents 5-1
rosters 5-1, 7-3, 7-7
summary list of sources for CPS population controls
C-12
weights 10-13
Housing units (HUs)
definition 3-7
frame omission 15-2
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Housing units (HUs)—Con.
measures 3-8
misclassification of 15-2
questionnaire information 5-1
selection of sample units 3-6−3-7
ultimate sampling units (USUs) 3-2
vacancy rates 11-2−11-4
Housing Vacancy Survey (HVS) 11-2−11-4, 11-10
I
Immigration
household population under 65 years of age
C-10−C-11
permanent residents C-2
population 65 years of age and over C-11
Imputation techniques 9-2−9-3, 15-8−15-9
Incomplete Address Locator Action Forms A-1
Industry and occupation data (I & O)
coding of 2-3−2-4, 8-2−8-3
data coding of 15-8
data questionnaire information 6-5
edits and codes 9-2−9-3
questionnaire information 5-3−5-4
verification 15-8
Inflation-deflation method 2-3−2-4
Initial second-stage adjustment factors 10-8
Institutional housing 3-7, 3-9
Institutional population
census base population C-5
change in the institutional population C-6
civilian institutional population by age, sex, race, and
Hispanic origin C-10
control universe C-2
definition C-2
population universe for CPS controls C-3−C-4
total population C-4
Internal consistency of a population
definition C-2
total population C-4−C-5
International migration
combined net international migration component C-9
definition C-2
international migration by age, sex, race, and Hispanic
origin C-9
net international movement C-6
total population C-4−C-5
Internet
CPS reports 12-1−12-2
questionnaire revision 6-3
Interview week 7-1
Interviewers (see also Field representatives)
assignments 4-1, 4-5−4-6
controlling response error 15-7
debriefings 6-3
evaluating performance D-1−D-5
interaction with respondents 15-7−15-8
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Interviewers (see also Field representatives)—Con.
performance guidelines D-3−D-4
Spanish-speaking 7-6
training procedures D-1−D-3
Interviews
beginning questions 7-5, 7-7
controlling response error 15-6
dependent interviewing 7-5−7-6
ending questions 7-5−7-7
household eligibility 7-1, 7-3
initial 7-3
noninterviews 7-1, 7-3−7-4
reinterviews 15-8, E-1−E-2
results 7-10
revision of 6-1−6-8
telephone interviews 7-4−7-10
transmitting results 8-1−8-3
Introductory letters 7-1−7-2
Item nonresponse 9-1−9-2, 10-2, 16-5
Item response analysis 6-3
Iterative proportional fitting 10-7
J
Job leavers 5-5
Job losers 5-5
Job seekers
composite estimators 10-10−10-11
definition 5-4, 5-5
duration of search 6-6−6-7
edits and codes 9-3
effect of Type A noninterviews on classification
16-4−16-5
family weights 10-13
first-stage ratio adjustment 10-3−10-4
gross flow statistics 10-14−10-15
household weights 10-13
interview information 7-3
longitudinal weights 10-14−10-15
methods of search 6-6
monthly estimates 10-13, 10-15
outgoing rotation weights 10-13−10-14
questionnaire information 5-2−5-6, 6-2
ratio estimation 10-3
seasonal adjustment 10-15−10-16
second-stage ratio adjustment 10-7−10-10
unbiased estimation procedure 10-1−10-2
L
Labor force participation rate, definition 5-5
LABSTAT 12-1,12-4
Layoffs 2-2, 5-5, 6-5−6-7
Legal permanent residents, definition C-2
Levitan Commission 2-4
Levitan Commission (see also National Commission on
Employment and Unemployment Statistics) 2-4, 6-1, 6-7
Listing activities 4-3
Index−5

Listing checks 15-4
Listing Sheets
reviews 15-3−15-4
Unit/Permit A-1−A-2
Longitudinal edits 9-2
Longitudinal weights 10-14−10-15
M
Maintenance reductions B-1−B-2
Major Labor Force Recode 9-3
March Supplement (see also Annual Social and Economic
Supplement or Annual Demographic Supplement)
11-5−11-10
Marital status categories 5-2
Maximum overlap procedure 3-4
Mean squared error 13-1−13-2
Measure of size (MOS) 3-8
Metropolitan Statistical Areas (MSAs) 3-2, 3-4
Microdata files 12-1−12-2, 12-4
Military housing/barracks 3-7, 3-9
Modeling errors 15-9
Modified age, race, and sex
census base population by age, sex, race, and Hispanic
origin modification of the census race distribution
C-7−C-8
civilian institutional population by age, sex, race, and
Hispanic origin C-10
deaths by age, sex, race, and Hispanic origin C-8
definition C-2
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
Month-in-sample (MIS) 7-1, 7-4−7-6, 11-5−11-10,
16-7−16-9
Monthly Labor Review 12-1, 12-3
Mover households 11-5−11-10
Multiple jobholders
definition 5-3
questionnaire information 6-2, 6-4
Multiunit structures
Multiunit Listing Aids A-1
Unit/Permit Listing Sheets A-1−A-2
Multiunits 4-3, 4-5
N
National Center for Health Statistics (NCHS)
births and deaths C-5−C-6
deaths by age, sex, race, and Hispanic origin C-8
distribution of births by sex, race, and Hispanic origin
C-8
household population under 65 years of age C-10
introduction C-1
National Commission on Employment and Unemployment
Statistics (see also Levitan Commission) 6-1, 6-7
National Park blocks 3-9
National Processing Center (NPC) 8-1−8-3, D-1

Index−6

Net recruits to the armed forces
migration of armed forces by age, sex, race, and
Hispanic origin C-9−C-10
net recruits to the armed forces from the civilian
population C-6−C-7
total population C-4−C-5
New entrants 5-5
New York substate areas 3-1
Noninstitutional housing 3-7−3-8
Noninterview
adjustments 10-3
American Time Use Survey (ATUS) 11-5
Annual Social and Economic Supplement (ASEC)
11-6−11-8
clusters 10-3
factors 10-3
Noninterviews 7-1, 7-3−7-4, 9-1, 16-2−16-5, 16-8
Nonmover households 11-5−11-8, 11-10
Nonresponse 9-1−9-2, 10-2, 15-4−15-5
Nonresponse errors
item nonresponse 16-5
mode of interview 16-7
proxy reporting 16-9
response variance 16-5−16-6
time in sample 16-7−16-9
type A noninterviews 16-2−16-5
Nonresponse rates 11-2
Nonsampling errors
controlling response error 15-6−15-8
coverage errors 15-2−15-4, 16-1
definitions 13-2−13-3
miscellaneous errors 15-8−15-9
nonresponse errors 15-4−15-5, 16-3−16-5
overview 15-1
response errors 15-6
Non-self-representing primary sampling units (NSR PSUs)
3-3−3-5, 10-4, 14-1−14-3
North American Industry Classification System 2-6
O
Occupational data
coding of 2-4 , 8-2−8-3, 9-1
edits and codes 9-3
questionnaire information 5-3−5-4, 6-5
Office of Management and Budget (OMB) 2-2, 2-7
Old construction frames 3-7−3-12
Outgoing rotation weights 10-13−10-14
P
Part-time workers
definition 5-3
economic reasons 5-3
monthly questions 2-2
noneconomic reasons 5-3
Performance Improvement Period (PIP) D-5
Performance Opportunity Period (POP) D-5
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Permit Address List (PAL) 4-2, 4-4−4-5
forms 4-6
operation 4-2, 4-4−4-5
Permit frames materials A-1−A-2
address identification 4-1−4-2
description of 3-9−3-10
listing in 4-6
Permit Sketch Map A-2
segment folder A-1
Unit/Permit Listing Sheets A-1−A-2
Permit Sketch Maps A-1
Persons on layoff, definition 5-5, 6-5−6-7
Population base
CPS control universe C-4
definition C-1
measuring the components of the balancing equation
C-5
Population Characteristics report 12-2
Population Control adjustments
American Time Use Survey (ATUS) 11-5
CPS population controls: estimates or projections
C-2−C-3
measuring the components of the balancing equation
C-5
nonsampling error 15-9
organization of this appendix C-1
population by age, sex, race, and Hispanic origin C-7
population controls for states C-10
population universe for CPS controls C-3
sources 10-8
summary list of sources for CPS population controls
C-12
total population C-4−C-5
Population data revisions 2-3
Population universe
births and deaths C-5−C-6
definition C-2
institutional population C-2
measuring the components of the balancing equation
C-5
population universe for CPS controls C-3−C-4
procedural revisions C-11
total population C-4−C-5
Post-sampling codes 3-9−3-10
President’s Committee to Appraise Employment and
Unemployment Statistics 2-3
Primary Sampling Units (PSUs)
basic components 3-10
definition 3-2
field 3-4
increase in number of 2-3
maximum overlap procedure 3-4
noninterview clusters 10-3
non-self-representing (NSR) 3-3−3-5, 10-4, 14-1−14-3
rules for defining 3-2−3-3
selection of 3-4−3-6
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Primary Sampling Units (PSUs)—Con.
self-representing (SR) 3-3−3-5, 10-4, 14-1−14-3
stratification of 3-3−3-4
Probability samples 10-1−10-2
Projections, population
births and deaths C-5−C-6
calculation of population projections for the CPS
universe C-4−C-5
CPS population controls: estimates or projections
C-2−C-3
definition C-2
measuring the components of the balancing equation
C-5
organization of this appendix C-1
procedural revisions C-11
total population C-4−C-5
Proxy reporting 16-9
Publications 12-1−12-4
Q
Quality control (QC)
reinterview program 15-8, E-1−E-2
Quality measures of statistical process
Questionnaires (see also interviews)
controlling response error 15-6
demographic information 5-1−5-2
household information 5-1−5-2
labor force information 5-2−5-6
reinterviews 15-8
revision of 2-1−2-3, 2-5, 6-1−6-8
structure of 5-1

13-1−13-3

R
Race
category modifications 2-4, 2-6
CPS population controls: estimates or projections
C-2−C-3
determination of 2-4
distribution of births by sex, race, and Hispanic origin
C-8
introduction C-1
modified race C-2
net international movement C-6
population by age, sex, race, and Hispanic origin C-7
population universe for CPS controls C-3−C-4
racial categories 5-2
total population C-4−C-5
update of the population by sex, race, and Hispanic
origin C-7
Raking ratio 10-7, 10-9
Random digit dialing sample (CATI/RDD) 6-2
Random groups 3-11
Random starts 3-10
Ratio adjustments
first-stage 10-3−10-4
general 10-3
Index−7

Ratio adjustments—Con.
second-stage 10-7−10-10
Ratio estimates
revisions to 2-1−2-4
two-level, first-stage procedure 2-4
Recycle reports 8-1−8-2
Reduction groups B-1−B-2
Reduction plans B-1−B-2
Reentrants 5-5
Reference persons 1-1, 2-5, 5-1−5-2
Reference week 5-2−5-3, 6-3−6-4, 7-1
Referral codes 15-8
Refusal rates 16-2−16-3
Regional offices (ROs)
organization and training of data collection staff
D-1−D-5
operations of 4-1, 4-6
transmitting interview results 8-1−8-3
Reinterviews 15-8, E-1−E-2
Relational imputation 9-2
Relationship information 5-1−5-2
Relative variances 14-4−14-5
Rental vacancy rates 11-2
Replicates, definition 14-1
Replication methods 14-1−14-2
Report series 12-2
Residence cells 10-3
Respondents
controlling response error 15-6−15-8
debriefings 6-3
interaction with interviewers 15-7−15-8
Response distribution analysis 6-3
Response error (RE) 6-1, 15-6−15-8, E-1
Response variance 13-1−13-2, 16-5−16-6
Retired persons, questionnaire information 6-7
Rotation chart 3-11
Rotation groups 3-11, 10-1,10-11, 10-13−10-14
Rotation system 2-1, 3-2, 3-13−3-14
S
Sample characteristics 3-1
Sample data revisions 2-3
Sample design
controlling coverage error 15-2−15-3
county selection 3-2−3-3
definition of the primary sampling units (PSUs)
field subsampling 3-13, 4-4−4-5
old construction frames 3-7−3-9
permit frame 3-9
permit frame materials A-1−A-2
post-sampling code assignment 3-11−3-13
sample housing unit selection 3-6−3-13
sampling frames 3-7−3-10
sampling procedure 3-10−3-11
sampling sources 3-7

Index−8

3-2

Sample design—Con.
selection of the sample primary sampling units (PSUs)
3-4−3-6
stratification of the PSUs 3-3−3-5
unit frame materials A-1
variance estimates 14-5−14-6
within primary sampling unit (PSU) sort 3-10
Sample preparation
address identification 4-1−4-2
components of 4-1
controlling coverage error 15-3−15-4
goals of 4-1
interviewer assignments 4-5
listing activities 4-3
Sample redesign 2-5
Sample size
determination of 3-1
maintenance reductions B-1−B-2
reduction in 2-3−2-5, B-1−B-2
Sample Survey of Unemployment 2-1
Sampling
bias 13-2
error 13-1−13-2
frames 3-7−3-10
intervals 3-6, 3-10
variance 13-1−13-2
School enrollment
edits and codes 9-3
general 2-4
Seasonal adjustment of employment 2-2, 2-7,
10-15−10-16
Second jobs 5-4
Second-stage ratio adjustment
armed forces 11-10
Annual Social and Economic Supplement (ASEC),
general 11-8
Housing Vacancy Survey (HVS) 11-14
Security
transmitting interview results 8-1
Segment folders
permit frame A-1
unit frame A-1
Segment number suffixes 3-11
Selection method revision 2-1
Self-employed definition 5-4
Self-representing primary sampling units (SR PSUs)
3-3−3-5, 10-4 , 14-1−14-3
Self-weighting samples 10-2
Senior field representatives (SFRs) D-1
Sex
calculation of population projections for the CPS
universe C-4
census base population by age, sex, race, and Hispanic
origin: modification of the census race distribution
C-7−C-8

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

Sex—Con.
CPS population controls: estimates or projections
C-2−C-3
deaths by age, sex, race, and Hispanic origin C-8
distribution of births by sex, race, and Hispanic origin
C-8
international migration by age, sex, race, and Hispanic
origin C-9
introduction C-1
net international movement C-6
population by age, sex, race, and Hispanic origin C-7
population controls for states C-10
population universe for CPS controls C-3−C-4
update of the population by sex, race, and Hispanic
origin C-7
Simple response variance 16-5−16-6
Simple weighted estimators 10-10, 10-12−10-13
Skeleton sampling 4-2
Sketch maps A-2
Spanish-speaking interviewers 7-6
Special studies report 12-2
Standard Error Computation Units (SECUs) 14-1−14-2
Standard Metropolitan Statistical Areas (SMSAs) 2-3
Standard Occupational Classification 2-6
Standards of Quality 11-1
Start-with procedure (SW) 3-13, 4-2
State Children’s Health Insurance Program (SCHIP)
adjustment 11-7−11-10
general 2-6, 3-1, 3-4, 3-6, 3-11, 11-7−11-10
State sampling intervals 3-6, 3-10
State supplementary samples 2-4
State-based design 2-4
States
coverage adjustment 10-6
exclusion of the armed forces population and inmates of
civilian institutions C-11
household population under 65 years of age
C-10−C-11
population controls for states C-10
two-stage ratio estimators 10-10
Statistical Policy Division 2-2
Statistics
quality measures 13-1−13-3
Stratification Search Program (SSP) 3-4
Subfamilies
definition 5-2
Subsampling 3-4, 4-4−4-5
Successive difference replication 14-2−14-4
Supplemental inquiries
American Time Use Survey (ATUS) 11-4−11-5
Annual Social and Economic Supplment (ASEC)
11-5−11-10
criteria for 11-1−11-2
Survey of Construction (SOC) 4-2
Survey week 2-2
Systematic sampling 4-2
Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

T
Take-every procedure (TE) 3-13
Telephone interviews 7-4−7-10, 16-7
Temporary jobs 5-5
The Employment Situation 12-1, 12-3
Type A noninterviews (see also Unit nonresponse)
7-3−7-4, 10-2−10-3, 16-2−16-5
Type B noninterviews 7-1, 7-3−7-4
Type C noninterviews 7-1, 7-3−7-4

7-1,

U
U.S. Bureau of Labor Statistics (BLS) 1-1, 12-1−12-2, 12-4
U.S. Census Bureau 1-1, 4-2, 12-1−12-2
Ultimate sampling units (USUs)
area frames 3-8, 3-10
description 3-2
group quarters frame 3-8−3-10
hit strings 3-11
permit frame 3-9−3-10
reduction groups B-1−B-2
selection of 3-10−3-11
special weighting adjustments 10-2
unit frame 3-8, 3-10
variance estimates 14-1−14-4
Unable to work persons, questionnaire information 6-7
Unbiased estimation procedure 10-1−10-2
Unbiased estimators 13-1−13-2
Unemployment
classification of 2-3
definition 5-5
duration of 5-5
early estimates of 2-1
household relationships and 2-3
monthly estimates 10-10−10-11
questionnaire information 6-5−6-7
reason for 5-5
seasonal adjustment 2-2, 10-15−10-16
variance on estimate of 3-6−3-8, 3-13
Union membership 2-4
Unit frame
address identification 4-1
description of 3-8
Incomplete Address Locator Action Forms A-1
listing in 4-4−4-5
materials A-1
Multiunit Listing Aid A-1
segment folders A-1
Unit/Permit Listing Sheets A-1
Unit nonresponse (see also Type A noninterviews) 10-2,
16-2−16-5
Unit/Permit 4-3−4-5
Unit/Permit Listing Sheets
general 4-5
permit frame A-1−A-2
unit frame A-1
Usual residence elsewhere (URE) 7-1
Index−9

V
Vacancy rates 11-2, 11-4
Variance estimation
definitions 13-1−13-2
design effects 14-7−14-9
determining optimum survey design 14-5−14-6
generalizing 14-3−14-5
objectives of 14-1
replication methods 14-1−14-2
state and local area estimates 14-3
successive difference replication 14-2−14-3
total variances as affected by estimation 14-6−14-7
Veterans, data for females 2-4
Veterans’ weights 10-14

W
Web sites
CPS reports 12-2
employment statistics 12-1
questionnaire revision 6-3
Weighting Procedure
American Time Use Survey (ATUS) 11-5
Annual Social and Economic Supplement (SEC)
11-6−11-10
Housing Vacancy Survey (HVS) 11-4
Weighting samples 15-8−15-9
Work Projects Administration (WPA) 2-1
X
X-11 and X-12 ARIMA program

Index−10

2-6, 10-15−10-16

Current Population Survey TP66
U.S. Bureau of Labor Statistics and U.S. Census Bureau

File Type	application/pdf
File Title	Design and Methodology
Subject	Current Population Survey
Author	U.S. Census Bureau and U.S. Bureau of Labor Statistics
File Modified	2011-01-26
File Created	2006-05-01