DW Source and Accuracy Statement 2012

ATTACHMENT 16
Source of the Data and Accuracy of the Estimates for the
January 2012 CPS Microdata File on Displaced Workers, Employee Tenure, and
Occupational Mobility

SOURCE OF THE DATA
The data in this microdata file are from the January 2012 Current Population Survey (CPS). The
U.S. Census Bureau conducts the CPS every month, although this file has only January data.
The January survey uses two sets of questions, the basic CPS and a set of supplemental
questions. The CPS, sponsored jointly by the Census Bureau and the U.S. Bureau of Labor
Statistics, is the country’s primary source of labor force statistics for the entire population. The
Census Bureau and the Bureau of Labor Statistics also jointly sponsor the supplemental
questions for January 2012.
Basic CPS. The monthly CPS collects primarily labor force data about the civilian
noninstitutionalized population living in the United States. The institutionalized population,
which is excluded from the population universe, is composed primarily of the population in
correctional institutions and nursing homes (91 percent of the 4.1 million institutionalized people
in Census 2000). Interviewers ask questions concerning labor force participation about each
member 15 years old and over in sample households. Typically, the week containing the
nineteenth of the month is the interview week. The week containing the twelfth is the reference
week (i.e., the week about which the labor force questions are asked).
The CPS uses a multistage probability sample based on the results of the decennial census, with
coverage in all 50 states and the District of Columbia. The sample is continually updated to
account for new residential construction. When files from the most recent decennial census
become available, the Census Bureau gradually introduces a new sample design for the CPS.
In April 2004, the Census Bureau began phasing out the 1990 sample1 and replacing it with the
2000 sample, creating a mixed sampling frame. Two simultaneous changes occurred during this
phase-in period. First, primary sampling units (PSUs)2 selected for only the 2000 design
gradually replaced those selected for the 1990 design. This involved 10 percent of the sample.
Second, within PSUs selected for both the 1990 and 2000 designs, sample households from the
2000 design gradually replaced sample households from the 1990 design. This involved about
90 percent of the sample. The new sample design was completely implemented by July 2005.
In the first stage of the sampling process, PSUs are selected for sample. The United States is
divided into 2,025 PSUs. The PSUs were redefined for this design to correspond to the Office of
Management and Budget definitions of Core-Based Statistical Area definitions and to improve
1

For detailed information on the 1990 sample redesign, please see reference [1].

2

The PSUs correspond to substate areas (i.e., counties or groups of counties) that are geographically contiguous.

16-1

efficiency in field operations. These PSUs are grouped into 824 strata. Within each stratum, a
single PSU is chosen for the sample, with its probability of selection proportional to its
population as of the most recent decennial census. This PSU represents the entire stratum from
which it was selected. In the case of strata consisting of only one PSU, the PSU is chosen with
certainty.
Approximately 72,000 housing units were selected for sample from the sampling frame in
January 2012. Based on eligibility criteria, 11 percent of these housing units were sent directly
to computer-assisted telephone interviewing (CATI). The remaining units were assigned to
interviewers for computer-assisted personal interviewing (CAPI).3 Of all housing units in
sample, about 59,000 were determined to be eligible for interview. Interviewers obtained
interviews at about 54,000 of these units. Noninterviews occur when the occupants are not
found at home after repeated calls or are unavailable for some other reason.
January 2012 Supplement. In January 2012, in addition to the basic CPS questions,
interviewers asked supplementary questions about displacement of workers, employee tenure,
and occupational mobility. Questions concerning displaced workers were asked of all
respondents who were at least 20 years old, and questions concerning job tenure and
occupational mobility were asked of all employed respondent who were at least 15 years old.
Estimation Procedure. This survey’s estimation procedure adjusts weighted sample results to
agree with independently derived population estimates of the civilian noninstitutionalized
population of the United States and each state (including the District of Columbia). These
population estimates, used as controls for the CPS, are prepared monthly to agree with the most
current set of population estimates that are released as part of the Census Bureau’s population
estimates and projections program.
The population controls for the nation are distributed by demographic characteristics in two
ways:
•
•

Age, sex, and race (White alone, Black alone, and all other groups combined).
Age, sex, and Hispanic origin.

The population controls for the states are distributed by race (Black alone and all other race
groups combined), age (0-15, 16-44, and 45 and over), and sex.
The independent estimates by age, sex, race, and Hispanic origin, and for states by selected age
groups and broad race categories, are developed using the basic demographic accounting formula
whereby the population from the latest decennial data is updated using data on the components
of population change (births, deaths, and net international migration) with net internal migration
as an additional component in the state population estimates.

3

For further information on CATI and CAPI and the eligibility criteria, please see reference [2].

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The net international migration component in the population estimates includes a combination of
the following:
•
•
•
•
•

Legal migration to the United States.
Emigration of foreign-born and native people from the United States.
Net movement between the United States and Puerto Rico.
Estimates of temporary migration.
Estimates of net residual foreign-born population, which include unauthorized
migration.

Because the latest available information on these components lags the survey date, it is necessary
to make short-term projections of these components to develop the estimate for the survey date.
ACCURACY OF THE ESTIMATES
A sample survey estimate has two types of error: sampling and nonsampling. The accuracy of an
estimate depends on both types of error. The nature of the sampling error is known given the
survey design; the full extent of the nonsampling error is unknown.
Sampling Error. Since the CPS estimates come from a sample, they may differ from figures
from an enumeration of the entire population using the same questionnaires, instructions, and
enumerators. For a given estimator, the difference between an estimate based on a sample and
the estimate that would result if the sample were to include the entire population is known as
sampling error. Standard errors, as calculated by methods described in “Standard Errors and
Their Use,” are primarily measures of the magnitude of sampling error. However, they may
include some nonsampling error.
Nonsampling Error. 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. There are several sources of nonsampling error that
may occur during the development or execution of the survey. It can occur because of
circumstances created by the interviewer, the respondent, the survey instrument, or the way the
data are collected and processed. For example, errors could occur because:
•

•
•
•
•

The interviewer records the wrong answer, the respondent provides incorrect
information, the respondent estimates the requested information, or an unclear
survey question is misunderstood by the respondent (measurement error).
Some individuals that should have been included in the survey frame were missed
(coverage error).
Responses are not collected from all those in the sample or the respondent is
unwilling to provide information (nonresponse error).
Values are estimated imprecisely for missing data (imputation error).
Forms may be lost, data may be incorrectly keyed, coded, or recoded, etc.
(processing error).
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To minimize these errors, the Census Bureau applies quality control procedures during all stages
of the production process including the design of the survey, the wording of questions, the
review of the work of interviewers and coders, and the statistical review of reports.
Two types of nonsampling error that can be examined to a limited extent are nonresponse and
undercoverage.
Nonresponse. The effect of nonresponse cannot be measured directly, but one indication of its
potential effect is the nonresponse rate. For the January 2012 basic CPS, the household-level
nonresponse rate was 9.57 percent. The person-level nonresponse rate for the displaced workers,
employee tenure, and occupational mobility supplement was an additional 5.0 percent.
Since the basic CPS nonresponse rate is a household-level rate and the displaced workers,
employee tenure, and occupational mobility supplement nonresponse rate is a person-level rate,
we cannot combine these rates to derive an overall nonresponse rate. Nonresponding households
may have fewer persons than interviewed ones, so combining these rates may lead to an
overestimate of the true overall nonresponse rate for persons for the displaced workers, employee
tenure, and occupational mobility supplement.
Coverage. The concept of coverage in the survey sampling process is the extent to which the
total population that could be selected for sample “covers” the survey’s target population.
Missed housing units and missed people within sample households create undercoverage in the
CPS. Overall CPS undercoverage for January 2012 is estimated to be about 13 percent. CPS
coverage varies with age, sex, and race. Generally, coverage is larger for females than for males
and larger for non-Blacks than for Blacks. This differential coverage is a general problem for
most household-based surveys.
The CPS weighting procedure partially corrects for bias from undercoverage, but biases may still
be present when people who are missed by the survey differ from those interviewed in ways
other than age, race, sex, Hispanic origin, and state of residence. How this weighting procedure
affects other variables in the survey is not precisely known. All of these considerations affect
comparisons across different surveys or data sources.
A common measure of survey coverage is the coverage ratio, calculated as the estimated
population before poststratification divided by the independent population control. Table 1
shows January 2012 CPS coverage ratios by age and sex for certain race and Hispanic groups.
The CPS coverage ratios can exhibit some variability from month to month.

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Table 1. CPS Coverage Ratios: January 2012
Total

White only

Black only

Residual race

Hispanic

All
Age
Male Female Male Female Male Female Male Female Male Female
group people
0-15
0.86
0.87
0.86
0.90
0.89
0.80
0.77
0.77
0.76
0.82
0.85
16-19 0.85
0.85
0.85
0.88
0.87
0.78
0.74
0.72
0.87
0.84
0.83
20-24 0.75
0.73
0.77
0.74
0.80
0.68
0.73
0.69
0.69
0.67
0.79
25-34 0.83
0.81
0.85
0.83
0.88
0.73
0.77
0.73
0.76
0.73
0.81
35-44 0.88
0.87
0.90
0.89
0.93
0.75
0.84
0.80
0.78
0.81
0.91
45-54 0.87
0.85
0.88
0.87
0.90
0.73
0.79
0.81
0.82
0.80
0.82
55-64 0.92
0.93
0.91
0.94
0.91
0.88
0.95
0.91
0.87
0.90
0.83
65+
0.92
0.92
0.91
0.92
0.92
0.96
0.90
0.87
0.88
0.78
0.78
15+
0.87
0.86
0.88
0.88
0.89
0.78
0.82
0.79
0.80
0.78
0.83
0+
0.87
0.86
0.87
0.88
0.89
0.78
0.81
0.78
0.79
0.80
0.84
Notes: (1) The Residual race group includes cases indicating a single race other than White or Black,
and cases indicating two or more races.
(2) Hispanics may be any race. For a more detailed discussion on the use of parameters for
race and ethnicity, please see the “Generalized Variance Parameters” section.

Comparability of Data. Data obtained from the CPS and other sources are not entirely
comparable. This results from differences in interviewer training and experience and in differing
survey processes. This is an example of nonsampling variability not reflected in the standard
errors. Therefore, caution should be used when comparing results from different sources.
Data users should be careful when comparing the data from this microdata file, which reflects
Census 2000-based controls, with microdata files from March 1994 through December 2002,
which reflect 1990 census-based controls. Ideally, the same population controls should be used
when comparing any estimates. In reality, the use of the same population controls is not
practical when comparing trend data over a period of 10 to 20 years. Thus, when it is necessary
to combine or compare data based on different controls or different designs, data users should be
aware that changes in weighting controls or weighting procedures can create small differences
between estimates. See the discussion following for information on comparing estimates derived
from different controls or different sample designs.
Microdata files from previous years reflect the latest available census-based controls. Although
the most recent change in population controls had relatively little impact on summary measures
such as averages, medians, and percentage distributions, it did have a significant impact on
levels. For example, use of Census 2000-based controls results in about a 1 percent increase
from the 1990 census-based controls in the civilian noninstitutionalized population and in the
number of families and households. Thus, estimates of levels for data collected in 2003 and later
years will differ from those for earlier years by more than what could be attributed to actual
changes in the population. These differences could be disproportionately greater for certain
population subgroups than for the total population.
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Note that certain microdata files from 2002, namely June, October, November, and the 2002
ASEC, contain both Census 2000-based estimates and 1990 census-based estimates and are
subject to the comparability issues discussed previously. All other microdata files from 2002
reflect the 1990 census-based controls.
Users should also exercise caution because of changes caused by the phase-in of the Census
2000 files (see “Basic CPS”). During this time period, CPS data were collected from sample
designs based on different censuses. Three features of the new CPS design have the potential of
affecting published estimates: (1) the temporary disruption of the rotation pattern from August
2004 through June 2005 for a comparatively small portion of the sample, (2) the change in
sample areas, and (3) the introduction of the new Core-Based Statistical Areas (formerly called
metropolitan areas). Most of the known effect on estimates during and after the sample redesign
will be the result of changing from 1990 to 2000 geographic definitions. Research has shown
that the national-level estimates of the metropolitan and nonmetropolitan populations should not
change appreciably because of the new sample design. However, users should still exercise
caution when comparing metropolitan and nonmetropolitan estimates across years with a design
change, especially at the state level.
Caution should also be used when comparing Hispanic estimates over time. No independent
population control totals for people of Hispanic origin were used before 1985.
A Nonsampling Error Warning. Since the full extent of the nonsampling error is unknown,
one should be particularly careful when interpreting results based on small differences between
estimates. The Census Bureau recommends that data users incorporate information about
nonsampling errors into their analyses, as nonsampling error could impact the conclusions drawn
from the results. Caution should also be used when interpreting results based on a relatively
small number of cases. Summary measures (such as medians and percentage distributions)
probably do not reveal useful information when computed on a subpopulation smaller than
75,000.
For additional information on nonsampling error including the possible impact on CPS
data when known, refer to references [2] and [3].
Standard Errors and Their Use. The sample estimate and its standard error enable one to
construct a confidence interval. A confidence interval is a range about a given estimate that has
a specified probability of containing the average result of all possible samples. For example, if
all possible samples were surveyed under essentially the same general conditions and using the
same sample design, and if an estimate and its standard error were calculated from each sample,
then approximately 90 percent of the intervals from 1.645 standard errors below the estimate to
1.645 standard errors above the estimate would include the average result of all possible samples.
A particular confidence interval may or may not contain the average estimate derived from all
possible samples, but one can say with specified confidence that the interval includes the average
estimate calculated from all possible samples.
16-6

Standard errors may also be used to perform hypothesis testing, a procedure for distinguishing
between population parameters using sample estimates. The most common type of hypothesis is
that the population parameters are different. An example of this would be comparing the
percentage of men who were part-time workers to the percentage of women who were part-time
workers.
Tests may be performed at various levels of significance. A significance level is the probability
of concluding that the characteristics are different when, in fact, they are the same. For example,
to conclude that two characteristics are different at the 0.10 level of significance, the absolute
value of the estimated difference between characteristics must be greater than or equal to 1.645
times the standard error of the difference.
The Census Bureau uses 90-percent confidence intervals and 0.10 levels of significance to
determine statistical validity. Consult standard statistical textbooks for alternative criteria.
Estimating Standard Errors. The Census Bureau uses replication methods to estimate the
standard errors of CPS estimates. These methods primarily measure the magnitude of sampling
error. However, they do measure some effects of nonsampling error as well. They do not
measure systematic biases in the data associated with nonsampling error. Bias is the average
over all possible samples of the differences between the sample estimates and the true value.
Generalized Variance Parameters. While it is possible to compute and present an estimate of
the standard error based on the survey data for each estimate in a report, 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. Additionally, data users have access to CPS microdata files, and it is
impossible to compute in advance the standard error for every estimate one might obtain from
those data sets. Moreover, variance estimates are based on sample data and have variances of
their own. Therefore, some methods of stabilizing these estimates of variance, for example, by
generalizing or averaging over time, may be used to improve their reliability.
Experience has shown that certain groups of estimates have similar relationships between their
variances and expected values. Modeling or generalizing may provide more stable variance
estimates by taking advantage of these similarities. The generalized variance function is a
simple model that expresses the variance as a function of the expected value of the survey
estimate. The parameters of the generalized variance function are estimated using direct
replicate variances. These generalized variance parameters provide a relatively easy method to
obtain approximate standard errors for numerous characteristics. In this source and accuracy
statement, Table 3 provides the generalized variance parameters for labor force estimates and
estimates from the January 2012 supplement.
The basic CPS questionnaire records the race and ethnicity of each respondent. With respect to
race, a respondent can be White, Black, Asian, American Indian and Alaskan Native (AIAN),
Native Hawaiian and Other Pacific Islander (NHOPI), or combinations of two or more of the
preceding. A respondent’s ethnicity can be Hispanic or non-Hispanic, regardless of race.
16-7

The generalized variance parameters to use in computing standard errors are dependent upon the
race/ethnicity group of interest. The following table summarizes the relationship between the
race/ethnicity group of interest and the generalized variance parameters to use in standard error
calculations.
Table 2. Estimation Groups of Interest and Generalized Variance Parameters
Generalized variance parameters to
use in standard error calculations

Race/ethnicity group of interest
Total population

Total or White

White alone, White AOIC, or White non-Hispanic population

Total or White

Black alone, Black AOIC, or Black non-Hispanic population

Black

Asian alone, Asian AOIC, or Asian non-Hispanic population
AIAN alone, AIAN AOIC, or AIAN non-Hispanic population

Asian, AIAN, NHOPI

NHOPI alone, NHOPI AOIC, or NHOPI non-Hispanic
population
Populations from other race groups

Asian, AIAN, NHOPI

Hispanic population

Hispanic

Two or more races – employment/unemployment and
educational attainment characteristics
Two or more races – all other characteristics

Black
Asian, AIAN, NHOPI

Notes: (1) AIAN is American Indian and Alaska Native and NHOPI is Native Hawaiian and Other
Pacific Islander.
(2) AOIC is an abbreviation for alone or in combination. The AOIC population for a race group
of interest includes people reporting only the race group of interest (alone) and people
reporting multiple race categories including the race group of interest (in combination).
(3) Hispanics may be any race.
(4) Two or more races refers to the group of cases self-classified as having two or more races.

Standard Errors of Estimated Numbers. The approximate standard error, sx, of an estimated
number from this microdata file can be obtained by using the formula:
s x  ax 2  bx

(1)

Here x is the size of the estimate and a and b are the parameters in Table 3 associated with the
particular type of characteristic. When calculating standard errors from cross-tabulations
involving different characteristics, use the set of parameters for the characteristic that will give
the largest standard error.

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Illustration 1
Suppose there were 6,818,000 unemployed men (ages 16 and up) in the civilian labor force. Use
the appropriate parameters from Table 3 and Formula (1) to get
Illustration 1
Number of unemployed males in the civilian
labor force (x)
a parameter (a)
b parameter (b)
Standard error
90-percent confidence interval

6,818,000
-0.000032
2,971
137,000
6,593,000 to 7,043,000

The standard error is calculated as:
0.000032 ∗ 6,818,000

2,971 ∗ 6,818,000

137,000

The 90-percent confidence interval is calculated as 6,818,000 ± 1.645 × 137,000.
A conclusion that the average estimate derived from all possible samples lies within a range
computed in this way would be correct for roughly 90 percent of all possible samples.
Standard Errors of Estimated Percentages. The reliability of an estimated percentage,
computed using sample data for both numerator and denominator, depends on both the size of
the percentage and its base. Estimated percentages are relatively more reliable than the
corresponding estimates of the numerators of the percentages, particularly if the percentages are
50 percent or more. When the numerator and denominator of the percentage are in different
categories, use the parameter from Table 3 as indicated by the numerator.
The approximate standard error, sy,p, of an estimated percentage can be obtained by using the
formula:
s y, p 

b
p 100  p 
y

(2)

Here y is the total number of people, families, households, or unrelated individuals in the base or
denominator of the percentage, p is the percentage 100*x/y (0  p 100), and b is the parameter
in Table 3 associated with the characteristic in the numerator of the percentage.

16-9

Illustration 2
Suppose of 13,001,000 displaced workers, 4,221,000, or 26.6 percent, lost their jobs when a
plant or company closed down or moved. Use the appropriate parameter from Table 3 and
Formula (2) to get
Illustration 2
Percentage of displaced workers who lost their
jobs when a plant or company closed down
or moved (p)
Base (y)
b parameter (b)
Standard error
90-percent confidence interval

26.6
13,001,000
3,096
0.68
25.5 to 27.7

The standard error is calculated as
,

,
,

,

∗ 26.6 ∗ 100

26.6

0.68

The 90-percent confidence interval for the percentage of displaced workers who lost their jobs
when a plant or company closed down or moved is from 25.5 to 27.7 percent (i.e., 26.6 ± 1.645 ×
0.68).
Standard Errors of Estimated Differences. The standard error of the difference between two
sample estimates is approximately equal to
s x1  x 2  s x1  s x 2
2

2

(3)

where sx1 and sx2 are the standard errors of the estimates, x1 and x2. The estimates can be
numbers, percentages, ratios, etc. This will result in accurate estimates of the standard error of
the same characteristic in two different areas, or for the difference between separate and
uncorrelated characteristics in the same area. However, if there is a high positive (negative)
correlation between the two characteristics, the formula will overestimate (underestimate) the
true standard error.

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Illustration 3
Suppose that of 7,988,000 employed men between 25-29 years of age, 88,000, or 1.1 percent,
were part-time workers, and of the 7,002,000 employed women between 25-29 years of age,
160,000, or 2.3 percent were part-time workers. Use the appropriate parameters from Table 3
and Formulas (2) and (3) to get
Illustration 3
Female (x2)
Male (x1)
Percentage working
part-time (p)
Base (y)
b parameter (b)
Standard error
90-percent confidence
interval

Difference

1.1

2.3

1.2

7,988,000
2,971
0.20

7,002,000
2,782
0.30

0.36

0.8 to 1.4

1.8 to 2.8

0.6 to 1.8

The standard error of the difference is calculated as
0.20

0.30

0.36

The 90-percent confidence interval around the difference is calculated as 1.2 ± 1.645 × 0.36.
Since the interval does not include zero, we can conclude with 90 percent confidence that the
percentage of part-time women workers between 25-29 years of age is significantly greater from
the percentage of part-time men workers between 25-29 years of age.
Standard Errors of Estimated Medians. The sampling variability of an estimated median
depends on the form of the distribution and the size of the base. One can approximate the
reliability of an estimated median by determining a confidence interval about it. (See “Standard
Errors and Their Use” for a general discussion of confidence intervals.)
Estimate the 68-percent confidence limits of a median based on sample data using the following
procedure:
1.

Determine, using Formula (2), the standard error of the estimate of 50 percent from the
distribution.

2.

Add to and subtract from 50 percent the standard error determined in step 1. These two
numbers are the percentage limits corresponding to the 68-percent confidence interval
about the estimated median.
16-11

3.

Using the distribution of the characteristic, determine upper and lower limits of the
68-percent confidence interval by calculating values corresponding to the two points
established in step 2.
Note: The percentage limits found in step 2 may or may not fall in the same
characteristic distribution interval.
Use the following formula to calculate the upper and lower limits:

Xp 

pN  N L
(U  L)  L
NU  N L

(4)

where
Xp =

estimated upper and lower limits for the confidence interval
(0  p  1). For purposes of calculating the confidence interval, p
takes on the values determined in step 2. Note that Xp estimates
the median when p = 0.50.

N =

for distribution of numbers: the total number of units (people,
households, etc.) for the characteristic in the distribution.

=
p =
L, U =

for distribution of percentages: the value 100.
the values obtained in Step 2.
the lower and upper boundaries, respectively, of the interval
containing Xp.
Note: For continuous data, i.e., income, time, etc., the upper bound
of the interval containing Xp and lower bound of the next interval
are essentially the same and will be treated as such in the
illustration.

NL, NU =

=

4.

for distribution of numbers: the estimated number of units
(people, households, etc.) with values of the characteristic less than
L and U, respectively.
for distribution of percentages: the estimated percentage of units
(people, households, etc.) having values of the characteristic less
than L and U, respectively.

Divide the difference between the two points determined in step 3 by 2 to obtain the
standard error of the median.
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Note: Medians and their standard errors calculated as below may differ from those in published
tables and reports showing medians, since narrower income intervals were used in those
calculations.

Illustration 4
Suppose you want to calculate the standard error of the estimated median of years on the lost job
for all displaced workers with the following distribution:

Years on lost job
<1
1-4.99
5-9.99
10-14.99
15-19.99
20+
Total

Illustration 4
Cumulative number
Number of persons
of persons
2,519,000
2,519,000
5,614,000
8,133,000
1,973,000
10,106,000
1,082,000
11,188,000
378,000
11,566,000
681,000
12,247,000
12,247,000

Cumulative percentage
of persons
20.57
66.41
82.52
91.35
94.44
100.00

1.

Using Formula (2) with b = 3,096 from Table 3, the standard error of 50 percent with a
base of 12,247,000 is 0.79 percent.

2.

To obtain a 68-percent confidence interval on an estimated median, add to and subtract
from 50 percent the standard error found in step 1. This yields percentage limits of 49.21
and 50.79.

3.

The lower and upper boundaries for the interval in which the percentage limits fall are L
= 1 year to U = 5 years, respectively.
Then the estimated number of displaced workers with years on the lost job between 1 and
5 are NL = 2,519,000 and NU = 8,133,000, respectively.
Using Formula (4), the lower limit for the confidence interval of the median is found to
be about
.

0.4921 ∗ 12,247,000 2,519,000
5
8,133,000 2,519,000

16-13

1

1

3.50

Similarly, the upper limit is found to be about
.

0.5079 ∗ 12,247,000 2,519,000
5
8,133,000 2,519,000

1

1

3.64

Thus, a 68-percent confidence interval for the median number of years on the lost job for
displaced workers is from 3.50 to 3.64.

4.

The standard error of the median is, therefore,
3.64

3.50
2

0.07

Standard Errors of Quarterly or Yearly Averages. For information on calculating standard
errors for labor force data from the CPS which involve quarterly or yearly averages, please see
the “Explanatory Notes and Estimates of Error: Household Data” section in Employment and
Earnings, a monthly report published by the U.S. Bureau of Labor Statistics.
Technical Assistance. If you require assistance or additional information, please contact the
Demographic Statistical Methods Division via e-mail at [email protected].

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Table 3. Parameters for Computation of Standard Errors for Labor Force Characteristics:
January 2012
Characteristic

a

b

Civilian labor force, employed
Not in labor force
Unemployed

-0.000016
-0.000009
-0.000016

3,068
1,833
3,096

Civilian labor force, employed, not in labor force, and unemployed
Men
Women
Both sexes, 16 to 19 years

-0.000032
-0.000031
-0.000022

2,971
2,782
3,096

-0.000151
-0.000311
-0.000252
-0.001632

3,455
3,357
3,062
3,455

-0.000141
-0.000253
-0.000266
-0.001528

3,455
3,357
3,062
3,455

-0.000346
-0.000729
-0.000659
-0.004146

3,198
3,198
3,198
3,198

Total or White

Black
Civilian labor force, employed, not in labor force, and unemployed
Total
Men
Women
Both sexes, 16 to 19 years
Hispanic, may be any race
Civilian labor force, employed, not in labor force, and unemployed
Total
Men
Women
Both sexes, 16 to 19 years
Asian, American Indian and Alaskan Native (AIAN), Native
Hawaiian and Other Pacific Islander (NHOPI)
Civilian labor force, employed, not in labor force, and unemployed
Total
Men
Women
Both sexes, 16 to 19 years

Notes: (1) These parameters are to be applied to basic CPS monthly labor force estimates.
(2) The Total or White, Black, and Asian, AIAN, NHOPI parameters are to be used for both alone and in
combination race group estimates.
(3) For nonmetropolitan characteristics, multiply the a and b parameters by 1.5. If the
characteristic of interest is total state population, not subtotaled by race or ethnicity, the a and
b parameters are zero.
(4) For foreign-born and noncitizen characteristics for Total and White, the a and b parameters
should be multiplied by 1.3. No adjustment is necessary for foreign-born and noncitizen
characteristics for Black, Hispanic, and Asian, AIAN, NHOPI parameters.
(5) For the groups self-classified as having two or more races, use the Asian, AIAN, NHOPI
parameters for all employment characteristics.

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REFERENCES
[1]

Bureau of Labor Statistics. 1994. Employment and Earnings. Volume 41 Number 5,
May 1994. Washington, DC: Government Printing Office.

[2]

U.S. Census Bureau. 2006. Current Population Survey: Design and Methodology.
Technical Paper 66. Washington, DC: Government Printing Office.
(http://www.census.gov/prod/2006pubs/tp-66.pdf)

[3]

Brooks, C.A. and Bailar, B.A. 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. (http://www.fcsm.gov/working-papers/spp.html)

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