Source and Accuracy Statement

Attachment 16
Source of the Data and Accuracy of the Estimates for the
May & September 2018 CPS Microdata File on
Unemployment Insurance Nonfilers

SOURCE OF THE DATA

The data in this microdata file are from the May and September 2018 Current Population
Survey (CPS). The U.S. Census Bureau conducts the CPS every month, although this file has
only May and September data. The May and September surveys use 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 civilian noninstitutionalized population. The Census Bureau
and the U.S. Bureau of Labor Statistics also jointly sponsor the supplemental questions for
May and September.

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 (98 percent of the 4.0 million
institutionalized people in Census 2010). Starting August 2017, college and university
dormitories were also excluded from the population universe because the majority of the
residents had usual residences elsewhere. 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.

Every ten years, the CPS first stage sample is redesigned1 reflecting changes based on the
most recent decennial census. In the first stage of the sampling process, primary sampling
units (PSUs)2 were selected for sample. In the 2010 sample design, the United States was
divided into 1,987 PSUs. These PSUs were then grouped into 852 strata. Within each
stratum, a single PSU was chosen for the sample, with its probability of selection
1

2

For detailed information on the 2010 sample redesign, please see Bureau of Labor Statistics (2014).
The PSUs correspond to substate areas (i.e., counties or groups of counties) that are geographically
contiguous.

16-1

proportional to its population as of the most recent decennial census. In the case of
strata consisting of only one PSU, the PSU was chosen with certainty.

Approximately 72,000 and 71,000 housing units were selected for sample from the
sampling frame in May and September, respectively. Based on eligibility criteria, eight
percent of these housing units were sent directly to computer‐assisted telephone
interviewing (CATI) in May and ten percent in September. The remaining units were
assigned to interviewers for computer‐assisted personal interviewing (CAPI).3 Of all
housing units in sample, about 60,000 were determined to be eligible for interview in each
month for May and September. Interviewers obtained interviews at about 51,000 of these
units in each of the two months. Noninterviews occur when the occupants are not found at
home after repeated calls or are unavailable for some other reason.

May and September 2018 Supplement. In May and September 2018, in addition to the
basic CPS questions, interviewers asked supplementary questions about Unemployment
Insurance to civilian noninstitutionalized persons age 16 or older who were unemployed,
as well as a subset of those classified as not in the labor force. Of the persons who
completed basic CPS interviews, about 62,000 persons were eligible to be interviewed for
the supplement in May and September combined. Interviewers obtained about 57,000
supplement interviews in May and September combined.

Estimation Procedure. This survey’s estimation procedure adjusts weighted sample
results to agree with independently derived population controls of the civilian
noninstitutionalized population of the United States, each state, and the District of
Columbia. These population controls4 are prepared by the Census Bureau’s Population
Estimates 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 2010 Census data is updated using
3

For further information on CATI and CAPI and the eligibility criteria, please see U.S. Census Bureau
(2006).
4
For additional information on population controls, including details on the demographic characteristics
used and net international components, please see Chapter 10 and Appendix C of U.S. Census Bureau
(2006).

16-2

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.

The net international migration component of the population estimates includes:
• Net international migration of the foreign born;
• Net migration between the United States and Puerto Rico;
• Net migration of natives to and from the United States; and
• Net movement of the Armed Forces population to and from the United States.

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 who 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 2018 basic CPS, the household‐level
unweighted nonresponse rate was 15.4 percent in May and 14.9 in September. The
person‐level unweighted nonresponse rate for the Unemployment Insurance supplement
was an additional 5.4 percent in May and 5.8 percent in September.

Since the basic CPS nonresponse rate is a household‐level rate and the Unemployment
Insurance 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 Unemployment Insurance
supplement.

Sufficient Partial Interview. A sufficient partial interview is an incomplete interview in
which the household or person answered enough of the questionnaire for the supplement
sponsor to consider the interview complete. The remaining supplement questions may
have been edited or imputed to fill in missing values. Insufficient partial interviews are
considered to be nonrespondents. Refer to the supplement overview attachment in the
technical documentation for the specific questions deemed critical by the sponsor as
necessary to be answered in order to be considered a sufficient partial interview.

As part of the nonsampling error analysis, the item response rates, item refusal rates, and
edits are reviewed. For the Unemployment Insurance supplement, the item refusal rates
range from 0.05 percent to 0.90 percent. The item nonresponse rates range from 2.14
percent to 22.28 percent.

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 May and September 2018 is
estimated to be about 11 percent for each month. CPS coverage varies with age, sex, and
race. Generally, coverage is higher for females than for males and higher 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
16-4

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. Tables
1 and 2 show the basic CPS coverage ratios by age and sex for certain race and Hispanic
groups for May 2018 and September 2018, respectively. The CPS coverage ratios can
exhibit some variability from month to month.

Table 1. Current Population Survey Coverage Ratios: September 2018
Total
Age
group
0‐15
16‐19
20‐24
25‐34
35‐44
45‐54
55‐64
65+
15+
0+

All
people
0.87
0.86
0.75
0.82
0.90
0.90
0.93
0.97
0.89
0.89

White only

Black only

Residual raceA

HispanicB

Male Female Male Female Male Female Male Female Male Female
0.88
0.88
0.74
0.80
0.87
0.89
0.92
0.97
0.88
0.88

0.86
0.84
0.77
0.85
0.93
0.91
0.94
0.98
0.90
0.90

0.92
0.91
0.79
0.84
0.90
0.93
0.95
0.98
0.91
0.91

0.91
0.86
0.79
0.89
0.96
0.94
0.97
0.99
0.94
0.93

0.70
0.75
0.55
0.64
0.72
0.71
0.77
0.96
0.73
0.72

0.67
0.68
0.71
0.71
0.80
0.75
0.81
0.92
0.78
0.76

0.84
0.85
0.66
0.72
0.80
0.85
0.81
0.85
0.79
0.80

0.83
0.93
0.68
0.75
0.84
0.85
0.81
0.85
0.81
0.82

0.79
0.81
0.69
0.72
0.78
0.80
0.88
0.86
0.78
0.78

0.82
0.81
0.75
0.84
0.89
0.87
0.89
0.88
0.85
0.84

Source: U.S. Census Bureau, Current Population Survey, May 2018.
A
The Residual race group includes cases indicating a single race other than White or Black, and cases
indicating two or more races.
B Hispanics may be any race.
Note: For a more detailed discussion on the use of parameters for race and ethnicity, please see the
“Generalized Variance Parameters” section.

Table 2. Current Population Survey Coverage Ratios: September 2018
Total
Age
group
0‐15
16‐19
20‐24
25‐34
35‐44
45‐54
55‐64
65+
15+
0+

All
people
0.88
0.87
0.77
0.82
0.90
0.90
0.92
0.97
0.89
0.89

White only

Black only

Residual raceA

HispanicB

Male Female Male Female Male Female Male Female Male Female
0.88
0.90
0.78
0.80
0.88
0.89
0.91
0.97
0.88
0.88

0.87
0.84
0.76
0.85
0.92
0.91
0.93
0.97
0.90
0.90

0.92
0.94
0.81
0.84
0.90
0.93
0.92
0.98
0.91
0.91

0.92
0.88
0.78
0.89
0.97
0.94
0.94
0.99
0.93
0.93

0.71
0.75
0.68
0.58
0.75
0.74
0.84
0.93
0.74
0.73

0.72
0.71
0.68
0.70
0.77
0.80
0.88
0.95
0.79
0.78

Source: U.S. Census Bureau, Current Population Survey, September 2018.
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0.82
0.83
0.74
0.79
0.87
0.82
0.85
0.85
0.82
0.82

0.83
0.75
0.78
0.80
0.84
0.87
0.85
0.82
0.83
0.82

0.82
0.93
0.78
0.72
0.79
0.80
0.81
0.87
0.80
0.80

0.82
0.83
0.76
0.83
0.92
0.86
0.84
0.86
0.85
0.84

A

The Residual race group includes cases indicating a single race other than White or Black, and cases
indicating two or more races.
B Hispanics may be any race.
Note: 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 2010 Census‐based controls, with microdata files from January 2003 through
December 2011, which reflect 2000 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 2010 Census‐based controls results in
about a 0.2 percent increase from the 2000 census‐based controls in the civilian
noninstitutionalized population and in the number of families and households. Thus,
estimates of levels for data collected in 2012 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.

Users should also exercise caution because of changes caused by the phase‐in of the Census
2010 files (see “Basic CPS”).5 During this time period, CPS data were collected from sample
designs based on different censuses. Two features of the new CPS design have the potential
of affecting published estimates: (1) the temporary disruption of the rotation pattern from
August 2014 through June 2015 for a comparatively small portion of the sample and (2)
the change in sample areas. Most of the known effect on estimates during and after the
sample redesign will be the result of changing from 2000 to 2010 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

5

The phase‐in process using the 2010 Census files began April 2014.
16-6

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 U.S. Census Bureau (2006) and Brooks & Bailar (1978).

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.

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.

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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.

There are two ways to calculate standard errors for the CPS microdata file on
Unemployment Insurance. They are:

• Direct estimates created from replicate weighting methods;
• Generalized variance estimates created from generalized variance function
parameters a and b.

While replicate weighting methods provide the most accurate variance estimates, this
approach requires more computing resources and more expertise on the part of the user.
The Generalized Variance Function (GVF) parameters provide a method of balancing
accuracy with resource usage as well as a smoothing effect on standard error estimates
across time. For more information on calculating direct estimates, see U.S. Census Bureau
(2009). For more information on GVF estimates, refer to the “Generalized Variance
Parameters” section.

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 GVF is a simple model
that expresses the variance as a function of the expected value of the survey estimate. The
parameters of the GVF are estimated using direct replicate variances. These GVF
parameters provide a relatively easy method to obtain approximate standard errors for
numerous characteristics.

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In this source and accuracy statement, Tables 4 through 6 provide illustrations for
calculating standard errors. Table 7 provides the GVF parameters for labor force
estimates, and Table 8 provides GVF parameters for characteristics from the May and
September 2018 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.

The GVF 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 GVF parameters to use in standard error
calculations.

Table 3. 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 alone or in combination (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

Asian, American Indian and Alaska
Native (AIAN), Native Hawaiian and
Other Pacific Islander (NHOPI)

AIAN alone, AIAN AOIC, or AIAN non‐Hispanic population

Asian, AIAN, NHOPI

NHOPI alone, NHOPI AOIC, or NHOPI non‐Hispanic
population

Asian, AIAN, NHOPI

Populations from other race groups

Asian, AIAN, NHOPI

HispanicA population

HispanicA

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

Black
Asian, AIAN, NHOPI

Source: U.S. Census Bureau, Current Population Survey, internal data files.
A
Hispanics may be any race.
B
Two or more races refers to the group of cases self‐classified as having two or more races.

When calculating standard errors for an estimate of interest from cross‐tabulations
involving different characteristics, use the set of GVF parameters for the characteristic that
will give the largest standard error. If the estimate of interest is strictly from basic CPS
data, the GVF parameters will come from the CPS GVF table (Table 7). If the estimate is
using Unemployment Insurance supplement data, the GVF parameters will come from the
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Unemployment Insurance supplement GVF table (Table 8). Do not use the
Unemployment Insurance Public Use File for basic CPS estimates of the CPS labor force;
only use it to analyze the supplement data. The basic CPS weights were not adjusted for
this 2‐month file.

Standard Errors of Estimated Numbers. The approximate standard error, , of an
estimated number from this microdata file can be obtained by using the formula:

(1)
√

Here x is the size of the estimate, and a and b are the parameters in Table 7 or 8 associated
with the particular type of characteristic.

Illustration 1
Suppose there were 3,510,000 unemployed persons aged 16 to 24 in the civilian labor
force. Use the appropriate parameters from Table 7 and Formula (1) to get

Table 4. Illustration of Standard Errors of Estimated Numbers
Number of unemployed persons aged 16 to 24
3,510,000
In the civilian labor force (x)
a‐parameter (a)
‐0.000017
b‐parameter (b)
3,244
Standard error
106,000
90‐percent confidence interval
3,336,000 to 3,684,000
Source: U.S. Census Bureau, Current Population Survey, Unemployment Insurance, May and September 2018.

The standard error is calculated as

0.000017

3,510,000
3,244 3,510,000,

which, rounded to the nearest thousand, is 106,000. The 90‐percent confidence interval is
calculated as 3,510,000 ± 1.645 × 106,000.

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 7 or 8 as indicated by
the numerator.

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The approximate standard error,
using the formula:

,

, of an estimated percentage can be obtained by

100

,

(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 7 or 8 associated with the characteristic in the numerator of the
percentage.

Illustration 2
Suppose that of the 3,510,000 unemployed persons aged 16 to 24, 1.2 percent were
compensated with unemployment insurance. Use the appropriate parameter from Table 8
and Formula (2) to get

Table 5. Illustration of Standard Errors of Estimated Percentages
Percentage of unemployed persons aged 16 to 24
1.2
compensated with unemployment insurance (p)
Base (y)
3,510,000
b‐parameter (b)
2,068
Standard error
0.26
90‐percent confidence interval
0.8 to 1.6
Source: U.S. Census Bureau, Current Population Survey, Unemployment Insurance, May and September 2018.

The standard error is calculated as

,

2,068
3,510,000

1.2

100.0

1.2

0.26

The 90‐percent confidence interval for the estimated percentage of unemployed persons
aged 16 to 24 compensated with unemployment insurance is from 0.8 to 1.6 percent (i.e.,
1.2 ± 1.645 × 0.26).

Standard Errors of Estimated Differences. The standard error of the difference between
two sample estimates is approximately equal to

(3)

where and are the standard errors of the estimates, and . 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
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positive (negative) correlation between the two characteristics, the formula will
overestimate (underestimate) the true standard error.

Illustration 3
Suppose that of the 3,510,000 unemployed persons aged 16 to 24, 1.2 percent were
compensated with unemployment insurance, and of the 8,012,000 unemployed persons
aged 25 and older, 9.7 percent were compensated. Use the appropriate parameters from
Table 8 and Formulas (2) and (3) to get

Table 6. Illustration of Standard Errors of Estimated Differences
Aged 16 to 24 (x1) Aged 25 and older (x2)
Percentage of unemployed who
1.2
9.7
were compensated (p)
Base (y)
3,510,000
8,012,000
b‐parameter (b)
2,068
1,963
Standard error
0.26
0.46
90‐percent confidence
0.8 to 1.6
8.9 to 10.5
interval

Difference
8.5
‐
‐
0.53
7.6 to 9.4

Source: U.S. Census Bureau, Current Population Survey, Unemployment Insurance, May and September 2018.

The standard error of the difference is calculated as

0.26
0.46
0.53

The 90‐percent confidence interval around the difference is calculated as 8.5 ± 1.645 ×
0.53. Since this interval does not include zero, we can conclude with 90‐percent confidence
that the percentage of unemployed persons between 16 and 24 years of age receiving
unemployment compensation is less than the percentage of unemployed persons aged 25
years and older receiving unemployment compensation.

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 Bureau of Labor Statistics (2006).

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 7. Parameters for Computation of Standard Errors for Labor Force Characteristics:
May and September 2018
Characteristic
a
b

Total or White

Civilian labor force, employed
‐0.000013
2,481
Unemployed
‐0.000017
3,244
Not in labor force
‐0.000013
2,432

Civilian labor force, employed, not in labor force, and unemployed

Men
‐0.000031
2,947
Women
‐0.000028
2,788
Both sexes, 16 to 19 years
‐0.000261
3,244

Black

Civilian labor force, employed, not in labor force, and unemployed

Total
‐0.000117
3,601
Men
‐0.000249
3,465
Women
‐0.000191
3,191
Both sexes, 16 to 19 years
‐0.001425
3,601

Asian, American Indian and Alaska Native (AIAN), Native

Hawaiian and Other Pacific Islander (NHOPI)
Civilian labor force, employed, not in labor force, and unemployed

Total
‐0.000245
3,311
Men
‐0.000537
3,397
Women
‐0.000399
2,874
Both sexes, 16 to 19 years
‐0.004078
3,311

Hispanic, may be of any race

Civilian labor force, employed, not in labor force, and unemployed

Total
‐0.000087
3,316
Men
‐0.000172
3,276
Women
‐0.000158
3,001
Both sexes, 16 to 19 years
‐0.000909
3,316

Source: U.S. Census Bureau, Internal Current Population Survey data files for the 2010 Design.
Notes: These parameters are to be applied to basic CPS monthly labor force estimates. The Total or White,
Black, and Asian, AIAN, NHOPI parameters are to be used for both alone and in combination race
group estimates. 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. 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. 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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 Table 8. Parameters for Computation of Standard Errors for Unemployment Insurance
Characteristics: May and September 2018
Total or White
Black
Asian, AIAN, NHOPIA
HispanicB
a
b
a
b
a
b
a
b
Did not apply for Unemployment Insurance
All Adults
‐0.000006 1,937 ‐0.000029 2,229 ‐0.000055 1,845 ‐0.000037 2,186
Sex
Male
‐0.000012 1,937 ‐0.000065 2,338 ‐0.000113 1,826 ‐0.000082 2,445
Female
‐0.000012 1,933 ‐0.000054 2,157 ‐0.000116 2,007 ‐0.000072 2,136
Age
16 to 24
‐0.000049 2,078 ‐0.000209 2,446 ‐0.000336 1,843 ‐0.000152 2,282
25 to 44
‐0.000024 2,038 ‐0.000106 2,323 ‐0.000172 1,718 ‐0.000133 2,363
Over 45
‐0.000013 1,742 ‐0.000079 2,030 ‐0.000190 1,997 ‐0.000128 2,101
Duration of Unemployment, in weeks
0 to 2
‐0.000006 1,808 ‐0.000029 2,218 ‐0.000055 1,829 ‐0.000036 2,164
3 to 4
‐0.000006 1,959 ‐0.000029 2,218 ‐0.000052 1,743 ‐0.000037 2,216
5 to 10
‐0.000006 2,061 ‐0.000034 2,620 ‐0.000060 1,990 ‐0.000041 2,432
11 to 26
‐0.000006 1,968 ‐0.000031 2,396 ‐0.000047 1,581 ‐0.000037 2,192
27 or more
‐0.000006 1,783 ‐0.000024 1,840 ‐0.000052 1,744 ‐0.000035 2,079
Applied for Unemployment Insurance
All Adults
‐0.000006 1,899 ‐0.000030 2,248 ‐0.000046 1,552 ‐0.000037 2,200
Sex
Male
‐0.000012 1,820 ‐0.000062 2,245 ‐0.000109 1,766 ‐0.000077 2,299
Female
‐0.000011 1,844 ‐0.000054 2,169 ‐0.000086 1,489 ‐0.000070 2,093
Age
16 to 24
‐0.000047 2,022 ‐0.000188 2,205 ‐0.000294 1,612 ‐0.000145 2,171
25 to 44
‐0.000023 1,937 ‐0.000108 2,366 ‐0.000174 1,740 ‐0.000126 2,249
Over 45
‐0.000013 1,789 ‐0.000077 1,972 ‐0.000152 1,601 ‐0.000144 2,368
Duration of Unemployment, in weeks
0 to 2
‐0.000006 1,863 ‐0.000029 2,204 ‐0.000045 1,490 ‐0.000031 1,868
3 to 4
‐0.000006 1,904 ‐0.000029 2,204 ‐0.000045 1,519 ‐0.000039 2,332
5 to 10
‐0.000006 1,896 ‐0.000029 2,204 ‐0.000048 1,608 ‐0.000037 2,188
11 to 26
‐0.000006 1,835 ‐0.000029 2,192 ‐0.000048 1,589 ‐0.000035 2,120
27 or more
‐0.000006 1,921 ‐0.000029 2,186 ‐0.000046 1,536 ‐0.000036 2,168
Received Unemployed Insurance
All Adults
‐0.000006 1,904 ‐0.000028 2,153 ‐0.000047 1,584 ‐0.000036 2,132
Sex
Male
‐0.000012 1,904 ‐0.000064 2,325 ‐0.000110 1,772 ‐0.000078 2,335
Female
‐0.000011 1,813 ‐0.000054 2,176 ‐0.000089 1,526 ‐0.000069 2,055
Age
16 to 24
‐0.000049 2,068 ‐0.000305 3,583 ‐0.000303 1,663 ‐0.000133 1,989
25 to 44
‐0.000023 1,963 ‐0.000104 2,290 ‐0.000168 1,679 ‐0.000126 2,238
Over 45
‐0.000014 1,830 ‐0.000076 1,958 ‐0.000158 1,663 ‐0.000137 2,244
Duration of Unemployment, in weeks
0 to 2
‐0.000005 1,738 ‐0.000024 1,827 ‐0.000045 1,513 ‐0.000035 2,113
3 to 4
‐0.000006 2,023 ‐0.000028 2,092 ‐0.000053 1,513 ‐0.000035 2,113
5 to 10
‐0.000006 1,832 ‐0.000025 1,876 ‐0.000045 1,783 ‐0.000033 1,965
11 to 26
‐0.000006 1,819 ‐0.000028 2,165 ‐0.000047 1,567 ‐0.000036 2,128
27 or more
‐0.000006 1,933 ‐0.000028 2,125 ‐0.000045 1,513 ‐0.000037 2,184
Source: U.S. Census Bureau, Current Population Survey, Internal data from the Unemployment Insurance, May and
September 2018.
Characteristics

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A

AIAN is American Indian and Alaska Native, and NHOPI is Native Hawaiian and Other Pacific Islander.
Hispanics may be any race.
Notes: These parameters are to be applied to the Unemployment Insurance data. The Total or White, Black,
and Asian, AIAN, NHOPI parameters are to be used for both alone and in combination race group estimates. 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. 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, Asian, AIAN, NHOPI, and
Hispanic parameters. For the group self‐classified as having two or more races, use the Asian, AIAN, NHOPI
parameters for all characteristics except employment, unemployment, and educational attainment, in which case
use Black parameters. For a more detailed discussion on the use of parameters for race and ethnicity, please see
the “Generalized Variance Parameters” section.

B

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REFERENCES

Brooks, C.A., & 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.
https://s3.amazonaws.com/sitesusa/wp‐
content/uploads/sites/242/2014/04/spwp3.pdf

Bureau of Labor Statistics, February 2006, “Household Data (“A” tables, monthly; “D”
tables, quarterly).” https://www.bls.gov/cps/eetech_methods.pdf

Bureau of Labor Statistics, April 2014, “Redesign of the Sample for the Current Population
Survey.” http://www.bls.gov/cps/sample_redesign_2014.pdf

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

U.S. Census Bureau. July 15, 2009. “Estimating ASEC Variances with Replicate Weights Part
I: Instructions for Using the ASEC Public Use Replicate Weight File to Create ASEC
Variance Estimates.”
http://usa.ipums.org/usa/resources/repwt/Use_of_the_Public_Use_Replicate_Weig
ht_File_final_PR.doc.

All online references accessed August 12, 2019.

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