Attachment I - Accuracy Statement

Source of the Data and Accuracy of the Estimates for the
August 2022 Current Population Survey Microdata File on Veterans
SOURCE OF THE DATA
The data in this microdata file are from the August 2022 Current Population Survey (CPS).
The U.S. Census Bureau conducts the CPS every month, although this file has only August
data. The August 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 civilian
noninstitutionalized population. The Department of Veterans Affairs and the Department
of Labor jointly sponsor the supplemental questions for August.

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 universe, consists primarily of the population in
correctional institutions and nursing homes (98 percent of the 4.0 million institutionalized
people in the 2010 Census). Starting in August 2017, college and university dormitories
were also excluded from the universe because most of the residents had usual residences
elsewhere. Interviewers ask questions concerning labor force participation of each
member 15 years old and older 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 redesigned 1 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
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 68,500 sampled addresses were selected from the sampling frame in
August. Based on eligibility criteria, eight percent of these sampled addresses were sent
directly to computer-assisted telephone interviewing (CATI). The remaining sampled
1
2

For detailed information on the 2010 sample redesign, please reference Bureau of Labor Statistics
(2014).
The PSUs correspond to substate areas (i.e., counties or groups of counties) that are geographically
contiguous.
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addresses were assigned to interviewers for computer-assisted personal interviewing
(CAPI).3 Of all addresses in sample, about 59,000 were determined to be eligible for
interview. Interviewers obtained interviews at about 43,500 of the housing units at these
addresses. Noninterviews occur when the occupants are not found at home after repeated
calls or are unavailable for some other reason.
August 2022 Supplement. In August 2022, in addition to the basic CPS questions,
interviewers asked supplementary questions of veterans on year of discharge, disability,
and job assistance.

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 controls 4 are prepared monthly as part of 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).
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 2020 Census 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
controls.
The net international migration component of the population controls includes:
•
•
•

3
4

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

For further information on CATI and CAPI and the eligibility criteria, please reference U.S. Census Bureau
(2019).
For additional information on population controls, including details on the demographic characteristics
used and net international components, please refer to Chapters 1-3 and Appendix: History of the
Current Population Survey of U.S. Census Bureau (2019).
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•

Net movement of the Armed Forces population to and from the United States.

Because the latest available information on these components lags behind 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, the estimation of standard errors 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. Some nonsampling errors,
and examples of each, include:
•

•
•
•
•

Measurement error: 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.
Coverage error: Some individuals who should have been included in the survey
frame were missed.
Nonresponse error: Responses are not collected from all those in the sample or
the respondent is unwilling to provide information.
Imputation error: Values are estimated imprecisely for missing data.
Processing error: Forms may be lost, data may be incorrectly keyed, coded, or
recoded, etc.

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.
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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 August 2022 basic CPS, the
household-level unweighted nonresponse rate was 26.3 percent. The person-level
unweighted nonresponse rate for the Veterans supplement was an additional 16.2 percent.
Since the basic CPS nonresponse rate is a household-level rate and the Veterans
supplement nonresponse rate is a person-level rate, we cannot combine these rates to
derive an overall nonresponse rate. Nonresponding households may have more or fewer
persons than interviewed ones, so combining these rates may lead to an under- or
overestimate of the true overall nonresponse rate for persons for the Veterans supplement.
Responses are made up of complete interviews and sufficient partial interviews. 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
answer in order to be considered a sufficient partial interview.

As a result of sufficient partial interviews being considered responses, individual
items/questions have their own response and refusal rates. As part of the nonsampling
error analysis, the item response rates, item refusal rates, and edits are reviewed. For the
Veterans supplement, the unweighted item refusal rates range from 0.0 percent to 4.4
percent. The unweighted item allocation rates range from 4.0 percent to 5.2 percent. The
unweighted item nonresponse rates range from 0.0 percent to 11.6 percent.

Undercoverage. The concept of coverage with a survey sampling process is defined as 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 August 2022 is estimated
to be about ten percent. 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 mitigates 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.
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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 August 2022 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.
Table 1. Current Population Survey Coverage Ratios: August 2022
Total

White alone

Black alone

Residual raceA

HispanicB

Age
All
Male Female Male Female Male Female Male Female Male Female
group people
0-15
0.85
0.86
0.83
0.91
0.87
0.72
0.68
0.79
0.78
0.83
0.81
16-19 0.82
0.84
0.80
0.86
0.84
0.77
0.68
0.78
0.73
0.89
0.82
20-24 0.76
0.76
0.77
0.79
0.79
0.58
0.70
0.77
0.72
0.80
0.77
25-34 0.82
0.81
0.82
0.87
0.85
0.57
0.65
0.77
0.86
0.80
0.83
35-44 0.88
0.86
0.91
0.88
0.93
0.68
0.77
0.89
0.90
0.79
0.93
0.87
0.90
0.89
0.93
0.76
0.80
0.90
0.80
0.78
0.87
45-54 0.89
55-64 0.92
0.89
0.95
0.91
0.97
0.80
0.85
0.88
0.93
0.75
0.87
65+
1.06
1.04
1.07
1.06
1.08
1.00
1.02
0.90
0.94
0.88
0.90
15+
0.90
0.89
0.92
0.91
0.95
0.73
0.80
0.85
0.86
0.81
0.86
0+
0.89
0.88
0.90
0.91
0.93
0.73
0.77
0.83
0.84
0.81
0.85
Source: U.S. Census Bureau, Current Population Survey, August 2022.
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 refer to the
“Generalized Variance Parameters” section.

Comparability of Data. Data obtained from the CPS and other sources are not entirely
comparable. This is due to differences in interviewer training and experience and in
differing survey processes. 5 These differences are examples 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 2020 Census-based controls 6, with microdata files which reflect 2010 Censusbased 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

5
6

Survey processes include, but are not limited to, question wording, universe, sampling frame, interview
modes, and weighting.
In recent decades, the decennial census has usually provided all the data necessary to produce the
population base used in the population controls. However, changes in disclosure avoidance practices and
delays in the 2020 Census necessitated changes to the data sources that produce the base population for
the Vintage 2021 population estimates. The updated population controls use a Blended Base that draws
on the 2020 Census, 2020 Demographic Analysis Estimates, and Vintage 2020 Postcensal Population
Estimates. More information on this methodology can be found at .
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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. The discussion following includes information on
comparing estimates derived from different populations 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 2020 Census-based controls results in
about a 0.7 percent increase from the 2010 Census-based controls in the civilian
noninstitutionalized population. 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 2010
Census files (refer to “Basic CPS”). 7 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 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
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 (2019) and Brooks & Bailar (1978).
7

The phase-in process using the 2010 Census files began April 2014.
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Standard Errors and Their Use. A 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 the 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.

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 estimate 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.
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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 (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.
In this source and accuracy statement:
•
•
•

Tables 3 through 5 provide illustrations for calculating standard errors;
Table 6 provides the GVF parameters for labor force estimates; and
Table 7 provides GVF parameters for characteristics from the August 2022
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. Table 2 summarizes the relationship between the
race/ethnicity group of interest and the GVF parameters to use in standard error
calculations.

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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

White alone, White alone or in combination (AOIC), or
White non-Hispanic population

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

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

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

Total or White
Total or White
Black

Asian, American Indian and Alaska
Native (AIAN), Native Hawaiian and
Other Pacific Islander (NHOPI)
Asian, AIAN, NHOPI
Asian, AIAN, NHOPI
Asian, AIAN, NHOPI
HispanicA
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 6). If the estimate is
using Veterans supplement data, the GVF parameters will come from the Veterans
supplement GVF table (Table 7).
Standard Errors of Estimated Numbers. The approximate standard error, 𝑠𝑠𝑥𝑥 , of an
estimated number from this microdata file can be obtained by using the formula:
𝑠𝑠𝑥𝑥 = √𝑎𝑎𝑥𝑥 2 + 𝑏𝑏𝑏𝑏

(1)

Here x is the size of the estimate, and a and b are the parameters in Table 6 or 7 associated
with the particular type of characteristic.

Illustration 1
Suppose there were 2,933,000 unemployed nonveterans, aged 18 to 34, in the civilian labor
force. Table 3 shows how to use the appropriate parameters from Table 6 and Formula (1)
to estimate the standard error and confidence interval.
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Table 3. Illustration of Standard Errors of Estimated Numbers

Number of unemployed nonveterans in the civilian labor
force (x)
a-parameter (a)
b-parameter (b)
Standard error
90-percent confidence interval

Source: U.S. Census Bureau, Current Population Survey, August 2022.

2,933,000

-0.000017
3,244
97,000
2,774,000 to 3,093,000

The standard error is calculated as

𝑠𝑠𝑥𝑥 = �−0.000017 × 2,933,0002 + 3,244 × 2,933,000,

which, rounded to the nearest thousand, is 97,000. The 90-percent confidence interval is
calculated as 2,933,000 ± 1.645 × 97,000.
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 6 or 7 as indicated by
the numerator.

The approximate standard error, 𝑠𝑠𝑦𝑦,𝑝𝑝, of an estimated percentage can be obtained by using
the formula:
𝑏𝑏

𝑠𝑠𝑦𝑦,𝑝𝑝 = � 𝑝𝑝(100 − 𝑝𝑝)
𝑦𝑦

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

Illustration 2
Suppose there were 1,424,000 Gulf War veterans aged 18 to 34 in the civilian labor force,
and 2.7 percent were unemployed. Table 4 shows how to use the appropriate parameters
from Table 7 and Formula (2) to estimate the standard error and confidence interval.
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Table 4. Illustration of Standard Errors of Estimated Percentages

Percentage of Gulf War veterans, aged 18-34,
unemployed (p)
Base (y)
b-parameter (b)
Standard error
90-percent confidence interval

2.7

Source: U.S. Census Bureau, Current Population Survey, Veterans, August 2022.

1,424,000
5,011
0.96
1.1 to 4.3

The standard error is calculated as

5,011
× 2.7 × (100.0 − 2.7) = 0.96
𝑠𝑠𝑦𝑦,𝑝𝑝 = �
1,424,000

and the 90-percent confidence interval for the estimated percentage of unemployed Gulf
War veterans aged 18 to 34 in the civilian labor force is from 1.1 to 4.3 percent (i.e., 2.7 ±
1.645 × 0.96).

Standard Errors of Estimated Differences. The standard error of the difference between
two sample estimates is approximately equal to
2

𝑠𝑠|𝑥𝑥1−𝑥𝑥2| = ��𝑠𝑠𝑥𝑥1 � + �𝑠𝑠𝑥𝑥2 �

2

(3)

Where 𝑠𝑠𝑥𝑥1 and 𝑠𝑠𝑥𝑥2 are the standard errors of the estimates, 𝑥𝑥1 and 𝑥𝑥2. 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.

Illustration 3
Suppose that of the 1,424,000 Gulf War veterans in the civilian labor force between 18 and
34 years of age, 2.7 percent were unemployed, and of the 54,510,000 nonveterans in the
civilian labor force between 18 and 34 years of age, 5.4 percent were unemployed. Table 5
shows how to use the appropriate parameters from Tables 6 and 7 and Formulas (2) and
(3) to estimate the standard error and confidence interval.

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Table 5. Illustration of Standard Errors of Estimated Differences
Percentage unemployed aged 18
to 34 (p)
Base (y)
b-parameter (b)
Standard error
90-percent confidence interval

Gulf War
Veterans (x1)

2.7

1,424,000
5,011
0.96
1.1 to 4.3

Nonveterans (x2)
5.4

54,510,000
3,244
0.17
5.1 to 5.7

Source: U.S. Census Bureau, Current Population Survey, Veterans, August 2022.

Difference

2.7

0.97
1.1 to 4.3

The standard error of the difference is calculated as

𝑠𝑠|𝑥𝑥1−𝑥𝑥2| = �0.962 + 0.172 = 0.97

and the 90-percent confidence interval around the difference is calculated as 2.7 ± 1.645 ×
0.97. Since this interval does not include zero, we can conclude with 90-percent confidence
that the percentage of unemployed Gulf War veterans in the civilian labor force between 18
and 34 years of age is lower than the percentage of unemployed nonveterans in the civilian
labor force between 18 and 34 years of age.
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 reference 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 6. Parameters for Computation of Standard Errors for Labor Force
Characteristics: August 2022

Characteristic

Total or White
Civilian labor force, employed
Unemployed
Not in labor force
Civilian labor force, employed, not in labor force, and unemployed
Men
Women
Both sexes, 16 to 19 years

Black
Civilian labor force, employed, not in labor force, and unemployed
Total
Men
Women
Both sexes, 16 to 19 years
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
Men
Women
Both sexes, 16 to 19 years
Hispanic, may be of any race
Civilian labor force, employed, not in labor force, and unemployed
Total
Men
Women
Both sexes, 16 to 19 years

a

b

-0.000013
-0.000017
-0.000013

2,481
3,244
2,432

-0.000031
-0.000028
-0.000261

2,947
2,788
3,244

-0.000117
-0.000249
-0.000191
-0.001425

3,601
3,465
3,191
3,601

-0.000245
-0.000537
-0.000399
-0.004078

3,311
3,397
2,874
3,311

-0.000087
-0.000172
-0.000158
-0.000909

3,316
3,276
3,001
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 bparameters 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 7. Parameters for Computation of Standard Errors for Veterans Characteristics:
August 2022

Characteristic
a
b
Total Employed and Nonagriculture Employed in Labor Force, Occupations, and Disability
Status of Employed
Total or Men
All Veterans
War Veterans
Gulf War Era Veterans
Other Service Veterans

-0.000266
-0.000339
-0.000599
-0.001228

4,885
4,885
4,885
4,885

Unemployed, Duration of Unemployment

-0.002408
-0.003015
-0.003558
-0.011944

4,885
4,885
4,885
4,885

Total or Men
All Veterans
War Veterans
Gulf War Era Veterans
Other Service Veterans

-0.000272
-0.000347
-0.000614
-0.001260

5,011
5,011
5,011
5,011

-0.002470
-0.003093
-0.003650
-0.012252

5,011
5,011
5,011
5,011

Women
All Veterans
War Veterans
Gulf War Era Veterans
Other Service Veterans

Women
All Veterans
War Veterans
Gulf War Era Veterans
Other Service Veterans

Source: U.S. Census Bureau, Current Population Survey, Internal data from the Veterans Supplement, August 2022.
Notes: These parameters are to be applied to the Veterans Supplement data. For foreign-born characteristics, the a- and
b-parameters should be multiplied by 1.3.

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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/wpcontent/uploads/sites/242/2014/04/spwp3.pdf
Bureau of Labor Statistics. (2006). Household Data (“A” tables, monthly; “D” tables,
quarterly). https://www.bls.gov/cps/eetech_methods.pdf

Bureau of Labor Statistics. (2014). Redesign of the Sample for the Current Population
Survey. http://www.bls.gov/cps/sample_redesign_2014.pdf

U.S. Census Bureau. (2012). Estimating Current Population Survey (CPS) Person Level
Supplement Variances Using Replicate Weights Part I: Instructions for Using CPS
Person Level Supplement Replicate Weights to Calculate Variances.
https://www2.census.gov/programs-surveys/cps/datasets/2018/supp/PERSONlevel_Use_of_the_Public_Use_Replicate_Weight_File.doc
U.S. Census Bureau. (2018). Estimating Current Population Survey (CPS) Household-level
Supplement Variances Using Replicate Weights Part I: Instructions for Using CPS
Household-level Supplement Replicate Weights to Calculate Variances.
https://www2.census.gov/programs-surveys/cps/datasets/2018/supp/HHlevel_Use_of_the_Public_Use_Replicate_Weight_File.doc

U.S. Census Bureau. (2019). Current Population Survey: Design and Methodology.
Technical Paper 77. Washington, DC: Government Printing Office.
https://www2.census.gov/programs-surveys/cps/methodology/CPS-Tech-Paper-77.pdf
All online references accessed February 6, 2023.

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