Att C - Source and Accuracy Statement

Source of the Data and Accuracy of the Estimates for the
2024 Annual Social and Economic Supplement Microdata File
Table of Contents
SOURCE OF THE DATA ..........................................................................................................................1
Basic CPS .................................................................................................................................................... 1
The 2024 Annual Social and Economic Supplement................................................................. 2
Estimation Procedure ........................................................................................................................... 4
ACCURACY OF THE ESTIMATES .........................................................................................................5
Sampling Error......................................................................................................................................... 5
Nonsampling Error ................................................................................................................................ 5
Nonresponse ............................................................................................................................................. 6
Undercoverage ......................................................................................................................................... 7
Comparability of Data ........................................................................................................................... 8
A Nonsampling Error Warning ........................................................................................................10
Estimation of Median Incomes ........................................................................................................10
Standard Errors and Their Use........................................................................................................11
Estimating Standard Errors ..............................................................................................................12
Replicate Weighting .............................................................................................................................13
Generalized Variance Parameters ..................................................................................................13
Standard Errors of Estimated Numbers ......................................................................................15
Standard Errors of Estimated Percentages ................................................................................16
Standard Errors of Estimated Differences ..................................................................................17
Standard Errors of Estimated Ratios ............................................................................................19
Standard Errors of Estimated Medians ........................................................................................20
Accuracy of State Estimates ..............................................................................................................23
Standard Errors of State Estimates................................................................................................24
Standard Errors of Regional Estimates ........................................................................................25
Standard Errors of Groups of States ..............................................................................................25
Standard Errors of Data for Combined Years ............................................................................26
Standard Errors of Quarterly or Yearly Averages ....................................................................28
Year-to-Year Factors............................................................................................................................28
Technical Assistance............................................................................................................................28
REFERENCES.......................................................................................................................................... 35

Tables
Table 1. Description of the March Basic Current Population Survey and Annual Social
and Economic Supplement Sample Cases ................................................................................ 3
Table 2. Current Population Survey Coverage Ratios: March 2024 ............................................... 7
Table 3. Estimation Groups of Interest and Generalized Variance Parameters .......................14
Table 4. Illustration of Standard Errors of Estimated Numbers ....................................................15
Table 5. Second Illustration of Standard Errors of Estimated Numbers ....................................16
Table 6. Illustration of Standard Errors of Estimated Percentages ..............................................17
Table 7. Illustration of Standard Errors of Estimated Differences ................................................18
Table 8. Second Illustration of Standard Errors of Estimated Differences ................................18
Table 9. Illustration of Standard Errors of Estimated Ratios ..........................................................19
Table 10. Second Illustration of Standard Errors of Estimated Ratios ..........................................20
Table 11. Distribution of Household Income for Illustration 8 .........................................................22
Table 12. Illustration of Standard Errors of State Estimates .............................................................24
Table 13. Illustration of Standard Errors of Regional Estimates ......................................................25
Table 14. Illustration of Standard Errors of Data for Combined Years ..........................................27
Table 15. Parameters for Computation of Standard Errors for Labor Force
Characteristics: March 2024 .......................................................................................................29
Table 16. Parameters for Computation of Standard Errors for People and Families:
2024 Annual Social and Economic Supplement .................................................................30
Table 17. Current Population Survey Year-to-Year Correlation Coefficients for Income
and Health Insurance Characteristics: Data Years 1960 to 2022 .................................31
Table 18. Current Population Survey Year-To-Year Correlation Coefficients for Poverty
Characteristics: Data Years 1970 to 2022 .............................................................................32
Table 19. Current Population Survey Correlation Coefficients Between Race and
Subgroups: 2024 Annual Social and Economic Supplement ..........................................32
Table 20. Factors and Populations for State Standard Errors and Parameters: 2024
Annual Social and Economic Supplement..............................................................................33
Table 21. Factors and Populations for Regional Standard Errors and Parameters: 2024
Annual Social and Economic Supplement..............................................................................34

Source of the Data and Accuracy of the Estimates for the
2024 Annual Social and Economic Supplement Microdata File
SOURCE OF THE DATA
The data in this microdata file and the estimates in the reports Income in the United States:
2023, Poverty in the United States: 2023, and Health Insurance Coverage in the United States:
2023 come from the 20241 Annual Social and Economic Supplement (ASEC) of the Current
Population Survey (CPS). The U.S. Census Bureau conducts the CPS ASEC over a 3-month
period in February, March, and April, with most of the data collection occurring in the
month of March. The CPS ASEC 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 U.S. Bureau of Labor Statistics also jointly
sponsor the CPS ASEC.
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 redesigned2 reflecting changes based on the
most recent decennial census. In the first stage of the sampling process, primary sampling
units (PSUs)3 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
3

For clarity and consistency throughout this report, the term “collection year” is the year the data is
collected (in this case, 2024), and “data year” is the year about which the data are obtained (in this case,
2023). The 2024 CPS ASEC asks questions of data year 2023, the 2023 CPS ASEC asks questions of data
year 2022, etc.
For detailed information on the 2010 sample redesign, please refer to Bureau of Labor Statistics (2014).
The PSUs correspond to substate areas (i.e., counties or groups of counties) that are geographically
contiguous.

2
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 69,500 sampled addresses were selected from the sampling frame for the
March basic CPS. Based on eligibility criteria, seven percent of these sampled addresses
were sent directly to computer-assisted telephone interviewing (CATI). The remaining
sampled addresses were assigned to interviewers for computer-assisted personal
interviewing (CAPI).4 Of all addresses in sample, about 60,000 were determined to be
eligible for interview. Interviewers obtained interviews at about 40,500 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. Table 1 summarizes
historical changes in the CPS design. 5
The 2024 Annual Social and Economic Supplement. In addition to the basic CPS
questions, interviewers asked supplementary questions for the CPS ASEC. They asked these
questions of the civilian noninstitutionalized population and also of military personnel who
live in households with at least one other civilian adult. The additional questions covered
the following topics:
•
•
•
•
•
•
•
•
•

Household and family characteristics.
Marital status.
Geographic mobility.
Foreign-born population.
Income from the previous calendar year.
Work status/occupation.
Health insurance coverage.
Program participation.
Educational attainment.

Including the basic CPS sample, approximately 89,500 addresses were in sample for the
CPS ASEC. About 78,500 sampled addresses were determined to be eligible for interview,
and about 56,500 interviews were conducted (refer to Table 1).
The additional sample for the CPS ASEC provides more reliable data than the basic CPS for
Hispanic households, non-Hispanic minority households, and non-Hispanic White
households with children 18 years or younger. These households were identified for
sample from previous months and the following April. For more information about the
households eligible for the CPS ASEC, please refer to U.S. Census Bureau (2019c).

4
5

For further information on CATI and CAPI and the eligibility criteria, please refer to U.S. Census Bureau
(2019c).
Counts and estimates throughout this source and accuracy statement are rounded according to
Disclosure Review Board rounding rules.

3
Table 1. Description of the March Basic Current Population Survey and Annual Social and
Economic Supplement Sample Cases
Time period
2024
2023
2022E
2021
2020
2019
2018
2017
2016
2015
2014 RedesignF
2014 TraditionalG
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1990 to 1994
1989

Total (CPS ASECC/ADSD + basic
Number of Basic CPSB sampled addresses
sample
eligible
CPS) sampled addresses eligible
PSUsA
Interviewed Not interviewed Interviewed
Not interviewed
852
40,500
19,500
56,500
22,500
852
40,500
18,500
57,000
21,000
852
42,500
16,500
59,000
19,000
852
44,900
14,100
62,800
16,500
852
43,600
16,100
60,400
19,000
852
48,900
11,100
68,300
13,600
852
50,800
9,900
67,900
11,500
852
52,400
9,300
70,000
10,900
852
52,000
9,100
69,500
10,600
852
52,900
8,200
74,300
10,300
824
17,200
2,200
22,700
2,600
824
35,500
4,600
51,500
5,800
824
52,700
6,800
--824
52,900
6,400
75,500
7,700
824
53,300
5,800
75,100
7,200
824
53,400
5,300
75,900
6,500
824
54,100
4,600
77,000
5,700
824
54,100
4,600
76,200
5,700
824
53,800
5,100
75,900
6,400
824
53,700
5,600
75,500
7,100
824
54,000
5,400
76,000
7,100
H754/824
54,400
5,700
76,500
7,500
754
55,000
5,200
77,700
7,000
754
55,500
4,500
78,300
6,800
754
55,500
4,500
78,300
6,600
754
46,800
3,200
49,600
4,300
754
46,800
3,200
51,000
3,700
754
46,800
3,200
50,800
4,300
754
46,800
3,200
50,400
5,200
754
46,800
3,200
50,300
3,900
754
46,800
3,200
49,700
4,100
792
56,700
3,300
59,200
3,800
729
57,400
2,600
59,900
3,100
729
53,600
2,500
56,100
3,000

4
Table 1, cont. Description of the March Basic Current Population Survey and Annual Social
and Economic Supplement Sample Cases
Time period

Basic CPSB sampled addresses
Total (CPS ASECC/ADSD + basic
Number of
eligible
CPS) sampled addresses eligible
sample
PSUsA
Interviewed
Not interviewed Interviewed
Not interviewed

1986 to 1988
729
57,000
2,500
59,500
3,000
I629/729
1985
57,000
2,500
59,500
3,000
1982 to 1984
629
59,000
2,500
61,500
3,000
1980 to 1981
629
65,500
3,000
68,000
3,500
1977 to 1979
614
55,000
3,000
58,000
3,500
1976
624
46,500
2,500
49,000
3,000
1973 to 1975
461
46,500
2,500
49,000
3,000
J449/461
1972
45,000
2,000
45,000
2,000
1967 to 1971
449
48,000
2,000
48,000
2,000
1963 to 1966
357
33,400
1,200
33,400
1,200
1960 to 1962
333
33,400
1,200
33,400
1,200
1959
330
33,400
1,200
33,400
1,200
Source: U.S. Census Bureau, Current Population Survey, 1959-2024 Annual Social and Economic Supplement.
A
PSUs are primary sampling units.
B
CPS is the Current Population Survey.
C
CPS ASEC is the Annual Social and Economic Supplement of the Current Population Survey.
D
The CPS ASEC was referred to as the Annual Demographic Supplement (ADS) until 2002.
E
Starting with 2022, the number of interviewed and not interviewed cases are rounded to the nearest 500
due to disclosure review board policy. Therefore, numbers may not sum to totals due to rounding.
F
The 2014 CPS ASEC Redesign indicates the subsample of the basic CPS households which received the
redesigned ASEC questionnaire incorporating new income and health insurance questions.
G
The 2014 CPS ASEC Traditional indicates the subsample of the basic CPS households which received the
same ASEC questionnaire that was used in the 2013 CPS ASEC.
H
The Census Bureau redesigned the CPS following the Census 2000. During phase-in of the new design,
addresses from the new and old designs were in the sample.
I
The Census Bureau redesigned the CPS following the 1980 Decennial Census of Population and Housing.
J
The Census Bureau redesigned the CPS following the 1970 Decennial Census of Population and Housing.

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 are prepared monthly as part of the Census Bureau’s
Population Estimates Program.6
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:
6

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 (2019c).

5
•
•
•

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:
•
•
•
•

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 behind the survey date,
it is necessary to make short-term projections of these components to develop the estimate
for the survey date.
The estimation procedure of the CPS ASEC includes a further adjustment to give married
and unmarried partners the same weight.
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

6
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.
Answers to questions about money income often depend on the memory or knowledge of
one person in a household. Recall problems can cause underestimates of income in survey
data because it is easy to forget minor or irregular sources of income. Respondents may
also misunderstand what the Census Bureau considers money income or may simply be
unwilling to answer these questions correctly because the questions are considered too
personal. For more details, please refer to Appendix C of U.S. Census Bureau (1993).
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 cases eligible for the 2024 ASEC, the
basic CPS household-level unweighted nonresponse rate was 28.5 percent. The householdlevel unweighted nonresponse rate for the ASEC was an additional 17.1 percent. These two
nonresponse rates lead to a combined supplement unweighted nonresponse rate of 40.7
percent.7
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

7

Because the ASEC is at the household level, the overall/combined ASEC response rate is a product of the
basic CPS response rate and the ASEC response rate.

7
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
CPS ASEC, the unweighted item refusal rates range from 0.0 percent to 2.0 percent. The
unweighted item allocation rates range from 21.1 percent to 73.9 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 March 2024 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.
A common measure of survey coverage is the coverage ratio, calculated as the estimated
population before poststratification divided by the independent population control. Table 2
shows March 2024 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 2. Current Population Survey Coverage Ratios: March 2024
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
0.85
0.84
0.91
0.90
0.69
0.61
0.78
0.79
0.80
0.81
16-19 0.80
0.82
0.78
0.85
0.82
0.72
0.62
0.75
0.76
0.84
0.80
20-24 0.79
0.81
0.77
0.81
0.80
0.69
0.61
0.92
0.82
0.88
0.76
25-34 0.82
0.82
0.81
0.87
0.86
0.58
0.58
0.84
0.82
0.87
0.85
35-44 0.89
0.87
0.91
0.91
0.95
0.66
0.70
0.85
0.93
0.85
0.94
45-54 0.92
0.90
0.94
0.93
0.99
0.75
0.76
0.84
0.82
0.78
0.94
55-64 0.93
0.90
0.96
0.93
1.00
0.76
0.84
0.80
0.84
0.86
0.97
65+
1.08
1.06
1.09
1.08
1.12
1.04
1.04
0.96
0.90
0.98
1.00
15+
0.92
0.90
0.93
0.94
0.97
0.74
0.76
0.85
0.85
0.86
0.90
0+
0.90
0.89
0.91
0.93
0.96
0.73
0.73
0.84
0.84
0.84
0.88
Source: U.S. Census Bureau, Current Population Survey, March 2024.
A
The Residual race group includes cases indicating a single race other than White or Black, and cases
indicating two or more races.

8
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.
B

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.8 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 aware that estimates in the reports, Income in the United States: 2023,
Poverty in the United States: 2023, and Health Insurance Coverage in the United States: 2023
use the internal CPS ASEC file. The Census Bureau must keep survey responses
confidential, so disclosure avoidance techniques are applied to files prior to public release.
Therefore, some estimates using the microdata files may differ from the estimates provided
in the reports.
Caution should be used when comparing estimates of the Hispanic population over time.
No independent population control totals for people of Hispanic origin were used before
1985.
Caution should also be used when comparing CPS ASEC results from different years. Below,
more detail is provided on several reasons for caution when comparing estimates across
years.
Nonresponse Bias in the CPS ASEC. Data users should exercise caution when comparing
estimates for data years 2019, 2020, and 2021 from the reports or from the microdata files
to those from other years due to the effects that the coronavirus (COVID-19) had on
interviewing and response rates. The Census Bureau administers the CPS ASEC each year
between February and April by telephone and in-person interviews, with most data
collected in March. In 2020, data collection faced extraordinary circumstances due to the
onset of the COVID-19 pandemic; the Census Bureau suspended in-person interviews and
closed telephone contact centers. The response rate for the CPS basic household survey
declined to 73 percent in March 2020, from 82 percent in March 2019. Pre-pandemic
response rates were regularly above 80 percent.
Although standard collection procedures have resumed, response rates remain below prepandemic levels. The response rate for the CPS basic household survey was 67 percent in
March 2024.9 Lower response rates could affect estimates if respondents differ from
nonrespondents. Using administrative data, Census Bureau researchers have documented
that nonrespondents in the 2020 to 2023 surveys are less similar to respondents than in

8
9

Survey processes include, but are not limited to, question wording, universe, sampling frame, interview
modes, and weighting.
This response rate is specifically for the March CPS and differs from the response rate obtained using the
value in the “Nonresponse” section that is for the full CPS sample that was eligible for ASEC.

9
earlier years.10 For more details on how sample differences and the associated
nonresponse bias impact income and official poverty estimates, refer to U.S. Census Bureau
(2023b). The effects of data collection issues on 2020 health insurance coverage estimates
are detailed in the working paper, U.S. Census Bureau (2020).
Change in Processing System. Data users should exercise caution when comparing estimates
from the CPS ASEC for data years 2018 through 2023 to estimates from earlier years. An
updated data processing system was implemented beginning with data year 2018 estimates.
This system introduced demographic edit changes to account for same-sex couples, revised
procedures for editing income and health insurance variables, and added several new income
and health insurance variables. Changes to the editing procedures encompassed both changes
to the resolution of logically inconsistent data and changes to the imputation methods. The
2019 through 2024 CPS ASEC estimates for data years 2018 through 2023 can be compared to
the 2018 CPS ASEC Bridge Files11, which contain data year 2017 estimates, and to the 2017
CPS ASEC Research Files12, which contain estimates for data year 2016. The 2017 Research File
and the 2018 Bridge File both use the new processing system and serve as a bridge between
the legacy production files and the updated processing system. Data users should be aware
that the estimates from the 2017 and 2018 CPS ASEC Files for data years 2016 and 2017 using
the legacy processing system are not directly comparable to estimates from the 2019 CPS ASEC
through 2024 CPS ASEC.
Change in Questionnaire. In 2014, the ASEC questionnaire was redesigned to incorporate new
income and health insurance questions. Due to the differences in measurement, health
insurance estimates for 2014-2017 CPS ASEC for data years 2013-2016 are not directly
comparable to health insurance estimates for previous years.13 For income and poverty
estimates, when survey changes had statistically significant impacts, comparisons should be
made by adjusting historical published estimates to approximate the magnitude of those
impacts.14
Change in Census-Based Controls. Data users should exercise caution when comparing
estimates for 2023 from the microdata file or from the ASEC reports, Income in the United
States: 2023, Poverty in the United States: 2023, and Health Insurance Coverage in the United
States: 2023 (which reflect 2020 Census-based controls15), with estimates from 2020 or
10
11
12
13
14
15

For additional information, please refer to Rothbaum & Bee (2021), U.S. Census Bureau (2021c), U.S.
Census Bureau (2022c), and U.S. Census Bureau (2023b).
For additional information on the 2018 CPS ASEC Bridge Files, please refer to the Documentation and
User Notes in U.S. Census Bureau (2019b).
For additional information on the 2017 CPS ASEC Research Files, please refer to the Documentation and
User Notes in U.S. Census Bureau (2019a).
For more information, refer to U.S. Census Bureau (2019d).
For more details on the adjustment for these comparisons, refer to U.S. Census Bureau (2019e).
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. Please refer to U.S. Census (2021d) for more information on this methodology.

10
earlier microdata files or ASEC Reports. Estimates from data years 2021 through 2023
(March 2022 CPS through March 2024 CPS, respectively) reflect 2020 Census-based
controls. Estimates from data years 2011 through 2020 (March 2012 CPS through March
2021 CPS) reflect 2010 Census-based controls. Estimates from data years 2001 through
2010 (March 2002 CPS through March 2011 CPS) reflect 2000 Census-based controls and
estimates from data years 1993 through 2000 (March 1994 CPS through March 2001 CPS)
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
could create small differences between estimates.
Users should also exercise caution because of changes caused by the phase-in of the 2010
Census files (refer to “Basic CPS”).16 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.
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 (2019c) and Brooks & Bailar (1978).
Estimation of Median Incomes. The Census Bureau has changed the methodology for
computing median income over time. The Census Bureau has computed medians using
either Pareto interpolation or linear interpolation. Currently, we are using linear
interpolation to estimate all medians. Pareto interpolation assumes a decreasing density of
population within an income interval, whereas linear interpolation assumes a constant
density of population within an income interval.
16

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

11
The Census Bureau calculated estimates of median income and associated standard
errors for 1979 through 1987 using Pareto interpolation if the estimate was larger than
$20,000 for people or $40,000 for families and households. We calculated estimates of
median income and associated standard errors for 1976, 1977, and 1978 using Pareto
interpolation if the estimate was larger than $12,000 for people or $18,000 for families and
households. All other estimates of median income and associated standard errors for 1976
through 2023 (2024 CPS ASEC), and almost all of the estimates of median income and
associated standard errors for 1975 and earlier, were calculated using linear interpolation.
Thus, use caution when comparing median incomes above $12,000 for people or $18,000
for families and households for different years. Median incomes below those levels are
more comparable from year to year since they have always been calculated using linear
interpolation. For an indication of the comparability of medians calculated using Pareto
interpolation with medians calculated using linear interpolation, refer to U.S. Census
Bureau (1978) and U.S. Census Bureau (1993).
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.
The tables in Income in the United States: 2023, Poverty in the United States: 2023, and
Health Insurance Coverage in the United States: 2023 list estimates followed by a number
labeled “Margin of error (±).” This number can be added to and subtracted from the

12
estimates to calculate upper and lower bounds of the 90-percent confidence interval. For
example, Health Insurance Coverage in the United States: 2023 shows the numbers for
health insurance. For the statement, “8.0 percent of people were uninsured for the
entire calendar year,” the 90-percent confidence interval for the estimate, 8.0 percent, is
8.0 (± 0.2) percent, or 7.8 percent to 8.2 percent.17
Estimating Standard Errors. The Census Bureau uses replication methods to estimate the
standard errors of CPS and ASEC 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 2024 CPS ASEC microdata file.
1. Direct estimates created from replicate weighting methods;
2. Generalized variance estimates created from generalized variance function
(GVF) 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 GVF parameters provide a method of balancing accuracy with resource usage as well
as a smoothing effect on standard error estimates. For more information on calculating
direct estimates, refer to the “Replicate Weighting” section. For more information on GVF
estimates, refer to the “Generalized Variance Parameters” section.
The Income in the United States: 2023, Poverty in the United States: 2023, and Health
Insurance Coverage in the United States: 2023 reports use replicate weights to calculate the
margins of error of the estimates seen in tables and throughout the reports. In 2009, the
Census Bureau released replicate weights for the 2005 through 2009 CPS ASEC collection
years and has released replicate weights for each year since with the release of the CPS
ASEC public use data. Since the published GVF parameters generally underestimated
standard errors, standard errors produced using direct estimates may be higher than in
previous reports. For most CPS ASEC estimates, the increase in standard errors from GVF
to direct estimates will not alter the findings. However, marginally significant differences
using the GVF may not be significant using replicate weights.
The examples in this source and accuracy statement are for guidance calculating standard
errors using the generalized variance parameters. The use of generalized variance
parameters is the recommended method of calculating standard errors for data users who
do not have the ability to calculate the standard errors using replicate weights.
17

Note that the confidence interval here is calculated in a different way than the confidence interval given
in Illustration 3. The margin of errors within the tables in the reports are calculated using direct
estimates, whereas the standard errors within the illustrations later in this document are calculated
using generalized variance estimates.

13
Replicate Weighting. The Census Bureau is releasing public use replicate weight files
for the 2024 CPS ASEC that can be matched to the microdata files.
Replicate estimates are created using each of the 160 weights independently to create 160
replicate estimates. For point estimates, multiply the replicate weights by the item of
interest at the record level (either an indicator variable to determine the number of people
with a characteristic or a variable that contains some value) and tally the weighted values
to create the 160 replicate estimates. Use these replicate estimates in formula (1) below to
calculate the total variance for the item of interest. For example, say that the item of
interest is the number of males. Tally the weights for all the records that indicated male to
create the 160 replicate estimates of the number of males. Then use these estimates in the
formula to calculate the total variance for the number of males.
Calculate variance estimates for the estimates using:
4
̂
̂ 2
var(𝜃̂0 ) = 160 ∑160
𝑖=1 (𝜃𝑖 − 𝜃0 )

(1)

where 𝜃̂0 is the estimate of the statistic of interest, such as a point estimate or proportion,
using the weight for the full sample, and 𝜃̂𝑖 are the replicate estimates of the same statistic
using the replicate weights. The standard error is the square root of the variance.
For more information on using replicate weights and calculating direct estimates, refer to
U.S. Census Bureau (2021b).
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.
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.
In this source and accuracy statement:
•

Tables 4 through 14 provide illustrations for calculating standard errors;

14
•
•
•
•
•

Table 15 provides the GVF parameters for labor force estimates;
Table 16 provides GVF parameters for characteristics from the 2024 CPS ASEC;
Tables 17 and 18 provide correlation coefficients for comparing estimates from
consecutive years;
Table 19 provides correlation coefficients between race and subgroups; and
Tables 20 and 21 provide factors and population controls to derive state and
regional parameters.

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 3 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
Race/ethnicity group of interest
use in standard error calculations
Total populationA

Total

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

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

HispanicB population
Two or more racesC – employment/unemployment and
educational attainment characteristics
Two or more racesC – all other characteristics

HispanicB
Black
Asian, AIAN, NHOPI

Source: U.S. Census Bureau, Current Population Survey, internal data files.
A
For standard error calculations using parameters from the CPS GVF table (Table 15), ‘Total or White’
should be used.
B
Hispanics may be any race.
C
Two or more races refers to the group of cases self-classified as having two or more races.

15
Note: 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).

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 15). If the estimate is
using ASEC data, the GVF parameters will come from the ASEC GVF table (Table 16).
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:
𝑠𝑥 = √𝑎𝑥 2 + 𝑏𝑥

(2)

Here x is the size of the estimate, and a and b are the parameters in Table 15 or 16
associated with the particular type of characteristic.
Illustration 1
Suppose there were 2,998,000 unemployed females (ages 16 and up) in the civilian labor
force. Table 4 shows how to use the appropriate parameters from Table 15 and Formula
(2) to estimate the standard error and confidence interval.
Table 4. Illustration of Standard Errors of Estimated Numbers
Number of unemployed females in the civilian labor force (x)
2,998,000
a-parameter (a)
-0.000028
b-parameter (b)
2,788
Standard error
90,000
90-percent confidence interval
2,850,000 to 3,146,000
Source: U.S. Census Bureau, Current Population Survey, March 2024.

The standard error is calculated as
𝑠𝑥 = √−0.000028 × 2,998,0002 + 2,788 × 2,998,000
which, rounded to the nearest thousand, is 90,000. The 90-percent confidence interval is
calculated as 2,998,000 ± 1.645 × 90,000.
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.
Illustration 2
Suppose there were 62,300,000 married-couple family households. Table 5 shows how to
use the appropriate parameters from Table 16 and Formula (2) to estimate the standard
error and confidence interval.

16
Table 5. Second Illustration of Standard Errors of Estimated Numbers
Number of married-couple family households (x)
62,300,000
a-parameter (a)
-0.000008
b-parameter (b)
2,587
Standard error
361,000
90-percent confidence interval
61,710,000 to 62,890,000
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.

The standard error is calculated as
𝑠𝑥 = √−0.000008 × 62,300,0002 + 2,587 × 62,300,000
which, rounded to the nearest thousand, is 361,000. The 90-percent confidence interval is
calculated as 62,300,000 ± 1.645 × 361,000.
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 15 or 16 as indicated
by the numerator.
The approximate standard error, 𝑠𝑦,𝑝 , of an estimated percentage can be obtained by using
the formula:
𝑏

𝑠𝑦,𝑝 = √𝑦 𝑝(100 − 𝑝)

(3)

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 15 or 16 associated with the characteristic in the numerator of the
percentage.
Illustration 3
The report, Health Insurance Coverage in the United States: 2023, shows that there were
26,440,000 out of 331,700,000 people, or 8.0 percent, who did not have health insurance.
Table 6 shows how to use the appropriate parameter from Table 16 and Formula (3) to
estimate the standard error and confidence interval.

17
Table 6. Illustration of Standard Errors of Estimated Percentages
8.0
Percentage of people without health insurance (p)
Base (y)
331,700,000
b-parameter (b)
4,825
Standard error
0.10
90-percent confidence interval
7.8 to 8.2
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.

The standard error is calculated as

𝑠𝑦,𝑝 = √

4,825

331,700,000

× 8.0 × (100.0 − 8.0) = 0.10

and the 90-percent confidence interval for the estimated percentage of people without
health insurance is from 7.8 to 8.2 percent (i.e., 8.0 ± 1.645 × 0.10).
Standard Errors of Estimated Differences. The standard error of the difference between
two sample estimates is approximately equal to
𝑠|𝑥1−𝑥2| = √𝑠𝑥1 2 + 𝑠𝑥2 2 − 2𝑟𝑠𝑥1 𝑠𝑥2

(4)

where 𝑠𝑥1 and 𝑠𝑥2 are the standard errors of the estimates, 𝑥1 and 𝑥2 . The estimates can be
numbers, percentages, ratios, etc. Tables 17 and 18 contain the correlation coefficient, r, for
CPS year-to-year comparisons for CPS poverty, income, and health insurance estimates of
numbers and proportions. Table 19 contains the correlation coefficient r for making
comparisons between race categories that are subsets of one another. For example, to
compare the number of people in poverty who listed White as their only race to the
number of people in poverty who are White alone or in combination with another race, a
correlation coefficient is needed to account for the large overlap between the two groups.
For making other comparisons (including race overlapping where one group is not a
complete subset of the other), assume that r equals zero. Making this assumption will result
in accurate estimates of standard errors for the difference between two estimates 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 4
Suppose there were 28,480,000 men over age 24 who were never married and 11,160,000
men over age 24 who were divorced. The apparent difference is 17,320,000. Table 7 shows
how to use Formulas (2) and (4) with r = 0 and the appropriate parameters from Table 16
to estimate the standard errors and confidence intervals.

18
Table 7. Illustration of Standard Errors of Estimated Differences
Difference
Never married (x1)
Divorced (x2)
Number of males over age 24
28,480,000
11,160,000
17,320,000
a-parameter (a)
-0.000008
-0.000008
b-parameter (b)
2,642
2,642
Standard error
262,000
169,000
312,000
90-percent confidence
28,050,000 to
10,880,000 to
16,810,000 to
interval
28,910,000
11,440,000
17,830,000
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.

The standard error of the difference is calculated as
𝑠|𝑥1−𝑥2| = √262,0002 + 169,0002
which, rounded to the nearest thousand, is 312,000. The 90-percent confidence interval
around the difference is calculated as 17,320,000 ± 1.645 × 312,000. Since this interval
does not include zero, we can conclude with 90-percent confidence that the number of
never-married men over age 24 was higher than the number of divorced men over age 24.
Illustration 5
The report, Poverty in the United States: 2023, shows that 10,780,000 out of 71,950,000
children, or 15.0 percent, were reported as in poverty in 2022, and that 11,020,000 out of
72,220,000 children, or 15.3 percent, were in poverty in 2023. The apparent difference is
0.3 percent. Table 8 shows how to use the appropriate parameter from Table 16, the
correlation coefficient from Table 18, and Formulas (3) and (4) to estimate the standard
errors and confidence intervals.
Table 8. Second Illustration of Standard Errors of Estimated Differences
Difference
2022 (x1)
2023 (x2)
Percentage of children in poverty (p)
15.0
15.3
0.3
Base (y)
71,950,000
72,220,000
b-parameter (b)
4,295A
5,813
Correlation coefficient (r)
0.45
Standard error
0.28
0.32
0.32
90-percent confidence interval
14.5 to 15.5
14.8 to 15.8
-0.2 to 0.8
Source: U.S. Census Bureau, Current Population Survey, 2023-2024 Annual Social and Economic Supplement.
A This value comes from the Source and Accuracy Statement for the 2023 Annual Social and Economic
Supplement, Appendix H, Table 16 in U.S. Census Bureau (2023a). For additional information, refer to the
“Year-to-Year Factors” section.

The standard error of the difference is calculated as
𝑠|𝑥1−𝑥2| = √0.282 + 0.322 − 2 × 0.45 × 0.28 × 0.32 = 0.32
and the 90-percent confidence interval around the difference is calculated as 0.3 ± 1.645 ×
0.32. Since this interval includes zero, we conclude with 90-percent confidence that the

19
percentage of children in poverty in 2022 is not significantly different than the
percentage of children in poverty in 2023.
Standard Errors of Estimated Ratios. Certain estimates may be calculated as the ratio of
two numbers. Compute the standard error of a ratio, x/y, using
𝑥

𝑠

2

𝑠𝑦 2

𝑠𝑥⁄𝑦 = 𝑦 √( 𝑥𝑥 ) + ( 𝑦 ) − 2𝑟

𝑠𝑥 𝑠𝑦

(5)

𝑥𝑦

The standard error of the numerator, sx, and that of the denominator, sy, may be calculated
using formulas described earlier. In Formula (5), r represents the correlation between the
numerator and the denominator of the estimate.
For one type of ratio, the denominator is a count of families or households and the
numerator is a count of people in those families or households with a certain characteristic.
If there is at least one person with the characteristic in every family or household, use 0.7
as an estimate of r. An example of this type is the average number of children per family
with children.
For all other types of ratios, r is assumed to be zero. Examples are the average number of
children per family and the family poverty rate. If r is actually positive (negative), then this
procedure will provide an overestimate (underestimate) of the standard error of the ratio.
Note: For estimates expressed as the ratio of x per 100 y or x per 1,000 y, multiply
Formula (5) by 100 or 1,000, respectively, to obtain the standard error.
Illustration 6
Suppose there were 11,790,000 males working part-time and 18,250,000 females working
part-time. The ratio of males working part-time to females working part-time would be
0.646 or 64.6 percent. Table 9 shows how to use the appropriate parameters from Table 15
and Formulas (2) and (5) with r = 0 to estimate the standard errors and confidence
intervals.
Table 9. Illustration of Standard Errors of Estimated Ratios
Males (x)
Females (y)
Number who work part-time
11,790,000
18,250,000
a-parameter (a)
-0.000031
-0.000028
b-parameter (b)
2,947
2,788
Standard error
174,000
204,000
90-percent confidence interval 11,500,000 to 12,080,000 17,910,000 to 18,590,000
Source: U.S. Census Bureau, Current Population Survey, March 2024.

The standard error is calculated as

Ratio
0.646
0.0120
0.626 to 0.666

20

𝑠𝑥⁄𝑦

11,790,000
174,000 2
204,000 2
√
(
) +(
) = 0.0120
=
18,250,000 11,790,000
18,250,000

and the 90-percent confidence interval is calculated as 0.646 ± 1.645 × 0.0120.
Illustration 7
The report, Poverty in the United States: 2023, shows that the number of families below the
poverty level was 7,009,000 and the total number of families was 84,710,000. The ratio of
families below the poverty level to the total number of families would be 0.083 or 8.3
percent. Table 10 shows how to use the appropriate parameters from Table 16 and
Formulas (2) and (5) with r = 0 to estimate the standard errors and confidence intervals.
Table 10. Second Illustration of Standard Errors of Estimated Ratios
In poverty (x)
Total (y)
Ratio (in percent)
Number of families
7,009,000
84,710,000
8.3
a-parameter (a)
-0.000011
-0.000008
b-parameter (b)
2,767
2,587
Standard error
137,000
402,000
0.17
90-percent confidence interval
6,784,000 to 7,234,000 84,050,000 to 85,370,000
8.0 to 8.6
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.

The standard error is calculated as

𝑠𝑥⁄𝑦

7,009,000
137,000 2
402,000 2
√
(
) +(
) = 0.0017 = 0.17%
=
84,710,000 7,009,000
84,710,000

and the 90-percent confidence interval of the percentage is calculated as 8.3 ± 1.645 × 0.17.
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. (Refer to
the “Standard Errors and Their Use” section 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.

Using Formula (3) and the base of the distribution, calculate the standard error of
50 percent.

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.

21
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−𝑁1
𝑁2 −𝑁1

(𝐴2 − 𝐴1 ) + 𝐴1

(6)

where

4.

Xp

=

estimated upper and lower bounds 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.

=

for distribution of percentages: the value 100.

p

=

the values obtained in Step 2.

A1, A2

=

the lower and upper bounds, respectively, of the interval
containing Xp.

N1, N2

=

for distribution of numbers: the estimated number of units
(people, households, etc.) with values of the characteristic less
than or equal to A1 and A2, respectively.

=

for distribution of percentages: the estimated percentage of
units (people, households, etc.) having values of the
characteristic less than or equal to A1 and A2, respectively.

Divide the difference between the two points determined in step 3 by 2 to obtain the
standard error of the median.

Note: Median incomes and their standard errors calculated as below may differ from
those in published tables and reports showing income, since narrower income
intervals were used in those calculations.

22
Illustration 8
The report, Income in the United States: 2023, shows that there were 132,200,000
households, and their income was distributed as shown in Table 11.
Table 11. Distribution of Household Income for Illustration 8
Number of
Cumulative number of
Cumulative percent
Income level
households
households
of households
Under $5,000
4,068,000
4,068,000
3.08%
$5,000 to $9,999
1,782,000
5,850,000
4.43%
$10,000 to $14,999
3,972,000
9,822,000
7.43%
$15,000 to $24,999
8,852,000
18,670,000
14.12%
$25,000 to $34,999
9,189,000
27,860,000
21.07%
$35,000 to $49,999
13,560,000
41,420,000
31.33%
$50,000 to $74,999
20,740,000
62,160,000
47.02%
$75,000 to $99,999
16,030,000
78,190,000
59.15%
A
$100,000 and over
54,020,000
132,200,000
100.00% A
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.
A May not sum to totals due to rounding.

1.

Using Formula (3) with b = 3,190 from Table 16, the standard error of 50 percent on
a base of 132,200,000 is about 0.25 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 49.75 and 50.25.

3.

The lower and upper limits for the interval in which the percentage limits falls are
$75,000 and $100,000, respectively.
Then the estimated numbers of households with an income less than or equal to
$75,000 and $100,000 are 62,160,000 and 78,190,000, respectively.
Using Formula (6), the lower limit for the confidence interval of the median is found
to be about (rounded to four significant digits)
𝑋0.4975 =

0.4975 × 132,200,000 − 62,160,000
(100,000 − 75,000) + 75,000 = 80,630
78,190,000 − 62,160,000

23
Similarly, the upper limit is found to be about
𝑋0.5025 =

0.5025 × 132,200,000 − 62,160,000
(100,000 − 75,000) + 75,000 = 81,660
78,190,000 − 62,160,000

Thus, a 68-percent confidence interval for the median income for households is
from $80,630 to $81,66018.
4.

The standard error of the median is, therefore,
81,660 − 80,630
= 515.0
2

Accuracy of State Estimates. The redesign of the CPS following the 1980 census provided
an opportunity to increase efficiency and accuracy of state data. All strata are now defined
within state boundaries. The sample is allocated among the states to produce state and
national estimates with the required accuracy while keeping total sample size to a
minimum. Improved accuracy of state data was achieved with about the same sample size
as in the 1970 design.
Since the CPS is designed to produce both state and national estimates, the proportion of
the total population sampled and the sampling rates differ among the states. In general, the
smaller the population of the state the larger the sampling proportion. For example, in
Vermont, approximately 1 in every 250 households is sampled each month. In New York,
the sample is about 1 in every 2,000 households. Nevertheless, the size of the sample in
New York is four times larger than in Vermont because New York has a larger population.
Note: The Census Bureau recommends the use of 3-year averages to compare estimates
across states and 2-year averages to evaluate changes in state income and poverty
estimates over time. Refer to the “Standard Errors of Data for Combined Years”
section. Further, the Income in the United States and Poverty in the United States
reports no longer present state estimates. Therefore, the Census Bureau
recommends the American Community Survey (ACS) microdata file as the preferred
source for income and poverty state data in years 2006 (2005 estimates) to the
present.19 A questionnaire redesign introduced with the 2014 CPS ASEC and an
updated processing system introduced with the 2019 CPS ASEC each mark the start
of new time series for health insurance estimates in the CPS ASEC, so data users
should not create multiyear averages across these years.

18

19

Note that the median and confidence interval here does not match the median and confidence interval
given in the report, Income in the United States: 2023, because the mean and standard errors/margin of
errors were calculated in two different ways. The mean and margin of errors within the tables in the
report are calculated using direct estimates, whereas the mean and standard errors within the
illustration are calculated using generalized variance estimates.
For additional information on the American Community Survey, please refer to U.S. Census Bureau
(2022a).

24
Standard Errors of State Estimates. The standard error for a state may be obtained by
determining new state-level a- and b-parameters and then using these adjusted parameters
in the standard error formulas mentioned previously. To determine a new state-level bparameter (bstate), multiply the b-parameter from Table 15 or 16 by the state factor from
Table 20. To determine a new state-level a-parameter (astate), use the following:
(1)

If the a-parameter from Table 15 or 16 is positive, multiply it by the state
factor from Table 20.

(2)

If the a-parameter in Table 15 or 16 is negative, calculate the new state-level
a-parameter as follows:
−𝑏

𝑎𝑠𝑡𝑎𝑡𝑒 = 𝑃𝑂𝑃𝑠𝑡𝑎𝑡𝑒

𝑠𝑡𝑎𝑡𝑒

(7)

where POPstate is the state population found in Table 20.
Illustration 9
Suppose there were 14,510,000 people living in New York state who were born in the
United States. Table 12 shows how to use Formulas (2) and (7) and the appropriate
parameter, factor, and population from Tables 16 and 20 to estimate the standard error
and confidence interval.
Table 12. Illustration of Standard Errors of State Estimates
Number of people in New York born in the U.S. (x)
14,510,000
b-parameter (b)
2,642
New York state factor
1.19
State population
19,316,535
State b-parameter (bstate)
3,144
State a-parameter (astate)
-0.000163
Standard error
106,000
90-percent confidence interval
14,340,000 to 14,680,000
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.

Obtain the state-level b-parameter by multiplying the b-parameter, 2,642 by the state
factor, 1.19. This gives bstate = 2,642 × 1.19 = 3,144. Obtain the needed state-level aparameter by
𝑎𝑠𝑡𝑎𝑡𝑒 =

−3,144
= −0.000163
19,316,535

The standard error of the estimate of the number of people in New York state who were
born in the United States can then be found by using Formula (2) and the new state-level aand b- parameters, -0.000163 and 3,144, respectively. The standard error is given by
𝑠𝑥 = √−0.000163 × 14,510,0002 + 3,144 × 14,510,000

25
which, rounded to the nearest thousand, is 106,000.
Standard Errors of Regional Estimates. To compute standard errors for regional
estimates, follow the steps for computing standard errors for state estimates found in
“Standard Errors for State Estimates” using the regional factors and populations found in
Table 21.
Illustration 10
The report, Poverty in the United States: 2023, shows that there were 16,040,000 of
128,959,76020 people, or 12.4 percent, living in poverty in the South. Table 13 shows how
to use Formula (3) and the appropriate parameter, factor, and population from Tables 16
and 21 to estimate the standard error and confidence interval.
Table 13. Illustration of Standard Errors of Regional Estimates
Poverty rate in the South (p)
12.4
Base (y)
128,959,760
b-parameter (b)
5,368
South regional factor
1.13
Regional b-parameter (bregion)
6,066
Standard error
0.23
90-percent confidence interval
12.0 to 12.8
Source: U.S. Census Bureau, Current Population Survey, 2024 Annual Social and Economic Supplement.

Obtain the region-level b-parameter by multiplying the b-parameter, 5,368, by the South
regional factor, 1.13. This gives bregion = 5,368 × 1.13 = 6,066.
The standard error of the estimate of the poverty rate for people living in the South can
then be found by using Formula (3) and the new region-level b-parameter, 6,066. The
standard error is given by

𝑠𝑦,𝑝 = √

6,066
× 12.4 × (100 − 12.4) = 0.23
128,959,760

and the 90-percent confidence interval of the poverty rate for people living in the South is
calculated as 12.4  1.645  0.23.
Standard Errors of Groups of States. The standard error calculation for a group of states
is similar to the standard error calculation for a single state. First, calculate a new state
group factor for the group of states. Then, determine new state group a- and b-parameters.
Finally, use these adjusted parameters in the standard error formulas mentioned
previously.

20

Note that the populations provided in Table 21 are population controls and do not need to be rounded.
The population controls will differ from the population estimates provided in the reports.

26
Use the following formula to determine a new state group factor:
𝑠𝑡𝑎𝑡𝑒 𝑔𝑟𝑜𝑢𝑝 𝑓𝑎𝑐𝑡𝑜𝑟 =

∑𝑛
𝑖=1 𝑃𝑂𝑃𝑖 ×𝑠𝑡𝑎𝑡𝑒 𝑓𝑎𝑐𝑡𝑜𝑟𝑖

(8)

∑𝑛
𝑖=1 𝑃𝑂𝑃𝑖

where POPi and state factori are the population and factor for state i from Table 20. To
obtain a new state group b-parameter (bstate group), multiply the b-parameter from Table 15
or 16 by the state group factor obtained by Formula (8). To determine a new state group aparameter (astate group), use the following:
(1)

If the a-parameter from Table 15 or 16 is positive, multiply it by the state
group factor determined by Formula (8).

(2)

If the a-parameter in Table 15 or 16 is negative, calculate the new state group
a-parameter as follows:
𝑎𝑠𝑡𝑎𝑡𝑒 𝑔𝑟𝑜𝑢𝑝 =

−𝑏𝑠𝑡𝑎𝑡𝑒 𝑔𝑟𝑜𝑢𝑝

(9)

∑𝑛
𝑖=1 𝑃𝑂𝑃𝑖

Illustration 11
Suppose the state group factor for the state group Illinois-Indiana-Michigan was required.
The appropriate factor would be
𝑠𝑡𝑎𝑡𝑒 𝑔𝑟𝑜𝑢𝑝 𝑓𝑎𝑐𝑡𝑜𝑟 =

12,368,973 × 1.17 + 6,792,112 × 1.11 + 9,939,507 × 1.11
= 1.14
12,368,973 + 6,792,112 + 9,939,507

Standard Errors of Data for Combined Years. Sometimes estimates for multiple years
are combined to improve precision. For example, suppose x is an average derived from n
n

consecutive years’ data, i.e., x = 
i =1

xi
, where the xi are the estimates for the individual
n

years. Use the formulas described previously to estimate the standard error, s xi , of each
year’s estimate. Then the standard error of x is
𝑠𝑥̅ =

𝑠𝑥
𝑛

(10)

where
𝑠𝑥 = √∑𝑛𝑖=1 𝑠𝑥2𝑖 + 2𝑟 ∑𝑛−1
𝑖=1 𝑠𝑥𝑖 𝑠𝑥𝑖+1

(11)

and s xi are the standard errors of the estimates xi. Tables 17 and 18 contain the correlation
coefficients, r, for the correlation between consecutive years i and i+1. Correlation between
nonconsecutive years is zero. The correlations were derived for income, poverty, and

27
health insurance estimates, but they can be used for other types of estimates where the
year-to-year correlation between identical households is high.
The Census Bureau recommends the use of 3-year average estimates for certain small
population subgroups21 (refer also to the “Accuracy of State Estimates” section). Two-year
moving averages are recommended for these small population subgroups for comparisons
across adjacent years.
Illustration 12
The report, Poverty in the United States: 2023, provides the percentages of families in
poverty. Suppose the 2021-2023 3-year average percentage of families with female
householder, no spouse present, in poverty was 22.6. Suppose the percentages and bases
for 2021, 2022, and 2023 were 23.0, 23.0, and 21.8 percent and 15,620,000, 15,040,000,
and 15,180,000, respectively. Table 14 shows how to use the appropriate parameters and
correlation coefficients from Tables 16 and 18 and Formulas (3), (10), and (11) to estimate
the standard errors and confidence intervals.
Table 14. Illustration of Standard Errors of Data for Combined Years
2021-2023
2021
2022
2023
Average
Percentage of families with female
householder, no spouse
present, in poverty (p)
23.0
23.0
21.8
22.6
Base (y)
15, 620,000
15,040,000
15,180,000
A
B
b-parameter (b)
5,073
5,660
2,767
Correlation coefficient (r)
0.35
Standard error
0. 76
0. 82
0.56
0.51
90-percent confidence interval
21.7 to 24.3
21.7 to 24.3
20.9 to 22.7
21.8 to 23.4
Source: U.S. Census Bureau, Current Population Survey, 2022-2024 Annual Social and Economic Supplement.
A This value comes from the Source and Accuracy Statement for the 2022 Annual Social and Economic
Supplement, Appendix G, Table 16 in U.S. Census Bureau (2022b). For additional information, refer to the
“Year-to-Year Factors” section.
B This value comes from the Source and Accuracy Statement for the 2023 Annual Social and Economic
Supplement, Appendix H, Table 16 in U.S. Census Bureau (2023a). For additional information, refer to the
“Year-to-Year Factors” section.

The standard error of the 3-year average is calculated as
𝑠𝑥̅ =

1.52
= 0.51
3

where
𝑠𝑥 = √0.762 + 0.822 + 0.562 + (2 × 0.35 × 0.76 × 0.82) + (2 × 0.35 × 0.82 × 0.56) = 1.52
21

Estimates of characteristics of the Native Hawaiian and Other Pacific Islander (NHOPI) population based
on a single-year sample would be unreliable due to the small size of the sample that can be drawn from
either population. Accordingly, such estimates are based on multiyear averages.

28
The 90-percent confidence interval for the 3-year average percentage of families with a
female householder, no spouse present, in poverty is 22.6  1.645  0.51.
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 refer to Bureau of Labor Statistics (2006).
Year-to-Year Factors. In past years, the Census Bureau published a table of year factors
for the CPS ASEC Supplement in the Source and Accuracy Statement. User demand for these
factors has diminished with the introduction of replicate weights. Data users producing
estimates from prior years should consult the Source and Accuracy Statements covering
the years of their analysis to estimate standard errors.
Technical Assistance. If you require assistance or additional information, please contact
the Demographic Statistical Methods Division via e-mail at
dsmd.source.and.accuracy@census.gov.

29
Table 15. Parameters for Computation of Standard Errors for Labor Force Characteristics:
March 2024
Characteristic

a

b

-0.000013
-0.000013
-0.000017

2,481
2,432
3,244

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

-0.000031
-0.000028
-0.000261

2,947
2,788
3,244

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

-0.000117
-0.000249
-0.000190
-0.001425

3,601
3,465
3,191
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
Men
Women
Both sexes, 16 to 19 years

-0.000245
-0.000537
-0.000399
-0.004078

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

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

-0.000087
-0.000172
-0.000158
-0.000909

3,316
3,276
3,001
3,316

Total or White
Civilian labor force, employed
Not in labor force
Unemployed

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 the groups self-classified as having two or more races, use the Asian, AIAN,
NHOPI parameters for all employment characteristics.

30
Table 16. Parameters for Computation of Standard Errors for People and Families: 2024 Annual
Social and Economic Supplement
Total

Characteristics

White

Black

Asian, AIAN,
NHOPIA
a
b

a

b

a

b

a

b

Educational Attainment

-0.000011

3,777

-0.000014

4,081

-0.000045

3,665

-0.000089

Employment

-0.000013

2,481

-0.000013

2,481

-0.000117

3,601

-0.000245

People by family income

-0.000020

6,581

-0.000021

6,096

-0.000083

6,856

-0.000010
-0.000021
-0.000019

3,344
3,362
3,155

-0.000012
-0.000025
-0.000023

3,428
3,515
3,302

-0.000043
-0.000090
-0.000080

-0.000082
-0.000038
-0.000040
-0.000054
-0.000015

3,588
3,365
3,283
3,176
4,825

-0.000103
-0.000045
-0.000049
-0.000064
-0.000018

3,847
3,402
3,490
3,380
5,092

-0.000008
-0.000012

2,642
3,903

-0.000010
-0.000014

-0.000023

7,468

-0.000024

HispanicB
a

b

3,341

-0.000051

3,331

3,311

-0.000087

3,316

-0.000145

5,432

-0.000092

6,018

3,568
3,528
3,434

-0.000078
-0.000155
-0.000144

2,933
2,829
2,764

-0.000041
-0.000084
-0.000072

2,678
2,755
2,333

-0.000287
-0.000178
-0.000171
-0.000244
-0.000043

3,569
4,245
3,146
2,522
3,578

-0.000473
-0.000266
-0.000364
-0.000593
-0.000114

2,866
2,926
2,884
2,606
4,260

-0.000162
-0.000141
-0.000192
-0.000385
-0.000064

2,643
2,680
2,684
2,215
4,216

2,993
4,054

-0.000052
-0.000046

4,274
3,778

-0.000071
-0.000096

2,659
3,598

-0.000038
-0.000056

2,492
3,673

-0.000022

6,268

-0.000099

8,151

-0.000174

6,518

-0.000120

7,875

7,834

-0.000024

7,006

-0.000089

7,296

-0.000169

6,337

-0.000120

7,823

-0.000016
-0.000030
-0.000029

5,368
4,861
4,892

-0.000016
-0.000031
-0.000029

4,685
4,435
4,162

-0.000071
-0.000128
-0.000130

5,813
5,034
5,572

-0.000130
-0.000221
-0.000249

4,875
4,024
4,780

-0.000077
-0.000147
-0.000129

5,022
4,827
4,204

-0.000115
-0.000072
-0.000020
-0.000106
-0.000042
-0.000048
-0.000062
-0.000017

6,877
5,813
5,385
4,652
3,739
3,877
3,687
3,244

-0.000110
-0.000068
-0.000021
-0.000114
-0.000047
-0.000061
-0.000086
-0.000017

5,543
4,644
4,994
4,287
3,572
4,325
4,585
3,244

-0.000439
-0.000283
-0.000076
-0.000353
-0.000176
-0.000187
-0.000265
-0.000117

8,107
6,786
4,931
4,392
4,189
3,440
2,740
3,601

-0.000658
-0.000498
-0.000171
-0.000793
-0.000291
-0.000443
-0.000702
-0.000245

6,036
5,686
5,017
4,807
3,199
3,512
3,085
3,311

-0.000403
-0.000284
-0.000083
-0.000248
-0.000174
-0.000235
-0.000496
-0.000087

6,780
6,067
4,561
4,053
3,313
3,288
2,853
3,316

PEOPLE

Income characteristics
Total
Male
Female
Age
15 to 24
25 to 44
45 to 64
65 and over
Health insurance
Marital status, household and family
Some household members
All household members
Mobility (movers)
Educational attainment, labor force,
marital status, household, family,
and income
US, county, state, region, or
metropolitan statistical areas
Below poverty
Total
Male
Female
Age
Under 15
Under 18
15 and over
15 to 24
25 to 44
45 to 64
65 and over
Unemployment

FAMILIES, HOUSEHOLDS, OR UNRELATED INDIVIDUALS
Income
Marital status, household and family,
educational attainment, population
by age/sex
Poverty

-0.000015

3,190

-0.000027

3,445

-0.000097

3,222

-0.000133

2,615

-0.000053

2,499

-0.000008
-0.000011

2,587
2,767

-0.000013
-0.000019

2,329
2,781

-0.000046
-0.000069

2,454
2,830

-0.00047
-0.000094

1,809
2,305

-0.000020
-0.000031

1,937
2,093

Source: U.S. Census Bureau, Current Population Survey, External data from the 2024 Annual Social and Economic
Supplement.
A AIAN is American Indian and Alaska Native, and NHOPI is Native Hawaiian and Other Pacific Islander.
B Hispanics may be any race.
Notes: These parameters are to be applied to the 2024 Annual Social and Economic Supplement data. The White, Black,
and Asian, AIAN, NHOPI parameters are calculated using the non-hispanic population, but are to be used for both
alone and in combination race group estimates. 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

31
attainment, in which case use Black parameters. For a more detailed discussion on the use of
parameters for race and ethnicity, please refer to the “Generalized Variance Parameters” section.

Table 17. Current Population Survey Year-to-Year Correlation Coefficients for Income and Health
Insurance Characteristics: Data Years 1960 to 2023
1960-2000 (basic)
Characteristics or 2000 (expanded)-2023

Total
White
Black
Other
HispanicA

1999 (basic)2000 (expanded)

People

Families

People

Families

0.30
0.30
0.30
0.30
0.45

0.35
0.35
0.35
0.35
0.55

0.19
0.20
0.15
0.15
0.36

0.22
0.23
0.18
0.17
0.28

Source: U.S. Census Bureau, Current Population Survey, Internal data files.
A
Hispanics may be any race.
Notes: Correlation coefficients are not available for income data before 1960. These correlation coefficients
are for comparisons of consecutive years. For comparisons of nonconsecutive years, assume the
correlation is zero. For households and unrelated individuals, use the correlation coefficient for
families. For a more detailed discussion on the use of parameters for race and ethnicity, please refer
to the “Generalized Variance Parameters” section.

32
Table 18. Current Population Survey Year-to-Year Correlation Coefficients for Poverty
Characteristics: Data Years 1970 to 2023
1972-83, 19842000 (basic)
1999 (basic)1983-1984
1971-1972
1970-1971
or
2000
2000
(expanded)
Characteristics
(expanded)-2023
People Families People Families People Families People Families People Families
Total
White
Black
Other
HispanicA

0.45
0.35
0.45
0.45
0.65

0.35
0.30
0.35
0.35
0.55

0.29
0.23
0.23
0.22
0.52

0.22
0.20
0.18
0.17
0.40

0.39
0.30
0.39
0.30
0.56

0.30
0.26
0.30
0.30
0.47

0.15
0.14
0.17
0.17
0.17

0.14
0.13
0.16
0.16
0.16

0.31
0.28
0.35
0.35
0.35

0.28
0.25
0.32
0.32
0.32

Source: U.S. Census Bureau, Current Population Survey, Internal data files.
A
Hispanics may be any race.
Notes: Correlation coefficients are not available for poverty data before 1970. These correlation coefficients
are for comparisons of consecutive years. For comparisons of nonconsecutive years, assume the
correlation is zero. For households and unrelated individuals, use the correlation coefficient for
families. For a more detailed discussion on the use of parameters for race and ethnicity, please refer
to the “Generalized Variance Parameters” section.

Table 19. Current Population Survey Correlation Coefficients Between Race and Subgroups:
2024 Annual Social and Economic Supplement
Race 1 (subgroup)

Race 2

White alone, not Hispanic ..........
White alone, not Hispanic ..........
Black alone ........................................
Asian alone........................................

White alone ........................................................................
White alone or in combination, not Hispanic .....
Black alone or in combination ...................................
Asian alone or in combination ...................................

𝒓
0.82
0.98
0.95
0.92

Source: U.S. Census Bureau, Current Population Survey, Internal data files.
Notes: For a more detailed discussion on the use of parameters for race and ethnicity, please refer to the
“Generalized Variance Parameters” section.

33
Table 20. Factors and Populations for State Standard Errors and Parameters: 2024 Annual
Social and Economic Supplement
State

Factor

Population

State

Factor

Population

Alabama
1.11
5,051,530
Montana
0.21
1,123,479
Alaska
0.18
704,341
Nebraska
0.52
1,958,099
Arizona
1.25
7,376,639
Nevada
0.77
3,170,057
Arkansas
0.73
3,030,127
New Hampshire
0.33
1,389,319
California
1.28
38,483,741
New Jersey
1.15
9,228,962
Colorado
1.22
5,813,968
New Mexico
0.51
2,080,435
Connecticut
0.86
3,584,153
New York
1.19
19,316,535
Delaware
0.22
1,027,605
North Carolina
1.18
10,727,187
District of Columbia
0.17
675,375
North Dakota
0.17
770,097
Florida
1.14
22,551,398
Ohio
1.10
11,641,369
Georgia
1.15
10,915,712
Oklahoma
1.06
3,995,675
Hawaii
0.32
1,383,204
Oregon
1.07
4,191,810
Idaho
0.41
1,957,312
Pennsylvania
1.11
12,781,261
Illinois
1.17
12,368,973
Rhode Island
0.28
1,082,819
Indiana
1.11
6,792,112
South Carolina
1.07
5,344,538
Iowa
0.77
3,169,821
0.22
906,902
South Dakota
Kansas
0.82
2,883,377
Tennessee
1.10
7,085,668
Kentucky
1.13
4,452,141
Texas
1.32
30,365,478
Louisiana
1.01
4,474,104
Utah
0.53
3,417,702
Maine
0.39
1,383,836
Vermont
0.18
642,663
Maryland
1.15
6,103,837
Virginia
1.19
8,546,597
Massachusetts
1.10
6,953,070
Washington
1.18
7,733,730
Michigan
1.11
9,939,507
West Virginia
0.48
1,736,719
Minnesota
1.13
5,692,661
Wisconsin
1.13
5,868,245
Mississippi
0.69
2,876,069
Wyoming
0.16
576,509
Missouri
1.13
6,116,818
Source: U.S. Census Bureau, Current Population Survey, Internal data files for the 2010 Design; U.S. Census
Bureau, Population Estimates, March 2024.
Notes: The state population counts in this table are for the 0+ population.

34
Table 21. Factors and Populations for Regional Standard Errors and Parameters: 2024 Annual
Social and Economic Supplement
Region
Midwest
Northeast
South
West

Factor
1.06
1.07
1.13
1.12

Population
68,107,981
56,362,618
128,959,760
78,012,927

Source: U.S. Census Bureau, Current Population Survey, Internal data files for the 2010 Design; U.S. Census
Bureau, Population Estimates, March 2024.
Notes: The region population counts in this table are for the 0+ population.

35
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