Attachment J - CWS Source and Accuracy Statement

ATTACHMENT J
Source and Accuracy Statement for the
May 2017 CPS Microdata File on Contingent Workers

SOURCE OF THE DATA
The data in this microdata file are from the May 2017 Current Population Survey (CPS). The
U.S. Census Bureau conducts the CPS every month, although this file has only May 2017 data.
The May 2017 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 U.S. Bureau of Labor Statistics sponsors the supplemental
questions on Contingent Workers for May 2017.
Basic CPS. The monthly CPS collects primarily labor force data about the civilian
noninstitutionalized population living in the United States. The institutionalized population,
which is excluded from the population universe, is composed primarily of the population in
correctional institutions and nursing homes (98 percent of the 4.0 million institutionalized people
in Census 2010). Starting August 2017, college and university dormitories were also excluded
from the population universe because the majority of the residents had usual residences
elsewhere. Interviewers ask questions concerning labor force participation about each member
15 years old and over in sample households. Typically, the week containing the nineteenth of
the month is the interview week. The week containing the twelfth is the reference week (i.e., the
week about which the labor force questions are asked).
The CPS uses a multistage probability sample based on the results of the decennial census, with
coverage in all 50 states and the District of Columbia. The sample is continually updated to
account for new residential construction. When files from the most recent decennial census
become available, the Census Bureau gradually introduces a new sample design for the CPS.
Every ten years, the CPS first stage sample is redesigned1 reflecting changes based on the most
recent decennial census. In the first stage of the sampling process, primary sampling units
(PSUs)2 were selected for sample. In the 2010 sample design, the United States was divided into
1,987 PSUs. These PSUs were then grouped into 852 strata. Within each stratum, a single PSU
was chosen for the sample, with its probability of selection 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 74,000 housing units were selected for sample from the sampling frame in May.
Based on eligibility criteria, nine percent of these housing units were sent directly to

1
2

For detailed information on the 2000 sample redesign, please see reference [1].
The PSUs correspond to substate areas (i.e., counties or groups of counties) that are geographically contiguous.

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computer-assisted telephone interviewing (CATI). The remaining units were assigned to
interviewers for computer-assisted personal interviewing (CAPI).3 Of all housing units in
sample, about 61,000 were determined to be eligible for interview. Interviewers obtained
interviews at about 52,000 of these units. Noninterviews occur when the occupants are not
found at home after repeated calls or are unavailable for some other reason.
May 2017 Supplement. In May 2017, in addition to the basic CPS questions, interviewers
asked supplementary questions on contingent workers in three-fourths of the sample households.
Estimation Procedure. This survey’s estimation procedure adjusts weighted sample results to
agree with independently derived population estimates of the civilian noninstitutionalized
population of the United States and each state (including the District of Columbia). These
population estimates, used as controls for the CPS, are prepared monthly to agree with the most
current set of population estimates that are released as part of the Census Bureau’s population
estimates and projections program.
The population controls for the nation are distributed by demographic characteristics in two
ways:
•
•

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

The population controls for the states are distributed by race (Black alone and all other race
groups combined), age (0-15, 16-44, and 45 and over), and sex.
The independent estimates by age, sex, race, and Hispanic origin, and for states by selected age
groups and broad race categories, are developed using the basic demographic accounting formula
whereby the population from the 2010 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 estimates.
The net international migration component of the population estimates includes:
•
•
•
•

Net international migration of the foreign born;
Net migration between the United States and Puerto Rico;
Net migration of natives to and from the United States; and
Net movement of the Armed Forces population to and from the United States.

Because the latest available information on these components lags the survey date, it is necessary
to make short-term projections of these components to develop the estimate for the survey date.

3

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

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

The interviewer records the wrong answer, the respondent provides incorrect
information, the respondent estimates the requested information, or an unclear survey
question is misunderstood by the respondent (measurement error).
Some individuals who should have been included in the survey frame were missed
(coverage error).
Responses are not collected from all those in the sample or the respondent is
unwilling to provide information (nonresponse error).
Values are estimated imprecisely for missing data (imputation error).
Forms may be lost, data may be incorrectly keyed, coded, or recoded, etc. (processing
error).

To minimize these errors, the Census Bureau applies quality control procedures during all stages
of the production process including the design of the survey, the wording of questions, the
review of the work of interviewers and coders, and the statistical review of reports.
Two types of nonsampling error that can be examined to a limited extent are nonresponse and
undercoverage.

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Nonresponse. The effect of nonresponse cannot be measured directly, but one indication of its
potential effect is the nonresponse rate. For the May 2017 basic CPS, the household-level
nonresponse rate was 14.8 percent. The person-level nonresponse rate for the Contingent
Worker supplement was an additional 9.6 percent.
Since the basic CPS nonresponse rate is a household-level rate and the Contingent Worker
supplement nonresponse rate is a person-level rate, we cannot combine these rates to derive an
overall nonresponse rate. Nonresponding households may have fewer persons than interviewed
ones, so combining these rates may lead to an overestimate of the true overall nonresponse rate
for persons for the Contingent Worker supplement.
Sufficient Partial Interview. A sufficient partial interview is an incomplete interview in which
the household or person answered enough of the questionnaire for the supplement sponsor to
consider the interview complete. The remaining supplement questions may have been edited or
imputed to fill in missing values. Insufficient partial interviews are considered to be
nonrespondents. Refer to the supplement overview attachment in the technical documentation
for the specific questions deemed critical by the sponsor as necessary to be answered in order to
be considered a sufficient partial interview.
As part of the nonsampling error analysis, the item response rates, item refusal rates, and edits
are reviewed. For the Contingent Worker supplement, the item refusal rates range from 0.0
percent to 4.5 percent. The item nonresponse rates range from 0.3 percent to 17.4 percent.
Coverage. The concept of coverage in the survey sampling process is the extent to which the
total population that could be selected for sample “covers” the survey’s target population.
Missed housing units and missed people within sample households create undercoverage in the
CPS. Overall CPS undercoverage for May 2017 is estimated to be about 11 percent. CPS
coverage varies with age, sex, and race. Generally, coverage is larger for females than for males
and larger for non-Blacks than for Blacks. This differential coverage is a general problem for
most household-based surveys.
The CPS weighting procedure partially corrects for bias from undercoverage, but biases may still
be present when people who are missed by the survey differ from those interviewed in ways
other than age, race, sex, Hispanic origin, and state of residence. How this weighting procedure
affects other variables in the survey is not precisely known. All of these considerations affect
comparisons across different surveys or data sources.
A common measure of survey coverage is the coverage ratio, calculated as the estimated
population before poststratification divided by the independent population control. Table 1
shows May 2017 CPS coverage ratios by age and sex for certain race and Hispanic groups. The
CPS coverage ratios can exhibit some variability from month to month.

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Table 1. Current Population Survey Coverage Ratios: May 2017
Total
Age
All
group people
0.87
0-15
16-19 0.86
20-24 0.76
25-34 0.82
35-44 0.91
45-54 0.91
55-64 0.92
0.97
65+
0.89
15+
0.89
0+

White only

Black only

Residual raceA

HispanicB

Male

Female

Male

Female

Male

Female

Male

Female

Male

Female

0.88
0.89
0.74
0.80
0.88
0.90
0.92
0.97
0.88
0.88

0.87
0.83
0.78
0.85
0.93
0.92
0.93
0.97
0.90
0.90

0.92
0.93
0.79
0.84
0.91
0.93
0.93
0.98
0.91
0.91

0.92
0.85
0.82
0.90
0.96
0.95
0.95
0.98
0.93
0.93

0.75
0.78
0.57
0.63
0.73
0.80
0.87
0.92
0.76
0.75

0.70
0.74
0.67
0.68
0.81
0.79
0.85
0.94
0.79
0.77

0.77
0.79
0.66
0.72
0.84
0.78
0.85
0.82
0.78
0.78

0.77
0.84
0.70
0.69
0.88
0.84
0.85
0.85
0.81
0.80

0.82
0.86
0.70
0.72
0.78
0.83
0.84
0.81
0.79
0.79

0.83
0.76
0.82
0.84
0.88
0.85
0.84
0.83
0.84
0.83

Source: U.S. Census Bureau, Current Population Survey, May 2017
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. For a more detailed discussion on the use of parameters for race and ethnicity,
please see the “Generalized Variance Parameters” section.

Comparability of Data. Data obtained from the CPS and other sources are not entirely
comparable. This results from differences in interviewer training and experience and in differing
survey processes. This is an example of nonsampling variability not reflected in the standard
errors. Therefore, caution should be used when comparing results from different sources.
Data users should be careful when comparing the data from this microdata file, which reflects
2010 Census-based controls, with microdata files from January 2003 through December 2011,
which reflect 2000 Census-based controls. Ideally, the same population controls should be used
when comparing any estimates. In reality, the use of the same population controls is not
practical when comparing trend data over a period of 10 to 20 years. Thus, when it is necessary
to combine or compare data based on different controls or different designs, data users should be
aware that changes in weighting controls or weighting procedures can create small differences
between estimates. See the discussion following for information on comparing estimates derived
from different controls or different sample designs.
Microdata files from previous years reflect the latest available census-based controls. Although
the most recent change in population controls had relatively little impact on summary measures
such as averages, medians, and percentage distributions, it did have a significant impact on
levels. For example, use of 2010 Census-based controls results in about a 0.2 percent increase
from the 2000 census-based controls in the civilian noninstitutionalized population and in the
number of families and households. Thus, estimates of levels for data collected in 2012 and later
years will differ from those for earlier years by more than what could be attributed to actual
changes in the population. These differences could be disproportionately greater for certain
population subgroups than for the total population.
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Users should also exercise caution because of changes caused by the phase-in of the Census
2010 files (see “Basic CPS”).4 During this time period, CPS data were collected from sample
designs based on different censuses. Two features of the new CPS design have the potential of
affecting published estimates: (1) the temporary disruption of the rotation pattern from August
2014 through June 2015 for a comparatively small portion of the sample and (2) the change in
sample areas. Most of the known effect on estimates during and after the sample redesign will
be the result of changing from 2000 to 2010 geographic definitions. Research has shown that the
national-level estimates of the metropolitan and nonmetropolitan populations should not change
appreciably because of the new sample design. However, users should still exercise caution
when comparing metropolitan and nonmetropolitan estimates across years with a design change,
especially at the state level.
Caution should also be used when comparing Hispanic estimates over time. No independent
population control totals for people of Hispanic origin were used before 1985.
A Nonsampling Error Warning. Since the full extent of the nonsampling error is unknown,
one should be particularly careful when interpreting results based on small differences between
estimates. The Census Bureau recommends that data users incorporate information about
nonsampling errors into their analyses, as nonsampling error could impact the conclusions drawn
from the results. Caution should also be used when interpreting results based on a relatively
small number of cases. Summary measures (such as medians and percentage distributions)
probably do not reveal useful information when computed on a subpopulation smaller than
75,000.
For additional information on nonsampling error, including the possible impact on CPS
data, when known, refer to references [2] and [3].
Standard Errors and Their Use. The sample estimate and its standard error enable one to
construct a confidence interval. A confidence interval is a range about a given estimate that has
a specified probability of containing the average result of all possible samples. For example, if
all possible samples were surveyed under essentially the same general conditions and using the
same sample design, and if an estimate and its standard error were calculated from each sample,
then approximately 90 percent of the intervals from 1.645 standard errors below the estimate to
1.645 standard errors above the estimate would include the average result of all possible samples.
A particular confidence interval may or may not contain the average estimate derived from all
possible samples, but one can say with specified confidence that the interval includes the average
estimate calculated from all possible samples.
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.
4

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

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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.
There are two ways to calculate standard errors for the CPS microdata file on Contingent
Workers. They are:
•
•

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

While replicate weighting methods provide the most accurate variance estimates, this approach
requires more computing resources and more expertise on the part of the user. The Generalized
Variance Function (GVF) parameters provide a method of balancing accuracy with resource
usage as well as a smoothing effect on standard error estimates across time. For more
information on calculating direct estimates, see reference [4]. For more information on GVF
estimates, refer to the “Generalized Variance Parameters” section.
Generalized Variance Parameters. While it is possible to compute and present an estimate of
the standard error based on the survey data for each estimate in a report, there are a number of
reasons why this is not done. A presentation of the individual standard errors would be of
limited use, since one could not possibly predict all of the combinations of results that may be of
interest to data users. Additionally, data users have access to CPS microdata files, and it is
impossible to compute in advance the standard error for every estimate one might obtain from
those data sets. Moreover, variance estimates are based on sample data and have variances of
their own. Therefore, some methods of stabilizing these estimates of variance, for example, by
generalizing or averaging over time, may be used to improve their reliability.
Experience has shown that certain groups of estimates have similar relationships between their
variances and expected values. Modeling or generalizing may provide more stable variance
estimates by taking advantage of these similarities. The GVF is a simple model that expresses
the variance as a function of the expected value of the survey estimate. The parameters of the
GVF are estimated using direct replicate variances. These GVF parameters provide a relatively
easy method to obtain approximate standard errors for numerous characteristics.
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In this source and accuracy statement, Tables 3 through 5 provide illustrations for calculating
standard errors. Table 6 provides GVF parameters for labor force estimates, and Table 7
provides GVF parameters for characteristics from the May 2017 Contingent Worker supplement.
The basic CPS questionnaire records the race and ethnicity of each respondent. With respect to
race, a respondent can be White, Black, Asian, American Indian and Alaskan Native (AIAN),
Native Hawaiian and Other Pacific Islander (NHOPI), or combinations of two or more of the
preceding. A respondent’s ethnicity can be Hispanic or non-Hispanic, regardless of race.
The GVF parameters to use in computing standard errors are dependent upon the race/ethnicity
group of interest. The following table summarizes the relationship between the race/ethnicity
group of interest and the GVF parameters to use in standard error calculations.

Table 2. Estimation Groups of Interest and Generalized Variance Parameters
Generalized variance parameters to
use in standard error calculations

Race/ethnicity group of interest
Total population

Total or White

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

Total or White

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

Black

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

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

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

Asian, AIAN, NHOPI

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

Asian, AIAN, NHOPI

Populations from other race groups

Asian, AIAN, NHOPI

HispanicA population

HispanicA

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

Black
Asian, AIAN, NHOPI

Source: U.S. Census Bureau, Current Population Survey, internal data files.
A

Hispanics may be any race. 
B
Two or more races refers to the group of cases self‐classified as having two or more races.

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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 Contingent
Worker supplement data, the GVF parameters will come from the Contingent Worker
supplement GVF table (Table 7).
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:
√

(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 3,436,000 unemployed men (ages 16 and up) in the civilian labor force. Use
the appropriate parameters from Table 6 and Formula (1) to get
Table 3. Illustration of Standard Errors of Estimated Numbers
Number of unemployed females in the civilian labor force (x)
a-parameter (a)
b-parameter (b)
Standard error
90-percent confidence interval

3,436,000
-0.000031
2,947
99,000
3,273,000 to 3,599,000

Source: U.S. Census Bureau, Current Population Survey, May 2017

The standard error is calculated as
0.000031

3,436,000

2,947

3,436,000

99,000

The 90-percent confidence interval is calculated as 3,436,000 ± 1.645 × 99,000.
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, , , of an estimated percentage can be obtained by using
the formula:
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100

,

(2)

Here y is the total number of people, families, households, or unrelated individuals in the base or
denominator of the percentage, p is the percentage 100*x/y (0 ≤ p ≤ 100), and b is the parameter
in Table 6 or 7 associated with the characteristic in the numerator of the percentage.
Illustration 2
Suppose that of 5,858,000 contingent workers, 1,419,000, or 24.2 percent, were 25 to 34 years of
age. Use the appropriate parameter from Table 7 and Formula (2) to get
Table 4. Illustration of Standard Errors of Estimated Percentages
Percentage of contingent workers were 25 to 34 years of age (p)
Base (y)
b-parameter (b)
Standard error
90-percent confidence interval

24.2
5,858,000
4,475
1.18
22.3 to 26.1

Source: U.S. Census Bureau, Current Population Survey, Contingent Worker Supplement, May 2017

The standard error is calculated as

,

4,475
5,858,000

24.2

100.0

24.2

1.18

The 90-percent confidence interval for the estimated percentage is from 22.3 to 26.1 percent (i.e.,
24.2 ± 1.645 × 1.18).
Standard Errors of Estimated Differences. The standard error of the difference between two
sample estimates is approximately equal to
(3)
where
and
are the standard errors of the estimates, and . The estimates can be
numbers, percentages, ratios, etc. This will result in accurate estimates of the standard error of
the same characteristic in two different areas or for the difference between separate and
uncorrelated characteristics in the same area. However, if there is a high positive (negative)
correlation between the two characteristics, the formula will overestimate (underestimate) the
true standard error.

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Illustration 3
Suppose that of 7,344,000 employed men between 20 and 24 years of age, 1,871,000, or 25.5
percent, were part-time workers, and of the 6,786,000 employed women between 20
and 24 years of age, 2,581,000, or 38.0 percent, were part-time workers. Use the appropriate
parameters from Table 6 and formulas (2) and (3) to get
Table 5. Illustration of Standard Errors of Estimated Differences
Percentage working part-time (p)
Base
b-parameter (b)
Standard error
90-percent confidence interval

Men (x1)
25.5
7,344,000
2,947
0.87
24.1 to 26.9

Women (x2)
38.0
6,786,000
2,788
0.98
36.4 to 39.6

Difference
12.5
1.31
10.3 to 14.7

Source: U.S. Census Bureau, Current Population Survey, Contingent Worker Supplement, May 2017

The standard error of the difference is calculated as
0.87

0.98

1.31

The 90-percent confidence interval around the difference is calculated as 12.5±1.645×1.31.
Since this interval does not include zero, we can conclude with 90 percent confidence that the
percentage of part-time women workers between 20-24 years of age is greater than the
percentage of part-time men workers between 20-24 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 see
reference [5].
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.

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Table 6. Parameters for Computation of Standard Errors for Labor Force
Characteristics: May 2017
Characteristic

a

b

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

-0.000013
-0.000017
-0.000013

2,481
3,244
2,432

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
Total
Men
Women
Both sexes, 16 to 19 years

-0.000117
-0.000249
-0.000191
-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
Total
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
Total
Men
Women
Both sexes, 16 to 19 years

-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 bparameters 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 Contingent Worker Characteristics:
May 2017
Asian, AIAN,
Total or White
Black
HispanicB
NHOPIA
Characteristics
a
b
a
b
a
b
a
b
Contingent Workers
All Adults
-0.000016 4053 -0.000093 5226 -0.000201 4776 -0.000117 4835
Sex
Male
-0.000032 3999 -0.000196 5091 -0.000504 5696 -0.000236 4842
Female
-0.000031 4101 -0.000187 5605 -0.000354 4425 -0.000226 4678
Age
16 to 19
-0.000087 3741 -0.000502 5861 -0.000652 3457 -0.000344 5022
20 to 24
-0.000111 4764 -0.000507 5922 -0.001151 6107 -0.000357 5210
25 to 34
-0.000102 4475 -0.000456 5194 -0.000985 5015 -0.000510 4573
35 to 44
-0.000097 3875 -0.000449 4376 -0.000884 3927 -0.000557 4592
45 to 54
-0.000048 3974 -0.000286 4888 -0.000623 4256 -0.000435 4959
55 to 64
-0.000042 3511 -0.000234 4004 -0.000295 2013 -0.000329 3755
65 and over
-0.000056 2777 -0.000521 3986 -0.000802 2473 -0.001001 4104
Full-or Part-time
Status
Part-time
-0.000016 4113 -0.000092 5171 -0.000230 5485 -0.000122 5024
Full-time
-0.000016 4115 -0.000094 5261 -0.000189 4498 -0.000115 4753
Education
-0.000016 4172 -0.000098 5460 -0.000210 4994 -0.000116 4804
Occupation
-0.000015 3867 -0.000092 5169 -0.000213 5064 -0.000115 4739
Industry
-0.000015 3894 -0.000091 5068 -0.000197 4677 -0.000114 4694
Workers with Alternative Arrangements
All Adults
-0.000015 3823 -0.000088 4942 -0.000184 4378 -0.000111 4574
Sex
Male
-0.000033 4047 -0.000195 5068 -0.000406 4585 -0.000259 5306
Female
-0.000029 3757 -0.000158 4751 -0.000378 4732 -0.000208 4314
Age
16 to 19
-0.000076 3255 -0.000395 4608 -0.000833 4419 -0.000251 3675
20 to 24
-0.000098 4203 -0.000481 5621 -0.000833 4419 -0.000340 4964
25 to 34
-0.000094 4145 -0.000478 5435 -0.000894 4554 -0.000579 5192
35 to 44
-0.000098 3917 -0.000509 4962 -0.000983 4369 -0.000573 4723
45 to 54
-0.000046 3816 -0.000277 4744 -0.000603 4116 -0.000419 4775
55 to 64
-0.000043 3600 -0.000226 3872 -0.000561 3827 -0.000398 4535
65 and over
-0.000069 3386 -0.000534 4089 -0.001487 4589 -0.000891 3652
Full-or Part-time
Status
Part-time
-0.000015 3772 -0.000085 4779 -0.000179 4255 -0.000114 4682
Full-time
-0.000017 4220 -0.000094 5259 -0.000196 4658 -0.000122 5044
Education
-0.000015 3912 -0.000087 4861 -0.000192 4563 -0.000116 4783
Occupation
-0.000015 3734 -0.000085 4745 -0.000182 4334 -0.000114 4707
Industry
-0.000015 3788 -0.000085 4734 -0.000174 4148 -0.000113 4677
Source: U.S. Census Bureau, Current Population Survey, Internal data from the Contingent Worker Supplement,
May 2017
A
AIAN is American Indian and Alaska Native, and NHOPI is Native Hawaiian and Other Pacific Islander.
B
Hispanics may be any race. For a more detailed discussion on the use of parameters for race and ethnicity,
please see the “Generalized Variance Parameters” section.

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Notes: These parameters are to be applied to the Contingent Worker Supplement data. The Total or White, Black, and
Asian, AIAN, NHOPI parameters are to be used for both alone and in combination race group estimates. For
nonmetropolitan characteristics, multiply the a- and b-parameters by 1.5. If the characteristic of interest is total state
population, not subtotaled by race or ethnicity, the a- and b-parameters are zero. For foreign-born and noncitizen
characteristics for Total and White, the a- and b-parameters should be multiplied by 1.3.
No adjustment is necessary for foreign-born and noncitizen characteristics for Black, Asian, AIAN,
NHOPI, and Hispanic parameters. For the group self-classified as having two or more races, use the Asian,
AIAN, NHOPI parameters for all characteristics except employment, unemployment, and educational
attainment, in which case use Black parameters.

REFERENCES
[1]

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

[2]

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

[3]

Brooks, C.A. and Bailar, B.A. 1978. Statistical Policy Working Paper 3 - An Error
Profile: Employment as Measured by the Current Population Survey. Subcommittee on
Nonsampling Errors, Federal Committee on Statistical Methodology, U.S. Department of
Commerce, Washington, DC. https://s3.amazonaws.com/sitesusa/wpcontent/uploads/sites/242/2014/04/spwp3.pdf

[4]

U.S. Census Bureau, July 15, 2009, “Estimating ASEC Variances with Replicate Weights
Part I: Instructions for Using the ASEC Public Use Replicate Weight File to Create
ASEC Variance Estimates.”
http://thedataweb.rm.census.gov/pub/cps/march/Use_of_the_Public_Use_Replicate_Wei
ght_File_final.doc

[5]

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

All online references accessed November 21, 2018.

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