Chapter 1.
Labor Force Data Derived from the Current Population Survey
Limitations of the Data
Geographic. Although the present CPS sample is a
Statebased design, the sample size of the CPS is sufficient to
produce reliable monthly estimates at the national level only.
The sample does not permit the production of reliable monthly
estimates for the States. However, demographic, social, and
economic detail is published annually for the census regions and
divisions, all States and the District of Columbia, 50 large
metropolitan areas, and selected central cities. The production of
subnational labor force and unemployment estimates is discussed in
more detail in chapter 4 of this bulletin.
Sources of errors in the survey estimates. There are two types
of errors possible in an estimate based on a sample survey — sampling
and nonsampling. The mathematical discipline of sampling theory
provides methods for estimating standard errors when the probability
of selection of each member of a population can be specified.
The standard error, a measure of sampling variability, can be used to
compute confidence intervals that indicate a range of differences
from true population values that can be anticipated because only a
sample of the population has been surveyed. Nonsampling errors such
as response variability, response bias, and other types of bias
occur in complete censuses as well as sample surveys. In some
instances, nonsampling error may be more tightly controlled in a
wellconducted survey, through which it is feasible to collect and
process the data more skillfully. Estimation of other types of bias
is one of the most difficult aspects of survey work, and adequate
measures of bias often cannot be made.
Nonsampling error. The full extent of nonsampling error is
unknown, but special studies have been conducted to quantify some
sources of nonsampling error in the CPS. The effect of nonsampling
error should be small on estimates of relative change, such as
monthtomonth change. Estimates of monthly levels would be more
severely affected by nonsampling error.
Nonsampling errors in surveys can be attributed to many sources,
including the inability to obtain information about all persons in
the sample; differences in the interpretation of questions;
inability or unwillingness of respondents to provide correct
information; inability to recall information; errors made in
collecting and processing the data; errors made in estimating
values for missing data; and failure to represent all sample
households and all persons within sample households
(undercoverage).
The effects of some components of nonsampling error in the
CPS data are reflected in the variation in some labor force
measures among the rotation groups, each of which is designed to be
a representative sample of the population. For example,
unemployment estimates from a rotation group tend to be higher in
the first and fifth months of interviewing.
Undercoverage in the CPS results from missed housing units and
missed persons within sample households. The noninterview adjustment
procedure accounts for missed households. It also is known that
the CPS undercoverage of persons varies with age, sex, race, and
Hispanic ethnicity. Generally, undercoverage is greater for men
than for women and greater for blacks, Hispanics, and other races
than for whites. Ratio adjustment to independent agesexraceorigin
population controls, as described previously, partially corrects
for the biases due to survey undercoverage. Biases still exist in
the estimates to the extent that persons in missed households or
missed persons in interviewed households have characteristics
different from those of interviewed persons in the same
agesexraceorigin group.
The independent population estimates used in the estimation
procedure may be a source of error, although, on balance, their
use substantially improves the statistical reliability of many of
the figures. Errors may arise in the independent population
estimates because of underenumeration of certain population groups
or errors in age reporting in the decennial census
(which serves as the base for the estimates) or similar problems in
the components of population change (mortality, immigration, and so
forth) since that date.
Sampling error. When a sample, rather than the entire population,
is surveyed, estimates differ from the true population values that
they represent. This difference, or sampling error, occurs by chance,
and its variability is measured by the standard error of the
estimate. Sample estimates from a given survey design are unbiased
when an average of the estimates from all possible samples would
yield, hypothetically, the true population value. In this case,
the sample estimate and its standard error can be used to construct
approximate confidence intervals, or ranges of values, that include
the true population value with known probabilities. If the process
of selecting a sample from the population were repeated many times
and an estimate and its standard error were calculated for each
sample, then:
 Approximately 68 percent of the intervals from 1 standard
error below the estimate to 1 standard error above the estimate
would include the true population value.
 Approximately 90 percent of the intervals from 1.6 standard
errors below the estimate to 1.6 standard errors above the estimate
would include the true population value.
 Approximately 95 percent of the intervals from 2 standard errors
below the estimate to 2 standard errors above the estimate would
include the true population value.
Although the estimating methods used in the CPS do not produce
unbiased estimates, biases for most estimates are believed to be
small enough that these confidence interval statements are
approximately true.
Standard error estimates computed using generalized variance
functions are provided in Employment and Earnings and other
publications. Using replicate variance techniques, standard
error estimates are generated. As computed, these standard error
estimates reflect contributions not only from sampling error, but
also from some types of nonsampling error, particularly response
variability. Because replicate variance techniques are somewhat
cumbersome, simplified formulas called generalized variance
functions (GVFs) have been developed for various types of labor
force characteristics. The GVF can be used to approximate an
estimate's standard error, but this indicates only the general
magnitude of the standard error, rather than a precise value.
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