Chapter 2.
Employment, Hours, and Earnings from the Establishment
Survey
Reliability
of Estimates
The establishment survey, like other sample surveys, is
subject to two types of error, sampling and nonsampling
error. The magnitude of sampling error, or variance, is
directly related to the size of the sample and the
percentage of universe coverage achieved by the sample.
The establishment survey sample covers over one-third of
total universe employment; this yields a very small
variance on the total nonfarm estimates.
Unlike most sample surveys that publish sampling error
as their only measure of error, the CES can derive an
annual approximation of total error, on a lagged basis,
because of the availability of the independently derived
universe data. While the benchmark error is used as a
measure of total error for the CES survey estimate, it
actually represents the difference between two independent
estimates derived from separate survey processes
(specifically, the CES sample process and the UI
administrative process) and thus reflects the errors
present in each program. Historically, benchmark
revisions have been very small for total nonfarm
employment. Over the past decade, percentage benchmark
error has averaged 0.3 percent, with an absolute range from
less than 0.05 percent to 0.7 percent.
The estimation of sample variance for the CES survey is
accomplished through use of the method of Balanced Half
Samples (BHS). This replication technique uses half
samples of the original sample and calculates estimates
using those subsamples. The sample variance is calculated
by measuring the variability of the subsample estimates.
The weighted link estimator is used to calculate both half
sample estimates and the variances estimates. The sample
units in each cell; where a cell is based on State, industry,
and size classification; are divided into two random
groups. The basic BHS method is applied to both groups.
The subdivision of the cells is done systematically,
in the same order as the initial sample selection.
Weights for units in the half sample are multiplied by a
factor of 1+y, where weights for units not in the half
sample are multiplied by a factor of 1-y. Estimates from
these subgroups are calculated using the estimation
formula described above.
The formula used to calculate CES variances is as follows:
![](https://webarchive.library.unt.edu/eot2008/20090507015636im_/http://www.bls.gov/opub/hom/images/homch2_f5.gif)
where
![](https://webarchive.library.unt.edu/eot2008/20090507015636im_/http://www.bls.gov/opub/hom/images/homch2_f6.gif)
is the half-sample estimator
![](https://webarchive.library.unt.edu/eot2008/20090507015636im_/http://www.bls.gov/opub/hom/images/homch2_f7.gif)
k is the number of half-samples
![](https://webarchive.library.unt.edu/eot2008/20090507015636im_/http://www.bls.gov/opub/hom/images/homch2_f8.gif)
is the original full sample estimates
Variances statistics are useful for comparison purposes,
but they do have some limitations. Variances reflect the
error component of the estimates that is due to surveying
only a subset of the population, rather than conducting a
complete count of the entire population. However,
they do not reflect the nonsampling error, such as
response errors and bias due to nonresponse. The overall
performance of the CES employment estimates is best measured
in terms of the benchmark revisions. The variances of the
over-the-month change estimates are very useful in
determining when changes are significant at some level
of confidence.
Next: Technical References
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