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:

where

is the half-sample estimator

k is the number of half-samples

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.

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