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The Bureau of Labor Statistics (BLS) collects data each month on employment, hours, and earnings from a sample of nonfarm establishments through the Current Employment Statistics (CES) program. The CES survey includes about 119,000 businesses and government agencies, which cover approximately 629,000 individual worksites drawn from a sampling frame of Unemployment Insurance (UI) tax accounts covering roughly 11.3 million establishments. The active CES sample includes approximately 30 percent of all nonfarm payroll employees in the 50 states and the District of Columbia. From these data, a large number of employment, hours, and earnings series in considerable industry and geographic detail are prepared and published each month. Historical statistics for the nation are available on the CES-National data webpage. Information and data for states and metropolitan areas are available on the CES-State and Metro Area data webpage.
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Each year, CES-National estimates are benchmarked to the most recent Quarterly Census of Employment and Wage (QCEW) data, based on Unemployment Insurance records, along with a small amount of employment data that is not covered by QCEW. Benchmarking is a standard part of any sample-based survey and is meant to more closely align the CES estimates to population totals. These technical notes will provide background and analysis about the benchmarking process, the data used in that process, and the effects that updates to population data have on CES estimates.
The methodology used in the Current Employment Statistics (CES) programs concepts, data sources, design, calculations, and presentation of data is described in the CES Handbook of Methods. This webpage supplements the Handbook by providing additional detail and information specific to the most recent CES benchmark.
The Current Employment Statistics (CES) sample is a stratified, simple random sample of worksites, clustered by Unemployment Insurance (UI) account number. The UI account number is a major identifier on the Bureau of Labor Statistics (BLS) Longitudinal Database (LDB) of employer records, which serves as both the sampling frame and the benchmark source for the CES employment estimates.
More information about the CES sample design, including frame and sample selection, selection weighting, frame maintenance and sample updates, and the inclusion of a model component to account for units that cannot be sampled, is available in the CES Handbook of Methods under Design.
Table 1 shows the 2023 benchmark employment levels and the approximate proportion of total universe employment coverage at the total nonfarm and major industry sector levels. The sample distribution by industry reflects the goal of minimizing the sampling error in the total nonfarm employment estimate, while also providing reliable employment estimates by industry. Sample coverage rates vary by industry as a result of building a design to meet these goals.
The CES government sample is not part of the program's probability-based design, which is used to estimate employment for all private industries. A very high level of universe employment coverage is achieved by obtaining full payroll employment counts for many government agencies. The private and government estimates are summed to derive total nonfarm employment estimates.
The employment universe that the CES sample is estimating is highly skewed towards employers with fewer than 10 employees, as shown by table 2. CES samples larger firms at a higher rate than smaller firms, a standard technique used in business establishment surveys.
Table 3 shows the distribution of the active CES sample units. A much greater proportion of large UIs are selected; however, that does not create a bias in either the sample or the estimates made from the sample. The use of sample weights in the estimation process prevents a large (or small) firm bias in the estimates.
The CES survey, like other sample surveys, is subject to two types of error, sampling and non-sampling 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 CES survey sample covers over 30 percent of total universe employment; this yields a very small variance on the total nonfarm estimates. Measurements of error associated with sample estimates are provided in table 4 and the all employee (AE), production employee (PE), and women employee (WE) standard error tables.
First preliminary estimates of employment, hours, and earnings, based on less than the total sample, are published immediately following the reference month and are revised with each of the 2 following months to incorporate additional sample received. Table 4 presents the standard error and the relative standard error of CES sample-based estimates for a 1-month change for total nonfarm, total private, and aggregate industries. Standard and relative standard errors for detailed CES industries also are available as variance tables for AE, PE, and WE. An explanation of variance estimation and the uses of standard and relative standard errors for CES estimates is available in the Variance estimation section under Reliability in the CES Handbook of Methods.
The sum of sampling and non-sampling error can be considered total survey error. Unlike most sample surveys that publish sampling error as their only measure of error, the CES derives 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 often used as a proxy measure of total error for the CES survey estimate, it actually represents the difference between two employment estimates derived from separate statistical processes (the CES sample process and the UI administrative process) and therefore reflects the sum of the errors present in each program. Historically, the benchmark revision has been small for total nonfarm employment. Over the prior 10 years, absolute percentage benchmark error has averaged 0.1 percent, with an absolute range from less than 0.05 percent to 0.3 percent. Further discussion about CES annual benchmarks can be found under Benchmark in the CES Handbook of Methods.
Certain aspects of CES estimation are updated each year during benchmark processing. With the benchmark release, updates to the list of published industries are incorporated. The previous years business net birth-death forecasts are replaced with new forecasted values for the post-benchmark period. Seasonal adjustment models are reselected. Also, any changes deemed necessary for published estimates including reconstructions, corrections, and NAICS updates are all implemented with the benchmark. The latest of these changes are described below.
All CES series are evaluated annually for sample size, coverage, and response rate adequacy and to ensure respondent identifying information cannot be ascertained from estimates. All changes resulting from a re-evaluation of the sample and universe coverage for CES industries, which are based on the 2022 North American Industry Classification System (NAICS) industries, are published on the Notice of Publication Changes webpage.
Some small industries no longer have sufficient sample to be estimated and published separately and are combined with other similar industries for estimation and publication purposes. A list of currently published CES series is available on the CES Published Series webpage.
CES estimates series at the basic cell level and then aggregates these estimates to higher industry levels. Aggregation procedures are specific to the data type and published level of precision. For detailed descriptions of CES aggregation procedures for all data types, see Aggregation procedures under the Calculations section of the CES Handbook of Methods.
The CES sample alone is not sufficient for estimating the total employment level because each month new firms generate employment that cannot be captured through the sample. There is an unavoidable lag between a firm opening for business and its appearance on the CES sample frame.
A parallel though somewhat different issue exists in capturing employment loss from business deaths through monthly sample collection. Businesses that have closed are unlikely to respond to the survey, and data collectors may not be able to ascertain until after the monthly collection period that firms have in fact gone out of business.
CES adjusts for these limitations explicitly, using a statistical modeling technique in conjunction with the sample. Without the net birth-death model-based adjustment, the CES nonfarm payroll employment estimates would be considerably less accurate.
Table 9 in the Benchmark Article shows the net birth-death model forecasts for the post-benchmark period from April to October of the benchmark year. For more recent months of birth-death information, see the CES net birth-death webpage.
The CES program employs a concurrent seasonal adjustment methodology to seasonally adjust its national estimates of employment, hours, and earnings each month. Each year, new seasonal adjustment models are re-specified and implemented with the benchmark update. All seasonally adjusted series are updated for the latest 5 years based on the newly selected models. For more about seasonal adjustment methodology in the CES program, see Seasonal adjustment in the CES Handbook of Methods. For files and information used to calculate seasonal adjusted CES estimates, see the CES seasonal adjustment webpage.
For national CES estimates, annual benchmarks are constructed in order to realign the sample-based employment totals for March of each year with the Unemployment Insurance (UI) based population counts for March. These population counts are much less timely than sample-based estimates and are used to provide an annual point-in-time census for employment. Only the March sample-based estimates are replaced with UI counts. Other months are revised based on that March universe level back to the previous April and forward to the current month. 152ee80cbc
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