1

Christopher D. Manning

manning.chris@bls.gov

Christopher D. Manning is an economist in the 

Office of Employment and Unemployment 

Statistics, U.S. Bureau of Labor Statistics.

John R. Stewart

stewart.john@bls.gov

John R. Stewart is a supervisory economist in the 

Office of Employment and Unemployment 

Statistics, U.S. Bureau of Labor Statistics.

Benchmarking the Current Employment Statistics 

national estimates

The Current Employment Statistics (CES) survey is a large 

monthly survey of approximately 147,000 businesses and 

government agencies that represent about 634,000 

individual worksites. It is used to produce detailed industry 

estimates of employment, hours, and earnings for the 

nation, states, and metropolitan areas. The CES program 

benchmarks its all-employee series annually to reanchor 

sample-based employment estimates to full population 

counts. This process improves the accuracy of the CES allemployee series by replacing estimates with full population 

counts that are not subject to the sampling or modeling 

errors inherent in the CES monthly estimates. These 

population counts are derived from administrative records 

and are much less timely than the sample-based estimates. 

However, they provide a near census of establishment 

employment. The authors describe the procedures currently 

used to benchmark the national CES all-employee 

estimates.

The Current Employment Statistics (CES) survey, 

conducted by the U.S. Bureau of Labor Statistics (BLS), is 

a monthly panel survey of approximately 147,000 

businesses and government agencies representing, 

approximately 634,000 individual worksites. The survey 

provides detailed industry data on employment, hours, and 

earnings of workers on nonfarm payrolls.1 As some of the 

most timely and sensitive economic indicators published by 

the federal government, national CES estimates are widely viewed as key measures of the health of the U.S. 

economy and are tracked closely by public policymakers, businesses, and academia.

The CES program reanchors its sample-based employment estimates to full population counts for March of each 

year. This process—known as benchmarking—improves the accuracy of the CES all-employee series, since these 

counts are not subject to sampling or modeling errors inherent in the CES monthly estimates. The CES program 

October 2017
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derives population counts from administrative records that provide a near total count of nonfarm employment on a 

lagged basis.

The Quarterly Census of Employment and Wages (QCEW) program,2 which collects employment and wage data 

from states’ unemployment insurance (UI) tax systems, is the primary source of benchmark data for the CES 

survey. All businesses and government agencies that are subject to UI tax laws must report employment and wage 

information quarterly to their states’ Labor Market Information (LMI) agencies. These UI records cover about 97 

percent of nonfarm wage and salary jobs on civilian payrolls. The remaining 3 percent of jobs that are within scope 

for CES estimates are exempt from UI tax law. An estimate for these employees, collectively referred to as 

“noncovered employment,” is constructed from other sources.

The size of the benchmark revision is widely regarded as a measure of the accuracy of the CES estimates. For 

national total nonfarm employment, the absolute average annual benchmark revision has averaged about twotenths of 1.0 percent (0.2 percent) over the past decade, with a range from −0.7 percent to +0.3 percent. (See 

table 1.)

Source: U.S. Bureau of Labor Statistics.

Benchmark data are available only for the all-employee series. However, most other series are recalculated during 

the benchmarking process because the all-employee series are used as inputs to their calculations.

Benchmark processing

The benchmark process involves several steps that help ensure the integrity of our time series. The first step 

involves the collection of all the necessary data in establishing the new benchmark level for March. Once this step 

is done, the next step involves deriving estimates for months other than March that account for the new March 

level and create a continuous time series. These changes will affect employment estimates as well as other data 

types such as production and women employees.

Establishing the benchmark level

The benchmark procedure for CES national estimates replaces the March sample-based all-employee estimates 

for each estimating cell with full population counts for that March. The difference between the previously published 

sample-based estimate and the benchmark level is called the benchmark error or the benchmark revision. For 

most industries, the March benchmark level is essentially the March QCEW employment figure. However, UI laws 

may not cover some groups of workers in certain industries and, hence, these groups are not included in the 

QCEW figures. Estimates for the noncovered employees are developed from other sources.

Type 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Percent –0.2 –0.1 –0.7 –0.3 0.1 0.3 –0.1 0.0 –0.1 –0.1

Level –293,000 –89,000 –902,000 –378,000 162,000 424,000 –119,000 67,000 –172,000 –81,000

Table 1. Percent and level differences between nonfarm employment benchmarks and estimates, total 

nonfarm, March 2007–March 2016
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Noncovered employment results from a difference in scope between the CES program and the QCEW program. 

The QCEW data include all employment covered by each state’s given UI tax laws. CES includes all nonfarm 

wage and salary workers for businesses and government agencies (excluding households) regardless of whether 

they are covered by UI tax laws. For example, students who participate in workstudy programs through school, 

elected officials, those working for religious organizations, and employees of railroad companies are typically 

exempt from UI tax coverage.3 Since such data are outside the scope of the QCEW, CES must look to other data 

sources to account for this employment.

No single source of noncovered data exists. As a result, CES uses multiple sources to produce the employment 

counts. These sources include County Business Patterns and the Annual Survey of Public Employment and 

Payroll (both from the U.S. Census Bureau), the Railroad Retirement Board, and individual state LMI agencies.4

Table 2 lists industries that include noncovered employment.

Notes:

Industry code Industry title

524113 Direct life insurance carriers

524114 Direct health and medical insurance carriers

524126 Direct property and casualty insurers

524127 Direct title insurance carriers

524128 Direct title insurance and other direct insurance carriers

524130 Reinsurance carriers

524210 Insurance agencies and brokerages

531210 Offices of real estate agents and brokers

611110 Elementary and secondary schools

611210 Junior colleges

611310 Colleges universities, and professional schools

611410 Business and secretarial schools

611420 Business and secretarial schools and computer training

611430 Professional and management development training

611511 Cosmetology and barber schools

611512 Flight training

611513 Apprenticeship training

611519 Other technical and trade schools

611610 Fine arts schools

622110 General medical and surgical hospitals(2)

622210 Psychiatric and substance abuse hospitals(2)

622310 Specialty (except psychiatric and substance abuse) hospitals(2)

624310 Vocational rehabilitation services

624410 Child day care services

813110 Religious organizations

813211 Grantmaking foundations

813312 Environment, conservation and wildlife organizations

813410 Civic and social organizations

813910 Business associations

813940 Political organizations

813990 Miscellaneous professional and similar organizations

Table 2. Industries with noncovered employment(1)

See footnotes at end of table.
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1.

(1) Noncovered employment is defined as jobs exempt from unemployment insurance law.

(2) Indicates that noncovered employment is calculated for firms privately owned and state and local government owned.

Source: U.S. Bureau of Labor Statistics.

The Federal Unemployment Insurance Tax Act defines federal minimum standards for UI coverage that all states 

must follow. Some states, however, go beyond those standards and cover additional workers, which means that 

some groups of employees are covered in one state but not covered in another. The corporate officers group is a 

prominent example—in North Dakota, Oregon, Washington, and Idaho, corporate officers are exempt from UI 

coverage. Consequently, noncovered employment exists in most industries in those states. Employment levels for 

corporate officers and other state-specific UI-exempt employees are provided annually by the individual state LMI 

offices. In addition, BLS and staff in state LMI offices review the “Comparison of State UI Laws” publication each 

year from the U.S. Department of Labor’s Employment and Training Administration to identify changes in UI 

coverage that affects benchmark employment counts.5

Distribution of unclassified employment

QCEW and CES data are classified according the North American Industry Classification System (NAICS). When 

first added to the QCEW files, some establishments are assigned an unclassified industry code, indicating 

insufficient information exists to assign an initial industry code. These records represent less than 1 percent of 

reports. For benchmarking purposes, the CES program distributes employment in the unclassified group to all 

other industries in proportion to their respective percentage of total nonfarm employment. For example, if an 

industry represents 4 percent of total nonfarm employment, that industry will be assigned 4 percent of the total 

unclassified employment. This step ensures that the unclassified employment is included in the benchmark level.

Reestimation for months other than March

Once the March benchmark level has been established, the monthly sample-based estimates for the 11 months 

preceding and the 9 months following the March benchmark are subject to revision. For example, March 2016 

benchmark revisions (published in February 2017) resulted in revised estimates (not seasonally adjusted) from 

April 2015 through December 2016.

The monthly sample-based estimates for the 11 months preceding the March benchmark are revised with the use 

of a “wedge-back” procedure. The difference between the March population or benchmarked employment value 

and the March sample-based estimate is calculated and spread back across the previous 11 months for each 

basic estimating cell.6 Employment estimates for basic cells are then summed to derive the benchmarked series 

for all higher level series up to total nonfarm. CES assumes the total benchmark error accumulated at a steady 

rate since the prior year’s March benchmarked employment, resulting in a linear application (or wedge) of that 

error to the prior April. Specifically, 11/12ths of the March error (or difference) is added to the February estimate, 

10/12ths to the January estimate, and so on, back to the previous April estimate, which receives 1/12th of the 

March difference.

Estimates for the 7 months following March (April–October) also are subject to revision. These estimates are 

adjusted by

applying the previously published sample-based monthly links to the new March benchmark level and
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2.

1.

2.

calculating new business birth/death factors for each month, which are revised to incorporate information 

from the most recent year of QCEW data. The next section titled “Updating the net birth/death model” 

provides more information on the recalculation of business net birth/death factors.

Estimates for the November and December subsequent to the March benchmark are revised because of the 

benchmark adjustments mentioned earlier as well as the receipt of additional sample.

Revisions to other data types

Data types other than all employees also are revised. Although no data are available from the QCEW or 

noncovered sources to benchmark these series directly, they are derived in part from all-employee counts and 

must be recalculated based on benchmarked employment estimates. Ratios of employment used in the monthly 

calculation of the number of women employees and production and nonsupervisory employees are preserved and 

applied to the revised all-employee counts to yield a revised estimate for those data types. These series are 

revised at the basic estimating cell level and aggregated to the summary level.

The basic-level series for average weekly hours, average hourly earnings, and average weekly overtime hours (in 

manufacturing) do not change with the benchmark revision. However, the aggregate levels of these data series 

can change. Reaggregation of these series uses either revised all-employee [series] or production and 

nonsupervisory-employee series, which serve as weights in the calculations. All other derivative series (such as 

real earnings and payroll indexes) also are recalculated.

Updating the net birth/death model

One limitation of the CES probability-based sample is the lack of timely data on business births. This limitation is 

because there is a lag between when a firm opens for business and when it appears on the CES sample frame. 

Another complication is that business death units are difficult to collect; often during monthly collection, CES 

cannot distinguish business deaths from nonrespondents. CES adjusts for these limitations using a statistical 

modeling technique that is based on two principals:

Overall employment change is largely driven by the net difference between expanding and contracting 

continuous businesses.

Employment associated with business births and deaths is substantial (both are between 1 and 2 million 

employees in size). However, the difference between the two is relatively small and stable, which permits 

one to be used as a proxy for the other, leaving a small residual that can be modeled.

Modeling is accomplished through a two-step process. In the first step, CES uses employment associated with 

business deaths as a proxy for business birth employment. In particular, employment from business death units is 

imputed during estimation in subsequent months by not reflecting the establishment as going out of business in the 

estimate but rather by replacing its death employment with the employment growth rate of reporting units. This 

implicit imputation of employment for business deaths is meant to account for the missing birth employment. 

However, the relationship is not one to one. Therefore, in the second step, CES models that difference using 

actual residual net birth/death figures derived from historical QCEW data. The model calculates a forecast of the 

residual for future months, which is then applied to the sample-based portion of the estimate each month. The 
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forecasts are produced once a year with the annual benchmark processing and are updated quarterly, with the 

addition of each new quarter of QCEW data.

Other activities during benchmark

The reanchoring of the CES estimates to full population counts drives the annual revision process. However, 

additional processes are performed that also contribute to the overall revision of the data.

Seasonal adjustment

Over the year, regular seasonal events, such as major holidays, weather, and school schedules, have large 

impacts on CES data. For most series that the CES program publishes, a regular and discernable seasonal 

pattern can be extracted from the time series to reveal underlying trends and cyclical patterns. The seasonally 

adjusted series are useful to data users and are highlighted in CES news releases.

CES uses concurrent methodology to seasonally adjust estimates each month.7 As such, all controllable variables 

in the seasonal adjustment process remain fixed during the year. Each year during benchmark processing, model 

specifications—such as ARIMA (autoregressive integrated moving average) models, moving averages, historical 

outliers, and time series transformations—are determined and remain fixed throughout the year. In addition, 

seasonal factors derived with the use of the new specifications are applied to estimates for the 5 most recent 

years. For example, the March 2016 benchmark revisions (published in February 2017) resulted in revised 

seasonally adjusted series from January 2012 through December 2016. After 5 years of revisions, seasonally 

adjusted data are frozen, unless the historical data are changed because of other factors.

Changes to historical data

For most of its history, the CES program also has timed the release of any changes in publication status, methods, 

or procedures to coincide with the publishing of the benchmark revisions. The timing of these changes coincides 

with benchmark revisions because the changes often require revisions to historical time series. The coincident 

timing is efficient for both BLS data producers and the users of CES data because historical data revisions occur 

only once a year. Some examples of these types of changes follow.

Each year, CES reviews its sample by industry and may eliminate or combine the existing series or add a new 

series based on accuracy and confidentiality measures. When possible, historical series for all newly defined 

industry series are reconstructed.

Likewise, periodic changes are made to the NAICS, which CES uses to define industry classifications and 

publication structures. NAICS updates usually occur every 5 years. Generally, based on the new industry code, 

affected series are re-created, or reconstructed, back to 1990, although in some cases longer reconstructions may 

be made when longer historical data are available.

Methodological changes, while infrequent, are implemented with the annual benchmark processing because they 

can result in revisions to historical data. For example, several series in health care, state government education, 

and local government education were corrected, reconstructed, and introduced with the March 2016 benchmark 

revision released in February 2017.8
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Conclusion

CES data provide users with a wide range of statistics on current labor conditions, the complexities of which 

necessitate the wide-ranging and comprehensive benchmark methodology outlined in this article. The processes 

associated with the CES annual benchmark methodology are done with the goal of ensuring data quality for our 

users. This goal is why the methodology is always being reviewed for improvements.

SUGGESTED CITATION

Christopher D. Manning and John R. Stewart, "Benchmarking the Current Employment Statistics national 

estimates," Monthly Labor Review, U.S. Bureau of Labor Statistics, October 2017, https://doi.org/10.21916/mlr.

2017.25

NOTES

1 CES estimates are classified in accordance with the North American Industry Classification System (NAICS) 2012. Establishments 

are classified into industries based on their primary activity. Those establishments that use comparable inputs (capital equipment, 

labor, and raw materials) are classified together.

2 The Quarterly Census of Employment and Wages also serves as the sampling frame for the CES.

3 Employees of railroad companies are covered by a different unemployment insurance system administered by the Railroad 

Retirement Board.

4 For more information on the County Business Patterns, see https://www.census.gov/programs-surveys/cbp.html, and for more 

information on the Annual Survey of Public Employment and Payroll, see https://www.census.gov/programs-surveys/apes.html.

5 For more information on the “Comparison of State UI Laws” publication from the U.S. Department of Labor’s Employment and 

Training Administration, see https://workforcesecurity.doleta.gov/unemploy/statelaws.asp.

6 Basic cells are defined primarily by detailed industry at the 3-, 4-, 5-, or 6-digit NAICS level.

7 In concurrent seasonal adjustment, seasonal factors are updated every month, incorporating all observations of the not seasonally 

adjusted series, up to and including the current month value. For more information on seasonal adjustment in the CES program, see 

https://www.bls.gov/web/empsit/cesseasadj.htm.

8 For more information on reconstruction, see https://www.bls.gov/web/empsit/cesbmart.htm.

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Benchmarking the Current Employment Statistics survey: the past, present, and future, Monthly Labor Review, October 2017.

Benchmarking the Current Employment Statistics state and area estimates, Monthly Labor Review, November 2017.

Benchmarking the Current Employment Statistics survey: perspectives on current research, Monthly Labor Review, November 2017.

A quarterly benchmarking procedure for the Current Employment Statistics program, Monthly Labor Review, November 2017.

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