Technical Notes for the Current Employment Statistics Survey

Introduction

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 122,000 businesses and government agencies, which cover approximately 666,000 individual worksites drawn from a sampling frame of Unemployment Insurance (UI) tax accounts covering roughly 11.0 million establishments. The active CES sample includes approximately one-third 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 homepage. Historical statistics for states and metropolitan areas are available on the CES State and Metro Area data homepage.

Table of Contents

Use the links below to skip to specific topics about the CES sample, industry classification, available statistics, data collection, microdata review, estimation, and revisions. A link is included to skip to a list of equations, tables, and figures included in the CES Technical Notes.

• The Sample
• Design
• Coverage
• Reliability
• Classification
• Industry Classification
• Major Industry Groups
• Available Data
• Employment
• Hours and Earnings
• Derivative Series
• Forms of Publication
• Statistics for States and Areas
• Data Collection
• Collection Methods
• Collection Forms
• Microdata Review
• Editing and Screening of Microdata
• Analyst Review
• Estimation Methods
• Monthly Estimation
• Small Domain Model
• Birth-Death Model
• Aggregation Procedures
• Seasonal Adjustment
• Revisions
• Sample-based Revisions
• Benchmarks
• Historical Reconstructions
• Other Factors Contributing to Revisions
• Table of Figures
  • Equations
  • Tables
  • Figures

The Sample

Design

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. The sample strata, or subpopulations, are defined by state, industry, and employment size, yielding a state-based design. The sampling rates for each stratum are determined through a method known as optimum allocation, which distributes a fixed number of sample units across a set of strata to minimize the overall variance, or sampling error, on the primary estimate of interest. The total nonfarm employment level is the primary estimate of interest, and the CES sample design gives top priority to measuring it as precisely as possible, or minimizing the statistical error around the statewide total nonfarm employment estimates.

Frame and sample selection

The LDB is the universe from which CES draws the establishment survey sample. The LDB contains data on the roughly 11.0 million U.S. business establishments covered by UI, representing nearly all elements of the U.S. economy. The Quarterly Census of Employment and Wages (QCEW) program collects these data from employers on a quarterly basis in cooperation with Labor Market Information Agencies (LMIs). The LDB contains employment and wage information from employers, as well as name, address, and location information. It also contains identification information such as UI account number and reporting unit or worksite number.

The LDB contains records of all employers covered under the UI tax system. That system covers 97 percent of all employment within the scope of CES in the 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. There are a few sections of the economy that are not covered by the QCEW, including the self-employed, unpaid family workers, railroads, religious organizations, small agricultural employers, and elected officials. Data for employers generally are reported at the worksite level. Employers that have multiple establishments within a state usually report data for each individual establishment. The LDB tracks establishments over time and links them from quarter to quarter.

The total private and government portions of the CES sample are selected using two different methods. Private establishments in the CES sample frame are stratified by state, industry, and size. Stratification groups population members together for the purpose of sample allocation and selection. The strata, or groups, are composed of homogeneous units. With 13 industries (treating manufacturing as one industry and not including government) and 8 size classes, there are 104 total allocation cells per state. The sampling rate for each stratum is determined through a method known as optimum allocation. Optimum allocation minimizes variance at a fixed cost or minimizes cost for a fixed variance. Under the CES probability design, a fixed number of sample units for each state is distributed across the allocation strata in such a way as to minimize the overall variance, or sampling error, of the total state employment over-the-month change. The number of sample units in the CES probability sample are fixed according to available program resources and are reviewed and updated every 5 years to reflect each state's share of employment. The optimum allocation formula places more sample in cells that have more units and cells that have a larger variance.

The CES government sample is not part of the program's probability-based design. CES is able to achieve a very high level of universe employment coverage in government industries by obtaining full payroll employment counts for many government agencies, eliminating the need for a probability-based sample design. Government estimates are combined with the total private estimates to obtain values for total nonfarm.

Annual sample selection helps keep the CES survey current with respect to employment from business births and business deaths. In addition, the updated universe files provide the most recent information about industry, size, and metropolitan area designation. Each year the CES sample is drawn from the first quarter Longitudinal Database (LDB) data in the fall of that year. A birth update is added in the early summer from the third quarter of the previous year.

After all out-of-scope records are removed, the sampling frame is separated into allocation cells. Within each allocation cell, units are grouped by metropolitan statistical area (MSA), and these MSAs are sorted by the size of the MSA, defined as the number of UI accounts in that MSA. As the sampling rate is uniform across the entire allocation cell, implicit stratification by MSA ensures that a proportional number of units are sampled from each MSA. Some MSAs may have too few UI accounts in the allocation cell; these MSAs are collapsed and treated as a single MSA.

Permanent Random Numbers (PRNs) are assigned to all UI accounts on the sampling frame. As new units appear on the frame, random numbers are assigned to those units as well. As records are linked across time, the PRN is carried forward in the linkage. Within each selection cell, the units are sorted by PRN, and units are selected according to the specified sample selection rate. The number of units selected randomly from each selection cell is equal to the product of the sample selection rate and the number of eligible units in the cell plus any carryover from the prior selection cell. The result is rounded to the nearest whole number. Carryover is defined as the amount that is rounded up or down to the nearest whole number.

Because of the cost and workload associated with enrolling new sample units, all units remain in the sample for a minimum of 2 years. To ensure all units meet this minimum requirement, the CES program has established a "swapping" procedure. The procedure allows units to be swapped into the sample that were newly selected during the previous sample year and not reselected as part of the current probability sample. The procedure removes a unit within the same selection cell and places the newly selected unit from the previous year back into the sample. To reduce respondent burden, a similar procedure swaps units out of the sample that have been sample members for 4 or more consecutive years. The swap-out procedure removes an old unit within the same selection cell and replaces it with a new unit. In order to maintain an implicit, proportional allocation across MSAs in the same strata, the ideal unit swap would occur within the same stratum and MSA. On rare occasions, a swap may involve a unit from a different MSA, but the stratum must remain the same. If a unit has been identified for swapping, and there are no units available in the same stratum, then the swap will not take place. Approximately 66 percent of the CES sample for private industries overlaps from the previous sample to the current sample.

Selection weights

Once the sample is drawn, sample selection weights are calculated based on the number of UI accounts actually selected within each allocation cell. The sample selection weight is approximately equal to the inverse of the probability of selection, or the inverse of the sampling rate, shown in equation 1.

Equation 1. Sample selection weights

Sample selection weight = N_h / n_h

where:

N_h = the number of noncertainty UI accounts within the allocation cell that are eligible for sample selection

n_h = the number of noncertainty UI accounts selected within the allocation cell

To Table of Figures

Frame maintenance and sample updates

Due to the dynamic economy, there is a constant cycle of business openings (births) and closings (deaths). A sample update is performed during the summer each year drawing from the previous year's third quarter LDB data. This update selects units from the population of openings and other units not previously eligible for selection and includes them as part of the sample. Location, contact, and administrative information are updated for all establishments that were selected as part of the annual sample.

Coverage

Table 1 shows the 2022 benchmark employment levels and the approximate proportion of total universe employment coverage at the total nonfarm and major industry sector levels. The coverage for individual industries within the supersectors may vary from the proportions shown. The UI counts and establishment numbers shown in table 1 are from the benchmark year, not the current sample year, and therefore differ from UI and establishment totals for the current sample year.

Table 1. Employment benchmarks and approximate coverage of BLS employment and payrolls sample, March 2022

CES Industry Code	CES Industry Title	Employment Benchmarks (in thousands)	Sample Coverage
			Unemployment Insurance Counts (UI)(1)	Number of Establishments	Employees
					Number (in thousands)(2)	Percent of Benchmark Employment Level
00-000000	Total nonfarm	150,411	122,308	653,228	42,213	28
10-000000	Mining and logging	583	799	2,434	127	22
20-000000	Construction	7,463	8,471	11,573	682	9
30-000000	Manufacturing	12,673	6,852	15,081	2,293	18
40-000000	Trade, transportation, and utilities	28,327	19,354 (3)	200,918	9,238	33
41-420000 (4)	Wholesale trade	5,890	6,098	15,436	610	10
42-000000 (4)	Retail trade	15,352	9,463	168,589	6,651	43
43-000000 (4)	Transportation and warehousing	6,534	3,972 (3)	13,374	1,798	28
44-220000 (4)	Utilities	551	329	3,519	180	33
50-000000	Information	3,006	2,432	11,943	718	24
55-000000	Financial activities	8,949	6,357	82,064	1,792	20
60-000000	Professional and business services	22,207	17,877	55,580	3,424	15
65-000000	Private education and health services	24,162	14,286	60,879	5,604	23
70-000000	Leisure and hospitality	15,103	13,390	61,733	2,346	16
80-000000	Other services	5,612	4,647	13,821	330	6
90-000000	Government	22,326	30,395	137,202	15,659	70
Footnotes: (1) Counts reflect active sample reports. Because not all establishments report payroll and hours information, hours and earnings estimates are based on a smaller sample than are the employment estimates. (2) Employment of reported values for March 2022. (3) The Surface Transportation Board provides a complete count of employment for Class I railroads plus Amtrak. A small sample is used to estimate hours and earnings data. (4) Indented industries are a part of trade, transportation, and utilities.

To Table of Figures

CES sample by industry

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 (see table 1). For example, manufacturing and leisure and hospitality industries are of similar size. Manufacturing has 12.7 million employees while leisure and hospitality has about 15.1 million employees. However, their relative sample sizes are different. Manufacturing has about 15,100 sample establishments with nearly 2.3 million employees while leisure and hospitality has many more sample establishments, about 61,700 sample establishments, but covers only about 2.3 million employees. The manufacturing sample therefore covers about 18 percent of all employment in manufacturing while the leisure and hospitality sample covers about 16 percent of all employment in that industry. Some of the difference can be attributed to manufacturing having a much larger average firm size than leisure and hospitality. These types of differences do not cause a bias in the CES employment estimates because of the use of industry sampling strata and sampling weights which ensure each firm is properly represented in the estimates.

Government sample

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 (70 percent) is achieved by obtaining full payroll employment counts for many government agencies. Consequently, a probability-based sample design is not necessary for this industry. The high coverage rate ensures a high degree of reliability for the government employment estimates. Because it is used to estimate only the government portion of total nonfarm employment, the large government sample does not bias the total nonfarm employment estimates. The private and government estimates are summed to derive total nonfarm employment estimates.

Sample implementation

CES enrollment efforts begin immediately after a sample is selected, and collection generally begins in the first month after enrollment. Prior to the July 2014 first preliminary release, CES incorporated the new sample units for all industries once a year, starting with the third release of November estimates. (More information about first, second, and third preliminary releases of CES estimates is available in the Sample-based Revisions section of this document.) Each January, the new sample is used for the first time to estimate November third preliminary estimates of the previous year, December second preliminary estimates of the previous year, and January first preliminary estimates for the current year. Waiting to introduce new sample for all industries simultaneously meant newly enrolled respondents that started reporting payroll data immediately after the sample draw had provided useful data for almost a year before the data were used to produce CES estimates. The annual implementation schedule also contributed in part to revisions in national CES estimates between the November second preliminary and final releases and between the December first and second preliminary estimates. In the past, implementation of new sample units into the CES survey took a large amount of resources and time. CES updated processes for several years to improve the efficiency of sample updates and researched the effects of this change on the estimates.

Beginning with the July 2014 first preliminary release, CES began a quarterly sample implementation schedule. Under the quarterly sample implementation schedule, all industries have been classified into four groups that begin enrollment and data collection at a specific quarter after the sample is drawn for the year. Each group of industries begins enrollment and data collection procedures the quarter prior to being used in estimation and are used in estimation on the first reference month of the following quarter (see table 2). All birth units selected as part of the semi-annual update are implemented in the last group, regardless of industry. Each reference month is estimated using the same sample from the estimation of the first preliminary estimate through the third preliminary estimate.

Because quarterly sample implementation began with the July 2014 first preliminary estimates, the first implementation included the industries identified in groups 1 and 2.

Table 2. Industry groupings for the CES quarterly sample implementation

Group	CES Industry Code	Major Industry Sector	Timing
			Enrollment	Estimation
Group 1	10-000000	Mining and logging	First Quarter	Beginning in Q2 with the April preliminary estimate release (1)
	41-420000	Wholesale trade
	42-000000	Retail trade
	43-000000	Transportation and warehousing
	44-220000	Utilities
	55-000000	Financial activities
Group 2	20-000000	Construction	Second Quarter	Beginning in Q3 with the July preliminary estimate release (1)
	70-000000	Leisure and hospitality
Group 3	50-000000	Information	Third Quarter	Beginning in Q4 with the October preliminary estimate release
	60-000000	Professional and business services
	80-000000	Other services
Group 4	30-000000	Manufacturing (durable and nondurable goods)	Fourth Quarter	Beginning in Q1 with the January preliminary estimate release
	65-000000	Private education and health services
	Birth units for all private industries sampled from the third quarter of the LDB that did not exist on the first quarter of the LDB
Footnotes (1) Because quarterly sample implementation began with the July 2014 first preliminary estimates, the first implementation of sample drawn in 2013 included the industries identified in groups 1 and 2. Subsequent quarters implemented new sample units one group at a time.

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Under the quarterly sample implementation schedule, any quarterly sample implementation group can have an effect on industries outside the group. All the worksites associated with a UI account that are being implemented in a group are introduced into the sample at the same time, even if they are classified under a different industry.

The switch to quarterly sample implementation allows units not in the new sample to be dropped at the same time as the new sample is introduced. The quarterly sample implementation process is expected to reduce respondent burden.

CES sample by employment size class

The employment universe that the CES sample is estimating is highly skewed, as shown by table 3. The largest UI accounts (those with 1,000 employees or more) comprise only 0.2 percent of all UI accounts but contain approximately 28.7 percent of total private employment. The smallest size class (0-9 employees) contains nearly 72.7 percent of all UIs but only about 10.5 percent of total private employment. CES samples larger firms at a higher rate than smaller firms, which is a standard technique commonly used in business establishment surveys.

Table 3. Total private universe employment by size of UI, March 2022

Size Class	Percent of All UIs	Percent of Employment
1 (0-9 employees)	72.7	10.5
2 (10-19 employees)	12.9	7.8
3 (20-49 employees)	8.7	12.3
4 (50-99 employees)	3.0	9.7
5 (100-249 employees)	1.7	12.9
6 (250-499 employees)	0.5	9.3
7 (500-999 employees	0.3	8.8
8 (1000+ employees)	0.2	28.7
Total	100.0	100.0

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Table 4 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. Each sample unit selected is assigned a weight based on its probability of selection, which ensures that all firms of its size are properly represented in the estimates. For example, if 1 in every 100 firms are selected from UIs in the smallest firm stratum, they are assigned a weight of 100 because they represent themselves and 99 other firms that were not sampled. The use of sample weights in the estimation process prevents a large (or small) firm bias in the estimates.

Table 4. Total private CES sample employment by size of UI, March 2022

Size Class	Percent of All UIs	Percent of Employment
1 (0-9 employees)	31.6	0.3
2 (10-19 employees)	12.5	0.5
3 (20-49 employees)	15.5	1.5
4 (50-99 employees)	10.3	2.3
5 (100-249 employees)	11.5	5.8
6 (250-499 employees)	6.9	8.1
7 (500-999 employees	5.6	13.7
8 (1000+ employees)	6.1	67.8
Total	100.0	100.0

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Reliability

Measurements of error

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. Measurements of error associated with sample estimates are provided in table 5 and the all employee (AE), production employee (PE), and women employee (WE) standard error tables.

Table 5. Standard and relative standard errors of CES sample-based estimates for a 1-month change

CES Industry Code	CES Industry Title	Standard Error(1)	Relative Standard Error
00-000000	Total nonfarm	79,928	0.2
05-000000	Total private	74,599	0.2
06-000000	Goods-producing	23,431	0.4
07-000000	Service-providing	75,475	0.2
08-000000	Private service-providing	69,807	0.2
10-000000	Mining and logging	3,329	3.4
20-000000	Construction	14,934	0.7
30-000000	Manufacturing	16,512	0.4
31-000000	Durable goods	13,580	0.6
32-000000	Nondurable goods	9,510	0.7
40-000000	Trade, transportation, and utilities	21,833	0.3
41-420000 (2)	Wholesale trade	9,862	0.6
42-000000 (2)	Retail trade	14,970	0.4
43-000000 (2)	Transportation and warehousing	11,055	0.7
44-220000 (2)	Utilities	1,621	1.8
50-000000	Information	13,192	2.3
55-000000	Financial activities	10,708	0.5
60-000000	Professional and business services	34,805	0.5
65-000000	Private education and health services	29,295	0.4
70-000000	Leisure and hospitality	37,763	0.8
80-000000	Other services	14,530	0.8
90-000000	Government	28,697	0.5
90-910000	Federal	(3)	(3)
90-911000	Federal, except U.S. Postal Service	(3)	(3)
90-919120	U.S. Postal Service	(3)	(3)
90-920000	State government	13,669	1
90-921611	State government education	8,905	1.3
90-922000	State government, excluding education	9,068	1.3
90-930000	Local government	25,233	0.7
90-931611	Local government education	17,784	0.8
90-932000	Local government, excluding education	15,336	0.9
Footnotes (1) Variance for total private is calculated using Fay's Balanced Half Samples (BHS) replication technique. Replicate estimates are derived by perturbing the original sampling weights and using the same estimation structure and weighted-link-relative formula used in the original estimator. The variance is computed by measuring the variability of the replicate estimates. Variances for state and local government are based on a regression formula that uses relationships between sampling variability and employment level. For more information, see the Reliability section in the CES Handbook of Methods at www.bls.gov/opub/hom/ces/calculation.htm#reliability. (2) Indented industries are part of trade, transportation, and utilities. (3) Federal government is estimated from a nearly complete population count of employment, so these industries have zero variance.

To Table of Figures

Benchmark revision as a measure of survey error

The sum of sampling and nonsampling error can be considered total survey error. 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 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 the CES annual benchmark can be found in the Revisions section of this document under Benchmarks.

Revisions between preliminary and final data

First preliminary estimates of employment, hours, and earnings, based on less than the total sample, are published immediately following the reference month. Final revised sample-based estimates are published 2 months later when nearly all the reports in the sample have been received. Table 5 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.

Revisions of preliminary hours and earnings estimates are normally not greater than 0.1 hour for weekly hours and 1 cent for hourly earnings at the total private level and may be slightly larger for the more detailed industry groupings. Further discussion about the CES sample-based monthly revisions to estimates can be found in the Revisions section of this document under Sample-based Revisions.

Variance estimation

The estimation of sample variance for AE, PE, and WE for the CES survey is accomplished through the 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 estimates and variances. 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 + γ where weights for units not in the half sample are multiplied by a factor of 1 − γ. Estimates from these subgroups are calculated using the estimation formula described in equation 2.

The formula used to calculate CES variances is as follows:

Equation 2. CES variance

,

where

•   is the half-sample estimator
• γ = ½  
• k is the number of half samples
•   is the original full-sample estimate.

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Appropriate uses of sampling variances

Variance statistics are useful for comparison purposes, but they 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 nonsampling error, such as response errors, and bias due to nonresponse. The variances of the over-the-month change estimates are very useful in determining when changes are significant at some level of confidence. Variance statistics for first and second preliminary estimates are available for AE, PE, and WE, and third closing variances are available upon request.

Sampling errors

The sampling errors shown for all private industries and total nonfarm have been calculated for estimates that follow the benchmark employment revision by a period of 16 to 20 months. The errors are presented as median values of the observed error estimates. These variances have been estimated using the method of BHS with the probability sample data and sample weights assigned at the time of sample selection.

Illustration of the use of relative standard error tables

AE, PE, and WE standard error tables provide a reference for relative standard errors of all major series developed from the CES. The errors in these tables are presented as relative standard errors (rse) that are derived as the standard error divided by the level estimate (Y) and expressed as a percent. Multiplying the relative standard error by its estimated level value gives the estimate of the standard error: S = Y x (rse/100).

Suppose S₁ and S₂ are standard errors in two non-overlapping (independent) industries. The standard errors of differences between estimates in these industries are calculated as

Equation 3. CES relative standard error

To Table of Figures

Suppose that the level of all employees for financial activities in a given month at first closing is estimated at 8,863,000. The approximate relative standard error of this estimate (0.5 percent) is provided in the AE standard error tables. A 90-percent confidence interval is then the following interval:

8,863,000 ± (1.645 × 0.005 × 8,863,000) = 8,863,000 ± 72,898 = 8,790,102 to 8,935,898

Illustration of the use of standard error tables

AE, PE, and WE standard error tables provide a reference for the standard errors of 1-, 3-, and 12-month changes in the employment, hours, and earnings series. The errors are presented as standard errors of the changes. The standard and relative standard errors for AE, PE, and WE are appropriate for use with both seasonally adjusted and not seasonally adjusted CES data. Suppose that the over-the-month change in all employee average hourly earnings (AHE) from a given month to the next in coal mining at second closing is −$0.64. The standard error for a 1-month change for coal mining from the table is $0.43. The interval estimate of the over-the-month change in AHE that will include the true over-the-month change with 90-percent confidence is calculated as follows:

−$0.64 ± (1.645 × $0.43) = −$0.64 ± $0.71 = [−$1.35, $0.07]

The true value of the over-the-month change is in the interval −$1.35 to $0.07. Because this interval includes $0.00 (no change), the change of −$0.64 shown is not significant at the 90-percent confidence level. Alternatively, the estimated absolute change of $0.64 does not exceed $0.71 (1.645 × $0.43); therefore, one could conclude from these data that the change is not significant at the 90-percent confidence level.

Classification

Industry Classification

All data on employment, hours, and earnings for the nation, states, and metropolitan areas are classified in accordance with the North American Industry Classification System (NAICS) 2022, specified by the U.S. Office of Management and Budget (OMB). The United States, Canada, and Mexico share this classification system, which allows a direct comparison of economic data across the three countries. Information about the use of NAICS in the Current Employment Statistics (CES) program is available on the CES NAICS homepage.

Establishments are classified into industries on the basis of their primary activity. Those that use comparable capital equipment, labor, and raw material inputs are classified together. This information is collected as a supplement to the quarterly Unemployment Insurance (UI) tax reports filed by employers. For an establishment engaging in more than one activity, the entire employment of the establishment is included under the industry indicated by the principal activity.

Major Industry Groups

CES aggregates estimates for detailed industries into 1 of 17 major industry sectors. Major industry sectors are defined in table 6 below. All major industry sectors include only privately owned establishments, except for 90-910000 federal government, 90-920000 state government, and 90-930000 local government.

Table 6. Major industry sectors

CES Industry Code	Major Sector Name	NAICS Codes Included / Ownership
10-000000	Mining and logging	1133, 21 / Private
20-000000	Construction	23 / Private
31-000000	Durable goods manufacturing	33, 32(1) / Private
32-000000	Nondurable goods manufacturing	31, 32(1) / Private
41-420000	Wholesale trade	42 / Private
42-000000	Retail trade	44-45 / Private
43-000000	Transportation and warehousing	48-49 / Private
44-220000	Utilities	22 / Private
50-000000	Information	51 / Private
55-000000	Financial activities	52,53 / Private
60-000000	Professional and business services	54,55,56 / Private
65-000000	Private education and health services	61,62 / Private
70-000000	Leisure and hospitality	71,72 / Private
80-000000	Other services	811,812,813 / Private
90-910000	Federal government	All in-scope NAICS / Federal government
90-920000	State government	All in-scope NAICS / State government
90-930000	Local government	All in-scope NAICS / Local government
Footnotes (1) CES allocates 3-digit NAICS industries to this major industry sector based on industry description.

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Aggregate industry sectors group the major industry sectors into higher levels of detail, as defined in table 7 below. Together, the major industry and aggregate industry sectors are referred to as supersectors.

Table 7. Aggregate industry sectors

CES Industry Code	Aggregate Sector Name	Sectors Included
00-000000	Total nonfarm	05-000000 Total private, 90-000000 Government
05-000000	Total private	06-000000 Goods-producing, 08-000000 Private service-providing
06-000000	Goods-producing	10-000000 Mining and logging, 20-000000 Construction, 30-000000 Manufacturing
07-000000	Service-providing	40-000000 Trade, transportation, and utilities, 50-000000 Information, 55-000000 Financial activities, 60-000000 Professional and business services, 65-000000 Private education and health services, 70-000000 Leisure and hospitality, 80-000000 Other services, 90-000000 Government
08-000000	Private service-providing	40-000000 Trade, transportation, and utilities, 50-000000 Information, 55-000000 Financial activities, 60-000000 Professional and business services, 65-000000 Private education and health services, 70-000000 Leisure and hospitality, 80-000000 Other services
30-000000	Manufacturing	31-000000 Durable goods, 32-000000 Nondurable goods
40-000000	Trade, transportation, and utilities	41-420000 Wholesale trade, 42-000000 Retail trade, 43-000000 Transportation and warehousing, 44-220000 Utilities
90-000000	Government	90-910000 Federal government, 90-920000 State government, 90-930000 Local government

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Available Data

National data availability

The Current Employment Statistics (CES) program produces nonfarm employment series for all employees (AE), production and nonsupervisory employees (PE), and women employees (WE). For AE and PE, CES also produces average hourly earnings (AHE), average weekly hours (AWH), and, in manufacturing industries only, average weekly overtime hours (AWOH). Most detailed employment series begin in 1990, although employment by aggregate industry sector and most major industry sectors is published as far back as 1939. A list of currently published CES series is available on the CES Published Series page.

Nearly 2,000 not seasonally adjusted employment series for AE, PE, and WE are published monthly. The series for AE include nearly 900 industries at various levels of aggregation.

Approximately 2,300 AE and PE series for AWH, AHE, and, in manufacturing, AWOH are published monthly on a not seasonally adjusted basis and cover about 600 industries.

Over 4,200 seasonally adjusted employment series for AE, PE, and WE and hours and earnings series for AE and PE are published.

About 7,800 not seasonally adjusted special derivative series such as average weekly earnings (AWE), indexes, and constant dollar series for AE and PE are also published for approximately 600 industries.

State and area data availability

For states and metropolitan areas, the CES program produces nonfarm industry employment, hours, and earnings series for AE and PE. Most employment series begin in 1990. Metropolitan areas are defined by the U.S. Office of Management and Budget (OMB). Further information about state and metropolitan area data is available in the Statistics for States and Areas section of this document.

Employment

Employment data refer to persons on establishment payrolls who worked or received pay for any part of the pay period that includes the 12th day of the month.

The data exclude proprietors, the unincorporated self-employed, unpaid volunteer or family employees, farm employees, and domestic employees. Salaried officers of corporations are included. Government employment covers only civilian employees; military personnel are excluded. Employees of the Central Intelligence Agency, the National Security Agency, the National Imagery and Mapping Agency, and the Defense Intelligence Agency also are excluded.

Persons on establishment payrolls who are on paid sick leave (for cases in which pay is received directly from the firm), on paid holiday, or on paid vacation, or who work during a part of the pay period even though they are unemployed or on strike during the rest of the period are counted as employed. Not counted as employed are persons who are on layoff, on leave without pay, or on strike for the entire period, or who were hired but have not yet reported during the period.

Production and nonsupervisory employees (PE) are defined differently for certain major industry sectors. In manufacturing and in mining and logging, PE includes only production and related employees. In construction, PE includes only construction employees. In private service-providing industries, PE includes all nonsupervisory employees.

Production and related employees

This category includes working supervisors and all nonsupervisory employees (including group leaders and trainees) engaged in fabricating, processing, assembling, inspecting, receiving, storing, handling, packing, warehousing, shipping, trucking, hauling, maintenance, repair, janitorial, guard services, product development, auxiliary production for plant's own use (for example, power plant), recordkeeping, and other services closely associated with the described production operations.

Construction employees

This group includes the following employees in the construction sector: working supervisors, qualified craft employees, mechanics, apprentices, helpers, laborers, and so forth, engaged in new work, alterations, demolition, repair, maintenance, and the like, whether they work at the site of construction or in shops or yards at jobs (such as precutting and preassembling) ordinarily performed by members of the construction trades.

Nonsupervisory employees

These are employees (not above the working-supervisor level) such as office and clerical employees, repairers, salespersons, operators, drivers, physicians, lawyers, accountants, nurses, social employees, research aides, teachers, drafters, photographers, beauticians, musicians, restaurant employees, custodial employees, attendants, line installers and repairers, laborers, janitors, guards, and other employees at similar occupational levels whose services are closely associated with those of the employees listed.

Hours and Earnings

Concurrent with the release of January 2010 data, the CES program began publishing all employee hours and earnings as official BLS series. These series were developed to measure the AHE and AWH of all nonfarm private sector employees and the AWOH of all manufacturing employees. AE hours and earnings were first released as experimental series in April 2007 and included national level estimates at a total private sector level and limited industry detail.

Historically, the CES program has published average hours and earnings series for production employees in the goods-producing industries and for nonsupervisory employees in the service-providing industries. These employees account for about 81 percent of total private nonfarm employment. The AE hours and earnings series are more comprehensive in coverage, covering 100 percent of all paid employees in the private sector, thereby providing improved information for analyzing economic trends and for constructing other major economic indicators, including nonfarm productivity and personal income.

AE average hours and earnings data are derived from reports of hours and payrolls for all employees. PE average hours and earnings data are derived from reports of production and related employees in manufacturing and mining and logging, construction employees in construction, and nonsupervisory employees in private service-providing industries.

Hours

These are the hours worked or for which pay was received during the pay period that includes the 12th of the month for all employees, production, construction, and nonsupervisory employees. Included are hours paid for holidays, for vacations, and for sick leave when pay is received directly from the firm.

Payroll

Payroll refers to dollars paid for full- and part-time all employees, production, construction, and nonsupervisory employees who received pay for any part of the pay period that includes the 12th day of the month. The payroll is reported before deductions of any kind, such as those for old-age and unemployment insurance, group insurance, withholding tax, bonds, or union dues; also included is pay for overtime, tips, holidays, and vacation and for sick leave paid directly by the firm. Excluded from the payroll are bonuses (unless earned and paid regularly each pay period); other pay not earned in the pay period reported (such as retroactive pay); and the value of free rent, fuel, meals, or other payment in kind. Commissions are also included if paid at least monthly.

Overtime hours

These are hours worked by all employees, production and related employees, and nonsupervisory employees in manufacturing for which overtime premiums were paid because the hours were in excess of the number of hours of either the straight-time workday or the workweek during the pay period that included the 12th of the month. Weekend and holiday hours are included only if overtime premiums were paid. Hours for which only shift differential, hazard, incentive, or other similar types of premiums were paid are excluded.

Average weekly hours

The workweek information relates to the average hours for which pay was received and is different from standard or scheduled hours. Such factors as unpaid absenteeism, labor turnover, part-time work, and stoppages cause average weekly hours to be lower than scheduled hours of work for an establishment. Industry supersector averages further reflect changes in the workweek of component industries.

Average hourly earnings

Average hourly earnings are collected as "gross" earnings. They reflect not only changes in basic hourly and incentive wage rates, but also such variable factors as premium pay for overtime and late-shift work and changes in output of employees paid on an incentive plan. They also reflect shifts in the number of employees between relatively high-paid and low-paid work and changes in employees' earnings in individual establishments. Averages for groups and divisions further reflect changes in AHE for individual industries.

The earnings series do not measure the level of total labor costs on the part of the employer because the following are excluded: benefits, irregular bonuses, retroactive items, and payroll taxes paid by employers.

Average overtime hours

Overtime hours represent that portion of weekly hours that exceeded regular hours and for which overtime premiums were paid in the manufacturing sector. If an employee were to work on a paid holiday at regular rates, receiving as total compensation his holiday pay plus straight-time pay for hours worked that day, no overtime hours would be reported. This applies to both AE and PE average overtime hours.

Because overtime hours are premium hours by definition, weekly hours and overtime hours do not necessarily move in the same direction from month to month. Such factors as work stoppages, absenteeism, and labor turnover may not have the same influence on overtime hours as on average hours. Diverse trends at the industry group level also may be caused by a marked change in hours for a component industry in which little or no overtime was worked in both the previous and current months.

Derivative Series

Three-month moving average

These estimates are an average of the over-the-month change for the most recent 3 months calculated only at the total nonfarm and total private levels. The current month's employment change as well as the previous 2 months' employment changes are averaged to create the 3-month moving average. Each month, the average is moved forward 1 month.

Average weekly earnings

These estimates are derived by multiplying AWH estimates by AHE estimates. Therefore, AWE are affected not only by changes in AHE but also by changes in the length of the workweek. Monthly variations in such factors as the proportion of part-time employees, stoppages for varying reasons, labor turnover during the survey period, and absenteeism for which employees are not paid may cause the average workweek to fluctuate.

Long-term trends of AWE can be affected by structural changes in the makeup of the workforce. For example, persistent long-term increases in the proportion of part-time employees in retail trade and many of the services industries have reduced average workweeks in these industries and have affected the average weekly earnings series.

Real earnings

These earnings are in constant dollars and are calculated from the earnings averages for the current month using a deflator. The Consumer Price Index (CPI) for All Urban Consumers (CPI-U) is used to deflate the earnings series for AE, while the CPI for Urban Wage Earners and Clerical employees (CPI-W) is used to deflate the earnings series for PE. The scope of the CPI-W is similar to that of PE earnings, both in the type of employee who is covered and the amount of the population that is covered by these series. The CPI-U used to deflate AE earnings is more inclusive than the CPI-W. Since AE earnings include all private sector employees, the more inclusive deflator is used in the calculation. The reference base for the CPI series is the 36-month period covering the years 1982, 1983, and 1984.

For more information about real earnings, see the real earnings technical notes.

Average hourly earnings, excluding overtime

Average hourly earnings, excluding overtime-premium pay, are produced for manufacturing only and are computed by dividing the total AE or PE payroll for the industry group by the corresponding sum of total AE or PE hours and one-half of total AE or PE overtime hours. No adjustments are made for other premium payment provisions, such as holiday pay, late-shift premiums, and overtime rates other than time and one-half.

Indexes of aggregate weekly hours and payrolls

For basic estimating industries, aggregate hours are the product of AWH for AE times the employment for AE or AWH for PE times the employment for PE. At all higher levels of industry aggregation, aggregate hours are the sum of the component aggregates. The indexes for AE aggregate weekly hours are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for 2007. The indexes of aggregate weekly hours for PE are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for 2002.

For basic industries, the aggregate payroll is the product of AHE for AE and aggregate weekly hours for AE or AHE for PE and aggregate weekly hours for PE. At all higher levels of industry aggregation, aggregate payroll is the sum of the component aggregates. The indexes of aggregate weekly payrolls are calculated by dividing the current month's aggregate by the average of the 12 monthly figures for 2007 for AE and 2002 for PE.

Indexes of diffusion of employment change

Diffusion indexes measure the dispersion of employment change across industries over a specified time span (1-, 3-, 6-, or 12-month). The overall indexes are calculated from 250 employment series that are seasonally adjusted for 1-, 3-, and 6-month indexes and are not seasonally adjusted for 12-month indexes. The employment series are primarily 4-digit NAICS industries and cover nonfarm payroll employment in the private sector. The manufacturing diffusion indexes are based on 72 4-digit NAICS industries, seasonally adjusted for 1-, 3-, and 6-month indexes and not seasonally adjusted for 12-month indexes.

To derive the indexes, each component industry is assigned a value of 0, 50, or 100 percent, depending on whether its employment showed a decrease, no change, or an increase, respectively, over the time span. The average value (mean) is then calculated, and this percent is the diffusion index number.

The reference point for diffusion analysis is 50 percent, the value indicating that the same number of component industries had increased as had decreased. Index numbers above 50 show that more industries had increasing employment and values below 50 indicate that more had decreasing employment. The margin between the percent that increased and the percent that decreased is equal to the difference between the index and its complement - that is, 100 minus the index. For example, an index of 65 percent means that 30 percent more industries had increasing employment than had decreasing employment (65-(100-65) = 30). However, for dispersion analysis, the distance of the index number from the 50-percent reference point is the most significant observation.

Although diffusion indexes commonly are interpreted as showing the percent of components that increased over the time span, the index reflects half of the unchanged components as well. (This is the effect of assigning a value of 50 percent to the unchanged components when computing the index.)

Forms of Publication

The Employment Situation

Each month, usually 3 weeks after the end of the reference period including the 12^th of the month, BLS releases The Employment Situation, which contains CES national first preliminary (first closing) estimates of employment, hours, and earnings for all 3-digit NAICS series. The remaining series published by CES are released with the following month's news release. For a list of CES published series, see the CES Published Series page.

Real Earnings

Each month, coincident with the CPI release, BLS releases Real Earnings, which contains CES earnings data indexed to the CPI. For more information about real earnings, see Real Earnings in this document or visit the real earnings technical notes page.

Other forms of publication

CES data are also available in the following forms of publication:

• Employment and Earnings Online
• CES Databases
• CES Highlights

Statistics for States and Areas

BLS independently produces CES national and CES state and area employment, hours, and earnings series. Both sets of estimates are based on the same establishment reports; however, the CES national program uses the full establishment survey sample to produce monthly national employment estimates, while the CES state and area program uses only the state-specific portion of the sample to develop state employment estimates. The CES area statistics relate to metropolitan areas, using the most recent OMB bulletin regarding statistical area definitions to define metropolitan statistical areas and metropolitan divisions. The OMB definitions currently being used by the CES state and area program (SAE) are available at www.bls.gov/sae/additional-resources/metropolitan-statistical-area-definitions.htm. The CES SAE program also produces area statistics for nonstandard areas (areas which are not defined in the OMB Bulletin), noted at the non-standard CES areas page. Changes in definitions are noted as they occur. Estimates for states and areas are produced using two methods. The majority of state and area estimates are produced using direct sample-based estimation. However, published area and industry combinations (domains) that do not have a large enough sample to support estimation using only sample responses have been estimated using modeling techniques. For more state and area employment information, see the CES SAE homepage.

The state and area estimates use smaller amounts of sample by industry than the national industry estimates. This increases the error component associated with state and metropolitan level estimates. For this reason, aggregating state data to the national level will also sum this error component, resulting in different estimates of U.S. employment, hours, and earnings. Summed state level CES estimates should not be compared with national CES estimates.

Data Collection

Collection Methods

Each month, the Bureau of Labor Statistics (BLS) collects data on employment, payroll, and paid hours from a sample of establishments. Prior to 1991, most of the Current Employment Statistics (CES) sample was collected by mail in a decentralized environment by each Labor Market Information (LMI) Agency. CES has gradually centralized collection and adopted automated sample collection methods with the result that collection rates have gradually risen over time. Now, CES has a comprehensive program of new sample unit solicitation in four CES Regional Data Collection Centers (DCCs). The DCCs perform initial enrollment of each establishment via telephone, collect the data for several months via Computer Assisted Telephone Interviewing (CATI), and, where possible, transfer respondents to web reporting. In addition, the DCCs conduct an ongoing program of refusal conversion. The Electronic Data Interchange (EDI) Center enrolls large firms that submit electronic files for processing. Under EDI, the firm provides an electronic file to CES each month in a prescribed file format. This file includes data for all of the firm's worksites. The file is received, processed, and edited by the CES-operated EDI Center.

Offering survey respondents a choice of reporting methods helps sustain response rates to this voluntary survey. The largest portion of the CES sample is collected via EDI (55 percent), while web collection is used for approximately 30 percent, and CATI is used for approximately 10 percent of all reports. Web is one of the fastest growing collection methods. Under web collection, the respondent links to a secure website that contains an image of the questionnaire where the respondent can enter their data. The data are subject to a series of edit checks before being transmitted to CES.

Touchtone Data Entry (TDE), another self-reporting mode, is used to collect about 1 percent of the monthly reports. Under the TDE system, the respondent uses a touchtone telephone to call a toll-free number and activate an interview session. The questionnaire resides on the computer in the form of prerecorded questions that are read to the respondent. The respondent enters numeric responses by pressing the touchtone phone buttons. Each answer is read back for respondent verification.

For the remaining establishments that do not use these methods, data are collected using other methods, which mostly consist of nonstandard electronic files that require custom processing (4 percent).

Figure 1 shows the percentage of the establishments using different data collection methods.

Figure 1. Current Employment Statistics survey data collection methods by percent

To Table of Figures

Collection Forms

The CES collection forms are separated by broad industry group and number of pay groups. Each form asks of an establishment how often employees receive pay, if they receive commissions and how often, and the total number of employees, production employees, women employees, payroll, commission, and hours. This list of questions is repeated for each month in a 12-month period; a new form is required for the next 12-month period. Respondents receive a booklet with space to complete these questions. A complete list of CES report forms is available on the CES report forms page.

Microdata Review

Editing and Screening of Microdata

The CES program tests all respondent data, collectively known as microdata, in order to generate accurate, timely, and relevant monthly employment estimates. These tests, also called microdata screening tests, compare all new data reported by survey respondents to the respondent’s historically reported data. Data that fail these microdata screening tests are then reviewed by analysts to determine whether the microdata should be used in the estimation of employment, hours, and earnings.

Respondents report data for the pay period that includes the 12th of the month. Employee counts are requested for all paid employees and women employees, as well as production employees in goods-producing industries and nonsupervisory workers in private service-providing industries. Total payrolls, commissions, and hours paid (including those for overtime and paid leave) are requested for all employees and for production and nonsupervisory employees. Overtime hours are requested for manufacturing industries only.

All edit and screening tests for a given report use only that specific respondent’s historically reported and current data. The tests do not incorporate data from other respondents, estimates, or sources. All payroll, commissions, and hours are normalized to weekly equivalents based on a respondent’s reported length of pay period. CES derives additional data types for each respondent when possible based on reported data. For example, average hourly earnings for a respondent is derived by dividing reported payroll by reported hours. Table 8 describes each data type or variable used in the edit and screening process.

Table 8. CES data types and other payroll variables

Abbreviation	Basic Data Types
AE	All employees
Commissions	Total commissions of all employees
Hours	Total hours of all employees
OT	Total overtime hours of all employees (manufacturing only)
AE Payroll	Total payroll of all employees
PE	Production and nonsupervisory employees
PE Commissions	Total commissions of production and nonsupervisory employees
PE Hours	Total hours of production and nonsupervisory employees
PE OT	Total overtime hours of production employees (manufacturing only)
PE Payroll	Total payroll of production and nonsupervisory employees
WE	Women employees
	Derivative Data Types
AHE	Average hourly earnings of all employees (corresponding payroll divided by hours (Payroll/Hours))
AWOH	Average weekly overtime hours of all employees (corresponding normalized overtime hours divided by all employee count (OT/AE))
AWH	Average weekly hours of all employees (corresponding normalized total hours divided by all employee count (Hours/AE))
PE AHE	Average hourly earnings of production and nonsupervisory employees (corresponding payroll divided by hours (PE payroll/PE hours))
PE AWOH	Average weekly overtime hours of production and nonsupervisory employees (corresponding normalized overtime hours divided by production and nonsupervisory employee count (PE OT/PE))
PE AWH	Average weekly hours of production and nonsupervisory employees (corresponding total normalized hours divided by production and nonsupervisory employee count (PE hours/PE))
PE ratio	Production and nonsupervisory employee-to-all employee ratio (PE/AE)
WE ratio	Women employee-to-all employee ratio (WE/AE)
	Other Payroll Variables - Length of Pay Period (LP)
Commissions LP	LP for commissions for all employees
AE LP	LP for all employees
PE Commissions LP	LP for commissions for production and nonsupervisory employees
PE LP	LP for production and nonsupervisory employees

To Table of Figures

Each reported or derived data type must pass a series of edit and screening tests to determine the validity of the record. The tests are divided into four sequential categories: strict edit tests, nonstrict edit tests, screening tests for all data types, and screening tests for specific data types. Table 9 shows the data types that must be reported to produce CES estimates. See the Estimation Methods section of the CES Technical Notes for more information.

Table 9. Sample required to produce basic estimates, shown in the order estimated

Basic Estimate		Required Data Types
1	All employees	AE
2	Women employees	AE, WE
3	Average weekly hours of all employees	AE, Hours
4	Average hourly earnings of all employees	AE, Hours, Payroll, Commissions(1)
5	Average weekly overtime hours of all employees (manufacturing only)	AE, OT
6	production and nonsupervisory employees	AE, PE
7	Average weekly hours of production and nonsupervisory employees	AE, PE, PE Hours
8	Average hourly earnings of production and nonsupervisory employees	AE, PE, PE Hours, PE Payroll, PE Commissions(1)
9	Average weekly overtime hours of production and nonsupervisory employees (manufacturing only)	AE, PE, PE OT
Footnotes (1) Commissions are used in estimating average hourly earnings only if they are paid at least monthly or more frequently. Both commissions and payrolls are normalized to weekly equivalents and are added to get total payroll for estimation purposes.

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Strict edit tests

Several conditions known as strict edits must be met for microdata to be used in CES estimates. For example, the number of women employees cannot be greater than the total of all employees reported for the reference pay period. All microdata are processed to identify strict edit errors. If a microdata record fails any of the strict edit tests, all data types used in the specific test fail, become excluded from estimation, and are returned to the data collection group for correction. See table 10 for a comprehensive list of strict edit tests used by CES.

Table 10. Strict edit tests

Data Types	Condition Required (microdata will be eligible for use in estimation)
AE, Payroll, Hours, OT, PE, PE Payroll, PE Hours, PE OT, WE	AE must be reported
PE, PE Payroll, PE Hours	If PE Payroll and PE Hours reported, then PE must be reported
Payroll, Hours	If Hours reported, then Payroll must be reported
Hours, Payroll	If Payroll reported, then Hours must be reported
Payroll, Hours, Commissions	If Commissions reported, then both Payroll and Hours must be reported
Payroll, Hours, OT	If OT reported, then both Payroll and Hours must be reported
PE Payroll, PE Hours	If PE Hours reported, then PE Payroll must be reported
PE Hours, PE Payroll	If PE Payroll reported, then PE Hours must be reported
PE Payroll, PE Hours, PE Commissions	If PE Commissions reported, then PE Payroll and PE Hours must be reported
PE Payroll, PE Hours, PE OT	If PE OT reported, then both PE Payroll and PE Hours must be reported
Payroll	If Payroll reported, then LP factor is required
PE Payroll	If PE Payroll reported, then PE LP factor is required
Commissions	If Commissions reported, then Commissions LP is required
PE Commissions	If PE Commissions reported, then PE Commissions LP factor is required
AE, Payroll, Hours	If AE or Payroll or Hours equals zero, then all must equal zero
AE, WE	AE must be greater than or equal to WE
AE, Commissions	If Commissions positive, then AE must be greater than zero
AE, OT	If OT positive, then AE must be greater than zero
PE, PE Payroll, PE Hours	If PE or PE Payroll or PE Hours equals zero, then all must equal zero
PE, PE Commissions	If PE Commissions positive, then PE must be greater than zero
PE, PE OT	If PE OT positive, then PE must be greater than zero
AE, PE	AE must be greater than or equal to PE
PE Payroll, Payroll	Payroll must be greater than or equal to PE Payroll
Commissions	If Commissions LP indicates 'no commissions' are paid or Commissions is paid less frequently than once a month, then Commissions should not be reported
PE Commissions	If PE Commissions LP indicates 'no commissions' are paid or PE Commissions are paid less frequently than once a month, then PE Commissions should not be reported
PE Hours, Hours	Hours must be greater than or equal to PE Hours
OT, Hours	Hours must be greater than OT
PE OT, OT	OT must be greater than or equal to PE OT
PE OT, PE Hours	PE Hours must be greater than PE OT
AE	AE should be less than 199,999
PE	PE should be less than 199,999
AWH	AWH must be less than or equal to 168 hours
AHE	AHE in any private-sector industry, except food services and drinking places, must be no lower than 50 cents below the Federal minimum wage
AHE	AHE in food services and drinking places must be no lower than 70 percent of the federal minimum wage
PE AWH	PE AWH must be less than or equal to 168 hours
PE AHE	PE AHE in any private-sector industry, except food services and drinking places, must be no lower than 50 cents below the federal minimum wage
PE AHE	PE AHE in food services and drinking places must be no lower than 70 percent of the federal minimum wage

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Nonstrict edit tests

Nonstrict edit tests, shown in table 11, are designed to identify microdata reports that are possible, but highly unlikely. For example, one test condition asks if average hourly earnings can be 25 times greater than the minimum wage. If a test condition is true and no explanatory comment code has been entered, the data type fails and, therefore, the microdata record fails. An analyst then reviews the failed record to determine whether follow up with the respondent is necessary or if the record can be used in estimation.

Table 11. Nonstrict edit tests

Data Types	Test Condition
AE, Hours, LP	AWH greater than 65 hours
Payroll, Hours	AHE greater than 25 times the federal minimum wage
PE, PE Hours, LP	PE AWH greater than 65 hours
PE Payroll, PE Hours	PE AHE greater than 15 times the federal minimum wage
AE, OT, LP	AWOH greater than 25 hours
AE, Hours, OT, LP	AWOH greater than one-half AWH
PE, PE OT, PE LP	PE AWOH greater than 25 hours
PE, PE Hours, PE OT, PE LP	PE AWOH to be greater than one-half PE AWH
PE	PE equals zero and AE greater than zero

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Screening tests for all data types

All microdata records that pass the first two categories of edit tests are then screened for unusual percentage and level changes for all data types. (See table 12.) Failure of all screening tests for the data types that the respondent has reported results in that data type record being sent to an analyst for review. If any data type passes the test, that data type record passes to the final category of screening tests. If a respondent has not reported all of the historical data required to perform a screening test, that test does not factor into the results as a pass or failure. Furthermore, if a respondent is reporting their first month of data, their microdata is not used in the current month’s estimation since they would not be included in the matched sample. (See Estimation Methods below for more information about matched samples.)

There are some exceptions to failures of screening tests for all data types. For example, if microdata provided by a respondent that previously reported only one set of payroll numbers suddenly provided a breakout of microdata for multiple worksites, it would likely fail the screening test for several of the respondent's data types. However, if the data were reported along with a code that indicated a change in their basis of reporting, the data would be considered correct for the current month and would pass screening. In this example the data will not be used in the current month of estimation, but it may be matched with the following month’s reported data if the subsequent month of data is reported with the same basis of reporting. (See Estimation Methods below for more information about matched samples.)

Microdata reports that indicate a changed basis of reporting, by breaking out payrolls for multiple worksites after previously reporting only one set of payroll numbers for example, are considered correct for the current month and pass screening. The data will not be used in the current month of estimation, but may be matched with the following month’s reported data if the subsequent month of data is reported with the same basis of reporting.

Table 12. Screening tests for all data types

Condition Required	Variables Tested
	AE	WE	WE ratio	AWH	AHE	AWOH	PE	PE ratio	PE AWH	PE AHE	PE AWOH
1-month percent change less than X1i	ü		ü	ü	ü	ü		ü	ü	ü	ü
2-month percent change less than X1i	ü		ü	ü	ü	ü		ü	ü	ü	ü
1-month percent change differs by no more than X2i percentage points from the 1-month percent change 12 months ago	ü		ü	ü	ü	ü		ü	ü	ü	ü
2-month percent change differs by no more than X2i percentage points from the 2-month percent change 12 months ago	ü		ü	ü	ü	ü		ü	ü	ü	ü
1-month percent change differs by no more than X2i percentage points from the 1-month percent change 13 months ago	ü		ü	ü	ü	ü		ü	ü	ü	ü
1-month percent change differs by no more than X2i percentage points from the 1-month percent change 11 months ago	ü		ü	ü	ü	ü		ü	ü	ü	ü
1-month change is less than K1i	ü	ü		ü	ü	ü	ü		ü	ü	ü
2-month change is less than K1i	ü	ü		ü	ü	ü	ü		ü	ü	ü
12-month change is less than K1i	ü	ü		ü	ü	ü	ü		ü	ü	ü
1-month change is less than a tolerance value defined by the product of the average maximum and minimum 1-month change, the critical T value, and the D factor(1)	ü		ü	ü	ü	ü		ü	ü	ü	ü
12-month change is less than a tolerance value defined by the product of the average maximum and minimum 12-month change, the critical T value, and the D factor(1)	ü		ü	ü	ü	ü		ü	ü	ü	ü
Comment code indicates changed basis of reporting	ü	ü		ü	ü	ü	ü		ü	ü	ü
Footnotes (1) Tcritical is based on the .975 percentile of a standard t-distribution. In order to process the statistical t-tests, a respondent must have reported data for the prior 6 months, at a minimum. D factors are used to provide estimates of the variance using a respondent's range of employment values.

To Table of Figures

Screening tests for all data types use a large set of variable (X) and constant (K) factors that vary by industry and by data type. Industry analysts adjust these factors if screening tests result in excessive amounts of microdata failures that are determined to be correct or excessive amounts of bad microdata passing through the screening tests. For example, data from a large, multiple establishment business or government agency may regularly fail screening tests. If the respondent regularly confirms the accuracy of their data, the industry analyst may adjust an X or K factor to allow the respondent’s data to pass. These X and K factors are not available to the public in order to prevent disclosure of CES respondents and their payroll.

Screening tests for specific data types

The final category of screening tests are thresholds and conditions for the specific data type being tested. (See table 13.) Failure of all tests results in a record being sent to an industry analyst for review. Tests not processed due to insufficient historical data for a piece of microdata are excluded from counts of passed and failed tests.

Table 13. Screening tests for specific data types

Data Types	Condition Required
AE	Comment code corroborates AE increase or decrease
AE, PE	The 1-month percent change in non-production worker employment is less than or equal to factor X4
AE, PE	The 1-month change in non-production worker employment is less than or equal to factor K2
AE, AWH	The relative 1-month change in AE is greater than K3 and the 1-month change in AWH is less than K4
AE, AHE	The relative 1-month change in AE is greater than K5 and the 1-month change in AHE is less than K6
AE, AWOH	The relative 1-month change in AE is greater than K5 and the 1-month change in AWOH is less than K7
PE, PE AWH	The relative 1-month change in PE is greater than K3 and the 1-month change in PE AWH is less than K4
PE, PE AHE	The relative 1-month change in PE is greater than K5 and the 1-month change in PE AHE is less than K6
PE, PE AWOH	The relative 1-month change in PE is greater than K5 and the 1-month change in PE AWOH is less than K7

To Table of Figures

Analyst Review

An analyst reviews microdata that failed all screening tests for either all data types or for specific data types. The analyst considers the failed data, the respondent’s historical data, comment codes, and any information gleaned from data reconciliation contacts. Based on this information, the analyst may accept the microdata as originally reported for use in estimation or exclude the microdata from the sample and the current month's estimation. If excluded, the analyst may also request additional clarification or correction from data reconciliation contacts.

Estimation Methods

Monthly Estimation

The Current Employment Statistics (CES) program uses a matched sample concept and weighted link relative estimator to produce employment, hours, and earnings estimates. These methods are described in table 14. A matched sample is defined to be all sample members that have reported data for the reference month and the month prior. Excluded from the matched sample is any sample unit that reports that it is out of business and has zero employees. This aspect of the estimation methodology is more fully described below in the section on Birth-Death Model estimation.

Table 14. Summary of methods for computing industry statistics on employment, hours, and earnings estimates

Employment, Hours, and Earnings	Basic Estimating Cell (industry, 6-digit published level)	Aggregate Industry Level (super sector and, where stratified, industry)	Annual Average Data
All employees	All employee estimate for previous month multiplied by weighted ratio of all employees in current month to all employees in previous month, for sample establishments that reported for both months, plus net birth-death model estimate.	Sum of all employee estimates for component cells.	Sum of monthly estimates divided by 12.
Average weekly hours of all employees	All employee hours divided by number of all employees.	Average, weighted by all employees, of the average weekly hours for component cells.	Annual total of aggregate hours (all employees multiplied by average weekly hours) divided by annual sum of all employees.
Average weekly overtime hours of all employees	All employee overtime hours divided by number of all employees.	Average, weighted by all employees, of the average weekly overtime hours for component cells.	Annual total of aggregate overtime hours (all employees multiplied by average weekly overtime hours) divided by annual sum of all employees.
Average hourly earnings of all employees	All employee payroll divided by all employee hours.	Average, weighted by aggregate hours, of the average hourly earnings for component cells.	Annual total of aggregate payrolls (all employees multiplied by weekly hours and hourly earnings) divided by annual aggregate hours.
Average weekly earnings of all employees	Product of all employee average weekly hours and all employee average hourly earnings.	Product of all employee average weekly hours and all employee average hourly earnings.	Sum of monthly all employee aggregate payrolls divided by the sum of monthly all employees.
Production or nonsupervisory employees	All employee estimate for current month multiplied by weighted ratio of production or nonsupervisory employees to all employees in sample establishments for current month.	Sum of estimates of production or nonsupervisory employees for component cells.	Sum of monthly estimates divided by 12.
Women employees	All employee estimate for current month multiplied by weighted ratio of women employees to all employees in sample establishments for current month.	Sum of estimates of women employees for component cells.	Sum of monthly estimates divided by 12.
Average weekly hours of production or nonsupervisory employees	Production or nonsupervisory employee hours divided by number of production or nonsupervisory employees.	Average, weighted by production or nonsupervisory employment, of the average weekly hours for component cells.	Annual total of aggregate hours (production or nonsupervisory employment multiplied by average weekly hours) divided by annual sum of production or nonsupervisory employment.
Average weekly overtime hours of production employees (manufacturing industries only)	Production employee overtime hours divided by number of production employees.	Average, weighted by production employment, of the average weekly overtime hours for component cells.	Annual total of aggregate overtime hours (production employment multiplied by average weekly overtime hours) divided by annual sum of production employment.
Average hourly earnings of production or nonsupervisory employees	Total production or nonsupervisory employee payroll divided by total production or nonsupervisory employee hours.	Average, weighted by aggregate hours, of the average hourly earnings for component cells.	Annual total of aggregate payrolls (production or nonsupervisory employment multiplied by weekly hours and hourly earnings) divided by annual aggregate hours.
Average weekly earnings of production or nonsupervisory employees	Product of production or nonsupervisory employee average weekly hours and production or nonsupervisory employee average hourly earnings.	Product of production or nonsupervisory employee average weekly hours and production or nonsupervisory employee average hourly earnings.	Sum of monthly aggregate payrolls divided by the sum of monthly production employees or nonsupervisory.

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Stratification

The sample is stratified into 549 basic estimation cells for purposes of computing national all employee (AE) estimates. Estimating cell structures may differ for production and nonsupervisory employees (PE), women employees (WE), and hours and earnings for both AE and PE. Cells are defined primarily by detailed industry. In the construction supersector, geographic stratification is also used. The estimation cells can be defined at the 3-, 4-, 5-, and 6-digit North American Industry Classification System (NAICS) level.

In addition to the estimation cells mentioned above, there are 29 independently estimated cells that do not aggregate to the summary cell levels.

Weighted link-relative technique

The estimator for the AE series uses the sample trend in the cell to move the previous level to the current-month estimated level. A model-based component is applied to account for the net employment resulting from business births and deaths not captured by the sample.

The basic formula for estimating AE is:

Equation 4. Current month estimate of all employees

,

where:

i = matched sample unit;

j = matched sample unit where the current month is atypical;

w_i = weight associated with the CES report;

ae_c,i = current month reported all employees;

ae_p,i = previous month reported all employees;

w_j = weight associated with the CES report where the current month is atypical;

ae_c,j = current month reported all employees where the current month is atypical;

ae_p,j = previous month reported all employees where the current month is atypical;

= current month estimated all employees;

= previous month estimated all employees; and

b_c = current month birth-death estimate.

To Table of Figures

Weighted link and taper technique

The estimator used for all data types other than AE accounts for the over-the-month change in the sampled units, but it also includes a tapering feature used to keep the estimates close to the overall sample average over time. The taper is considered to be a level correction. This estimator uses matched sample data; it tapers the estimate toward the sample average for the previous month of the current matched sample before applying the current month's change; and it promotes continuity by heavily favoring the estimate for the previous month when applying the numerical factors. Variables used in these equations are defined below equation 7.

Current month estimate of PE is defined as:

Equation 5. Current month estimate of production and nonsupervisory employees

To Table of Figures

Current month estimate of women employees (WE)

Estimation of the series for WE is identical to that described for PE with the appropriate substitution of WE values for the PE values in the previous formulas.

Current month estimate of Hours and Earnings series

The same estimation formulas currently used for the published PE hours and earnings series are used for the AE hours and earnings series. Within the formulas, simply substitute AE references for PE references.

Current month estimate of average weekly hours (AWH) is defined as:

Equation 6. Current month estimate of average weekly hours

To Table of Figures

Current month estimate of average hourly earnings (AHE) is defined as:

Equation 7. Current month estimate of average hourly earnings

where:

i = a matched CES report

I = the set of all matched CES reports

j = a matched CES report where the current month is atypical

J = the set of all matched CES reports where the current month is atypical (Note: J is a subset of I)

* = indicates an atypical matched CES report

α = 0.9

β = 0.1

c = indicates current month sample or estimate

p = indicates previous month sample or estimate

w = weight associated with a CES report

ae = reported all employees

pe = reported production and nonsupervisory employees

we = reported women employees

= estimated employment for all employees (or production and nonsupervisory or women employees if PE or WE)

= estimated average weekly hours for all employees (or production and nonsupervisory employees when estimating PE hours)

= estimated average hourly earnings for all employees (or production and nonsupervisory employees when estimating PE earnings)

= estimated ratio of production and nonsupervisory (or women) employees to all employees

wh = reported weekly hours for all employees (or production and nonsupervisory employees when estimating PE hours)

pr = reported weekly payroll for all employees (or production and nonsupervisory employees when estimating PE earnings)

= estimated aggregate weekly hours for all employees (or production and nonsupervisory employees) derived from estimates of average weekly hours and employment

b = net birth-death forecast for the current month

For all variables used in the equations above:

• All estimated values are shown in upper case.
• All sample measures are shown in lower case and are based on a matched sample.
• The estimator for women employees takes the same form as the estimator for production and nonsupervisory employees, where PE and PER are the estimates for women employees and women-to-all employee ratio, respectively, and matched sample totals pe are the matched sample totals for women.
• The estimator for average weekly hours for production and nonsupervisory employees takes the same form as average weekly hours for all employees, where AE and AWH represent estimates of production and nonsupervisory employees and average weekly hours of production and nonsupervisory employees, respectively, and the matched sample totals ae and wh represent matched sample totals for production employees and weekly hours for production and nonsupervisory employees, respectively.
• The estimator for average hourly earnings for production and nonsupervisory employees takes the same form as average hourly earnings for all employees, where AE, AWH, and AHE represent estimates of production and nonsupervisory employees and their hours and earnings, and the matched sample totals pr and wh represent matched sample totals of payroll and work hours for production and nonsupervisory employees.
• The estimators for average weekly overtime take the same form as average weekly hours, where AWH represents the estimates of average weekly overtime hours and wh represents the matched sample for total overtime hours reported. Overtime estimates are calculated for manufacturing industries only.

To Table of Figures

Current month estimate of average weekly overtime hours (AWOH)

Estimation of average weekly overtime hours is identical to that described for AWH with the appropriate substitution of overtime hours values for the weekly hours values in the previous formula.

Residential and nonresidential specialty trade contractors estimates

Residential and nonresidential employment estimates in specialty trade contractors (NAICS 238) are produced as breakouts under the standard NAICS coding structure. Benchmarks for these series are developed from the Quarterly Census of Employment and Wages (QCEW) data and independent estimates for these series are made on a monthly basis and proportionally distributed, or raked, to the estimates produced under the standard structure to ensure that the sum of the residential specialty trade contractors and nonresidential specialty trade contractors series is consistent with the published total for specialty trade contractors at the 3-digit NAICS level.

The raking adjustment uses the following methodology:

Estimates are derived independently for the residential and nonresidential groups at the 4-digit NAICS level for each region. The regional estimates are rounded and summed to the 4-digit NAICS level for both the residential and nonresidential groups. Within each 4-digit NAICS series, ratios of residential-to-total employment and nonresidential-to-total employment are calculated.

At the 4-digit NAICS level, the sum of the residential/nonresidential series is subtracted from the official industry-region cell structure total to determine the amount that must be raked. The total amount that must be raked is multiplied by the ratios to determine what percentage of the raked amount should be applied to the residential group and what percentage should be applied to the nonresidential group.

Once the residential and nonresidential groups receive their proportional amount of raked employment, the two groups are aggregated again to the 4-digit NAICS level. At this point they are equal to the 4-digit NAICS total derived from the official industry-region cell structure. This raking process also forces additivity at the 3-digit NAICS level.

Only estimates of AE are made for the residential and nonresidential specialty trade contractor series. Estimates of construction employees, women employees, and hours and earnings are not produced.

Small Domain Model

The small domain model

The CES Small Domain Model is a weighted least squares model with two employment inputs: (1) an estimate based on available CES sample for that series, and (2) an Autoregressive Integrated Moving Average (ARIMA) projection based on trend from 10 years of historical QCEW data. These two over-the-month change estimates are then weighted based on the variance of each of the estimates. This version of small domain is used for national and state estimation of a small number of series with sampling limitations.

Small domain for metropolitan statistical areas (MSAs) consists of a weighted sum of three different relative over-the-month change estimates , , and , calculated from the two employment inputs. These three relative over-the-month estimates are then weighted based on the variance of each of the three estimates. The larger the variance of each estimate relative to the other variances, the smaller the weight. The resulting estimate of current month employment is defined as:

Equation 8. Employment calculated using small domain model

where:

i = the CES industry.

a = the geographic location for that series. For national, a is the nation as a whole. For states, a is the state as a whole. For MSAs, a is the metropolitan area.

= current month t employment estimate for domain ia defined by the intersection of industry i and geographic location a.

= current month relative over-the-month change estimate based on available sample responses for domain ia.

W_iat,1 = current month weight assigned to based on the variances , , and . The weights W_iat,2 and W_iat,3 are defined similarly.

= current month relative over-the-month change estimate based on time series forecasts using historical universe employment counts for domain ia. These historical universe employment counts are available from January 1990 to 12 months prior to the current month t.

= current month relative over-the-month change estimate based on a synthetic estimate of the relative change that uses all sample responses in the state that includes the MSA's geographic location a for industry i. This variable and its corresponding weight are only used in conjunction with MSA level small domain estimation.

= previous month employment estimate for domain ia from the small domain model.

To Table of Figures

It is possible that for a given industry i and geographic location a, one or even two of the inputs to the model are assigned weights of zero. The reasons for assigning a weight of zero to a model input are due to concerns regarding the stability of the inputs. For example, if or has five or fewer responses, then it is assigned a weight of zero. If exhibits an unstable variance or has extremely poor model fit, then it may also be assigned a weight of zero. In these cases, the small domain model estimate may be based on only one or two of the three described inputs.

The model defined above is employed for both state and area and national estimation, but the CES national program does not identify the inputs to the model by state or MSA, only by industry. Consequently, national estimates have only one geographic location a that includes all 50 states and the District of Columbia.

Sampling errors are not applicable to the estimates made using small domain. The measure available to judge the reliability of these modeled estimates is their performance over past periods compared with the universe values for those periods. These measures are useful, but it is not certain that the past performance of the modeled estimates accurately reflects their current performance.

It should also be noted that extremely small estimates of 2,000 employees or less are potentially subject to large percentage revisions that are caused by occurrences such as the relocation of one or two businesses, or a change in the activities of one or two businesses. These are noneconomic classification changes that relate to the activity or location of businesses and will be present for sample-based estimates as well as the model-based estimates.

Small domain model in CES estimation

The CES state and area program has been using the model since 2003 for some state and metropolitan area employment series that have small samples. The CES national program began using small domain in 2007.

National employment estimates for two industries are produced using CES small domain. Relatively small sample sizes in these industries limit the reliability of the weighted-link-relative estimator for estimates of all employees (see table 15).

Table 15. National small domain model industries

CES Industry Code	CES Industry Title
55-533000	Lessors of nonfinancial intangible assets (except copyrighted works)
60-541213	Tax preparation services

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Birth-Death Model

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. The sample frame is built from Unemployment Insurance (UI) quarterly tax records. These records cover virtually all U.S. employers and include business births, but they only become available for updating the CES sampling frame 7 to 9 months after the reference month. After the births appear on the frame, there is also time required for sampling, contacting, and soliciting cooperation from the firm, and verifying the initial data provided. In practice, CES cannot sample and begin to collect data from new firms until they are at least a year old.

There is a parallel though somewhat different issue 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. As with business births, hard information on business deaths eventually becomes available from the lagged UI tax records.

Difficulty in capturing information from business birth and death units is not unique to the CES; virtually all current business surveys face these limitations. Unlike in many surveys, CES adjusts for these limitations explicitly, using a statistical modeling technique. Other surveys that do not explicitly adjust for business births and deaths are implicitly using the continuing sample units to represent birth and death units. This approach is viable when the primary characteristic of interest is an average measure of some type. However, because the goal of the CES program is to estimate an employment total each month and business births and deaths are important components contributing to these totals, CES uses a model-based adjustment in conjunction with the sample. Without the net birth-death model-based adjustment, the CES nonfarm payroll employment estimates would be considerably less accurate.

CES birth-death modeling technique

Prior to the Current Employment Statistics (CES) program adopting the current birth-death modeling technique, research using historical information indicated that the business birth and death portions of total employment were substantial, but the net contribution of, or the difference between, the two components was relatively small and stable. The research was done using the nearly complete counts of employment developed from the UI tax records that are tabulated under the BLS Quarterly Census of Employment and Wages (QCEW) (www.bls.gov/osmr/research-papers/2002/pdf/st020090.pdf). These QCEW tabulations also form the basis for both the sample frame and annual benchmark for the CES program.

Beyond the research cited above, the Business Employment Dynamics (BED) series published quarterly by BLS, also illustrate how business birth and death employment substantially offset each other. The BED series are also derived from the QCEW. The BED series demonstrate that most of the net employment change each quarter is generated by the expansions and contractions in employment of the continuing businesses and a relatively smaller piece from business openings and closings (CES refers to business openings and closings as net business births and deaths). As shown in figure 2 below, continuing businesses that are adding employees (expansions) or subtracting employees (contractions) over the quarter comprise the vast majority of total change; these movements are measured by the CES sample. Employment change contributions from openings (or births) and closings (or deaths) are much smaller and more stable, and the two series offset each other to a large degree. It is these underlying relationships among the components of net employment change that allow the CES to produce accurate estimates using a current monthly sample of continuing businesses and a model-based approach for the residual of net business births and deaths.

Figure 2. Total private Business Employment Dynamics series (not seasonally adjusted, in thousands)

To Table of Figures

Birth-death modeling methodology

The CES birth-death methodology has two steps.

Step One — Employment losses from business deaths are excluded from the sample in order to offset the missing employment gains from new business births. Because employment increases from births nearly offset employment decreases from deaths in most months (as illustrated above by the BED data), this step accounts for most of the net of business birth and death employment.

Operationally, each month, business deaths that are nonrespondents to the survey are automatically excluded because they have no current month data. Death establishments that report zero employment to the survey for the current month are treated the same as nonrespondents and also excluded. As a result, the over-the-month change calculation from the sample is based solely on continuing businesses.

For the months subsequent to a business death, the deaths are "kept alive" in the CES estimation process; the growth rate of the continuing units in the sample is applied to them each month. This estimates for the growth of the new business births in the months after their birth but before they can be brought into the sample.

This step accounts for most of the birth-death employment but not all of it. The residual net employment that is not captured by this step is estimated through an econometric model, described below as step two.

Step Two — Modeling for the residual of birth-death employment change. In this step, the CES adjusts its sample-based estimates for the net birth-death employment that step one misses. This adjustment is derived from an econometric technique known as ARIMA modeling. ARIMA is a standard econometric modeling technique that is often used to estimate relatively stable series. Outliers, level shifts, and temporary ramps are automatically identified. CES refits the ARIMA models each year for each basic estimation cell as part of its annual benchmarking process. Table 16 shows the net birth-death model forecasts for the post-benchmark period of the benchmark from April to October of the benchmark year. For more recent months of birth-death information, see the CES net birth-death page.

Table 16. Net birth-death forecasts by industry supersector, April to December 2022 (in thousands)

CES Industry Code	CES Industry Title	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec	Cumulative Total
10-000000	Mining and logging	-1	1	0	0	1	0	1	0	0	2
20-000000	Construction	33	41	22	17	10	-4	31	-9	-17	124
30-000000	Manufacturing	-2	11	7	3	5	-1	11	4	1	39
40-000000	Trade, transportation, and utilities	4	32	19	39	23	-3	112	23	14	263
41-420000(1)	Wholesale trade	-4	6	-3	6	2	-11	21	2	0	19
42-000000(1)	Retail trade	5	19	13	17	11	0	31	0	-3	93
43-000000(1)	Transportation and warehousing	3	7	9	16	10	8	59	21	17	150
44-220000(1)	Utilities	0	0	0	0	0	0	1	0	0	1
50-000000	Information	9	6	3	10	4	0	14	6	2	54
55-000000	Financial activities	8	8	-5	18	4	-16	45	1	8	71
60-000000	Professional and business services	111	37	-8	85	26	-33	142	10	-21	349
65-000000	Private education and health services	45	18	-31	57	19	-35	102	12	-14	173
70-000000	Leisure and hospitality	99	90	82	88	22	-52	31	-24	-6	330
80-000000	Other services	17	10	4	10	6	-9	22	-2	-4	54
Total private net birth-death forecast		323	254	93	327	120	-153	511	21	-37	1,459
Footnotes (1) Indented industries are part of trade, transportation, and utilities.

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The inputs to the ARIMA model are historical observations of the net birth-death employment that are not captured by either the sample or the step one imputation described earlier. These historical observations are derived empirically from the most recent 5 years of QCEW historical data. From the QCEW universe employment series, CES classifies each establishment each month as a continuing unit, a birth, or a death. Then sample-based estimates are simulated using the month-to-month change of the continuing units and using the deaths-to-impute-for-births technique described in step one. The difference between these simulated estimates and the actual total employment measured by the QCEW each month is the net birth-death employment. The net birth-death series assumed the following form:

Equation 9. Net birth-death

Net birth-death = Population − Sample-based estimate + Error

During the net birth-death modeling process, simulated monthly probability estimates over a 5-year period are created and compared with population employment levels. Moving from a simulated benchmark, the differences between the series across time represent a cumulative birth-death component. Those residuals are converted to month-to-month differences and used as input series to the modeling process.

Because the net birth-death employment component is relatively stable, the ratio of it to total employment change can vary substantially from year to year. In slower growth years (for example, March 2003 to March 2004), the ratio is much different than in stronger growth years (for example, March 2004 to March 2005). Put another way, the net birth-death amount itself is relatively stable but its relationship to overall net employment change varies, depending on the magnitude of the overall change, almost by definition.

Year one and year two models

The birth-death model is forecast using 24-month long spans of input data, representing historical net births and deaths. These spans are separated into two models referred to as year 1 (Y1) and year 2 (Y2) models. The age of the firms that contribute to the imputation step (step 1) of the birth-death process impact the trend calculation. Y2 models are forecast using a sample that is a year older (relative to the reference month) than the Y1 models. While the results of the two models are similar, there are differences.

Birth-death model under quarterly sample rotation

Using quarterly sample rotation, different industries have differently aged samples. Therefore, the mix of Y1 and Y2 models used varies by quarter. Y1 birth-death values are appropriate for the newest sample, and Y2 values are phased in as the sample ages. Table 17 shows the forecast value used with each rotation group for each quarter.

Table 17. Net birth-death forecast year of industry groupings for CES quarterly sample rotation

Group	CES Industry Code	Major Industry Sector	Estimate
			Q2	Q3	Q4	Q1
Group 1	10-000000	Mining and logging	Y1	Y1	Y1	Y1
	41-420000	Wholesale trade
	42-000000	Retail trade
	43-000000	Transportation and warehousing
	44-220000	Utilities
	55-000000	Financial activities
Group 2	20-000000	Construction	Y2
	70-000000	Leisure and hospitality
Group 3	50-000000	Information		Y2
	60-000000	Professional and business services
	80-000000	Other services
Group 4	30-000000	Manufacturing (durable and nondurable goods)			Y2
	65-000000	Private education and health services

To Table of Figures

Quarterly updates to the CES birth-death model

Prior to the release of preliminary January 2011 employment estimates in February 2011, net birth-death forecasts were calculated on an annual basis and then applied each month during development of monthly estimates. With the release of the January 2011 preliminary estimates, CES began updating the net birth-death model component of the estimation process on a quarterly basis instead of annually. This change allows for the incorporation of QCEW data into the birth-death model as soon as it becomes available and reduces the post-benchmark revision due to net birth-death forecasts in the CES series. This change does not impact the timing or frequency of CES monthly and annual releases or when benchmarking is done. More information is available on the CES quarterly net birth-death forecasting page.

Quarterly and annual net birth-death forecasts

Table 18 shows a comparison of the CES birth-death model adjustment using either a quarterly or annual forecasting frequency. The March 2003 benchmark is the first in which all industries were estimated using annually updated net birth-death forecasts, and quarterly updated net birth-death forecasts have been used in estimates from January 2011 forward. The differences between annual and quarterly forecasting of birth-death are small in most cases. However, the CES estimates reflect more current business openings and closings more rapidly by increasing the frequency of updates to inputs to the net birth-death model. More information is available on the CES quarterly net birth-death forecasting page. Historical comparisons, including simulated quarterly net birth-death forecasts for years before 2011 and simulated annual net birth-death forecasts for years after 2011, are available on the CES quarterly net birth-death comparison page.

Table 18. Comparison of annual net birth-death to quarterly net birth-death forecasts for 2014

Industry	Birth-Death	April	May	June	July	August	September	October	November	December	Cumulative
Mining and logging	Simulated Annual BD	1	2	3	2	2	1	2	1	1	15
	Production Quarterly BD	1	2	2	2	1	1	2	0	0	11
	Difference	0	0	1	0	1	0	0	1	1	4
Construction	Simulated Annual BD	33	35	23	6	8	7	9	-16	-21	84
	Production Quarterly BD	32	36	23	9	12	6	8	-11	-21	94
	Difference	1	-1	0	-3	-4	1	1	-5	0	-10
Manufacturing	Simulated Annual BD	-2	6	3	-5	4	0	2	1	0	9
	Production Quarterly BD	-1	6	4	-5	4	1	1	1	0	11
	Difference	-1	0	-1	0	0	-1	1	0	0	-2
Trade, transportation, and utilities	Simulated Annual BD	18	24	13	3	18	13	35	7	5	136
	Production Quarterly BD	17	25	12	-1	14	9	22	8	3	109
	Difference	1	-1	1	4	4	4	13	-1	2	27
Information	Simulated Annual BD	0	4	0	0	3	-2	5	4	-1	13
	Production Quarterly BD	1	5	-1	-1	3	-1	4	4	-1	13
	Difference	-1	-1	1	1	0	-1	1	0	0	0
Financial activities	Simulated Annual BD	4	8	4	1	6	-1	16	2	10	50
	Production Quarterly BD	6	8	3	0	4	-1	13	3	10	46
	Difference	-2	0	1	1	2	0	3	-1	0	4
Professional and business services	Simulated Annual BD	74	27	10	27	18	-15	70	5	-12	204
	Production Quarterly BD	75	21	3	25	19	-14	68	12	-13	196
	Difference	-1	6	7	2	-1	-1	2	-7	1	8
Private education and health services	Simulated Annual BD	20	18	-12	7	19	13	46	11	-2	120
	Production Quarterly BD	18	17	-15	3	22	11	40	13	-3	106
	Difference	2	1	3	4	-3	2	6	-2	1	14
Leisure and hospitality	Simulated Annual BD	78	79	90	48	20	-44	-30	-25	1	217
	Production Quarterly BD	75	79	84	52	21	-35	-23	-22	4	235
	Difference	3	0	6	-4	-1	-9	-7	-3	-3	-18
Other services	Simulated Annual BD	10	7	6	-3	1	-2	4	-2	0	21
	Production Quarterly BD	10	6	6	-4	2	-3	2	0	1	20
	Difference	0	1	0	1	-1	1	2	-2	-1	1
Total Private	Simulated Annual BD	236	210	140	86	99	-30	159	-12	-19	869
	Production Quarterly BD	234	205	121	80	102	-26	137	8	-20	841
	Difference	2	5	19	6	-3	-4	22	-20	1	28

To Table of Figures

Limitations

The primary limitation stems from the fact that the model is, of necessity, based on historical data. If there is a substantial departure from historical patterns of employment changes in net business births and deaths, as occurred from 2008 into 2009 during the 2009 benchmark, the model's contribution to error reduction can erode. As with any model that is based on historical data, turning points that do not resemble historical patterns are difficult to incorporate in real time. Because there is no current monthly information available on business births, and because only incomplete sample data is available on business deaths, estimation of this component will always be potentially more problematic than estimation of change from continuing businesses.

The net birth-death model and seasonal adjustment

The birth-death model component is added to the sample-based component to form the not seasonally adjusted employment estimate for each month, as described above. These employment estimates are subsequently seasonally adjusted. Seasonal adjustment smooths the employment series by removing normal seasonal variations due to factors such as weather and holidays; therefore the seasonally adjusted over-the-month employment changes are generally much smaller than the unadjusted changes.

Users who wish to compare the model's contribution to overall employment change reported for a month should compare against the unadjusted estimates, not the seasonally adjusted series. Comparing the model amounts to seasonally adjusted estimates generally results in an overstatement of the model-based component's contribution to over-the-month employment change.

The birth-death model component generally shows the same overall seasonal patterns as the sample-based component. For example, total nonfarm employment shows a large seasonal increase in employment each April; the model also shows a relatively large net addition to employment each April. Similarly, total nonfarm employment records a large drop in employment each January and the model estimates a substantial drop in net birth-death employment each January. An example of the net birth-death model components versus overall net employment change from April 2021 to March 2022 (prior to the March 2022 benchmark implementation) is shown in table 19. The April 2021 model amount of 309,000 should be viewed as a component of the 1,050,000 not seasonally adjusted employment change, rather than as a component of the 263,000 seasonally adjusted change.

Table 19. Net birth-death forecasts and over-the-month change in total nonfarm employment (in thousands)

	April 21	May	June	July	August	September	October	November	December	January	February	March 22
Birth-death model amount	309	239	106	264	146	-87	379	17	-42	-114	156	23
Not seasonally adjusted total employment change	1,050	946	1,189	-41	495	704	1,659	900	142	-2,847	1,638	762
Seasonally adjusted total employment change	263	447	557	689	517	424	677	647	588	504	714	398

To Table of Figures

Aggregation Procedures

CES estimates at the basic estimating level and then aggregates these estimates to higher industry levels. Aggregation procedures are specific to the data type and published level of precision (i.e. the degree of rounding).

Publication precision

For employment data types, CES publishes estimates for major industry and aggregate industry sectors in thousands rounded to the nearest whole number, except for major industry sectors 41-420000 wholesale trade, 42-000000 retail trade, 43-000000 transportation and warehousing, and 44-220000 utilities, which are published in thousands rounded to one decimal. More detailed employment estimates are published in thousands rounded to one decimal.

For hours and earnings data types, estimates are published using the same procedures for all levels of detail. Hours data types are published in hours rounded to one decimal. Earnings data types are published in dollars rounded to the cent.

Employment (AE, PE, and WE)

AE, PE, and WE data types use the same method for aggregation. Basic-level estimates are in thousands and rounded to one decimal. They are aggregated to summary-level estimates up to and including major industry sectors and are then rounded to one decimal for summary series and to whole numbers for major industry sectors. Aggregate industry sector estimates are then calculated by summing the rounded major industry sector estimates that make up each aggregate industry sector.

Average weekly hours (AE and PE)

The aggregation method for average weekly hours (AWH) of AE and PE is identical with the appropriate substitution of AE values or PE values in equation 10 and equation 11. AWH are estimated at the basic level and multiplied by employment estimates for the same basic level to calculate aggregate employee hours. Aggregate employee hours (AH) at the basic estimating level are calculated as shown:

Equation 10. Aggregate hours

AH = AWH × Emp

where:

AH = current month aggregate employee hours calculation for the basic level

AWH = current month AWH estimate for the basic level rounded as published

Emp = current month employment estimate for the basic level rounded as published

To Table of Figures

Next, aggregate employee hours are added up to the summary levels. Average weekly hours rounded to the tenths are calculated for the summary level by:

Equation 11. Summary level average weekly hours

AWH = AH ÷ Emp

where:

AWH = current month average weekly hours estimate for the summary level rounded to the tenths

AH = current month aggregate employee hours calculation for the summary level

Emp = current month employment estimate for the summary level rounded according to published precision

To Table of Figures

Average hourly earnings (AE and PE)

The aggregation method for average hourly earnings (AHE) of AE and PE is identical, with the appropriate substitution of AE values or PE values in equation 12 and equation 13. AHE are estimated at the basic level and multiplied by AH estimates (see equation 10) for the same basic level to calculate aggregate payroll (PR). The calculation of AHE at the summary level is identical to that described for AHE at the basic level.

Aggregate payroll (PR) is calculated using basic-level AWH, AHE, and employment. Aggregate PR calculations are in dollars and cents and are defined in equation 12.

Equation 12. Aggregate payroll

PR = AHE × AWH × Emp

where:

PR = current month aggregate payroll calculation for the basic level

AHE = current month average hourly earnings estimate for the basic level rounded to the cent

AWH = current month average weekly hours estimate for the basic level rounded to one decimal place

Emp = current month employment estimate for the basic level rounded according to published precision

To Table of Figures

To calculate the summary-level estimates, summarize the aggregate employee hours and aggregate payroll to the summary level. Average hourly earnings rounded to the cent are calculated for the summary level using equation 13.

Equation 13. Summary level average hourly earnings

AHE = PR ÷ AH

where:

AHE = current month average hourly earnings estimate for the summary level rounded to the cent

AH = current month aggregate employee hours calculation for the summary level

PR = current month aggregate payroll calculation for the summary level

To Table of Figures

Average weekly overtime hours (AE and PE)

Aggregation of average weekly overtime hours is identical to that described for AWH with the appropriate substitution of overtime hours values for the weekly hours values in the previous formula.

Caution in aggregating state data

The national estimation procedures used by CES are designed to produce accurate national data by detailed industry; correspondingly, the state estimation procedures are designed to produce accurate data for each individual state. State estimates are not forced to sum to national totals nor vice versa. Because each state series is subject to larger sampling and nonsampling errors than the national series, summing them cumulates individual state level errors and can cause distortion at an aggregate level. For more information about state and metropolitan area level CES data, see the state and area employment website.

Seasonal Adjustment

The CES program employs a concurrent seasonal adjustment methodology to seasonally adjust its national estimates of employment, hours, and earnings. Under concurrent methodology, new seasonal factors are calculated each month using all relevant data up to and including the current month period.

Many CES data users are interested in the seasonally adjusted over-the-month changes as a primary measure of overall national economic trends. Therefore, accurate seasonal adjustment is an important component in the usefulness of these monthly data. The following section discusses in detail the seasonal adjustment methodology and software employed by CES. It is important to note that this describes seasonal adjustment only as it relates to the CES program's implementation. There are other aspects of seasonal adjustment that are not discussed here.

Seasonal adjustment and X‑13ARIMA‑SEATS

The CES program uses X‑13ARIMA‑SEATS software developed by the U.S. Census Bureau to seasonally adjust the monthly estimates. The X‑13ARIMA‑SEATS software is available on the U.S. Census Bureau web site at www.census.gov/data/software/x13as.html. The site contains the following information:

Effective with the February 6, 2015 release of January 2015 data, the Current Employment Statistics (CES) survey transitioned from using X‑12‑ARIMA to X‑13ARIMA‑SEATS to produce seasonally adjusted series and net birth-death forecasts. For more information about X‑13ARIMA‑SEATS please visit the U.S. Census Bureau website at www.census.gov/data/software/x13as.html. The following files are available from the Census website:

• Program files for the latest PC version of X‑13ARIMA‑SEATS
• Program files for the latest UNIX workstation version of X‑13ARIMA‑SEATS
• Program files for X‑13‑Graph, a companion graphics package
• Installation instructions
• Reference manual

Historical data will not be revised to be seasonally adjusted using X‑13ARIMA‑SEATS. The CES program has been running parallel seasonal adjustment using X‑13ARIMA‑SEATS, and no differences were observed. Examples of the specification files used by X‑13ARIMA‑SEATS can be found in the CES spec examples zip file.

The remainder of this section describes how the CES program employs X‑13ARIMA‑SEATS for seasonal adjustment purposes. Specifically, it describes the input files used in the CES program's implementation and commands used to invoke the software. This is not a substitute for formal X‑13ARIMA‑SEATS training. There are other uses and features of X‑13ARIMA‑SEATS that are not discussed in this section. The U.S. Census Bureau offers more intensive training for X‑13ARIMA‑SEATS and seasonal adjustment. Contact the Census Bureau or visit their website at www.census.gov for more details.

Seasonally adjusting CES data

For published AE series, the CES program seasonally adjusts many series at the 3-, 4-, 5-, and 6-digit NAICS level. However, only the seasonally adjusted 3-digit NAICS-level estimates are used to aggregate to the higher levels. The seasonally adjusted series that are published at more detailed levels than the 3-digit NAICS are considered to be independent series and are not included in aggregation of seasonally adjusted series. For example, seasonally adjusted data at the 5-digit NAICS are not aggregated to form seasonally adjusted 4-digit NAICS series. Instead the 4-digit NAICS and the 5-digit NAICS series are independently seasonally adjusted.

Most series are seasonally adjusted by directly applying the seasonal adjustment factors to the series with the exception of the component series used in indirect seasonal adjustment. In some cases, 3-digit NAICS series are indirectly seasonally adjusted by aggregating the seasonally adjusted employment level of their component series. For indirectly seasonally adjusted 3-digit NAICS series, the seasonal adjustment factors are applied to the component series rather than to the 3-digit NAICS series. The component series are then aggregated to create the 3-digit NAICS series. Indirectly seasonally adjusted series are noted in table 21.

For published PE series and for published hours and earnings series for both PE and AE, the CES program seasonally adjusts at the major industry sector level for all industries except manufacturing, which is seasonally adjusted at the 3-digit NAICS level. The seasonally adjusted PE, seasonally adjusted hours and earnings for PE, and seasonally adjusted hours and earnings for AE are aggregated from the 3-digit level in manufacturing industries and are aggregated from the major industry sector level for all other industries to get seasonally adjusted aggregate sectors.

For published PE and AE overtime series, the CES program seasonally adjusts manufacturing series at the 2-digit NAICS level, or the durable goods and nondurable goods levels. These seasonally adjusted overtime series are aggregated to the manufacturing level.

For published WE series, the CES program seasonally adjusts at the major industry sector level for all industries. The seasonally adjusted WE are aggregated from the major industry sector level for all industries.

Special model adjustments

The CES program's current implementation of seasonal adjustment controls for several calendar effects, explained below.

Variable survey intervals. Beginning with the release of the 1995 benchmark, BLS refined the seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. Although the CES survey is referenced to a consistent concept—the pay period including the 12th of each month—inconsistencies arise because there are sometimes 4 and sometimes 5 weeks between the weeks including the 12th in a given pair of months. In highly seasonal industries, these variations can be an important determinant of the magnitude of seasonal hires or layoffs that have occurred at the time the survey is taken, thereby complicating seasonal adjustment.

Standard seasonal adjustment methodology relies heavily on the experience of the most recent 3 years to determine the expected seasonal change in employment for each month of the current year. Prior to the implementation of the adjustment, the procedure did not distinguish between 4- and 5-week survey intervals, and the accuracy of the seasonal expectation depended in large measure on how well the current year's survey interval corresponded with those of the previous 3 years. All else the same, the greatest potential for distortion occurred when the current month being estimated had a 5-week interval but the 3 years preceding it were all 4-week intervals; or, conversely, when the current month had a 4-week interval but the 3 years preceding it were all 5-week intervals.

BLS adopted REGARIMA (regression with auto-correlated errors) modeling to identify the estimated size and significance of the calendar effect for each published series. REGARIMA combines standard regression analysis, which measures correlation among two or more variables, with ARIMA modeling, which describes and predicts the behavior of data series based on its own past history. For many economic time series, including nonfarm payroll employment, observations are auto-correlated over time; each month's value is significantly dependent on the observations that precede it. These series, therefore, usually can be successfully fit using ARIMA models. If auto-correlated time series are modeled through regression analysis alone, the measured relationships among other variables of interest may be distorted due to the influence of the auto-correlation. Thus, the REGARIMA technique is appropriate for measuring relationships among variables of interest in series that exhibit auto-correlation, such as nonfarm payroll employment.

In this application, the correlations of interest are those between employment levels in individual calendar months and the lengths of the survey intervals for those months. The REGARIMA models evaluate the variation in employment levels attributable to 11 separate survey interval variables, 1 specified for each month, except March. March is excluded because there are almost always 4 weeks between the February and March surveys. Models for individual basic series are fit with the most recent 10 years of data available, the standard time span used for CES seasonal adjustment.

The REGARIMA procedure yields regression coefficients for each of the 11 months specified in the model. These coefficients provide estimates of the strength of the relationship between employment levels and the number of weeks between surveys for the 11 modeled months. The X‑13ARIMA‑SEATS software also produces diagnostic statistics that permit the assessment of the statistical significance of the regression coefficients, and all series are reviewed for model adequacy.

Because the 11 coefficients derived from the REGARIMA models provide an estimate of the magnitude of variation in employment levels associated with the length of the survey interval, these coefficients are used to adjust the CES data to remove the calendar effect. These "filtered" series then are seasonally adjusted using the standard X‑13ARIMA‑SEATS software.

Weather-related outliers in construction series. Beginning with the 1996 benchmark revision, BLS utilized special treatment to adjust construction industry series. In the application of the interval effect modeling process to the construction series, there initially was difficulty in accurately identifying and measuring the effect because of the strong influence of variable weather patterns on employment movements in the industry. Further research allowed BLS to incorporate interval effect modeling for the construction industry by disaggregating the construction series into its finer industry and geographic estimating cells and tightening outlier designation parameters. This allowed a more precise identification of weather-related outliers that had masked the interval effect and clouded the seasonal adjustment patterns in general. With these outliers removed, interval effect modeling became feasible. The result is a seasonally adjusted series for construction that is improved because it is controlled for two potential distortions: unusual weather events and the 4- versus 5-week effect.

Length of pay adjustment. With the release of the 1997 benchmark, BLS implemented refinements to the seasonal adjustment process for the hours and earnings series to correct for distortions related to the method of accounting for the varying length of payroll periods across months. There is a significant correlation between over-the-month changes in both the average weekly hours (AWH) and the average hourly earnings (AHE) series and the number of weekdays in a month, resulting in noneconomic fluctuations in these two series. Both AWH and AHE show more growth in "short" months (20 or 21 weekdays) than in "long" months (22 or 23 weekdays). The effect is stronger for the AWH than for the AHE series.

The calendar effect is traceable to response and processing errors associated with converting payroll and hours information from sample respondents with semi-monthly or monthly pay periods to a weekly equivalent. The response error comes from sample respondents reporting a fixed number of total hours for workers regardless of the length of the reference month, while the CES conversion process assumes that the hours reporting will be variable. A constant level of hours reporting most likely occurs when employees are salaried rather than paid by the hour, as employers are less likely to keep actual detailed hours records for such employees. This causes artificial peaks in the AWH series in shorter months that are reversed in longer months.

The processing error occurs when respondents with salaried workers report hours correctly (vary them according to the length of the month), which dictates that different conversion factors be applied to payroll and hours. The CES processing system uses the hours conversion factor for both fields, resulting in peaks in the AHE series in short months and reversals in long months.

REGARIMA modeling is used to identify, measure, and remove the length-of-pay-period effect for seasonally adjusted average weekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaining AWH movements in all the service-providing industries except utilities. For AHE, the length-of-pay-period variable is significant for wholesale trade, retail trade, information, financial activities, professional and business services, and other services. All AWH series in the service-providing industries except utilities have been adjusted from January 1990 forward. The AHE series for wholesale trade, retail trade, information, financial activities, professional and business services, and other services have been adjusted from January 1990 forward as well. For this reason, calculations of over-the-year changes in the establishment hours and earnings series should use seasonally adjusted data.

The series to which the length-of-pay-period adjustment is applied are not subject to the 4- versus 5-week adjustment, as the modeling cannot support the number of variables that would be required in the regression equation to make both adjustments.

Poll workers in local government series. A special adjustment is made in November each year to account for variations in employment due to the presence or absence of poll workers in local government, excluding educational services. This procedure was first introduced in November 1988 to prevent fluctuations in seasonally adjusted local government, excluding education series, resulting from the short-term employment of poll workers during presidential election years. Initially this effect was estimated using an X-11 ARIMA extension analogous to the early method used to adjust for the floating holiday effect described below.

This is not a true seasonal effect because it occurs only once every 4 years in November. In addition, according to the CES definition, poll workers who receive even just one day's pay are correctly counted as employed. However, a decision was made by BLS to remove this effect due to its confounding the analysis of economic trends in total nonfarm employment. The adjustment procedure is now accomplished through X‑13ARIMA‑SEATS; it removes an estimate of the number of poll workers in the series prior to seasonal adjustment in order to prevent November spikes in total nonfarm employment that result from the 1-day employment of many thousands of poll workers.

The current procedure was introduced with the first preliminary release of May 1998 data and is used for the national local government, excluding education employment series only.

Floating holiday adjustment. This adjustment to average weekly hours and average weekly overtime series accounts for significant effects due to the timing of the survey reference period (the pay period including the 12th of the month) overlapping with the Good Friday (Easter) and Labor Day holidays. These holidays do not occur at exactly the same time every year—sometimes they occur during the survey reference period and sometimes not—which complicates the seasonal adjustment process. The presence or absence of these holidays in the survey reference period causes a significant variation in hours reported by respondents in some industries (more hours are reported when the holiday does not fall in the week of the 12th). The special adjustment procedure identifies the magnitude of the effect and adjusts for it prior to seasonally adjusting the series, thereby neutralizing the effect. The floating holiday adjustment is accomplished through the REGARIMA option within the X‑12 procedure. Essentially, a regression model estimate of the significance of the presence or absence of the holiday during the week of the 12th is made using a dummy variable to indicate in which years the holiday is present or absent. For industry series where the dummy variable test is significant, an adjustment is made to the original series before it is input into the seasonal adjustment procedure, using the estimated regression parameters.

The floating holiday procedure was first introduced in 1990, pre-dating X‑12 REGARIMA availability. The adjustment was accomplished using an extension of the X-11 ARIMA procedure. This process was based on the same concepts described above and yielded similar results to the procedure currently in use. X‑12‑ARIMA was introduced with the release of first preliminary May 1997 estimates in June 1997. With the 2015 benchmark release, CES transitioned from using X‑12‑ARIMA to X‑13ARIMA‑SEATS to produce seasonally adjusted series and net birth-death forecasts. For more information about X‑13ARIMA‑SEATS please visit the U.S. Census Bureau website at www.census.gov/data/software/x13as.html.

More information about the calendar-related fluctuations in CES data is available at the CES fluctuations in hours and earnings page.

Residential and nonresidential specialty trade contractors raking procedure. Concurrent with the release of the 2004 benchmark, the CES Program began producing and publishing employment series for residential specialty trade contractors (20-238001) and nonresidential specialty trade contractors (20-238002). The two employment series are derived independently from the traditionally published 3-digit NAICS series specialty trade contractors (20-238000). A raking procedure is used to ensure that the sum of the seasonally adjusted residential specialty trade contractors and seasonally adjusted nonresidential specialty trade contractors series is consistent with the published seasonally adjusted total for specialty trade contractors at the 3-digit NAICS level.

The raking procedure begins by seasonally adjusting the two series independently for the residential and nonresidential groups at the 3-digit NAICS level. The seasonally adjusted residential and nonresidential series are summed at the 3-digit NAICS level to get a 3-digit total. Ratios of seasonally adjusted residential-to-total employment and seasonally adjusted nonresidential-to-total employment are calculated. The sum of the seasonally adjusted residential/nonresidential series is subtracted from the official 3-digit seasonally adjusted estimate for specialty trade contractors to determine the amount that must be raked. The total amount that must be raked is multiplied by the ratios to determine what percentage of the raked amount should be applied to the residential group and what percentage should be applied to the nonresidential group. Once the seasonally adjusted residential and nonresidential groups receive their proportional amount of raked employment, the two groups are aggregated again to get a 3-digit total. At this point their sum should be equal to the official 3-digit seasonally adjusted estimate for specialty trade contractors.

Additive and multiplicative models. Prior to the March 2002 benchmark release in June 2003, all CES series were adjusted using multiplicative seasonal adjustment models. Although the X‑13ARIMA‑SEATS seasonal adjustment program provides for either an additive or a multiplicative adjustment depending on which model best fits the individual series, the previous CES processing system was unable to use additive seasonal adjustments. A new processing system, introduced simultaneously with the conversion to NAICS in June 2003, is able to use both additive and multiplicative adjustments. The seasonal adjustment website) contains a list of which series are adjusted with additive or multiplicative seasonal adjustment models.

Special notice regarding seasonal adjustment for AE hours and earnings

Concurrent with the release of January 2010 data, the CES program began publishing AE hours and earnings as official BLS series. The AE hours and earnings series are published at the same level of industry detail as PE hours and earnings series and are published on both a not seasonally adjusted and a seasonally adjusted basis.

CES has at least 10 full years of history for all series, which allows for incorporating the special model adjustments for variation due to the calendar effects (4- vs. 5-week, 10- vs. 11-day). CES generally uses 10 years of not seasonally adjusted data as an input to seasonal adjustment.

CES seasonal adjustment input files

All controllable variables remain fixed during the year. For example, the ARIMA model, outliers, transformation specification, and historical data are held constant, and the same calendar treatments are used throughout the year. Once a year, as part of the annual CES benchmark procedure, all seasonal adjustment specifications are reviewed for each series. Any changes are implemented and kept constant until the next annual benchmark. Also during the annual benchmark, estimates for the 5 most recent years are re-seasonally adjusted using the new specifications. After 5 years of revisions, seasonally adjusted data are generally not updated again. However, if a series is reconstructed further back than 5 years, the full time span of the reconstruction is seasonally adjusted again.

The CES program uses the following input files when seasonally adjusting estimates:

• Specification file
• Input data file
• Prior-adjustment file
• User-defined regression variables (dummy variables) file
• Metafile
• Recent outliers

More details on each input follow.

Specification file

An input specification file, or a "spec" file, is a text file used to specify program operations. The spec file is composed of functional units called specifications (or "specs"). Each spec unit comprising the spec file controls the options for a specific function. There are 15 different specs that can be used in a spec file; however, the CES program's implementation typically employs only 8 specs for most series and 9 specs for indirectly seasonally adjusted series. These specs are the following:

• SERIES spec — this specifies the location and format of the data
• TRANSFORM spec — this specifies a data transformation
• REGRESSION spec — this specifies any regression components
• ARIMA spec — this specifies the ARIMA model to be used
• ESTIMATE spec — this estimates the regARIMA model
• FORECAST spec — this generates forecasts of seasonal factors
• OUTLIER spec — this specifies automatic outlier detection
• X11 spec — this generates and controls the seasonal adjustment process
• COMPOSITE spec — this is a special spec used only during indirect seasonal adjustment

Each spec used by the CES program is covered in greater detail at the end of this section in Anatomy of a Spec File.

In the CES program, each seasonally adjusted employment series has its own spec file ending in a ".spc" file extension. The ".spc" extension is not recognizable by all operating systems and usually needs to be opened with a text editor such as TextPad, Wordpad, or Notepad. Also, it is important to remember that when running X‑13ARIMA‑SEATS in DOS, the name of the spec file must be 8 characters or less. This is a limitation of DOS, not X‑13ARIMA‑SEATS. All of the spec files currently used in production can be downloaded from the CES seasonal adjustment page.

Input data file

The input data file consists of not seasonally adjusted CES estimates for all series that have a corresponding seasonally adjusted series and is referred to in the SERIES spec of the spec file. The CES implementation reads input data from a text file in "free format" style. In the free-format style, data are delimited with either tabs or spaces, and only the input data are included—dates and other descriptive information are excluded. Instead, information describing the data is specified in the SERIES spec using the START and PERIOD arguments. The full path and name of the input data file is specified using the FILE argument (see figure 3).

Figure 3. Input data file specifications

To Table of Figures

CES data can be extracted from the BLS website from the CES data homepage. However, in some cases, not seasonally adjusted data extracted from the BLS website will differ from what the CES program actually uses in seasonal adjustment. In particular, data extracted from the BLS website will reflect any strikes or other prior adjustments that have taken place. Before running seasonal adjustment, the CES program will reverse these effects so that they will not be considered when calculating the seasonal factors. Also, the CES program uses unrounded data when running seasonal adjustment, while published data on the BLS website are rounded.

Prior adjustment file

As mentioned in the previous section, in some cases the CES program will modify the not seasonally adjusted estimates (input data) before running X‑13ARIMA‑SEATS. This is done to ensure that nonseasonal events such as strikes are not included in the calculation of the seasonal factors. Once the seasonal factors are calculated, they are applied to the not seasonally adjusted data used as inputs. Then the prior adjustments that were removed before running X‑13ARIMA‑SEATS are incorporated to create the seasonally adjusted estimates. To read more about the impact of strikes on CES data, visit the CES strike report page.

The latest prior adjustment file used in the seasonal adjustment of CES data can be downloaded from the CES seasonal adjustment page. The prior adjustment file is updated annually to reflect the series structure adopted with the benchmark, and it is updated monthly with each release of CES national estimates to include strike data. In the example shown below in figure 4, the first column contains the 14-digit CES NAICS tabcode. This tabcode identifies the series by an 8-digit industry code, followed by three zeros used as placeholders, a 2-digit data type code, and a single digit indicating seasonal adjustment (3 for not seasonally adjusted, 5 for seasonally adjusted). The tabcode structure is similar to the CES series ID structure, described on the CES NAICS webpage (www.bls.gov/ces/naics/home.htm#2.3). The second column contains the year, and the next 12 columns represent the months of the year in sequential order (January through December). The file contains both positive and negative numbers. The positive numbers reflect a strike and are added to the not seasonally adjusted data before running X‑13ARIMA‑SEATS. The negative numbers reflect the buildup of employment associated with the decennial census and are added to the not seasonally adjusted data before calculating the seasonal factors.

Figure 4. Prior adjustment file format⁽¹⁾

Footnotes (1) The prior adjustment file contains unrounded data and must be adjusted to the thousands rounded to one decimal place to be comparable to CES employment estimates.

To Table of Figures

User-defined regression variable file

As mentioned earlier, the CES program's current implementation of seasonal adjustment controls for several non-economic calendar related fluctuations in the estimates. This is done with the inclusion of user-defined regression (or "dummy") variables. The dummy variables are defined in the REGRESSION spec of the spec file. The dummy files vary depending upon the type of calendar event being treated. Table 20 lists the dummy files used and the calendar event(s) they are used to treat.

Table 20. Dummy files with calendar treatment

Dummy File	Calendar Event Treated
Fdum8606.dat	4 vs. 5 week effect
Fdumpc96.dat	4 vs. 5 week effect plus a special adjustment for the presence/absence of poll workers in local government
Fdumpcw6.dat	4 vs. 5 week effect plus a special adjustment for the presence/absence of poll workers in local government (only applies to women employee series)
Fdumw96.dat	4 vs. 5 week effect plus a special adjustment for the presence/absence of an annual increase in postal employment in December (only applies to U.S. Postal Services, 90-919120)
Fdumel06.dat	Good Friday (Easter)/Labor Day adjustment
Fdumel96.dat	4 vs. 5 week effect plus Good Friday (Easter)/Labor Day adjustment
Dumlp06.dat	10/11 day effect
Dumlpel6.dat	10/11 day effect plus Good Friday (Easter)/Labor Day adjustment

To Table of Figures

The dummy values are usually 1 and 0, with weights assigned so that the effect over a 10-year period sums to zero. The latest user-defined regression files used in the seasonal adjustment of CES data can be downloaded from the CES seasonal adjustment page.

Metafile

The metafile is a text file ending in a ".mta" file extension and is used when running X‑13ARIMA‑SEATS on more than one series. It is essentially a list of the complete path and filename—without the extension—of all of the input spec files. Only one spec file is listed per row. As with the individual spec files, it is important to remember that when running X‑13ARIMA‑SEATS in DOS, the name of the metafile must be 8 characters or less.

Recent outliers

The latest outliers used in the seasonal adjustment of CES data can be downloaded from the CES seasonal adjustment page in the ces.spec.others.zip file. Outliers are any data point that falls outside of the normal monthly values for CES data. The list of these outliers is updated monthly with each release of CES national estimates as an excel table called outliers.xlsx that lists the month, year, and industry code of recent outliers manually identified during analyst review. The file contains outliers from the November following the most recent benchmark to the present month.

Until the 2021 benchmark, BLS included only point outliers (also known as additive outliers, or AO) in its annual seasonal adjustment processing. Point outliers only affect the month in which they occur and do not have a persistent influence over subsequent months. Only using AOs allowed for more control over outliers from the end of the input series, in the most recent year of data. However, research into the effects of the COVID-19 pandemic on seasonal patterns has resulted in changes to how BLS will treat outliers in CES seasonal adjustment from January 2022 forward.

BLS will allow other types of outliers, known as temporary change (TC) and level shift (LS) outliers, to be included in seasonal adjustment of series. Point outliers indicate that a shock only affected that particular point in time. Temporary change outliers show a shock to one month of data followed by an effect on subsequent months that diminishes over time. Level shift outliers show a shock that interrupts a normal seasonal pattern that is then continued on the new post-shock level. Any shock at the end of a time series must be an AO because TC and LS outliers can only be identified after enough time has passed to recognize either a diminishing effect (for a TC) or a continuation of seasonal pattern at the new level (for an LS).

A seasonal adjustment run selecting only AOs is run initially. The other two outlier types, in addition to the AO outliers, are automatically chosen by X13‑ARIMA‑SEATS and used in a second seasonal adjustment run. Both the AO-only and additional outlier runs are manually reviewed by CES analysts for each basic level series, and the seasonally adjusted series with the best fit is chosen. The outliers.xlsx file will contain a full account of AO, TC, and LS outliers chosen for each month.

Running X‑13 on a single series

Use the following command at the DOS prompt when running X‑13ARIMA‑SEATS on a single series:

{path1\}x13as {path2\}spec file name -options

where {path1\}	= path of the X‑13ARIMA‑SEATS program
x13as	= command informing X‑13 program to execute
{path2\}	= path of the spec file
spec file name	= name of the input spec file you want to adjust (without the extension)
options	= see X‑13 manual for list of options

Example: At the DOS prompt, type:

c:\x13as\x13as c:\x13\seasadj\AE113310 -w

(where AE113310.spc is the series you want to adjust)

Running X‑13 on multiple series

Use the following command at the DOS prompt when running X‑13ARIMA‑SEATS on more than one series:

{path1\}x13as -m {path2\}metafile name -options

where {path1\}	= path of the X‑13ARIMA‑SEATS program
x13as	= command informing X‑13 program to execute
-m	= flag that informs X‑13 that the subsequent named file is a metafile
{path2\}	= path of the metafile
metafile name	= name of the metafile (without the extension) containing the input spec files
options	= see X‑13 manual for list of options

Example: At the DOS prompt, type:

c:\x13as\x13as -m c:\x13\seasadj\pubAE -w

(where pubAE.mta is the metafile you are using)

Output from X‑13ARIMA‑SEATS

When X‑13ARIMA‑SEATS is run, several output files are generated by default. The output files are saved in the same location as the input specification files.

• Main output file (*.out)
• Error output file (*.err)
• Log output file (*.log)

More details follow on each of the output files.

Main output file (*.out)

The X‑13ARIMA‑SEATS output is written to a text file ending in a ".out" extension. Output from the CES implementation contains many different tables and statistics, including:

• Table displaying the original, not seasonally adjusted series
• Table displaying the final seasonally adjusted series
• Table displaying the final seasonal factors
• Statistics related to model selection
• Statistics related to outlier detection
• A summary of seasonal adjustment diagnostics
• Quality control statistics

Individual specs in the spec file control their contribution to this output using optional PRINT arguments. For example, within the X11 spec, BRIEF specifies that only certain tables or plots are printed, while the minus sign in front of a name (such as -SPECSA or -SPECIRR) means that particular table or plot should be suppressed from the output. In this example, without the options -SPECSA and -SPECIRR, both of the plots would be printed by default under the BRIEF option.

Figure 5. The PRINT argument in the X11 spec

To Table of Figures

It is important to remember that every time X‑13ARIMA‑SEATS is run on a particular series, the *.out file is overwritten, unless an alternate name or directory is specified.

Error output file (*.err)

Input errors are written to a text file ending in an ".err" extension. If the error is fatal, ERROR: will be displayed before the error message. If the error is not fatal, WARNING: will be printed before the message. Nonfatal errors (or warnings) will not stop the program, but they should be an alert to use caution and to check input and output carefully.

It is important to remember that, as is the case with all output files, every time X‑13ARIMA‑SEATS is run on a particular series, the *.err file is overwritten, unless an alternate name or directory is specified.

Log output file (*.log)

A summary of modeling and seasonal adjustment diagnostics are written to a text file ending in a ".log" extension. Individual specs in the specification file control their contribution to this output using optional SAVELOG arguments. When X‑13ARIMA‑SEATS is run on an individual spec file, the log file is stored with the same name and directory as the spec file. However, when X‑13 is run using a metafile, the log file is stored with the same name and directory as the metafile. As is with all output files, every time X‑13ARIMA‑SEATS is run, the *.log file is overwritten unless an alternate name or directory is specified.

Other output files

Other output files are generated as specified in the spec file using the SAVE argument. In the CES program's implementation, the following additional output files are generated:

• *.a1 – This file contains the not seasonally adjusted data with associated dates and is specified in the SERIES spec
• *.ao – This file contains outlier factors with associated dates and is specified in the REGRESSION spec
• *.d10 – This file contains final seasonal factors with associated dates and is specified in the X11 spec
• *.d11 – This file contains final seasonally adjusted data with associated dates and is specified in the X11 spec
• *.d16 – This file contains combined seasonal and trading day factors with associated dates and is specified in the X11 spec
• *.td – This file contains final trading day factors with associated dates and is specified in the REGRESSION spec

Indirect seasonal adjustment

The CES program generally seasonally adjusts published series directly at the 3-digit NAICS level and aggregates to the higher levels. However, there are some exceptions to this rule. In a few of the AE series, the CES program will seasonally adjust at a level lower than the 3-digit NAICS level. In these instances, the CES program seasonally adjusts the 3-digit series indirectly, meaning all of the component (lower level) series are seasonally adjusted directly and aggregated up to the composite (3-digit) level. Indirect seasonal adjustment is performed on these series because some of the individual component series that aggregate to the composite series exhibit different seasonal patterns that may be masked if the data are seasonally adjusted directly at the aggregate level.

The spec file for the composite series differs somewhat from normal CES implementation. The most significant difference is at the beginning of the spec file, where the SERIES spec is replaced with the COMPOSITE spec. Running X‑13 employing the COMPOSITE spec produces an indirect seasonal adjustment of the composite series as well as a direct adjustment. Output from the indirect adjustment is saved under nonstandard file extensions.

• Aggregated not seasonally adjusted data with associated dates are saved in a text file with the extension *.cms (instead of *.a1 under direct seasonal adjustment)
• Final indirect (aggregated) seasonally adjusted data with associated dates are saved in a text file with the extension *.isa (instead of *.d11 under direct seasonal adjustment)
• Final seasonal factors for aggregated series with associated dates are saved in a text file with the extension *.isf (instead of *.d16 under direct seasonal adjustment)

The COMPOSITE spec is covered in greater detail at the end of this section in Anatomy of a Spec File. Seasonal adjustment of the component series that go into a composite series is run using X‑13ARIMA‑SEATS in the same way as a standard seasonally adjusted series, but is then summed to the composite level. A metafile listing the file locations and names (without the .spc extension) of the composite series followed by all of its component series is used to seasonally adjust a composite series.

A current list of industries that are indirectly seasonally adjusted follows in table 21, along with their component series. For any given series, not all of the component series are published at first closing. Some series are published during a later release. In the table below, component series published with the second preliminary data release are denoted with a footnote.

Table 21. Indirectly seasonally adjusted CES series

Composite Series		Component Series(1)
CES Industry Code	CES Industry Title
10‑212000	Mining (except oil and gas)	10-212100, 10-212200, 10-212300
20-236100(2)	Residential building construction	20-236115, 20-236116, 20-236117, 20-236118
20-236200(2)	Nonresidential building construction	20-236210, 20-236220
20-238000(2)	Specialty trade contractors	20-238110, 20-238120, 20-238130, 20-238140, 20-238150, 20-238160, 20-238170, 20-238190, 20-238210, 20-238220, 20-238290, 20-238310, 20-238320, 20-238330, 20-238340, 20-238350, 20-238390, 20-238910, 20-238990
31-334000	Computer and electronic product manufacturing	31-334100, 31-334200, 31-334400, 31-334500, 31-334600
42-441000	Motor vehicle and parts dealers	42-441100, 42-441200, 42-441300
42-455000	General merchandise retailers	42-455100, 42-455200
55-522000	Credit intermediation and related activities	55-522100, 55-522200, 55-522300
60-540000	Professional, scientific, and technical services	60-541100, 60-541200, 60-541300, 60-541400, 60-541500, 60-541600, 60-541700, 60-541800, 60-541900
60-561000	Administrative and support services	60-561100, 60-561200, 60-561300, 60-561400, 60-561500, 60-561600, 60-561700, 60-561900
65-621000	Ambulatory health care services	65-621100, 65-621200, 65-621300, 65-621400, 65-621500, 65-621600, 65-621900
65-623000	Nursing and residential care facilities	65-623100, 65-623200, 65-623300, 65-623900
65-624000	Social assistance	65-624100, 65-624200, 65-624300, 65-624400
Footnotes (1) For CES industry titles of the component series, see the CES published series page. (2) The component series for this industry are published with the second preliminary release.

To Table of Figures

Anatomy of a spec file

For published series, the CES program generally seasonally adjusts at the 3-digit NAICS level and aggregates to the higher levels. A small number of series are independently seasonally adjusted at a higher level of detail, but these are not included in the aggregation of seasonally adjusted data. One of the main inputs to the seasonal adjustment process is a unique file called a spec file. The spec file contains a set of specs that give X‑13ARIMA‑SEATS various information about the data and the desired seasonal adjustment options and output. Each specification inside the spec file controls options for a specific function. For example, the SERIES spec contains specifications on the location and format of the data, while the X11 spec sets seasonal adjustment options such as seasonal adjustment transformation mode, output files to save, and diagnostic statistics to print.

Figure 6. CES seasonal adjustment spec file

To Table of Figures

The spec file is free format, and blank spaces, tabs, and blank lines may be used as desired to make the spec file more readable. The order of the specification statements in the spec file (with one exception) and the order of the arguments within the braces of any spec do not matter. The only requirement is that the SERIES spec or COMPOSITE spec must be the first spec.

More detail on each spec used by CES follows.

1. SERIES spec

SERIES{

TITLE = "Logging"

START = 1993.01

PERIOD = 12

SAVE = A1

PRINT = BRIEF

NAME = '10113310 – AE'

FILE = 'c:\AE10113310.dat'}

The main function of the SERIES spec is to specify details about the input data series such as the name, format, and location of the data. The CES implementation employs seven options or arguments with the SERIES spec.

• TITLE — A descriptive title for the series. In this example, the title is "Logging".
• START — The start date of the time series being adjusted. In this example, the start date is January, 1993.
• PERIOD — Seasonal period of the series. In this example, the period is 12 (which means monthly).
• SAVE — Specifies output to be saved. In this example, the time series data with associated dates will be saved in an output file called AE10113310.A1.
• PRINT — Specifies output to be printed. In this example, BRIEF specifies that only certain tables are printed.
• NAME — The name of the time series. In this example, the name is "10113310 – AE".
• FILE — The complete path and name of the file containing the time series data. In this example, the complete path and filename is "c:\AE10113310.dat".

2. TRANSFORM spec

TRANSFORM{FUNCTION = LOG}

The main function of the TRANSFORM spec is to transform or adjust the time series prior to estimating a regARIMA model. The CES implementation employs one argument with the TRANSFORM spec.

• FUNCTION — Specifies the method to transform the time series. In this example, the transformation method is log transformation, which means X‑13 will compute a multiplicative seasonal decomposition.

3. REGRESSION spec

REGRESSION{

VARIABLES = (AO2012.10 AO2020.04 AO2020.05 AO2020.06 AO2020.07 AO2020.10)

USER = (dum1 dum2 dum3 dum4 dum5 dum6 dum7 dum8 dum9 dum10 dum11)

START = 1986.01

FILE = 'c:\FDUM8606.dat'

USERTYPE = TD

SAVE = (TD AO TC LS) }

The main function of the REGRESSION spec is to specify the regression components of a regARIMA model. The CES implementation employs up to six options with the REGRESSION spec.

• VARIABLES — Specifies any predefined regression variables to be included in the model. In the CES implementation, predetermined outliers are listed after the VARIABLES argument. In this example, predetermined outliers include AO2012.10 (October 2012), AO2020.04 (April 2020), AO2020.05 (May 2020), AO2020.06 (June 2020), AO2020.07 (July 2020), and AO2020.10 (October 2020).
• USER — Specifies the names for any user-defined regression variables. CES defines regression variables to adjust for significant effects associated with calendar related events such as (1) the relative timing of the reference period of the survey and the Good Friday (Easter) and Labor Day holidays; (2) variations of 4 or 5 weeks between reference periods in any given pair of months, and; (3) differences in the number of working days in a pay period from month-to-month. In this example, the regression variables are named dum1, dum2, dum3, dum4, dum5, dum6, dum7, dum8, dum9, dum10, and dum11.
• START — Specifies the start date for the data values for the user-defined regression variables. In this example, the start date is January, 1986.
• FILE — The complete name of the file containing the data values for the user-defined regression variables, including the path. In this example, the filename, including the path, is "c:\FDUM8606.dat".
• USERTYPE — Specifies a type of model-estimated regression effect to each user-defined regression variable. In this example, the type of model-estimated regression effect is defined as TD, or trading day.
• SAVE — Specifies output to be saved. In this example, trading day factors with associated dates will be saved in an output file called AE10113310.TD, and point outlier factors with associated dates will be saved in an output file called AE10113310.AO. Had there been TC or LS outliers, they would have been saved in files called AE10113310.TC and AE10113310.LS.

Note: Not every option is used in every spec file. For example, if no predetermined outliers exist, then the VARIABLES argument will not be used. Likewise, if we are not treating a particular series for calendar effects, then the USER, START, FILE, and USERTYPE arguments will not be used.

4. ARIMA spec

ARIMA{MODEL = (1 1 0) (1 0 0)}

The main function of the ARIMA spec is to specify the ARIMA part of a regARIMA model. The CES implementation employs one option with the ARIMA spec.

• MODEL — Specifies the actual ARIMA model to be used. In this example, the model is (1 1 0) (1 0 0).

5. ESTIMATE spec

ESTIMATE{MAXITER = 1000}

The main function of the ESTIMATE spec is to estimate the regARIMA model specified by the REGRESSION and ARIMA specs. The CES implementation employs one argument with the ESTIMATE spec.

• MAXITER — Specifies the maximum number allowed of autoregressive moving average (ARMA) nonlinear iterations. ARMA is a time-series model that includes both autoregressive (AR) and moving average (MA) nonlinear components. In this example, the maximum number allowed of ARMA iterations is 1,000.

6. FORECAST spec

FORECAST{MAXLEAD = 24}

The main function of the FORECAST spec is to generate forecasts (and/or backcasts) for the time series model given in the SERIES spec using the estimated regARIMA model. The CES implementation employs one argument with the FORECAST spec.

• MAXLEAD — Specifies the number of forecasts produced. In this example, the number of forecasts specified is 24 months.

7. OUTLIER spec

OUTLIER{

CRITICAL = 3.5

TYPES = (AO TC LS) }

The main function of the OUTLIER spec is to perform automatic detection of point outliers (AO), temporary change (TC) outliers, level shifts (LS), or any combination of the three. The CES implementation uses this spec to automatically detect all three types of outliers. CES employs two arguments with the OUTLIER spec.

• CRITICAL — Specifies the value to which the absolute values of the outlier t-statistics are compared to detect outliers. In this example, the critical value is 3.5.
• TYPES — Specifies the types of outliers to detect. The CES implementation uses the OUTLIER spec to automatically detect AO, TC, and LS outliers.

8. X11 spec

X11{

MODE = MULT

PRINT = (BRIEF -SPECSA -SPECIRR)

SAVE = (D10 D11 D16)

APPENDFCST = YES

FINAL = USER

SAVELOG = (Q Q2 M7 FB1 FD8 MSF) }

The function of the X11 spec is to control certain aspects of the seasonal adjustment process. For example, the CES implementation uses the X11 spec to control the type of seasonal adjustment decomposition calculated (mode). CES employs six arguments with the X11 spec.

• MODE — Specifies the mode of the seasonal adjustment decomposition to be performed. There are four choices: multiplicative, additive, pseudo-additive, and log-additive. In the CES implementation, only the multiplicative or additive modes are employed. In this example, the mode specified is multiplicative (MULT).
• PRINT — Specifies output to be printed. In this example, BRIEF specifies that only certain tables or plots are printed. The minus sign in front of a name means that particular table or plot should be suppressed. In this example, -SPECSA specifies that a spectral plot of differenced, seasonally adjusted series be suppressed, while -SPECIRR specifies that a spectral plot of outlier-modified irregular series be suppressed. Without these options, both plots would be printed under the BRIEF option by default.
• SAVE — Specifies output to be saved. In this example, final seasonal factors with associated dates will be saved in an output file called AE10113310.D10; the final seasonally adjusted series with associated dates will be saved in an output file called AE10113310.D11; and combined seasonal and trading day factors with associated dates will be saved in an output file called AE10113310.D16.
• APPENDFCST — Determines if forecasts of seasonal factors will be included in the X‑13 output files and tables that were selected in the SAVE option. If APPENDFCST = yes, then forecasted seasonal factors will be stored. In this example, the APPENDFCST value is YES.
• FINAL — Specifies the types of prior adjustment factors (obtained from the REGRESSION and OUTLIER specs) that are to be applied to the final seasonally adjusted series. In this example, FINAL = USER, which means that factors derived from user-defined regressors (or in this example, the dummy variables) are to be applied to the final seasonally adjusted series, removing significant effects associated with calendar related events.
• SAVELOG — Specifies the diagnostic statistics to be printed to the log file. In this example, the following diagnostics will be printed:
  • Q, which is the overall index of the acceptability of the seasonal adjustment. The adjustment may be poor if Q > 1.
  • Q2, which is the Q-statistic computed without the M2 Quality Control Statistic. The M2 values can sometimes be misleading if the trend shows several changes of direction.
  • M7, which measures the moving seasonality relative to the stable seasonality found in the series. Any M > 1 indicates a source of potential problems for the adjustment procedure.
  • FB1, which is an F-test for stable seasonality, performed on the original series.
  • FB8, which is an F-test for stable seasonality, performed on the final ratio of the seasonal-to-irregular components.
  • MSF, which is an F-test for moving seasonality.

As previously mentioned, the CES program generally seasonally adjusts published series at the 3-digit NAICS level and aggregates to the higher levels. However, there are a few cases in which CES seasonally adjusts published series at a level lower than the 3-digit NAICS level. In these instances, CES seasonally adjusts the 3-digit NAICS level indirectly, meaning all of the component or lower level series are seasonally adjusted directly and then aggregated up to the 3-digit level. When this happens, the SERIES spec is replaced by the COMPOSITE spec in the specification file of the 3-digit series.

9. COMPOSITE spec

COMPOSITE{

TITLE = "Construction of buildings"

SAVE = (ISF ISA CMS)

PRINT = BRIEF

NAME = '20236000 - AE'

SAVELOG = (INDTEST INDQ) }

The COMPOSITE spec is used as part of the procedure for obtaining both indirect and direct adjustments of a composite series data series. This spec is required for obtaining composite adjustments and is used in place of the SERIES spec. The COMPOSITE spec can also specify details about the input data series such as the name of the series and which tables are to be printed or stored. The CES implementation employs five options or arguments with the COMPOSITE spec.

• TITLE — A descriptive title for the series. In this example, the title is "Construction of buildings".
• SAVE — Specifies output to be saved. In this example, the aggregated time series data with associated dates will be saved in an output file called AE20236000.CMS, the final seasonal factors for the indirect adjustment with associated dates will be saved in an output file called AE20236000.ISF, and the final indirect seasonally adjusted series with associated dates will be saved in an output file called AE20236000.ISA.
• PRINT — Specifies output to be printed. In this example, BRIEF specifies that only certain tables are printed.
• NAME — The name of the time series. In this example, the name is "20236000 – AE".
• SAVELOG — Specifies the diagnostic statistics to be printed to the log file. In this example, the following diagnostics will be printed:
  • IND TEST, which is a test for adequacy of composite adjustment.
  • IND Q, which is an overall index of the acceptability of the indirect seasonal adjustment.

Revisions

Sample-based Revisions

Effect of sample receipts

CES data users typically are most concerned with revisions to over-the-month changes. This section profiles these monthly revisions of CES seasonally adjusted over-the-month changes and the sample collection rates that underlie the revisions.

CES begins collecting sample reports for a reference month as soon as the reference period—the establishment's pay period that includes the 12th of the month—is complete. Collection time available for first preliminary estimates ranges from 9 to 15 days, depending on the scheduled date for the Employment Situation news release. The Employment Situation is scheduled for the third Friday following the week including the 12th of the prior month, with an exception for January. (For January, the news release is delayed a week if the third Friday following the week of the 12th occurs on January 1, 2, or 3.)

Given this short collection cycle for the first preliminary estimates, many establishments are not able to provide their payroll information in time to be included in these estimates. Therefore, CES sample responses for the reference month continue to be collected for 2 more months and are incorporated into the second preliminary and final sample-based estimates published in subsequent months. (Second preliminary estimates for a reference month are published the month following the initial release, and final sample-based estimates are published 2 months after the initial release.) Additional sample receipts are the primary source of the monthly CES employment revisions.

Sample-based estimates remain final until employment levels are reset to universe employment counts, or benchmarks, for March of each year; the benchmarks are primarily derived from Unemployment Insurance (UI) tax records. The annual benchmarking process results in revised data back to the last annual benchmark for not seasonally adjusted series and back 5 years for seasonally adjusted series.

Monthly revisions

Revisions to CES over-the-month changes are calculated by comparing each month's second preliminary over-the-month change to the first preliminary over-the-month change, the final sample-based over-the month change with the second preliminary over-the-month change, and the final sample-based over-the-month change to the first preliminary over-the-month change.

See the CES revisions page for a table of revisions to seasonally adjusted total nonfarm over-the-month changes from January 1979 forward. The monthly employment change figures shown in the table do not reflect subsequent changes due to the introduction of benchmark revisions, seasonal adjustment, or other updates. Mean revisions and mean absolute revisions for each calendar year are included in the table. Mean absolute revisions indicate the overall magnitude of change to the estimates, while the mean revisions are a measure of whether there is a bias in direction of the revisions. The closer the mean revision is to zero, the less indication that revisions are predominantly either upward or downward. For example, if in a given year there were six upward revisions of 50,000 and six downward revisions of 50,000, the mean revision would be zero; however, the mean absolute revision would be 50,000.

Collection rates

Collection rates are defined as the percent of reports received for a monthly estimate compared to the total number of actively-reporting sample units on the sample registry.

CES collection rates back to 1981 can be found on the CES registry receipts page.

Much of the month-to-month variation in the first preliminary collection rates is a function of the number of collection days in the individual months. The overall upward trend over time is attributable to replacing decentralized mail collection with automated techniques.

For more information about the methods used to calculate CES estimates of employment, hours, and earnings at all closings, see the section on Monthly Estimation in this documentation.

Benchmarks

For the CES survey, 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. For national series, only the March sample-based estimates are replaced with UI counts. For state and metropolitan area series, all available months of UI data are used to replace sample-based estimates. State and area series are based on smaller samples and are therefore more vulnerable to both sampling and nonsampling errors than national estimates.

Population counts are derived from the administrative file of employees covered by UI. All employers covered by UI laws are required to report employment and wage information to the appropriate state Unemployment Insurance agency four times per year. About 97 percent of private and total nonfarm employment within the scope of the establishment survey is covered by UI. The UI data are obtained and edited by each state's Labor Market Information (LMI) agency, and are tabulated and published through the BLS Quarterly Census of Employment and Wages (QCEW) program. A benchmark for the remaining 3 percent is constructed from alternate sources, primarily records from the Railroad Retirement Board (RRB) and County Business Patterns (CBP). This 3 percent is collectively referred to as noncovered employment and is explained further in the calculating noncovered employment section of this document. The full benchmark developed for March replaces the March sample-based estimate for each basic cell. The monthly sample-based estimates for the year preceding and the year following the benchmark are also then subject to revision. Each annual benchmark revision affects 21 months of data for not seasonally adjusted series and 5 years of data for seasonally adjusted series.

Monthly estimates for the year preceding the March benchmark are readjusted using a "wedge back" procedure. The difference between the final benchmark level and the previously published March sample estimate is calculated and spread back across the previous 11 months. The wedge is linear; eleven-twelfths of the March difference is added to the February estimate, ten-twelfths to the January estimate, and so on, back to the previous April estimate, which receives one-twelfth of the March difference. This assumes that the total estimation error since the last benchmark accumulated at a steady rate throughout the current benchmark year.

Estimates for the 7 months following the March benchmark (April through October) also are recalculated each year. These post-benchmark estimates reflect the application of sample-based monthly changes to new benchmark levels for March and the recomputation of business birth-death forecasts for each month.

Following the revision of basic employment estimates, all other derivative series also are recalculated. New seasonal adjustment factors are calculated and all data series for the previous 5 years are re-seasonally adjusted before full publication of all revised data in February of each year.

Estimates for the November and December following the March benchmark are revised due to both impacts of benchmarking and additional sample. Additionally, new sample units are rotated into the survey starting with November.

As an example of benchmark effects, the March 2022 benchmark revisions (published in February 2023) resulted in revised series from April 2021 through December 2022 on a not seasonally-adjusted-basis and revised series from January 2018 through December 2022 on a seasonally-adjusted-basis for all series.

Annual CES benchmark revisions are published along with January first preliminary estimates in February of each year. For example, the annual CES benchmark revisions for March 2022 were published along with the January 2023 first preliminary estimates on February 3, 2023.

The benchmark revision is the difference between the universe count of employment for March and its corresponding sample-based estimate after removing the effect of any changes in employment scope. The benchmark revisions from 1979 forward are included in table 22. See the CES National Benchmark Article for more details about the benchmarking process.

Table 22. CES total nonfarm benchmark revisions⁽¹⁾

Year	Percent difference	Difference in thousands
1979	0.5	447
1980	-0.1	-63
1981	-0.4	-349
1982	-0.1	-113
1983	(2)	36
1984	0.4	353
1985	(2)	-3
1986	-0.5	-467
1987	(2)	-35
1988	-0.3	-326
1989	(2)	47
1990	-0.2	-229
1991	-0.6	-640
1992	-0.1	-59
1993	0.2	263
1994	0.7	747
1995	0.5	542
1996	(2)	57
1997	0.4	431
1998	(2)	44
1999	0.2	258
2000	0.4	468
2001	-0.1	-123
2002(3)	-0.2	-203
2003	-0.1	-122
2004	0.2	203
2005	-0.1	-158
2006	0.6	752
2007	-0.2	-293
2008	-0.1	-89
2009	-0.7	-902
2010(4)	-0.3	-378
2011(5)	0.1	162
2012	0.3	424
2013(6)	-0.1	-119
2014	(2)	67
2015(7)	-0.1	-172
2016	-0.1	-81
2017(8)	0.1	135
2018	(2)	-16
2019 (9)	-0.3	-489
2020	-0.1	-121
2021	(2)	-7
2022 (10)	0.3	506
Footnotes (1) The table reflects the benchmark revisions after removing the effect of any changes in employment scope. (2) Absolute revision is less than 0.05 percent. (3) With the conversion from SIC to NAICS, support activities for animal production (NAICS 1152) was removed from CES scope. Also, the federal government employment level derivations were changed from end-of-month counts provided by the Office of Personnel Management that excluded some workers, mostly employees of U.S. Department of Defense-owned establishments such as military base commissaries, to QCEW-derived benchmark employment levels. For more information, see the 2002 CES Benchmark Article. (4) With the 2010 benchmark, BLS reconstructed historical national levels of all employees for other federal government (91-999900) to reflect corrections to initial counts for temporary and intermittent workers for the 2010 Census. The reconstructions resulted in about 4,000 in employment being added to other federal government. For more information, see the Reconstructions section in the 2010 CES Benchmark Article. (5) A review of industries for the possible presence of noncovered employment yielded 13 additional industries. As a result of including these industries, employment in the amount of 95,000 was added to the benchmark nonfarm level. For more information, see the Changes to noncovered employment section in the 2011 CES Benchmark Article. (6) With the 2013 benchmark, BLS reconstructed several national employment series. Each first quarter, the Quarterly Census of Employment and Wages (QCEW) program, whose data account for approximately 97 percent of the CES universe scope (see The Sample section of the CES Technical Notes), incorporates updated industry assignments. In 2013, these updates included two substantial groups of nonrandom, noneconomic code changes, one to funds, trusts, and other financial vehicles (NAICS 525), and the other, a reclassification of approximately 466,000 in employment from private households (NAICS 814), which is out of scope for CES, to services for the elderly and persons with disabilities (NAICS 62412), which is in scope. These changes also had an impact, beyond what would be considered typical for a given benchmark year, on corresponding CES series. For more information about the changes to these industries, see the QCEW First Quarter 2013 News Release or the Special notice regarding reconstructed data section in the 2013 CES Benchmark Article. (7) With the 2015 benchmark, BLS reconstructed the national employment series services for the elderly and persons with disabilities (65-624120) back to January 2000. BLS previously reconstructed this series with the 2013 benchmark; however, between the 2013 and 2015 benchmark, a better source of information for the employment within NAICS 62412 for the state of California was found. The inclusion of the reconstructed series resulted in total nonfarm and total private employment that was 27,000 less than the originally published March 2015 estimate level. The difference between the benchmarked and originally published March 2015 estimate level is −199,000 or −0.1 percent. This table displays March 2015 data after accounting for the decrease of 27,000 from the reconstructed series. Similarly, for the education and health services supersector, this table displays March 2015 data after incorporating the reconstructed series. For more information, see the Reconstructions section in the 2015 CES Benchmark Article. (8) With the 2017 benchmark, BLS reconstructed the national employment series security guards and patrols and armored car services (60-561613) back to October 2016 to correct a microdata error. The inclusion of the reconstructed series resulted in total nonfarm and total private employment that was 3,000 more than the originally published March 2017 estimate level. The difference between the benchmarked and originally published March 2017 estimate level is 138,000 or 0.1 percent. This table displays March 2017 data after accounting for the increase of 3,000 from the reconstructed series. Similarly, for the professional and business services supersector, this table displays March 2017 data after incorporating the reconstructed series. For more information, see the Reconstructions section in the 2017 CES Benchmark Article. (9) With the 2019 benchmark, BLS reconstructed some national employment series in transportation to correct a processing error in rail transportation (43-482000), which had resulted in 16,000 employment being double counted. The reconstruction removed the doubled-counted employment and affected aggregates of rail transportation, up to and including total nonfarm, back to January 1990. While the difference between the benchmarked and originally published March 2019 estimate level is −505,000, or −0.3 percent, this table displays March 2019 data after accounting for the removal of 16,000 from the published series. For more information, see the Reconstructions section in the 2019 CES Benchmark Article. (10) With the 2022 benchmark, BLS reconstructed several national employment series. A recoding effort in the QCEW resulted in about 68,000 in employment in electronic shopping and mail-order houses (42-454100) being moved into corporate, subsidiary, and regional managing offices (60-551114). Affected series were reconstructed for their entire history going back to January 1990. Additionally, the CES program found that some QCEW employment microdata submitted for services for the elderly and persons with disabilities (NAICS 624120) was erroneously reported for the first quarter of 2022. CES imputed the March 2022 level for this industry, and the new level was approximately 83,000 greater than the originally reported QCEW level. For more information, see the Reconstructions and Benchmark level adjustment to services for the elderly and persons with disabilities sections in the 2022 CES Benchmark Article.

To Table of Figures

Calculating noncovered employment

Noncovered employment results from a difference in scope between the CES program and the Quarterly Census of Employment and Wages (QCEW) program. The QCEW employment counts are derived from UI tax reports that individual firms file with their State Employment Security Agency (SESA). Most firms are required to pay UI tax for their employees; however, there are some types of employees that are exempt from UI tax law, but are still within scope for the CES estimates. Examples of the types of employees that are exempt are students paid by their school as part of a work study program; interns of hospitals paid by the hospital for which they work; employees paid by state and local government and elected officials; independent or contract insurance agents; employees of nonprofits and religious organizations (this is the largest group of employees not covered); and railroad employees covered under a different system of UI administered by the Railroad Retirement Board (RRB). This employment needs to be accounted for in order to set the benchmark level for CES employment.

No single source of noncovered data exists; therefore, CES uses a number of sources to generate the employment counts, including County Business Patterns (CBP) and the Annual Survey of Public Employment and Payroll (ASPEP) both from the US Census Bureau, the RRB, and the Labor Market Information Agencies (LMIs).

The majority of noncovered employment is calculated using CBP data. Noncovered industries whose employment is derived from the CBP are provided in table 23. The CBP—which draws from Social Security filings and other records that include those employees not covered by UI tax laws—is lagged in its publication by approximately 2 years (for example, in 2014 the 2012 CBP data were published). To adjust for this lag, CES assumes that the noncovered portion of employment grows or declines at the same rate as the covered portion and trends the CBP data forward using the QCEW trend. The current QCEW employment level is subtracted from the trended CBP figure, and the residual is the noncovered employment level.

Noncovered employment for all CBP-based industries, with the exception of religious organizations, direct life and health insurance carriers, direct property and casualty insurers, and direct title and other direct insurance carriers, is calculated using equation 14.

Equation 14. Noncovered employment for CBP-based industries, except religious organizations, direct life and health insurance carriers, direct property and casualty insurers, and direct title and other direct insurance carriers

where:

N = Noncovered employment estimate

C = CBP employment data for North American Industry Classification System (NAICS) code

E = QCEW employment for NAICS code

t = Benchmark year

Noncovered employment for religious organizations is calculated using equation 15.

Equation 15. Noncovered employment for religious organizations

where:

N = Noncovered employment estimate

C = CBP employment data for NAICS 813110

E = QCEW employment for NAICS 813110

t = Benchmark year

Noncovered employment for direct life and health insurance carriers, direct property and casualty insurers, and direct title insurance and other direct insurance carriers is calculated using equation 16.

Equation 16. Noncovered employment for direct life and health insurance carriers, direct property and casualty insurers, and direct title insurance and other direct insurance carriers

where:

N = Noncovered employment estimate

C = CBP employment data for NAICS 524113, 524114, 524126, and 524128

E = QCEW employment for NAICS 524113, 524114, 524126, and 524128

t = Benchmark year

Table 23. Noncovered industries calculated using CBP data

NAICS Code	NAICS Industry Title
524113	Direct life insurance carriers
524114	Direct health and medical insurance carriers
524126	Direct property and casualty insurance carriers
524127	Direct title insurance carriers
524128	Other direct insurance carriers, except life, health, & medical
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 and universities
611410	Business and secretarial schools
611420	Computer training
611430	Management 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(1)
622210	Psychiatric and substance abuse hospitals(1)
622310	Other hospitals(1)
624310	Vocational rehabilitation services
624410	Child day care services
813110	Religious organizations
813211	Grantmaking foundations
813312	Environment and conservation organizations
813410	Civic and social organizations
813910	Business associations
813940	Political organizations
813990	Other similar organizations
Footnotes (1) Indicates that noncovered employment is calculated for firms owned both privately and by state and local government.

To Table of Figures

The estimated employment for industries listed in table 24 is calculated from the ASPEP data using equation 17.

Equation 17. Noncovered employment for ASPEP-based industries

where:

N = Noncovered employment estimate

E = Public employment data for higher education*

t = Benchmark year

*Public employment data for higher education is the sum of institutional full-time and part-time employment and noninstitutional full-time and part-time employment.

Table 24. Noncovered industries calculated using ASPEP data⁽¹⁾

NAICS Code	NAICS Industry Title
611210	Junior colleges
611310	Colleges and universities
Footnotes (1) Noncovered employment is calculated only for businesses owned by state and local government.

To Table of Figures

Railroad employment estimates are developed based on data provided by the RRB. The RRB data is broken out by railroad class rather than industry so CES prorates the class data out to NAICS code (see table 25). These data are lagged by 1 year and are trended forward using a ratio based on the benchmark year and the previous year for the CES series rail transportation (NAICS 482). This ratio is applied to the RRB data and then mapped to the corresponding NAICS codes.

Table 25. Noncovered industries calculated using RRB data

Rail Class	Rail Class Description	NAICS Code	NAICS Industry Title
A	Class 1 line-haul railroads	482111	Line-haul railroads
B	Non-Class 1 line-haul railroads and switching & terminal companies	488210	Support activities for rail transportation
		482112	Short line railroads
C	Commuter railroads (includes Amtrak)	482111	Line-haul railroads
		485111	Mixed mode transit systems
D	Car-loan railroads	532411	Commercial air, rail, and water transportation equipment rental and leasing
E	Labor organizations	813930	Labor unions and similar labor organizations
F	Miscellaneous employers	488210	Support activities for rail transportation

To Table of Figures

Over time some sources from which CES draws input data have become unreliable. Where possible, CES has tried to find new sources of input data, but for series that no longer have reliable input data, CES trends forward the previous year's noncovered employment levels using a ratio derived from QCEW employment data. These industries are contained in table 26 and are calculated using equation 18.

Equation 18. Noncovered employment for QCEW-trend-based industries

where:

N = noncovered employment estimate

E = QCEW employment

t = Benchmark year

Table 26. Noncovered industries calculated using QCEW trend

NAICS Code	NAICS Industry Title
513110	Newspaper publishers
513120	Periodical publishers
513130	Book publishers
513190	Directory, mailing list, and other publishers
512230	Music publishers
516200	Media streaming distribution services, social networks, and other media networks and content providers
519290	Web search portals and all other information services
921140	Executive and legislative offices(1)
922190	Other justice, public order, and safety activities(1)
923110	Administration of education programs(1)
924110	Administration of air and water resource and solid waste management programs(1)
925110	Administration of housing programs(1)
926110	Administration of general economic programs(1)
927110	Space research and technology(1)
928110	National security(1)
Footnotes: (1) Noncovered employment is calculated only for firms owned by state and local government.

To Table of Figures

Corporate officers are one of the largest exemptions outside of the industries listed. In several states, corporate officers are exempt from UI coverage and as a result noncovered employment exists in most NAICS industries in those states. Corporate officers and other state-specific employment exemptions outside of those listed above are collected from state offices annually by CES.

Noncovered employment industries are reviewed and refined periodically. This review is done to identify any changes in state UI coverage, as well as to ensure that CES captures all exempted employment within the scope of the CES survey and that our methodology and external data sources are as accurate as possible. When additions and changes are identified during review, they are incorporated with the following March benchmark.

Changing data ratios for education and religious organizations

Due to the small sample in religious organizations (NAICS 8131) and definitional exclusions in the collection of data for educational services (NAICS 611), certain ratios for these series are recalculated with each benchmark to allow for the creation of aggregate totals. Production and nonsupervisory employee (PE) and women employee (WE) ratios, all employee (AE) average hourly earnings (AHE) and average weekly hours (AWH), and PE AHE and AWH for these series are calculated based on the weighted average of the previous year's professional and technical services, private education and health services, leisure and hospitality, and other services supersectors' annual averages. This year the March 2022 values were set based on the 2021 annual averages.

The educational services series uses the PE ratio, AHE, and AWH calculated from the weighted average. The religious organizations series uses the PE ratio, WE ratio, AHE, and AWH calculated from the weighted average. In both cases, the ratios, AHE, and AWH for AE and PE are held constant through the next benchmark.

Historical Reconstructions

Beyond the monthly revisions and the benchmark revisions, CES employment, hours, and earnings estimates have been reconstructed several times in order to avoid series breaks and to provide users with continuous, comparable employment time series suitable for economic analysis when incorporating methodological changes. The major reconstruction efforts are briefly described below.

Improvement to seasonal adjustment methodology

With the release of the 1995 benchmark revision (in June 1996), CES refined its seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. This improvement mitigated the effects that a variable number of weeks between surveys had on the measurement of employment change, thus improving the measurement of true economic trends. At that time, data for 1988 forward were revised to incorporate this new methodology.

CES sample redesign

Over a 4-year period, CES introduced a new probability-based sample design; it replaced an outmoded and less scientific quota sample-based design. The new design was phased in by major industry division with the June 2000 through June 2003 benchmark releases (see table 27). As each industry was phased in, the post-benchmark estimates for that year were affected by the new sample composition.

Table 27. CES sample redesign phase-in schedule

Year	Industries converted to new sample design
2000	Wholesale trade
2001	Mining, construction, manufacturing
2002	Transportation and public utilities; finance, insurance, and real estate; retail trade
2003	Services

To Table of Figures

Industry reclassification

CES periodically updates the national nonfarm payroll series to revised NAICS structures. This update usually occurs every 4 to 5 years. For all NAICS updates, affected series are reconstructed back to at least 1990, and in some cases, where longer histories are available, they are reconstructed back further.

With the release of the 2022 benchmark in February 2023, CES converted from NAICS 2017 to NAICS 2022. The conversion to NAICS 2022 will result in minor revisions reflecting content and coding changes within the mining and logging, manufacturing, wholesale trade, financial activities, and other services sectors, as well as major revisions reflecting content and coding changes in the retail trade and information sectors. Many industry titles and descriptions also will be updated to better reflect official NAICS titles. Prior to NAICS 2022, CES estimates were classified under NAICS 2017, preceded by NAICS 2012, the NAICS 2007, and NAICS 2002. The NAICS system was updated from NAICS 2002 to NAICS 2007 in early 2008, from NAICS 2007 to NAICS 2012 in early 2011, and from NAICS 2012 to NAICS 2017 in early 2017. Before switching to NAICS 2002, the CES estimates were classified under the Standard Industrial Classification (SIC) system. CES estimates were converted from SIC to NAICS 2002 in mid-2003. For more information about NAICS in the CES program, see the CES NAICS homepage.

Other Factors Contributing to Revisions

Over the period covered by the revision and collection rate tables, CES has introduced many program improvements; some of these affect the revision patterns observed over time.

Monthly revisions

As noted above, the overall magnitude of these revisions has trended down over time mainly due to automated and improved data collection techniques which raised the collection rates for the first and second preliminary estimates. Other factors of note include the following:

Timing of benchmark revisions

Between 1980 and 2003, annual benchmark revision updates were introduced in June of each year, concurrent with the March final sample-based estimates and the April second preliminary estimates. The monthly revisions for March and April for these years were often larger than for other months, because the March final and April second preliminary estimates were incorporating not only additional sample but also other benchmark-related changes.

Beginning with the 2003 benchmark revision (published in 2004), CES reduced the time required to produce the annual revisions by 4 months and thus began publishing benchmark revisions in February rather than June. Therefore from 2004 forward, the November final and December second preliminary estimates are affected by benchmark revision updates, rather than the March final and April second preliminary estimates.

Timing of seasonal adjustment updates

Between 1980 and June 1996 seasonal factors were updated on an annual basis along with the benchmark revisions. Therefore, March final and April second preliminary estimates were affected by the recomputation of seasonal factors as well as other benchmarking procedures and additional sample receipts.

Between November 1996 and November 2002, CES updated seasonal factors on a semi-annual basis, meaning that September final and October second preliminary estimates as well as March final and April second preliminary revisions were affected by seasonal factor updates.

Since June 2003 the CES program has used a concurrent seasonal adjustment procedure, meaning that seasonal adjustment is rerun every month using all available months of estimates including the month currently being estimated for first preliminary. This technique yields the best possible seasonal adjustment for the current month and reduces benchmark revisions to over-the-month changes. In the application of the concurrent procedure, the previous 2 months are revised to incorporate not only additional sample receipts but also new seasonal factors. Therefore, there are no longer individual months that are more affected than others by seasonal factor updates. However, this practice does mean that revisions from second preliminary to final sample-based estimates for each month are affected by the CES replacement policy. Because CES revises only 2 months of estimates each month, the fourth month back from the current first preliminary estimate is adjusted using a different set of seasonal factors than the third month back. For example, with the release of October first preliminary data, factors are revised for September and August, but not for July.

Table of Figures

Use the links below to skip to specific equations, tables, and figures describing the CES sample, data collection, available statistics, estimation, and revisions.

Equations

• Equation 1. Sample selection weights
• Equation 2. CES variance
• Equation 3. CES relative standard error
• Equation 4. Current month estimate of all employees
• Equation 5. Current month estimate of production and nonsupervisory employees
• Equation 6. Current month estimate of average weekly hours
• Equation 7. Current month estimate of average hourly earnings
• Equation 8. Employment calculated using small domain model
• Equation 9. Net birth-death
• Equation 10. Aggregate hours
• Equation 11. Summary level average weekly hours
• Equation 12. Aggregate payroll
• Equation 13. Summary level average hourly earnings
• Equation 14. Noncovered employment for CBP-based industries, except religious organizations, direct life and health insurance carriers, direct property and casualty insurers, and direct title insurance and other direct insurance carriers
• Equation 15. Noncovered employment for religious organizations
• Equation 16. Noncovered employment for direct life and health insurance carriers, direct property and casualty insurers, and direct title insurance and other direct insurance carriers
• Equation 17. Noncovered employment for ASPEP-based industries
• Equation 18. Noncovered employment for QCEW-trend-based industries

Tables

• Table 1. Employment benchmarks and approximate coverage of CES employment and payrolls sample
• Table 2. Industry groupings for the CES quarterly sample implementation
• Table 3. Total private universe employment by size of UI
• Table 4. Total private CES sample employment by size of UI
• Table 5. Standard and relative standard errors of CES sample-based estimates for a 1-month change
• Table 6. Major industry sectors
• Table 7. Aggregate industry sectors
• Table 8. CES data types and other payroll variables
• Table 9. Sample required to produce basic estimates, shown in the order estimated
• Table 10. Strict edit tests
• Table 11. Nonstrict edit tests
• Table 12. Screening tests for all data types
• Table 13. Screening tests for specific data types
• Table 14. Summary of methods for computing industry estimates of employment, hours, and earnings
• Table 15. National small domain model industries
• Table 16. Net birth-death forecasts by supersector, April to December (in thousands)
• Table 17. Net birth-death forecast year of industry groupings for the CES quarterly sample rotation
• Table 18. Comparison of annual net birth-death to quarterly net birth-death forecasts for 2014
• Table 19. Net birth-death forecasts and over-the-month change in total nonfarm employment (in thousands)
• Table 20. Dummy files with calendar treatment
• Table 21. Indirectly seasonally adjusted CES series
• Table 22. CES total nonfarm benchmark revisions
• Table 23. Noncovered industries calculated using CBP data
• Table 24. Noncovered industries calculated using the ASPEP data
• Table 25. Noncovered industries calculated using RRB data
• Table 26. Noncovered industries calculated using the QCEW trend
• Table 27. CES sample redesign phase-in schedule

Figures

• Figure 1. Current Employment Statistics survey data collection methods by percent
• Figure 2. Total private Business Employment Dynamics series (not seasonally adjusted, in thousands)
• Figure 3. Input data file specifications
• Figure 4. Prior adjustment file format
• Figure 5. The PRINT argument in the X11 spec
• Figure 6. CES seasonal adjustment spec file

Last Modified Date: June 2, 2023