Benchmark Article 

BLS Establishment Estimates Revised to Incorporate March 2009 Benchmarks 

• Introduction• Summary of the benchmark revisions• Summary of research into possible sources of the 2009 benchmark revision• Revisions in the post-benchmark period• Why benchmarks differ from estimates• Benchmark revisions for other data types• Methods• Addition of all employee hours and earnings as official BLS series• Other changes to the CES published series • Availability of revised data• Small domain model• Seasonal adjustment procedure• Special model adjustments

Introduction Victoria Battista Victoria Battista is an economist in the Division of Current Employment Statistics, Office of Employment and UStatistics, Bureau of Labor Statistics. Telephone: (202) 691-6555; e-mail: CESInfo@bls.govWith the release of data for January 2010, the Bureau of Labor Statistics (BLS) introduced its annual revision of employment, hours, and earnings from the Current Employment Statistics (CES) monthly survey of nonfarm estayear, the CES survey realigns its sample-based estimates to incorporate universe counts of employment—a procebenchmarking. Comprehensive counts of employment, or benchmarks, are derived primarily from unemploymenreports that nearly all employers are required to file with State Workforce Agencies. Summary of the benchmark revisions 
The March 2009 benchmark level for total nonfarm employment is 131,175,000; this figure is 902,000 below the 

estimate for March 2009, an adjustment of -0.7 percent. Table 1 shows the total nonfarm percentage benchmark rten years. The twelve months ending with March 2009 experienced steep declines in employment rarely seen in the U.S. lain an over-the-year decrease in employment of almost 5.8 million, or -4.2 percent, as measured by the universe e

from the BLS Quarterly Census of Employment and Wages (QCEW). As of March 2009, this was the largest 12-

employment in the history of the CES total nonfarm series, which dates back to 1939. In percentage terms, the -4

the largest negative 12-month percent change since April 1958. While the latest benchmark revision to the CES temployment estimates represents the largest divergence between CES and QCEW in many years, CES employm84 percent of the nonfarm payroll decline. Table 2 shows the nonfarm employment benchmarks for March 2009, not seasonally adjusted, by industry. Near

had downward revisions, with the exception of government. The largest downward revision occurred in trade, trautilities with a revision of -300,000, or -1.2 percent. Within this sector, the revision is concentrated in discount derevised by -28,700 or -3.1 percent, all other general merchandise, revised by -24,700 or -7.7 percent, and departmdiscount revised by -24,200 or -4.6 percent. Construction was revised by -171,000, or -2.9 percent, while professional and business services was revised -137Within construction, the largest revision was in new single-family general contractors, which was revised downw15.3 percent. Manufacturing had a downward revision of 84,000, or 0.7 percent. The revision in manufacturing wspread across industries with 43,000 of the downward revision in durable goods (-0.6 percent) and 41,000 of the 

nondurable goods (-0.9 percent). Leisure and hospitality had a revision of -72,000, or -0.6 percent. Within this se

concentrated in full-service restaurants, which was revised down by 71,600, or 1.6 percent. Education and health services had a downward revision of 56,000, or 0.3 percent. The revision in this sector was general medical and surgical hospitals, with a downward revision of 42,800, or 1.0 percent. Both information and 

revised downward by 42,000. For information this was a -1.5 percent change, while for other services this was a Mining and logging revised down by 25,000, or 3.5 percent. In mining and logging, the largest revision was in sumining in the amount of -13,700, or -4.6 percent. Financial activities revised down by 4,000, or 0.1 percent. Only government had an upward revision of 31,000, or 0.1 percent. 
Summary of research into possible sources of the March 2009 benchmark revision 

The net difference between CES estimates and QCEW-based benchmark, observed as the CES benchmark revisisources and disaggregating it into components is complex. BLS examined potential error sources in both the CES 

QCEW-based employment benchmark. Both series are subject to nonresponse and imputation error, and reportinfor the CES estimates, sampling error and the business birth/death modeling errors are a factor. 

Profiling the divergence between CES and QCEW BLS began its search for causes by studying the profile of the divergence between CES and QCEW: 1) by industbenchmark year. Insights from these analyses follow. 1) CES benchmark revisions by industry The large downward revision in employment is widespread, and is seen in all super sectors but government, althovaries by industry. Typically, percent benchmark revisions at many industry levels are larger than at the total nonoffsetting. Revisions at the supersector level were not offsetting this year (with the exception of government), resthan-normal revision at the total nonfarm level. Percentage benchmark revisions by supersector

Industry Year

2007 2008 

Total nonfarm ‐0.2 ‐0.1 Mining and logging (1) 0.4  Construction 0.1 0.7 Manufacturing ‐1  ‐0.1  Trade, transportation, utilities 0.5 0.2 Information ‐1.8 0.3  Financial activities ‐1.3 ‐0.3 Professional and business services 0.2  ‐0.4  
Education and health services ‐0.2 ‐0.1 

Leisure and hospitality ‐0.8  ‐1.1  Other services 0.3 0.2 Government ‐0.2 0.2 (1)Less than 0.05 percent.The largest revisions are somewhat correlated with the over-the-year declines in the QCEW employment, as showFor example, trade, transportation, and utilities has the largest downward benchmark revision (-300,000), and alsover-the-year decline in employment (-1,457,000). Likewise, professional and business services has the third largbenchmark revision (-137,000) and the third largest over-the-year decline in employment (-1,179,000). This suggis not concentrated in one particular industry or groups of industries. Benchmark revision and over‐the‐month change in population employment, level and percen

2009 by supersector (in thousands)

Industry 2009

Benchmark Estimate Revision Over‐the‐year Change in Po

Level Percent Level Perce

Total nonfarm ‐902  ‐0.7  ‐5769  ‐4.2Mining and logging ‐25 ‐3.5 ‐27 ‐3.6Construction ‐171  ‐2.9  ‐1097  ‐15.Manufacturing ‐84 ‐.7 ‐1447 ‐10.

Trade, transportation, utilities ‐300  ‐1.2  ‐1457  ‐5.5

Information ‐42 ‐1.5 ‐156 ‐5.2Financial activities ‐4  ‐.1  ‐357  ‐4.4Professional and business services ‐137 ‐.8 ‐1179 ‐6.6Education and health services ‐56  ‐.3 397 2.1Leisure and hospitality ‐72 ‐.6 ‐408 ‐3.1Other services ‐42  ‐.8  ‐158  ‐2.9Government 31 .1 120 0.52) CES-QCEW tracking over the benchmark year 
CES estimates and QCEW employment counts generally track within a relatively predictable range from quarter

have somewhat different seasonal patterns, and therefore it is more informative to compare them on an over-the-when examining trend differences. From the last benchmark month, March 2008, through December 2008, CES nonfarm employment were tracking within a normal 0.3 percent range. Substantial divergence between the two sthe first quarter of 2009, as illustrated by the table below. Monthly over‐the‐year change for total nonfarm employment (CES scope) from April 2008 to 

seasonally adjusted (in thousands)

Month Apr‐08 May‐08 Jun‐08 Jul‐08 Aug‐08 Sep‐08 Oct‐08 Nov‐08 Dec‐08 Jan‐0QCEW 449 180 ‐373 ‐438 ‐676 ‐1035 ‐1440 ‐2432 ‐3146 ‐413CES* 208 ‐87 ‐328 ‐329 ‐486 ‐927 ‐1294 ‐2208 ‐2958 ‐353Difference 241 267 ‐45 ‐109 ‐190 ‐108 ‐146 ‐224 ‐188 ‐595 * CES estimates are pre‐March 2009 benchmark revision.Business Employment Dynamics (BED) To gain insight into the nature of the first quarter 2009 divergences, BLS examined its Business Employment DyThe BED series disaggregate the QCEW employment data into gross job gains from business expansions and oplosses from business contractions and closings. These series reveal which of the underlying flows are driving the The BED series indicated a decrease in gross job gains rather than an increase in gross job losses drove the net em

the first quarter of 2009. The BED series illustrate the first quarter decline in gross job gains. In addition, there is overall openings in first quarter 2009. Nevertheless, the continuous establishments – not the opening and closing driving the overall net change in employment. 

Three‐month private sector gross job gains and losses, seasonally adjusted (BED table)

Category Levels (in thousands) Rate

3 months ended Mar‐

08

Jun‐

08

Sep‐

08

Dec‐

08

Mar‐

09

Mar‐

08

Jun‐

08

Gross job gains Total 7,167 7,296 6,884 6,738 5,746 6.3 6.5 

At expanding

establishments

5,781 5,869 5,520 5,363 4,603 5.1 5.2 

At opening

establishments

1,386 1,427 1,364 1,375 1,143 1.2 1.3 

Gross job losses Total 7,447 7,832 7,851 8,539 8,486 6.5 6.9 

At contracting

establishments

6,090 6,334 6,461 7,038 7,045 5.3 5.6 

At closing establishments 1,357 1,498 1,390 1,501 1,441 1.2 1.3 

Net employment

change(1)

‐280 ‐536 ‐967 ‐1,801 ‐2,740 ‐0.2 ‐0.4 

(1)The net employment change is the difference between total gross job gains and total gross job losses. For more informa

https://www.bls.gov/bdm/.

Possible causes of the CES-QCEW difference Numerous statistical error sources exist in both the CES and QCEW and are potential contributors to the larger th

benchmark revision as noted above. BLS research examined both: 

• Whether the change in the QCEW from March 2008 to March 2009 was impacted by noneconomic data reporting 

and 

• If the QCEW March 2008 to March 2009 change is taken as correct, why the CES estimation process failed to mea

historical ranges. 

A description of key aspects of the research follows. QCEW imputations 
Every quarter the QCEW program imputes employment for UI accounts where reports were not received or were 

contained only wage information and no employment data. Typically about 10 percent of the worksites and 5 pertotal employment is imputed. BLS reviewed key aspects of the QCEW imputation process to see if it could be a f

divergence between CES and QCEW over-the-year employment trends. The review included: the functioning of and edit checks, instances of long term imputations (more than two quarters), and the number and trend of imputhistorical norms. After extensive examination, no problems or changes to the QCEW imputations were found tha

the large CES benchmark revision. Possible change in payroll processing firm reporting for the QCEW A substantial percentage of firms do not file their own Quarterly Contributions Report (QCR) with State UI agenpayroll processing firms (PPFs) to do it for them. The PPFs also derive the employment counts that are reported therefore become the basis for the QCEW employment series. Using the QCEW microdata file, BLS examined a 

tabulations to see whether there might be obvious issues with PPF reporting. The review was somewhat limited bfirm records do not carry a code that indicates whether the report is filed by a PPF. The analysis for the March 20

not reveal any issues that could be a factor in the larger than normal benchmark revision. In addition to the QCEW review work described above, BLS examined major aspects of the CES survey process methods. Results are described below. CES nonresponse A review of collection rates for the January 2007-March 2009 period indicates rates have been trending higher ovyears. Final collection rates averaged 91 percent in 2008, up from 87 percent the prior year, and averaged 93 percthree months of 2009. Thus there is no indication that the large benchmark revision was caused by problems with CES birth/death modeling CES uses a two-step method to estimate net business birth/death employment. Step 1 excludes employment lossedeaths from sample-based estimation in order to offset the missing employment gains from business births. This the sample-based estimate procedure by simply not reflecting sample units going out of business, but imputing thas the responding firms in the sample. Step 2 is an ARIMA-based model intended to estimate the residual net birnot accounted for by Step 1. Only the Step 2 error is directly measurable. Error from this component is measured 
actual residual from March 2008-09 once it becomes available, with the model-based estimate. As the table below 

net birth/death residual for April 2008 to March 2009 had a significant contribution to the large benchmark revis

birth/death residual was approximately 779,000 below the forecasted amount used in the CES monthly estimates 

These errors started to grow in the fourth quarter of 2008 and got significantly larger in the first quarter of 2009. 

Differences between forecasted and actual net birth/death from April 2008 to March 2009 

Benchmark 2009 Apr‐

08

May‐

08

Jun‐

08

Jul‐

08

Aug‐

08

Sep‐

08

Oct‐

08

Nov‐

08

Dec‐

08

Jan‐

09

Actual Net Birth/Death 46 184 108  ‐28 109 5 25  ‐48  ‐9  ‐570 Forecast NetBirth/Death

174 177 164 24 93 19 95 17 59 ‐355 

Difference ‐128 7  ‐56  ‐52 16  ‐14  ‐70  ‐65  ‐68  ‐215  Cumulative Difference ‐128 ‐121 ‐177 ‐229 ‐213 ‐227 ‐297 ‐362 ‐430 ‐645 Until this year, the contribution of the actual net birth/death component had been relatively stable over time, regacycle. As the following chart illustrates, the pronounced recession led to a breakdown in that stability from MarcThe graph displays the over-the-year change in total nonfarm employment, the actual net birth/death residual, anbirth/death residual, and also includes a trend line for the over-the-year employment change. In the graph, we typ

relatively stability of the actual residual, as represented by the red (or middle) bars. 

The graph below underscores the historical stability of the net birth/death residual by comparing the deviation of 

average level to the deviation of over-the-year total nonfarm employment change from its average level. During the annual net birth/death residual component was no more than 20 percent below its average, whereas the over-temployment change was 351 percent below its average during the corresponding year (March 2001 to 2002). At expansionary period (March 2005 to 2006), the over-the-year employment change was 253 percent above averagbirth/death residual was only 48 percent above average. Mild correlation exists between the actual birth/death resthe-year change in total nonfarm employment; yet its strength is inconsistent. 

This relative stability in the net birth/death component has allowed the model to perform reasonably well over th

the past year of unprecedented steep job loss, the adjustment did not work as effectively as it traditionally has wowas not sensitive to the unusually large economic shock that was experienced in the last quarter of 2008 and the Conclusions regarding the March 2009 benchmark BLS has researched potential sources identified for the large CES benchmark revision to the extent possible sincmagnitude of the revision was announced in October 2009. Typically it is an accumulation of differences from se

sources - sampling error, reporting error, nonresponse, net birth/death - that compose the overall benchmark revimajority of the 2009 benchmark revision is linked to error in the CES model-based estimation process for birth eResearch improvements to CES net birth/death modeling BLS continues to research possible improvements to the CES net birth/death model in an effort to better understa

limitations and to improve its sensitivity to economic shocks. Nevertheless, the net birth/death model will likely component of the CES methodology. Because there is no current information available on business births, model

historical information will continue to be used. The basic modeling method currently used is simple and has been industry estimates were generated using the CES redesign methodology in 2000. However, improvements to the 

may be possible in several areas. For example, CES is investigating the benefit of increasing the frequency with 

run. As the model is dependent upon QCEW data (available on a quarterly basis), CES may opt to update the mo

to incorporate more recent information. Also under investigation is the option of adding independent regression v

to improve its responsiveness to business cycle changes; in particular, sample links derived from CES data are bepotential regressors. Several additional years of real time experience with the model are now available and can be 

additional evaluation and research testing. Revisions in the post-benchmark period Post-benchmark period estimates from April 2009 to December 2009 were calculated for each month based on nAlso, beginning in April, model-based estimates for the net of birth/death employment were revised to incorpora

the most recent year of universe employment counts. Text table A shows the net birth/death model figures for the 

the post-benchmark period. From April 2009 to December 2009, the cumulative net birth/death model added 585990,000 in the previously published April to December estimates. 
2009

Mining&

logging

Construction

Manufacturing

Trade,

transportation, & utilities

Information

Financial

activities

Professional&

business

services

Education&

health services

Leisure&

April ‐1 22 ‐14 8 ‐4 ‐5 35 9 73 May 1 41 4 26 3 8 11 7 78 June 1 24 3 15 0 5 10 ‐11 80 

July 2  ‐8  ‐14  ‐9  ‐3  ‐9  ‐4 2 38

August 1 12 5 17 5 4 18 10 23 

September 1 7 1 15 0 3 0 13  ‐4

October 2 ‐2 ‐6 16 2 6 41 31 ‐39 

November 0  ‐17 1 3 1  ‐1 2 8  ‐19

December 0 ‐21 1 12 3 12 2 6 7 

Cumulative total 7 58  ‐19 103 7 23 115 75 20

Revisions to November and December also reflect incorporation of the annual CES sample update and the routinadditional sample units not available for the respective months' preliminary estimates. Why benchmarks differ from estimates A benchmark revision is the difference between the benchmark employment level for a given March and its correbased estimate. The overall accuracy of the establishment survey is usually gauged by the size of this difference. revision often is regarded as a proxy for total survey error, but this does not take into account error in the universemployment counts obtained from quarterly unemployment insurance tax forms are administrative data that refle

keeping practices and differing State laws and procedures. The benchmark revision can be more precisely interprbetween two independently derived employment counts, each subject to its own error sources. Like all sample surveys, the establishment survey is susceptible to two sources of error: sampling error and nonsSampling error is present any time a sample is used to make inferences about a population. The magnitude of the 

variance, relates directly to sample size and the percentage of the universe covered by that sample. The CES mon
slightly under one-third of the universe, exceptionally high by usual sampling standards. This coverage insures a 

at the total nonfarm employment level. Both the universe counts and the establishment survey estimates are subject to nonsampling errors common to al

response, and processing errors. The error structures for both the CES monthly survey and the UI universe are coprograms generally produce consistent total employment figures, each validating the other. Over the last decade, revisions at the total nonfarm level have averaged 0.3 percent, with an absolute range of 0.1 percent to 0.7 percen

Benchmark revision effects for other data types The routine benchmarking process results in revisions to the series for production and nonsupervisory employees 

employees. There are no benchmark employment levels for these series; they are revised by preserving ratios of 

particular data type to all employees employment prior to benchmarking, and then applying these ratios to the revfigures. These figures are calculated at the basic cell level and then aggregated to produce the summary estimates

Average weekly hours and average hourly earnings are not benchmarked; they are estimated solely from reports 

respondents at the basic estimating cell level. The aggregate industry level of the hours and earnings series are derived as a weighted average. The production o

employment estimates for the basic cells are used as weights for the hours and earnings estimates for broader ind

Adjustments of the all employee estimates to new benchmarks may alter the weights, which, in turn, may change 

hours and earnings of production or nonsupervisory workers at higher levels of aggregation. Generally, new employment benchmarks have little effect on hours and earnings estimates for major groupings. Tand earnings estimates of a broader group, employment revisions have to be relatively large and must affect indu

or earnings averages that are substantially different from those of other industries in their group. Table 4 gives in

levels of specific hours and earnings series resulting from the March 2009 benchmark. At the total private level, 

in average weekly hours from the previously published level, while average hourly earnings increased from the plevel by 3 cents. 

Methods 
Benchmark adjustment procedure. Establishment survey benchmarking is done on an annual basis to a populatio

from the administrative file of employees covered by unemployment insurance (UI). The time required to complprocess--from the full collection of the UI population data to publication of the revised industry estimates--is abobenchmark adjustment procedure replaces the March sample-based employment estimates with UI-based populaThe benchmark therefore determines the final employment levels, while sample movements capture month-to-mBenchmarks are established for each basic estimating cell and are aggregated to develop published levels. On a nbasis, the sample-based estimates for the year preceding and the year following the benchmark month also are thEmployment estimates for the months between the most recent March benchmark and the previous year's benchmusing a "wedge-back" procedure. In this process, the difference between the benchmark level and the previously estimate for each estimating cell is computed. This difference, or error, is linearly distributed across the 11 month

subsequent to the previous benchmark; eleven-twelfths of the March difference is added to February estimates, teestimates, and so on, ending with the previous April estimates, which receive one-twelfth of the March differencprocedure assumes that the total estimation error accumulated at a steady rate since the last benchmark. Applying over-the-month sample changes to the revised March level yields revised estimates for the months following the 

New net birth/death model estimates also are calculated and applied during post-benchmark estimation, and new from the annual update. Benchmark source material. The principal source of benchmark data for private industries is the Quarterly Censu

Wages (QCEW). These employment data are provided to State Employment Security Agencies by employers colaws. BLS uses several other sources to establish benchmarks for the remaining industries partially covered or exUI coverage, accounting for nearly 3 percent of the nonfarm employment total. Data on employees covered under Social Security laws, published by the U.S. Census Bureau in County Business 

augment UI data for industries not fully covered by the UI scope, such as nonoffice insurance sales workers, chil

religious organizations, and private schools and hospitals. Benchmarks for State and local government hospitals institutions are based on the Annual Census of Governments conducted by the Census Bureau. Benchmark data favailable only on a lagged basis. Extrapolation to a current level is accomplished by applying the employment tre

covered part of the population in these industries to the noncovered part. Universe data for interstate railroads are Railroad Retirement Board. Business birth and death estimation. Regular updating of the CES sample frame with information from the UI unkeep the CES survey current with respect to employment from business births and business deaths. The timeliest 
available, however, always will be a minimum of 9 months out of date. The CES survey thus cannot rely on regu

maintenance alone to provide estimates for business birth and death employment contributions. BLS has researchbased and model-based approaches to measuring birth units that have not yet appeared on the UI universe frame. 

demonstrated that sampling for births was not feasible in the very short CES production timeframes, the Bureau based approach for this component. Earlier research indicated that while both the business birth and death portions of total employment are generally 

contribution is relatively small and stable. To account for this net birth/death portion of total employment, BLS iestimation procedure with two components. The first component excludes employment losses from business deatbased estimation in order to offset the missing employment gains from business births. This is incorporated into relative estimate procedure by simply not reflecting sample units going out of business, but imputing to them the responding firms in the sample. The second component is an ARIMA time series model designed to estimate the birth/death employment not accounted for by the imputation. The historical time series used to create and test the derived from the UI universe micro level database, and reflects the actual residual net of births and deaths over th

net birth/death model component figures are unique to each month and include negative adjustments in some mo

these figures exhibit a seasonal pattern similar to the seasonal patterns of the continuing businesses. 

Addition of all employee hours and earnings as official BLS series Concurrent with the release of January 2010 data, the CES program began publishing all employee hours and earseries. These series were developed to measure the average hourly earnings and average weekly hours of all nonf

employees and the average overtime hours of all manufacturing employees. All employee hours and earnings weexperimental series in April 2007, and included National level estimates at a total private sector level and limited month, new monthly estimates for the experimental estimates were released on the same date as the Employment release (typically the first Friday of the month). The experimental series were not seasonally adjusted. Now that tseries, all employee hours and earnings series will be published at the same level of industry detail as production earnings series, and will be published on both a not seasonally adjusted and a seasonally adjusted basis. Historically, the CES program has published average hours and earnings series for production workers in the gooindustries and for nonsupervisory workers in the service-providing industries. These workers account for about 8private nonfarm employment. The all employee hours and earnings series are more comprehensive in coverage, cof all paid employees in the private sector, thereby providing improved information for analyzing economic trendconstructing other major economic indicators, including nonfarm productivity and personal income. 
In order to publish all employee average weekly hours, average hourly earnings, and average weekly earnings, B

all employees total payroll and all employees total hours from survey respondents in late 2005. The definitions oparallel the definitions used for the production worker payroll and hours data; the only difference is that they covrather than just production or nonsupervisory workers (for a sample collection form, see www.bls.gov/ces/idcf/forme_mp.pdf).Additionally, the same estimation formulas currently used for the publishe

and nonsupervisory workers are used for the all employee hours and earnings series. The new series start in Marc

For more information on the CES All Employee Hours and Earnings series, please see www.bls.gov/ces/cesaeppOther changes to the CES published series All CES series are evaluated annually for sample size, coverage, and response rates. The series changes detailed changes for the 2009 benchmark webpage (www.bls.gov/ces/cesnewseries2009.htm) result from a re-evaluation 

universe coverage for NAICS industries. Some small industries no longer have sufficient sample to be estimated separately and have been combined with other similar industries for estimation and publication purposes. Most o

deleted series are in the manufacturing sector where employment has been declining over a number of years. His

series with changed scope were reconstructed to provide consistent time series. 

Availability of revised data LABSTAT, the BLS public database on the Internet, contains all historical employment, hours, and earnings data 

this benchmark, including both unadjusted and seasonally adjusted data. The data can be accessed at https://www

Current Employment Statistics homepage. Small domain model The CES Small Domain Model (SDM) is used for industries where the sample alone is insufficient for reliable esSDM is a Weighted Least Squares model with two employment inputs: (1) an estimate based on available CES sand (2) an ARIMA projection based on 10 years of historical QCEW data. Further background on the SDM is prtechnical notes. 
There are six industries estimated by using the SDM. These industries are lessors of nonfinancial intangible asset

medical insurance carriers, tax preparation services, other technical consulting services, remediation services, anvacation camps. Seasonal adjustment procedure BLS uses X-12 ARIMA software developed by the U.S. Census Bureau to seasonally adjust national employmenseries derived from the CES program. Individual series are seasonally adjusted using either a multiplicative or an (Exhibit 1), and seasonal adjustment factors are directly applied to the component levels. For employment, indivi

levels are seasonally adjusted, and higher level aggregates are formed by summing these components. Seasonally hours and earnings are obtained by taking weighted averages of the seasonally adjusted data for the component s

Special model adjustments Variable survey intervals. Beginning with the release of the 1995 benchmark, BLS refined the seasonal adjustmecontrol for survey interval variations, sometimes referred to as the 4- versus 5-week effect. Although the CES suconsistent concept — the pay period including the 12th of each month — inconsistencies arise because there are 

sometimes 5 weeks between the week including the 12th in a given pair of months. In highly seasonal industries, 

be an important determinant of the magnitude of seasonal hires or layoffs that have occurred at the time the survecomplicating seasonal adjustment. Standard seasonal adjustment methodology relies heavily on the experience of the most recent 3 years to determi

seasonal change in employment for each month of the current year. Prior to the implementation of the adjustmen

interval variations, the procedure did not distinguish between 4- and 5-week survey intervals, and the accuracy oexpectation depended in large measure on how well the current year’s survey interval corresponded with those o

All else the same, the greatest potential for distortion occurred when the current month being estimated had a 5-w

years preceding it were all 4-week intervals, or conversely when the current month had a 4-week interval but the were all 5-week intervals. BLS adopted REGARIMA (regression with auto-correlated errors) modeling to identify the estimated size and sicalendar effect for each published series. REGARIMA combines standard regression analysis, which measures cor more variables, with ARIMA modeling, which describes and predicts the behavior of data series based on its omany economic time series, including nonfarm payroll employment, observations are auto-correlated over time; 
value is significantly dependent on the observations that precede it. These series, therefore, usually can be succes

ARIMA models. If auto-correlated time series are modeled through regression analysis alone, the measured relatvariables of interest may be distorted due to the influence of the auto-correlation. Thus, the REGARIMA techniq

measuring relationships among variables of interest in series that exhibit auto-correlation, such as nonfarm payroIn this application, the correlations of interest are those between employment levels in individual calendar monththe survey intervals for those months. The REGARIMA models evaluate the variation in employment levels attrisurvey interval variables, one specified for each month, except March. March is excluded because there are almobetween the February and March surveys. Models for individual basic series are fit with the most recent 10 years 

standard time span used for CES seasonal adjustment. The REGARIMA procedure yields regression coefficients for each of the 11 months specified in the model. The

provide estimates of the strength of the relationship between employment levels and the number of weeks betwee

modeled months. The X-12 ARIMA software also produces diagnostic statistics that permit the assessment of the 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 varialevels associated with the length of the survey interval, these coefficients are used to adjust the CES data to remoeffect. These "filtered" series then are seasonally adjusted using the standard X-12 ARIMA software. 

For a few series, REGARIMA models do not fit well; these series are seasonally adjusted with the X-12 software 

interval effect adjustment. There are several additional special effects modeled through the REGARIMA process

below. Construction series. Beginning with the 1996 benchmark revision, BLS utilized special treatment to adjust constseries. In the application of the interval effect modeling process to the construction series, there initially was diffidentifying and measuring the effect because of the strong influence of variable weather patterns on employment industry. Further research allowed BLS to incorporate interval effect modeling for the construction industry by dconstruction series into its finer industry and geographic estimating cells and tightening outlier designation parammore precise identification of weather-related outliers that had masked the interval effect and clouded the seasonin general. With these outliers removed, interval effect modeling became feasible. The result is a seasonally adjuconstruction that is improved because it is controlled for two potential distortions: unusual weather events and th

effect. 
Floating holidays. BLS is continuing the practice of making special adjustments for average weekly hours and av

overtime series to account for the presence or absence of religious holidays in the April survey reference period a

Labor Day in the September reference period, back to the start date of each series. 

Local government series. A special adjustment also is made in November each year to account for variations in e

presence or absence of poll workers in the local government, excluding educational services series. Refinements in hours and earnings seasonal adjustment. With the release of the 1997 benchmark, BLS implementhe seasonal adjustment process for the hours and earnings series to correct for distortions related to the method ovarying length of payroll periods across months. There is a significant correlation between over-the-month changaverage weekly hours (AWH) and the average hourly earnings (AHE) series and the number of weekdays in a mnoneconomic fluctuations in these two series. Both AWH and AHE show more growth in "short" months (20 or "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 insample respondents with semi-monthly or monthly pay periods to a weekly equivalent. The response error comes respondents reporting a fixed number of total hours for workers regardless of the length of the reference month, wconversion process assumes that the hours reporting will be variable. A constant level of hours reporting most likemployees are salaried rather than paid by the hour, as employers are less likely to keep actual detailed hours recemployees. 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 month), which dictates that different conversion factors be applied to payroll and hours. The CES processing sysconversion factor for both fields, resulting in peaks in the AHE series in short months and reversals in long montREGARIMA modeling is used to identify, measure, and remove the length-of-pay-period effect for seasonally adweekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaimovements in all the service-providing industries except utilities. For AHE, the length-of-pay-period variable is wholesale trade, retail trade, information, financial activities, professional and business services, and other servicin the service-providing industries except utilities have been adjusted from January 1990 forward. The AHE serieretail trade, information, financial activities, professional and business services, and other services have been adj

1990 forward as well. For this reason, calculations of over-the-year change in the establishment hours and earninseasonally adjusted data. 
The series to which the length-of-pay-period adjustment is applied are not subject to the 4- versus 5-week adjustm

cannot support the number of variables that would be required in the regression equation to make both adjustmen

Seasonal adjustment of new all employee hours and earning series Although the seasonal adjustment process used by CES has proven effective for many years, the limited history oemployees hours and earnings series estimates complicates the use of existing adjustment techniques. CES has lehistory for the new hours and earnings series; in contrast, CES uses 10 years of data when seasonally adjusting aThe X-12 ARIMA software requires at least 3 years of data to perform seasonal adjustment; the new series meet 

consisting of 46 months of data at initial publication (March 2006 through January 2010). Nevertheless, there are and concerns associated with seasonally adjusting the new series with such a limited amount of input data. For ex

history is not sufficient to utilize the automatic model selection feature of X-12, which is typically used to select 

for other data types. The CES program typically uses the automatic selection feature once a year, during the annuprocessing, and keep the automatically selected model constant throughout the next year. Secondly, there is a risk 

component affecting the seasonal component. But the most significant limitation is that there is insufficient histo

the existing calendar effects treatment method described above. In particular, at least 5 years of data are required due to the calendar effects (4 vs. 5 week, 10 vs. 11 day). Therefore, an alternative approach has been implemente

concerns until sufficient history is available to use the current procedure. 

ARIMA Model Selection Unable to utilize X-12 ARIMA's automatic model selection due to the short length of the new series, CES electe

1)(0 1 1) model for these series in the interim. This model diminishes noise in time series by using an exponentia

average of past values. As hours and earnings data tend to exhibit fluctuations around slowly-varying means, use 

smoothing model is a sensible choice. Experimental results confirmed that this model is highly effective and will 

employees hours and earnings series reach the necessary length to allow the integration of automatic model selecChoice of Seasonal Filter By default, X-12 ARIMA uses a 3x3 moving average to calculate the initial seasonal factors in each iteration, anaverage to calculate the final seasonal averages. However, the shortness of the new series imposes a technical limonly available option is to utilize a stable seasonal filter. A stable seasonal filter computes a single seasonal facto
first calculating the average of all the values for each month, which reduces the chance of the irregular componen

seasonal component -- a concern with short series. Treatment of Calendar Effects The existing seasonal adjustment method for production employees' hours and earnings relies on the REGARIMdescribed earlier that implicitly treats each month; the alternate technique treats only the months for which calendata. Affected months are manually treated as outliers when defining parameters for X-12 ARIMA. This treatmecalendar effects, but spikes from the 10 versus 11 day effect are still present in the adjusted series. Based on manually-designated outliers, X-12 ARIMA produces a prior adjusted series by imputing for outlier ob

the trend of the non-outlier observations. By default, X-12 ARIMA relies upon this prior-adjusted series when deseasonality and estimating seasonal factors for time series; however, it then applies those factors to the original ti

the prior-adjusted series. Under normal circumstances, application of seasonal factors to the original time series ioriginal time series consists of the series true values. In the context of CES' efforts to eliminate calendar effects from these estimates, it is undesirable to apply the sea

original time series. Because the calendar effects are part of the original series - and because we cannot remove t

standard treatment procedures - it is therefore necessary that we apply the seasonal factors to the prior-adjusted sAn additional problem is that the series' average for seasonally adjusted data is not preserved relative to the unadbecause the adjustment is applied outside of X-12 ARIMA. A common goal when performing seasonal adjustmeseries’ seasonality without altering series’ levels. This goal is accomplished by smoothing seasonal fluctuations aspan such that some observations are elevated while others are diminished depending on the direction of the seas

net change is offsetting. Therefore, application of seasonal factors to prior-adjusted series - series which often ex

than their unadjusted analogues - may result in seasonally adjusted series with lower means than those of corresp

series. Such outcomes are contradictory to established CES practices in that CES takes series’ true levels to be deunadjusted data. Therefore, a second step is required to preserve the series' average. A correction factor forces the average of each series adjusted for calendar effects to the average of the correspon

series. The factor is defined as: 
Correction Factor = 

∑∑

==

n

t

t

n

t

t

XX

11

ˆ

Xt = Unadjusted value of series at month tXt

ˆ

= Adjusted, calendar-effect-treated value of series at month t

The factor is applied multiplicatively to all observations in the seasonally-adjusted series. Testing has shown that effectively reconciles level shifts introduced by the modified calendar effects treatment procedure, thereby maintbetween series' unadjusted and seasonally-adjusted levels. See Exhibit 1 for series that have the calendar effects modeling described above. 

Table 1. Percent differences between nonfarm employment benchmarks and estimates by industry supersector, March 

Industry 2000 2001 2002 2003 2004 2005 2006 2007 2

Total 0.4 ‐ 0.1 ‐ 0.2 ‐ 0.1 0.2 ‐ 0.1 0.6 ‐0.2 Total private .3 ‐ .2 ‐ .4 ‐ .2 .2 ‐ .2 .7 ‐.2 Mining and logging .9 .7 ‐.3 1.2 (2) Construction ‐.8 .6 .5 2.6 .1 Manufacturing ‐1.1 ‐.4 ‐.3 ‐.1 ‐1.0 Trade, transportation, and utilities (2) .2 .3 .6 .5 Information ‐2.6 ‐1.0 ‐2.1 ‐.5 ‐1.8 Financial activities .2 .1 ‐.8 .4 ‐1.3 
Professional and business services ‐.7 ‐.2 ‐.4 1.3 .2 

Education and health services .3 .2 (2) .5 ‐.2 Leisure and hospitality .5 1.2 .4 .3 ‐.8 Other services 1.4 .5 ‐1.3 .5 .3 Government .6 .3 1.0 .3 .1 (2) (2) ‐.2 (1) Differences are based on comparisons of final published March estimates and benchmark levels, as originally (2) Less than 0.05 percent. 

Table 2. Nonfarm employment benchmarks by industry for March 2009 (in thousands)

Industry Benchmark Estimate Difference

Amount Percent

Total nonfarm 131,175 132,077 ‐902 ‐0.7Total private 108,215 109,148 ‐933 ‐0.9Goods-producing 18,776 19,056 ‐280 ‐1.5Mining and logging 714 739 ‐25 ‐3.5Logging 48 49 ‐1 ‐2.1Mining 666 689 ‐23 ‐3.5Oil and gas extraction 161 165 ‐4 ‐2.5Mining, except oil and gas 208 214 ‐6 ‐2.9Coal mining 85 83 2 2.4Support activities for mining 297 311 ‐14 ‐4.7Construction 5,950 6,121 ‐171 ‐2.9Construction of buildings 1,364 1,421 ‐57 ‐4.2Heavy and civil engineering construction 808 827 ‐19 ‐2.4Specialty trade contractors 3,778 3,874 ‐96 ‐2.5Manufacturing 12,112 12,196 ‐84 ‐0.7Durable goods 7,532 7,575 ‐43 ‐0.6
Wood products 365 377 ‐12 ‐3.3

Nonmetallic mineral products 394 404 ‐10 ‐2.5Primary metals 381 386 ‐5 ‐1.3Fabricated metal products 1,360 1,363 ‐3 ‐0.2Machinery 1,075 1,069 6 0.6Computer and electronic products 1,173 1,185 ‐12 ‐1Computer and peripheral equipment 175 173 2 1.1Communications equipment 123 128 ‐5 ‐4.1Semiconductors and electronic components 394 396 ‐2 ‐0.5Electronic instruments 429 431 ‐2 ‐0.5Electrical equipment and appliances 386 388 ‐2 ‐0.5Transportation equipment 1,407 1,403 4 0.3Furniture and related products 400 405 ‐5 ‐1.3Miscellaneous manufacturing 591 597 ‐6 ‐1Nondurable goods 4,580 4,621 ‐41 ‐0.9Food manufacturing 1,423 1,435 ‐12 ‐0.8Beverages and tobacco products 184 186 ‐2 ‐1.1Textile mills 128 127 1 0.8Textile product mills 128 129 ‐1 ‐0.8Apparel 174 172 2 1.1Leather and allied products 30 32 ‐2 ‐6.7Paper and paper products 412 415 ‐3 ‐0.7Printing and related support activities 539 539 0 (1)Petroleum and coal products 113 112 1 0.9Chemicals 812 821 ‐9 ‐1.1Plastics and rubber products 638 654 ‐16 ‐2.5Service-providing 112,399 113,021 ‐622 ‐0.6Private service-providing 89,439 90,092 ‐653 ‐0.7Trade, transportation, and utilities 24,873 25,173 ‐300 ‐1.2Wholesale trade 5,638 5,706 ‐68 ‐1.2
Electronic markets and agents and brokers 814 837 ‐23 ‐2.8

Retail trade 14,405 14,640 ‐235 ‐1.6Motor vehicle and parts dealers 1,641 1,684 ‐43 ‐2.6Automobile dealers 1,029 1,059 ‐30 ‐2.9Furniture and home furnishings stores 452 490 ‐38 ‐8.4Electronics and appliance stores 485 514 ‐29 ‐6Building material and garden supply stores 1,152 1,169 ‐17 ‐1.5Food and beverage stores 2,814 2,802 12 0.4Health and personal care stores 982 980 2 0.2Gasoline stations 817 821 ‐4 ‐0.5Clothing and clothing accessories stores 1,327 1,379 ‐52 ‐3.9Sporting goods, hobby, book, and music stores 605 591 14 2.3General merchandise stores 2,945 3,014 ‐69 ‐2.3Department stores 1,446 1,499 ‐53 ‐3.7Miscellaneous store retailers 771 788 ‐17 ‐2.2Nonstore retailers 415 410 5 1.2Transportation and warehousing 4,267 4,258

9 0.2

Air transportation 466 472 ‐6 ‐1.3Rail transportation 224 219 5 2.2Water transportation 63 57

6 9.5

Truck transportation 1,268 1,275 ‐7 ‐0.6Transit and ground passenger transportation 436 420 16 3.7Pipeline transportation 42 43 ‐1 ‐2.4Scenic and sightseeing transportation 21 21

0 (1)

Support activities for transportation 556 550

6 1.1

Couriers and messengers 547 555 ‐8 ‐1.5Warehousing and storage 644 647 ‐3 ‐0.5Utilities 562 569 ‐7 ‐1.2Information 2,860 2,902 ‐42 ‐1.5Publishing industries, except Internet 819 826 ‐7 ‐0.9
Motion picture and sound recording industries 360 393 ‐33 ‐9.2

Broadcasting, except Internet 307 298

9 2.9

Telecommunications 990 997 ‐7 ‐0.7Data processing, hosting and related services 251 255 ‐4 ‐1.6Other information services 134 134

0 (1)

Financial activities 7,814 7,818 ‐4 ‐0.1Finance and insurance 5,826 5,827 ‐1 (1)Monetary authorities - central bank 21 21

0 (1)

Credit intermediation and related activities 2,624 2,635 ‐11 ‐0.4Depository credit intermediation 1,766 1,780 ‐14 ‐0.8Commercial banking 1,324 1,332 ‐8 ‐0.6Securities, commodity contracts, investments 826 807 19 2.3Insurance carriers and related activities 2,265 2,277 ‐12 ‐0.5Funds, trusts, and other financial vehicles 89 88 1 1.1Real estate and rental and leasing 1,988 1,990 ‐2 ‐0.1Real estate 1,412 1,399 13 0.9Rental and leasing services 550 563 ‐13 ‐2.4Lessors of nonfinancial intangible assets 27 28 ‐1 ‐3.7Professional and business services 16,554 16,691 ‐137 ‐0.8Professional and technical services 7,652 7,749 ‐97 ‐1.3Legal services 1,131 1,139 ‐8 ‐0.7Accounting and bookkeeping services 1,041 1,038 3 0.3Architectural and engineering services 1,339 1,356 ‐17 ‐1.3Computer systems design and related services 1,417 1,452 ‐35 ‐2.5Management and technical consulting services 984 1,006 ‐22 ‐2.2Management of companies and enterprises 1,882 1,850 32 1.7Administrative and waste services 7,021 7,092 ‐71 ‐1Administrative and support services 6,678 6,739 ‐61 ‐0.9Employment services 2,412 2,448 ‐36 ‐1.5Temporary help services 1,754 1,736 18 1
Business support services 833 805 28 3.4

Services to buildings and dwellings 1,634 1,657 ‐23 ‐1.4Waste management and remediation services 342 353 ‐11 ‐3.2Education and health services 19,230 19,286 ‐56 ‐0.3Educational services 3,233 3,223 10 0.3Health care and social assistance 15,998 16,063 ‐65 ‐0.4Ambulatory health care services 5,718 5,763 ‐45 ‐0.8Offices of physicians 2,260 2,302 ‐42 ‐1.9Outpatient care centers 539 537 2 0.4Home health care services 1,003 992 11 1.1Hospitals 4,661 4,705 ‐44 ‐0.9Nursing and residential care facilities 3,060 3,035 25 0.8Nursing care facilities 1,633 1,618 15 0.9Social assistance 2,559 2,560 ‐1 (1)Child day care services 874 874 0 (1)Leisure and hospitality 12,748 12,820 ‐72 ‐0.6Arts, entertainment, and recreation 1,778 1,776 2 0.1Performing arts and spectator sports 375 378 ‐3 ‐0.8Museums, historical sites, zoos, and parks 120 121 ‐1 ‐0.8Amusements, gambling, and recreation 1,282 1,277 5 0.4Accommodation and food services 10,970 11,044 ‐74 ‐0.7Accommodation 1,709 1,673 36 2.1Food services and drinking places 9,260 9,371 ‐111 ‐1.2Other services 5,360 5,402 ‐42 ‐0.8Repair and maintenance 1,160 1,164 ‐4 ‐0.3Personal and laundry services 1,281 1,294 ‐13 ‐1Membership associations and organizations 2,919 2,944 ‐25 ‐0.9Government 22,960 22,929 31 0.1Federal 2,779 2,787 ‐8 ‐0.3Federal, except U.S. Postal Service 2,061 2,069 ‐8 ‐0.4
U.S. Postal Service 718 718 0 (1)

State government 5,320 5,323 ‐3 ‐0.1State government education 2,511 2,525 ‐14 ‐0.6State government, excluding education 2,809 2,798 11 0.4Local government 14,861 14,819 42 0.3Local government education 8,471 8,445 26 0.3Local government, excluding education 6,390 6,375 15 0.2(1)Less than 0.05 percent. 

Table 3. Differences in seasonally adjusted levels and over‐the‐month changes, total nonfarm employment, January 20

2009 Levels Over‐the‐month chang

As previously published As revised Difference As previously published As revis

January 134,333 133,549 ‐784 ‐741 ‐779 February 133,652 132,823 ‐829 ‐681 ‐726 March 133,000 132,070 ‐930 ‐652 ‐753 April 132,481 131,488 ‐993 ‐519 ‐582 May 132,178 131,141 ‐1,037 ‐303 ‐347 June 131,715 130,637 ‐1,078 ‐463 ‐504 July 131,411 130,293 ‐1,118 ‐304 ‐344 August 131,257 130,082 ‐1,175 ‐154 ‐211 September 131,118 129,857 ‐1,261 ‐139 ‐225 October 130,991 129,633 ‐1,358 ‐127 ‐224 November 130,995 129,697 ‐1,298 4 64 December(p) 130,910 129,547 ‐1,363 ‐85 ‐150 (p) Preliminary 
Table 4. Effect of March 2009 benchmark revisions on hours and earnings estimates, selected indust

Industry Average weekly hours Average hou

As previously

published

As

revised

Difference As previously

published

Total private 33.1 33.1 0 $18.57 Goods-producing 38.7 38.7 0 19.74 Mining and logging 42.9 42.9 0 23.40 Construction 37.3 37.2 ‐0.1 22.45 Manufacturing 39.2 39.2 0 18.09 Durable goods 39.2 39.2 0 19.17 Wood products 36.2 36.2 0 14.67 Nonmetallic mineral products 39.2 39.2 0 17.19 Primary metals 40.3 40.3 0 19.69 Fabricated metal products 38.8 38.8 0 17.29 Machinery 40.0 40.0 0 18.26 Computer and electronic products 39.8 39.8 0 21.71 Electrical equipment and appliances 38.6 38.6 0 15.95 Transportation equipment 40.0 40.0 0 24.80 Furniture and related products 37.5 37.5 0 15.02 Miscellaneous manufacturing 38.3 38.3 0 16.02 Nondurable goods 39.2 39.2 0 16.43 Food manufacturing 39.6 39.5 ‐0.1 14.24 Beverages and tobacco products 35.8 35.8 0 20.40 Textile mills 36.2 36.2 0 13.88 Textile product mills 37.0 37.1 0.1 11.34 Apparel 36.2 36.2 0 11.26 Leather and allied products 33.1 33.1 0 14.21 Paper and paper products 40.7 40.7 0 18.90 Printing and related support 37.6 37.6 0 16.69 
activities 

Petroleum and coal products 43.3 43.3 0 29.80 Chemicals 40.9 40.9 0 19.93 Plastics and rubber products 39.3 39.3 0 16.20 Private service-providing 32.1 32.1 0 18.31 Trade, transportation, and utilities 32.7 32.7 0 16.45 Wholesale trade 37.9 37.9 0 20.64 Retail trade 29.5 29.5 0 13.02 Transportation and warehousing 35.7 35.7 0 18.64 Utilities 42.2 42.2 0 29.42 Information 36.8 36.9 0.1 25.40 Financial activities 36.5 36.5 0 20.67 Professional and business services 34.9 34.9 0 22.52 Education and health services 32.4 32.4 0 19.23 Leisure and hospitality 24.8 24.8 0 11.00 Other services 30.5 30.5 0 16.33 

Exhibit 1. Seasonal Adjustment ‐ AE

NAICS Tabcode Tabcode title Mode 4/5 week adj Other adj

1011330000 Logging MULT X1021100000 Oil and gas extraction MULT X1021200000 Mining, except oil and gas ‐ X Indirect(1)1021210000 Coal mining MULT X1021300000 Support activities for mining MULT X2023610000 Residential building ‐ X Raked(2)2023620000 Nonresidential building ‐ X Raked2023700000 Heavy and civil engineering construction ADD X
2023800000 Specialty trade contractors ‐ X Indirect

2023800100 Residential specialty trade contractors ADD X2023800200 Nonresidential specialty trade contractors ADD X3132100000 Wood products ADD X3132700000 Nonmetallic mineral products ADD X3133100000 Primary metals ADD X3133200000 Fabricated metal products ADD X3133300000 Machinery ADD X3133400000 Computer and electronic products ‐ X Indirect3133410000 Computer and peripheral equipment MULT X3133420000 Communications equipment MULT X3133440000 Semiconductors and electronic components MULT X3133450000 Electronic instruments MULT X3133500000 Electrical equipment and appliances MULT X3133600000 Transportation equipment ADD3133600100 Motor vehicles and parts ADD3133700000 Furniture and related products ADD X3133900000 Miscellaneous manufacturing MULT X3231100000 Food manufacturing MULT X3231200000 Beverages and tobacco products MULT X3231300000 Textile mills MULT X3231400000 Textile product mills MULT X3231500000 Apparel MULT X3231600000 Leather and allied products MULT X3232200000 Paper and paper products MULT X3232300000 Printing and related support activities ADD X3232400000 Petroleum and coal products MULT X3232500000 Chemicals MULT X3232600000 Plastics and rubber products ADD X4142300000 Durable goods MULT X4142400000 Nondurable goods MULT X4142500000 Electronic markets and agents and brokers MULT X4244100000 Motor vehicle and parts dealers ‐ X Indirect
4244110000 Automobile dealers ADD X

4244200000 Furniture and home furnishings stores ADD X4244300000 Electronics and appliance stores MULT X4244400000 Building material and garden supply stores MULT X4244500000 Food and beverage stores MULT X4244600000 Health and personal care stores MULT X4244700000 Gasoline stations MULT X4244800000 Clothing and clothing accessories stores MULT X4245100000 Sporting goods, hobby, book, and music stores MULT X4245200000 General merchandise stores ‐ X Indirect4245210000 Department stores MULT X4245300000 Miscellaneous store retailers MULT X4245400000 Nonstore retailers MULT X4348100000 Air transportation MULT X4348200000 Rail transportation MULT X4348300000 Water transportation MULT X4348400000 Truck transportation ADD X4348500000 Transit and ground passenger transportation ADD4348600000 Pipeline transportation MULT X4348700000 Scenic and sightseeing transportation MULT X4348800000 Support activities for transportation MULT X4349200000 Couriers and messengers MULT X4349300000 Warehousing and storage ADD X4422000000 Utilities MULT X5051100000 Publishing industries, except Internet ADD X5051200000 Motion picture and sound recording industries MULT X5051500000 Broadcasting, except Internet MULT X5051700000 Telecommunications MULT X5051800000 Data processing, hosting and related services MULT X5051900000 Other information services MULT X5552100000 Monetary authorities ‐ central bank MULT X5552200000 Credit intermediation and related activities ‐ X Indirect5552210000 Depository credit intermediation MULT X
5552211000 Commercial banking MULT X

5552300000 Securities, commodity contracts, investments MULT X5552400000 Insurance carriers and related activities MULT X5552500000 Funds, trusts, and other financial vehicles MULT X5553100000 Real estate MULT X5553200000 Rental and leasing services ADD X5553300000 Lessors of nonfinancial intangible assets MULT X6054000000 Professional and technical services ‐ X Indirect6054110000 Legal services MULT X6054120000 Accounting and bookkeeping services ADD X6054130000 Architectural and engineering services MULT X6054150000 Computer systems design and related services ADD X6054160000 Management and technical consulting services MULT X6055000000 Management of companies and enterprises MULT X6056100000 Administrative and support services ‐ X Indirect6056130000 Employment services ADD X6056132000 Temporary help services ADD X6056140000 Business support services ADD X6056170000 Services to buildings and dwellings MULT X6056200000 Waste management and remediation services ADD X6561000000 Educational services ADD X6562100000 Ambulatory health care services ‐ X Indirect6562110000 Offices of physicians MULT X6562140000 Outpatient care centers MULT X6562160000 Home health care services ADD X6562200000 Hospitals MULT X6562300000 Nursing and residential care facilities ‐ X Indirect6562310000 Nursing care facilities MULT X6562400000 Social assistance ‐ X Indirect6562440000 Child day care services ADD X7071100000 Performing arts and spectator sports MULT X7071200000 Museums, historical sites, zoos, and parks MULT X7071300000 Amusements, gambling, and recreation ADD X
7072100000 Accommodation MULT X

7072200000 Food services and drinking places ADD X8081100000 Repair and maintenance MULT X8081200000 Personal and laundry services MULT X8081300000 Membership associations and organizations ADD9091100000 Federal, except U.S. Postal Service MULT X9091912000 U.S. Postal Service MULT X9092161100 State government education ADD X9092200000 State government, excluding education MULT X9093161100 Local government education ADD X9093200000 Local government, excluding education ADD X Election adjustment(3)

Seasonal Adjustment ‐ AE AWH

NAICS Tabcode Tabcode title Mode 4/5 week adj 10/11 day adj Easter/Labor Day adj

1000000000 Mining and logging MULT X2000000000 Construction MULT X3132100000 Wood products MULT3132700000 Nonmetallic mineral products MULT3133100000 Primary metals MULT X3133200000 Fabricated metal products MULT X3133300000 Machinery MULT X3133400000 Computer and electronic products MULT X3133500000 Electrical equipment and appliances MULT X3133600000 Transportation equipment MULT X3133600100 Motor vehicles and parts MULT X3133700000 Furniture and related products MULT X3133900000 Miscellaneous manufacturing MULT X3231100000 Food manufacturing MULT X3231200000 Beverages and tobacco products MULT X3231300000 Textile mills MULT X3231400000 Textile product mills MULT X3231500000 Apparel MULT X
3231600000 Leather and allied products MULT X

3232200000 Paper and paper products MULT X3232300000 Printing and related support activities MULT X3232400000 Petroleum and coal products MULT3232500000 Chemicals MULT3232600000 Plastics and rubber products MULT X4142000000 Wholesale trade MULT X4200000000 Retail trade MULT X4300000000 Transportation and warehousing MULT X4422000000 Utilities MULT5000000000 Information MULT X5500000000 Financial activities MULT X6000000000 Professional and business services MULT X X6500000000 Education and health services MULT X7000000000 Leisure and hospitality MULT X8000000000 Other services MULT X

Seasonal Adjustment ‐ AE AHE

NAICS Tabcode Tabcode title Mode 4/5 week adj 10/11 day adj

1000000000 Mining and logging MULT2000000000 Construction MULT3100000000 Durable goods MULT3200000000 Nondurable goods MULT4142000000 Wholesale trade MULT X4200000000 Retail trade MULT X4300000000 Transportation and warehousing MULT X4422000000 Utilities MULT5000000000 Information MULT X5500000000 Financial activities ADD X6000000000 Professional and business services ADD X6500000000 Education and health services ADD7000000000 Leisure and hospitality MULT
8000000000 Other services MULT X

Seasonal Adjustment Comparison ‐ AE AOH

NAICS Tabcode Tabcode title Mode 4/5 week adj 10/11 day adj Easter/Labor Day adj

3100000000 Durable goods MULT X3200000000 Nondurable goods MULT X

Seasonal Adjustment ‐ PE

NAICS Tabcode Tabcode title Mode 4/5 week adj Other adj

1000000000 Mining and logging MULT X2000000000 Construction ADD X3132100000 Wood products ADD X3132700000 Nonmetallic mineral products ADD X3133100000 Primary metals ADD X3133200000 Fabricated metal products ADD X3133300000 Machinery ADD X3133400000 Computer and electronic products MULT X3133500000 Electrical equipment and appliances MULT X3133600000 Transportation equipment ADD3133600100 Motor vehicles and parts ADD3133700000 Furniture and related products ADD X3133900000 Miscellaneous manufacturing ADD X3231100000 Food manufacturing MULT X3231200000 Beverages and tobacco products ADD X3231300000 Textile mills MULT X3231400000 Textile product mills ADD X3231500000 Apparel MULT X3231600000 Leather and allied products MULT X3232200000 Paper and paper products MULT X3232300000 Printing and related support activities ADD X3232400000 Petroleum and coal products MULT X
3232500000 Chemicals ADD X

3232600000 Plastics and rubber products ADD X4142000000 Wholesale trade MULT X4200000000 Retail trade MULT X4300000000 Transportation and warehousing MULT X4422000000 Utilities MULT X5000000000 Information MULT X5500000000 Financial activities MULT X6000000000 Professional and business services MULT X6500000000 Education and health services ADD X7000000000 Leisure and hospitality ADD X8000000000 Other services MULT X

Seasonal Adjustment ‐ PE AWH

NAICS Tabcode Tabcode title Mode 4/5 week adj 10/11 day adj Easter/Labor Day adj

1000000000 Mining and logging MULT X X2000000000 Construction ADD X X3132100000 Wood products MULT X X3132700000 Nonmetallic mineral products MULT X X3133100000 Primary metals MULT X X3133200000 Fabricated metal products MULT X X3133300000 Machinery MULT X X3133400000 Computer and electronic products MULT X X3133500000 Electrical equipment and appliances MULT X X3133600000 Transportation equipment MULT X X3133600100 Motor vehicles and parts ADD X X3133700000 Furniture and related products MULT X X3133900000 Miscellaneous manufacturing MULT X X3231100000 Food manufacturing MULT X X3231200000 Beverages and tobacco products MULT X X3231300000 Textile mills ADD X X
3231400000 Textile product mills MULT X X

3231500000 Apparel MULT X X3231600000 Leather and allied products ADD X X3232200000 Paper and paper products MULT X X3232300000 Printing and related support activities MULT X X3232400000 Petroleum and coal products MULT X X3232500000 Chemicals MULT X3232600000 Plastics and rubber products MULT X X4142000000 Wholesale trade MULT X X4200000000 Retail trade MULT X4300000000 Transportation and warehousing MULT X X4422000000 Utilities MULT X5000000000 Information MULT X5500000000 Financial activities MULT X6000000000 Professional and business services MULT X X6500000000 Education and health services MULT X7000000000 Leisure and hospitality MULT X8000000000 Other services MULT X X

Seasonal Adjustment ‐ PE AHE

NAICS Tabcode Tabcode title Mode 4/5 week adj 10/11 day adj

1000000000 Mining and logging MULT X2000000000 Construction MULT X3100000000 Durable goods ADD X3200000000 Nondurable goods MULT X4142000000 Wholesale trade MULT X4200000000 Retail trade MULT X4300000000 Transportation and warehousing MULT X4422000000 Utilities ADD X5000000000 Information MULT X5500000000 Financial activities MULT X6000000000 Professional and business services MULT X
6500000000 Education and health services ADD X

7000000000 Leisure and hospitality MULT X8000000000 Other services MULT X

Seasonal Adjustment Comparison ‐ PE AOH

NAICS Tabcode Tabcode title Mode 4/5 week adj 10/11 day adj Easter/Labor Day adj

3100000000 Durable goods MULT X X3200000000 Nondurable goods ADD X X

Seasonal Adjustment ‐ WE

NAICS Tabcode Tabcode title Mode 4/5 week

adj

Other adj

1000000000 Mining and logging MULT X1021000000 Mining MULT X

2000000000 Construction MULT X

3100000000 Durable goods ADD X

3200000000 Nondurable goods MULT X

4142000000 Wholesale trade MULT X

4200000000 Retail trade MULT X

4300000000 Transportation and warehousing MULT X

4422000000 Utilities MULT X

5000000000 Information MULT X

5552000000 Finance and insurance MULT X

5553000000 Real estate and rental and leasing MULT X

6054000000 Professional and technical services ADD X

6055000000 Management of companies and enterprises MULT X

6056000000 Administrative and waste services MULT X

6561000000 Educational services ADD X

6562000000 Health care and social assistance ADD X

7071000000 Arts, entertainment, and recreation MULT X

7072000000 Accommodation and food services ADD X
8000000000 Other services MULT X

9091000000 Federal MULT X9092000000 State government ADD X

9093000000 Local government ADD X Election adjustment(3)

(1) Seasonal adjustment occurs at the lowest available industry level.(2) Residential and nonresidential specialty trade estimates are raked to the specialty trade estimates to ensure consistenc(3)Special adjustment for the presence/absence of poll workers in local government.Last Modified Date: February 23, 2010