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BLS National Establishment Estimates Revised to Incorporate March 2006 BenMichele Eickman Economist 

With the release of data for January 2007, the Bureau of Labor Statistics (BLS) introduced its annual revi

estimates of employment, hours, and earnings from the Current Employment Statistics (CES) monthly survey of establishments. Each year, the CES survey realigns its sample-based estimates to reflect more currently availabl

employment—a process known as benchmarking. Comprehensive counts of employment, or benchmarks, are de

the unemployment insurance (UI) tax reports that nearly all employers are required to file with State Workforce ASummary of the benchmark revisions The March 2006 benchmark level for total nonfarm employment is 134,868,000, a figure that is 752,000 above th

based estimate for March 2006, an adjustment of 0.6 percent. Table 1 shows the total nonfarm percentage bench

past 10 years. This year’s revision is large by recent historical standards. BLS has researched many potential southan usual revision, reviewing potential error sources in both the CES estimates and the UI-based universe count

in the next section. 
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Table 2 shows the nonfarm employment benchmarks for March 2006, not seasonally adjusted, by industry. 

supersector dominated in terms of the size of the revision. The largest upward revision occurred in professional with a revision of 230,000, or 1.3 percent. Construction estimates were raised by 189,000, or 2.6 percent, followetransportation, and utilities supersector with an upward revision of 158,000 or 0.6 percent. Education and health were revised upward by 96,000 or 0.5 percent. 

Other supersectors had upward revisions of a smaller magnitude. Estimates in leisure and hospitality were r

0.3 percent, while estimates were revised by 35,000 in financial activities and 27,000 in other services, or 0.4 and respectively. Within leisure and hospitality, the accommodations and food services industry was revised 37,000, Within the financial activities supersector, direct life insurance carriers was revised by 44,700, or 12.2 percent, wsimilar organizations, a part of other services, was revised by 17,800, or 3.6 percent. 

Mining estimates were raised by 8,000, or 1.2 percent, and government estimates were revised upward by 3,

percent. 

Manufacturing had a small revision of –21,000, or –0.1 percent, with nondurable goods accounting for the m

revision of –23,000, or –0.4 percent. A downward revision was also made in information (–15,000, or–0.5 perceSummary of research into possible sources of the March 2006 benchmark revision 

Michele Eickman is an economist in the Division of Current Employment Statistics, Office of Employment and Unemployment S
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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 CEQCEW-based employment benchmark. Both series are subject to non-response and imputation error, and reportiAdditionally for the CES estimates, sampling error and the business birth/death modeling errors are a factor. Profiling the CES-QCEW divergence BLS began its search for causes by studying the profile of the CES-QCEW divergences: 1) by industry; and 2) ov

year. Insights from these analyses follow. 1) CES benchmark revisions by industry The large revision at total nonfarm is mostly attributable to larger than usual revisions in construction and professervices, and a substantial revision in trade, transportation, and utilities. 

 Percentage benchmark revisions by super sector 

2004 2005 2006 

2006 bmk revision 

Total Nonfarm 0.2 -0.1 0.6 752Natural resources and mining 0.7 -0.3 1.2 8Construction 0.6 0.5 2.6 189Manufacturing -0.4 -0.3 -0.1 -21Trade, transportation, utilities 0.2 0.3 0.6 158Information -1.0 -2.1 -0.5 -15Financial activities 0.1 -0.8 0.4 35

Statistics. Telephone: (202) 691-6555; e-mail: CESInfo@bls.gov. 
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Professional and business services -0.2 -0.4 1.3 230Education and health services 0.2 0.0 0.5 96Leisure and hospitality 1.2 0.4 0.3 42Other services 0.5 -1.3 0.5 27Government 0.1 0.0 0.0 32) 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 2005, through December 2005, CES private employment were tracking within a normal + 0.3% range. Throughout this period, CES was tracking con

QCEW but not by a large magnitude. The magnitude of difference more than doubled in the first quarter of 2006

table below. 

 Over the Year Change by Month, Total Private (CES scope), 

 Apr 05-Mar 06, not seasonally adjusted; CES estimates are pre-March 06 benchmark revision  Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar CES 1891 1701 1862 1863 2013 1861 1610 1856 1794 1863 1858 1920 QCEW 2193 2052 1989 2044 2160 2308 1810 2031 2029 2335 2323 2681 QCEWCES 302 351 127 181 147 447 200 175 235 472 465 761 
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Business Employment Dynamics (BED) To gain insight into the nature of the first quarter 2006 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 that it was not gross job gains driving the employment strength in first quarter 2

slowdown in gross job losses from contractions and closings. The BED chart and table below illustrate the first qgross job losses. In addition, the BED data by industry show sharp fall-offs in contractions in most of the industrbenchmark revisions, including Construction and Professional and Business Services. There is a notable drop in 

fourth quarter 05 as well as first quarter 06. 
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Three-month private sector gross job gains and losses, seasonally adjusted -------------------------------------------------------------------------  | 3 months ended  |--------------------------------------  Category | Mar. | June | Sept. | Dec. | Mar.  | 2005 | 2005 | 2005 | 2005 | 2006  |--------------------------------------  | Levels (in thousands) ----------------------------------|--------------------------------------  | | | | | Gross job gains...................| 7,635| 7,932| 8,055| 7,818 | 7,556  At expanding establishments.....| 6,171| 6,311| 6,423| 6,293 | 6,205  At opening establishments.......| 1,464| 1,621| 1,632| 1,525 | 1,351 Gross job losses..................| 7,310| 7,358| 7,427| 7,267 | 6,772  At contracting establishments...| 5,852| 5,873| 5,915| 5,888 | 5,536  At closing establishments.......| 1,458| 1,485| 1,512| 1,379 | 1,236 Net employment change (1).........| 325| 574| 628| 551 | 784  |--------------------------------------  | Rates (percent)  |-------------------------------------- Gross job gains...................| 6.9| 7.2| 7.3| 7.1 | 6.7  At expanding establishments.....| 5.6| 5.7| 5.8| 5.7 | 5.5  At opening establishments.......| 1.3| 1.5| 1.5| 1.4 | 1.2 Gross job losses..................| 6.6| 6.6| 6.8| 6.5 | 6.0  At contracting establishments...| 5.3| 5.3| 5.4| 5.3 | 4.9 
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 At closing establishments.......| 1.3| 1.3| 1.4| 1.2 | 1.1 Net employment change(1)..........| 0.3| 0.6| 0.5| 0.6 | 0.7  | | | | | -------------------------------------------------------------------------  1 The net employment change is the difference between total gross job gains and total gross job losses. 

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 to date has looked at both: 

- Whether the change in the QCEW from March 2005 to March 2006 was impacted by noneconomic data r

processing changes, and 

- If the QCEW March 2005 to March 2006 change is taken as correct, why the CES estimation process fail

within usual historical ranges. 

A description of key aspects of the research and validation completed to date 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% of the worksites and 5% of the employment is imputed. BLS reviewed key aspects of the QCEW imputation process to see if it could be a factor CES/QCEW over-the-year employment trends. The review included: the functioning of processing systems and 
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of long term imputations (more than two quarters), and the number and trend of imputed accounts versus historicextensive examination, no problems or changes to the QCEW imputations were found that could help explain the 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 agen

payroll processing firms (PPFs). The PPFs also derive the employment counts that are reported on the QCR, and basis for the QCEW employment series. Using the QCEW microdata file, BLS examined a number of tabulationmight be obvious issues with PPF reporting. The review was limited because of time constraints and the fact tharecords do not carry the agent code that indicates whether the report is filed by a PPF. While BLS is planning a m

quality control review of PPF-supplied data, the limited review done for the March 2006 benchmark did not reve

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. In addition to directly reviewing estimation components, BLS also completed a series of estimate simu

vary one component of the estimation process at a time in order to observe possible effects on the estimates. Resbelow. 
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CES nonresponse A review of response rates for the March 2005-06 benchmark year indicates rates mostly in the normal historical 

August, October, and November 2005 had slightly lower than usual rates. The major reason for this is a combina

effects in the Gulf Coast area and the introduction of a more extensive CES collection form which included fieldpayroll and hours data. There is no indication that these slightly below normal response rates for a few months a

to the benchmark revision magnitude. Experimental estimates done with a simulated 100% response rate (using 

to fill in for CES non-respondents) did not substantially shrink the observed benchmark revision. Reporting differences between CES and QCEWBoth the CES survey and the QCEW program use the same pay period including the 12th of the month reference 

employment. However individual firms sometimes report different counts to the two programs. This can contrib

the aggregate employment series measured by the two programs and therefore to the benchmark revision. EstimQCEW responses were substituted for CES responses did not indicate any substantial difference in benchmark reHowever for some industries with large benchmark revisions, most notably construction and professional and busubstituting QCEW for CES responses did shrink the revision substantially. 
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CES data edit and review effectsDuring the estimation process CES sample data are edited for potential errors and atypical reports. In order to exproblematic effects from this process, estimate simulations were produced omitting these treatments. These expereveal any problems with the data editing and review process. Changes to the CES and QCEW measurement processes following Hurricane Katrina Both the CES and QCEW used modified procedures in the aftermath of Hurricane Katrina, for parts of the Gulf Cprograms modified procedures for imputing for non-respondents; CES additionally changed other aspects of its eThe end result of these changes were that CES estimates showed a sharper employment drop over the benchmark eventually reported by the QCEW. These differences are mostly concentrated in September and October, the tw

used modified estimation procedures. Without the special procedures, both programs would have understated thfollowing Katrina. Under the assumption that the final QCEW (including its special imputation procedures) is enCES national estimates overstated the employment loss by approximately 60,000. (If CES had not used special pnational estimates would have understated the QCEW-reported loss by about 90,000.) Thus the special procedurthe CES result as compared to using standard estimation procedures. However because this year’s benchmark is direction, the sharper drop measured by the CES is a contributor to the net benchmark revision. The 60,000 diffe

product of two factors. CES imputed zeros for non-respondents in heavily damaged areas (as did QCEW); howe
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CES non-respondents were later to be found still in business by QCEW. This accounts for about half of the diffe

shown by the two programs. The other half of the 60,000 difference resulted from respondents reporting zero empositive employment to the QCEW. CES also re-weighted sample units in the heavily damaged areas; this had a the estimates. In sum the measurable impact on the benchmark revision of the post-Katrina measurement probleCES birth/death modelingCES uses a two-step method to estimate net business birth/death employment. Step 1 uses business deaths to im

can not be picked up in the sample on a real time basis. Step 2 is an ARIMA-based model intended to estimate tbirth/death employment not accounted for by Step 1. Only the Step 2 error is directly measurable. Error from thimeasured by comparing the actual residual from March 2005-06 once it becomes available, with the model-based for the 2006 benchmark year at approximately 300,000 was larger as compared with the most recent two years, emeasured an error of approximately 100,000. For 2006 these larger errors were concentrated in July and Octobe

actual birth/death residual measured from the QCEW differed substantially from prior years, and consequently waccurately by the model. Conclusions regarding the March 2006 benchmark 
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BLS has researched potential sources identified for the large CES benchmark revision to the extent possible sincmagnitude of the revision was announced in October 2006. A small portion of the benchmark revision can be tra

estimation of employment for September-October 2005 for the areas impacted by Hurricane Katrina. A larger pobenchmark is linked to error in the CES model-based estimation process for birth employment. The largest portiolikely results from an accumulation of differences from several other error sources. Plans for further research and modification to methods Research into the March 2006 CES benchmark revision sources was limited by the short period of time available computation of the first quarter 2006 QCEW employment level and publication of the CES benchmark revision. conduct more research and implement several improvements to CES and QCEW procedures to facilitate the mor

and analysis of discrepancies between the two employment measures. These plans are described below. -- Response Analysis Survey (RAS) for Payroll Processing Firms and Payroll Software Developers These companies exert a substantial influence on the QCEW employment series because many thousands of indiemployment counts are directly derived and/or reported by these firms. The RAS will be geared towards understand methods used to develop the QCEW employment counts. 
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-- CES/QCEW Response Analysis Survey for a sample of individual companies Prior to the March 2006 benchmark issue, BLS was already planning a Response Analysis Survey (RAS) to bettesource of reporting discrepancies between the CES and QCEW at the individual firm level, and their potential codifferences in the series. A small pilot RAS of about 200 firms will be completed in early 2007, and a larger RA

planned for 2008. -- Integration of CES/QCEW microdata editingBLS will pursue editing individual CES sample reports against their QCEW counterparts on a regular basis; this uncover any systemic reporting problems in either program. -- Phase-in of new CES sample units more quickly Presently CES solicits new sample units each month, but only brings new sample into live estimation once a yeasample sooner would make better use of this valuable information and should contribute to more accurate estimalogistical and operational issues that BLS is working to resolve in order to make this feasible. -- Research improvements to CES net birth/death modeling
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 BLS will research possible improvements to the CES net birth/death model, although this will always remain a dCES methodology. Because there is no current information available on business births, models based in part on 

information will continue to be used. The basic modeling method currently used is simple and has been in use siestimates were converted to the CES redesign methodology in 2000. Several additional years of real time experi

are now available and can be used as a basis for additional evaluation and research testing. -- Quarterly benchmarkingSome type of quarterly adjustment of the CES estimates to the QCEW employment counts may help prevent the from diverging substantially. BLS is exploring the feasibility of more frequent benchmarking. Changes to the CES published series Each year, in addition to the benchmarking process, the CES program conducts a review of the adequacy of its sa

and publication cells and makes warranted adjustments. This year, deletion of several published series resulted freview of sample employment and universe coverage. 

Industry series discontinued for production workers, average weekly hours, average hourly earnings, averag

and average overtime hours estimates are shown on Exhibits 1 and 2. Exhibit 1. Discontinued publication of production worker, hours, and earnings series
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Industry title NAICS code CES industry code

Other pressed and blown glass and glassware 327211,2 31327212 Fluid power valves and hose fittings 332912 31332912 Photographic and photocopying equipment 333315 31333315 Miscellaneous commercial and service industry machinery 333311,2,4,9 31333319 Motor vehicle seating and interior trim 33636 31336360 Flour milling, malt, starch, and vegetable oil 31121,2 32311220 Miscellaneous paperboard containers 322213,4,5 32322215 Coated and laminated package materials and paper 322221,2 32322222 Miscellaneous coated and treated paper and paper bags 322223,4,5,6 32322226 Tires 32621 32326210 Department stores 4521 42452100 Department stores, except discount 452111 42452111 Discount department stores 452112 42452112 Other general merchandise stores 4529 42452900 Warehouse clubs and supercenters 45291 42452910 All other general merchandise stores 45299 42452990 

Exhibit 2. Discontinued publication of average overtime series
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Industry title NAICS code CES industry code

Lighting fixtures 33512 31335120 Switchgear and switchboard apparatus 335313 31335313 Truck trailers 336212 31336212 Inclusion of tips and reconstruction of earnings in full-service restaurants and cafeterias In September 2005, BLS began collecting payroll and hours information for all employees, in addition to producnonsupervisory worker payroll and hours information from survey respondents. The all employee payroll was de

at the same time, respondents were also requested to begin including tips in the nonsupervisory worker payrolls. include tips in the nonsupervisory worker payrolls did not result in substantial changes to reported payroll for mohad already been including tips. For two industries, full-service restaurants (70-722100) and cafeterias (70-72221

reporting instructions did make a notable difference and published earnings estimates were adjusted to remove thearnings levels. Effective with the March 2006 benchmark release, earnings data for these series were reconstruc

level that accounts for the inclusion of tips in the historical earnings data. 

Reconstruction of average hourly earnings data from January 1990 (the first month of publication for these two 
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Reconstruction of the two basic series back to January 1990 was completed as follows: 

- Revised September and October 2005 Average Hourly Earnings estimates were derived 

 from the weighted sample average of the responding sample. 

- Revised estimates for August 2005 were calculated by subtracting the average  monthly growth for the series over the July 2004 to July 2005 annual period and the 

normal seasonal movement for the month from the reconstructed September 2005 estimate. 

- Revised estimates for July 2005 were calculated by subtracting the average  monthly growth for the series over the July 2004 to July 2005 annual period and the 

 normal seasonal movement for the month from the reconstructed August 2005 estimate. - Estimates for months prior to July 2005 were calculated by applying the previously 

published over-the-month percentage change back from the recalculated July 2005 average hourly earnings level 

- For estimates from November 2005 forward, the standard weighted-link-and-taper  estimation was run using the collected sample - Average Hourly Earnings estimates for higher level industry series (leisure and hospitality, private service-

private) were recomputed using the newly reconstructed series for full-service restaurants and cafeterias as input
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Reconstruction of average hourly earnings data prior to January 1990 Prior to 1990, average hourly earnings are published only at the higher industry level, leisure and hospitality

cafeterias and full-service restaurants. 

Reconstruction of leisure and hospitality (70-000000) and higher level series prior to 1990 was completed as 

- The ratio between the reconstructed average hourly earnings series for leisure and hospitality and the p

average hourly earnings for the series was calculated for January through March 1990. 

- The average ratio for the three months (January through March) was calculated. - The average ratio calculated in step 2 was applied to all previously published leisure and hospitality av

estimates going back to January 1964. 

- Total private and private service-providing average hourly earnings series were recomputed using the 

and hospitality series. 

Revisions to earnings series At the total private level, average hourly earnings increased by 5 cents over the previously published estimate for 

Estimates for private service-providing and leisure and hospitality increased as well, 5 cents and 20 cents, respec
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restaurants estimates rose from $8.63 to $9.13, an increase of 50 cents. The cafeteria series had a smaller revision previous estimate. These revisions are a combined result of both the routine benchmark processing and the reconimpacted series. Routine benchmark effects on earnings series are described below in the section “effect of bench

other data types”. Exhibit 3. Effect of March 2006 benchmark revisions and reconstruction on average hourly earnings estim

industries Industry title Previous Estimate Revised Estimate DifferencTotal private $16.51 $16.56 $.05 Private service-providing 16.19 16.24 .05 Leisure and hospitality 9.43 9.63 .20 

Full-service restaurants 8.63 9.13 .50 Cafeterias 8.01 8.10 .09 Small domain model The employment estimator for five industries has been changed from the standard CES weighted-link-relative tecSmall Domain Model (SDM). Relatively small sample sizes in these industries limit the reliability of the weighteestimator. There was no change in the estimation of nonsupervisory workers, average weekly hours, and average earnings. BLS has been using the CES SDM for several years for some State and metropolitan area employment small samples. 
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Exhibit 4. Series estimated using a Small Domain Model Industry title CES Industry Code Employment, 2005 annual ave

in thousands 

Direct health and medical insurance carriers 55524114 427.4 Tax preparation services 60541213 97.3 Other technical consulting services 60541690 89.6 Remediation services 60562910 69.8 Recreational and vacation camps 70721214 27.8 

The CES Small Domain Model (SDM) is a Weighted Least Squares model with two employment inputs: (1) 

available CES sample for that series, and (2) an ARIMA projection based on 10 years of historical QCEW data. Revisions in the postbenchmark period Postbenchmark-period estimates from April 2006 to October 2006 were calculated for each month on the basis olevels and new model-based estimates for the net birth/death employment. 

Text table A shows the net birth/death model figures for the supersectors over the postbenchmark period. Fr

December 2006, the cumulative net birth/death model added 1,002,000 compared with 906,000 in the previously December estimates. 
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Table 3 presents revised total nonfarm employment data on a seasonally adjusted basis for January through O

revised data for April 2006 forward incorporate the effect of applying the rate of change measured by the sample 

benchmark level, as well as updated net birth /death model adjustments, and new seasonal adjustment factors. Why benchmarks differ from estimatesA benchmark revision is the difference between the benchmark employment level of a particular series for a give

corresponding sample-based estimate. The overall accuracy of the establishment survey is usually gauged by the difference. The benchmark revision often is regarded as a proxy for total survey error, but this approach does noerror in the universe data. The employment counts obtained from quarterly unemployment insurance tax forms athat reflect employer recordkeeping practices and differing State laws and procedures. The benchmark revision cinterpreted as the difference between two independently derived employment counts, each subject to its own erro

Like all sample surveys, the establishment survey is susceptible to two sources of error: sampling error and 

Sampling error is present any time a sample is used to make inferences about a population. The magnitude of thvariance, relates directly to the sample size and the percentage of the universe covered by the sample. The CES m

captures slightly under one-third of the universe, which is exceptionally high by usual sampling standards. This 

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

coverage, response, and processing errors. The error structures for both the CES monthly survey and the UI univStill, the two programs generally produce consistent total employment figures, each validating the other. Over thbenchmark revisions at the total nonfarm level have averaged 0.2 percent, with an absolute range from less than percent. (See table 1.) 

Effects of benchmark revisions on other data types The routine benchmarking process results in revisions to the series for women workers and production or nonsup

There are no benchmark employment levels for these series; they are revised by preserving ratios of employment data type to all-employee employment prior to benchmarking and then applying these ratios to the revised all-em

latter figures are calculated at the basic cell level and then are aggregated to produce the summary estimates. 

Average weekly hours and average hourly earnings are not benchmarked; instead they are estimated solely f

by survey respondents at the basic estimating cell level. 

The aggregate industry levels of the hours and earnings series are derived as a weighted average. The produ

nonsupervisory worker employment estimates for the basic cells are used as weights for the hours and earnings eindustry groupings. Adjustments of the all-employee estimates to new benchmarks may alter the weights, which the estimates for hours and earnings of production or nonsupervisory workers at higher levels of aggregation. 
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Generally, new employment benchmarks have little effect on hours and earnings estimates for major groupin

hours and earnings estimates of a broader group, employment revisions have to be relatively large and must affec

have hours or earnings averages that are substantially different from those of other industries in their group. Tab

on the levels of specific hour and earnings series resulting from the March 2006 benchmark. At the total private change in average weekly hours from the previously published level, while average hourly earnings was increasepublished level by 5 cents. As described earlier, this revision is affected by the inclusion of tips in certain industabove. Methods Benchmark adjustment procedure. Establishment survey benchmarking is done on an annual basis for a populati

from the administrative files of employees covered by unemployment insurance (UI). The time required to compprocess—from the full collection of the UI population data to publication of the revised industry estimates—is abbenchmark 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-m

Benchmarks are established for each basic estimating cell and are aggregated to develop published levels. O

adjusted basis, the sample-based estimates for the year preceding and the year following the benchmark also are 
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revision. Employment estimates for the months between the most recent March benchmark and the previous yeaadjusted with the use of a “wedge-back” procedure in which the difference between the benchmark level and the March estimate for each estimating cell is computed. This difference, or error, is then linearly distributed across 

estimates subsequent to the previous benchmark; eleven-twelfths of the March difference is added to the Februartwelfths to the January estimates, ending with the previous April estimates, which receive one-twelfth of the Marwedge-back procedure assumes that the total estimation error has accumulated at a steady rate since the last bencpreviously derived over-the-month sample changes to the revised March level yields revised estimates for the moMarch benchmark. New net birth/death model estimates also are calculated and applied during postbenchmark esample is introduced from the annual update. Benchmark source material. The principal source of benchmark data for private industries is the Quarterly Censand Wages (QCEW), also known as the ES-202. The QCEW contains employment data provided to State Workemployers covered by State UI laws. BLS uses several other sources to establish benchmarks for the remaining ipartially covered or exempt from mandatory UI coverage, accounting for 3 percent of the nonfarm employment t

Data on employees covered under Social Security laws are published by the U.S. Census Bureau in County B

are used to augment UI data on nonoffice insurance sales workers, child day care workers, religious organization

and hospitals. Benchmarks for State and local government hospitals and educational institutions are based on the 
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Governments conducted by the U.S. Census Bureau. Benchmark data from these sources are available only on a Extrapolation to a current level is accomplished by assuming and applying the employment trends from the UI-cpopulation in these industries to the noncovered part. Universe data for interstate railroads are obtained from the Board. Business birth and death estimation. Regular updating of the CES sample frame with information from the UI u

keep the CES survey current with respect to employment resulting from business births and business deaths. Eve

universe files available, however, always will be a minimum of 9 months out of date. The CES survey thus cannframe maintenance alone to provide estimates for business birth and death employment contributions. BLS has rsample-based and model-based approaches to measuring birth units that have not yet appeared on the UI univers

research demonstrated that sampling for births was not feasible over the very short CES production timeframes, ta model-based approach for this component. 

Earlier research indicated that, while both the business birth and death portions of total employment are gene

net contribution is relatively small and stable. To account for this net birth/death portion of total employment, Bestimation procedure with two components. The first component uses business deaths to impute employment for component is incorporated into the sample-based link relative estimate procedure by simply not reflecting sampl
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business, but instead imputing the same trend to them as seen in the other firms in the sample. The second comp(Auto-Regressive Integrated Moving Average) time-series model designed to estimate the residual net birth/deataccounted for by the imputation. The historical time series used to create and test the ARIMA model was deriveuniverse microlevel database and it reflects the actual residual net of births and deaths over the past 5 years. The component is reviewed on a quarterly basis. The net birth/death model component figures are unique to each mo

negative adjustments in some months. Furthermore, these figures may exhibit a seasonal pattern that also is obseUI universe data series. 

The most significant potential drawback to this, or any model-based approach, is that time-series modeling a

continuation of historical patterns and relationships, and therefore, is likely to have some difficulty producing releconomic turning points or during periods when there are sudden changes in trend. BLS will continue researchin

based techniques for the net birth/death component; it is likely to remain the most problematic part of the estimat

Availability of revised dataLABSTAT, the BLS public database on the Internet, contains all historical employment, hours, and earnings dataseasonally adjusted, that have been revised as a result of this benchmark. The data can be accessed at https://ww
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Current Employment Statistics program homepage. Employment, hours, and earnings estimates also are publish

Employment and Earnings.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. BLS computes seasonal factors concurrently with the monthly estimate prseries are seasonally adjusted with either a multiplicative or an additive model (Exhibit 5), and seasonal adjustmeapplied to the component levels. For employment, individual three-digit NAICS series are seasonally adjusted, aaggregates are formed by the summation of these components series. Seasonally adjusted totals for hours and ea

by taking weighted averages of the seasonally adjusted data for the component series. Special model adjustments Variable survey intervals. Beginning with the release of the 1995 benchmark, BLS refined the seasonal adjustm

control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. Although the CES su

a consistent concept—the pay period that includes the 12th of each month—inconsistencies arise because there asometimes 5 weeks between the weeks that include the 12th in a given pair of months. In highly seasonal industcan be an important determinant of the magnitude of seasonal hires or layoffs that have occurred at the time the sthereby complicating seasonal adjustment. 
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Standard seasonal adjustment methodology relies heavily on the experience of the most recent 3 years to det

seasonal change in employment for each month of the current year. Prior to the implementation of the adjustmennot distinguish between 4- and 5-week survey intervals, and the accuracy of the seasonal expectation depended in how well the current year’s survey interval corresponded with those from the previous 3 years. All else being thpotential for distortion occurred when the current month being estimated had a 5-week interval, but the 3 years pweek intervals, or, conversely, when the current month had a 4-week interval, but the 3 years preceding it were a

BLS has adopted REGARIMA (regression with autocorrelated errors) modeling to identify the estimated siz

the calendar effect on each published series. REGARIMA combines standard regression analysis, which measur

two or more variables, with ARIMA modeling, which describes and predicts the behavior of data series based on history. For many economic time series, including nonfarm payroll employment, observations are autocorrelated each month’s value is significantly dependent on the observations that precede it. These series, thus, usually can ARIMA models. If autocorrelated time series are modeled through regression analysis alone, however, the measamong other variables of interest may be distorted due to the influence of the autocorrelation. Thus, the REGARappropriate to measuring relationships among variables of interest in series that exhibit autocorrelation, such as n

employment. 

In this application, the correlations of interest are those between employment levels in individual calendar m

of the survey intervals for those months. The REGARIMA models evaluate the variation in employment levels t
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11 separate survey interval variables, one specified for each month except March, which is excluded because theweeks between the February and March surveys. Models for individual basic series were fitted with the most recavailable, 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. 

provide estimates of the strength of the relationship between employment levels and the number of weeks betweemonths that are modeled. The X-12-ARIMA software also produces diagnostic statistics that permit assessment 

significance of the regression coefficients, and all series are reviewed for adequacy with respect to the model. 

Because the 11 coefficients derived from the REGARIMA models provide an estimate of the magnitude of v

employment levels associated with the length of the survey interval, these coefficients are used to adjust the CES 

calendar effect. These “filtered” series then are seasonally adjusted with 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 soft

interval-effect adjustment. Several additional special effects modeled through the REGARIMA process are descConstruction series. Beginning with the 1996 benchmark revision, BLS utilized special treatment to adjust consseries. In the application of the interval-effect modeling process to the construction series, there initially was difand measuring the effect accurately because of the strong influence of variable weather patterns on employment industry. Further research enabled BLS to apply interval-effect modeling to the construction industry by disaggr
31 

construction series into its finer industry and geographic estimating cells and tightening outlier designation param

procedure allowed a more precise identification of weather-related outliers that had masked the interval effect anseasonal adjustment patterns in general. With these outliers removed, interval-effect modeling became feasible. seasonally adjusted series for construction that is improved because it is controlled for two potential distortions; events and the 4- versus 5-week effect. Floating holidays. BLS also continues the practice of making special adjustments for average weekly hours and overtime hours series to account for the presence or absence of religious holidays in the April survey reference poccurrence of Labor Day in the September reference period, back to the starting date of each series. 

Local government series. A special adjustment also is made in November each year to account for variations in the presence or absence of poll workers in the local government, excluding educational services, series. Refinements in seasonal adjustment for hours and earnings. With the release of the 1997 benchmark, BLS imple

to the seasonal adjustment process for the hours and earnings series to correct for distortions related to the metho

the varying length of payroll periods across months. There is a significant correlation between over-the-month c

average weekly hours and the average hourly earnings series and the number of weekdays in a month, resulting i
32 

fluctuations in these two series. Both series show more growth in “short” months (months with 20 or 21 weekdaymonths (months with 22 or 23 weekdays). The effect is stronger for the hours series than for the earning series. 

This calendar effect is traceable to response and processing errors associated with converting payroll and ho

sample respondents with semimonthly 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 reported will be varied. A constant level of hours reported most likely employees are salaried rather than paid by the hour, because employers are less likely to keep actual detailed reco

for such employees. This causes artificial peaks in the hours series in shorter months that are reversed in longer 

The processing error occurs when respondents with salaried workers report hours correctly (varying them ac

of the month), which dictates that different conversion factors be applied to payroll and hours. The CES process

hours-conversion factor for both fields, resulting in peaks in the hourly earnings series in short months and troug

REGARIMA modeling is used to identify, measure, and remove the length-of-pay-period effect for seasonal

weekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaaverage weekly hours in all the service-providing industries, except retail trade. For average hourly earnings, the 

variable is significant in wholesale trade, in financial activities, in professional and business services, and in otheaverage weekly hours series in the service-producing industries, except retail trade, have been adjusted from JanuThe average hourly earnings series for wholesale trade, financial activities, professional and business services, an
33 

been adjusted from January 1990 forward as well. For this reason, calculations of over-the-year change in the es

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 ad

modeling cannot support the number of variables that would be required in the regression equation to make both Exhibit 5 for series that have the calendar-effect modeling described in this section.) Exhibit 5. Model specifications

Seasonal Adjustment - AE 

NAICS 

Tabcode Tabcode title Mode 

4/5 

week 

adj 

Other adj 

10113310 Logging MULT X 10211000 Oil and gas extraction MULT X 10212000 Mining, except oil and gas - X Indirect1 10212100 Coal mining MULT X 10213000 Support activities for mining ADD X 20236100 Residential building - X Indirect 20236200 Nonresidential building - X Indirect 
34 

20237000 

Heavy and civil 

engineering 

construction 

ADD X 

20238000 Specialty trade contractors - X Indirect 20238001 Residential specialty trade contractors MULT X Raked220238002 

Nonresidential 

specialty trade 

contractors 

ADD X Raked 

31321000 Wood products ADD X 31327000 Nonmetallic mineral products ADD X 31331000 Primary metals MULT X 31332000 Fabricated metal products MULT X 31333000 Machinery MULT X 31334000 Computer and electronic products - X Indirect 31334100 Computer and peripheral equipment MULT X 31334200 Communications equipment MULT X 31334400 

Semiconductors and 

electronic 

components 

MULT X 

31334500 Electronic instruments MULT X 31335000 Electrical equipment and appliances MULT X 31336000 Transportation equipment ADD 31336001 Motor vehicles and ADD 
35 

parts 

31337000 Furniture and related products MULT X 31339000 Miscellaneous manufacturing MULT X 32311000 Food manufacturing MULT X 32312000 Beverages and tobacco products ADD X 32313000 Textile mills MULT X 32314000 Textile product mills ADD X 32315000 Apparel MULT X 32316000 Leather and allied products MULT X 32322000 Paper and paper products MULT X 32323000 Printing and related support activities MULT X 32324000 Petroleum and coal products MULT X 32325000 Chemicals MULT X 32326000 Plastics and rubber products MULT X 41423000 Durable goods MULT X 41424000 Nondurable goods MULT X 41425000 

Electronic markets 

and agents and 

brokers 

MULT X 

42441000 Motor vehicle and parts dealers - X Indirect 42441100 Automobile dealers MULT X 42442000 Furniture and home furnishings stores MULT X 42443000 Electronics and MULT X 
36 

appliance stores 

42444000 

Building material 

and garden supply 

stores 

MULT X 

42445000 Food and beverage stores MULT X 42446000 Health and personal care stores MULT X 42447000 Gasoline stations MULT X 42448000 

Clothing and 

clothing accessories 

stores 

MULT X 

42451000 

Sporting goods, 

hobby, book, and 

music stores 

MULT X 

42452000 General merchandise stores - X Indirect 42452100 Department stores MULT X 42453000 Miscellaneous store retailers MULT X 42454000 Nonstore retailers MULT X 43481000 Air transportation MULT X 43482000 Rail transportation ADD X 43483000 Water transportation MULT X 43484000 Truck transportation ADD X 43485000 

Transit and ground 

passenger 

transportation 

ADD 

43486000 Pipeline transportation MULT X 43487000 

Scenic and 

sightseeing 

transportation 

MULT X 
37 

43488000 Support activities for transportation MULT X 43492000 Couriers and 

messengers 

MULT X 

43493000 Warehousing and storage MULT X 44221000 Utilities MULT X 50511000 

Publishing 

industries, except 

Internet 

MULT X 

50512000 

Motion picture and 

sound recording 

industries 

MULT X 

50515000 Broadcasting, except Internet MULT X 50516000 Internet publishing and broadcasting MULT X 50517000 Telecommunications MULT X 50518000 ISPs, search portals, and data processing MULT X 50519000 Other information services MULT X 55521000 Monetary authorities - central bank MULT X 55522000 Credit intermediation and related activities - X Indirect 55522100 Depository credit intermediation MULT X 55522110 Commercial banking MULT X 55523000 

Securities, 

commodity 

contracts, 

investments 

MULT X 
38 

55524000 Insurance carriers and related activities MULT X 55525000 

Funds, trusts, and 

other financial 

vehicles 

MULT X 

55531000 Real estate MULT X 55532000 Rental and leasing services MULT X 55533000 

Lessors of 

nonfinancial 

intangible assets 

MULT X 

60541000 Professional and technical services - X Indirect 60541100 Legal services MULT X 60541200 

Accounting and 

bookkeeping 

services 

 ADD X 

60541300 Architectural and engineering services MULT X 60541500 

Computer systems 

design and related 

services 

MULT X 

60541600 

Management and 

technical consulting 

services 

ADD X 

60551000 

Management of 

companies and 

enterprises 

MULT X 

60561000 Administrative and support services - X Indirect 60561300 Employment services MULT X 60561320 Temporary help MULT X 
39 

services 

60561400 Business support services ADD X 60561700 Services to buildings and dwellings MULT X 60562000 

Waste management 

and remediation 

services 

MULT X 

65611000 Educational services MULT X 65621000 Ambulatory health care services - X Indirect 65621100 Offices of physicians MULT X 65621400 Outpatient care centers MULT X 65621600 Home health care services ADD X 65622000 Hospitals MULT X 65623000 

Nursing and 

residential care 

facilities 

- X Indirect 

65623100 Nursing care facilities MULT X 65624000 Social assistance - X Indirect 65624400 Child day care services ADD X 70711000 Performing arts and spectator sports MULT X 70712000 Museums, historical sites, zoos, and parks MULT X 70713000 

Amusements, 

gambling, and 

recreation 

ADD X 

70721000 Accommodations MULT X 
40 

70722000 Food services and drinking places ADD X 

80811000 Repair and maintenance MULT X 

80812000 Personal and laundry services MULT X 80813000 

Membership 

associations and 

organizations 

ADD 

90911000 Federal, except U.S. Postal Service MULT X 90919120 U.S. Postal Service MULT X 90921611 State government education ADD X 90922000 State government, excluding education MULT X 90931611 Local government education ADD X 90932000 Local government, excluding education MULT X Election adjustment3 

Seasonal Adjustment - WW 

NAICS 

Tabcode Mode 

4/5 

week 

adj 

Other adj 

10000000 Natural resources and mining MULT X 10210000 Mining MULT X 20000000 Construction MULT X 31000000 Durable goods MULT X 
41 

32000000 Nondurable goods MULT X 41420000 Wholesale trade MULT X 42000000 Retail trade MULT X 43000000 Transportation and warehousing ADD X 44220000 Utilities MULT X 50000000 Information MULT X 55520000 Finance and insurance MULT X 55530000 Real estate and rental and leasing MULT X 60540000 Professional and technical services MULT X 60550000 

Management of 

companies and 

enterprises 

ADD X 

60560000 Administrative and waste services MULT X 65610000 Educational services MULT X 65620000 Health care and social assistance MULT X 70710000 Arts, entertainment, and recreation MULT X 70720000 Accommodations and food services ADD X 80000000 Other services MULT X 90910000 Federal MULT X 90920000 State government MULT X 90930000 Local government MULT X Election adjustment 
42 

Seasonal Adjustment - PW 

NAICS 

Tabcode Mode 

4/5 

week 

adj 

Other adj 

10000000 Natural resources and mining ADD X 20000000 Construction ADD X 31321000 Wood products ADD X 31327000 Nonmetallic mineral products ADD X 31331000 Primary metals MULT X 31332000 Fabricated metal products MULT X 31333000 Machinery MULT X 31334000 Computer and electronic products MULT X 31335000 Electrical equipment and appliances MULT X 31336000 Transportation equipment MULT 31336001 Motor vehicles and parts MULT 31337000 Furniture and related products MULT X 31339000 Miscellaneous manufacturing MULT X 32311000 Food manufacturing MULT X 32312000 Beverages and tobacco products ADD X 32313000 Textile mills MULT X 
43 

32314000 Textile product mills ADD X 32315000 Apparel MULT X 32316000 Leather and allied products MULT X 32322000 Paper and paper products MULT X 32323000 Printing and related support activities MULT X 32324000 Petroleum and coal products MULT X 32325000 Chemicals ADD X 32326000 Plastics and rubber products MULT X 41420000 Wholesale trade MULT X 42000000 Retail trade MULT X 43000000 Transportation and warehousing MULT X 44220000 Utilities MULT X 50000000 Information MULT X 55000000 Financial activities ADD X 60000000 Professional and business services MULT X 65000000 Education and health services MULT X 70000000 Leisure and hospitality MULT X 80000000 Other services ADD X 

Seasonal Adjustment - AWH 

NAICS 

Tabcode Mode 

4/5 

week 

adj 

10/11 

day adj 

East

Lab

Day 
44 

adj 

10000000 Natural resources and mining MULT X X 20000000 Construction ADD X X 31321000 Wood products MULT X X 31327000 Nonmetallic mineral products MULT X X 31331000 Primary metals MULT X X 31332000 Fabricated metal products MULT X X 31333000 Machinery MULT X X 31334000 Computer and electronic products MULT X X 31335000 Electrical equipment and appliances MULT X X 31336000 Transportation equipment MULT X X 31336001 Motor vehicles and parts MULT X X 31337000 Furniture and related products MULT X X 31339000 Miscellaneous manufacturing MULT X X 32311000 Food manufacturing MULT X X 32312000 Beverages and tobacco products MULT X X 32313000 Textile mills ADD X X 32314000 Textile product mills MULT X X 32315000 Apparel MULT X X 32316000 Leather and allied products MULT X X 
45 

32322000 Paper and paper products MULT X X 32323000 Printing and related support activities MULT X X 32324000 Petroleum and coal products MULT X X 32325000 Chemicals MULT X 32326000 Plastics and rubber products MULT X X 41420000 Wholesale trade MULT X X 42000000 Retail trade MULT X 43000000 Transportation and warehousing MULT X X 44220000 Utilities MULT X 50000000 Information MULT X 55000000 Financial activities MULT X 60000000 Professional and business services MULT X X 65000000 Education and health services MULT X 70000000 Leisure and hospitality MULT X 80000000 Other services MULT X X 
46 

Seasonal Adjustment - AHE 

NAICS 

Tabcode Mode 

4/5 

week 

adj 

10/11 

day adj 

10000000 Natural resources and mining MULT X 20000000 Construction MULT X 31000000 Durable goods ADD X 32000000 Nondurable goods MULT X 41420000 Wholesale trade ADD X 42000000 Retail trade MULT X 43000000 Transportation and warehousing MULT X 44220000 Utilities MULT X 50000000 Information MULT X 55000000 Financial activities MULT X 60000000 Professional and business services MULT X 65000000 Education and health services MULT X 70000000 Leisure and hospitality ADD X 80000000 Other services MULT X 
47 

Seasonal Adjustment Comparison - AOT 

NAICS 

Tabcode Mode 

4/5 

week 

adj 

10/11 day 

adj 

East

Lab

Day 

31000000 Durable goods MULT X X 32000000 Nondurable goods MULT X X 

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 cons3 Special adjustment for the presence/absence of poll workers in local government 
48 

48 

Table 1. Percent differences between nonfarm employment benchmarks and est

industry supersector, March 1997-2006 (1)

Industry 1997 1998 1999 2000 2001 2002 2003 2004 

 Total 0.4 -2 0.2 0.4 -0.1 -0.2 -0.1 0.2 Total Private 0.5 0.1 0.2 0.3 -0.2 -0.4 -0.2 0.2 Natural Resources & Mining 0.9 0.7 Construction -0.8 0.6 Manufacturing -1.1 -0.4 Trade, Transportation, & Utilities -2 0.2 Information -2.6 -1 Financial Activities 0.2 0.1 Professional & Business Services -0.7 -0.2 Education & Health Services 0.3 0.2 Leisure & Hospitality 0.5 1.2 Other Services 1.4 0.5 Government -0.4 -0.2 0.1 0.6 0.3 1 0.3 0.1 

(1) Differences are based on comparisons of final published March estimates and benchmark levels, as orig(2) Less than 0.05 percent. 
49 

49 

Table 2. Nonfarm employment benchmarks by industry, March 2006(Numbers in thousands) 

Difference

 Industry Benchmark Estimate 

Amount Percent

Total nonfarm 134,868 134,116 752 Total private 112,561 111,812 749 Goods-producing 22,132 21,956 176 Service-providing 112,736 112,160 576 Private service-providing 90,429 89,856 573 Natural resources and mining 654 646 8 Logging 62 59 3 
50 

50 

Mining 592 588 4 Oil and gas extraction 130 131 -1 -Mining, except oil and gas 213 211 2 Coal mining 78 78 0 Support activities for mining 249 246 3 Construction 7,337 7,148 189 Construction of buildings 1,751 1,696 55 Heavy and civil engineering construction 899 904 -5 -Specialty trade contractors 4,688 4,548 140 Manufacturing 14,141 14,162 -21 -Durable goods 8,969 8,967 2 Wood products 563 550 13 Nonmetallic mineral products 503 496 7 Primary metals 464 473 -9 -
51 

51 

Fabricated metal products 1,540 1,531 9 

Machinery 1,177 1,172 5 Computer and electronic products 1,306 1,319 -13 -Computer and peripheral equipment 198 203 -5 -Communications equipment 145 149 -4 -Semiconductors and electronic components 455 452 3 Electronic instruments 435 444 -9 -Electrical equipment and appliances 433 440 -7 -Transportation equipment 1,772 1,776 -4 -Furniture and related products 564 557 7 Miscellaneous manufacturing 648 654 -6 -Nondurable goods 5,172 5,195 -23 -Food manufacturing 1,451 1,434 17 Beverages and tobacco products 189 189 0 Textile mills 203 204 -1 -Textile product mills 163 171 -8 -Apparel 244 253 -9 -
52 

52 

Leather and allied products 38 37 1 

Paper and paper products 473 474 -1 -Printing and related support activities 637 642 -5 -Petroleum and coal products 109 111 -2 -Chemicals 865 887 -22 -Plastics and rubber products 801 792 9 Trade, transportation, and utilities 25,928 25,770 158 Wholesale trade 5,844 5,797 47 Electronic markets and agents and brokers 772 748 24 Retail trade 15,145 15,067 78 Motor vehicle and parts dealers 1,898 1,899 -1 -Automobile dealers 1,242 1,240 2 Furniture and home furnishings stores 580 586 -6 -Electronics and appliance stores 541 532 9 Building material and garden supply stores 1,302 1,290 12 
53 

53 

Food and beverage stores 2,789 2,776 13 

Health and personal care stores 950 955 -5 -Gasoline stations 855 855 0 Clothing and clothing accessories stores 1,384 1,383 1 Sporting goods, hobby, book, and music stores 636 620 16 General merchandise stores 2,915 2,866 49 Department stores 1,531 1,555 -24 -Miscellaneous store retailers 873 883 -10 -Nonstore retailers 421 422 -1 -Transportation and warehousing 4,394 4,349 45 Air transportation 483 485 -2 -Rail transportation 224 226 -2 -Water transportation 60 60 0 Truck transportation 1,398 1,383 15 Transit and ground passenger transportation 411 409 2 Pipeline transportation 38 38 0 Scenic and sightseeing transportation 21 25 -4 -1
54 

54 

Support activities for transportation 565 560 5 

Couriers and messengers 571 572 -1 -Warehousing and storage 623 592 31 Utilities 546 557 -11 -Information 3,048 3,063 -15 -Publishing industries, except Internet 903 902 1 Motion picture and sound recording industries 377 383 -6 -Broadcasting, except Internet 328 324 4 Internet publishing and broadcasting 34 30 4 1Telecommunications 972 993 -21 -ISPs, search portals, and data processing 385 382 3 Other information services 51 49 2 Financial activities 8,282 8,247 35 Finance and insurance 6,153 6,119 34 
55 

55 

Monetary authorities - central bank 21 21 0 

Credit intermediation and related activities 2,921 2,911 10 Depository credit intermediation 1,792 1,808 -16 -Commercial banking 1,311 1,317 -6 -Securities, commodity contracts, investments 809 800 9 Insurance carriers and related activities 2,311 2,299 12 Funds, trusts, and other financial vehicles 92 89 3 Real estate and rental and leasing 2,129 2,128 1 Real estate 1,471 1,467 4 Rental and leasing services 631 633 -2 -Lessors of nonfinancial intangible assets 27 28 -1 -Professional and business services 17,225 16,995 230 Professional and technical services 7,369 7,241 128 Legal services 1,168 1,155 13 Accounting and bookkeeping services 996 967 29 
56 

56 

Architectural and engineering services 1,349 1,337 12 

Computer systems design and related services 1,252 1,233 19 Management and technical consulting services 900 871 29 Management of companies and enterprises 1,788 1,764 24 Administrative and waste services 8,068 7,990 78 Administrative and support services 7,727 7,660 67 Employment services 3,514 3,505 9 Temporary help services 2,519 2,485 34 Business support services 786 765 21 Services to buildings and dwellings 1,674 1,649 25 Waste management and remediation services 341 330 11 Education and health services 17,862 17,766 96 Educational services 3,052 2,998 54 
57 

57 

Health care and social assistance 14,810 14,768 42 

Ambulatory health care services 5,235 5,224 11 Offices of physicians 2,127 2,151 -24 -Outpatient care centers 488 489 -1 -Home health care services 856 834 22 Hospitals 4,390 4,395 -5 -Nursing and residential care facilities 2,869 2,872 -3 -Nursing care facilities 1,571 1,577 -6 -Social assistance 2,317 2,277 40 Child day care services 826 809 17 Leisure and hospitality 12,674 12,632 42 Arts, entertainment, and recreation 1,747 1,742 5 Performing arts and spectator sports 366 343 23 Museums, historical sites, zoos, and parks 113 113 0 Amusements, gambling, and recreation 1,268 1,286 -18 -
58 

58 

Accommodations and food services 10,926 10,889 37 

Accommodations 1,767 1,746 21 Food services and drinking places 9,160 9,144 16 Other services 5,410 5,383 27 Repair and maintenance 1,246 1,247 -1 -Personal and laundry services 1,276 1,265 11 Membership associations and organizations 2,887 2,872 15 Government 22,307 22,304 3 Federal 2,715 2,692 23 Federal, except U.S. Postal Service 1,947 1,927 20 U.S. Postal Service 768 765 3 State government 5,202 5,168 34 State government education 2,432 2,399 33 
59 

59 

State government, excluding education 2,771 2,769 2 

Local government 14,390 14,444 -54 -Local government education 8,255 8,281 -26 -Local government, excluding education 6,134 6,164 -30 -(1) Less than 0.05 percent. 
60 

60 

Table 3. Differences in seasonally adjusted levels and over-the-month changes, employment, January 2006-October 2006(In thousands) 

Levels Over-the-month changes

As previously 

published As revised As previously published As revised Difference

2006: January 134,530 135,110 154 206 52February 134,730 135,410 200 300 100March 134,905 135,659 175 249 74April 135,017 135,803 112 144 32May 135,117 135,906 100 103 3June 135,251 136,030 134 124 -10July 135,374 136,252 123 222 99August 135,604 136,438 230 186 -44September 135,807 136,636 203 198 -5October 135,893 136,745 86 109 23
61 

61 

Table 4. Hours and earnings estimates, selected industries, March 2006

Industry Average Weekly Hours Average Hourly Earnings

Total private 33.6 $16.56Goods-producing 40.2 17.73Natural resources and mining 44.7 19.57Construction 38.4 19.53Manufacturing 41.0 16.69Durable goods 41.4 17.52Wood products 40.0 13.14Nonmetallic mineral products 42.4 16.60Primary metals 43.5 19.21Fabricated metal products 41.4 16.08Machinery 42.2 16.99Computer and electronic products 40.6 18.58Electrical equipment and appliances 41.0 15.42Transportation equipment 42.9 22.31
62 

62 

Furniture and related products 38.4 13.52Miscellaneous manufacturing 38.8 14.30Nondurable goods 40.3 15.27Food manufacturing 39.3 13.04Beverages and tobacco products 40.1 18.12Textile mills 40.6 12.40Textile product mills 39.8 11.79Apparel 36.3 10.62Leather and allied products 39.8 11.11Paper and paper products 42.0 17.81Printing and related support activities 39.1 15.77Petroleum and coal products 44.3 24.58Chemicals 42.8 19.66Plastics and rubber products 40.7 14.84Private service-providing 32.1 16.24Trade, transportation, and utilities 33.0 15.23
63 

63 

Wholesale trade 37.6 18.60Retail trade 30.1 12.49Transportation and warehousing 36.4 17.05Utilities 40.7 27.55Information 36.2 22.85Financial activities 35.3 18.47Professional and business services 34.3 18.83Education and health services 32.3 17.21Leisure and hospitality 25.3 9.63Other services 30.7 14.69
64 

64 

Text Table A. Net Birth/Death Estimates, Post-Benchmark 2006 

(In thousands) 

Natural 

Resources 

& Mining 

Construction Manufacturing 

Trade, 

Transportation, 

& Utilities 

Information Financial Activities Professional & Business Services Education & Health Services 

H

April 1 36 -1 23 7 19 62 31 

May 1 37 4 24 4 4 33 13 

June 1 25 5 17 -2 4 28 -4 

July 1 4 -12 -15 -2 3 -8 -4 

August 1 15 2 21 3 9 22 16 

September 1 9 1 16 -1 3 10 11 

October 1 16 -4 27 3 25 30 33 

November 0 -2 2 17 2 8 7 7 

2006 

December 0 -7 3 19 2 17 3 7 

Cumulative Total 7 133 0 149 16 92 187 110