Benchmark Article 

BLS Establishment Estimates Revised to Incorporate March 2010 Benchmarks

Introduction

Summary of the benchmark revisions

Revisions in the post-benchmark period

Corrected historical CES data for the other federal government series

Other changes to the CES published series

Why benchmarks differ from estimates

Benchmark revision effects for other data types

Methods

Introduction of quarterly net birth/death adjustments

Availability of revised data

Small domain model

Seasonal adjustment procedure

Special model adjustments

Seasonal adjustment of new all employee hours and earning series

Introduction

Jennifer M. Harris

Jennifer M. Harris is an economist in the Division of Current Employment Statistics, Office of Employment and Unemployment 

Statistics, Bureau of Labor Statistics. Telephone: (202) 691-6555; e-mail: CESInfo@bls.gov

With the release of data for January 2011, the Bureau of Labor Statistics (BLS) introduced its annual revision of national estimates of 

employment, hours, and earnings from the Current Employment Statistics (CES) monthly survey of nonfarm establishments. Each 

year, the CES survey realigns its sample-based estimates to incorporate universe counts of employment—a process known as 

1
benchmarking. Comprehensive counts of employment, or benchmarks, are derived primarily from unemployment insurance (UI) tax 

reports that nearly all employers are required to file with State Workforce Agencies. 

Summary of the benchmark revisions

The March 2010 benchmark level for total nonfarm employment is 128,584,000; this figure is 378,000 below the sample-based 

estimate for March 2010, an adjustment of -0.3 percent. Table 1 shows the total nonfarm percentage benchmark revisions for the past 

ten years.

Table 1. Percent differences between nonfarm employment benchmarks and estimates by industry supersector, March 2001-

2010 (1)

Industry 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Total - 0.1 - 0.2 - 0.1 0.2 - 0.1 0.6 -0.2 -0.1 -0.7 -0.3

Total private - .2 - .4 - .2 .2 - .2 .7 -.2 -.1 -.9 -.4

Mining & logging .9 .7 -.3 1.2 (2) .4 -3.5 -3.0

Construction -.8 .6 .5 2.6 .1 .7 -2.9 -1.3

Manufacturing -1.1 -.4 -.3 -.1 -1.0 -.1 -.7 -1.0

Trade, transportation, & utilities (2) .2 .3 .6 .5 .2 -1.2 -.6

Information -2.6 -1.0 -2.1 -.5 -1.8 .3 -1.5 -.4

Financial activities .2 .1 -.8 .4 -1.3 -.3 -.1 .4

Professional & business services -.7 -.2 -.4 1.3 .2 -.4 -.8 (2)

Education & health services .3 .2 (2) .5 -.2 -.1 -.3 (2)

Leisure & hospitality .5 1.2 .4 .3 -.8 -1.1 -.6 -.6

Other services 1.4 .5 -1.3 .5 .3 .2 -.8 .2

Government .3 1.0 .3 .1 (2) (2)

-.2 .2 .1 .1

(1) Differences are based on comparisons of final published March estimates and benchmark levels, as originally published.

(2) Less than 0.05 percent.

Table 2 shows the nonfarm employment benchmarks for March 2010, not seasonally adjusted, by industry. The majority of super 

sectors had downward revisions, with the exception of government, financial activities, professional and business services, and other 

services. The largest downward revision occurred in trade, transportation, and utilities with a revision of -143,000, or -0.6 percent. 

Within this sector, the revision is concentrated in wholesale trade, which revised by -124,500, or -2.3 percent. It is of note that within 

2
trade, transportation, and utilities there were a series of large offsetting revisions made to utilities. Utilities as a whole was only 

revised downwards 3,400, or 0.6 percent, but electric power generation was revised by -67,800, or -39.9 percent. This revision was 

offset by a positive revision in electric power transmission and distribution, revised upwards by 65,600, or 28.7 percent. 

Manufacturing had a downward revision of 119,000, or 1.0 percent, and leisure and hospitality had a revision of -80,000, or -0.6 

percent. The revision in manufacturing was concentrated in durable goods with a downward revision of 82,000, or 1.2 percent, while 

nondurable goods was revised downward by 37,000, or 0.8 percent. The largest downward revision within leisure and hospitality was 

food services and drinking places with a revision of 92,700, or 1.0 percent. 

Table 2. Nonfarm employment benchmarks by industry for March 2010 (in thousands)

Industry Benchmark Estimate

Differences

Amount Percent

Total nonfarm 128,584 128,962 -378 -0.3

Total private 105,671 106,062 -391 -.4

Goods-producing 17,248 17,454 -206 -1.2

Service-providing 111,336 111,508 -172 -.2

Private service-providing 88,423 88,608 -185 -.2

Mining and logging 668 688 -20 -3.0

Logging 48 46 2 4.2

Mining 621 642 -21 -3.4

Oil and gas extraction 156 161 -5 -3.2

Mining, except oil and gas 194 205 -11 -5.7

Coal mining 79 81 -2 -2.5

Support activities for mining 271 276 -5 -1.8

Construction 5,213 5,280 -67 -1.3

Construction of buildings 1,186 1,214 -28 -2.4

Heavy and civil engineering construction 735 721 14 1.9

Specialty trade contractors 3,292 3,345 -53 -1.6

Manufacturing 11,367 11,486 -119 -1.0

Durable goods 6,960 7,042 -82 -1.2

Wood products 333 339 -6 -1.8

Nonmetallic mineral products 358 369 -11 -3.1

3
Table 2. Nonfarm employment benchmarks by industry for March 2010 (in thousands) — continued

Industry Benchmark Estimate

Differences

Amount Percent

Primary metals 353 362 -9 -2.5

Fabricated metal products 1,253 1,273 -20 -1.6

Machinery 978 980 -2 -.2

Computer and electronic products 1,093 1,091 2 .2

Computer and peripheral equipment 159 158 1 .6

Communications equipment 116 119 -3 -2.6

Semiconductors and electronic components 366 362 4 1.1

Electronic instruments 406 405 1 .2

Electrical equipment and appliances 353 364 -11 -3.1

Transportation equipment 1,319 1,336 -17 -1.3

Furniture and related products 356 356 0

(1)

Miscellaneous manufacturing 564 572 -8 -1.4

Nondurable goods 4,407 4,444 -37 -.8

Food manufacturing 1,418 1,427 -9 -.6

Beverages and tobacco products 178 179 -1 -.6

Textile mills 119 123 -4 -3.4

Textile product mills 118 121 -3 -2.5

Apparel 159 165 -6 -3.8

Leather and allied products 27 29 -2 -7.4

Paper and paper products 393 394 -1 -.3

Printing and related support activities 487 494 -7 -1.4

Petroleum and coal products 110 110 0

(1)

Chemicals 785 781 4 .5

Plastics and rubber products 614 622 -8 -1.3

Trade, transportation, and utilities 24,278 24,421 -143 -.6

Wholesale trade 5,410 5,535 -125 -2.3

Electronic markets and agents and brokers 799 823 -24 -3.0

Retail trade 14,204 14,222 -18 -.1

Motor vehicle and parts dealers 1,599 1,608 -9 -.6

Automobile dealers 997 1,009 -12 -1.2

4
Table 2. Nonfarm employment benchmarks by industry for March 2010 (in thousands) — continued

Industry Benchmark Estimate

Differences

Amount Percent

Furniture and home furnishings stores 432 435 -3 -.7

Electronics and appliance stores 492 479 13 2.6

Building material and garden supply stores 1,132 1,154 -22 -1.9

Food and beverage stores 2,779 2,776 3 .1

Health and personal care stores 974 968 6 .6

Gasoline stations 804 809 -5 -.6

Clothing and clothing accessories stores 1,318 1,334 -16 -1.2

Sporting goods, hobby, book, and music stores 585 593 -8 -1.4

General merchandise stores 2,935 2,903 32 1.1

Department stores 1,453 1,448 5 .3

Miscellaneous store retailers 747 754 -7 -.9

Nonstore retailers 408 410 -2 -.5

Transportation and warehousing 4,111 4,108 3 .1

Air transportation 462 452 10 2.2

Rail transportation 211 215 -4 -1.9

Water transportation 60 61 -1 -1.7

Truck transportation 1,208 1,201 7 .6

Transit and ground passenger transportation 437 429 8 1.8

Pipeline transportation 42 39 3 7.1

Scenic and sightseeing transportation 21 22 -1 -4.8

Support activities for transportation 532 534 -2 -.4

Couriers and messengers 514 517 -3 -.6

Warehousing and storage 625 639 -14 -2.2

Utilities 553 556 -3 -.5

Information 2,715 2,726 -11 -.4

Publishing industries, except Internet 762 762 0

(1)

Motion picture and sound recording industries 365 342 23 6.3

Broadcasting, except Internet 294 295 -1 -.3

Telecommunications 913 942 -29 -3.2

Data processing, hosting and related services 244 249 -5 -2.0

5
Table 2. Nonfarm employment benchmarks by industry for March 2010 (in thousands) — continued

Industry Benchmark Estimate

Differences

Amount Percent

Other information services 138 136 2 1.4

Financial activities 7,606 7,572 34 .4

Finance and insurance 5,695 5,656 39 .7

Monetary authorities - central bank 21 21 0

(1)

Credit intermediation and related activities 2,544 2,567 -23 -.9

Depository credit intermediation 1,727 1,748 -21 -1.2

Commercial banking 1,304 1,310 -6 -.5

Securities, commodity contracts, investments 796 791 5 .6

Insurance carriers and related activities 2,249 2,194 55 2.4

Funds, trusts, and other financial vehicles 86 85 1 1.2

Real estate and rental and leasing 1,911 1,915 -4 -.2

Real estate 1,379 1,368 11 .8

Rental and leasing services 506 523 -17 -3.4

Lessors of nonfinancial intangible assets 25 25 0

(1)

Professional and business services 16,343 16,346 -3

(1)

Professional and technical services 7,468 7,469 -1

(1)

Legal services 1,109 1,101 8 .7

Accounting and bookkeeping services 1,005 1,025 -20 -2.0

Architectural and engineering services 1,261 1,261 0

(1)

Computer systems design and related services 1,417 1,429 -12 -.8

Management and technical consulting services 982 974 8 .8

Management of companies and enterprises 1,850 1,815 35 1.9

Administrative and waste services 7,025 7,062 -37 -.5

Administrative and support services 6,681 6,722 -41 -.6

Employment services 2,517 2,571 -54 -2.1

Temporary help services 1,908 1,922 -14 -.7

Business support services 809 799 10 1.2

Services to buildings and dwellings 1,591 1,576 15 .9

Waste management and remediation services 344 340 4 1.2

Education and health services 19,599 19,592 7

(1)

6
Table 2. Nonfarm employment benchmarks by industry for March 2010 (in thousands) — continued

Industry Benchmark Estimate

Differences

Amount Percent

Educational services 3,285 3,283 2 .1

Health care and social assistance 16,314 16,309 5

(1)

Ambulatory health care services 5,924 5,873 51 .9

Offices of physicians 2,306 2,308 -2 -.1

Outpatient care centers 594 548 46 7.7

Home health care services 1,066 1,056 10 .9

Hospitals 4,666 4,697 -31 -.7

Nursing and residential care facilities 3,105 3,102 3 .1

Nursing care facilities 1,649 1,646 3 .2

Social assistance 2,620 2,637 -17 -.6

Child day care services 868 882 -14 -1.6

Leisure and hospitality 12,578 12,658 -80 -.6

Arts, entertainment, and recreation 1,740 1,737 3 .2

Performing arts and spectator sports 370 371 -1 -.3

Museums, historical sites, zoos, and parks 119 121 -2 -1.7

Amusements, gambling, and recreation 1,251 1,245 6 .5

Accommodation and food services 10,838 10,920 -82 -.8

Accommodation 1,679 1,668 11 .7

Food services and drinking places 9,159 9,252 -93 -1.0

Other services 5,304 5,293 11 .2

Repair and maintenance 1,128 1,139 -11 -1.0

Personal and laundry services 1,259 1,265 -6 -.5

Membership associations and organizations 2,917 2,890 27 .9

Government 22,913 22,900 13 .1

Federal 2,905 2,895 10 .3

Federal, except U.S. Postal Service 2,244 2,235 9 .4

U.S. Postal Service 661 661 0

(1)

State government 5,280 5,315 -35 -.7

State government education 2,507 2,540 -33 -1.3

State government, excluding education 2,774 2,775 -1

(1)

7
Table 2. Nonfarm employment benchmarks by industry for March 2010 (in thousands) — continued

Industry Benchmark Estimate

Differences

Amount Percent

Local government 14,728 14,690 38 .3

Local government education 8,432 8,379 53 .6

Local government, excluding education 6,296 6,312 -16 -.3

(1)Less than 0.05 percent.

Table 3. Differences in seasonally adjusted levels and over-the-month changes, total nonfarm employment, January 2010-

December 2010 (in thousands)

2010

Levels Over-the-month changes

As previously 

published As revised Difference

As previously 

published As revised Difference

January 129,602 129,281 -321 14 -39 -53

February 129,641 129,246 -395 39 -35 -74

March 129,849 129,438 -411 208 192 -16

April 130,162 129,715 -447 313 277 -36

May 130,594 130,173 -421 432 458 26

June 130,419 129,981 -438 -175 -192 -17

July 130,353 129,932 -421 -66 -49 17

August 130,352 129,873 -479 -1 -59 -58

September 130,328 129,844 -484 -24 -29 -5

October 130,538 130,015 -523 210 171 -39

November 130,609 130,108 -501 71 93 22

December(p) 130,712 130,229 -483 103 121 18

(p) Preliminary

Construction was revised by -67,000, or -1.3 percent, while mining and logging was revised -20,000, or -3.0 percent. One of the 

largest revisions within construction was to building equipment contractors, which was revised downward by 32,700, or 2.1 percent. 

Information had a downward revision of 11,000, or 0.4 percent. Within the supersector the largest revision was in telecommunications 

with a revision of -29,000, or -3.2 percent. 

8
Four sectors saw positive revisions. Financial activities was revised upward by 34,000, or 0.4 percent. The revision in financial 

activities was concentrated in insurance carriers and related activities which was revised upward by 55,000, or 2.5 percent. 

Government was revised upward by 13,000, or 0.1 percent, and other services had a revision of 11,000, or 0.2 percent. Education and 

health services had a negligible revision upward of 7,000, or less than 0.05 percent.

Revisions in the post-benchmark period

Post-benchmark period estimates from April 2010 to December 2010 were calculated for each month based on new benchmark levels. 

Also, beginning in April, model-based estimates for the net birth/death employment were revised to incorporate information from the 

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

post-benchmark period. From April 2010 to December 2010, the cumulative net birth/death model added 537,000, compared with 

759,000 in the previously published April to December estimates. 

Text Table A. Net Birth/Death Estimates, Post-Benchmark 2010 (in thousands)

2010

Mining & 

Logging

Construction

Manufacturing

Trade, 

Transportation, 

& Utilities

Information

Financial Activities

Professional & 

Business 

Services

Education & 

Health Services

Leisure & 

Hospitality

Other Services

Monthly 

Amount 

Contributed

April 0 8 -9 -1 2 -2 50 20 66 7 141

May 1 36 7 21 3 6 18 15 78 7 192

June 2 21 5 12 1 5 15 -7 72 5 131

July 1 -18 -11 -19 -3 -11 -1 2 29 -7 -38

August 1 5 3 11 4 5 19 19 21 3 91

September 1 4 0 6 0 -1 -9 11 -36 -1 -25

October 1 0 -3 16 4 11 42 45 -44 -1 71

November 0 -16 1 -2 1 0 2 7 -24 -1 -32

December 0 -19 0 6 2 11 -2 2 4 2 6

Cumulative 

Total 7 21 -7 50 14 24 134 114 166 14 537

9
Revisions to November and December also reflect incorporation of the annual CES sample update and the routine inclusion of 

additional sample units not available for the respective months' preliminary estimates.

Corrected historical CES data for the other federal government series

With the 2010 benchmark revision BLS corrected historical CES employment levels for all employees and women employees for the

other federal government series (91-999900). CES data series on hours and earnings for all employees and for production and 

nonsupervisory employees were not impacted. The corrections affected April 2009 to April 2010 data, and reflect corrections to initial 

counts for Census temporary and intermittent workers for Census 2010. These corrections were applied before any benchmark 

processing was done. The corrected March 2010 value was used to calculate the benchmark revision for this industry, and the 

corrected values from April 2009 to February 2010 were then subject to the normal wedging process. (For more information on the 

wedging process please reference the section on benchmark methods.) The corrected values for March and April 2010 were used to 

calculate a sample link for April 2010, which was applied to the new benchmark level during the post-benchmark processing to 

generate the April 2010 post-benchmark estimate. These corrections did not impact employment levels after April 2010; postbenchmark estimates for May through October 2010 were calculated using a sample link generated from previously published values. 

Exhibit 1 shows the previously published employment level, the corrected value applied before benchmark processing, and the 

difference between the two for all employees. Exhibit 2 shows the same for women employees.

Exhibit 1. Corrections to all employees for other federal government (91-999900)

Month

Previously published 

employment

Corrected value applied before 

benchmark processing Difference

Apr-09 1,377.2 1,373.4 -3.8

May-09 1,330.4 1,324.2 -6.2

Jun-09 1,289.5 1,292.3 2.8

Jul-09 1,298.8 1,301.6 2.8

Aug-09 1,296.3 1,300.3 4.0

Sep-09 1,287.9 1,292.3 4.4

Oct-09 1,296.9 1,301.4 4.5

Nov-09 1,285.3 1,288.8 3.5

Dec-09 1,287.3 1,291.0 3.7

Jan-10 1,289.4 1,291.9 2.5

10
Exhibit 1. Corrections to all employees for other federal government (91-999900) — continued

Month

Previously published 

employment

Corrected value applied before 

benchmark processing Difference

Feb-10 1,312.9 1,314.5 1.6

Mar-10 1,367.3 1,371.6 4.3

Apr-10 1,452.5 1,443.7 -8.8

Exhibit 2. Corrections to women employees for other federal government (91-999900)

Month Previously published employment

Corrected value applied before 

benchmark processing Difference

Apr-09 654.3 652.5 -1.8

May-09 626.8 623.9 -2.9

Jun-09 603.9 605.2 1.3

Jul-09 606.3 607.6 1.3

Aug-09 604.9 606.8 1.9

Sep-09 602.1 604.2 2.1

Oct-09 607.9 610.0 2.1

Nov-09 604.0 605.7 1.7

Dec-09 605.0 606.7 1.7

Jan-10 608.2 609.4 1.2

Feb-10 623.0 623.7 0.7

Mar-10 650.6 652.6 2.0

Apr-10 691.3 686.9 -4.4

Other changes to the CES published series

All CES series are evaluated annually for sample size, coverage, and response rates. The following series changes result from a reevaluation of the sample and universe coverage for NAICS industries. Some small industries no longer have sufficient sample to be 

estimated and published separately and have been combined with other similar industries for estimation and publication purposes, as 

shown below. Most of the collapsed and deleted series are in the manufacturing sector where employment has been declining over a 

number of years. Historical data for the series with changed scope were reconstructed to provide consistent time series.

11
Exhibit 3. Series with changed scope effective with the March 2010 benchmark revisions 

Industry title

NAICS 

code

CES 

industry 

code Industries collapsed

Prefabricated metal 

buildings, components, and 

plate work

332311,3 31-332313 Prefabricated metal buildings and components (31-332311) is collapsed 

into Plate work (31-332313). The collapsed series is renamed 

Prefabricated metal buildings, components, and plate work.

Miscellaneous electronic 

instruments

334512,4,6-

9

31-334519 Automatic environmental controls (31-334512) is collapsed into 

Miscellaneous electronic instruments (31-334519).

Railroad rolling stock and 

other transportation 

equipment

3365,9 31-336900 Railroad rolling stock (31-336500) is collapsed into Other transportation 

equipment (31-336900). The collapsed series is renamed Railroad rolling 

stock and other transportation equipment.

Stationery and other 

converted paper products

32223,9 32-322290 Stationery products (32-322230) is collapsed into Other converted paper 

products (32-322290). The series is renamed Stationery and other 

converted paper products.

Miscellaneous commercial 

printing

323115-9 32-323119 Manifold business forms printing (32-323116) is collapsed into 

Miscellaneous commercial printing (32-323119). 

All other nondurable goods 

wholesalers

42494,5,9 41-424990 Tobacco and tobacco products (41-424940) is collapsed into Paint, 

painting supplies, and other nondurable goods (41-424990). The series is 

renamed All other nondurable goods wholesalers.

Urban, interurban, rural, and 

charter bus transportation

4851,2,5 43-485500 Urban transit, interurban and rural bus transportation (43-485200) is 

collapsed into Charter bus industry (43-485500). The series is renamed 

Urban, interurban, rural, and charter bus transportation.

Exhibit 4. Discontinued all employee series 

Industry title

NAICS 

code

CES industry 

code Next highest published level

Hand and edge tools 332212 31-332212 Cutlery and hand tools (31-332200)

Conveyor and conveying equipment 333922 31-333922 Material handling equipment (31-333920)

12
Exhibit 4. Discontinued all employee series — continued

Industry title

NAICS 

code

CES industry 

code Next highest published level

Dairy products, except frozen 31151 32-311510 Dairy products (32-311500)

Ice cream and frozen dessert 31152 32-311520 Dairy products (32-311500)

Broadwoven fabric finishing mills 313311 32-313311 Textile and fabric finishing mills (32-313300)

Coated and laminated package materials and 

paper

322221,2 32-322222 Paper bags and coated and treated paper (32-

322220)

Miscellaneous coated and treated paper and 

paper bags

322223-6 32-322226 Paper bags and coated and treated paper (32-

322220)

Exhibit 5. Discontinued production employment, hours, and earnings series 

Industry title

NAICS 

code

CES industry 

code Next highest published level

Metal ore mining 2122 10-212200 Mining, except oil and gas (10-212000)

Nonmetallic mineral mining and quarrying 2123 10-212300 Mining, except oil and gas (10-212000)

Other nonmetallic mineral mining 21239 10-212390 Mining, except oil and gas (10-212000)

Audio and video equipment 3343 31-334300 Computer and electronic products (31-334000)

Miscellaneous electronic instruments 334514,6-9 31-334519 Electronic instruments (31-334500)

Magnetic media manufacturing and 

reproduction

3346 31-334600 Computer and electronic products (31-334000)

Railroad rolling stock 3365 31-336500 Transportation equipment (31-336000)

Other transportation equipment 3369 31-336900 Transportation equipment (31-336000)

Miscellaneous commercial printing 323111,5,7-

9

32-323119 Commercial lithograph printing (32-323110)

Paint, painting supplies, and other nondurable 

goods

42499 41-424990 Miscellaneous nondurable goods (41-424900)

Scenic and sightseeing transportation 487 43-487000 Transportation and warehousing (43-000000)

Credit card issuing 52221 55-522210 Nondepository credit intermediation (55-

522200)

Other nondepository credit intermediation 52229 55-522290 Nondepository credit intermediation (55-

13
Exhibit 5. Discontinued production employment, hours, and earnings series — continued

Industry title

NAICS 

code

CES industry 

code Next highest published level

522200)

Miscellaneous nondepository credit 

intermediation

522293,4,8 55-522298 Nondepository credit intermediation (55-

522200)

Miscellaneous intermediation 52391 55-523910 Other financial investment activities (55-

523900)

All other financial investment activities 52399 55-523990 Other financial investment activities (55-

523900)

Amusement parks and arcades 7131 70-713100 Amusements, gambling, and recreation (70-

713000)

Other amusement and recreation industries 7139 70-713900 Amusements, gambling, and recreation (70-

713000)

All other amusement and recreation industries 71399 70-713990 Amusements, gambling, and recreation (70-

713000)

Exhibit 6. Discontinued all employee and production employee average overtime series 

Industry title NAICS code CES industry code

Audio and video equipment 3343 31-334300

Miscellaneous electronic instruments 334514,6-9 31-334519

Magnetic media manufacturing and reproduction 3346 31-334600

Miscellaneous commercial printing 323111,5,7-9 32-323119

Why benchmarks differ from estimates

A benchmark revision is the difference between the benchmark employment level for a given March and its corresponding samplebased estimate. The overall accuracy of the establishment survey is usually gauged by the size of this difference. The benchmark 

revision often is regarded as a proxy for total survey error, but this does not take into account error in the universe data. The 

employment counts obtained from quarterly unemployment insurance tax forms are administrative data that reflect employer record14
keeping practices and differing State laws and procedures. The benchmark revision can be more precisely interpreted as the difference 

between 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 nonsampling 

error. Sampling error is present any time a sample is used to make inferences about a population. The magnitude of the sampling error, 

or variance, relates directly to sample size and the percentage of the universe covered by that sample. The CES monthly survey 

captures slightly under one-third of the universe, exceptionally high by usual sampling standards. This coverage ensures a small 

sampling error at the total nonfarm employment level.

Both the universe counts and the establishment survey estimates are subject to nonsampling errors common to all surveys—coverage, 

response, and processing errors. The error structures for both the CES monthly survey and the UI universe are complex. Still, the two 

programs generally produce consistent total employment figures, each validating the other. Over the last decade, annual benchmark 

revisions at the total nonfarm level have averaged 0.3 percent (in absolute terms), with an absolute range of 0.1 percent to 0.7 percent.

Benchmark revision effects for other data types

The routine benchmarking process results in revisions to the series for production and nonsupervisory workers. There are no 

benchmark employment levels for these series; they are revised by preserving ratios of employment for the particular data type to all 

employees employment prior to benchmarking, and then applying these ratios to the revised all employee figures. 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 supplied by survey 

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 or nonsupervisory 

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

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

estimates for hours and earnings of production or nonsupervisory employees at higher levels of aggregation.

Generally, new employment benchmarks have little effect on hours and earnings estimates for major groupings. To influence the 

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

hours or earnings averages that are substantially different from those of other industries in their group. Table 4A and Table 4B give 

information on the levels of specific hours and earnings series resulting from the March 2010 benchmark. At the total private level, 

15
there was no change in average weekly hours for all employees and production and nonsupervisory employees from the previously

published level. Average hourly earnings increased by 3 cents for all employees and 4 cents for production and nonsupervisory 

employees from the previously published level.

Table 4a. Effect of March 2010 benchmark revisions to all employee hours and earnings estimates, 

selected industries

Industry

Average weekly hours Average hourly earnings

As previously 

published

As 

revised Difference

As previously 

published

As 

revised Difference

Total private 33.8 33.8 0.0 $22.48 $22.51 $0.03

Goods-producing 39.1 39.1 0 23.95 23.97 .02

Private service-providing 32.8 32.7 -.1 22.13 22.18 .05

Mining and logging 42.7 42.8 .1 27.38 27.34 -.04

Construction 37.0 36.9 -.1 25.26 25.23 -.03

Manufacturing 39.9 39.9 0 23.18 23.22 .04

Durable goods 40.2 40.2 0 24.66 24.72 .06

Wood products 38.3 38.3 0 17.31 17.24 -.07

Nonmetallic mineral products 38.6 38.6 0 20.22 20.22 0

Primary metals 42.2 42.2 0 23.46 23.55 .09

Fabricated metal products 39.6 39.6 0 21.19 21.21 .02

Machinery 40.6 40.6 0 24.14 24.14 0

Computer and electronic 

products 40.2 40.2 0 32.13 32.12 -.01

Electrical equipment and 

appliances 39.9 39.9 0 21.51 21.51 0

Transportation equipment 42.2 42.2 0 28.40 28.56 .16

Furniture and related products 38.1 38.1 0 18.41 18.46 .05

Miscellaneous manufacturing 38.2 38.2 0 23.01 23.02 .01

Nondurable goods 39.4 39.4 0 20.78 20.81 .03

Food manufacturing 39.2 39.2 0 17.22 17.21 -.01

Beverages and tobacco 36.9 36.9 0 23.51 23.51 0

16
Table 4a. Effect of March 2010 benchmark revisions to all employee hours and earnings estimates, 

selected industries — continued

Industry

Average weekly hours Average hourly earnings

As previously 

published

As 

revised Difference

As previously 

published

As 

revised Difference

products

Textile mills 40.8 40.9 .1 16.50 16.48 -.02

Textile product mills 37.2 37.2 0 14.54 14.55 .01

Apparel 36.2 36.2 0 15.43 15.42 -.01

Leather and allied products 37.4 37.4 0 16.94 16.94 0

Paper and paper products 40.6 40.6 0 22.73 22.75 .02

Printing and related support 

activities 37.3 37.3 0 20.56 20.56 0

Petroleum and coal products 41.5 41.5 0 34.05 33.99 -.06

Chemicals 40.3 40.3 0 27.32 27.31 -.01

Plastics and rubber products 41.3 41.3 0 19.76 19.77 .01

Trade, transportation, and 

utilities 33.8 33.7 -.1 19.68 19.61 -.07

Wholesale trade 37.6 37.6 0 25.99 25.99 0

Retail trade 30.8 30.9 .1 15.59 15.56 -.03

Transportation and 

warehousing 37.7 37.6 -.1 20.88 20.86 -.02

Utilities 40.4 40.3 -.1 32.75 32.39 -.36

Information 36.2 36.2 0 30.07 30.13 .06

Financial activities 36.7 36.7 0 26.88 26.96 .08

Professional and business 

services 35.0 35.1 .1 27.17 27.19 .02

Education and health services 32.7 32.6 -.1 22.62 22.70 .08

Leisure and hospitality 25.6 25.6 0 13.13 13.15 .02

Other services 31.5 31.3 -.2 19.97 20.25 .28

17
Table 4b. Effect of March 2010 benchmark revisions to production employee hours and earnings estimates, 

selected industries

Industry

Average weekly hours Average hourly earnings

As previously 

published

As 

revised Difference

As previously 

published

As 

revised Difference

Total private 33.1 33.1 0.0 $18.91 $18.95 $0.04

Goods-producing 39.9 39.9 0 20.05 20.05 0

Private service-providing 32.0 32.0 0 18.68 18.72 .04

Mining and logging 43.6 43.7 .1 24.10 24.10 0

Construction 37.4 37.3 -.1 23.04 23.01 -.03

Manufacturing 40.8 40.9 .1 18.44 18.47 .03

Durable goods 41.1 41.1 0 19.63 19.67 .04

Wood products 38.7 38.7 0 14.80 14.76 -.04

Nonmetallic mineral products 40.4 40.4 0 17.30 17.30 0

Primary metals 43.3 43.3 0 20.11 20.19 .08

Fabricated metal products 40.8 40.9 .1 17.92 17.91 -.01

Machinery 41.8 41.7 -.1 18.56 18.55 -.01

Computer and electronic 

products 41.2 41.2 0 22.45 22.44 -.01

Electrical equipment and 

appliances 41.0 41.0 0 16.72 16.72 0

Transportation equipment 42.7 42.7 0 24.94 25.09 .15

Furniture and related products 38.4 38.5 .1 14.89 14.90 .01

Miscellaneous manufacturing 38.7 38.7 0 16.38 16.39 .01

Nondurable goods 40.5 40.5 0 16.65 16.67 .02

Food manufacturing 40.4 40.4 0 14.35 14.33 -.02

Beverages and tobacco 

products 35.6 35.6 0 22.13 22.13 0

Textile mills 41.2 41.3 .1 13.50 13.49 -.01

Textile product mills 39.6 39.6 0 11.61 11.61 0

Apparel 36.4 36.4 0 11.32 11.32 0

18
Table 4b. Effect of March 2010 benchmark revisions to production employee hours and earnings estimates, 

selected industries — continued

Industry

Average weekly hours Average hourly earnings

As previously 

published

As 

revised Difference

As previously 

published

As 

revised Difference

Leather and allied products 38.6 38.6 0 13.19 13.19 0

Paper and paper products 42.3 42.3 0 19.78 19.80 .02

Printing and related support 

activities 38.0 38.0 0 17.04 17.04 0

Petroleum and coal products 42.4 42.3 -.1 31.56 31.48 -.08

Chemicals 42.1 42.1 0 20.55 20.55 0

Plastics and rubber products 41.9 41.9 0 15.65 15.65 0

Trade, transportation, and 

utilities 32.9 32.8 -.1 16.76 16.71 -.05

Wholesale trade 37.5 37.5 0 21.26 21.25 -.01

Retail trade 29.8 29.8 0 13.18 13.16 -.02

Transportation and 

warehousing 36.4 36.2 -.2 19.13 19.10 -.03

Utilities 41.4 41.2 -.2 30.02 29.73 -.29

Information 36.2 36.2 0 25.52 25.53 .01

Financial activities 35.8 35.8 0 21.35 21.42 .07

Professional and business 

services

34.8 34.8 0 22.66 22.66 0

Education and health services 32.0 32.0 0 19.80 19.93 .13

Leisure and hospitality 24.7 24.7 0 11.33 11.34 .01

Other services 30.6 30.5 -.1 16.87 17.13 .26

Methods

Benchmark adjustment procedure. Establishment survey benchmarking is done on an annual basis to a population derived primarily 

from the administrative file of employees covered by unemployment insurance (UI). The time required to complete the revision 

process—from the full collection of the UI population data to publication of the revised industry estimates—is about 10 months. The 

19
benchmark adjustment procedure replaces the March sample-based employment estimates with UI-based population counts for 

March. The benchmark therefore determines the final employment levels, while sample movements capture month-to-month trends. 

Benchmarks are established for each basic estimating cell and are aggregated to develop published levels. On a not-seasonallyadjusted basis, the sample-based estimates for the year preceding and the year following the benchmark also are then subject to 

revision. Employment estimates for the months between the most recent March benchmark and the previous year's benchmark are 

adjusted using a "wedge-back" procedure. In this process, the difference between the benchmark level and the previously published 

March estimate for each estimating cell is computed. This difference, or error, is linearly distributed across the 11 months of estimates 

subsequent to the previous benchmark; eleven-twelfths of the March difference is added to February estimates, ten-twelfths to January 

estimates, and so on, ending with the previous April estimates, which receive one-twelfth of the March difference. The wedge 

procedure assumes that the total estimation error accumulated at a steady rate since the last benchmark. Applying previously derived 

over-the-month sample changes to the revised March level yields revised estimates for the months following the March benchmark.

New net birth/death model estimates also are calculated and applied during post-benchmark estimation, and new sample is introduced 

from the annual update.

Benchmark source material. The principal source of benchmark data for private industries is the Quarterly Census of Employment and 

Wages (QCEW). These employment data are provided to State Employment Security Agencies by employers covered by State UI 

laws. BLS uses several other sources to establish benchmarks for the remaining industries partially covered or exempt from 

mandatory UI 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 Patterns, are used to 

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

religious organizations, and private schools and hospitals. Benchmarks for State and local government hospitals and educational 

institutions are based on the Annual Census of Governments conducted by the Census Bureau. Benchmark data from these sources 

are available only on a lagged basis. Extrapolation to a current level is accomplished by applying the employment trends from the UIcovered part of the population in these industries to the noncovered part. Universe data for interstate railroads are obtained from the 

Railroad Retirement Board. 

Business birth and death estimation. Regular updating of the CES sample frame with information from the UI universe files helps to 

keep the CES survey current with respect to employment from business births and business deaths. The timeliest UI universe files 

available, however, always will be a minimum of 9 months out of date. The CES survey thus cannot rely on regular frame 

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

20
demonstrated that sampling for births was not feasible in the very short CES production timeframes, the Bureau is utilizing a modelbased approach for this component. 

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

contribution is relatively small and stable. To account for this net birth/death portion of total employment, BLS is utilizing an 

estimation procedure with two components. The first component excludes employment losses from business deaths from samplebased estimation in order to offset the missing employment gains from business births. This is incorporated into the sample-based link 

relative estimate procedure by simply not reflecting sample units going out of business, but imputing to them the same trend as the 

other firms in the sample. The second component is an ARIMA time series model designed to estimate the residual net birth/death 

employment not accounted for by the imputation. The historical time series used to create and test the ARIMA model was derived 

from the UI universe micro level database, and reflects the actual residual net of births and deaths over the past five years. The net 

birth/death model component figures are unique to each month and include negative adjustments in some months. Furthermore, these 

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

Only error from the second component is directly measurable. Error from this component is measured by comparing the actual 

residual from March 2009-10—once it becomes available—with the model-based estimate. As Exhibit 7 shows, the actual net 

birth/death residual for April 2009 to March 2010 was approximately 285,000 below the forecasted amount used in the CES monthly 

estimates for the time period.

Exhibit 7. Differences between forecasted and actual net birth/death from April 2009 to March 2010 (in thousands) 

Benchmark 2010

Apr-09

May-09

Jun-09

Jul-09

Aug-09

Sep-09Oct-09

Nov-09

Dec-09

Jan-10

Feb-10

Mar-10

Total

Actual Net Birth/Death -3 172 127 -70 81 -53 96 -35 0 -469 89 110 46

Forecast Net Birth/Death 124 185 134 -12 97 0 50 -23 23 -428 97 83 331

Difference -127 -13 -7 -58 -16 -53 46 -12 -23 -41 -8 27 -285

Cumulative Difference -127 -140 -147 -205 -221 -274 -228 -240 -263 -304 -312 -285

Introduction of quarterly net birth/death adjustments

Also with the release of the January 2010 estimates BLS will begin updating the CES net birth/death model component of the 

estimation process on a quarterly basis instead of annually. This change allows for the incorporation of QCEW into the birth/death 

21
model as soon as it becomes available and will reduce the post-benchmark revision in the CES series. This will not impact the timing 

or frequency of CES monthly and annual releases or when benchmarking is done. The first estimates that will be affected will be for 

the reference month of April 2011.

Methods and Timing. As described earlier, on a not seasonally adjusted basis, the sample-based estimates for the year preceding and 

the 9 months following the March benchmark are subject to revision. The 9 months following the March benchmark are referred to as 

the "post-benchmark" period. These 9 months of data are revised by applying previously derived over-the-month sample changes and 

newly calculated net birth/death residuals to the March benchmark level. The change between the original birth/death residuals used 

for this time period and the new residuals calculated as part of the benchmark process is a major component of the post-benchmark 

revision.

Availability of revised data

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

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

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 estimates. The CES 

SDM is a Weighted Least Squares model with two employment inputs: (1) an estimate based on available CES sample for that series, 

and (2) an ARIMA projection based on 10 years of historical QCEW data. Further background on the SDM is provided in the CES 

technical notes.

There are six industries estimated by using the SDM. These industries are lessors of nonfinancial intangible assets, direct health and 

medical insurance carriers, tax preparation services, other technical consulting services, remediation services, and recreational and 

vacation camps. 

Seasonal adjustment procedure

BLS uses X-12 ARIMA software developed by the U.S. Census Bureau to seasonally adjust national employment, hours, and earnings 

series derived from the CES program. Individual series are seasonally adjusted using either a multiplicative or an additive model 

(Exhibit 8), and seasonal adjustment factors are directly applied to the component levels. For employment, individual 3-digit NAICS 

22
levels are seasonally adjusted, and higher level aggregates are formed by summing these components. Seasonally adjusted totals for 

hours and earnings are obtained 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 adjustment procedures to 

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

a consistent concept— the pay period including the 12th of each month— inconsistencies arise because there are sometimes 4 and 

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

be an important determinant of the magnitude of seasonal hires or layoffs that have occurred at the time the survey is taken, thereby 

complicating seasonal adjustment.

Standard seasonal adjustment methodology relies heavily on the experience of the most recent 3 years to determine the expected 

seasonal change in employment for each month of the current year. Prior to the implementation of the adjustment, the procedure did 

not distinguish between 4- and 5-week survey intervals, and the accuracy of the seasonal expectation depended in large measure on 

how well the current year’s survey interval corresponded with those of the previous 3 years. All else the same, the greatest potential 

for distortion occurred when the current month being estimated had a 5-week interval but the 3 years preceding it were all 4-week 

intervals, or conversely when the current month had a 4-week interval but the 3 years preceding it were all 5-week intervals. 

BLS adopted REGARIMA (regression with auto-correlated errors) modeling to identify the estimated size and significance of the 

calendar effect for each published series. REGARIMA combines standard regression analysis, which measures correlation among two 

or more variables, with ARIMA modeling, which describes and predicts the behavior of data series based on its own past history. For 

many economic time series, including nonfarm payroll employment, observations are auto-correlated over time; that is, each month’s 

value is significantly dependent on the observations that precede it. These series, therefore, usually can be successfully fit using 

ARIMA models. If auto-correlated time series are modeled through regression analysis alone, the measured relationships among other 

variables of interest may be distorted due to the influence of the auto-correlation. Thus, the REGARIMA technique is appropriate for 

measuring relationships among variables of interest in series that exhibit auto-correlation, such as nonfarm payroll employment. 

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

the survey intervals for those months. The REGARIMA models evaluate the variation in employment levels attributable to 11 

separate survey interval variables, one specified for each month, except March. March is excluded because there are almost always 4 

weeks between the February and March surveys. Models for individual basic series are fit with the most recent 10 years of data 

available, the standard time span used for CES seasonal adjustment. 

23
The REGARIMA procedure yields regression coefficients for each of the 11 months specified in the model. These coefficients 

provide estimates of the strength of the relationship between employment levels and the number of weeks between surveys for the 11 

modeled months. The X-12 ARIMA software also produces diagnostic statistics that permit the assessment of the statistical 

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

Because the 11 coefficients derived from the REGARIMA models provide an estimate of the magnitude of variation in employment 

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

effect. These "filtered" series then are seasonally adjusted using the standard X-12 ARIMA software. 

For a few series, REGARIMA models do not fit well; these series are seasonally adjusted with the X-12 software but without the 

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

below. 

Construction series. Beginning with the 1996 benchmark revision, BLS utilized special treatment to adjust construction industry 

series. In the application of the interval effect modeling process to the construction series, there initially was difficulty in accurately 

identifying and measuring the effect because of the strong influence of variable weather patterns on employment movements in the 

industry. Further research allowed BLS to incorporate interval effect modeling for the construction industry by disaggregating the 

construction series into its finer industry and geographic estimating cells and tightening outlier designation parameters. This allowed 

a more precise identification of weather-related outliers that had masked the interval effect and clouded the seasonal adjustment 

patterns in general. With these outliers removed, interval effect modeling became feasible. The result is a seasonally adjusted series 

for construction that is improved because it is controlled for two potential distortions: unusual weather events and the 4- versus 5-

week effect. 

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

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

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 employment due to the 

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 implemented refinements to 

the seasonal adjustment process for the hours and earnings series to correct for distortions related to the method of accounting for the 

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

24
average weekly hour (AWH) and the average hourly earnings (AHE) series and the number of weekdays in a month, resulting in 

noneconomic fluctuations in these two series. Both AWH and AHE show more growth in "short" months (20 or 21 weekdays) than in 

"long" months (22 or 23 weekdays). The effect is stronger for the AWH than for the AHE series. 

The calendar effect is traceable to response and processing errors associated with converting payroll and hours information from 

sample respondents with semi-monthly or monthly pay periods to a weekly equivalent. The response error comes from sample 

respondents reporting a fixed number of total hours for workers regardless of the length of the reference month, while the CES 

conversion process assumes that the hours reporting will be variable. A constant level of hours reporting most likely occurs when 

employees are salaried rather than paid by the hour, as employers are less likely to keep actual detailed hours records for such 

employees. This causes artificial peaks in the AWH series in shorter months that are reversed in longer months. 

The processing error occurs when respondents with salaried workers report hours correctly (vary them according to the length of the 

month), which dictates that different conversion factors be applied to payroll and hours. The CES processing system uses the hours 

conversion factor for both fields, resulting in peaks in the AHE series in short months and reversals in long months. 

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

weekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaining AWH 

movements in all the service-providing industries except utilities. For AHE, the length-of-pay-period variable is significant for 

wholesale trade, retail trade, information, financial activities, professional and business services, and other services. All AWH series 

in the service-providing industries except utilities have been adjusted from January 1990 forward. The AHE series for wholesale 

trade, retail trade, information, financial activities, professional and business services, and other services have been adjusted from 

January 1990 forward as well. For this reason, calculations of over-the-year change in the establishment hours and earnings series 

should use seasonally adjusted data. 

The series to which the length-of-pay-period adjustment is applied are not subject to the 4- versus 5-week adjustment, as the modeling 

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

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 of the new all 

employees hours and earnings series estimates complicates the use of existing adjustment techniques. CES has less than 5 years of 

history for the new hours and earnings series; in contrast, CES uses 10 years of data when seasonally adjusting all other data types. 

25
The X-12 ARIMA software requires at least 3 years of data to perform seasonal adjustment; the new series met this requirement, 

consisting of 46 months of data at initial publication (March 2006 through January 2010). Nevertheless, there are several limitations 

and concerns associated with seasonally adjusting the new series with such a limited amount of input data. For example, the limited 

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

for other data types. Secondly, there is a risk of the irregular component affecting the seasonal component. But the most significant 

limitation is that there is insufficient history to allow the use of the existing calendar effects treatment method described above. In 

particular, at least 5 years of data are required to adjust for variation due to the calendar effects (4 vs. 5 week, 10 vs. 11 day). 

Therefore, an alternative approach has been implemented to address these 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 

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

exponentially weighted moving average of past values. As hours and earnings data tend to exhibit fluctuations around slowly-varying 

means, use of the exponential smoothing model is a sensible choice. Experimental results confirmed that this model is highly effective 

and will suffice until the all employees hours and earnings series reach the necessary length to allow the integration of automatic 

model selection. 

Choice of Seasonal Filter. By default, X-12 ARIMA uses a 3x3 moving average to calculate the initial seasonal factors in each 

iteration, and a 3x5 moving average to calculate the final seasonal averages. However, the shortness of the new series imposes a 

technical limitation in that the only available option is to utilize a stable seasonal filter. A stable seasonal filter computes a single 

seasonal factor for each month by first calculating the average of all the values for each month, which reduces the chance of the 

irregular component affecting the seasonal component—a concern with short series. The effectiveness of the stable filter was 

demonstrated in experimental testing. 

Treatment of Calendar Effects. The existing seasonal adjustment method for production employees’ hours and earnings relies on the 

REGARIMA modeling approach described earlier that implicitly treats each month; the alternate technique treats only the months for 

which calendar effects impact the data. Affected months are manually treated as outliers when defining parameters for X-12 ARIMA. 

This treatment dampens the calendar effects, but spikes from the 10/11 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 observations based on 

the trend of the non-outlier observations. By default, X-12 ARIMA relies upon this prior-adjusted series when decomposing 

seasonality and estimating seasonal factors for time series; however, it then applies those factors to the original time series rather than 

26
the prior-adjusted series. Under normal circumstances, application of seasonal factors to the original time series i

original 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 seaoriginal time series. Because the calendar effects are part of the original series—and because we cannot remove tstandard 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 unad

because the adjustment is applied outside of X-12 ARIMA. A common goal when performing seasonal adjustme

series’ 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 exthan their unadjusted analogues—may result in seasonally adjusted series with lower means than those of correspseries. 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 = ∑ ௑೟ ೙೟సభ ∑ ௑෠೟ ೙೟సభ

ܺ௧= Unadjusted value of series at month tܺ

෠

௧= 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 maint

between series’ unadjusted and seasonally-adjusted levels. See Exhibit 8 for series that have the calendar effects modeling described above. 

27
Exhibit 8. Model specifications Seasonal Adjustment – AE

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

1011330000 Logging MULT X

1021100000 Oil and gas extraction MULT X

1021200000 Mining, except oil and gas X Indirect1

1021210000 Coal mining MULT X

1021300000 Support activities for mining MULT X

2023610000 Residential building X Indirect

2023620000 Nonresidential building X Indirect

2023700000 Heavy and civil engineering construction ADD X

2023800000 Specialty trade contractors X Indirect

2023800100 Residential specialty trade contractors ADD X Raked2

2023800200 Nonresidential specialty trade contractors ADD X Raked

3132100000 Wood products ADD X

3132700000 Nonmetallic mineral products ADD X

3133100000 Primary metals MULT X

3133200000 Fabricated metal products MULT X

3133300000 Machinery MULT X

3133400000 Computer and electronic products X Indirect

3133410000 Computer and peripheral equipment MULT X

3133420000 Communications equipment MULT X

3133440000 Semiconductors and electronic components MULT X

3133450000 Electronic instruments MULT X

3133500000 Electrical equipment and appliances MULT X

3133600000 Transportation equipment ADD

3133600100 Motor vehicles and parts ADD

3133700000 Furniture and related products ADD X

3133900000 Miscellaneous manufacturing MULT X

3231100000 Food manufacturing MULT X

3231200000 Beverages and tobacco products MULT X

3231300000 Textile mills MULT X

3231400000 Textile product mills MULT X

3231500000 Apparel MULT X

28
Exhibit 8. Model specifications Seasonal Adjustment – AE — continued

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

3231600000 Leather and allied products MULT X

3232200000 Paper and paper products MULT X

3232300000 Printing and related support activities MULT X

3232400000 Petroleum and coal products MULT X

3232500000 Chemicals MULT X

3232600000 Plastics and rubber products ADD X

4142300000 Durable goods MULT X

4142400000 Nondurable goods MULT X

4142500000 Electronic markets and agents and brokers MULT X

4244100000 Motor vehicle and parts dealers X Indirect

4244110000 Automobile dealers ADD X

4244200000 Furniture and home furnishings stores MULT X

4244300000 Electronics and appliance stores MULT X

4244400000 Building material and garden supply stores MULT X

4244500000 Food and beverage stores MULT X

4244600000 Health and personal care stores MULT X

4244700000 Gasoline stations MULT X

4244800000 Clothing and clothing accessories stores MULT X

4245100000 Sporting goods, hobby, book, and music stores MULT X

4245200000 General merchandise stores X Indirect

4245210000 Department stores MULT X

4245300000 Miscellaneous store retailers MULT X

4245400000 Nonstore retailers MULT X

4348100000 Air transportation MULT X

4348200000 Rail transportation MULT X

4348300000 Water transportation MULT X

4348400000 Truck transportation ADD X

4348500000 Transit and ground passenger transportation ADD

4348600000 Pipeline transportation MULT X

4348700000 Scenic and sightseeing transportation ADD X

4348800000 Support activities for transportation MULT X

29
Exhibit 8. Model specifications Seasonal Adjustment – AE — continued

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

4349200000 Couriers and messengers MULT X

4349300000 Warehousing and storage ADD X

4422000000 Utilities MULT X

5051100000 Publishing industries, except Internet MULT X

5051200000 Motion picture and sound recording industries MULT X

5051500000 Broadcasting, except Internet MULT X

5051700000 Telecommunications MULT X

5051800000 Data processing, hosting and related services MULT X

5051900000 Other information services MULT X

5552100000 Monetary authorities central bank MULT X

5552200000 Credit intermediation and related activities X Indirect

5552210000 Depository credit intermediation MULT X

5552211000 Commercial banking MULT X

5552300000 Securities, commodity contracts, investments MULT X

5552400000 Insurance carriers and related activities MULT X

5552500000 Funds, trusts, and other financial vehicles ADD X

5553100000 Real estate MULT X

5553200000 Rental and leasing services ADD X

5553300000 Lessors of nonfinancial intangible assets MULT X

6054000000 Professional and technical services X Indirect

6054110000 Legal services MULT X

6054120000 Accounting and bookkeeping services ADD X

6054130000 Architectural and engineering services MULT X

6054150000 Computer systems design and related services ADD X

6054160000 Management and technical consulting services MULT X

6055000000 Management of companies and enterprises MULT X

6056100000 Administrative and support services X Indirect

6056130000 Employment services ADD X

6056132000 Temporary help services ADD X

6056140000 Business support services ADD X

6056170000 Services to buildings and dwellings MULT X

30
Exhibit 8. Model specifications Seasonal Adjustment – AE — continued

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

6056200000 Waste management and remediation services MULT X

6561000000 Educational services ADD X

6562100000 Ambulatory health care services X Indirect

6562110000 Offices of physicians ADD X

6562140000 Outpatient care centers MULT X

6562160000 Home health care services ADD X

6562200000 Hospitals ADD X

6562300000 Nursing and residential care facilities X Indirect

6562310000 Nursing care facilities MULT X

6562400000 Social assistance X Indirect

6562440000 Child day care services ADD X

7071100000 Performing arts and spectator sports MULT X

7071200000 Museums, historical sites, zoos, and parks MULT X

7071300000 Amusements, gambling, and recreation ADD X

7072100000 Accommodation MULT X

7072200000 Food services and drinking places ADD X

8081100000 Repair and maintenance MULT X

8081200000 Personal and laundry services MULT X

8081300000 Membership associations and organizations ADD

9091100000 Federal, except U.S. Postal Service MULT X

9091912000 U.S. Postal Service MULT X

9092161100 State government education ADD X

9092200000 State government, excluding education MULT X

9093161100 Local government education ADD X

9093200000 Local government, excluding education MULT X Election adjustment3

31
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 X

2000000000 Construction MULT X

3132100000 Wood products MULT

3132700000 Nonmetallic mineral products MULT

3133100000 Primary metals MULT X

3133200000 Fabricated metal products MULT X

3133300000 Machinery MULT X

3133400000 Computer and electronic products MULT X

3133500000 Electrical equipment and appliances MULT X

3133600000 Transportation equipment MULT X

3133600100 Motor vehicles and parts ADD X

3133700000 Furniture and related products MULT X

3133900000 Miscellaneous manufacturing MULT X

3231100000 Food manufacturing MULT X

3231200000 Beverages and tobacco products MULT X

3231300000 Textile mills MULT X

3231400000 Textile product mills MULT X

3231500000 Apparel MULT X

3231600000 Leather and allied products MULT X

3232200000 Paper and paper products MULT X

3232300000 Printing and related support activities MULT X

3232400000 Petroleum and coal products MULT

3232500000 Chemicals MULT

3232600000 Plastics and rubber products MULT X

4142000000 Wholesale trade MULT X

4200000000 Retail trade MULT X

4300000000 Transportation and warehousing MULT X

4422000000 Utilities MULT

5000000000 Information ADD X

5500000000 Financial activities MULT X

32
Seasonal Adjustment - AE AWH — continued

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

Easter/Labor 

Day adj

6000000000 Professional and business services MULT X X

6500000000 Education and health services MULT X

7000000000 Leisure and hospitality MULT X

8000000000 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 ADD

2000000000 Construction MULT

3100000000 Durable goods ADD

3200000000 Nondurable goods ADD

4142000000 Wholesale trade ADD X

4200000000 Retail trade MULT X

4300000000 Transportation and warehousing MULT X

4422000000 Utilities ADD

5000000000 Information MULT X

5500000000 Financial activities ADD X

6000000000 Professional and business services MULT X

6500000000 Education and health services MULT

7000000000 Leisure and hospitality MULT

8000000000 Other services MULT X

33
Seasonal Adjustment - AE AOH 

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

3100000000 Durable goods MULT X

3200000000 Nondurable goods MULT X

Seasonal Adjustment – PE

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

1000000000 Mining and logging MULT X

2000000000 Construction ADD X

3132100000 Wood products ADD X

3132700000 Nonmetallic mineral products ADD X

3133100000 Primary metals ADD X

3133200000 Fabricated metal products MULT X

3133300000 Machinery MULT X

3133400000 Computer and electronic products MULT X

3133500000 Electrical equipment and appliances MULT X

3133600000 Transportation equipment ADD

3133600100 Motor vehicles and parts ADD

3133700000 Furniture and related products ADD X

3133900000 Miscellaneous manufacturing MULT X

3231100000 Food manufacturing MULT X

3231200000 Beverages and tobacco products MULT X

3231300000 Textile mills MULT X

3231400000 Textile product mills MULT X

3231500000 Apparel MULT X

3231600000 Leather and allied products MULT X

3232200000 Paper and paper products MULT X

3232300000 Printing and related support activities MULT X

3232400000 Petroleum and coal products ADD X

3232500000 Chemicals MULT X

34
Seasonal Adjustment – PE — continued

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

3232600000 Plastics and rubber products ADD X

4142000000 Wholesale trade MULT X

4200000000 Retail trade MULT X

4300000000 Transportation and warehousing ADD X

4422000000 Utilities MULT X

5000000000 Information MULT X

5500000000 Financial activities MULT X

6000000000 Professional and business services MULT X

6500000000 Education and health services ADD X

7000000000 Leisure and hospitality MULT X

8000000000 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 X

2000000000 Construction ADD X X

3132100000 Wood products MULT X X

3132700000 Nonmetallic mineral products ADD X X

3133100000 Primary metals ADD X X

3133200000 Fabricated metal products MULT X X

3133300000 Machinery MULT X X

3133400000 Computer and electronic products MULT X X

3133500000 Electrical equipment and appliances MULT X X

3133600000 Transportation equipment ADD X X

3133600100 Motor vehicles and parts ADD X X

3133700000 Furniture and related products MULT X X

3133900000 Miscellaneous manufacturing MULT X X

35
Seasonal Adjustment - PE AWH — continued

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

3231100000 Food manufacturing MULT X X

3231200000 Beverages and tobacco products ADD X X

3231300000 Textile mills MULT X X

3231400000 Textile product mills MULT X X

3231500000 Apparel MULT X X

3231600000 Leather and allied products ADD X X

3232200000 Paper and paper products MULT X X

3232300000 Printing and related support activities MULT X X

3232400000 Petroleum and coal products MULT X X

3232500000 Chemicals MULT X

3232600000 Plastics and rubber products MULT X X

4142000000 Wholesale trade MULT X X

4200000000 Retail trade MULT X

4300000000 Transportation and warehousing MULT X X

4422000000 Utilities MULT X

5000000000 Information MULT X

5500000000 Financial activities MULT X

6000000000 Professional and business services MULT X X

6500000000 Education and health services MULT X

7000000000 Leisure and hospitality MULT X

8000000000 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 X

2000000000 Construction MULT X

3100000000 Durable goods ADD X

3200000000 Nondurable goods MULT X

36
Seasonal Adjustment - PE AHE — continued

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

4142000000 Wholesale trade MULT X

4200000000 Retail trade MULT X

4300000000 Transportation and warehousing MULT X

4422000000 Utilities MULT X

5000000000 Information MULT X

5500000000 Financial activities MULT X

6000000000 Professional and business services MULT X

6500000000 Education and health services ADD X

7000000000 Leisure and hospitality MULT X

8000000000 Other services ADD X

Seasonal Adjustment - PE AOH 

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

3100000000 Durable goods MULT X X

3200000000 Nondurable goods ADD X X

Seasonal Adjustment – WE

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

1000000000 Mining and logging MULT X

1021000000 Mining MULT X

2000000000 Construction ADD X

3100000000 Durable goods MULT 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

37
Seasonal Adjustment – WE — continued

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

5000000000 Information MULT X

5552000000 Finance and insurance MULT X

5553000000 Real estate and rental and leasing MULT X

6054000000 Professional and technical services MULT 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 ADD X

7072000000 Accommodation and food services ADD X

8000000000 Other services MULT X

9091000000 Federal MULT X

9092000000 State government ADD X

9093000000 Local government ADD X Election adjustment3

(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 consistency.

(3)Special adjustment for the presence/absence of poll workers in local government.

Last Modified Date: February 11, 2011 

38