employment and earningsmonthly household data page historical a-l. employment status of the civilian...

EmploymentQ^EarningsU.S. Department of LaborBureau of Labor Statistics

In this issue:

2003 annual averages

" " ' ^ i & 0 » $ ? : | ' l§!&S % 11111111111181

Minimum wage

Digitized for FRASER http://fraser.stlouisfed.org/ Federal Reserve Bank of St. Louis

U.S. DEPARTMENT OF LABORElaine L. Chao, Secretary

BUREAU OF LABOR STATISTICSKathleen P. Utgoff, Commissioner

Employment & Earnings (ISSN 0013-6840; USPS 485-010),is published monthly and prepared in the Office ofEmployment and Unemployment Statistics in collaborationwith the Office of Publications. The data are collected bythe U.S. Census Bureau (Department of Commerce) andState Employment Security Agencies, in cooperation withthe Bureau of Labor Statistics. The State agencies are listedon the inside back cover.

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Communications on material in this publication should beaddressed to: Editors, Employment & Earnings, Bureau ofLabor Statistics, Washington, DC 20212. Specific questionsconcerning the data in this publication, or their availability,should be directed as follows:

Household data:Telephone: (202) 691-6378E-mail: [email protected]: http://www.bls.gov/cps/

National establishment data:Telephone: (202) 691-6555E-mail: [email protected]: http://www.bls.gov/ces/

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January 2004Vol. 51 No. 1

Calendar of Features

In addition to the monthly data appearing regularlyin Employment & Earnings, special features appearin most of the issues as shown below.

Household data

Revised seasonally adjusted series

Annual averages

Earnings by detailed occupation

Union affiliation

Minimum wage data

Employee absences

Quarterly averages: Seasonally adjusted data,persons of Hispanic or Latino ethnicity, andweekly earnings data

Establishment data

National annual averages:

Industry sectors (preliminary)

Industry detail

Women employees

National data revised to reflect new benchmarksand revised seasonally adjusted series

State and area annual averages

Area definitions

Region, State, and area labor force data

Annual averages

Jan.

Jan.

Jan.

Jan.

Jan.

Jan.

Jan., Apr., July, Oct.

Jan.

March

March

Feb.

May

May

May

Cover Design:Keith Tapscott


EMPLOYMENT&EARNINGS-Editor

John F. Stinson Jr.

Design and LayoutPhyllis L. Lott

Editor's NoteWith this issue, seasonally adjusted unemployment and other labor force seriesderived from the Current Population Survey (household survey) have been revisedto reflect updated seasonal adjustment factors which incorporate the experiencethrough December 2003. As a result, seasonally adjusted data for 1999-2003 aresubject to revision. Revised seasonally adjusted data appear in summary table A,tables A-l through A-12, and D-l through D-10. Historical seasonally adjustedmonthly and quarterly data also are available on the Internet at ftp://ftp.bls.gov/pub/special. requests/If/.

The article beginning on page 3 discusses the effect of the revisions, describes theseasonal adjustment method, and discusses the introduction of the use of concurrentseasonal adjustment for the household survey data.

Annual average data from the household survey also are published in this issue.The data reflect the introduction of data for Asians, new race definitions, and newoccupational and industry classification systems.

ContentsPage

List of statistical tables iiContents to the explanatory notes and estimates of error viiEmployment and unemployment developments, December 2003 1Revision of seasonally adjusted labor force series in 2004 3Summary tables and charts 10Explanatory notes and estimates of error 267Index to statistical tables 312

Statistical tables

Source HistoricalSeasonally

adjusted

Notseasonallyadjusted

Otherfeatures

Household data

Establishment data:Employment:

NationalStateArea

Hours and earnings:NationalState and area

Local area labor force data:RegionStateArea

Household data:Quarterly averagesAnnual averages

12

56

57

14

172

24

6168

77

159161

81102102

126156

166166

182194


Monthly Household Data

Page

HistoricalA-l. Employment status of the civilian noninstitutional population 16 years and over, 1969 to date 12A-2. Employment status of the civilian noninstitutional population 16 years and over by sex, 1992 to date 13

Seasonally Adjusted DataEmployment Status

A-3. Employment status of the civilian noninstitutional population by sex and age 14A-4. Employment status of the civilian noninstitutional population by race, sex, age,

and Hispanic or Latino ethnicity 1 5A-5. Employment status of the civilian noninstitutional population 25 years and over

by educational attainment 1 7A-6. Employed and unemployed full- and part-time workers by sex and age 18

Characteristics of the Employed

A-7. Employed persons by class of worker and part-time status 19

A-8. Employed persons by age, sex, and marital status 20

Characteristics of the Unemployed

A-9. Unemployed persons by age, sex, and marital status 21A-10. Unemployment rates by age, sex, and marital status 22A - l l . Unemployed persons by reason for unemployment 23A-12. Unemployed persons by duration of unemployment 23

Not Seasonally Adjusted DataEmployment Status

A-13. Employment status of the civilian noninstitutional population by age, sex, and race 24A-14. Employment status of the Hispanic or Latino population by age and sex 28A-l5. Employment status of the civilian noninstitutional population by race, sex, age,

and Hispanic or Latino ethnicity 29A-16. Employment status of the civilian noninstitutional population 16 to 24 years of age by

school enrollment, educational attainment, sex, race, and Hispanic or Latino ethnicity 3 0A-17. Employment status of the civilian noninstitutional population 25 years and over by

educational attainment, sex, race, and Hispanic or Latino ethnicity 32A-l8. Employed and unemployed full- and part-time workers by age, sex, race, and

Hispanic or Latino ethnicity 33Characteristics of the Employed

A-19. Employed persons by occupation, sex, and age 35A-20. Employed persons by occupation, race, Hispanic or Latino ethnicity, and sex 3 6A-21. Employed persons by industry and occupation 38A-22. Employed persons in agriculture and related and in nonagricultural industries

by age, sex, and class of worker 39A-23. Persons at work in agriculture and related and in nonagricultural industries by hours of work. 40A-24. Persons at work 1 to 34 hours in all and in nonagricultural industries by reason for

working less than 35 hours and usual full- or part-time status 40A-25. Persons at work in nonagricultural industries by class of worker and usual full- or part-time status 41A-26. Persons at work in nonagricultural industries by age, sex, race, Hispanic or

Latino ethnicity, marital status, and usual full- or part-time status 42A-27. Persons at work by occupation, sex, and usual full- or part-time status 43


A-28. Unemployed persons by marital status, race, Hispanic or Latino ethnicity, age, and sex 44A-29. Unemployed persons by occupation and sex 45A-30. Unemployed persons by industry and sex 46A-31. Unemployed persons by reason for unemployment, sex, and age 48A-32. Unemployed persons by reason for unemployment, race, and Hispanic or Latino ethnicity 49A-33. Unemployed persons by reason for unemployment, sex, age, and duration of unemployment 50A-34. Unemployed total and full-time workers by duration of unemployment 50A-35. Unemployed persons by age, sex, race, Hispanic or Latino ethnicity, marital status,

and duration of unemployment 5 1A-36. Unemployed persons by occupation, industry, and duration of unemployment 52

Persons Not in the Labor Force

A-37. Persons not in the labor force by desire and availability for work, age, and sex. 53

Multiple Jobholders

A-38. Multiple jobholders by selected demographic and economic characteristics 54


Monthly Establishment Data

Page

Historical

B-l. Employees on nonfarm payrolls by major industry sector, 1954 to date 56B-2. Average hours and earnings of production or nonsupervisory workers on private nonfarm

payrolls by major industry sector, 1964 to date 57

Seasonally Adjusted Data

Employment

National

B-3. Employees on nonfarm payrolls by major industry sector and selected industry detail 61B-4. Women employees on nonfarm payrolls by major industry sector and selected industry detail 65B-5. Production or nonsupervisory workers on private nonfarm payrolls by major industry sector

and selected industry detail 66

B-6. Diffusion indexes of employment change 67

States

B-7. Employees on nonfarm payrolls by State and major industry. 68

Hours and Earnings

National

B-8. Average weekly hours of production or nonsupervisory workers on private nonfarmpayrolls by major industry sector and selected industry detail. 77

B-9. Indexes of aggregate weekly hours of production or nonsupervisory workers on private nonfarmpayrolls by major industry sector and selected industry detail. 78

B-10. Hours of wage and salary workers on nonfarm payrolls by major industry 79B-ll. Average hourly and weekly earnings of production or nonsupervisory workers on private nonfarm

payrolls by major industry sector and selected industry detail. 80Not Seasonally Adjusted Data

Employment

National

B-12. Employees on nonfarm payrolls by detailed industry 81

B-l3. Women employees on nonfarm payrolls by major industry sector and selected industry detail 101

States and Areas

B-14. Employees on nonfarm payrolls in States and selected areas by major industry 102

Hours and Earnings

NationalB-l5. Average hours and earnings of production or nonsupervisory workers on private nonfarm

payrolls by detailed industry 126B-16. Average hourly earnings, excluding overtime, of production workers on manufacturing payrolls 154B-l 7. Average hourly and weekly earnings of production or nonsupervisory workers on private

nonfarm payrolls by major industry sector and selected industry detail, in currentand constant (1982) dollars 155

States and Areas

B-l 8. Average hours and earnings of production workers on manufacturing payrolls in Statesand selected areas 1 5 6

Monthly Regional, State, and Area Labor Force Data


C-l. Labor force status by census region and division. 159

C-2. Labor force status by State 161

Not Seasonally Adjusted Data

C-3. Labor force status by State and metropolitan area 166


Quarterly Household Data

Page


Employment Status

D-l. Employment status of the civilian noninstitutional population by sex and age 172D-2. Employment status of the civilian noninstitutional population by race, sex, age,

and Hispanic or Latino ethnicity 1 73D-3. Employment status of the civilian noninstitutional population 25 years and over

by educational attainment 175D-4. Employed and unemployed full- and part-time workers by sex and age 176


D-5. Employed persons by class of worker and part-time status 177D-6. Employed persons by age, sex, and marital status 178


D-7. Unemployed persons by age, sex, and marital status 179D-8. Unemployment rates by age, sex, and marital status 180D-9. Unemployed persons by reason for unemployment 181

D-10. Unemployed persons by duration of unemployment 181

Not Seasonally Adjusted Data

Employment Status

D-l l . Employment status of the civilian noninstitutional population by sex, age, and race 182D-12. Employment status of the Hispanic or Latino population by sex, age, and detailed ethnic group 183


D-13. Employed persons by sex, occupation, class of worker, full- or part-time status, and race 184D-l4. Employed Hispanic or Latino workers by sex, occupation, class of worker,

full- or part-time status, and detailed ethnic group 185D-15. Employed persons by age, sex, race, and Hispanic or Latino ethnicity 186


D-16. Unemployment rates by age, sex, race, and Hispanic or Latino ethnicity 187D-17. Unemployed persons by reason for unemployment, race, and Hispanic or Latino ethnicity 188D-l8. Unemployed persons by duration of unemployment, race, and Hispanic or Latino ethnicity 189

Weekly Earnings Data

D-19. Median weekly earnings of full-time wage and salary workers by selected characteristics. 190D-20. Median weekly earnings of part-time wage and salary workers by selected characteristics 191D-21. Median weekly earnings of full-time wage and salary workers by occupation and sex 192


Annual Averages—Household Data

Page

Employment Status

1. Employment status of the civilian noninstitutional population, 1940 to date 1942. Employment status of the civilian noninstitutional population 16 years and over by sex, 1971 to date 1953. Employment status of the civilian noninstitutional population by age, sex and race 1964. Employment status of the Hispanic or Latino population by age and sex 2005. Employment status of the civilian noninstitutional population by sex, age, and race 2016. Employment status of the Hispanic or Latino population by sex, age, and detailed ethnic group 2027. Employment status of the civilian noninstitutional population 25 years and over by educational

attainment, sex, race, and Hispanic or Latino ethnicity 2038. Employed and unemployed full- and part-time workers by age, sex, race, and Hispanic or Latino ethnicity. 204


9. Employed persons by occupation, sex, and age 20610. Employed persons by occupation, race, Hispanic or Latino ethnicity, and sex 20711. Employed persons by detailed occupation, sex, race, and Hispanic or Latino ethnicity. 20912. Employed persons by sex, occupation, class of worker, full- or part-time status, and race 21513. Employed Hispanic or Latino workers by sex, occupation, class of worker, full- or part-time status,

and detailed ethnic group 21 614. Employed persons in nonagricultural industries by age, sex, race, and Hispanic or Latino ethnicity 21715. Employed persons in agriculture and related and in nonagricultural industries by age, sex,

and class of worker 21916. Employed persons in nonagricultural industries by sex and class of worker 22017. Employed persons by industry, sex, race, and occupation 22218. Employed persons by detailed industry, sex, race, and Hispanic or Latino ethnicity 22519. Persons at work in agriculture and related and in nonagricultural industries by hours of work 23020. Persons at work 1 to 34 hours in all and in nonagricultural industries by reason for

working less than 35 hours and usual full- or part-time status 23021. Persons at work in nonagricultural industries by class of worker and usual full- or part-time status 23122. Persons at work in nonagricultural industries by age, sex, race, Hispanic or Latino ethnicity,

marital status, and usual full- or part-time status 23223. Persons at work by occupation, sex, and usual full- or part-time status 233


24. Unemployed persons by marital status, race, Hispanic or Latino ethnicity, age, and sex 23425. Unemployed persons by occupation and sex 23526. Unemployed persons by industry and sex 23627. Unemployed persons by reason for unemployment, sex, and age 23828. Unemployed persons by reason for unemployment, race, and Hispanic or Latino ethnicity 23929. Unemployed persons by reason for unemployment, sex, age, and duration ofunemployment 24030. Unemployed total and full-time workers by duration of unemployment 24031. Unemployed persons by age, sex, race, Hispanic or Latino ethnicity, marital status,and duration

of unemployment 24132. Unemployed persons by occupation, industry, and duration of unemployment 24233. Unemployed jobseekers by sex, age, race, Hispanic or Latino ethnicity, and activejobsearch

methods used 24334. Unemployed jobseekers by sex, reason for unemployment, and active jobsearchmethods used 244

Persons Not in the Labor Force

35. Persons not in the labor force by desire and availability for work, age, and sex 245

Multiple Jobholders

36. Multiple jobholders by selected demographic and economic characteristics 246

Weekly Earnings Data

37. Median weekly earnings of full-time wage and salary workers by selected characteristics 24738. Median weekly earnings of part-time wage and salary workers by selected characteristics 24839. Median weekly earnings of full-time wage and salary workers by detailed occupation and sex 249


Annual Averages—Household Data—Continued

Page

Union Affiliation Data

40. Union affiliation of employed wage and salary workers by selected characteristics 25441. Median weekly earnings of full-time wage and salary workers by union affiliation and

selected characteristics 25 542. Union affiliation of employed wage and salary workers by occupation and industry 25643. Median weekly earnings of full-time wage and salary workers by union affiliation, occupation,

and industry 25 8

Minimum Wage Data

44. Wage and salary workers paid hourly rates with earnings at or below the prevailing Federalminimum wage by selected characteristics 260

45. Wage and salary workers paid hourly rates with earnings at or below the prevailing Federalminimum wage by occupation and industry 261

Employee Absences Data

46. Absences from work of employed full-time wage and salary workers by age and sex. 26347. Absences from work of employed full-time wage and salary workers by occupation and industry. 264


Explanatory Notes and Estimates of Error

Page Page

Introduction 267Relationship between the household and establishmentseries 267

Comparability of household data with other series 268Comparability of payroll employment data withother series 268

Household data 269Collection and coverage 269Concepts and definitions 269Historical comparability 272

Changes in concepts and methods 272Noncomparability of labor force levels 273Changes in the occupational and industrialclassification systems 276

Sampling 276Selection of sample areas 277Selection of sample households 278Rotation of sample 278CPS sample, 1947 to present 278

Estimating methods 278Noninterview adjustment 279Ratio estimates 279

First stage 279Second stage 279

Composite estimation procedure 280Rounding of estimates 280Reliability of the estimates 280

Nonsampling error 280Sampling error 28 1

Tables 1-B through 1-H 281

Establishment data 287Data collection 287Concepts 287Estimating methods 290

Benchmarks 290

Establishment data—ContinuedStratification 290Monthly estimation 290Weighted link-relative technique 290Summary of methods table 291Weighted link and taper technique 291Business birth and death estimation 293

The sample 294Design 294

Frame and sample selection 294Frame maintenance and sample updates 295

Coverage 296Employment benchmarks and samplecoverage table 296

Reliability 296Benchmark revision as a measure of surveyerror 296

Revisions between preliminary and finaldata 296

Variance estimation 297Appropriate uses of sampling variances 297Sampling errors 297

Statistics for States and areas 298

Region, State, and area labor force data 306Federal-State cooperative program 306Estimating methods 306

Estimates for States 306Current monthly estimates 306Benchmark correction procedures 306

Estimates for substate areas 307Preliminary estimate:

Employment 307Unemployment 307

Substate adjustment for additivity 307Benchmark correction 307

Seasonal adjustment 308


Obtaining information from the Bureau of Labor Statistics

Office or Topic

Bureau of Labor StatisticsInformation services

Employment and unemploymentEmployment, hours, and earnings:

NationalState and local

Labor force statistics:NationalLocal

Ul-covered employment, wagesOccupational employmentMass layoffsLongitudinal data

Prices and living conditionsConsumer price indexesProducer price indexes)Import and export price indexesConsumer expenditures

Compensation and working conditionsNational Compensation Survey:

Employee benefitsEmployment cost trendsOccupational compensation

Occupational illnesses, injuriesFatal occupational injuriesCollective bargaining

ProductivityLaborIndustryMultifactor

ProjectionsEmploymentOccupation

International

Regional centersAtlantaBostonChicagoDallasKansas CityNew YorkPhiladelphiaSan Francisco

Other Federal statistical agencies

Internet address

http://www.bls.govhttp://www.bls.gov/opub/

http://www.bls.gov/ces/http://www.bls.gov/sae/

http://www.bls.gov/cps/http://www.bls.gov/lau/http://www.bls.gov/cew/http://www.bls.gov/oes/http://www.bls.gov/lau/http://www.bls.gov/nls/

http://www.bls.gov/cpi/http://www.bls.gov/ppi/http://www.bls.gov/mxp/http://www.bls.gov/cex/

http://www.bls.gov/ncs/http://www.bls.gov/ebs/http://www.bls.gov/ect/http://www.bls.gov/ncs/http://www.bls.gov/iii7http://stats.bls.gov/iif/http://www.bls.gov/cba/

http://www.bls.gov/lpc/http://www.bls.gov/lpc/http://www.bls.gov/mfp/

http://www.bls.gov/emp/http://www.bls.gov/oco/

http://www.bls.gov/fls/

http://www.bls.gov/ro4/http://www.bls.gov/ro 1 /http://www.bls.gov/ro5/http://www.bls.gov/ro6/http://www.bls.gov/ro7/http://www.bls.gov/ro2/http://www.bls.gov/ro3/http://www.bls.gov/ro9/

http://www.fedstats.gov/

E-mail

[email protected]

[email protected]@bls.gov

[email protected]@[email protected]@[email protected]@bls.gov

[email protected]@[email protected]@bls.gov

[email protected]@[email protected]@[email protected]@[email protected]

[email protected]@[email protected]

[email protected]@bls.gov

[email protected]

[email protected]@[email protected]@[email protected]@[email protected]@bls.gov


Employment and UnemploymentDevelopments, December 2003

Employment was virtually unchanged in Decemberwhile the unemployment rate, at 5.7 percent, continuedto trend down. Following increases that totaled277,000 in the prior 4 months, nonfarm payroll employmentwas flat in December (+1,000).

UnemploymentThe number of unemployed persons was 8.4 million inDecember and the unemployment rate was 5.7 percent. Bothmeasures continued to edge down from their recent highs inJune 2003. In December, the unemployment rates for adultmen (5.3 percent) and Hispanics or Latinos (6.6 percent)declined. The jobless rates for the other major workergroups—adult women (5.1 percent), teenagers (16.1 percent),whites (5.0 percent), and blacks (10.3 percent)—showed littleor no change from the previous month. The unemploymentrate for Asians was 5.3 percent in December, not seasonallyadjusted. (See tables A-3, A-4, and A-13.)

Total employment and the labor forceThe civilian labor force fell by 309,000 in December to 146.9million; the labor force participation rate decreased over themonth to 66.0 percent. Over the year, the participation ratedeclined by 0.4 percentage point. Both total employment(138.5 million) and the employment-population ratio (62.2percent) were about unchanged in December. (See tableA-3.)

Persons not in the labor forceIn December, about 1.5 million persons were marginallyattached to the labor force, about the same as a year earlier.(Data are not seasonally adjusted.) These individuals wantedand were available to work and had looked for a job sometimein the prior 12 months. They were not counted as unemployed,however, because they did not actively search for work inthe 4 weeks preceding the survey. There were 433,000discouraged workers in December, also about the same as inDecember 2002. Discouraged workers, a subset of themarginally attached, were not currently looking for workspecifically because they believed no jobs were available forthem. The other 1.1 million marginally attached had notsearched for work for other reasons such as school or familyresponsibilities. (Seetable A-37.)

Industry payroll employmentTotal nonfarm payroll employment was unchanged (+1,000)in December, at 130.1 million, seasonally adjusted.

Employment continued to rise in the temporary help,construction, and health care industries. Retail trade andmanufacturing lost jobs over the month. (See table B-3.)

In December, employment in retail trade declined by 38,000.Weak hiring for the holiday shopping period resulted inseasonally adjusted job losses in general merchandise stores;miscellaneous store retailers; and sporting goods, hobby,book, and music stores. Employment in gasoline stationsalso decreased over the month.

Manufacturing employment was down by 26,000 inDecember. From September to December, employment in thisindustry declined at a slower pace than during the first 8months of 2003. Employment in nondurable goodsmanufacturing decreased by 18,000 in December, with thelargest losses in printing and related support activities(-4,000) and in textile mills (-3,000). Manufacturing lost516,000 jobs in 2003 and has shed 2.8 million jobs since July2000, the last month it recorded a gain.

Within the financial activities industry, employment incredit intermediation declined for the third consecutive month,reflecting the reduced volume of mortgage refinancing. FromJuly 2000 through September 2003, the industry added 251,000jobs, but since then employment has fallen by 39,000.

Professional and business services added 45,000 jobs inDecember. Over the year, employment increases in thisindustry have totaled 252,000. The majority of this gainoccurred in temporary help services, which added 166,000jobs in 2003, including 30,000 in December. Employmentin education and health services also continued to riseover the month. Over the year, the industry added 301,000jobs.

Construction employment continued on a modest upwardtrend in December. The industry has added 173,000 jobs sinceFebruary.

Weekly hoursThe average workweek for production or nonsupervisoryworkers on private nonfarm payrolls decreased by 0.2 hourin December to 33.7 hours, seasonally adjusted. Themanufacturing workweek declined by 0.1 hour to 40.7 hours,and manufacturing overtime edged up by 0.1 hour to 4.6 hours.(See table B-8.)

The index of aggregate weekly hours of production ornonsupervisory workers on private nonfarm payrolls fell by0.6 percent to 98.8 in December (2002=100). Themanufacturing index decreased by 0.4 percent over the monthto 94.6. (See table B-9.)


Hourly and weekly earningsAverage hourly earnings of production or nonsupervisoryworkers on private nonfarm payrolls increased by 3 centsover the month to $15.50, seasonally adjusted. Average

weekly earnings fell by 0.4 percent in December to $522.35.Over the year, average hourly earnings increased by 2.0percent, and average weekly earnings rose by 1.7 percent.(See table B-11.)

Planned Changes in the Household Survey Data

Effective with the release of data for January 2004, revisions will be introduced into the populationcontrols for the household survey. These changes reflect the routine annual updating of intercensalpopulation estimates by the U.S. Census Bureau.

Revisions in the Establishment Survey Data

With the release of January data on February 6, BLS will introduce revisions in the establishment-basedseries on nonfarm payroll employment, hours, and earnings to reflect the annual benchmark adjustmentsfor March 2003 and updated seasonal adjustment factors. Unadjusted data since April 2002 and seasonallyadjusted data since January 1999 are subject to revision. Previously, the revised data were published inJune of each year; earlier receipt and tabulation of the benchmark source data now make it feasible toaccelerate the publication date to February.

Scheduled Release Dates

Employment and unemployment data are scheduled for initial release onthe following dates:

Reference month

January

February

March

Release date

February 6

March 5

April 2

Reference month

April

May

June

Release date

May 7

June 4

July 2


Revision of Seasonally AdjustedLabor Force Series in 2004

Richard B. Tiller and Thomas D. Evans

Short-run movements in labor force time series arestrongly influenced by seasonally, which refers toperiodic fluctuations that are associated with recurringcalendar-related events such as weather, holidays, and theopening and closing of schools. Seasonal adjustment is theprocess of estimating and removing these fluctuations to yielda seasonally adjusted series. The reason for doing so is tomake it easier for data users to observe fundamental changesin the level of the series, particularly those associated withgeneral economic expansions and contractions.

While seasonal adjustment is feasible only if the seasonaleffects are reasonably stable with respect to timing, direction,and magnitude, these effects are not necessarily fixed, butoften evolve over time. These evolving patterns are estimatedby the Bureau of Labor Statistics (BLS) using X-12, aprocedure based on moving averages, or "filters," thatsuccessively average a shifting timespan of data, therebyproviding estimates of seasonal factors that change in asmooth fashion from one year to the next.

For observations in the middle of the series, a set ofsymmetric moving averages with fixed weights produces finalseasonally adjusted estimates. A filter is referred to as beingsymmetric if it is centered around the time point beingadjusted with an equal amount of data preceding andfollowing that point. Standard seasonal adjustment optionsimply a symmetric filter using from 6 to 10 years of originaldata to produce a final seasonally adjusted estimate.Obviously, this final adjustment can be made only where thereis enough data beyond the time point in question to adjustwith the symmetric filter.

To seasonally adjust recent data, shorter, asymmetric filterswith less desirable properties must be used. These filters arereferred to as asymmetric because they use fewer observationsafter the reference point than preceding it. The weights forthese filters vary depending on how many observations areavailable beyond the time point for which estimates are to beadjusted.

Revisions to a seasonally adjusted estimate for a giventime point continue until enough future observations becomeavailable to use the symmetric weights. This effectively means

Richard B. Tiller and Thomas D. Evans are mathematical statisticianson the Statistical Methods Staff, Office of Employment andUnemployment Statistics, Bureau of Labor Statistics. Telephone: (202)691-6370 (Tiller) and 691-6354 (Evans); e-mail :77//[email protected];Evans. [email protected].

waiting up to 5 years for a final adjustment when usingstandard options.

Beginning with the release of estimates for December 2003in January 2004, BLS has adopted the practice of concurrentadjustment for seasonally adjusting current year labor forcedata from the Current Population Survey (CPS) data as itbecomes available each month. Under this practice, thecurrent month's seasonally adjusted estimate is computedusing all relevant original data up to and including those forthe current month. Revisions to estimates for previousmonths, however, are postponed until the end of the year.Previously, seasonal factors for the CPS labor force data wereprojected twice a year. With the introduction of concurrentseasonal adjustment, BLS will no longer publish projectedseasonal factors for CPS data. This procedure is discussedin more detail later in this article.

At the end of each calendar year, BLS reestimates theseasonal factors for the CPS series by including another fullyear of data in the estimation process. Based on this annualreestimation, BLS revises the historical seasonally adjusteddata for the last 5 years. As a result, each year's data aregenerally subject to five revisions before the values areconsidered final.

The fifth and final revisions to data for the earliest of the5 years are usually quite small, while the first-time revisionsto data for the most recent year are usually much larger. Forthe major aggregate labor force series, however, the first-time revisions rarely alter the essential trends observed inthe initial estimates.

Changes in 2004

Adoption of concurrent seasonal adjustmentAs indicated above, the new seasonal adjustmentmethodology replaces the projected factor method, whichupdated seasonal factors only twice a year. Under the latterprocedure, the seasonal adjustment program was run at theend of the year to update past estimates using all availabledata and produced a set of projected seasonal factors for thefirst 6 months of the upcoming year. These projected factorswere subsequently used to seasonally adjust the new originaldata as they were collected. At midyear, the historical serieswere updated with data for January through June and theseasonal adjustment program was rerun to produce projectedseasonal factors for July through December of the currentyear.


With concurrent seasonal adjustment, the seasonaladjustment program is rerun each month as the latest CPSdata become available. The seasonal factors for the mostrecent month are produced by applying a set of movingaverages to the entire data set, including data for the currentmonth. While all previous-month seasonally adjustedestimates are revised in this process, BLS policy is not torevise previous months' official seasonally adjusted estimatesas new data become available during the year. Revisionswill continue to be introduced for the most recent 5 years ofdata at the end of each year.

Numerous studies, including that discussed in a 1987 paperon the CPS labor force series,1 have indicated that the practiceof concurrent adjustment generally produces initial seasonallyadjusted estimates requiring smaller revisions than do thoseproduced using projected factors. Revisions to data forprevious months also may produce gains in accuracy,especially when the original data are themselves regularlyrevised on a monthly basis. Numerous revisions during theyear, however, should be avoided, because they tend toconfuse data users and substantially increase publicationcosts.

The case for revisions to previous-month seasonallyadjusted estimates is less compelling for CPS series, becausethe original sample data are normally not revised. Moreover,an empirical investigation indicated that there were nosubstantial gains in estimating month-to-month change byintroducing revisions to the data for the previous month. Forexample, it was found that if previous-month revisions weremade to the labor force series, the overall unemploymentrate would be different in only 2 months between January2001 and November 2002, in each case by only one-tenth ofa percentage point. (More detailed information about thisstudy is available from the authors upon request.)

Extension of seasonal adjustment to additionalseriesBeginning in January 2004, seasonal adjustment has beenextended to three series not previously adjusted. These arethe U-4, U-5, and U-6 alternative measures of laborunderutilization.2 These measures were substantially revisedafter the redesign of the CPS in 1994 and were published ona not seasonally adjusted basis because there was not a timeseries sufficiently long to permit evaluation of the quality ofthe seasonal adjustment for key components of thesemeasures. After careful study, BLS determined that the threelabor underutilization measures could be adequatelyseasonally adjusted, even though some of their componentscould not.

1 George R. Methee and Robert J. Mclntire, "An Evaluation ofConcurrent Seasonal Adjustment for the Major Labor Force Series," inthe 1987 Proceedings of the Business and Economic Statistics Section,American Statistical Association.

2 For a detailed discussion of these measures, see John E. Bregger andSteven E. Haugen, "BLS introduces new range of alternativeunemployment measures," Monthly Labor Review, October 1995, pp.19-26.

The U-4 measure is computed from the original CPS dataas the total unemployed plus discouraged workers as a percentof the civilian labor force plus discouraged workers.Diagnostic testing indicated that the discouraged workersseries is nonseasonal and therefore does not need to beseasonally adjusted. Thus, the seasonally adjusted U-4 isderived using the official adjustments for total employmentand unemployment with the original (not seasonally adjusted)discouraged worker series added.

The U-5 measure adds all other marginally attachedworkers to both the numerator and denominator of the U-4measure. Testing indicated that the all other marginallyattached worker series has seasonality that is weak andhard to estimate. Therefore, BLS did not seasonally adjustthis series, even though it is added to the seasonallyadjusted components of U-4 to derive an adjusted U-5.Analysis of the seasonally adjusted U-5 series indicated thatthis approach was acceptable because no residual seasonalitywas present.

Finally, the U-6 measure extends the U-5 measure toinclude workers employed part time for economic reasonsin the numerator. Because this latter series is alreadyseasonally adjusted, the seasonally adjusted U-6 measure iseasily derived.

Revisions to 2003 estimatesThis year's revisions incorporate data through December2003 and provide revised estimates for January 1999 throughDecember 2003 for all previously seasonally adjusted laborforce series. A total of 116 series are directly seasonallyadjusted and many more are indirectly adjusted. (See thesection below on aggregation.)

An important criterion for evaluating alternative methodsof seasonal adjustment is how close initial estimates are tothe results of subsequent revisions. Users of seasonallyadjusted data are often most interested in current information.Thus, it is desirable that the initial seasonally adjustedestimates be as close as possible to the improved estimatesmade after more data become available. Even though therevisions currently being released for the 2003 seasonallyadjusted data are not final, the first revisions are usually thelargest, and often indicate the direction of subsequentrevisions.

Table 1 shows the civilian unemployment rates for 2003as first computed and as revised. Rounded to one decimalplace as published, the rates were unchanged in 9 of the 12months, and changed by one-tenth of a percentage point inthe remaining 3 months.

Adjustment Methods and Procedures

Beginning in 2003, BLS adopted the use of X-12-ARIMAas the official seasonal adjustment procedure for CPS laborforce series, replacing the X-11-ARIMA program that hadbeen used since 1980. Both X-12- and X-11-ARIMA arebased on earlier versions of the widely used X-ll method


Table 1. Seasonally adjusted unemployment rates in 2003and change due to revision

Month

JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptember...OctoberNovember....December....

As firstcomputed

5.75.85.86.06.16.46.26.16.16.05.9

15.7

Asrevised

5.85.95.86.06.16.36.26.16.16.05.95.7

Change

0.1.1.0.0.0

-.1.0.0.0.0.0.0

1 This rate reflects the use of seasonal factors projected forDecember 2003 as published in the July 2003 issue of Employmentand Earnings and was subject to revision before regular publicationof December data.

developed at the U.S. Census Bureau in the 1960s.3 X-ll-ARIMA added to X-l 1 the ability to extend the time serieswith forward and backward extrapolations from Auto-Regressive Integrated Moving Average (ARIMA) models,prior to seasonal adjustment. The X-l 1 algorithm for seasonaladjustment is then applied to the extended series. The use offorward and backward extensions results in initial seasonaladjustments that are subject to smaller revisions, on average,when they are revised after future data become available.

Also developed at the U.S. Census Bureau, the X-l 2-ARIM A program includes all of the capabilities of the X-l 1-ARIMA program while also introducing major enhancements.These enhancements fall into three basic categories: (1)Enhanced ARIMA model selection and estimation, (2)detection and estimation of outlier, trading day, and holidayeffects, and (3) new postadjustment diagnostics.

For the majority of labor force series that are seasonallyadjusted by BLS, the main steps of the seasonal adjustmentprocess proceed in the following order:

• Times series modeling—a REGARIMA model (acombined regression and ARIMA model) is developedto account for the normal evolutionary behavior of thetime series and to control for outliers and other specialexternal effects that may exist in the series;

• Prior adjustments—given an adequate REGARIMAmodel, the series is modified by prior adjustments forexternal effects estimated from the regression part ofthe model and extrapolated forward 12 months by theARIMA part of the model;

• X-l 1 decomposition—the modified and extrapolatedseries is decomposed into trend, seasonal, and irregularcomponents using a series of moving averages,developed in the X-l 1 part of the program, to produceseasonal factors for implementing seasonal adjustment;and

• Evaluation—a battery of diagnostic tests is producedto evaluate the quality of the final seasonal adjustment.

For two series, the seasonal adjustment process beginswith special user-defined prior adjustments for Easter effects.(See section below on calendar adjustments.)

Time series modelingTime series models play an important role in seasonaladjustment. They are used to identify and correct the seriesfor aberrant observations and other external effects, as wellas to extend the original series with backcasts and forecastsso that less asymmetric filters can be used at the beginningand end of the series.

ARIMA models4 are designed to make forecasts of a timeseries based on only its past values. While these models canrepresent a wide class of evolving time series patterns, theydo not account for the presence of occasional outliers andother special external effects. An outlier represents a suddenbreak in the normal evolutionary behavior of a time series.Ignoring the existence of outliers may lead to seriousdistortions in the seasonally adjusted series.

A common form of outlier that presents a special problemfor seasonal adjustment is an abrupt shift in level that maybe either transitory or permanent. Three types are usuallydistinguished: (1) An additive change that affects only a singleobservation, (2) a temporary change having an effect thatdiminishes to zero over several periods, and (3) a level shiftor break in trend, which is a permanent increase or decreasein the underlying level of the series.

These three main types of outliers, as well as other typesof external effects, may be handled by the time seriesmodeling component of X-12. This is done by adding tothe ARIMA model appropriately defined regression vari-ables, based on intervention analysis originally proposed byGeorge E.R Box and George C. Tiao.5

The combined regression and ARIMA model is referredto as a REGARIMA model, and is represented by

Y,=PX,+Z,

where Y( is the original series or a log transformation of it, Xtis a set of fixed regression variables, ft represents the

3 For a detailed discussion of X-12-ARIMA, see David F. Findley, BrianC. Monsell, William R. Bell, Mark C. Otto, and Bor-Chung Chen, "NewCapabilities and Methods of the X-12-ARIMA Seasonal AdjustmentProgram," Journal of Business and Economic Statistics, April 1998, pp.127-52. For documentation on X-l 1-ARIMA, see Estela Bee Dagum, TheX-ll ARIMA Seasonal Adjustment Method, catalogue no. 12-564E(Ottawa, Statistics Canada, January 1983). The X-ll method is describedin Julius Shiskin, Alan Young, and John Musgrave, "The X-ll Variant ofthe Census Method II Seasonal Adjustment Program," Technical Paperno. 15 (Bureau of the Census, 1967).

4 For a more detailed discussion of ARIMA models, refer to GeorgeE.P. Box and Gwilym M. Jenkins, Time Series Analysis, Forecasting andControl (San Francisco, Holden Day, 1970); and Sir Maurice Kendalland J. Keith Ord, Time Series (New York, University Press, 1990).

D George E.P. Box and George C. Tiao, "Intervention Analysis withApplications to Economic and Environmental Problems," Journal of theAmerican Statistical Association, 1975, pp. 71-79.


regression coefficients, and Z is a standard seasonal ARIMAmodel described by the notation (p,d,q)(P,D,Q), where p isthe number of regular (nonseasonal) autoregressiveparameters; d is the number of regular differences; q is thenumber of regular moving average parameters; P is thenumber of seasonal autoregressive parameters; D is thenumber of seasonal differences; and Q is the number ofseasonal moving average parameters.

While the ARIMA model can theoretically be verycomplicated, in practice it takes a parsimonious forminvolving only a few estimated parameters. (See table 2.)There are well-developed methods for determining thenumber and types of parameters and the degree ofdifferencing appropriate for a given series.

With respect to specifying the regression component tocontrol for outliers, X-12 offers two approaches. Majorexternal events, such as breaks in trend, are usually associatedwith known events. In such cases, the user has sufficientprior information to specify special regression variables toestimate and control for these effects.

It is rare that there is sufficient prior information to locateand identify all of the aberrant observations that may exist ina time series. As a second approach to specifying theregression component, REGARIMA offers automatic outlierdetection based on work by I. Chang, G.C. Tiao, and C. Chen.6

This is especially useful when a large number of series mustbe processed. Of course, both of these approaches may becombined so that readily available prior information can beused directly while unknown substantial outliers may still bediscovered.

Model adequacy and length of series. The preference is touse relatively long series in fitting time series models, butwith some qualifications. Sometimes, the relevance of datain the distant past for seasonal adjustment is questionable.The implied X-l 1 moving average does not use much morethan 5 years of data before and after the central observationbeing adjusted. Using a sliding span of 10 years in length,never revising back more than 5 years at any point, issufficient to produce final revised seasonal factors.

Even though the X-12 filters have limited memory, thereare reasons for using longer series. First, for homogenoustime series, the more data used to identify and estimate amodel, the more likely that the model will represent thestructure of the data well and the more accurate the parameterestimates will be. The exact amount of data needed for time-series modeling depends on the properties of the seriesinvolved. Arbitrarily truncating the series, however, may leadto more frequent changes in model identification and to largechanges in estimated parameters, which in turn may lead tolarger-than-necessary revisions in forecasts.

Second, although level shifts and other types of outlierstend to occur more often in longer series, X-12 has thecapability of automatically controlling for these effects.

6 1 . Chang, G.C. Tiao, and C. Chen, "Estimation of Time Series Parametersin the Presence of Outliers," Technometrics, 1988, pp. 193-204.

Third, some very useful diagnostics available in X-12typically require a minimum of 11 years of data, and, in somecases, as much as 14 years of data.

Fourth, attempting to fit longer series often provides usefulinsights into the properties of the series, including its overallquality and the effects of major changes in survey design.

Based on the above considerations, REGARIMA modelsare initially estimated for series beginning in 1976 wheredata series of this length are available. Extensive use is madeof intervention analysis to estimate the magnitude of knownbreaks in CPS series and of automatic outlier detection toidentify and correct for the presence of additional aberrantobservations.

Once a model is estimated, it is evaluated in terms of itsadequacy for seasonal adjustment purposes. The criteriaessentially require a model to fit the series well (no systematicpatterns in the residuals) and to have low average forecastingerrors for the last 3 years of observed data. When there is atradeoff between the length of the series and the adequacy ofthe model, a shorter series is selected. If a shorter series isselected, the identification of the model is not changed withthe addition of new data unless the model fails diagnostictesting.

Acceptable REGARIMA models have been developed forall of the 116 labor force series that were directly adjusted atthe end of 2003. For each of the eight major civilian laborforce components, table 2 presents the form of the ARIMApart of the model, the transformation selected, and the startingdate of the series used to fit the model.

Prior adjustmentsPrior adjustments refer to adjustments made to the originaldata prior to seasonal adjustment. Their purpose is to correctthe original series for atypical observations and other externaleffects that otherwise would seriously distort the estimatesof the seasonal factors. These corrections, or prior adjustmentfactors, are subtracted from or used as divisors for the originalseries, depending on whether the seasonal adjustment isadditive or multiplicative.

Table 2. REGARIMA models used for the eight major civilianlabor force components

Series

Total employment:Men, 20 years and overWomen, 20 years and

overMen, 16 to 19 yearsWomen, 16 to 19 years

Total unemployment:Men, 20 years and overWomen, 20 years and

overMen, 16 to 19 yearsWomen, 16 to 19 years

Model

(0,1,2)(0,1,1)

(0,1,0)(0,1,1)(3,1,0X0,1,1)(0,1,1X0,1,1)

(0,1,3X0,1,1)

(1,1,0X0,1,1)(0,1,1X0,1,1)(0,1,1X0,1,1)

Trans-formation

LOG

LOGLOGLOG

LOG

LOGLOGLOG

Seriesstart date

1976

197619761976

1990

199019761976


Prior adjustment factors for CPS series may be based onspecial user-defined adjustments or handled more formallywith REGARIMA modeling. Most of the prior adjustmentfactors for the labor force series are estimated directly fromREGARIMA.

Level shifts. The most common type of outlier that occurs inCPS series is the permanent level shift. Most of these shiftshave been due to noneconomic methodological changesrelated to revisions in population controls and majormodifications to the CPS design.7 One notable economiclevel shift was due to the 2001 terrorist attacks. These levelshifts are discussed briefly below.

Population estimates extrapolated from the latest decennialcensus are used in the second-stage estimation procedure tocontrol CPS sample estimates to more accurate levels. Theseintercensal population estimates are regularly revised every10 years to reflect the latest census data and, less frequently,on other occasions.

During the 1990s, three breaks occurred in the intercensalpopulation estimates. Population controls based on the 1990census, adjusted for the estimated undercount, wereintroduced into the CPS series in 1994, and, in 1996, wereextended back to 1990. In January 1997 and again in January1999, the population controls were revised to reflect updatedinformation on international migration.

The most recent population revisions, which reflect theresults of the 2000 census, were introduced with the releaseof data for January 2003 and were extended back to databeginning in January 2000. Specifically, there was a netincrease in the total population, in large part due to growthin the numbers of Hispanics.

In 1994, major changes to the CPS were introduced, whichincluded a redesigned and automated questionnaire andrevisions to some of the labor force concepts and definitions.For data beginning in 2000, new industry and occupationalclassifications were introduced into the CPS.

To test for the possibility that revisions to the populationcontrols had important effects on those CPS series with largenumerical revisions in 1990, 1997, 1999, or 2000, as well asto test for effects due to the 1994 redesign, each REGARIMAmodel was modified to include intervention variables forthose years. The coefficients for these variables provideestimates of the direction and magnitude of the interventioneffects.

7 For further discussion of these changes, see the following articles inprevious issues of this publication: "Revisions in the Current PopulationSurvey Effective January 1994" in the February 1994 issue; "Revisionsin Household Survey Data Effective February 1996" in the March 1996issue; "Revisions in the Current Population Survey Effective January 1997"in the February 1997 issue; "Revision of Seasonally Adjusted Labor ForceSeries" in the January 1998 issue; "Revisions in the Current PopulationSurvey Effective January 1999" in the February 1999 issue; "New SeasonalAdjustment Factors for Household Data Series" in the July 1999 issue;and "Revisions to the Current Population Survey Effective in January2003" in the February 2003 issue, available on the Internet at http://www.bls.gov/cps/rvcpsO3.pdf.

Intervention effects for 2000 were necessary for selectedemployment series primarily related to Hispanic, adult, andagricultural categories. These effects mainly reflect increasesin adult and Hispanic employment due to the introduction ofCensus 2000-based population controls and the decline inagricultural employment caused by the change in the industryclassification system. (See the article, "Revisions to theCurrent Population Survey Effective in January 2003" in theFebruary 2003 issue of this publication.)

For those series with significant intervention effects, theestimated level shifts were removed prior to seasonaladjustment, thereby providing a smooth link to the pre-1990,pre-1994, pre-1997, pre-1999, and pre-2000 data. Theresulting "prior adjusted" series were then used to estimatethe seasonal factors. These factors were applied to theoriginal series, without prior adjustment, to obtain theseasonally adjusted series.

The prior adjustment factors used for all of the eight majorcivilian labor force component series are shown intable 3. Because all eight series are seasonally adjusted withthe multiplicative mode, the prior adjustments also aremultiplicative. That is, the original series is modified priorto seasonal adjustment by dividing it by its prior adjustmentfactor.

September 2001 effect. At the end of 2001, unemployed joblosers were identified as having had substantial upward levelshifts 1 month after the September 11,2001, terrorist attackson the World Trade Center in New York City. (See theseasonal adjustment article in the January 2002 issue of thispublication for more details.) Also, four additional series,related to workers employed part time for economic reasons,were identified as having substantial upward shifts at the timeof the terrorist attacks in September 2001.

Calendar effects. Calendar effects refer to transitory levelshifts in a series resulting from calendar events such as

Table 3. Prior adjustment factors for the eight major civilianlabor force components

Series

Total employment:Men, 20 years andover

Women, 20 years andover

Men, 16 to 19 yearsWomen, 16 to 19 years ..

Total unemployment:Men, 20 years andover

Women, 20 years andover

Men, 16 to 19 yearsWomen, 16 to 19 years ..

Mode ofadjustment

Multiplicative

MultiplicativeMultiplicativeMultiplicative

Multiplicative

MultiplicativeMultiplicativeMultiplicative

Prior adjustmentfactors

Pre-1990

.992

.940

Pre-1994

.957

Pre-2000

.983

.988


moving holidays or the differing composition of weekdaysin a month between years. These effects have differentinfluences on the same month across years, thereby distortingthe normal seasonal patterns for the given month.

Two CPS series related to persons at work have significanteffects in their April data due to the timing of Easter. Theseseries are persons at work on part-time schedules fornoneconomic reasons who usually work part time in allindustries and in nonagricultural industries. These series wereseasonally adjusted with multiplicative models using amoving-holiday correction. A detailed discussion of thenature of the Easter effect in these series and of the procedureused to control for it was included in the January 1990 versionof this article.

X-11 decompositionThe X-11 method of seasonal adjustment contained withinthe X-12-ARIMA procedure assumes that the original seriesis composed of three components—trend-cycle, seasonal, andirregular. Depending on the relationship between the originalseries and each of the components, the mode of seasonaladjustment may be additive or multiplicative. Formal testsare conducted to determine the appropriate mode ofadjustment.

The multiplicative mode assumes that the absolutemagnitudes of the components of the series are dependenton each other, which implies that the size of the seasonalcomponent increases and decreases with the level of theseries. With this mode, the monthly seasonal factors areratios, with all positive values centered around 1. Theseasonally adjusted series values are computed by dividingeach month's original value by the corresponding seasonalfactor.

In contrast, the additive mode assumes that the absolutemagnitudes of the components of the series are independentof each other, which implies that the size of the seasonalcomponent is independent of the level of the series. In thiscase, the seasonal factors represent positive or negativedeviations from the original series and are centered aroundzero. The seasonally adjusted series values are computed bysubtracting from each month's original value thecorresponding seasonal factor.

Given an appropriate choice for the mode of adjustment,the prior-adjusted and forecasted series is seasonally adjustedby the X-11 component of X-12. X-11 applies a sequence ofmoving average and smoothing calculations to estimate thetrend, seasonal, and irregular components. The method takeseither a ratio-to- or difference-from-moving-averageapproach, depending on whether the multiplicative or additivemodel is used. For observations in the middle of the series,a set of fixed symmetric moving averages (filters) is used toproduce final estimates. The implied length of the final filterunder standard options is 72 time points for the 3-by-5seasonal moving average or 120 time points for the 3-by-9moving average. That is, to obtain a final seasonally adjustedestimate for a single time point requires up to 5 years of

monthly data preceding and following that time point. Forrecent data, asymmetric filters, with less desirable propertiesthan symmetric filters, must be used.

All of the civilian labor force component series wereadjusted using the multiplicative mode. In previous years,unemployed teenagers, nonagricultural employment, andsome other series were additively adjusted. Formal testingfor the mode of seasonal adjustment with REGARIMAresulted in the rejection of all additive adjustments in favorof multiplicative adjustments.

EvaluationA series should be seasonally adjusted if three conditionsare satisfied: The series is seasonal, the seasonal effects canbe estimated reliably, and no residual seasonality is left inthe adjusted series. A variety of diagnostic tools is availablein X-12 to test for these conditions. These include the Ftest from the original X-11, the more extensive M and Qtests from X-l 1-ARIMA, and a set of tests first available inX-12. These X-12 tests include sliding-span diagnostics,frequency-spectrum estimates, and revision-history statistics.If diagnostic testing shows that any of the three conditionsfails to hold, a series is deemed not suitable for seasonaladjustment.

Aggregation proceduresBLS directly seasonally adjusts 116 series based on age, sex,industry, occupation, education, and other characteristics.BLS also provides seasonally adjusted totals, subtotals, andratios of selected series. It is possible to seasonally adjustan aggregate series either directly or indirectly by seasonallyadjusting its components and adding the results, or dividing,in the case of ratios. Indirect and direct adjustments usuallywill not give identical results. This is so because seasonalpatterns vary across series, there are inherent nonlinearitiesin X-12, many series are multiplicatively adjusted, and someseries are ratios.

BLS uses indirect seasonal adjustment for most of themajor labor force aggregates. Besides retaining, so far aspossible, the essential accounting relationships, the indirectapproach is needed because many of the aggregates includecomponents having different seasonal and trendcharacteristics that sometimes require different modes ofadjustment.

Examples of indirectly seasonally adjusted series are thelevels of total unemployment, employment, and the civilianlabor force, and the unemployment rate for all civilianworkers. These are produced by the aggregation of some orall of the seasonally adjusted series for the eight major civilianlabor force components. The seasonally adjusted level oftotal unemployment is the sum of the seasonally adjustedlevels of unemployment for four age-sex groups—men andwomen 16 to 19, and men and women 20 years and over.Likewise, seasonally adjusted civilian employment is the sumof employment in all industries for the same four age-sexgroups. The seasonally adjusted civilian labor force is the


sum of all eight components. The seasonally adjusted civilianunemployment rate is computed as the ratio of the totalseasonally adjusted unemployment level to the totalseasonally adjusted civilian labor force (expressed inpercentage form).

A problem with producing seasonally adjusted estimatesfor a series by aggregation is that seasonal adjustment factorscannot be directly computed for that series. Implicit seasonaladjustment factors, however, can be calculated after the factby taking the ratio of the unadjusted aggregate to theseasonally adjusted aggregate, or, for additive implicit factors,the difference between those two aggregates.

Availability of revised seriesThis issue of Employment and Earnings contains revisedmonthly and quarterly data for the most recent months andquarters for many seasonally adjusted labor forceseries. These revisions replace the seasonally adjustedestimates previously published. Revised historical sea-sonally adjusted labor force data also are available in variousforms on the BLS Internet site (www.bls.gov), includingftp access (ftp://ftp.bls.gov/pub/special.requests/lf/) to allof the revised data. The seasonally adjusted datalast published for 1998 and earlier years were not furtherrevised.


Summary table A. Major labor force status categories, seasonally adjusted

(Numbers in thousands)

Category

Civilian noninstitutional populationCivilian labor force

Percent of populationEmployed

Percent of populationU nemployed

Not in labor force

All workersMen, 20 years and overWomen, 20 years and overBoth sexes, 16 to 19 yearsWhiteBlack or African AmericanHispanic or Latino ethnicity

2002 2003

Dec. Jan. | Feb. | Mar. | Apr. | May | June | July | Aug. | Sept. | Oct. Nov. Dec.

Labor force status

218,741145,157

66.4136,459

62.48,698

73,584

219,897145,875

66.3137,447

62.58,428

74,022

220,114145,898

66.3137,318

62.48,581

74,216

220,317145,818

66.2137,300

62.38,519

74,499

220,540146,377

66.4137,578

62.48,799

74,163

220,768146,462

66.3137,505

62.38,957

74,306

221,014146,917

66.5137,673

62.39,245

74,097

221,252146,652

66.3137,604

62.29,048

74,600

221,507146,622

66.2137,693

62.28,929

74,884

221,779146,610

66.1137,644

62.18,966

75,168

222,039146,892

66.2138,095

62.28,797

75,147

222,279147,187

66.2138,533

62.38,653

75,093

222,509146,878

66.0138,479

62.28,398

75,631

Unemployment rates

6.05.65.2

16.75.2

11.48.0

5.85.54.8

17.05.1

10.57.9

5.95.55.1

17.35.1

10.77.7

5.85.45.1

17.65.1

10.37.7

6.05.75.1

17.85.2

10.87.6

6.15.85.1

18.15.4

10.78.1

6.36.05.2

19.05.5

11.68.2

6.25.95.2

18.25.4

11.18.1

6.15.85.2

16.95.4

10.97.8

6.15.85.3

17.55.3

11.17.5

6.05.65.2

17.15.1

11.47.3

5.95.65.1

15.75.2

10.47.4

5.75.35.1

16.15.0

10.36.6

NOTE: Beginning in January 2003, data reflect revised population controls used inthe household survey. Seasonally adjusted data have been revised back to January

1999 to reflect updated seasonal adjustment factors. See the article in this issue foradditional information.

Summary table B. Employment, hours, and earnings of employees on nonfarm payrolls, seasonally adjusted


Industry2002

Dec.

2003

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov.P Dec.P

Employment

Total nonfarmGoods-producing1

ConstructionManufacturing

Service-providing1

Retail tradeTransportation and warehousing ..

InformationFinancial activitiesProfessional and business servicesEducation and health servicesLeisure and hospitalityGovernment

130,19822,3236,731

15,020107,87515005.64170.73,3537,889

15,97216,37312,01921,556

130,35622,2886,738

14,982108,06815009.24174.6

3,3287,902

16,01516,40512,13221,576

130,23522,1916,700

14,922108,04414987.34166.73,3087,916

16,04316,43012,08421,588

130,08422,1596,720

14,874107,92514994.74153.83,3057,930

15,98016,45212,05021,547

130,06222,1196,760

14,795107,94314999.64136.33,3037,956

15,98916,48312,04321,526

129,98622,0986,786

14,746107,88814979.04128.53,2947,971

16,00216,50912,02621,484

129,90322,0616,800

14,692107,84214964.24113.9

3,2857,972

16,00616,50312,03921,476

129,84622,0016,804

14,631107,84514958.04103.73,2787,981

16,06316,48712,05121,458

129,88121,9826,825

14,592107,89914975.14101.2

3,2677,980

16,05416,54112,05121,470

129,98021,9786,841

14,573108,00214986.94114.13,2707,986

16,10716,57012,05621,456

130,08021,9666,845

14,556108,11414996.14116.73,2667,971

16,14216,62512,07121,473

130,12321,9546,859

14,530108,16914968.64122.03,2657,964

16,17916,65312,09121,472

130,12421,9426,873

14,504108,18214930.64112.3

3,2707,952

16,22416,67412,08721,468

Over-the-month change

Total nonfarmGoods-producing1

ConstructionManufacturing

Service-providing1

Retail tradeTransportation and warehousing

InformationFinancial activitiesProfessional and business servicesEducation and health servicesLeisure and hospitalityGovernment

-211-86-14-71

-125-8.4

-18.2-29

9-4216

-5016

158-35

7-381933.63.9-25134332

11320

-121-97-38-60-24

-21.9-7.9-20142825

-4812

-151-3220

-48-119

7.4-12.9

-314

-6322

-34-41

-22-4040

-7918

4.9-17.5

-226

931-7

-21

-76-2126

-49-55

-20.6-7.8

-9151326

-17-42

-83-3714

-54-46

-14.8-14.6

-914

-613-8

-57-60

4-61

3-6.2

-10.2-79

57-16

12-18

35-1921

-3954

17.1-2.5-11

-1-954

012

99-416

-1910311.812.9

36

5329

5-14

100-12

4-171129.22.6-4

-1535551517

43-1214

-2655

-27.55.3-1-7372820-1

1-1214

-2613

-38.0-9.7

5-124521-4-4

Hours of work2

Total privateManufacturing

Overtime

33.840.54.3

33.840.4

4.4

33.740.4

4.3

33.840.4

4.1

33.740.1

4.0

33.740.2

4.1

33.740.3

4.0

33.640.1

4.1

33.740.2

4.1

33.740.5

4.2

33.840.6

4.3

33.940.8

4.5

33.740.74.6

Indexes of aggregate weekly hours (2002=100)

Total private .Manufacturing

99.498.2

99.497.6

99.097.2

99.096.6

98.895.2

98.795.1

98.795.0

98.394.1

98.794.1

98.794.6

99.194.7

99.495.0

98.894.6

Earnings

Average hourly earnings, total private:Current dollarsConstants 982) dollars3

Average weekly earnings, total private .

$15.208.30

513.76

$15.228.28

514.44

$15.298.26

515.27

$15.298.22

516.80

$15.308.27

515.61

$15.358.31

517.30

$15.388.30

518.31

$15.438.32

518.45

$15.458.30

520.67

$15.448.27

520.33

$15.468.29

522.55

$15.478.32

524.43

$15.50N.A.

522.35

11ncludes other industries, not shown separately.Data relate to production or nonsupervisory workers.

3 The Consumer Price Index for Urban Wage Earners and Clerical Workers(CPI-W) is used to deflate this earnings series.N.A. = not available.

10

p= preliminary.NOTE: Establishment survey estimates are currently projected from

March 2002 benchmark levels. When more recent benchmark data areintroduced with the release of January 2004 estimates, all seasonally adjusteddata from January 1999 forward are subject to revision.Digitized for FRASER

http://fraser.stlouisfed.org/ Federal Reserve Bank of St. Louis

Chart 1. Nonfarm payroll employment, seasonally adjusted, 1999-2003

Thousands135,000

122,500

120,000

117,500 i , , , , , , , , , , , i , , i ,1999

, , ! , , , I

2000 2001 2002 2003

Thousands135,000

122,500

120,000

117,500

Chart 2. Unemployment rate, seasonally adjusted, 1999-2003Percent Percent

1999 2000 2001 2002 2003

4.5

4.0

^ 3.5

NOTE: Beginning in 1999, data incorporate revisions in the population controls. Beginning in 2000,data include the use of new population controls that reflect Census 2000 results. Beginning in January2003, data reflect an additional upward adjustment to population controls and other changes to the survey.These changes affect comparability with data for prior periods. Data have been revise to reflect updatedseasonal adjustment factors.

11


HOUSEHOLD DATAHISTORICAL

A-1. Employment status of the civilian noninstitutional population 16 years and over, 1969 to date


Yearand

month

1969

1970197119721

19731

197419751976197719781

1979

19801981198219831984198519861

198719881989

19901

19911992199319941

1995199619971

19981

19991

20001

2001200220031

2002:December

2003:January3

FebruaryMarchAprilMayJuneJulyAugustSeptember ....OctoberNovemberDecember

Civiliannoninsti-tutional

population

Civilian labor force

NumberPercent

ofpopulation

Employed

NumberPercent

ofpopulation

Unemployed

Number

Percent

oflaKrtriauorforce

Not inlaborforce

Annual averages

134,335

137,085140,216144,126147,096150,120153,153156,150159,033161,910164,863

167,745170,130172,271174,215176,383178,206180,587182,753184,613186,393

189,164190,925192,805194,838196,814198,584200,591203,133205,220207,753

212,577215,092217,570221,168

218,741

219,897220,114220,317220,540220,768221,014221,252221,507221,779222,039222,279222,509

80,734

82,77184,38287,03489,42991,94993,77496,15899,008

102,250104,962

106,940108,670110,204111,550113,544115,461117,834119,865121,669123,869

125,840126,346128,105129,200131,056132,304133,943136,297137,673139,368

142,583143,734144,863146,510

145,157

145,875145,898145,818146,377146,462146,917146,652146,622146,610146,892147,187146,878

60.1

60.460.260.460.861.361.261.662.363.263.7

63.863.964.064.064.464.865.365.665.966.5

66.566.266.466.366.666.666.867.167.167.1

67.166.866.666.2

77,902

78,67879,36782,15385,06486,79485,84688,75292,01796,04898,824

99,302100,39799,526

100,834105,005107,150109,597112,440114,968117,342

118,793117,718118,492120,259123,060124,900126,708129,558131,463133,488

136,891136,933136,485137,736

Monthly data, J

66.4

66.366.366.266.466.366.566.366.266.166.266.266.0

136,459

137,447137,318137,300137,578137,505137,673137,604137,693137,644138,095138,533138,479

58.0

57.456.657.057.857.856.156.857.959.359.9

59.259.057.857.959.560.160.761.562.363.0

62.861.761.561.762.562.963.263.864.164.3

64.463.762.762.3

2,832

4,0935,0164,8824,3655,1567,9297,4066,9916,2026,137

7,6378,273

10,67810,7178,5398,3128,2377,4256,7016,528

7,0478,6289,6138,9407,9967,4047,2366,7396,2105,880

5,6926,8018,3788,774

seasonally adjusted 2

62.4

62.562.462.362.462.362.362.262.262.162.262.362.2

8,698

8,4288,5818,5198,7998,9579,2459,0488,9298,9668,7978,6538,398

3.5

4.95.95.64.95.68.57.77.16.15.8

7.17.69.79.67.57.27.06.25.55.3

5.66.87.56.96.15.65.44.94.54.2

4.04.75.86.0

6.0

5.85.95.86.06.16.36.26.16.16.05.95.7

53,602

54,31555,83457,09157,66758,17159,37759,99160,02559,65959,900

60,80661,46062,06762,66562,83962,74462,75262,88862,94462,523

63,32464,57864,70065,63865,75866,28066,64766,83667,54768,385

69,99471,35972,70774,658

73,584

74,02274,21674,49974,16374,30674,09774,60074,88475,16875,14775,09375,631

1 Not strictly comparable with prior years. For anexplanation, see "Historical Comparability" under theHousehold Data section of the Explanatory Notes andEstimates of Error.

2 The population figures are not adjusted for seasonalvariation.

3 Beginning in January 2003, data are not strictlycomparable with data for 2002 and earlier years because of

the revisions in the population controls used in the householdsurvey. For additional information, see "Revisions to theCurrent Population Survey Effective in January 2003" in theFebruary 2003 issue of this publication. Seasonally adjusteddata have been revised back to January 1999 to reflectupdated seasonal adjustment factors. See the article in thisissue for additional information.

12


HOUSEHOLD DATAHISTORICAL

A-2. Employment status of the civilian noninstitutional population 16 years and over by sex, 1992 to date


Sex, year,and month

MEN199219931994119951996199711998119991

20001

o

o

oo

o

o

CM

C

M

CM

2002:December

2003:January3FebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember

WOMEN199219931994119951996199711998119991

200012001200220031

2002:December

2003:January3FebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecember

Civiliannoninsti-tutional

population

Civilian labor force

NumberPercent

ofpopulation

Employed

NumberPercent

ofpopulation

Unemployed

Number

Percentof

laborforce

Not inlaborforce

Annual averages

92,27093,33294,35495,17896,20697,71598,75899,722

101,964103,282104,585106,435

69,96470,40470,81771,36072,08673,26173,95974,512

76,28076,88677,50078,238

75.875.475.175.074.975.074.974.7

74.874.474.173.5

64,44065,34966,45067,37768,20769,68570,69371,446

73,30573,19672,90373,332

69.870.070.470.870.971.371.671.6

71.970.969.768.9

5,5235,0554,3673,9833,8803,5773,2663,066

2,9753,6904,5974,906

7.97.26.25.65.44.94.44.1

3.94.85.96.3

22,30622,92723,53823,81824,11924,45424,79925,210

25,68426,39627,08528,197

Monthly data, seasonally adjusted 2

105,195

105,767105,895106,005106,123106,238106,362106,475106,604106,744106,879107,003107,123

77,447

77,72277,91577,73178,09578,12178,33878,27778,25178,50478,53078,79978,661

73.6

73.573.673.373.673.573.773.573.473.573.573.673.4

72,615

72,95873,13273,01573,15073,04973,12473,14973,26373,48873,64373,91574,085

69.0

69.069.168.968.968.868.868.768.768.868.969.169.2

4,832

4,7644,7834,7164,9455,0725,2145,1284,9885,0164,8874,8834,576

6.2

6.16.16.16.36.56.76.66.46.46.26.25.8

27,749

28,04527,98028,27528,02828,11728,02328,19728,35328,24028,34828,20428,462

Annual averages

100,535101,506102,460103,406104,385105,418106,462108,031

110,613111,811112,985114,733

58,14158,79560,23960,94461,85763,03663,71464,855

66,30366,84867,36368,272

57.857.958.858.959.359.859.860.0

59.959.859.659.5

54,05254,91056,61057,52358,50159,87360,77162,042

63,58663,73763,58264,404

53.854.155.355.656.056.857.157.4

57.557.056.356.1

4,0903,8853,6293,4213,3563,1622,9442,814

2,7173,1113,7813,868

7.06.66.05.65.45.04.64.3

4.14.75.65.7

42,39442,71142,22142,46242,52842,38242,74843,175

44,31044,96245,62146,461

Monthly data, seasonally adjusted 2

113,546

114,130114,219114,312114,417114,531114,653114,778114,903115,035115,160115,276115,386

67,711

68,15367,98468,08868,28268,34268,57968,37468,37268,10668,36268,38868,217

59.6

59.759.559.659.759.759.859.659.559.259.459.359.1

63,844

64,48964,18664,28564,42764,45664,54864,45564,43164,15564,45264,61864,394

56.2

56.556.256.256.356.356.356.256.155.856.056.155.8

3,866

3,6653,7983,8033,8543,8854,0313,9203,9413,9513,9103,7703,823

5.7

5.45.65.65.65.75.95.75.85.85.75.55.6

45,835

45,97646,23646,22446,13546,18946,07446,40346,53246,92946,79946,88847,169

1 Not strictly comparable with prior years. For an explanation, see"Historical Comparability" under the Household Data section of the ExplanatoryNotes and Estimates of Error.

2 The population figures are not adjusted for seasonal variation.3 Beginning in January 2003, data are not strictly comparable with data for

2002 and earlier years because of the revisions in the population controls used

in the household survey. For additional information, see "Revisions to theCurrent Population Survey Effective in January 2003" in the February 2003issue of this publication. Seasonally adjusted data have been revised back toJanuary 1999 to reflect updated seasonal adjustment factors. See the article inthis issue for additional information.

13


HOUSEHOLD DATASEASONALLY ADJUSTED

A-3. Employment status of the civilian noninstitutional population by sex and age, seasonally adjusted


Employment status,sex, and age

2002

Dec.

2003

Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

TOTAL

Civilian noninstitutional population1

Civilian labor forcePercent of population

EmployedEmployment-population ratio

UnemployedUnemployment rate

Not in labor forcePersons who currently want a job

Men, 16 years and over

Civilian noninstitutional population1 .Civilian labor force


Employment-population ratioUnemployed

Unemployment rateNot in labor force

Men, 20 years and over





Women, 16 years and over





Women, 20 years and over



Employment-population ratioUnemployedUnemployment rate

Not in labor force

Both sexes, 16 to 19 years





218,741145,157

66.4136,459

62.48,698

6.073,5844,566

105,19577,447

73.672,615

69.04,832

6.227,749

97,13973,725

75.969,569

71.64,157

5.623,413

113,54667,711

59.663,844

56.23,866

5.745,835

105,67864,056

60.660,750

57.53,306

5.241,622

15,9257,37646.3

6,14138.6

1,23516.7

8,549

219,897145,875

66.3137,447

62.58,428

5.874,0224,644

105,76777,722

73.572,958

69.04,764

6.128,045

97,63574,014

75.869,940

71.64,075

5.523,620

114,13068,153

59.764,489

56.53,665

5.445,976

106,23564,490

60.761,391

57.83,100

4.841,745

16,0277,37146.0

6,11738.2

1,25417.0

8,656

220,114145,898

66.3137,318

62.48,581

5.974,2164,580

105,89577,915

73.673,132

69.14,783

6.127,980

97,76274,241

75.970,174

71.84,068

5.523,521

114,21967,984

59.564,186

56.23,798

5.646,236

106,32264,359

60.561,106

57.53,253

5.141,964

16,0307,298

45.56,039

37.71,260

17.38,731

220,317145,818

66.2137,300

62.38,519

5.874,4994,974

106,00577,731

73.373,015

68.94,716

6.128,275

97,86974,209

75.870,213

71.73,995

5.423,660

114,31268,088

59.664,285

56.23,803

5.646,224

106,41164,490

60.661,219

57.53,271

5.141,921

16,0387,12044.4

5,86836.6

1,25217.6

8,918

220,540146,377

66.4137,578

62.48,799

6.074,1634,462

106,12378,095

73.673,150

68.94,945

6.328,028

97,97974,510

76.070,290

71.74,220

5.723,469

114,41768,282

59.764,427

56.33,854

5.646,135

106,51064,632

60.761,343

57.63,289

5.141,878

16,0517,23545.1

5,94537.0

1,29017.8

8,816

220,768146,462

66.3137,505

62.38,957

6.174,3064,727

106,23878,121

73.573,049

68.85,072

6.528,117

98,08374,523

76.070,182

71.64,341

5.823,560

114,53168,342

59.764,456

56.33,885

5.746,189

106,61364,699

60.761,397

57.63,302

5.141,914

16,0727,24045.0

5,92636.9

1,31418.1

8,832

221,014146,917

66.5137,673

62.39,245

6.374,0974,687

106,36278,338

73.773,124

68.85,214

6.728,023

98,19674,675

76.070,190

71.54,485

6.023,521

114,65368,579

59.864,548

56.34,031

5.946,074

106,72464,989

60.961,610

57.73,379

5.241,735

16,0957,25445.1

5,87336.5

1,38119.0

8,841

221,252146,652

66.3137,604

62.29,048

6.274,6004,829

106,47578,277

73.573,149

68.75,128

6.628,197

98,30474,660

75.970,269

71.54,391

5.923,644

114,77868,374

59.664,455

56.23,920

5.746,403

106,83964,835

60.761,479

57.53,356

5.242,004

16,1097,15744.4

5,85636.4

1,30118.2

8,952

221,507146,622

66.2137,693

62.28,929

6.174,8844,826

106,60478,251

73.473,263

68.74,988

6.428,353

98,43474,682

75.970,324

71.44,358

5.823,751

114,90368,372

59.564,431

56.13,941

5.846,532

106,95764,836