Categories
Employment and Earnings

Explanatory notes and estimates of error

Explanatory notes and estimates of error

Introduction

The statistics in this periodical are compiled from two major sources: (1) household interviews, and (2) reports from employers.

Data based on household interviews are obtained from the Current Population Survey (CPS), a sample survey of the population 16 years of age and over. The survey is conducted each month by the U.S. Census Bureau for the Bureau of Labor Statistics and provides comprehensive data on the labor force, the employed, and the unemployed, classified by such characteristics as age, sex, race, family relationship, marital status, occupation, and industry attachment. The survey also provides data on the characteristics and past work experience of those not in the labor force. The information is collected by trained interviewers from a sample of about 60,000 households (beginning with July 2001 data) located in 754 sample areas. These areas are chosen to represent all counties and independent cities in the United States, with coverage in 50 States and the District of Columbia. The data collected are based on the activity or status reported for the calendar week including the 12th of the month.

Data based on establishment records are compiled each month through the use of touchtone data entry, computer-assisted telephone interviewing, and electronic data interchange, or by mail or fax, or on magnetic tape or computer diskette. The Current Employment Statistics (CES) survey is designed to provide industry information on nonfarm wage and salary employment, average weekly hours, average hourly earnings, and average weekly earnings for the Nation, States, and metropolitan areas. The employment, hours, and earnings series are based on payroll reports from a sample that includes about 160,000 businesses and government agencies covering approximately 400,000 individual worksites. The sample is drawn from a sampling frame of over 8 million unemployment insurance tax accounts. The active CES sample includes approximately one -third of all nonfarm payroll workers. The data relate to all workers, full or part time, who receive pay during the payroll period that includes the 12th of the month.

RELATIONSHIP BETWEEN THE HOUSEHOLD AND ESTABLISHMENT SERIES

The household and establishment data complement one another, each providing significant types of information that the other cannot suitably supply. Population characteristics, for example, are obtained only from the household survey, whereas detailed industrial classifications are much more reliably derived from establishment reports.

Data from these two sources differ from each other because of variations in definitions and coverage, source of information, methods of collection, and estimating procedures. Sampling variability and response errors are additional reasons for discrepancies. The major factors that have a differential effect on the levels and trends of the two data series are as follows.

Employment

Coverage. The household survey definition of employment comprises wage and salary workers (including domestics and other private household workers), self-employed persons, and unpaid workers who worked 15 hours or more during the reference week in family-operated enterprises. Employment in both agricultural and nonagricultural industries is included. The payroll survey covers only wage and salary employees on the payrolls of nonfarm establishments.

Multiple jobholding. The household survey provides information on the work status of the population without duplication, because each person is classified as employed, unemployed, or not in the labor force. Employed persons holding more than one job are counted only once. In the figures based on establishment reports, persons who worked in more than one establishment during the reporting period are counted each time their names appear on payrolls.

Unpaid absences from jobs. The household survey includes among the employed all civilians who had jobs but were not at work during the reference week–that is, were not working but had jobs from which they were temporarily absent because of illness, vacation, bad weather, childcare problems, or labor-management disputes, or because they were taking time off for various other reasons, even if they were not paid by their employers for the time off. In the figures based on payroll reports, persons on leave paid for by the company are included, but those on leave without pay for the entire payroll period are not.

Hours of work

The household survey measures hours worked for all workers, whereas the payroll survey measures hours for private production or nonsupervisory workers paid for by employers. In the household survey, all persons with a job but not at work are excluded from the hours distributions and the computations of average hours at work. In the payroll survey, production or nonsupervisory employees on paid vacation, paid holiday, or paid sick leave are included and assigned the number of hours for which they were paid during the reporting period.

Earnings

The household survey measures the earnings of wage and salary workers in all occupations and industries in both the private and public sectors. Data refer to the usual earnings received from the worker’s sole or primary job. Data from the establishment survey generally refer to average earnings of production and related workers in natural resources and mining and manufacturing; construction workers in construction; and nonsupervisory employees in private service-providing industries. For a comprehensive discussion of the various earnings series available from the household and establishment surveys, see BLS Measures of Compensation, Bulletin 2239 (Bureau of Labor Statistics, 1986).

COMPARABILITY OF HOUSEHOLD DATA WITH OTHER SERIES

Unemployment insurance data. The unemployed total from the household survey includes all persons who did not have a job during the reference week, were currently available for a job, and were looking for work or were waiting to be called back to a job from which they had been laid off, whether or not they were eligible for unemployment insurance. Figures on unemployment insurance claims, prepared by the Employment and Training Administration of the U.S. Department of Labor, exclude, in addition to otherwise ineligible persons who do not file claims for benefits, persons who have exhausted their benefit rights, new workers who have not earned rights to unemployment insurance, and persons losing jobs not covered by unemployment insurance systems (some workers in agriculture, domestic services, and religious organizations, and self-employed and unpaid family workers).

In addition, the qualifications for drawing unemployment compensation differ from the definition of unemployment used in the household survey. For example, persons with a job but not at work and persons working only a few hours during the week are sometimes eligible for unemployment compensation but are classified as employed, rather than unemployed, in the household survey.

Agricultural employment estimates of the U.S. Department of Agriculture. The principal differences in coverage are the inclusion of persons under 16 in the National Agricultural Statistics Service series and the treatment of dual jobholders, who are counted more than once if they work on more than one farm during the reporting period. There also are wide differences in sampling techniques and data collecting and estimating methods, which cannot be readily measured in terms of their impact on differences in the levels and trends of the two series.

COMPARABILITY OF PAYROLL EMPLOYMENT DATA WITH OTHER SERIES

Statistics on manufacturers and business, U.S. Census Bureau. BLS establishment statistics on employment differ from employment counts derived by the U.S. Census Bureau from its censuses or sample surveys of manufacturing and business establishments. The major reasons for non-comparability are different treatment of business units considered parts of an establishment, such as central administrative offices and auxiliary units; the industrial classification of establishments; and different reporting patterns by multiunit companies. There also are differences in the scope of the industries covered–for example, the Census of Business excludes professional services, public utilities, and financial establishments, whereas these are included in the BLS statistics.

County Business Patterns, U.S. Census Bureau. Data in County Business Patterns (CBP) differ from BLS establishment statistics in the treatment of central administrative offices and auxiliary units. Differences also may arise because of industrial classification and reporting practices. In addition, CBP excludes interstate railroads and most of government, and coverage is incomplete for some of the nonprofit agencies.

Employment covered by State unemployment insurance programs. Most nonfarm wage and salary workers are covered by the unemployment insurance programs. However, some employees, such as those working in parochial schools and churches, are not covered by unemployment insurance, whereas they are included in the BLS establishment statistics.

Household Data (“A” tables, monthly; “D” tables, quarterly)

COLLECTION AND COVERAGE

Statistics on the employment status of the population and related data are compiled by BLS using data from the Current Population Survey (CPS). This monthly survey of households is conducted for BLS by the U.S. Census Bureau through a scientifically selected sample designed to represent the civilian noninstitutional population. Respondents are interviewed to obtain information about the employment status of each member of the household 16 years of age and older. The inquiry relates to activity or status during the calendar week, Sunday through Saturday, that includes the 12th day of the month. This is known as the “reference week.” Actual field interviewing is conducted in the following week, referred to as the “survey week.”

Each month, about 60,000 occupied units are eligible for interview. Some 4,500 of these households are contacted but interviews are not obtained because the occupants are not at home after repeated calls or are unavailable for other reasons. This represents a noninterview rate for the survey that ranges between 7 and 8 percent. In addition to the 60,000 occupied units, there are about 12,000 sample units in an average month that are visited but found to be vacant or otherwise not eligible for enumeration. Part of the sample is changed each month. The rotation plan, as will be explained later, provides for three-fourths of the sample to be common from one month to the next, and one-half to be common with the same month a year earlier.

CONCEPTS AND DEFINITIONS

The concepts and definitions underlying labor force data have been modified, but not substantially altered, since the inception of the survey in 1940; those in use as of January 1994 are as follows:

Civilian noninstitutional population. Included are persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.

Employed persons. All persons who, during the reference week, (a) did any work at all (at least 1 hour) as paid employees, worked in their own business, profession, or on their own farm or worked 15 hours or more as unpaid workers in an enterprise operated by a member of the family, and (b) all those who were not working but who had jobs or businesses from which they were temporarily absent because of vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management dispute, job training, or other family or personal reasons, whether or not they were paid for the time off or were seeking other jobs.

Each employed person is counted only once, even if he or she holds more than one job. For purposes of occupation and industry classification, multiple jobholders are counted in the job at which they worked the greatest number of hours during the reference week.

Included in the total are employed citizens of foreign countries who are temporarily in the United States but not living on the premises of an embassy. Excluded are persons whose only activity consisted of work around their own house (painting, repairing, or own home housework) or volunteer work for religious, charitable, and other organizations.

Unemployed persons. All persons who had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment sometime during the 4-week period ending with the reference week. Persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work to be classified as unemployed.

Duration of unemployment. This represents the length of time (through the current reference week) that persons classified as unemployed had been looking for work. For persons on layoff, duration of unemployment represents the number of full weeks they had been on layoff. Mean duration is the arithmetic average computed from single weeks of unemployment; median duration is the midpoint of a distribution of weeks of unemployment.

Reason for unemployment. Unemployment also is categorized according to the status of individuals at the time they began to look for work. The reasons for unemployment are divided into five major groups: (1) Job losers, comprising (a) persons on temporary layoff, who have been given a date to return to work or who expect to return within 6 months (persons on layoff need not be looking for work to qualify as unemployed), and (b)permanent job losers, whose employment ended involuntarily and who began looking for work; (2) Job leavers, persons who quit or otherwise terminated their employment voluntarily and immediately began looking for work; (3) Persons who completed temporary jobs, who began looking for work after the jobs ended; (4) Reentrants, persons who previously worked but who were out of the labor force prior to beginning their job search; and (5) New entrants, persons who had never worked. Each of these five categories of the unemployed can be expressed as a proportion of the entire civilian labor force; the sum of the four rates thus equals the unemployment rate for all civilian workers. (For statistical presentation purposes, “job losers” and “persons who completed temporary jobs” are combined into a single category until seasonal adjustments can be developed for the separate categories.)

Jobseekers. All unemployed persons who made specific efforts to find a job sometime during the 4-week period preceding the survey week are classified as jobseekers. Jobseekers do not include persons classified as on temporary layoff, who, although often looking for work, are not required to do so to be classified as unemployed. Jobseekers are grouped by the methods used to seek work. Only active methods–which have the potential to result in a job offer without further action on the part of the jobseeker–qualify as job search. Examples include going to an employer directly or to a public or private employment agency, seeking assistance from friends or relatives, placing or answering ads, or using some other active method. Examples of the “other” category include being on a union or professional register, obtaining assistance from a community organization, or waiting at a designated labor pickup point. Passive methods, which do not qualify as job search, include reading (as opposed to answering or placing) “help wanted” ads and taking a job training course.

Labor force. This group comprises all persons classified as employed or unemployed in accordance with the criteria described above.

Unemployment rate. The unemployment rate represents the number unemployed as a percent of the labor force.

Participation rate. This represents the proportion of the population that is in the labor force.

Employment-population ratio. This represents the proportion of the population that is employed.

Not in the labor force. Included in this group are all persons in the civilian noninstitutional population who are neither employed nor unemployed. Information is collected on their desire for and availability to take a job at the time of the CPS interview, job search activity in the prior year, and reason for not looking in the 4-week period prior to the survey week. This group includes discouraged workers, defined as persons not in the labor force who want and are available for a job and who have looked for work sometime in the past 12 months (or since the end of their last job if they held one within the past 12 months), but who are not currently looking because they believe there are no jobs available or there are none for which they would qualify.

Persons classified as not in the labor force who are in the sample for either their fourth or eighth month are asked additional questions relating to job history and workseeking intentions. These latter data are available on a quarterly basis.

Occupation, industry, and class of worker. This information for the employed applies to the job held in the reference week. Persons with two or more jobs are classified in the job at which they worked the greatest number of hours. The unemployed are classified according to their last job. Beginning in 2003, the occupational and industrial classification of CPS data is based on the 2002 Census Bureau occupational and industrial classification systems which are derived from the 2000 Standard Occupational Classification (SOC) and the 2002 NorthAmerican Industry Classification System (NAICS). (See the following section on historical comparability for a discussion of previous classification systems used in the CPS.)

The class-of-worker breakdown assigns workers to the following categories: Private and government wage and salary workers, self-employed workers, and unpaid family workers. Wage and salary workers receive wages, salary, commissions, tips, or pay in kind from a private employer or from a government unit. Self-employed persons are those who work for profit or fees in their own business, profession, trade, or farm. Only the unincorporated self-employed are included in the self-employed category in the class-of-worker typology. Self-employed persons who respond that their businesses are incorporated are included among wage and salary workers because, technically, they are paid employees of a corporation. Unpaid family workers are persons working without pay for 15 hours a week or more on a farm or in a business operated by a member of the household to whom they are related by birth or marriage.

Multiple jobholders. These are employed persons who, during the reference week, either had two or more jobs as a wage and salary worker, were self-employed and also held a wage and salary job, or worked as an unpaid family worker and also held a wage and salary job. Excluded are self-employed persons with multiple businesses and persons with multiple jobs as unpaid family workers.

Hours of work. These statistics relate to the actual number of hours worked during the reference week. For example, persons who normally work 40 hours a week but were off on the Columbus Day holiday would be reported as working 32 hours, even though they were paid for the holiday. For persons working in more than one job, the published figures relate to the number of hours worked in all jobs during the week; all the hours are credited to the major job. Unpublished data are available for the hours worked in each job and for usual hours.

At work part time for economic reasons. Sometimes referred to as involuntary part time, this category refers to individuals who gave an economic reason for working 1 to 34 hours during the reference week. Economic reasons include slack work or unfavorable business conditions, inability to find full-time work, and seasonal declines in demand. Those who usually work part time must also indicate that they want and are available for full-time work to be classified as on part time for economic reasons.

At work part time for noneconomic reasons. This group includes those persons who usually work part time and were at work 1 to 34 hours during the reference week for a noneconomic reason. Noneconomic reasons include, for example: Illness or other medical limitations, childcare problems or other family or personal obligations, school or training, retirement or Social Security limits on earnings, and being in a job where full-time work is less than 35 hours. The group also includes those who gave an economic reason for usually working 1 to 34 hours but said they do not want to work full time or are unavailable for such work.

Usual full- or part-time status. Data on persons “at work” exclude persons who were temporarily absent from a job and therefore classified in the zero-hours-worked category, “with a job but not at work.” These are persons who were absent from their jobs for the entire week for such reasons as bad weather, vacation, illness, or involvement in a labor dispute. In order to differentiate a person’s normal schedule from his or her activity during the reference week, persons also are classified according to their usual full- or part-time status. In this context, full-time workers are those who usually worked 35 hours or more (at all jobs combined). This group will include some individuals who worked less than 35 hours in the reference week for either economic or noneconomic reasons and those who are temporarily absent from work. Similarly, part-time workers are those who usually work less than 35 hours per week (at all jobs), regardless of the number of hours worked in the reference week. This may include some individuals who actually worked more than 34 hours in the reference week, as well as those who are temporarily absent from work. The full-time labor force includes all employed persons who usually work full time and unemployed persons who are either looking for full-time work or are on layoff from full-time jobs. The part-time labor force consists of employed persons who usually work part time and unemployed persons who are seeking or are on layoff from part-time jobs. Unemployment rates for full- and part-time workers are calculated using the concepts of the full- and part-time labor force.

White, black or African American, and Asian. These are terms used to describe the race of persons. Persons in these categories are those who selected that race group only. Persons in the remaining race categories–American Indian or Alaska Native, Native Hawaiian or Other Pacific Islanders, and persons who selected more than one race category–are included in the estimates of total employment and unemployment but are not shown separately because the number of survey respondents is too small to develop estimates of sufficient quality for monthly publication. In the enumeration process, race is determined by the household respondent. (See the following section on historical comparability for a discussion of changes beginning in 2003 that affected how people are classified by race.)

Hispanic or Latino ethnicity. This refers to persons who identified themselves in the enumeration process as being Spanish, Hispanic, or Latino. Persons whose ethnicity is identified as Hispanic or Latino may be of any race. (See the following section on historical comparability for a discussion of changes beginning in 2003 that affected how people are classified by Hispanic or Latino ethnicity.)

Usual weekly earnings. Data represent earnings before taxes and other deductions, and include any overtime pay, commissions, or tips usually received (at the main job, in the case of multiple jobholders). Earnings reported on a basis other than weekly (for example, annual, monthly, hourly) are converted to weekly. The term “usual” is as perceived by the respondent. If the respondent asks for a definition of usual, interviewers are instructed to define the term as more than half the weeks worked during the past 4 or 5 months. Data refer to wage and salary workers (excluding all self-employed persons regardless of whether their businesses were incorporated) who usually work full time on their sole or primary job.

Median earnings. These figures indicate the value that divides the earnings distribution into two equal parts, one part having values above the median and the other having values below the median. The medians shown in this publication are calculated by linear interpolation of the $50 centered interval within which each median falls. Data expressed in constant dollars are deflated by the Consumer Price Index for All Urban Consumers (CPI-U).

Never married; married, spouse present; and other marital status. These are the terms used to define the marital status of individuals at the time of interview. Married, spouse present, applies to husband and wife if both were living in the same household, even though one may be temporarily absent on business, on vacation, on a visit, in a hospital, etc. Other marital status applies to persons who are married, spouse absent; widowed; or divorced. Married, spouse absent relates to persons who are separated due to marital problems, as well as to husbands and wives who are living apart because one or the other was employed elsewhere or was on duty with the Armed Forces, or for any other reasons.

Household. A household consists of all persons–related family members and all unrelated persons–who occupy a housing unit and have no other usual address. A house, an apartment, a group of rooms, or a single room is regarded as a housing unit when occupied or intended for occupancy as separate living quarters. A householder is the person (or one of the persons) in whose name the housing unit is owned or rented. The term is never applied to either husbands or wives in married-couple families but relates only to persons in families maintained by either men or women without a spouse.

Family. A family is defined as a group of two or more persons residing together who are related by birth, marriage, or adoption; all such persons are considered as members of one family. Families are classified either as married-couple families or as families maintained by women or men without spouses. A family maintained by a woman or a man is one in which the householder is either single, widowed, divorced, or married, spouse absent.

HISTORICAL COMPARABILITY

Changes in concepts and methods

While current survey concepts and methods are very similar to those introduced at the inception of the survey in 1940, a number of changes have been made over the years to improve the accuracy and usefulness of the data. Some of the most important changes include:

* In 1945, the questionnaire was radically changed with the introduction of four basic employment questions. Prior to that time, the survey did not contain specific question wording, but, rather, relied on a complicated scheme of activity prioritization.

* In 1953, the current 4-8-4 rotation system was adopted, whereby households are interviewed for 4 consecutive months, leave the sample for 8 months, and then return to the sample for the same 4 months of the following year. Before this system was introduced, households were interviewed for 6 consecutive months and then replaced. The new system provided some year-to-year overlap in the sample, thereby improving measurement over time.

* In 1955, the survey reference week was changed to the calendar week including the 12th day of the month, for greater consistency with the reference period used for other labor-related statistics. Previously, the calendar week containing the 8th day of the month had been used as the reference week.

* In 1957, the employment definition was modified slightly as a result of a comprehensive interagency review of labor force concepts and methods. Two relatively small groups of persons classified as employed, under “with a job but not at work,” were assigned to different classifications. Persons on layoff with definite instructions to return to work within 30 days of the layoff date, and persons volunteering that they were waiting to start a new wage and salary job within 30 days of interview, were, for the most part, reassigned to the unemployed classification. The only exception was the small subgroup in school during the reference week but waiting to start new jobs, which was transferred to not in the labor force.

* In 1967, more substantive changes were made as a result of the recommendations of the President’s Committee to Appraise Employment and Unemployment Statistics (the Gordon Committee). The principal improvements were as follows:

a) A 4-week job search period and specific questions on jobseeking activity were introduced. Previously, the questionnaire was ambiguous as to the period for jobseeking, and there were no specific questions concerning job search methods.

b) An availability test was introduced whereby a person must be currently available for work in order to be classified as unemployed. Previously, there was no such requirement. This revision to the concept mainly affected students, who, for example, may begin to look for summer jobs in the spring although they will not be available until June or July. Such persons, until 1967, had been classified as unemployed but since have been assigned to the “not in the labor force” category.

c) Persons “with a job but not at work” because of strikes, bad weather, etc., who volunteered that they were looking for work were shifted from unemployed status to employed.

d) The lower age limit for official statistics on employment, unemployment, and other labor force concepts was raised from 14 to 16 years. Historical data for most major series have been revised to provide consistent information based on the new minimum age limit.

e) New questions were added to obtain additional information on persons not in the labor force, including those referred to as “discouraged workers,” defined as persons who indicate that they want a job but are not currently looking because they believe there are no jobs available or none for which they would qualify.

f) New “probing” questions were added to the questionnaire in order to increase the reliability of information on hours of work, duration of unemployment, and self-employment.

* In 1994, major changes to the Current Population Survey (CPS) were introduced, which included a complete redesign of the questionnaire and the use of computer-assisted interviewing for the entire survey. In addition, there were revisions to some of the labor force concepts and definitions, including the implementation of some changes recommended in 1979 by the National Commission on Employment and Unemployment Statistics (NCEUS, also known as the Levitan Commission). Some of the major changes to the survey were:

a) The introduction of a redesigned and automated questionnaire. The CPS questionnaire was totally redesigned in order to obtain more accurate, comprehensive, and relevant information, and to take advantage of state-of-the-art computer interviewing techniques.

b) The addition of two, more objective, criteria to the definition of discouraged workers. Prior to 1994, to be classified as a discouraged worker, a person must have wanted a job and been reported as not currently looking because of a belief that no jobs were available or that there were none for which he or she would qualify. Beginning in 1994, persons classified as discouraged must also have looked for a job within the past year (or since their last job, if they worked during the year), and must have been available for work during the reference week (a direct question on availability was added in 1994; prior to 1994, availability had been inferred from responses to other questions). These changes were made because the NCEUS and others felt that the previous definition of discouraged workers was too subjective, relying mainly on an individual’s stated desire for a job and not on prior testing of the labor market.

c) Similarly, the identification of persons employed part time for economic reasons (working less than 35 hours in the reference week because of poor business conditions or because of an inability to find full-time work) was tightened by adding two new criteria for persons who usually work part time: They must want and be available for full-time work. Previously, such information was inferred. (Persons who usually work full time but worked part time for an economic reason during the reference week are assumed to meet these criteria.)

d) Specific questions were added about the expectation of recall for persons who indicate that they are on layoff. To be classified as “on temporary layoff,” persons must expect to be recalled to their jobs. Previously, the questionnaire did not include explicit questions about the expectation of recall.

e) Persons volunteering that they were waiting to start a new job within 30 days must have looked for work in the 4 weeks prior to the survey in order to be classified as unemployed. Previously, such persons did not have to meet the job search requirement in order to be included among the unemployed.

For additional information on changes in CPS concepts and methods, see “The Current Population Survey: Design and Methodology,” Technical Paper 63RV (Washington, U.S. Census Bureau and Bureau of Labor Statistics, March 2002), available on the Internet at www.bls.census.gov/cps/tp/ tp63.htm; “Overhauling the Current Population Survey–Why is it Necessary to Change?,” “Redesigning the Questionnaire,” and “Evaluating Changes in the Estimates,” Monthly Labor Review, September 1993; and “Revisions in the Current Population Survey Effective January 1994,” in the February 1994 issue of this publication.

Noncomparability of labor force levels

In addition to the refinements in concepts, definitions, and methods made over the years, other changes also have affected the comparability of the labor force data.

* Beginning in 1953, as a result of introducing data from the 1950 census into the estimating procedures, population levels were raised by about 600,000; labor force, total employment, and agricultural employment were increased by about 350,000, primarily affecting the figures for totals and for men; other categories were relatively unaffected.

* Beginning in 1960, the inclusion of Alaska and Hawaii resulted in increases of about 500,000 in the population and about 300,000 in the labor force. Four-fifths of the labor force increase was in nonagricultural employment; other labor force categories were not appreciably affected.

* Beginning in 1962, the introduction of data from the 1960 census reduced the population by about 50,000 and labor force and employment by about 200,000; unemployment totals were virtually unchanged.

* Beginning in 1972, information from the 1970 census was introduced into the estimation procedures, increasing the population by about 800,000; labor force and employment totals were raised by a little more than 300,000; unemployment levels and rates were essentially unchanged.

* In March 1973, a subsequent population adjustment based on the 1970 census was introduced. This adjustment, which affected the white and black-and-other groups but had little effect on totals, resulted in the reduction of nearly 300,000 in the white population and an increase of the same magnitude in the black-and-other population. Civilian labor force and total employment figures were affected to a lesser degree; the white labor force was reduced by 150,000, and the black-and-other labor force rose by about 210,000. Unemployment levels and rates were not significantly affected.

* Beginning in January 1974, the method used to prepare independent estimates of the civilian noninstitutional population was modified to an “inflation-deflation” approach. This change in the derivation of the estimates had its greatest impact on estimates of 20- to 24-year-old men–particularly those in the black-and-other population–but had little effect on estimates of the total population 16 years and over. Additional information on the adjustment procedure appears in “CPS Population Controls Derived from Inflation-Deflation Method of Estimation,” in the February 1974 issue of this publication.

* Effective in July 1975, as a result of the large inflow of Vietnamese refugees to the United States, the total and black-and-other independent population controls for persons 16 years and over were adjusted upward by 76,000-30,000 men and 46,000 women. The addition of the refugees increased the black-and-other population by less than 1 percent in any age-sex group, with all of the changes being cont-med to the “other” component of the population.

* Beginning in January 1978, the introduction of an expansion in the sample and revisions in the estimation procedures resulted in an increase of about 250,000 in the civilian labor force and employment totals; unemployment levels and rates were essentially unchanged. An explanation of the procedural changes and an indication of the differences appear in “Revisions in the Current Population Survey in January 1978” in the February 1978 issue of this publication.

* Beginning in October 1978, the race of the individual was determined by the household respondent for the incoming rotation group households, rather than by the interviewer as before. The purpose of this change was to provide more accurate estimates of characteristics by race. Thus, in October 1978, one-eighth of the sample households had race determined by the household respondent and seven-eighths of the sample households had race determined by interviewer observation. It was not until January 1980 that the entire sample had race determined by the household respondent. The new procedure had no significant effect on the estimates.

* Beginning in January 1979, the first-stage ratio adjustment method was changed in the CPS estimation procedure. Differences between the old and new procedures existed only for metropolitan and nonmetropolitan area estimates, not for the total United States. The reasoning behind the change and an indication of the differences appear in “Revisions in the Current Population Survey in January 1979” in the February 1979 issue of this publication.

* Beginning in January 1982, the second-stage ratio adjustment method was changed. The rationale for the change and an indication of its effect on national estimates of labor force characteristics appear in “Revisions in the Current Population Survey Beginning in January 1982” in the February 1982 issue of this publication. In addition, current population estimates used in the second-stage estimation procedure were derived from information obtained from the 1980 census, rather than the 1970 census. This change caused substantial increases in the total population and in the estimates of persons in all labor force categories. Rates for labor force characteristics, however, remained virtually unchanged. Some 30,000 labor force series were adjusted back to 1970 to avoid major breaks in series. The adjustment procedure used also is described in the February 1982 article cited above. The revisions did not, however, smooth out the breaks in series occurring between 1972 and 1979 (described above), and data users should consider them when comparing estimates from different periods.

* Beginning in January 1983, the first-stage ratio adjustment method was updated to incorporate data from the 1980 census. The rationale for the change and an indication of its effect on national estimates for labor force characteristics appear in “Revisions in the Current Population Survey Beginning in January 1983” in the February 1983 issue of this publication. There were only slight differences between the old and new procedures in estimates of levels for the various labor force characteristics and virtually no differences in estimates of participation rates.

* Beginning in January 1985, most of the steps of the CPS estimation procedure–the noninterview adjustment, the first- and second-stage ratio adjustments, and the composite estimator–were revised. These procedures are described in the Estimating Methods section. A description of the changes and an indication of their effect on national estimates of labor force characteristics appear in “Changes in the Estimation Procedure in the Current Population Survey Beginning in January 1985” in the February 1985 issue of this publication. Overall, the revisions had only a slight effect on most estimates. The greatest impact was on estimates of persons of Hispanic origin. Major estimates were revised back to January 1980.

* Beginning in January 1986, the population controls used in the second-stage ratio adjustment method were revised to reflect an explicit estimate of the number of undocumented immigrants (largely Hispanic) since 1980 and an improved estimate of the number of emigrants among legal foreign-born residents for the same period. As a result, the total civilian population and labor force estimates were raised by nearly 400,000; civilian employment was increased by about 350,000. The Hispanic-origin population and labor force estimates were raised by about 425,000 and 305,000, respectively, and Hispanic employment was increased by 270,000. Overall and subgroup unemployment levels and rates were not significantly affected. Because of the magnitude of the adjustments for Hispanics, data were revised back to January 1980 to the extent possible. An explanation of the changes and an indication of their effect on estimates of labor force characteristics appear in “Changes in the Estimation Procedure in the Current Population Survey Beginning in January 1986” in the February 1986 issue of this publication.

* Beginning in August 1989, the second-stage ratio estimation procedures were changed slightly to decrease the chance of very small cells occurring and to be more consistent with published age, sex, race cells. This change had virtually no effect on national estimates.

* Beginning in January 1994, 1990 census-based population controls, adjusted for the estimated undercount, were introduced into the second-stage estimation procedure. This change resulted in substantial increases in total population and in all major labor force categories. Effective February 1996, these controls were introduced into the estimates for 1990-93. Under the new population controls, the civilian noninstitutional population for 1990 increased by about 1.1 million, employment by about 880,000, and unemployment by approximately 175,000. The overall unemployment rate rose by about 0.1 percentage point. For further information, see “Revisions in the Current Population Survey Effective January 1994,” and “Revisions in Household Survey Data Effective February 1996” in the February 1994 and March 1996 issues, respectively, of this publication.

Additionally, for the period January through May 1994, the composite estimation procedure was suspended for technical and logistical reasons.

* Beginning in January 1997, the population controls used in the second-stage ratio adjustment method were revised to reflect updated information on the demographic characteristics of immigrants to, and emigrants from, the United States. As a result, the civilian noninstitutional population 16 years and over was raised by about 470,000. The labor force and employment levels were increased by about 320,000 and 290,000, respectively. The Hispanic-origin population and labor force estimates were raised by about 450,000 and 250,000, respectively, and Hispanic employment was increased by 325,000. Overall and subgroup unemployment rates and other percentages of labor market participation were not affected. An explanation of the changes and an indication of their effect on national labor force estimates appear in “Revisions in the Current Population Survey Effective January 1997” in the February 1997 issue of this publication.

* Beginning in January 1998, new composite estimation procedures and minor revisions in the population controls were introduced into the household survey. The new composite estimation procedures simplify processing of the monthly labor force data at BLS, allow users of the survey microdata to more easily replicate the official estimates released by BLS, and increase the reliability of the employment and labor force estimates. The new procedures also produce somewhat lower estimates of the civilian labor force and employment and slightly higher estimates of unemployment. For example, based on 1997 annual average data, the differences resulting from the use of old and new composite weights were as follows: Civilian labor force (-229,000), total employed (-256,000), and total unemployed (+27,000). Unemployment rates were not significantly affected.

Also beginning in January 1998, the population controls used in the survey were revised to reflect new estimates of legal immigration to the United States and a change in the method for projecting the emigration of foreign-born legal residents. As a result, the Hispanic-origin population was raised by about 57,000; however, the total civilian noninstitutional population 16 years and over was essentially unchanged. More detailed information on these changes and their effect on the estimates of labor force change and composition appear in “Revisions in the Current Population Survey Effective January 1998,” in the February 1998 issue of this publication.

* Beginning in January 1999, the population controls used in the survey were revised to reflect newly updated information on immigration. As a result, the civilian noninstitutional population 16 years and over was raised by about 310,000. The impact of the changes varied for different demographic groups. The civilian noninstitutional population for men 16 years and over was lowered by about 185,000, while that for women was increased by about 490,000. The Hispanic-origin population was lowered by about 165,000 while that of persons of non-Hispanic origin was raised by about 470,000. Overall labor force and employment levels were increased by about 60,000 each, while the Hispanic labor force and employment estimates were reduced by about 225,000 and 215,000, respectively. The changes had only a small impact on overall and subgroup unemployment rates and other percentages of labor market participation. An explanation of the changes and an indication of their effect on national labor force estimates appear in “Revisions in the Current Population Survey Effective January 1999” in the February 1999 issue of this publication.

* Beginning in January 2003, several major changes were introduced into the CPS. These changes included:

a) Population controls that reflected the results of Census 2000 were introduced into the monthly CPS estimation process. These new population controls substantially increased the size of the civilian noninstitutional population and the civilian labor force. Data from January 2000 through December 2002 were revised to reflect the higher population estimates from Census 2000 and the higher rates of population growth since the census. At the start of the revision period (January 2000), the new controls raised the civilian noninstitutional population and the civilian labor force by 2.6 and 1.6 million, respectively. By December 2002, the civilian population and labor force were 3.8 and 2.5 million, respectively, higher than originally estimated. In addition to these revisions, the U.S. Census Bureau introduced another large upward adjustment to the population controls as part of its annual update of population estimates for 2003. The entire amount of this adjustment was added to the labor force data in January 2003 resulting in increases of 941,000 to the civilian noninstitutional population and 614,000 to the civilian labor force. The unemployment rate and other ratios were not substantially affected by either of these population control adjustments.

b) The modification of the questions on race and Hispanic origin to comply with new standards for maintaining, collecting, and presenting Federal data on race and ethnicity for Federal statistical agencies. In accordance with the new standards, the following changes were made to the CPS questions: 1) Individuals were now asked whether they are of Hispanic ethnicity before being asked about their race. Prior to 2003, individuals were asked their ethnic origin after they were asked about their race. 2) Individuals were now asked directly if they are Spanish, Hispanic, or Latino. Previously, individuals were identified as Hispanic based on their, or their ancestors’, country of origin. 3) With respect to race, the response category of Asian and Pacific Islanders was split into two categories: a) Asian and b) Native Hawaiian or Other Pacific Islanders. 4) Individuals were allowed to choose more than one race category. Prior to 2003, individuals who considered themselves to belong to more than one race were required to select a single primary race. 5) The questions were reworded to indicate that individuals could select more than one race category and to convey more clearly that individuals should report their own perception of what their race is. These changes had no impact on the overall civilian noninstitutional population and civilian labor force but did reduce the population and labor force levels of whites, blacks or African Americans, and Asians beginning in January 2003. For whites and blacks, the differences resulted from the exclusion of individuals who reported more than one race from those groups. For Asians, the difference resulted from the same restriction as well as the split of the old Asian and Pacific Islander category into two separate categories. Analysis of data from a special CPS supplement conducted in May 2002 indicated that these changes reduced the population and labor force levels for whites by about 950,000 and 730,000, respectively, and for blacks and African Americans by about 320,000 and 240,000, respectively, while having little or no impact on their unemployment rates. For Asians, the changes had the effect of reducing the their population by about 1.1 million and their labor force by about 720,000, but did not have a statistically significant effect on their unemployment rate. The changes did not affect the size of the Hispanic or Latino population and had no significant impact on the size of their labor force, but did cause an increase of about half a percentage point in their unemployment rate.

c) Improvements were introduced to both the second-stage and composite weighting procedures. These changes adapted the weighting procedures to the new race/ethnic classification system and enhanced the stability over time of national and State/substate labor force estimates for demographic groups.

More detailed information on these changes and an indication of their effect on national labor force estimates appear in “Revisions to the Current Population Survey Effective in January 2003” in the February 2003 issue of this publication available on the Internet at http:// www.bls.gov/cps/rvcps03.pdf.

* Beginning in January 2004, the population controls used in the survey were updated to reflect revised estimates of net international migration for 2000 through 2003. The updated controls resulted in a decrease of 560,000 in the estimated size of the civilian noninstitutional population 16 years of age and over for December 2003. The civilian labor force and employment levels decreased by 437,000 and 409,000, respectively. The Hispanic or Latino population and labor force estimates declined by 583,000 and 446,000, respectively and Hispanic or Latino employment was lowered by 421,000. The updated controls had little or no affect on overall and subgroup unemployment rates and other measures of labor market participation. More detailed information on the effect of the updated controls on national labor force estimates appears in “Adjustments to Household Survey Population Estimates in January 2004” in the February 2004 issue of this publication available on the Internet at http://www.bls.gov/eps/cps04adj.pdf.

Changes in the occupational and industrial classification systems

Beginning in 1971, the comparability of occupational employment data was affected as a result of changes in the occupational classification system for the 1970 census that were introduced into the CPS. Comparability was further affected in December 1971, when a question relating to major activity or duties was added to the monthly CPS questionnaire in order to more precisely determine the occupational classification of individuals. As a result of these changes, meaningful comparisons of occupational employment levels could not be made between 1971-72 and prior years nor between those 2 years. Unemployment rates were not significantly affected. For a further explanation of the changes in the occupational classification system, see “Revisions in Occupational Classifications for 1971” and “Revisions in the Current Population Survey” in the February 1971 and February 1972 issues, respectively, of this publication.

Beginning in January 1983, the occupational and industrial classification systems used in the 1980 census were introduced into the CPS. The 1980 census occupational classification system evolved from the Standard Occupational Classification (SOC) system and was so radically different in concepts and nomenclature from the 1970 system that comparisons of historical data are not possible without major adjustments. For example, the 1980 major group “sales occupations” is substantially larger than the 1970 category “sales workers.” Major additions include “cashiers” from “clerical workers” and some self-employed proprietors in retail trade establishments from “managers and administrators, except farm.”

The industrial classification system used in the 1980 census was based on the 1972 Standard Industrial Classification (SIC) system, as modified in 1977. The adoption of the new system had much less of an adverse effect on historical comparability than did the new occupational system. The most notable changes from the 1970 system were the transfer of farm equipment stores from “retail” to “wholesale” trade and of postal service from “public administration” to “transportation,” and some interchange between “professional and related services” and “public administration.” Additional information on the 1980 census occupational and industrial classification systems appears in “Revisions in the Current Population Survey Beginning in January 1983” in the February 1983 issue of this publication.

Beginning in January 1992, the occupational and industrial classification systems used in the 1990 census were introduced into the CPS. (These systems were based largely on the 1980 Standard Occupational Classification (SOC) and 1987 Standard Industrial Classification (SIC) systems, respectively.) There were a few breaks in comparability between the 1980 and 1990 census-based systems, particularly within the “technical, sales, and administrative support” categories. The most notable changes in industry classification were the shift of several industries from “business services” to “professional services” and the splitting of some industries into smaller, more detailed categories. A number of industry titles were changed as well, with no change in content.

Beginning in January 2003, the 2002 Census Bureau occupational and industrial classification systems were introduced into the CPS. These systems were derived from the 2000 Standard Occupational Classification (SOC) and the 2002 North American Industry Classification System (NAICS). The composition of detailed occupational and industrial classifications in the new classification systems was substantially changed from the previous systems in use as was the structure for aggregating them into broad groups. Consequently, the use of the new classification systems created breaks in existing data series at all levels of aggregation. Additional information on the 2002 Census Bureau occupational and industrial classification systems appears in “Revisions to the Current Population Survey Effective in January 2003” in the February 2003 issue of this publication available on the Internet at http:// www.bls.gov/cps/rvcps03.pdf.

Sampling

Since the inception of the survey, there have been various changes in the design of the CPS sample. The sample traditionally is redesigned and a new sample selected after each decennial census. Also, the number of sample areas and the number of sample persons are changed occasionally. Most of these changes are made to improve the efficiency of the sample design, increase the reliability of the sample estimates, or control cost.

Changes in this regard since 1960 are as follows: When Alaska and Hawaii received statehood in 1959 and 1960, respectively, three sample areas were added to the existing sample to account for the population of these States. In January 1978, a supplemental sample of 9,000 housing units, selected in 24 States and the District of Columbia, was designed to provide more reliable annual average estimates for States. In October 1978, a coverage improvement sample of approximately 450 sample household units representing 237,000 occupied mobile homes and 600,000 new construction housing units was added. In January 1980, another supplemental sample of 9,000 households selected in 32 States and the District of Columbia was added. A sample reduction of about 6,000 units was implemented in May 1981. In January 1982, the sample was expanded by 100 households to provide additional coverage in counties added to the Standard Metropolitan Statistical Areas (SMSAs), which were redefined in 1973. In January 1985, a new State-based CPS sample was selected based on 1980 census information. A sample reduction of about 4,000 households was implemented in April 1988; the households were reinstated during the 8-month period, April-November 1989. A redesigned CPS sample based on the 1990 decennial census was selected for use during the 1990s. Households from this new sample were phased into the CPS between April 1994 and July 1995. The July 1995 sample was the first monthly sample based entirely on the 1990 census. For further information on the 1990 sample redesign, see “Redesign of the Sample for the Current Population Survey” in the May 1994 issue of this publication.

The original 1990 census-based sample design included about 66,000 housing units per month located in 792 selected geographic areas called primary sampling units (PSUs). The sample initially was selected to meet specific reliability criteria for the Nation, for each of the 50 States and the District of Columbia, and for the substate areas of New York City and the Los Angeles-Long Beach metropolitan area. In 1996, the original sample design reliability criteria were modified to reduce costs. In July 2001, the CPS sample was expanded to support the State Children’s Health Insurance Program. For further information on the sample expansion, see “Expansion of the Current Population Survey Sample Effective July 2001” in the August 2001 issue of this publication. The current criteria, given below, are based on the coefficient of variation (CV) of the unemployment level, where the CV is defined as the standard error of the estimate divided by the estimate, expressed as a percentage. These CV controls assume a 6-percent unemployment rate to establish a consistent specification of sampling error.

The current sample design, introduced in July 2001, includes about 72,000 “assigned” housing units from 754 sample areas. Sufficient sample is allocated to maintain, at most, a 1.9-percent CV on national monthly estimates of unemployment level, assuming a 6-percent unemployment rate. This translates into a change of 0.2 percentage point in the unemployment rate being significant at a 90-percent confidence level. For each of the 50 States and for the District of Columbia, the design maintains a CV of at most 8 percent on the annual average estimate of unemployment level, assuming a 6-percent unemployment rate. About 60,000 housing units are required in order to meet the national and State reliability criteria. Due to the national reliability criterion, estimates for several large States are substantially more reliable than the State design criterion requires. Annual average unemployment estimates for California, Florida, New York, and Texas, for example, carry a CV of less than 4 percent. In support of the State Children’s Health Insurance Program, about 12,000 additional housing units are allocated to the District of Columbia and 31 States. (These are generally the States with the smallest samples after the 60,000 housing units are allocated to satisfy the national and State reliability criteria.)

In the first stage of sampling, the 754 sample areas are chosen. In the second stage, ultimate sampling unit clusters composed of about four housing units each are selected. Each month, about 72,000 housing units are assigned for data collection, of which about 60,000 are occupied and thus eligible for interview. The remainder are units found to be destroyed, vacant, converted to nonresidential use, containing persons whose usual place of residence is elsewhere, or ineligible for other reasons. Of the 60,000 housing units, about 7.5 percent are not interviewed in a given month due to temporary absence (vacation, etc.), other failures to make contact after repeated attempts, inability of persons contacted to respond, unavailability for other reasons, and refusals to cooperate (about half of the noninterviews). Information is obtained each month for about 112,000 persons 16 years of age or older.

Selection of sample areas. The entire area of the United States, consisting of 3,141 counties and independent cities, is divided into 2,007 sample units (PSUs). In most States, a PSU consists of a county or a number of contiguous counties. In New England and Hawaii, minor civil divisions are used instead of counties.

Metropolitan areas within a State are used as a basis for forming PSUs. Outside of metropolitan areas, counties normally are combined except when the geographic area of an individual county is too large. Combining counties to form PSUs provides greater heterogeneity; a typical PSU includes urban and rural residents of both high and low economic levels and encompasses, to the extent feasible, diverse occupations and industries. Another important consideration is that the PSU be sufficiently compact so that, with a small sample spread throughout, it can be efficiently canvassed without undue travel cost.

The 2,007 PSUs are grouped into strata within each State. Then, one PSU is selected from each stratum with the probability of selection proportional to the population of the PSU. Nationally, there are a total of 428 PSUs in strata by themselves. These strata are self-representing and are generally the most populous PSUs in each State. The 326 remaining strata are formed by combining PSUs that are similar in such characteristics as unemployment, proportion of housing units with three or more persons, number of persons employed in various industries, and average monthly wages for various industries. The single PSU randomly selected from each of these strata is nonself-representing because it represents not only itself but the entire stratum. The probability of selecting a particular PSU in a nonself-representing stratum is proportional to its 1990 population. For example, within a stratum, the chance that a PSU with a population of 50,000 would be selected for the sample is twice that for a PSU having a population of 25,000.

Selection of sample households. Because the sample design is State based, the sampling ratio differs by State and depends on State population size as well as both national and State reliability requirements. The State sampling ratios range roughly from 1 in every 100 households to 1 in every 3,000 households. The sampling ratio occasionally is modified slightly to hold the size of the sample relatively constant given the overall growth of the population. The sampling ratio used within a sample PSU depends on the probability of selection of the PSU and the sampling ratio for the State. In a sample PSU with a probability of selection of 1 in 10 and a State sampling ratio of 3,000, a within-PSU sampling ratio of 1 in 300 achieves the desired ratio of 1 in 3,000 for the stratum.

The 1990 within-PSU sample design was developed using block-level data from the 1990 census. (The 1990 census was the first decennial census that produced data at the block level for the entire country.) Normally, census blocks are bounded by streets and other prominent physical features such as rivers or railroad tracks. County, minor civil division, and census place limits also serve as block boundaries. In cities, blocks can be bounded by four streets and be quite small in land area. In rural areas, blocks can be several square miles in size.

For the purpose of sample selection, census blocks were grouped into three strata: Unit, group quarters, and area. (Occasionally, units within a block were split between the unit and group-quarters strata.) The unit stratum contained regular housing units with addresses that were easy to locate (for example, most single-family homes, townhouses, condominiums, apartment units, and mobile homes). The group-quarters stratum contained housing units in which residents shared common facilities or received formal or authorized care or custody. Unit and group-quarters blocks exist primarily in urban areas. The area stratum contains blocks with addresses that are more difficult to locate. Area blocks exist primarily in rural areas.

To reduce the variability of the survey estimates and to ensure that the within-PSU sample would reflect the demographic and socioeconomic characteristics of the PSU, blocks within the unit, group-quarters, and area strata were sorted using geographic and block-level data from the census. Examples of the census variables used for sorting include proportion of minority renter-occupied housing units, proportion of housing units with female householders, and proportion of owner-occupied housing traits. The specific sorting variables used differed by type of PSU (urban or rural) and stratum.

Within each block, housing units were sorted geographically and grouped into clusters of approximately four units. A systematic sample of these clusters was then selected independently from each stratum using the appropriate within-PSU sampling ratio. The geographic clustering of the sample units reduces field representative travel costs. Prior to interviewing, special listing procedures are used to locate the particular sample addresses in the group-quarters and area blocks.

Units in the three strata described above all existed at the time of the 1990 decennial census. Through a series of additional procedures, a sample of building permits is included in the CPS to represent housing units built after the decennial census. Adding these newly built units keeps the sample up-to-date and representative of the population. It also helps to keep the sample size stable: Over the life of the sample, the addition of newly built housing units compensates for the loss of “old” units that may be abandoned, demolished, or converted to nonresidential use.

Rotation of sample. Part of the sample is changed each month. Each monthly sample is divided into eight representative subsamples or rotation groups. A given rotation group is interviewed for a total of 8 months, divided into two equal periods. It is in the sample for 4 consecutive months, leaves the sample during the following 8 months, and then returns for another 4 consecutive months. In each monthly sample, one of the eight rotation groups is in the first month of enumeration, another rotation group is in the second month, and so on. Under this system, 75 percent of the sample is common from month to month, and 50 percent is common from year to year for the same month. This procedure provides a substantial amount of month-to-month and year-to-year overlap in the sample, thus providing better estimates of change and reducing discontinuities in the data series without burdening any specific group of households with an unduly long period of inquiry.

CPS sample, 1947 to present. Table 1-A provides a description of some aspects of the CPS sample designs in use since 1947. A more detailed account of the history of the CPS sample design appears in chapter 2 of “The Current Population Survey: Design and Methodology,” Technical Paper 63RV, (Washington, U.S. Census Bureau and Bureau of Labor Statistics, March 2002), available on the Internet at www.bls.census.gov/cps/tp/tp63.htm. A description of the 1990 census-based sample design appears in “Redesign of the Sample for the Current Population Survey,” in the May 1994 issue of this publication and in chapter 3 of Technical Paper 63RV referenced above. A description of the sample expansion in support of the State Children’s Health Insurance Program appears in “Expansion of the Current Population Survey Sample Effective July 2001”, in the August 2001 issue of this publication and in Appendix J, “Changes to the Current Population Survey Sample in July 2001,” of Technical Paper 63RV referenced above.

ESTIMATING METHODS

Under the estimating methods used in the CPS, all of the results for a given month become available simultaneously and are based on returns from the entire panel of respondents. The estimation procedure involves weighting the data from each sample person by the inverse of the probability of the person being in the sample. This gives a rough measure of the number of actual persons that the sample person represents. Since 1985, most sample persons within the same State have had the same probability of selection. Some selection probabilities may differ within a State due to the sample design or for operational reasons. Field subsampling, for example, which is carried out when areas selected for the sample are found to contain many more households than expected, may cause probabilities of selection to differ for some sample areas within a State. Through a series of estimation steps (outlined below), the selection probabilities are adjusted for noninterviews and survey undercoverage; data from previous months are incorporated into the estimates through the composite estimation procedure.

1. Noninterview adjustment. The weights for all interviewed households

are adjusted to account for occupied sample households for which no information was obtained because of absence, impassable roads, refusals, or unavailability of the respondents for other reasons. This noninterview adjustment is made separately for clusters of similar sample areas that are usually, but not necessarily, contained within a State. Similarity of sample areas is based on Metropolitan Statistical Area (MSA) status and size. Within each cluster, there is a further breakdown by residence. Each MSA cluster is split by “central city” and “balance of the MSA.” Each non-MSA cluster is split by “urban” and “rural” residence categories. The proportion of sample households not interviewed varies from 7 to 8 percent, depending on weather, vacation, etc.

2. Ratio estimates. The distribution of the population selected for the sample may differ somewhat, by chance, from that of the population as a whole in such characteristics as age, race, sex, and State of residence. Because these characteristics are closely correlated with labor force participation and other principal measurements made from the sample, the survey estimates can be substantially improved when weighted appropriately by the known distribution of these population characteristics. This is accomplished through two stages of ratio adjustment, as follows:

a. First-stage ratio estimation. The purpose of the first-stage ratio adjustment is to reduce the contribution to variance that results from selecting a sample of PSUs rather than drawing sample households from every PSU in the Nation. This adjustment is made to the CPS weights in two race cells: Black and nonblack; it is applied only to PSUs that are not self-representing and for those States that have a substantial number of black households. The procedure corrects for differences that existed in each State cell at the time of the 1990 census between 1) the race distribution of the population in sample PSUs and 2) the race distribution of all PSUs. (Both 1 and 2 exclude self-representing PSUs.)

b. Second-stage ratio estimation. This procedure substantially reduces the variability of estimates and corrects, to some extent, for CPS undercoverage. A national-coverage step and a State-coverage step make preliminary corrections for undercoverage. The CPS sample weights are then adjusted to ensure that sample-based estimates of population match independent population controls. Three sets of controls are used in different steps of the procedure:

1) State step: Civilian noninstitutional population controls for 6 age-sex cells in the Los Angeles-Long Beach metropolitan area, the balance of California, New York City, the balance of New York State, each of the other 48 States, and the District of Columbia.

2) Ethnicity step: National civilian noninstitutional population controls for 26 Hispanic and 26 non-Hispanic age-sex cells.

3) Race step: National civilian noninstitutional population controls for 34 white, 26 black, and 26 Asian-plus-residual-race age-sex cells.

The independent population controls are prepared by projecting forward the resident population as enumerated on April 1, 2000. The projections are derived by updating demographic census data with information from a variety of other data sources that account for births, deaths, and net migration. Estimated numbers of resident Armed Forces personnel and institutionalized persons reduce the resident population to the civilian noninstitutional population. Prior to January 2003, the projections were based on earlier censuses. See “Revisions to the Current Population Survey Effective in January 2003,” in the February 2003 issue of this publication for a detailed discussion of changes to the second-stage weighting and composite estimating procedures that were introduced in January 2003.

3. Composite estimation procedure. The last step in the preparation of most CPS estimates makes use of a composite estimation procedure. The composite estimate consists of a weighted average of two factors: The two-stage ratio estimate based on the entire sample from the current month and the composite estimate for the previous month, plus an estimate of the month-to-month change based on the six rotation groups common to both months. In addition, a bias adjustment term is added to the weighted average to account for relative bias associated with month-in-sample estimates. This month-in-sample bias is exhibited by unemployment estimates for persons in their first and fifth months in the CPS being generally higher than estimates obtained for the other months.

The composite estimate results in a reduction in the sampling error beyond that which is achieved after the two stages of ratio adjustment. For some items, the reduction is substantial. The resultant gains in reliability are greatest in estimates of month-to-month change, although gains usually are also obtained for estimates of level in a given month, change from year to year, and change over other intervals of time.

Rounding of estimates

The sums of individual items may not always equal the totals shown in the same tables because of independent rounding of totals and components to the nearest thousand. Similarly, sums of percent distributions may not always equal 100 percent because of rounding. Differences, however, are insignificant.

Reliability of the estimates

An estimate based on a sample survey has two types of error sampling error and nonsampling error. The estimated standard errors provided in this publication are approximations of the true sampling errors. They incorporate the effect of some nonsampling errors in response and enumeration, but do not account for any systematic biases in the data.

Nonsampling error. The full extent of nonsampling error is unknown, but special studies have been conducted to quantify some sources of nonsampling error in the CPS. The effect of nonsampling error is small on estimates of relative change, such as month-to-month change; estimates of monthly levels tend to be affected to a greater degree.

Nonsampling errors in surveys can be attributed to many sources, for example, the inability to obtain information about all persons in the sample; differences in the interpretation of questions; inability or unwillingness of respondents to provide correct information; inability of respondents to recall information; errors made in collecting and processing the data; errors made in estimating values for missing data; and failure to represent all sample households and all persons within sample households (undercoverage).

Nonsampling errors occurring in the interview phase of the survey are studied by means of a reinterview program. This program is used to estimate various sources of error, as well as to evaluate and control the work of the interviewers. A random sample of each interviewer’s work is inspected through reinterview at regular intervals. The results indicate, among other things, that the data published from the CPS are subject to moderate systematic biases. A description of the CPS reinterview program may be found in Appendix G, “Reinterview: Design and Methodology,” of “The Current Population Survey: Design and Methodology,” Technical Paper 63RV (Washington, U.S. Census Bureau and Bureau of Labor Statistics, March 2002), available on the Internet at www.bls.census.gov/cps/tp/tp63.htm

The effects of some components of nonsampling error in the CPS data can be examined as a result of the rotation plan used for the sample, because the level of the estimates varies by rotation group. A description appears in Barbara A. Bailar, “The Effects of Rotation Group Bias on Estimates from Panel Surveys,” Journal of the American Statistical Association, March 1975, pp. 23-30.

Undercoverage in the CPS results from missed housing units and missed persons within sample households. The CPS covers about 92 percent of the decennial census population (adjusted for census undercount). It is known that the CPS undercoverage varies with age, sex, race, and Hispanic origin. Generally, undercoverage is larger for men than for women and is larger for blacks, Hispanics, and other races than for whites. Ratio adjustment to independent age-sex-race-origin population controls, as described previously, partially corrects for the biases due to survey undercoverage. However, biases exist in the estimates to the extent that missed persons in missed households or missed persons in interviewed households have characteristics different from those of interviewed persons in the same age-sex-race-origin group.

Additional information on nonsampling error in the CPS appears in Camilla Brooks and Barbara Bailar, “An Error Profile: Employment as Measured by the Current Population Survey,” Statistical Policy Working Paper 3 (Washington, U.S. Department of Commerce, Office of Federal Statistical Policy and Standards, September 1978); Marvin Thompson and Gary Shapiro, “The Current Population Survey: An Overview,” Annals of Economic and Social Measurement, Vol. 2, April 1973; and “The Current Population Survey: Design and Methodology,” Technical Paper 63RV referenced above. The last document includes a comprehensive discussion of various sources of errors and describes attempts to measure them in the CPS.

Sampling error. When a sample, rather than the entire population, is surveyed, estimates differ from the true population values that they represent. This difference, or sampling error, occurs by chance, and its variability is measured by the standard error of the estimate. Sample estimates from a given survey design are unbiased when an average of the estimates from all possible samples would yield, hypothetically, the true population value. In this case, the sample estimate and its standard error can be used to construct approximate confidence intervals, or ranges of values that include the true population value with known probabilities. If the process of selecting a sample from the population were repeated many times, an estimate made from each sample, and a suitable estimate of its standard error calculated for each sample, then:

1. Approximately 68 percent of the intervals from one standard error below the estimate to one standard error above the estimate would include the true population value.

2. Approximately 90 percent of the intervals from 1.645 standard errors below the estimate to 1.645 standard errors above the estimate would include the true population value.

3. Approximately 95 percent of the intervals from 1.96 standard errors below the estimate to 1.96 standard errors above the estimate would include the true population value.

These confidence interval statements are approximately true for the CPS. Although the estimating methods used in the CPS do not produce unbiased estimates, biases for most estimates are believed to be small. Methods for estimating standard errors reflect not only sampling errors but also some kinds of nonsampling error. Although both the estimates and the estimated standard errors depart from the theoretical ideal, the departures are minor and have little impact on the confidence interval statements. When clarity is needed, an estimated confidence interval is specified to be “approximate,” as is the estimated standard error used in the computation.

Tables 1-B through 1-D are provided so that approximate standard errors of estimates can be easily obtained. Tables 1-B and 1-C give approximate standard errors for estimated monthly levels and rates for selected employment status characteristics; the tables also provide approximate standard errors for consecutive month-to-month changes in the estimates. It is impractical to show approximate standard errors for all CPS estimates in this publication, so table 1-D provides parameters and factors that allow the user to calculate approximate standard errors for a wide range of estimated levels, rates, and percentages, and also changes over time. The parameters and factors are used in formulas that are commonly called generalized variance functions.

The approximate standard errors provided in this publication are based on the sample design and estimation procedures as of 1996, and reflect the population levels and sample size as of that year. Standard errors for years prior to 1996 may be roughly approximated by applying these adjustments to the standard errors presented here. (More accurate standard error estimates for historical CPS data may be found in previous issues of this publication.)

1. For the years 1967 through 1995, multiply the standard errors by 0.96.

2. For the years 1956 through 1966, multiply the standard errors by 1.17.

3. For years prior to 1956, multiply the standard errors by 1.44.

Use of tables 1-B and 1-C. These tables provide a quick reference for standard errors of major characteristics. Table 1-B gives approximate standard errors for estimates of monthly levels and consecutive month-to-month changes in levels for major employment status categories. Table 1-C gives approximate standard errors for estimates of monthly unemployment rates and consecutive month-to-month changes in unemployment rates for some demographic, occupational, and industrial categories. For characteristics not given in tables 1-B and 1-C, refer to table 1-D.

Illustration. Suppose that, for a given month, the number of women age 20 years and over in the civilian labor force is estimated to be 65,000,000. For this characteristic, the approximate standard error of 208,000 is given in table 1-B in the row “Women, 20 years and over; Civilian labor force.” To calculate an approximate 90-percent confidence interval, multiply the standard error of 208,000 by the factor 1.645 to obtain 342,000. This number is subtracted from and then added to 65,000,000 to obtain an approximate 90-percent confidence interval: 64,658,000 to 65,342,000. Concluding that the true civilian labor force level lies within an interval calculated in this way would be correct for roughly 90 percent of all possible samples that could have been selected for the CPS.

Use of table 1-D. This table gives a and b parameters that can be used with formulas to calculate approximate monthly standard errors for a wide range of estimated levels, proportions, and rates. Factors are provided to convert monthly measures into approximate standard errors of estimates for other periods (quarterly and yearly averages) and approximate standard errors for changes over time (consecutive monthly changes, changes in consecutive quarterly and yearly averages, and changes in monthly estimates 1 year apart).

The standard errors for estimated changes in level from one month to the next, one year to the next, etc., depend more on the monthly levels for characteristics than on the size of the changes. Likewise, the standard errors for changes in rates (or percentages) depend more on the monthly rates (or percentages) than on the size of the changes. Accordingly, the factors presented in table 1-D are applied to the monthly standard error approximations for levels, percentages, or rates; the magnitudes of the changes do not come into play. Factors are not given for estimated changes between nonconsecutive months (except for changes of monthly estimates 1 year apart); however, the standard errors may be assumed to be higher than the standard errors for consecutive monthly changes.

se(x) = [square root of (a[x.sup.2] + bx)]

Standard errors of estimated levels using table I-D. The approximate standard error se(x) of x, an estimated monthly level, can be obtained using the formula below, where a and b are the parameters from table 1-D associated with a particular characteristic.

Illustration. Assume that, in a given a month, there are an estimated 4 million unemployed men. Obtain the appropriate a and b parameters from table 1-D (Total or white; Men; Unemployed). Use the formula for se(x) to compute an approximate standard error on the estimate of x = 4,000,000.

a = -0.0000321 b = 2970.55

se(4,000,o00) = [square root of (- 0.0000321[(4,000,000).sup.2] + 2970.55(4,000,000)] [approximately equal to] 107,000

Procedure for using table 1-D factors for levels. Table 1-D gives factors that can be used to compute approximate standard errors of levels for other periods or for changes over time. For each characteristic, factors f are given for:

Consecutive month-to-month changes

Changes in monthly estimates 1 year apart

Quarterly averages

Changes in consecutive quarterly averages

Yearly averages

Changes in consecutive yearly averages

For a given characteristic, the table 1-D factor is used in the following formula, which also uses the a and b parameters from the same line of the table. A three-step procedure for using the formula is given. The f in the formula is frequently called an adjustment factor, because it appears to adjust a monthly standard error se(x). However, the x in the formula is not a monthly level, but an average of several monthly levels (see examples listed under Step 1, below).

se(x, f) = f * se(x) = f * [square root of ((a[x.sup.2] + bx))]

where x is an average of monthly levels over a designated period.

Step 1. Average monthly levels appropriately in order to obtain x. Levels for 3 months are averaged for quarterly averages, and those for 12 months are averaged for yearly averages. For changes in consecutive averages, average over the 2 months, 2 quarters, or 2 years involved. For changes in monthly estimates 1 year apart, average the 2 months involved.

Step 2. Calculate an approximate standard error se(x), treating the average x from step 1 as if it were an estimate of level for a single month. Obtain parameters a and b from table 1-D. (Note that, for some characteristics, an approximate standard error of level could instead be obtained from table 1-B and used in place of se(x) in the formula.)

Step 3. Determine the standard error se (x, f) on the average level or on the change in level. Multiply the result from step 2 by the appropriate factor f. The a and b parameters used in step 2 and the factor fused in this step come from the same line in table 1-D.

Illustration of a standard error computation for consecutive month change in level. Continuing the previous example, suppose that in the next month the estimated number of unemployed men increases by 150,000, from 4,000,000 to 4,150,000.

Step 1. The average of the two monthly levels is x = 4,075,000.

Step 2. Apply the a and b parameters from table 1-D (Total or white; Men; Unemployed) to the average x, treating it like an estimate for a single month.

a = -0.0000321 b = 2970.55

se(4,075,000) = [square root of (-0.0000321[(4,075,000).sup.2] + 2970.55(4,075,000)] [approximately equal to] 108,000

Step 3. Obtain f = 1.27 from the same row of table 1-D in the column “Consecutive month-to-month change,” and multiply the factor by the result from step 2.

se(150,000) = f * se(4,075,000) = 1.27 * 108,000 [approximately equal to] 137,000

For an approximate 90-percent confidence interval, compute 1.645 * 137,000 [approximately equal to] 225,000. Subtract the number from and add the number to 150,000 to obtain an interval of -75,000 to 375,000. This is an approximate 90-percent confidence interval for the true change, and since this interval includes zero, one cannot assert at this level of confidence that any real change has occurred in the unemployment level. The result also can be expressed by saying that the apparent change of 150,000 is not significant at a 90-percent confidence level.

Illustration of a standard error computation for quarterly average level. Suppose that an approximate standard error is desired for a quarterly average of the black or African American employment level. Suppose that the estimated employment levels for the 3 months making up the quarter are 14,900,000, 15,000,000, and 15,100,000.

Step 1. The average of the three monthly levels is x = 15,000,000.

Step 2. Apply the a and b parameters from table 1-D (Black; Total; Civilian labor force, employed, and not in labor force) to the average x, treating it like an estimate for a single month.

a = -0.0001514 b = 3454.72

se(15,000.000) = [square root of (-0.0001514[(15,000,000).sup.2] + 3454.72 (15,000,000)] [approximately equal to] 133,000

Step 3. Obtain f = .86 from the same row of table 1-D in the column “Quarterly averages,” and multiply the factor by the result from step 2.

se(15,000,O00) = .86 * 133,000 [approximately equal to] 114,000

Illustration of a standard error computation for change in quarterly level. Continuing the example, suppose that, in the next quarter, the estimated average employment level for blacks is 15,400,000, based on monthly levels of 15,300,000, 15,400,000, and 15,500,000. This is an estimated increase of 400,000 over the previous quarter.

Step 1. The average of the two quarterly levels is x = 15,200,000.

Step 2. Apply the a and b parameters from table 1-D (Black; Total; Civilian labor force, employed, and not in labor force) to the average x, treating it like an estimate for a single month.

a = -0.0001514 b = 3454.72

se(15,200,000) = [square root of (-0.0001514[(15,200,000).sup.2] + 3454.72(15,200,000)] [approximately equal to] 132,000

Step 3. Obtain f = .78 from the same row of table 1-D in the column “Change in consecutive quarterly averages,” and multiply the factor by the result from step 2.

se(400,000) = .78 * se(15,200,000) = .78 * 132,000 [approximately equal to] 103,000

For an approximate 95-percent confidence interval, compute 1.96 * 103,000 [approximately equal to] 202,000. Subtract the number from and add the number to 400,000 to obtain an interval of 198,000 to 602,000. The interval excludes zero. Another way of stating this is to observe that the estimated change of 400,000 clearly exceeds 1.96 standard errors, or 202,000. One can conclude from these data that the change in quarterly averages is significant at a 95-percent confidence level.

Standard errors of estimated rates and percentages using table 1-D. As shown in the formula below, the approximate standard error se(p,y) of an estimated rate or percentage p depends, in part, upon the number of persons y in its base or denominator. Generally, rates and percentages are not published unless the monthly base is greater than 75,000 persons, the quarterly average base is greater than 60,000 persons, or the yearly average base is greater than 35,000 persons. The b parameter is obtained from table 1-D. When the base y and the numerator of p are from different categories within the table, use the b parameter from table 1-D relevant to the numerator of the rate or percentage.

se(p, y) = [square root of ([b/y]p(100 – p))]

Note that se(p,y) is in percent.

Illustration. For a given month, suppose y = 6,200,000 women 20 to 24 years of age are estimated to be employed. Of this total, 2,000,000, or p = 32 percent, are classified as part-time workers. Obtain the parameter b = 3095.55 from the table 1-D row (Employment; Part-time workers) that is relevant to the numerator of the percentage. Apply the formula to obtain:

se(p, y) = [square root of ([3095.55/6,200,000]((32)(100 – 32)] [approximately equal to] 1.0 percent)]

For an approximate 95-percent confidence interval, compute 1.96 * 1.0 percent, and round the result to 2 percent. Subtract this from and add this to the estimate of p = 32 percent to obtain an interval of 30 percent to 34 percent.

Procedure for using table 1-D factors for rates an d percentages. Table 1-D factors can be used to compute approximate standard errors on rates and percentages for other periods or for changes over time. As for levels, there are three steps in the procedure for using the formula.

se(p, y, f)= f * se(p, y) = f * [square root of ([b/y]p(100 – p))]

where p and y are averages of monthly estimates over a designated period. Note that se (p, y, f) is in percent.

Step 1. Appropriately average estimates of monthly rates or percentages to obtain p, and also average estimates of monthly levels to obtain y. Rates for 3 months are averaged for quarterly averages, and those for 12 months are averaged for yearly averages. For changes in consecutive averages, average over the 2 months, 2 quarters, or 2 years involved. For changes in monthly estimates 1 year apart, average the 2 months involved.

Step 2. Calculate an approximate standard error se (p, y), treating the averages p and y from step 1 as if they were estimates for a single month. Obtain the b parameter from the table 1-D row that describes the numerator of the rate or percentage. (Note that, for some characteristics, an approximate standard error could instead be obtained from table 1-C and used in place of se (p, y) in the formula.)

Step 3. Determine the standard error se (p, y, f) on the average level or on the change in level. Multiply the result from step 2 by the appropriate factor f. The b parameter used in step 2 and the factor fused in this step come from the same line in table 1-D.

Illustration of a standard error computation for consecutive month change in percentage. Continuing the previous example, suppose that, in the next month, 6,300,000 women 20 to 24 years of age are reported employed, and that 2,150,000, or 34 percent, are part-time workers.

Step 1. The month-to-month change is 2 percent = 34 percent–32 percent. The average of the two monthly percentages of 32 percent and 34 percent is needed (p = 33 percent), as is the average of the two bases of 6,200,000 and 6,300,000 (y = 6,250,000).

Step 2. Apply the b = 3095.55 parameter from table 1-D (Employment; Part-time workers) to the averaged p and y, treating the averages like estimates for a single month.

se(p, y) = [square root of] ([3095.55/6,250,000]((33)(100 – 33)] [approximately equal to] 1.0 percent

Step 3. Obtain f= .65 from the same row of table 1-D in the column “Consecutive month-to-month change,” and multiply the factor by the result from step 2.

se(2%) = .65 * 1.0 percent = .65 percent

For an approximate 95-percent confidence interval, compute 1.96 * .65 percent, and round the result to 1.3 percent. Subtract this from and add this to the 2-percent estimate of change to obtain an interval of 0.7 percent to 3.3 percent. Because this interval excludes zero, it can be concluded at a 95-percent confidence level that the change is significant.

Establishment Data (“B” tables)

DATA COLLECTION

BLS cooperates with State employment security agencies in the Current Employment Statistics (CES) or establishment survey to collect data each month on employment, hours, and earnings from a sample of nonfarm establishments (including government). The sample includes about 160,000 businesses and government agencies covering approximately 400,000 individual worksites. The sample is drawn from a sampling frame of over 8 million unemployment insurance tax accounts. The active CES sample includes approximately one-third of all nonfarm payroll workers. From these data, a large number of employment, hours, and earnings series in considerable industry and geographic detail are prepared and published each month. Historical statistics are available at http://www.bls.gov, the BLS Internet site.

Each month, BLS and the State agencies collect data on employment, payrolls, and paid hours from a sample of establishments. BLS has established a comprehensive program of new sample unit solicitation in the three BLS regional office data collection centers (DCCs). The DCCs perform initial enrollment of each firm via telephone, collect the data for several months via computer assisted telephone interviewing (CATI), and, where possible, transfer respondents to a self-reporting mode such as touch data entry (TDE), FAX, or Web. In addition, the DCCs conduct an ongoing program of refusal conversion. Very large firms are often enrolled via personal visit and ongoing reporting is established via electronic data interchange (EDI).

EDI and TDE are the two most frequently used collection modes. Under EDI, the firm provides an electronic file to BLS each month in a prescribed file format. This file includes data for all of the firms’ worksites. The file is received, processed, and edited by the BLS operated EDI Center. Under the TDE system, the respondent uses a touchtone telephone to call a toll-free number and activate an interview session. The questionnaire resides on the computer in the form of prerecorded questions that are read to the respondent. The respondent enters numeric responses by pressing the touchtone phone buttons. Each answer is read back for respondent verification.

CATI and FAX collection through the regional BLS DCCs combined account for most of the remainder of the reports. For establishments that do not use the above methods, data are collected by the State agency using mail, FAX, transcript, magnetic tape, or computer diskette. BLS is also pilot testing reporting via the World Wide Web with about 1,600 firms providing data via this mode.

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

CONCEPTS

Industrial classification

All data on employment, hours, and earnings for the Nation and for States and areas are classified in accordance with the 2002 North American Industry Classification System (NAICS), U.S. Office of Management and Budget. The United States, Canada, and Mexico share this classification system, and thus it allows a direct comparison of economic data between the three countries.

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

Industry employment

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

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

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

Industry hours and earnings

Average hours and earnings data are derived from reports of payrolls and hours for production and related workers in natural resources and mining and manufacturing, construction workers in construction, and nonsupervisory employees in private service-providing industries.

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

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

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

Payroll. This refers to the payroll for full- and part-time production, construction, or nonsupervisory workers who received pay for any part of the pay period that includes the 12th day of the month. The payroll is reported before deductions of any kind, such as those for old-age and unemployment insurance, group insurance, withholding tax, bonds, or union dues; also included is pay for overtime, holidays, and vacation, and for sick leave paid directly by the firm. Bonuses (unless earned and paid regularly each pay period); other pay not earned in the pay period reported (such as retroactive pay); tips; and the value of free rent, fuel, meals, or other payment in kind are excluded. Employee benefits (such as health and other types of insurance, contributions to retirement, and so forth, paid by the employer) also are excluded.

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

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

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

Indexes of aggregate weekly hours and payrolls. The indexes of aggregate weekly hours are calculated by dividing the current month’s aggregate by the average of the 12 monthly figures for 2002. For basic industries, the hours aggregates are the product of average weekly hours and production worker or nonsupervisory worker employment. At all higher levels of industry aggregation, hours aggregates are the sum of the component aggregates.

The indexes of aggregate weekly payrolls are calculated by dividing the current month’s aggregate by the average of the 12 monthly figures for 2002. For basic industries, the payroll aggregates are the product of average hourly earnings and aggregate weekly hours. At all higher levels of industry aggregation, payroll aggregates are the sum of the component aggregates.

Average overtime hours. Overtime hours represent that portion of average weekly hours that exceeded regular hours and for which overtime premiums were paid. If an employee were to work on a paid holiday at regular rates, receiving as total compensation their holiday pay plus straight-time pay for hours worked that day, no overtime hours would be reported.

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

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

Averages of hourly earnings differ from wage rates. Earnings are the actual return to the worker for a stated period; rates are the amount stipulated for a given unit of work or time. The earnings series do not measure the level of total labor costs on the part of the employer because the following are excluded: Benefits, irregular bonuses, retroactive items, payroll taxes paid by employers, and earnings for those employees not covered under production worker, construction worker, or nonsupervisory employee definitions.

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

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

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

Real earnings. These earnings are in constant dollars and are calculated from the earnings averages for the current month using a deflator derived from the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPIW). The reference year for these series is 1982.

Indexes of diffusion of employment change. These indexes measure the dispersion of change in employment among industries over the specified timespan. The overall indexes are calculated from 278 seasonally adjusted employment series (4-digit NAICS industries) covering all nonfarm payroll employment in the private sector. The manufacturing diffusion indexes are based on 84 4-digit NAICS industries.

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

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

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

ESTIMATING METHODS

The Current Employment Statistics (CES) or establishment survey estimates of employment are generated through an annual benchmark and monthly sample link procedure. Annual universe counts or benchmark levels are generated primarily from administrative records on employees covered by unemployment insurance (UI) tax laws. These annual benchmarks, established for March of each year, are projected forward for each subsequent month based on the trend of the sample employment, using an estimation procedure called the link relative. Benchmarks and sample link relatives are computed for each basic estimating cell and summed to create aggregate-level employment estimates.

Benchmarks

For the establishment survey, annual benchmarks are constructed in order to realign the sample-based employment totals for March of each year with the UI-based population counts for March. These population counts are much less timely than sample-based estimates and are used to provide an annual point-in-time census for employment. For national series, only the March sample-based estimates are replaced with UI counts. For State and metropolitan area series, all available months of UI data are used to replace sample-based estimates. State and area series are based on smaller samples and are therefore more vulnerable to both sampling and nonsampling errors than national estimates.

Population counts are derived from the administrative file of employees covered by UI. All employers covered by UI laws are required to report employment and wage information to the appropriate State employment security agency four times a year. Approximately 97 percent of private employment within the scope of the establishment survey is covered by UI. A benchmark for the remaining 3 percent is constructed from alternate sources, primarily records from the Railroad Retirement Board and County Business Patterns. The full benchmark developed for March replaces the March sample-based estimate for each basic cell. The monthly sample-based estimates for the year preceding and the year following the benchmark also are subject to revision.

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

Estimates for the 7 months following the March benchmark also are recalculated each year. These post-benchmark estimates reflect the application of sample-based monthly changes to new benchmark levels for March and the recomputation of net birth/death model factors for each month.

Following the revision of basic employment estimates, all other derivative series (such as the number of production workers and average hourly earnings) also are recalculated. New seasonal adjustment factors are calculated and all data series for the previous 5 years are re-seasonally adjusted before full publication of all revised data in February of each year.

Monthly estimation

CES uses a matched sample concept and weighted link relative estimator to produce employment, hours, and earnings estimates. These methods are described in table 2A. A matched sample is defined to be all sample members that have reported data for the reference month and the previous month. Excluded from the matched sample is any sample unit that reports that it is out of business. This aspect of the estimation methodology is more fully described in the section on estimation of business births and deaths below.

Stratification. The sample is stratified into 682 estimation cells for purposes of computing national employment, hours, and earnings estimates. Cells are defined primarily by detailed industry. In the construction supersector, geographic stratification also is used. The estimation cells can be defined at the 3-, 4-, 5-, and 6-digit NAICS levels.

In addition to the estimation cells mentioned above, there are 40 independently estimated cells which do not aggregate to the summary cell levels.

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

The basic formula for estimating all employees is:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]

where:

i = matched sample unit;

[w.sub.i] = weight associated with the CES report;

[ae.sub.c,i] = current-month reported all employees;

[ae.sub.p,i] = previous-month reported all employees;

[AE.sub.c] = current-month estimated all employees; and

[AE.sub.p] = previous-month estimated all employees.

Weighted link and taper technique. The estimator used for all non-all-employee data types accounts for the over-the-month change in the sampled units, but also includes a tapering feature used to keep the estimates close to the overall sample average over time. The taper is considered to be a level correction. This estimator uses matched sample data; it tapers the estimate toward the sample average for the previous month of the current matched sample before applying the current month’s change; and it promotes continuity by heavily favoring the estimate for the previous month when applying the numerical factors.

Current-month estimate of production or nonsupervisory workers (PW) is defined as:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]

where:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]

for all i [member of] I and j [member of] J

Current-month estimate of women workers (WW) is identical to that described for production workers, with the appropriate substitution of women worker values for the production worker values in the previous formulas.

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

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]

for all i [member of] I and j [member of] J

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

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]

for all i [member of] I and j [member of] J

where:

i = a matched CES report;

I = the set of all matched CES reports;

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

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

[w.sub.i] = weight associated with the CES report;

[pw.sub.c,i] = current-month reported production workers;

[pw.sub.p,i] = previous-month reported production workers;

[pw.sup.*.sub.c,j] = current-month reported production workers, atypical record;

[pw.sup.*.sub.p,j] = previous-month reported production workers, atypical record;

[pw.sup.*(WH).sub.c,j] = current-month reported production workers, atypical weekly hours (WH) record;

[pw.sup.*(WH).sub.p,j] = previous-month reported production workers, atypical weekly hours (WH) record;

[P[??].sub.c,i] = current-month estimated production workers;

[P[??].sub.p,i] = previous-month estimated production workers;

[wh.sub.c,i] = current-month reported weekly hours;

[wh.sub.p,i] = previous-month reported weekly hours;

[wh.sup.*.sub.c,j] = current-month reported weekly hours, atypical record;

[wh.sup.*.p,j] = previous-month reported weekly hours, atypical record;

[wh.sup.*(PR).sub.c,j] = current-month reported weekly hours, atypical payroll (PR) record;

[wh.sup.*(PR).sub.p,j] = previous-month reported weekly hours, atypical payroll (PR) record;

[W[??].sub.c,i] = current-month estimated weekly hours;

[W[??].sub.p,i] = previous-month estimated weekly hours;

[A[??]H.sub.c,i] = current-month estimated average weekly hours;

[A[??]H.sub.p,i] = previous-month estimated average weekly hours;

[pr.sub.c,i] = current-month reported weekly payroll;

[pr.sub.p,i] = previous-month reported weekly payroll;

[pr.sup.*.sub.c,j] = current-month reported weekly payroll, atypical record;

[pr.sup.*.sub.p,j] = previous-month reported weekly payroll, atypical record;

[A[??]E.sub.c,i] = current-month estimated average hourly earnings; and

[A[??]E.sub.p,i] = previous-month estimated average hourly earnings.

Current-month estimate of overtime hours (OT) is identical to that described for weekly hours, with the appropriate substitution of overtime hours values for the weekly hours values in the previous formula.

Business birth and death estimation. In a dynamic economy, firms are continually opening and closing. These two occurrences offset each other to some extent. That is, firms that are born replace firms that die. CES uses this fact to account for a large proportion of the employment associated with business births. This is accomplished by excluding such business death units from the matched sample definition. Effectively, business deaths are not included in the sample-based link portion of the estimate, and the implicit imputation of their previous month’s employment is assumed to offset a portion of the employment associated with births.

There is an operational advantage associated with this approach as well. Most firms will not report that they have gone out of business; rather, they simply cease reporting and are excluded from the link, as are all other nonrespondents. As a result, extensive follow-up with monthly nonrespondents to determine whether a company is out of business or simply did not respond is not required.

Employment associated with business births will not exactly equal that associated with business deaths. The amount by which it differs varies by month and by industry. As a result, the residual component of the birth/ death offset must be accounted for by using a model-based approach.

With any model-based approach, it is desirable to have 5 or more years of history to use in developing the models. Due to the absence of reliable counts of monthly business births and deaths, development of an appropriate birth/death residual series assumed the following form:

Birth-death residual = Population – Sample-based estimate + Error

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

Models are fit using X-12 ARIMA (Auto-Regressive Integrated Moving Average). Outliers, level shifts, and temporary ramps are automatically identified. Seven models are tested, and the model exhibiting the lowest average forecast error is selected for each series. Table 2-B shows the net birth/death model figures for the post-benchmark period of April 2003 to October 2003 by supersector.

THE SAMPLE

Design

The CES sample is a stratified, simple random sample of worksites, clustered by UI account number. The UI account number is a major identifier on the BLS longitudinal database of employer records, which serves as both the sampling frame and the benchmark source for the CES employment estimates. The sample strata, or subpopulations, are defined by State, industry, and employment size, yielding a State-based design. The sampling rates for each stratum are determined through a method known as optimum allocation, which distributes a fixed number of sample units across a set of strata to minimize the overall variance, or sampling error, on the primary estimate of interest. The total nonfarm employment level is the primary estimate of interest, and the CES sample design gives top priority to measuring it as precisely as possible, or, in other words, minimizing the statistical error around the statewide total nonfarm employment estimates.

Frame and sample selection. The longitudinal data base (LDB) is the universe from which BLS draws the CES sample. The LDB contains data on approximately 8 million U.S. business establishments, representing nearly all elements of the U.S. economy. The Quarterly Census of Employment and Wages (QCEW), or ES-202, program collects these data from employers, on a quarterly basis, in cooperation with State employment security agencies (SESAs). The LDB contains employment and wage information from employers, as well as name, address, and location information. It also contains identification information such as unemployment insurance (UI) account number and reporting unit or worksite number.

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

Permanent random numbers (PRNs) have been assigned to all UI accounts on the sampling frame. As new units appear on the frame, random numbers are assigned to those units as well. As records are linked across time, the PRN is carried forward in the linkage.

The CES sample is stratified by State, industry, and size. Stratification groups population members together for the purpose of sample allocation and selection. The strata, or groups, are composed of homogeneous units. With 13 industries and 8 size classes, there are 104 total allocation cells per State. The sampling rate for each stratum is determined through a method known as optimum allocation. Optimum allocation minimizes variance at a fixed cost or minimizes cost for a fixed variance. Under the CES probability design, a fixed number of sample units for each State is distributed across the allocation strata in such a way as to minimize the overall variance, or sampling error, of the total State employment level. The number of sample units in the CES probability sample was fixed according to available program resources. The optimum allocation formula places more sample in cells for which data cost less to collect, cells that have more units, and cells that have a larger variance.

During the first quarter of each year, a new sample is drawn from the LDB. Annual sample selection helps keep the CES survey current with respect to employment from business births and business deaths. In addition, the updated universe files provide the most recent information on industry, size, and metropolitan area designation.

After all out-of-scope records are removed, the sampling frame is sorted into allocation cells. Within each allocation cell, units are sorted by metropolitan statistical area (MSA) and by the size of the MSA, defined as the number of UI accounts in that MSA. As the sampling rate is uniform across the entire allocation cell, implicit stratification by MSA ensures that a proportional number of units are sampled from each MSA. Some MSAs may have too few UI accounts in the allocation cell; these MSAs are collapsed and treated as a single MSA. Within each selection cell, the units are sorted by PRN, and units are selected according to the specified sample selection rate. The number of units selected randomly from each selection cell is equal to the product of the sample selection rate and the number of eligible units in the cell, plus any carryover from the prior selection cell. The result is rounded to the nearest whole number. Carryover is defined as the amount that is rounded up or down to the nearest whole number.

As a result of the cost and workload associated with enrolling new sample units, all units remain in the sample for a minimum of 2 years. To insure that all units meet this minimum requirement, BLS has established a “swapping in” procedure. The procedure allows units to be swapped into the sample that were newly selected during the previous sample year and not reselected as part of the current probability sample. The procedure removes a unit within the same selection cell and places the newly selected unit from the previous year back into the sample.

Selection weights. Once the sample is drawn, sample selection weights are calculated based on the number of UI accounts actually selected within each allocation cell. The sample selection weight is approximately equal to the inverse of the probability of selection, or the inverse of the sampling rate. It is computed as:

Sample selection weight = [N.sub.h] / [n.sub.h]

where:

[N.sub.h] = the number of noncertainty UI accounts within the allocation cell that are eligible for sample selection; and

[n.sub.h] = the number of noncertainty UI accounts selected within the allocation cell

Sample Rotation. Sample rotation eases the burden on respondents who have been participating in the survey for an extended time period. A 25-percent rotation is utilized in selection cells with weights greater than 2.00. Units that rotate out of the sample will not be reselected as part of the sample for 3 years. In an effort to keep units from moving back into the sample after a single year, a “swap out” procedure has been established. The “swap out” procedure removes units from the current sample that had been rotated out of the sample within the last 3 years and replaces them with other units within the selection cell eligible for sample selection. As a result of sample rotation, approximately 68 percent of the Current Employment Statistics sample for the private industries overlaps from one year to the next.

Frame maintenance and sample updates. Due to the dynamic economy, there is a constant cycle of business births and deaths. A semiannual update is performed during the third quarter of each year. This update selects units from the population of births and other units not previously eligible for selection, and includes them as part of the sample. Updated location, contact, and administrative information is provided for all establishments that were selected in the annual sample selection.

Subsampling. The primary enrollment of new establishments takes place in BLS regional office data collection centers (DCCs). After the sample has been sent to the DCCs, interviewers enroll the selected establishments. While the UI account is the sample unit, interviewers attempt to collect the data for all individual establishments within a UI account.

For multiple-worksite UI accounts, it is sometimes necessary to subsample employers. This occurs when:

–the company cannot report for all worksites from a central location;

–the company cannot provide an aggregate report for the entire UI account; or

–there are too many individual worksites to make it practical to contact each of them.

With subsampling of a smaller number of worksites, both interviewer workload and respondent burden are reduced without significantly reducing the accuracy of the estimates, but this technique will result in a small increase in variance. In the event that a UI account is subsampled, weight adjustments are made to reflect each of the worksites’ probability of selection.

Coverage

Table 2-C shows the latest benchmark employment levels and the approximate proportion of total universe employment coverage at the total nonfarm and major industry supersector levels. The coverage for individual industries within the supersectors may vary from the proportions shown.

Reliability

The establishment survey, like other sample surveys, is subject to two types of error, sampling and nonsampling error. The magnitude of sampling error, or variance, is directly related to the size of the sample and the percentage of universe coverage achieved by the sample. The establishment survey sample covers over one-third of total universe employment; this yields a very small variance for the total nonfarm estimates. Measurements of error associated with sample estimates are provided in tables 2-D through 2-F.

Benchmark revision as a measure of survey error. The sum of sampling and nonsampling error can be considered total survey error. Unlike most sample surveys which publish sampling error as their only measure of error, the CES can derive an annual approximation of total error, on a lagged basis, because of the availability of the independently derived universe data. While the benchmark error is used as a measure of total error for the CES survey estimate, it actually represents the difference between two independent estimates derived from separate survey processes (specifically, the CES sample process and the UI administrative process) and thus reflects the errors present in each program. Historically, the benchmark revision has been very small for total nonfarm employment. Over the past decade, percentage benchmark error has averaged 0.3 percent, with an absolute range from less than 0.05 percent to 0.7 percent.

Revisions between preliminary and final data. First preliminary estimates of employment, hours, and earnings, based on less than the total sample, are published immediately following the reference month. Final revised sample-based estimates are published 2 months later when nearly all the reports in the sample have been received. Table 2-D presents the root-mean-square error, the mean percent, and the mean absolute percent revision that may be expected between the preliminary and final employment estimates.

Revisions of preliminary hours and earnings estimates are normally not greater than 0.1 hour for weekly hours and 1 cent for hourly earnings, at the total private nonfarm level, and may be slightly larger for the more detailed industry groupings.

Variance estimation. The estimation of sample variance for the CES survey is accomplished through use of the method of balanced half samples (BHS). This replication technique uses half samples of the original sample and calculates estimates using those subsamples. The sample variance is calculated by measuring the variability of the subsample estimates. The weighted link estimator is used to calculate both estimates and variances. The sample units in each cell–where a cell is based on State, industry, and size classification–are divided into two random groups. The basic BHS method is applied to both groups. The subdivision of the cells is done systematically, in the same order as the initial sample selection. Weights for units in the half sample are multiplied by a factor of 1 + [gamma] where weights for units not in the half sample are multiplied by a factor of 1 – [gamma]. Estimates from these subgroups are calculated using the estimation formula described previously.

The formula used to calculate CES variances is as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]

where:

[[??].sup.+.sub.a] = [theta]([[??].sup.+.sub.a],[[??].sup.+.sub.a], …..) is the half-sample estimator;

[gamma] = 1/2;

k = number of half-samples; and

[??] = original full-sample estimates.

Appropriate uses of sampling variances. Variance statistics are useful for comparison purposes, but they do have some limitations. Variances reflect the error component of the estimates that is due to surveying only a subset of the population, rather than conducting a complete count of the entire population. However, they do not reflect nonsampling error, such as response errors, and bias due to nonresponse. The overall performance of the CES employment estimates is best measured in terms of the benchmark revisions. The variances of the over-the-month change estimates are very useful in determining when changes are significant at some level of confidence.

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

Illustration of the use of table 2-E. Table 2-E provides a reference for relative standard errors of three major series developed from the CES–estimates of the number of all employees (AE), of average hourly earnings (AHE), and of average weekly hours (AWH) within the same industry.

The standard errors of differences between estimates in two non-overlapping industries are calculated as:

S difference = [square root of ([s.sup.2.sub.1] + [s.sup.2.sub.2])

since the two estimates are independent.

The errors are presented as relative standard errors (standard error divided by the estimate and expressed as a percent). Multiplying the relative standard error by its estimated value gives the estimate of the standard error.

Suppose that the level of all employees for financial activities in a given month is estimated at 7,819,000. The approximate relative standard error of this estimate (0.5 percent) is provided in table 2-E. A 90-percent confidence interval would then be the interval:

7,819,000 +/- (1.645*.005*7,819,000) = 7,819,000 +/- 64,311 = 7,754,689 to 7,883,311

Illustration of the use of table 2-F. Table 2-F provides a reference for the standard errors of 1-, 3-, and 12-month changes in AE, AHE, and AWH. The errors are presented as standard errors of the changes.

Suppose that the over-the-month change in AHE from January to February in coal mining is $0.11. The standard error for a 1-month change for coal mining from the table is $0.24. The interval estimate of the over-the-month change in AHE that will include the true over-the-month change with 90-percent confidence is calculated as:

$0.11 +/-(1.645 * $0.24) = $0.11 +/- $0.39 = -$0.28 to $0.50

The true value of the over-the-month change is in the interval -$0.28 to $0.50. Because this interval includes $0.00 (no change), the change of $0.11 shown is not significant at the 90-percent confidence level. Alternatively, the estimated change of $0.11 does not exceed $0.39 (1.645 * $0.24); therefore, one could conclude from these data that the change is not significant at the 90-percent confidence level.

STATISTICS FOR STATES AND AREAS

(Tables B-7, B-14, and B-18)

As explained earlier, State agencies in cooperation with BLS collect and prepare State and area employment, hours, and earnings data. These statistics are based on the same establishment reports used by BLS. However, BLS uses the full CES sample to produce monthly national employment estimates, while each State agency uses its portion of the sample to independently develop a State employment estimate.

The CES area statistics relate to metropolitan areas. Definitions for all areas are published each year in the issue of Employment and Earnings that contains State and area annual averages (usually the May issue). Changes in definitions are noted as they occur.

Estimates for States and areas are produced using two methods. The majority of State and area estimates are produced using direct sample-based estimation. However, published area and industry combinations (domains) that do not have a large enough sample to support estimation using only sample responses are estimated by using a small-domain model.

Small-domain model. The small-domain model consists of a weighted sum of three different relative over-the-month change estimates, [[??].sub.1], [[??].sub.2], and [[??].sub.3]. These three relative over-the-month change estimates are then weighted based on the variance of each of the three estimates. The larger the variance of each [[??].sub.k] estimate relative to the other [[??].sub.k] variances, the smaller the weight. The resulting estimate of current-month employment [[??].sub.at] is defined as:

[[??].sub.iat] = ([W.sub.iat,1] [??].sub.iat,1] + [W.sub.iat,2] [??].sub.iat,2] + [W].sub.iat,3][??].sub.iat,3])[[??].sub.ia,t-1]

where:

[[??].sub.iat] = current-month t employment estimate for domain ia defined by the intersection of industry i and area a;

[[??].sub.iat,1] = current-month relative over-the-month change estimate based on available sample responses for domain ia;

[W.sub.iat,1] = current-month weight assigned to [[??].sub.iat,1] based on the variances of [[??].sub.int,1], [[??].sub.int,2], and [[??].sub.iat,3] (The weights [W.sub.iat,2] and [W.sub.iat,3] are defined similarly.);

[[??].sub.int,2] = current-month relative over-the-month change estimate based on time series forecasts using historical universe employment counts for domain ia (These historical universe employment counts are available from January 1990 to 12 months prior to the current month t.);

[[??].sub.iat,3] = current-month relative over-the-month change estimate based on a synthetic estimate of the relative change that uses all sample responses in the State that includes area a, for industry i; and

[[??].sub.ia,t-1] = previous-month employment estimate for domain ia from the small-domain model.

It is possible that for a given industry i and area a, one or even two of the inputs [[??].sub.iat,k] to the model are assigned weights of 0. The reasons for assigning a weight of 0 to a model input are due to concerns regarding the stability of the inputs. For example, if [[??].sub.iat,1] or [[??].sub.iat,3] has five or fewer responses, then it is assigned a weight of 0. If [[??].sub.iat,2] exhibits an unstable variance or has an extremely poor model fit, then it may also be assigned a weight of 0. In these cases, the small-domain model estimate may be based on only one or two of the three described inputs.

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

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

Error measures for State and area estimates are available on the BLS Web site at http://www.bls.gov/sae/ 790stderr.htm.

Caution in aggregating State data. The national estimation procedures used by BLS are designed to produce accurate national data by detailed industry; correspondingly, the State estimation procedures are designed to produce accurate data for each individual State. State estimates are not forced to sum to national totals nor vice versa. Because each State series is subject to larger sampling and nonsampling errors than the national series, summing them cumulates individual State level errors and can cause distortion at an aggregate level. This has been a particular problem at turning points in the U.S. economy, when the majority of the individual State errors tend to be in the same direction. Due to these statistical limitations, the Bureau does not compile or publish a “sum-of-States” employment series. Additionally, BLS cautions users that such a series is subject to a relatively large and volatile error structure, particularly at turning points.

Region, State, and Area Labor Force Data (“C” tables)

FEDERAL-STATE COOPERATIVE PROGRAM

Labor force and unemployment estimates for States, labor market areas (LMAs), and other areas covered under Federal assistance programs are developed by State employment security agencies under a Federal-State cooperative program, The local unemployment estimates, which derive from standardized procedures developed by BLS, are the basis for determining eligibility of an area for benefits under Federal programs such as the Workforce Investment Act.

Annual average data for the States and 337 areas shown in table C-3 are published in Employment and Earnings (usually the May issue). For regions, States, selected metropolitan areas, and central cities, annual average data classified by selected demographic, social, and economic characteristics are published in the BLS bulletin, Geographic Profile of Employment and Unemployment.

Labor force estimates for counties, cities, and other small areas have been prepared for administration of various Federal economic assistance programs and are available on the Internet at http://www.bls.gov/lau or by subscription by calling 202-691-6392.

ESTIMATING METHODS

Monthly labor force, employment, and unemployment estimates are prepared for the 50 States, the District of Columbia, Puerto Rico, and over 7,000 areas, including nearly 2,400 LMAs, all counties, and cities with a population of 25,000 or more. Regional aggregations are derived by summing the State estimates. The estimation methods are described below for States (and the District of Columbia) and for sub-state areas. At the sub-LMA (county and city) level, estimates are prepared using disaggregation techniques based on decennial and annual population estimates and current unemployment insurance data. A more detailed description of the estimation procedure is contained in the BLS document, Manual for Developing Local Area Unemployment Statistics.

Estimates for States

Current monthly estimates. Effective January 1996, civilian labor force and unemployment estimates for all States and the District of Columbia are produced using models based on a “signal-plus-noise” approach. The model of the signal is a time series model of the true labor force which consists of three components: A variable coefficient regression, a flexible trend, and a flexible seasonal component. The regression techniques are based on historical and current relationships found within each State’s economy as reflected in the different sources of data that are available for each State–the Current Population Survey (CPS), the Current Employment Statistics (CES) survey, and the unemployment insurance (UI) system. The noise component of the models explicitly accounts for auto correlation in the CPS sampling error and changes in the average magnitude of the error. In addition, the models can identify and remove the effects of outliers in the historical CPS series. While all the State models have important components in common, they differ somewhat from one another to better reflect individual State characteristics.

Two models–one for the employment-to-population ratio and one for the unemployment rate–are used for each State. The employment-to-population ratio, rather than the employment level, and the unemployment rate, rather than the unemployment level, are estimated primarily because these ratios are usually more meaningful for economic analysis.

The employment-to-population ratio models use the relationship between the State’s monthly employment from the CES and the CPS. The models also include trend and seasonal components to account for movements in the CPS not captured by the CES series. The seasonal component accounts for the seasonality in the CPS not explained by the CES, while the trend component adjusts for long-run systematic differences between the two series.

The unemployment rate models use the relationship between the State’s monthly unemployment insurance (UI) claims data and the CPS unemployment rate, along with trend and seasonal components.

In both the employment-to-population ratio and unemployment rate models, an important feature is the use of a technique that allows the equations to adjust automatically to structural changes that occur. The regression portion of the model includes a built-in tuning mechanism, known as the Kalman Filter, which revises a model’s coefficients when the new data that become available each month indicate that changes in the data relationships have taken place. Once the estimates are developed from the models, revels are calculated for employment, unemployment, and labor force.

Benchmark correction procedures. Once each year, monthly estimates for all States and the District of Columbia are adjusted, or benchmarked, by BLS to the annual average CPS estimates. The benchmarking technique employs a procedure (called the Denton method) which adjusts the annual average of the models to equal the CPS annual average, while preserving, as much as possible, the original monthly seasonal pattern of the model estimates.

Estimates for substate areas

Monthly labor force, employment, and unemployment estimates for two large substate areas–New York City and the Los Angeles-Long Beach metropolitan area–are obtained using the same modeling approach as for states. Estimates for the nearly 2,400 remaining LMAs, are prepared through indirect estimation techniques, described below.

Preliminary estimate–employment. The total civilian employment estimates are based largely on CES data. These “place-of-work” estimates must be adjusted to refer to place of residence as used in the CPS. Factors for adjusting from place of work to place of residence have been developed on the basis of employment relationships at the time of the 1990 decennial census. These factors are applied to the CES estimates for the current period to obtain adjusted employment estimates, to which are added estimates for employment not represented in the CES–agricultural employees, nonagricultural self-employed and unpaid family workers, and private household workers.

Preliminary estimate–unemployment. In the current month, the estimate of unemployment is an aggregate of the estimates for each of two categories: (1) Persons who were previously employed in industries covered by State UI laws; and (2) those who were entering the civilian labor force for the first time or reentering after a period of separation.

Substate adjustment for additivity. Estimates of employment and unemployment are prepared for the State and all LMAs within the State. The LMA estimates geographically exhaust the entire State. Thus, a proportional adjustment is applied to all substate preliminary LMA estimates to ensure that they add to the independently estimated State totals for employment and unemployment. For California and New York, the proportional adjustment is applied to all LMAs other than the two modeled areas, to ensure that the LMA estimates sum to an independent model-based estimate for the balance of State.

Benchmark correction. At the end of each year, substate estimates are revised. The revisions incorporate any changes in the inputs, such as revisions in the CES-based employment figures, corrections in UI claims counts, and updated historical relationships. The updated estimates are then readjusted to add to the revised (benchmarked) State estimates of employment and unemployment.

Seasonal Adjustment

Over the course of a year, the size of the Nation’s labor force, the levels of employment and unemployment, and other measures of labor market activity undergo sharp fluctuations due to such seasonal events as changes in weather, reduced or expanded production, harvests, major holidays, and the opening and closing of schools. Because these seasonal events follow a more or less regular pattern each year, their influence on statistical trends can be eliminated by adjusting the statistics from month to month. These adjustments make it easier to observe the cyclical and other nonseasonal movements in the series. Seasonally adjusted series for selected labor force and establishment-based data are published monthly in Employment and Earnings.

Household data

Beginning in January 2003, BLS started using the X-12-ARIMA (Auto-Regressive Integrated Moving Average) seasonal adjustment program to seasonally adjust national labor force data from the Current Population Survey (CPS), or household survey. This program replaced the X-11 ARIMA program which had been used since January 1980. For a detailed description of the X-12-AR/MA program and its features, see D.F. Findley, B.C. Monsell, W.R. Bell, M.C. Otto, and B.C. Chen, “New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program,” Journal of Business and Economic Statistics, April 1998, Vol. 16, No. 2, pp. 127-152. See “Revision of Seasonally Adjusted Labor Force Series in 2003,” in the February 2003 issue of this publication for a discussion of the introduction of the use of X-12 ARIMA for seasonal adjustment of the labor force data and the effects that it had on the data.

Beginning in January 2004, BLS converted to the use of concurrent seasonal adjustment to produce seasonally adjusted labor force estimates from the household survey. Concurrent seasonal adjustment uses all available monthly estimates, including those for the current month, in developing seasonal factors. Previously, seasonal factors for the CPS data had been projected twice a year. As a result of this change in methodology, BLS no longer publishes seasonal factors for the labor force data. For more information on the adoption of concurrent seasonal adjustment for the labor force data, see “Revision of Seasonally Adjusted Labor Force Series in 2004,” in the January 2004 issue of this publication available on the Internet at http://www.bls.gov/ cps/cpsrs2004.pdf.

Revisions of historical data, usually for the most recent 5 years, are made only at the beginning of each calendar year. However, as a result of the revisions to the estimates for 1970-81 based on 1980 census population counts, revisions to seasonally adjusted series in early 1982 were carried back to 1970. In 1994, data were revised only for that year because of the major survey redesign and the introduction of 1990 census-based population controls, adjusted for the estimated undercount, into the Current Population Survey. In 1996, 1990-93 data also were revised to incorporate these 1990 census-based population controls and seasonally adjusted series were revised back to 1990. Subsequent revisions were carried back only to 1994 through 1998, when the standard 5-year revision period was reinstated.

All labor force and unemployment rate statistics, as well as the major employment and unemployment estimates, are computed by aggregating independently adjusted series. For example, for each of the major labor force components–employment, and unemployment–data for four sex-age groups (men and women under and over 20 years of age) are separately adjusted for seasonal variation and are then added to derive seasonally adjusted total figures. The seasonally adjusted figure for the labor force is a sum of four seasonally adjusted civilian employment components and four seasonally adjusted unemployment components. The total for unemployment is the sum of the four unemployment components, and the unemployment rate is derived by dividing the resulting estimate of total unemployment by the estimate of the labor force. Because of the independent seasonal adjustment of various series, components will not necessarily add to totals.

Each January issue (March issue in 1996 and February issue in 2003) of Employment and Earnings contains revised seasonally adjusted data for selected labor force series based on the experience through December and a description of the current seasonal adjustment procedure.

National establishment data

BLS also uses the X-12-ARIMA seasonal adjustment program to seasonally adjust national establishment-based employment, hours, and earnings series derived from the Current Employment Statistics (CES) program. (Use of X-12 ARIMA to seasonally adjust the CES data began in June 1996, with the release of the March 1995 benchmark revisions.) Individual series are seasonally adjusted using either a multiplicative or an additive model. For employment, seasonal adjustment factors are directly applied to the component levels. Individual 3-digit NAICS levels are seasonally adjusted, and higher-level aggregates are formed by the summation of these components. Seasonally adjusted totals for hours and earnings are obtained by taking weighted averages of the seasonally adjusted data for the component series.

Revised seasonally adjusted national establishment-based series based on the experience through January 2004 and a detailed description of the current seasonal adjustment procedure appear in the February 2004 issue of Employment and Earnings.

Concurrent seasonal adjustment. Beginning in June 2003 with the May 2003 first preliminary estimates, BLS began computing seasonal factors concurrently with the monthly estimate production. Previously, the factors were forecasted twice a year. Concurrent seasonal adjustment is expected to provide a more accurate seasonal adjustment, and smaller revisions from the first preliminary estimates to the final benchmarked estimates, than the semiannual updates. As a result of the adoption of concurrent seasonal adjustment, the CES program has discontinued the publication of projected seasonal factors.

Additive and multiplicative models. Prior to the March 2002 benchmark release in June 2003, all CES series were adjusted using multiplicative seasonal adjustment models. Although the X-12-ARIMA seasonal adjustment program provides for either an additive or a multiplicative adjustment depending on which model best fits the individual series, the previous CES processing system was unable to utilize additive seasonal adjustments. A new processing system, introduced simultaneously with the conversion to NAICS in June 2003, is able to utilize both additive and multiplicative adjustments. The article, “Revisions to the Current Employment Statistics National Estimates Effective May 2003,” published in the June 2003 issue of this publication contains a list of which series are adjusted with additive seasonal adjustment models and which series are adjusted with multiplicative models. The article also lists which series are subject to the calendar-effects modeling described below.

Variable survey intervals. Beginning with the release of the 1995 benchmark, BLS refined the seasonal adjustment procedures to control for survey interval variations, sometimes referred to as the 4- versus 5-week effect. Although the CES survey is referenced to a consistent concept–the pay period including the 12th of each month-inconsistencies arise because there are sometimes 4 and sometimes 5 weeks between the 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 from the previous 3 years. All else being 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 uses REGARIMA (regression with autocorrelated 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 autocorrelated over time. That is, each month’s value is significantly dependent on the observations that precede it; these series, thus, usually can be successfully fit using ARIMA models. If autocorrelated 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 autocorrelation. Thus, the REGARIMA technique is appropriate to measuring relationships among variables of interest in series that exhibit autocorrelation, 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 is almost always 4 weeks between the February and March surveys. Models for individual basic series are fitted with the most recent 10 years of data available, the standard time span used for CES seasonal adjustment.

The REGARIMA procedure yields regression coefficients for each of the 11 months specified in the model. These coefficients provide estimates of the strength of the relationship between employment levels and the number of weeks between surveys for the 11 modeled months. The X-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 previously used.

For a few series, REGARIMA models did 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 which are described below.

Construction series. BLS continues its special treatment in seasonally adjusting the construction industry series, which began with the 1996 benchmark revision. 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 freer 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 also makes special adjustments for average weekly hours and average weekly overtime series to account for the presence or absence of religions holidays in the April survey reference period and the occurrence of Labor Day in the September reference period.

Local government series. A special adjustment also is made in the local government, excluding education series in November each year to account for variations in employment due to the presence or absence of poll workers.

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 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 semimonthly 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. Currently, the CES processing system can accommodate only one conversion factor per reporter.

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

State establishment data

Seasonally adjusted nonfarm payroll employment data by selected industry supersectors for all States and the District of Columbia are presented in table B-7 of this publication. As with the national establishment data, the State establishment data are seasonally adjusted with the X-12-ARIMA seasonal adjustment program. Seasonal adjustment factors are applied directly to the employment estimates at the supersector level and then aggregated to the State totals for most States. For a few States that do not have many publishable seasonally adjusted supersectors, however, total nonfarm data are seasonally adjusted directly at the aggregate level. The recomputation of seasonal factors and historical revisions are made coincident with the annual benchmark adjustments.

Region and State labor force data

Beginning in 1992, BLS introduced publication of seasonally adjusted labor force data for the census regions and divisions, the 50 States, and the District of Columbia (tables C-1 and C-2). Beginning in 1998, regional aggregations are derived by summing the State estimates.

Seasonal adjustment of the State labor force data is done in two steps. First, a signal plus noise model is fit to the data series to filter out the effects of sampling errors that result from the small sample size of the State estimates. In the second step, the error-corrected labor force series is then seasonally adjusted with the X-12-ARIMA seasonal adjustment program.

Seasonal adjustment factors are computed and applied independently to the component employment and unemployment levels and then aggregated to regional or State totals. Current seasonal adjustment factors are produced for 6-month periods twice a year. Historical revisions usually are made at the beginning of each calendar year. Because of the separate processing procedures, totals for the Nation, as a whole, differ from the results obtained by aggregating regional or State data.

Table 1-A. Characteristics of the CPS sample, 1947 to present

Households

eligible

Number of sample

Period areas Interviewed

Aug. 1947 to Jan. 1954 68 21,000

Feb. 1954 to Apr. 1956 230 21,000

May 1956 to Dec. 1959 (1) 330 33,500

Jan. 1960 to Feb. 1963 (2) 333 33,500

Mar. 1963 to Dec. 1966 357 33,500

Jan. 1967 to July 1971 449 48,000

Aug. 1971 to July 1972 449 45,000

Aug. 1972 to Dec. 1977 461 45,000

Jan. 1978 to Dec. 1979 614 53,500

Jan. 1980 to Apr. 1981 629 62,200

May 1981 to Dec. 1984 629 57,800

Jan. 1985 to Mar. 1988 729 57,000

Apr. 1988 to Mar. 1989 729 53,200

Apr. 1989 to Oct. 1994 (3) 729 57,400

Nov. 1994 to Aug. 1995 (4) 792 54,500

Sept. 1995 to Dec. 1995 792 52,900

Jan. 1996 to June 2001 754 46,250

July 2001 to present (5) 754 55,500

Households

eligible

Households visited

Period Not interviewed but not eligible

Aug. 1947 to Jan. 1954 500-1,000 3,000-3,500

Feb. 1954 to Apr. 1956 500-1,000 3,000-3,500

May 1956 to Dec. 1959 1,500 6,000

Jan. 1960 to Feb. 1963 1,500 6,000

Mar. 1963 to Dec. 1966 1,500 6,000

Jan. 1967 to July 1971 2,000 8,500

Aug. 1971 to July 1972 2,000 8,000

Aug. 1972 to Dec. 1977 2,000 8,000

Jan. 1978 to Dec. 1979 2,500 10,000

Jan. 1980 to Apr. 1981 2,800 12,000

May 1981 to Dec. 1984 2,500 11,000

Jan. 1985 to Mar. 1988 2,500 11,000

Apr. 1988 to Mar. 1989 2,600 11,500

Apr. 1989 to Oct. 1994 (3) 2,600 11,800

Nov. 1994 to Aug. 1995 (4) 3,500 10,000

Sept. 1995 to Dec. 1995 3,400 9,700

Jan. 1996 to June 2001 3,750 10,000

July 2001 to present (5) 4,500 120,001

(1) Beginning in May 1956, these areas were chosen to provide

coverage in each State and the District of Columbia.

(2) Three sample areas were added in 1960 to represent Alaska

and Hawaii after statehood.

(3) The sample was increased incrementally during the 8-month

period, April-November 1989.

(4) Includes 2,000 additional assigned housing units from

Georgia and Virginia that were gradually phased in during the

10-month period, October 1994-August 1995.

(5) Includes 12,000 assigned housing units in support of the

StateChildren’sHealthInsuranceProgram.

Table 1-B. Approximate standard errors for major employment

status categories

(In thousands)

Consecutive

Characteristic Monthly month-to-

level month change

Total

Total, 16 years and over:

Civilian labor force 303 197

Employed 326 212

Unemployed 160 204

Men, 20 years and over:

Civilian labor force 166 108

Employed 190 124

Unemployed 108 138

Women, 20 years and over:

Civilian labor force 208 135

Employed 218 142

Unemployed 94 120

Both sexes, 16 to 19 years:

Civilian labor force 145 139

Employed 132 127

Unemployed 62 102

Black or African American

Total, 16 years and over:

Civilian labor force 125 82

Employed 134 87

Unemployed 75 96

Men, 20 years and over:

Civilian labor force 61 40

Employed 84 55

Unemployed 48 61

Women, 20 years and over:

Civilian labor force 87 57

Employed 86 56

Unemployed 47 60

Both sexes, 16 to 19 years:

Civilian labor force 41 39

Employed 37 35

Unemployed 28 45

Hispanic or Latino ethnicity

Total, 16 years and over:

Civilian labor force 122 80

Employed 132 86

Unemployed 68 87

Table 1-C. Approximate standard errors for enemployment

rates by major characteristics

(In percent)

Consecutive

Monthly month-to-

Characteristics rate month change

Total 0.11 0.14

Men .15 .19

Men, 20 years and over .14 .18

Women .15 .19

Women, 20 years and over .14 .18

Both sexes, 16 to 19 years .79 1.30

White .11 .14

Black or African American .45 .57

Hispanic or Latino ethnicity .36 .46

Married men, spouse present .15 .19

Married women, spouse present .17 .21

Women who maintain families .48 .61

Table 1-D. Parameters and factors for computation of approximate

standard errors

Parameters Factors

Consecutive

month-to-

Characteristic a b month

change

Total or white

Total:

Civilian labor force and

employed -0.0000167 3067.77 0.65

Unemployed -.0000164 3095.55 1.27

Not in labor force -.0000087 1833.31 .65

Men:

Civilian labor force, employed,

and not in labor force -.0000321 2970.55 .65

Unemployed -.0000321 2970.55 1.27

Women:

Civilian labor force, employed,

and not in labor force -.0000304 2782.44 .65

Unemployed -.0000304 2782.44 1.27

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force -.0000225 3095.55 .96

Unemployed -.0000225 3095.55 1.65

Black or African American

Total:

Civilian labor force, employed,

and not in labor force -.0001514 3454.72 .65

Unemployed -.0001514 3454.72 1.28

Men:

Civilian labor force, employed,

and not in labor force -.0003109 3356.66 .65

Unemployed -.0003109 3356.66 1.27

Women:

Civilian labor force, employed,

and not in labor force -.0002516 3061.85 .65

Unemployed -.0002516 3061.85 1.27

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force -.0016321 3454.72 .96

Unemployed -.0016321 3454.72 1.65

Hispanic or Latino ethnicity

Total:

Civilian labor force, employed,

and not in labor force -.0001412 3454.72 .65

Unemployed -.0001412 3454.72 1.28

Men:

Civilian labor force, employed,

and not in labor force -.0002528 3356.66 .65

Unemployed -.0002528 3356.66 1.29

Women:

Civilian labor force, employed,

and not in labor force -.0002664 3061.85 .65

Unemployed -.0002664 3061.85 1.27

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force -.0015280 3454.72 .96

Unemployed -.0015280 3454.72 1.65

Employment

Educational attainment -0.0000164 3095.55 0.65

Marital status, men -.0000321 2970.55 .65

Marital status, women -.0000304 2782.44 .65

Women who maintain families -.0000304 2782.44 .65

Nonagricultural industries:

Total -.0000164 3095.55 .65

Wage and salary workers -.0000164 3095.55 .65

Self-employed workers -.0000164 3095.55 .65

Unpaid family workers -.0000164 3095.55 .65

Full-time workers -.0000164 3095.55 .65

Part-time workers -.0000164 3095.55 .65

Multiple jobholders -.0000164 3095.55 1.27

At work

Total and nonagricultural

industries:

Total -.0000164 3095.55 .65

1 to 4 and 5 to 14 hours -.0000164 3095.55 1.65

15 to 29 hours -.0000164 3095.55 1.27

30 to 34 or 35 to 39 hours -.0000164 3095.55 1.65

1 to 34 or 40 hours -.0000164 3095.55 1.27

41 to 48 or 49 to 59 hours -.0000164 3095.55 1.65

35+, 41+, or 60+ hours -.0000164 3095.55 1.27

Part time for economic

reasons -.0000164 3095.55 1.47

Part time for noneconomic

reasons -.0000164 3095.55 1.27

Unemployment

Educational attainment -0.0000164 3095.55 1.27

Marital status, men -0.0000321 2970.55 1.27

Marital status, women -0.0000304 2782.44 1.27

Women who maintain families -0.0000304 2782.44 1.27

Industries and occupations -0.0000164 3095.55 1.27

Full-time workers -0.0000164 3095.55 1.27

Part-time workers -0.0000164 3095.55 1.65

Less than 5 weeks -0.0000164 3095.55 1.27

5 to 14 weeks -0.0000164 3095.55 1.65

15 to 26 week -0.0000164 3095.55 1.65

15+ or 27+ weeks -0.0000164 3095.55 1.27

All reasons for unemployment,

except temporary layoff -0.0000164 3095.55 1.27

On temporary layoff -0.0000164 3095.55 1.65

Not in the labor force

Total -0.0000087 1833.31 .65

Persons who currently want

a job and discouraged

workers -0.0000164 3095.55 1.65

Factors

Year-to-year

change Quarterly

Characteristic of monthly averages

estimates

Total or white

Total:

Civilian labor force and

employed 1.22 0.87

Unemployed 1.38 .72

Not in labor force 1.22 .87

Men:

Civilian labor force, employed,

and not in labor force 1.23 .86

Unemployed 1.39 .72

Women:

Civilian labor force, employed,

and not in labor force 1.22 .87

Unemployed 1.39 .71

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force 1.32 .81

Unemployed 1.37 .68

Black or African American

Total:

Civilian labor force, employed,

and not in labor force 1.22 .86

Unemployed 1.38 .73

Men:

Civilian labor force, employed,

and not in labor force 1.25 .84

Unemployed 1.37 .73

Women:

Civilian labor force, employed,

and not in labor force 1.27 .84

Unemployed 1.39 .71

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force 1.33 .80

Unemployed 1.37 .68

Hispanic or Latino ethnicity

Total:

Civilian labor force, employed,

and not in labor force 1.20 .86

Unemployed 1.38 .71

Men:

Civilian labor force, employed,

and not in labor force 1.26 .84

Unemployed 1.38 .71

Women:

Civilian labor force, employed,

and not in labor force 1.21 .86

Unemployed 1.38 .71

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force 1.34 .81

Unemployed 1.42 .70

Employment

Educational attainment 1.11 0.87

Marital status, men 1.15 .86

Marital status, women 1.18 .85

Women who maintain families 1.18 .85

Nonagricultural industries:

Total 1.15 .88

Wage and salary workers 1.13 .88

Self-employed workers 1.15 .87

Unpaid family workers 1.26 .81

Full-time workers 1.17 .85

Part-time workers 1.27 .81

Multiple jobholders 1.29 .78

At work

Total and nonagricultural

industries:

Total 1.21 .84

1 to 4 and 5 to 14 hours 1.36 .67

15 to 29 hours 1.33 .73

30 to 34 or 35 to 39 hours 1.34 .67

1 to 34 or 40 hours 1.30 .76

41 to 48 or 49 to 59 hours 1.34 .71

35+, 41+, or 60+ hours 1.25 .78

Part time for economic

reasons 1.37 .67

Part time for noneconomic

reasons 1.29 .74

Unemployment

Educational attainment 1.38 .72

Marital status, men 1.39 .72

Marital status, women 1.39 .71

Women who maintain families 1.39 .71

Industries and occupations 1.38 .72

Full-time workers 1.38 .72

Part-time workers 1.40 .69

Less than 5 weeks 1.38 .72

5 to 14 weeks 1.37 .66

15 to 26 week 1.39 .67

15+ or 27+ weeks 1.42 .75

All reasons for unemployment,

except temporary layoff 1.38 .72

On temporary layoff 1.35 .68

Not in the labor force

Total 1.22 .87

Persons who currently want

a job and discouraged

workers 1.41 .63

Factors

Change in Change in

consecutive Yearly conse-

Characteristic quarterly averages cutive

averages averages

Total or white

Total:

Civilian labor force and

employed 0.77 0.68 0.81

Unemployed .91 .42 .57

Not in labor force .77 .68 .81

Men:

Civilian labor force, employed,

and not in labor force .79 .66 .80

Unemployed .91 .43 .57

Women:

Civilian labor force, employed,

and not in labor force .78 .67 .81

Unemployed .90 .41 .55

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force .87 .55 .71

Unemployed .88 .40 .53

Black or African American

Total:

Civilian labor force, employed,

and not in labor force .78 .66 .80

Unemployed .90 .43 .58

Men:

Civilian labor force, employed,

and not in labor force .82 .62 .76

Unemployed .91 .43 .58

Women:

Civilian labor force, employed,

and not in labor force .80 .64 .78

Unemployed .90 .41 .56

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force .85 .56 .70

Unemployed .86 .41 .52

Hispanic or Latino ethnicity

Total:

Civilian labor force, employed,

and not in labor force .82 .65 .78

Unemployed .90 .42 .56

Men:

Civilian labor force, employed,

and not in labor force .82 .62 .76

Unemployed .90 .41 .55

Women:

Civilian labor force, employed,

and not in labor force .84 .63 .76

Unemployed .89 .41 .55

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force .84 .58 .73

Unemployed .89 .41 .55

Employment

Educational attainment 0.92 0.61 0.74

Marital status, men .93 .59 .72

Marital status, women .94 .57 .72

Women who maintain families .94 .57 .72

Nonagricultural industries:

Total .75 .71 .83

Wage and salary workers .84 .67 .79

Self-employed workers .96 .58 .71

Unpaid family workers .95 .50 .65

Full-time workers .92 .59 .72

Part-time workers .89 .55 .69

Multiple jobholders .91 .50 .64

At work

Total and nonagricultural

industries:

Total .77 .66 .79

1 to 4 and 5 to 14 hours .86 .38 .51

15 to 29 hours .88 .45 .58

30 to 34 or 35 to 39 hours .86 .39 .51

1 to 34 or 40 hours .87 .51 .64

41 to 48 or 49 to 59 hours .86 .45 .57

35+, 41+, or 60+ hours .86 .53 .65

Part time for economic

reasons .87 .39 .52

Part time for noneconomic

reasons .85 .49 .62

Unemployment

Educational attainment .91 .42 .57

Marital status, men .91 .43 .57

Marital status, women .90 .41 .55

Women who maintain families .90 .41 .55

Industries and occupations .91 .42 .57

Full-time workers .91 .42 .57

Part-time workers .88 .40 .53

Less than 5 weeks .91 .42 .57

5 to 14 weeks .88 .35 .50

15 to 26 week .89 .36 .50

15+ or 27+ weeks .93 .44 .60

All reasons for unemployment,

except temporary layoff .91 .42 .57

On temporary layoff .87 .40 .53

Not in the labor force .77 .68 .81

Total

Persons who currently want

a job and discouraged

workers .83 .36 .48

Table 2-A. Summary of methods for computing industry statistics

on employment, hours, and earnings estimates

Aggregate industry

Employment, Basic estimating cell level (supersector and,

hours, and (industry, 6-digit where stratified,

earnings published level industry)

All employees All-employee estimate Sum of all-employee

for previous month estimates for

multiplied by weighted component cells.

ratio of all employees

in current month to all

employees in previous

month, for sample

establishments that

reported for both

months plus net birth/

death model estimate.

Production or All-employee estimate Sum of production or

nonsupervisory for current month nonsupervisory worker

workers, women multiplied by (1) estimates, or estimates

employees weighted ratio of of women employees,

production or for component cells.

nonsupervisory workers

to all employees in

sample establishments

for current month, (2)

estimated weighted ratio

of women employees to

all employees.

Average weekly Production or Average, weighted by

hours nonsupervisory worker production or

hours divided by number nonsupervisory worker

of production or employment, of the

nonsupervisory workers. average weekly hours

for component cells.

Average weekly Production worker Average, weighted by

overtime hours overtime hours divided production worker

by number of production employment, of the

workers. average weekly overtime

hours for component

cells.

Average hourly Total production or non- Average, weighted by

earnings supervisory worker aggregate hours, of the

payroll divided by total average hourly earnings

production or for component cells.

nonsupervisory worker

hours.

Average weekly Product of average Product of average

earnings weekly hours and average weekly hours and

hourly earnings. average hourly

earnings.

Employment,

hours, and Annual average data

earnings

All employees Sum of monthly estimates

divided by 12.

Production or Sum of monthly estimates

nonsupervisory divided by 12.

workers, women

employees

Average weekly Annual total of aggregate

hours hours (production or

nonsupervisory worker

employment multiplied by

average weekly hours)

divided by annual sum of

production worker

employment.

Average weekly Annual total of aggregate

overtime hours overtime hours (production

worker employment

multiplied by average

weekly overtime hours)

divided by annual sum of

production worker

employment.

Average hourly Annual total of aggregate

earnings payrolls (production or

nonsupervisory worker

employment multiplied by

weekly hours and hourly

earnings) divided by

annual aggregate hours.

Average weekly Product of average weekly

earnings hours annual average and

average hourly earnings

annual average.

Table 2-B. Net birth/death estimates for private nonfarm industries,

post-benchmark 2003

(In thousands)

Natural Trade,

re- trans-

Year and month sources Con- Manu- portation,

and struction facturing and

mining utilities

2003:

April -1 13 -15 -4

May 1 35 5 21

June 1 28 5 18

July 0 -8 -29 -19

August 1 16 6 17

September 1 9 3 17

October 1 8 -7 13

November -1 -7 3 17

December 0 -8 1 18

Cumulative Total 3 86 -28 98

Profes-

sional Educa-

Year and month Infor- Financial and tion and

mation activities business health

services services

2003:

April -3 9 61 32

May 4 8 32 6

June 0 6 21 -4

July -4 -11 -22 -20

August 2 8 31 14

September 0 4 15 12

October -1 14 18 26

November 3 7 10 10

December 3 13 9 7

Cumulative Total 4 58 175 83

Total

Leisure monthly

Year and month and Other amount

hos- services con-

pitality tributed

2003:

April 29 7 128

May 72 8 192

June 83 6 164

July 40 -10 -83

August 24 5 124

September -29 1 33

October -27 0 45

November -14 2 30

December 15 4 62

Cumulative Total 193 23 695

Table 2-C. Employment benchmarks and approximate coverage

of BLS employment and payrolls sample, March 2003

Sample coverage

Unemployment

Employment insurance Number of

Industry benchmarks counts establish-

(thousands) (UI) (1) ments (1)

Total 129,148 149,590 381,139

Natural resources and mining 556 1,255 2,371

Construction 6,319 12,631 24,925

Manufacturing 14,654 18,258 25,176

Trade, transportation, and

utilities 24,994 (3) 24,978 (3) 103,163

Information 3,214 2,944 12,861

Financial activities 7,910 7,631 51,127

Professional and business

services 15,700 19,848 37,881

Education and health

services 16,632 16,317 35,305

Leisure and hospitality 11,769 15,205 37,617

Other services 5,383 7,164 16,499

Government 22,017 23,359 34,214

Sample coverage

Employees

Number Percent of

Industry (thousands) (2) employment

benchmarks

Total 41,497 32

Natural resources and mining 163 29

Construction 767 12

Manufacturing 5,014 34

Trade, transportation, and

utilities 6,227 25

Information 895 28

Financial activities 1,823 23

Professional and business

services 3,071 20

Education and health

services 5,448 33

Leisure and hospitality 2,163 18

Other services 394 7

Government 15,533 71

(1) Counts reflect active sample reports. Because not all

establishments report payroll and hours information, hours and

earnings estimates are based on a smaller sample than are the

employment estimates.

(2) Average employment of reported values for 2003.

(3) The Surface Transportation Board provides a complete count of

employment for Class I railroads plus Amtrak. A small sample is

used to estimate hours and earnings data.

Table 2-D. Errors of preliminary employment estimates

Mean percent

revision

Root-mean-square

Industry error of monthly Actual Absolute

level (1)

Total 54,300 0 0

Total private 43,500 0 0

Government 26,800 0 .1

Federal government 13,900 .1 .3

Federal government, except

U.S. Postal Service 12,000 .2 .4

U.S. Postal Service 6,800 -.2 .4

State government 11,600 0 .2

State government

education 11,400 .1 .5

State government, excluding

education 5,300 0 .1

Local government 19,200 0 .1

Local government

education 19,500 0 .2

Local government, excluding

education 8,600 .1 .1

(1) The root-mean-square error is the square root of the mean

squared error. The mean squared error is the square of the difference

between the final and preliminary estimates averaged across a series

of monthly observations.

NOTE: Errors are based on differences from January 1999 through

October 2003.

Table 2-E. Relative standard errors for estimates of employment,

hours, and earnings in selected industries (1)

(Percent)

Relative standard error

Average Average

All hourly weekly

Industry employees earnings hours

Total nonfarm 0.2 (2) (2)

Total private 0.2 0.2 0.2

Goods-producing 0.3 0.3 0.2

Natural resources and mining 2.1 1.6 1.4

Logging 7.1 6.6 3.7

Mining 2.0 1.7 1.4

Oil and gas extraction 4.5 3.8 4.2

Mining, except oil and

gas 2.8 1.8 1.2

Coal mining 4.5 2.6 2.5

Support activities for

mining 3.3 3.5 3.2

Construction 0.8 0.5 0.5

Construction of buildings 1.4 1.1 0.9

Heavy and civil

engineering construction 1.9 1.2 1.0

Specialty trade

contractors 1.0 0.7 0.5

Manufacturing 0.4 0.5 0.3

Durable goods 0.6 0.7 0.5

Wood products 1.7 1.1 1.1

Nonmetallic mineral

products 1.4 1.7 1.3

Primary metals 1.3 1.2 1.4

Fabricated metal products 0.8 0.8 0.7

Machinery 0.9 1.4 0.7

Computer and electronic

products 1.5 3.9 1.1

Computer and peripheral

equipment 5.1 19.7 3.5

Communications equipment 5.9 6.2 3.2

Semiconductors and

electronic components 2.1 2.9 2.1

Electronic instruments 1.3 3.0 1.7

Electrical equipment and

appliances 1.3 1.2 1.8

Transportation equipment 2.7 2.2 1.2

Furniture and related

products 1.8 1.3 1.2

Miscellaneous

manufacturing 1.4 1.2 1.3

Nondurable goods 0.5 0.5 0.5

Food manufacturing 1.3 1.1 1.1

Beverages and tobacco

products 2.7 4.6 3.0

Textile mills 2.5 1.5 2.2

Textile product mills 2.8 1.9 2.0

Apparel 3.3 2.2 1.9

Leather and allied

products 6.1 6.2 2.1

Paper and paper products 1.4 1.3 1.1

Printing and related

support activities 1.2 1.3 1.1

Petroleum and coal

products 3.2 2.7 2.9

Chemicals 1.2 2.1 1.2

Plastics and rubber

products 1.5 1.3 1.0

Private service-providing 0.2 0.3 0.2

Trade, transportation, and

utilities 0.3 0.6 0.3

Wholesale trade 0.7 0.9 0.6

Durable goods 0.9 1.6 0.5

Nondurable goods 0.9 2.0 1.0

Electronic markets and

agents and brokers 2.4 3.6 2.8

Retail trade 0.4 0.8 0.6

Motor vehicle and parts

dealers 0.5 1.6 1.2

Automobile dealers 0.6 1.8 1.7

Furniture and home

furnishings stores 1.8 5.6 2.4

Electronics and appliance

stores 2.8 4.6 5.5

Building material and

garden supply stores 1.1 1.9 1.2

Food and beverage stores 0.9 2.6 1.7

Health and personal care

stores 1.2 3.5 1.9

Gasoline stations 1.5 1.6 1.5

Clothing and clothing

accessories stores 1.5 2.1 2.8

Sporting goods, hobby,

book, and music stores 1.9 2.1 2.0

General merchandise

stores 1.3 1.9 1.2

Department stores 2.0 1.0 1.3

Miscellaneous store

retailers 1.3 1.5 2.0

Nonstore retailers 3.6 2.6 2.5

Transportation and

warehousing 0.7 1.3 1.2

Air transportation 1.1 3.2 2.5

Rail transportation 1.6 (3) (3)

Water transportation 5.8 13.7 5.3

Truck transportation 1.0 2.9 1.2

Transit and ground

passenger transportation 3.2 1.9 2.8

Pipeline transportation 5.2 4.0 6.0

Scenic and sightseeing

transportation 21.4 8.9 19.3

Support activities for

transportation 2.0 2.2 2.3

Couriers and messengers 1.7 4.7 6.7

Warehousing and storage 2.2 3.2 1.3

Utilities 0.9 2.2 1.3

Information 1.0 2.0 1.0

Publishing industries,

except Internet 1.2 5.0 1.3

Motion picture and sound

recording industries 3.8 6.6 6.4

Broadcasting, except

Internet 3.4 2.7 1.6

Internet publishing and

broadcasting 9.2 9.7 9.2

Telecommunications 1.5 2.3 1.2

ISPs, search portals, and

data processing 2.4 3.3 1.7

Other information services 3.2 8.4 7.5

Financial activities 0.5 1.0 0.5

Finance and insurance 0.6 1.2 0.5

Monetary authorities –

central bank 1.5 8.7 3.0

Credit intermediation and

related activities 1.0 2.5 0.7

Depository credit

intermediation 0.8 2.0 0.7

Commercial banking 1.0 2.7 0.9

Securities, commodity

contracts, investments 1.6 4.0 1.7

Insurance carriers and

related activities 0.9 1.8 0.7

Funds, trusts, and other

financial vehicles 3.8 11.5 4.2

Real estate and rental and

leasing 1.0 1.2 1.1

Real estate 1.4 1.4 1.2

Rental and leasing

services 1.9 1.6 1.9

Lessors of nonfinancial

intangible assets 6.6 6.1 3.6

Professional and business

services 0.8 0.8 0.5

Professional and technical

services 0.6 0.9 0.4

Legal services 0.7 1.8 0.7

Accounting and

bookkeeping services 2.3 2.0 1.8

Architectural and

engineering services 1.9 2.1 0.8

Computer systems design

and related services 1.9 2.3 1.3

Management and technical

consulting services 1.9 2.7 1.4

Management of companies and

enterprises 1.7 1.5 0.9

Administrative and waste

services 1.8 1.2 0.8

Administrative and support

services 1.8 1.3 0.9

Employment services 3.8 2.7 1.6

Temporary help

services 4.7 3.0 1.2

Business support

services 1.9 2.0 1.8

Services to buildings

and dwellings 1.3 1.6 1.2

Waste management and

remediation services 2.3 2.3 1.9

Education and health services 0.3 0.7 0.5

Educational services 1.3 1.1 0.7

Health care and social

assistance 0.3 0.8 0.6

Ambulatory health care

services 0.5 1.9 0.6

Offices of physicians 0.6 3.7 0.7

Outpatient care centers 1.4 2.0 1.8

Home health care

services 1.6 2.4 2.6

Hospitals 0.3 2.0 1.3

Nursing and residential

care facilities 0.5 0.6 0.7

Nursing care facilities 0.6 1.0 0.9

Social assistance 0.9 1.0 1.0

Child day care services 1.5 1.6 1.3

Leisure and hospitality 0.4 0.8 0.6

Arts, entertainment, and

recreation 1.6 2.1 1.5

Performing arts and

spectator sports 4.8 5.6 3.4

Museums, historical sites,

zoos, and parks 3.4 2.7 3.2

Amusements, gambling, and

recreation 1.5 1.5 1.7

Accommodations and food

services 0.4 0.7 0.6

Accommodations 1.0 2.0 1.0

Food services and drinking

places 0.4 0.8 0.6

Other services 1.3 1.1 0.9

Repair and maintenance 1.0 1.3 0.9

Personal and laundry

services 0.9 1.7 1.4

Membership associations

and organizations 2.4 1.9 1.9

(1) Estimates of variance are not available for government sectors

due to lack of historical probability-based estimates.

(2) Hours and earnings are not published.

(3) Estimates are not available as a result of confidentiality

standards.

Table 2-F. Standard errors for change in levels estimates of

employment, hours, and earnings in selected industries (1)

Standard error

1-month change

Average Average

All weekly hourly

Industry employees hours earnings

Total nonfarm 67,693 (2) (2)

Total private 64,337 0.03 0.01

Goods-producing 23,015 0.06 0.02

Natural resources and mining 2,793 0.37 0.10

Logging 1,107 1.13 0.24

Mining 2,774 0.38 0.11

Oil and gas extraction 999 1.07 0.18

Mining, except oil and gas 1,076 0.40 0.11

Coal mining 1,056 0.73 0.24

Support activities for mining 1,901 0.89 0.21

Construction 13,325 0.10 0.04

Construction of buildings 6,525 0.22 0.08

Heavy and civil engineering

construction 4,845 0.26 0.09

Specialty trade contractors 12,508 0.13 0.06

Manufacturing 18,967 0.07 0.02

Durable goods 11,818 0.09 0.03

Wood products 2,214 0.27 0.05

Nonmetallic mineral products

manufacturing 2,196 0.30 0.07

Primary metals 1,787 0.29 0.10

Fabricated metal products 3,462 0.18 0.05

Machinery 2,387 0.21 0.07

Computer and electronic products 2,550 0.23 0.11

Computer and peripheral

equipment 1,274 0.39 0.44

Communications equipment 1,089 0.59 0.27

Semiconductor and electronic

components 1,228 0.44 0.14

Electronic instruments 1,224 0.32 0.09

Electrical equipment and

appliances 1,183 0.32 0.06

Transportation equipment 8,687 0.19 0.08

Furniture and related products 2,412 0.28 0.06

Miscellaneous manufacturing 2,197 0.26 0.07

Nondurable goods 11,894 0.12 0.03

Food manufacturing 8,685 0.32 0.06

Beverages and tobacco products 1,540 0.76 0.35

Textile mills 2,154 0.41 0.07

Textile product mills 1,491 0.47 0.10

Apparel 3,214 0.41 0.08

Leather and allied products 389 0.73 0.13

Paper and paper products 1,800 0.28 0.10

Printing and related support

activities 2,323 0.26 0.07

Petroleum and coal products 599 0.91 0.21

Chemicals 2,324 0.28 0.08

Plastics and rubber products 3,013 0.19 0.05

Private service-providing 60,401 0.03 0.01

Trade, transportation, and utilities 24,211 0.05 0.02

Wholesale trade 8,319 0.10 0.07

Durable goods 5,714 0.14 0.10

Nondurable goods 5,636 0.18 0.07

Electronic markets and agents

and brokers 2,923 0.52 0.27

Retail trade 17,620 0.06 0.02

Motor vehicle and parts dealers 3,373 0.17 0.12

Automobile dealers 2,546 0.21 0.17

Furniture and home furnishings

stores 2,671 0.25 0.13

Electronics and appliance stores 3,098 0.36 0.18

Building material and garden

supply stores 3,894 0.23 0.08

Food and beverage stores 5,216 0.11 0.03

Health and personal care stores 3,399 0.18 0.09

Gasoline stations 3,234 0.18 0.04

Clothing and clothing accessories

stores 7,122 0.30 0.09

Sporting goods, hobby, book and

music stores 4,295 0.25 0.07

General merchandise stores 7,386 0.13 0.03

Department stores 7,137 0.13 0.04

Miscellaneous store retailers 4,550 0.25 0.06

Nonstore retailers 4,017 0.33 0.12

Transportation and warehousing 9,845 0.15 0.05

Air transportation 1,705 0.48 0.20

Rail transportation 3,258 (3) (3)

Water transportation 1,447 0.91 0.24

Truck transportation 5,187 0.26 0.09

Transit and ground passenger

transportation 3,885 0.48 0.10

Pipeline transportation 274 0.64 0.32

Scenic and sightseeing

transportation 1,617 1.50 0.43

Support activities for

transportation 3,865 0.38 0.14

Couriers and messengers 2,887 0.35 0.13

Warehousing and storage 2,572 0.37 0.08

Utilities 834 0.19 0.09

Information 6,751 0.12 0.10

Publishing industries, except

Internet 2,075 0.20 0.20

Motion picture and sound recording

industries 4,746 0.52 0.37

Broadcasting, except Internet 1,238 0.28 0.16

Internet publishing and

broadcasting 461 0.74 0.56

Telecommunications 3,046 0.25 0.13

ISPs, search portals, and data

processing 2,199 0.21 0.18

Other information services 431 0.52 0.17

Financial activities 8,938 0.07 0.08

Finance and insurance 7,309 0.08 0.11

Monetary authorities – central

bank 76 0.40 0.27

Credit intermediation and related

activities 5,619 0.14 0.22

Depository credit intermediation 2,675 0.17 0.08

Commercial banking 2,163 0.22 0.09

Securities, commodity contracts,

investments 1,990 0.33 0.24

Insurance carriers and related

activities 4,011 0.09 0.07

Funds, trusts, and other financial

vehicles 529 0.58 0.21

Real estate and rental and leasing 6,596 0.18 0.05

Real estate 5,378 0.19 0.07

Rental and leasing services 3,596 0.37 0.09

Lessors of nonfinancial intangible

assets 595 0.71 0.42

Professional and business services 29,520 0.06 0.04

Professional and technical services 8,935 0.07 0.05

Legal services 2,675 0.13 0.09

Accounting and bookkeeping

services 5,076 0.21 0.10

Architectural and engineering

services 3,341 0.16 0.09

Computer systems design and

related services 4,907 0.21 0.16

Management and technical

consulting services 3,766 0.23 0.15

Management of companies and

enterprises 6,027 0.21 0.12

Administrative and waste services 24,109 0.10 0.05

Administrative and support

services 23,689 0.10 0.05

Employment services 21,047 0.15 0.09

Temporary help services 17,220 0.15 0.09

Business support services 3,715 0.27 0.08

Services to buildings and

dwellings 5,707 0.16 0.05

Waste management and remediation

services 3,239 0.39 0.14

Education and health services 16,025 0.06 0.02

Educational services 12,697 0.14 0.06

Health care and social assistance 10,135 0.06 0.02

Ambulatory health care services 6,600 0.11 0.07

Offices of physicians 3,720 0.18 0.13

Outpatient care centers 1,607 0.21 0.09

Home health care services 4,633 0.23 0.09

Hospitals 1,786 0.17 0.07

Nursing and residential care

facilities 3,619 0.09 0.02

Nursing care facilities 3,037 0.13 0.04

Social assistance 6,125 0.12 0.03

Child day care services 4,360 0.28 0.05

Leisure and hospitality 18,446 0.06 0.02

Arts, entertainment, and recreation 10,429 0.24 0.08

Performing arts and spectator

sports 6,297 0.54 0.27

Museums, historical sites, zoos,

and parks 1,300 0.36 0.11

Amusements, gambling, and

recreation 7,284 0.23 0.05

Accommodation and food services 15,850 0.06 0.02

Accommodation 7,122 0.16 0.04

Food services and drinking places 13,401 0.06 0.02

Other Services 20,325 0.12 0.05

Repair and maintenance 4,171 0.18 0.06

Personal and laundry services 3,810 0.21 0.07

Membership associations and

organizations 19,388 0.21 0.08

Standard error

3-month change

Average Average

All weekly hourly

Industry employees hours earnings

Total nonfarm 124,081 (2) (2)

Total private 117,694 0.03 0.02

Goods-producing 40,842 0.06 0.03

Natural resources and mining 4,158 0.52 0.16

Logging 1,711 1.24 0.63

Mining 4,017 0.53 0.16

Oil and gas extraction 1,718 0.76 0.29

Mining, except oil and gas 2,197 0.44 0.22

Coal mining 1,326 0.93 0.32

Support activities for mining 2,115 1.11 0.30

Construction 22,572 0.12 0.06

Construction of buildings 11,922 0.27 0.12

Heavy and civil engineering

construction 8,923 0.39 0.13

Specialty trade contractors 18,147 0.14 0.06

Manufacturing 30,570 0.08 0.03

Durable goods 18,981 0.10 0.04

Wood products 3,885 0.36 0.08

Nonmetallic mineral products

manufacturing 3,374 0.38 0.09

Primary metals 2,620 0.38 0.12

Fabricated metal products 6,375 0.23 0.06

Machinery 4,475 0.24 0.08

Computer and electronic products 6,637 0.33 0.14

Computer and peripheral

equipment 5,012 1.09 0.69

Communications equipment 2,125 0.86 0.34

Semiconductor and electronic

components 2,427 0.52 0.24

Electronic instruments 2,032 0.35 0.18

Electrical equipment and

appliances 2,888 0.49 0.08

Transportation equipment 17,628 0.35 0.16

Furniture and related products 3,870 0.36 0.08

Miscellaneous manufacturing 3,861 0.32 0.10

Nondurable goods 23,085 0.15 0.04

Food manufacturing 19,376 0.36 0.07

Beverages and tobacco products 2,542 0.83 0.49

Textile mills 4,891 0.44 0.10

Textile product mills 4,050 0.82 0.17

Apparel 5,443 0.50 0.10

Leather and allied products 526 1.00 0.20

Paper and paper products 3,393 0.37 0.12

Printing and related support

activities 3,354 0.25 0.10

Petroleum and coal products 1,614 1.33 0.34

Chemicals 3,730 0.36 0.11

Plastics and rubber products 4,386 0.29 0.07

Private service-providing 99,190 0.04 0.02

Trade, transportation, and utilities 35,620 0.07 0.03

Wholesale trade 14,046 0.14 0.10

Durable goods 8,797 0.17 0.15

Nondurable goods 8,767 0.26 0.11

Electronic markets and agents

and brokers 5,032 0.53 0.28

Retail trade 26,978 0.08 0.03

Motor vehicle and parts dealers 4,852 0.21 0.14

Automobile dealers 3,498 0.27 0.20

Furniture and home furnishings

stores 3,540 0.36 0.23

Electronics and appliance stores 5,940 0.45 0.20

Building material and garden

supply stores 6,109 0.25 0.11

Food and beverage stores 10,513 0.18 0.06

Health and personal care stores 4,435 0.28 0.10

Gasoline stations 4,880 0.28 0.05

Clothing and clothing accessories

stores 10,932 0.39 0.10

Sporting goods, hobby, book and

music stores 5,986 0.31 0.11

General merchandise stores 15,876 0.16 0.06

Department stores 15,310 0.22 0.05

Miscellaneous store retailers 7,345 0.31 0.10

Nonstore retailers 4,979 0.47 0.16

Transportation and warehousing 13,112 0.19 0.07

Air transportation 3,896 0.60 0.24

Rail transportation 1,629 (3) (3)

Water transportation 2,376 1.10 0.70

Truck transportation 7,115 0.37 0.11

Transit and ground passenger

transportation 7,049 0.77 0.16

Pipeline transportation 358 1.03 0.50

Scenic and sightseeing

transportation 3,059 2.96 0.89

Support activities for

transportation 4,971 0.49 0.18

Couriers and messengers 5,573 0.50 0.22

Warehousing and storage 4,380 0.43 0.22

Utilities 1,579 0.23 0.18

Information 9,977 0.18 0.18

Publishing industries, except

Internet 3,753 0.29 0.26

Motion picture and sound recording

industries 6,820 0.64 0.76

Broadcasting, except Internet 1,921 0.32 0.27

Internet publishing and

broadcasting 687 1.16 0.84

Telecommunications 5,323 0.36 0.20

ISPs, search portals, and data

processing 4,503 0.38 0.39

Other information services 702 0.63 0.29

Financial activities 14,823 0.11 0.11

Finance and insurance 13,196 0.11 0.15

Monetary authorities – central

bank 153 0.76 0.30

Credit intermediation and related

activities 9,322 0.17 0.29

Depository credit intermediation 4,598 0.17 0.10

Commercial banking 4,072 0.23 0.10

Securities, commodity contracts,

investments 3,655 0.45 0.33

Insurance carriers and related

activities 5,792 0.12 0.10

Funds, trusts, and other financial

vehicles 937 0.77 0.51

Real estate and rental and leasing 9,506 0.26 0.08

Real estate 6,743 0.24 0.09

Rental and leasing services 7,294 0.39 0.17

Lessors of nonfinancial intangible

assets 780 1.03 0.56

Professional and business services 45,241 0.09 0.06

Professional and technical services 16,561 0.09 0.08

Legal services 4,042 0.15 0.13

Accounting and bookkeeping

services 6,068 0.28 0.12

Architectural and engineering

services 5,947 0.20 0.17

Computer systems design and

related services 8,545 0.25 0.25

Management and technical

consulting services 5,497 0.26 0.20

Management of companies and

enterprises 11,793 0.28 0.21

Administrative and waste services 40,127 0.15 0.06

Administrative and support

services 40,472 0.15 0.07

Employment services 38,002 0.25 0.13

Temporary help services 32,505 0.23 0.14

Business support services 6,870 0.42 0.12

Services to buildings and

dwellings 10,845 0.20 0.07

Waste management and remediation

services 4,256 0.53 0.19

Education and health services 30,076 0.09 0.03

Educational services 25,836 0.62 0.13

Health care and social assistance 17,161 0.07 0.03

Ambulatory health care services 10,765 0.12 0.07

Offices of physicians 5,039 0.15 0.11

Outpatient care centers 2,494 0.36 0.16

Home health care services 6,033 0.34 0.17

Hospitals 3,015 0.18 0.08

Nursing and residential care

facilities 4,923 0.11 0.03

Nursing care facilities 3,719 0.13 0.05

Social assistance 9,522 0.19 0.05

Child day care services 7,031 0.32 0.06

Leisure and hospitality 30,172 0.09 0.03

Arts, entertainment, and recreation 20,559 0.28 0.12

Performing arts and spectator

sports 9,494 0.78 0.39

Museums, historical sites, zoos,

and parks 1,587 0.52 0.17

Amusements, gambling, and

recreation 17,219 0.31 0.12

Accommodation and food services 24,380 0.09 0.04

Accommodation 15,008 0.20 0.08

Food services and drinking places 19,672 0.09 0.03

Other Services 48,672 0.21 0.07

Repair and maintenance 5,963 0.21 0.09

Personal and laundry services 5,749 0.27 0.09

Membership associations and

organizations 47,774 0.36 0.12

Standard error

12-month change

Average Average

All weekly hourly

Industry employees hours earnings

Total nonfarm 181,069 (2) (2)

Total private 164,702 0.06 0.03

Goods-producing 68,921 0.09 0.04

Natural resources and mining 9,180 0.66 0.24

Logging 4,115 1.76 0.95

Mining 8,366 0.76 0.24

Oil and gas extraction 4,668 1.75 0.37

Mining, except oil and gas 4,067 0.63 0.28

Coal mining 2,819 1.21 0.32

Support activities for mining 4,753 1.61 0.55

Construction 48,889 0.17 0.10

Construction of buildings 20,669 0.43 0.20

Heavy and civil engineering

construction 13,238 0.40 0.18

Specialty trade contractors 42,109 0.21 0.13

Manufacturing 49,559 0.10 0.05

Durable goods 47,004 0.13 0.08

Wood products 7,262 0.53 0.12

Nonmetallic mineral products

manufacturing 6,389 0.60 0.20

Primary metals 4,817 0.67 0.17

Fabricated metal products 9,327 0.27 0.11

Machinery 9,300 0.31 0.16

Computer and electronic products 13,464 0.57 0.44

Computer and peripheral

equipment 7,062 1.55 2.84

Communications equipment 6,929 1.39 0.88

Semiconductor and electronic

components 6,774 1.12 0.52

Electronic instruments 4,960 0.54 0.46

Electrical equipment and

appliances 5,392 0.65 0.12

Transportation equipment 44,030 0.38 0.28

Furniture and related products 6,931 0.45 0.14

Miscellaneous manufacturing 7,487 0.44 0.12

Nondurable goods 22,055 0.18 0.06

Food manufacturing 16,218 0.42 0.11

Beverages and tobacco products 3,908 1.23 0.66

Textile mills 7,773 0.48 0.14

Textile product mills 4,350 1.15 0.31

Apparel 9,778 0.70 0.17

Leather and allied products 2,329 1.05 0.42

Paper and paper products 5,474 0.47 0.18

Printing and related support

activities 7,622 0.41 0.16

Petroleum and coal products 2,682 1.35 0.58

Chemicals 9,038 0.50 0.30

Plastics and rubber products 9,300 0.40 0.14

Private service-providing 151,855 0.06 0.04

Trade, transportation, and utilities 58,617 0.10 0.05

Wholesale trade 27,391 0.23 0.17

Durable goods 19,265 0.25 0.27

Nondurable goods 14,822 0.38 0.25

Electronic markets and agents

and brokers 11,162 0.89 0.66

Retail trade 43,070 0.13 0.06

Motor vehicle and parts dealers 7,792 0.33 0.21

Automobile dealers 5,999 0.44 0.26

Furniture and home furnishings

stores 9,407 0.65 0.63

Electronics and appliance stores 12,934 1.04 0.46

Building material and garden

supply stores 10,665 0.41 0.18

Food and beverage stores 16,596 0.29 0.15

Health and personal care stores 9,972 0.55 0.41

Gasoline stations 11,347 0.37 0.09

Clothing and clothing accessories

stores 15,486 0.55 0.18

Sporting goods, hobby, book and

music stores 9,750 0.63 0.19

General merchandise stores 29,563 0.21 0.13

Department stores 29,381 0.21 0.06

Miscellaneous store retailers 10,368 0.47 0.17

Nonstore retailers 10,542 0.92 0.38

Transportation and warehousing 21,672 0.33 0.14

Air transportation 5,593 1.10 0.40

Rail transportation 3,258 (3) (3)

Water transportation 3,078 2.59 1.95

Truck transportation 11,982 0.55 0.27

Transit and ground passenger

transportation 8,729 1.04 0.27

Pipeline transportation 1,095 2.08 0.63

Scenic and sightseeing

transportation 4,302 4.04 1.33

Support activities for

transportation 10,082 0.77 0.33

Couriers and messengers 8,702 1.15 0.42

Warehousing and storage 6,259 0.65 0.25

Utilities 4,635 0.40 0.33

Information 29,853 0.29 0.47

Publishing industries, except

Internet 8,406 0.55 0.96

Motion picture and sound recording

industries 15,513 1.26 2.25

Broadcasting, except Internet 9,125 0.55 0.43

Internet publishing and

broadcasting 2,076 1.56 1.67

Telecommunications 11,416 0.48 0.36

ISPs, search portals, and data

processing 8,107 0.84 0.79

Other information services 850 1.22 1.04

Financial activities 29,085 0.20 0.15

Finance and insurance 25,984 0.21 0.20

Monetary authorities – central

bank 292 1.40 0.77

Credit intermediation and related

activities 21,316 0.34 0.29

Depository credit intermediation 10,997 0.35 0.22

Commercial banking 11,073 0.45 0.29

Securities, commodity contracts,

investments 12,213 0.54 0.73

Insurance carriers and related

activities 16,263 0.28 0.29

Funds, trusts, and other financial

vehicles 1,896 1.42 0.98

Real estate and rental and leasing 13,527 0.37 0.14

Real estate 12,008 0.45 0.18

Rental and leasing services 9,502 0.64 0.22

Lessors of nonfinancial intangible

assets 1,257 1.22 1.60

Professional and business services 115,818 0.18 0.12

Professional and technical services 38,689 0.15 0.16

Legal services 5,703 0.22 0.28

Accounting and bookkeeping

services 14,323 0.65 0.21

Architectural and engineering

services 18,932 0.26 0.41

Computer systems design and

related services 18,017 0.54 0.64

Management and technical

consulting services 10,196 0.58 0.54

Management of companies and

enterprises 21,217 0.34 0.35

Administrative and waste services 119,604 0.31 0.14

Administrative and support

services 119,733 0.33 0.15

Employment services 113,009 0.59 0.29

Temporary help services 102,784 0.51 0.26

Business support services 9,797 0.62 0.20

Services to buildings and

dwellings 15,444 0.37 0.14

Waste management and remediation

services 5,611 0.78 0.36

Education and health services 32,719 0.13 0.07

Educational services 22,680 0.20 0.16

Health care and social assistance 25,072 0.14 0.08

Ambulatory health care services 16,485 0.19 0.20

Offices of physicians 10,059 0.25 0.28

Outpatient care centers 4,518 0.51 0.34

Home health care services 11,580 0.58 0.36

Hospitals 7,908 0.39 0.29

Nursing and residential care

facilities 10,956 0.20 0.06

Nursing care facilities 8,353 0.25 0.09

Social assistance 13,127 0.27 0.09

Child day care services 7,313 0.49 0.12

Leisure and hospitality 36,432 0.13 0.06

Arts, entertainment, and recreation 18,213 0.33 0.24

Performing arts and spectator

sports 11,336 1.14 0.94

Museums, historical sites, zoos,

and parks 2,429 1.04 0.29

Amusements, gambling, and

recreation 12,200 0.32 0.12

Accommodation and food services 33,760 0.14 0.05

Accommodation 11,401 0.31 0.15

Food services and drinking places 27,125 0.16 0.06

Other Services 63,857 0.35 0.16

Repair and maintenance 10,157 0.34 0.16

Personal and laundry services 9,950 0.37 0.15

Membership associations and

organizations 60,211 0.59 0.31

(1) Estimates of variance are not available for government sectors

due to lack of historical probability-based estimates.

(2) Hours and earnings estimates are not published.

(3) Estimates are not available as a result of confidentiality

standards.

Chart 1. Distribution of CES sample by

collection mode

FAX 12%

Mail 9%

CATI 23%

TDE 27%

EDI 29%

Note: Table made from pie chart.

COPYRIGHT 2004 U.S. Department of Labor

COPYRIGHT 2004 Gale Group