Categories
Employment and Earnings

Seasonal adjustment

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. In evaluating changes in a seasonally adjusted series, it is important to note that seasonal adjustment is merely an approximation based on past experience. Seasonally adjusted estimates have a broader margin of possible error than the original data on which they are based, because they are subject not only to sampling and other errors but are also affected by the uncertainties of the seasonal adjustment process itself. 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. This program replaced the X-11 ARIMA program which had been used since January 1980. For a detailed description of the X-12-ARIMA program and its features, see D.E 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.

At the beginning of each calendar year, projected seasonal adjustment factors are calculated for use during the January-June period. In July of each year, BLS calculates and publishes in Employment and Earnings projected seasonal adjustment factors for use in the second half, based on the experience through June. 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 redesign and 1990 census-based population controls, adjusted for the estimated undercount, introduced 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.

In each January issue (March issue in 1996 and February issue in 2003), Employment and Earnings publishes revised seasonally adjusted data for selected labor force series based on the experience through December, new seasonal adjustment factors to be used to calculate the civilian unemployment estimate for the first 6 months of the following year, 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 May 2003 and a detailed description of the current seasonal adjustment procedure appear in the June 2003 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. For all employees, the series are transportation equipment, transit and ground passenger transportation, social assistance, and membership associations and organizations. The series for women workers, production or nonsupervisory workers, average weekly hours, average weekly overtime hours, and average hourly earnings also are adjusted with X-12 ARIMA including interval-effect modeling. As with the all-employee data, there are a few series which could not successfully be fitted to ARIMA/REGARIMA models and these do not include the interval-effect adjustment. These series are transportation equipment for women workers; wholesale trade, retail trade, transportation and warehousing, information, financial activities, professional and business services, education and health services, leisure and hospitality, and other services for average weekly hours; and wholesale trade, financial activities, professional and business services, and other services for average hourly earnings. All production or nonsupervisory worker and average overtime hours series have been successfully fitted to the models and include the interval-effect adjustment.

Construction series. Beginning with the 1996 benchmark revision, BLS instituted a special treatment in seasonally adjusting the construction industry series. In the application of the interval-effect modeling process to the construction series, there initially was difficulty in accurately identifying and measuring the effect because of the strong influence of variable weather patterns on employment movements in the industry. Further research allowed BLS to incorporate interval-effect modeling for the construction industry by disaggregating the construction series into its finer industry and geographic estimating cells and tightening outlier designation parameters. This allowed a more precise identification of weather-related outliers that had masked the interval effect and clouded the seasonal adjustment patterns in general. With these outliers removed, interval-effect modeling became feasible. The result is a seasonally adjusted series for construction that is improved because it is controlled for two potential distortions, unusual weather events and the 4- versus 5-week effect.

Floating holidays. BLS makes special adjustments for average weekly hours and average weekly overtime series to account for the presence or absence of religious holidays in the April survey reference period and the occurrence of Labor Day in the September reference period back to 1939, or when the series begins.

Local government series. A special adjustment also is made in November each year for poll workers in the local government, excluding education series; this adjustment is incorporated as part of the X-12 modeling process from 1988 forward. An X-11 ARIMA-based procedure is used for earlier years.

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). Much of the previously unexplained volatility in these series is attributable to this calendar effect. 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.

REGARIMA modeling is used to identify, measure, and remove the length-of-pay-period effect for seasonally adjusted average weekly hours and average hourly earnings series. The length-of-pay-period variable proves significant for explaining AWH movements in all the service-providing industries, except retail trade; these series have been adjusted from January 1990 forward. For AHE, the length-of-pay-period variable is significant for wholesale trade, financial activities, professional and business services, and other services; these series have been adjusted from January 1990 forward, as well. For this reason, calculations of over-the-year change in the establishment hours and earnings series should use seasonally adjusted data.

The series to which the length-of-pay-period adjustment is applied are not subject to the 4- versus 5-week adjustment, because the modeling cannot support the number of variables that would be required in the regression equation to make both adjustments. Because the 4- versus 5-week model shows only marginal significance in the service-providing industries, its replacement with the length-of-pay-period adjustment in those industries, with the exception of retail trade, is a viable trade-off. The 4- versus 5-week adjustment is most significant in manufacturing hours and earnings series; it will continue to be applied there and in other divisions not affected by the length-of-pay-period variable.

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. Using the X-Il ARIMA procedure, 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 12,000

(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 State

Children’s Health Insurance Program.

Table 1-B. Approximate standard errors for major employment

status categories

(In thousands)

Consecutive

Monthly month-to-

Characteristic level month change

Total

Total, 16 years and over:

Civilian labor force 267 174

Employed 273 177

Unemployed 131 166

Men, 20 years and over:

Civilian labor force 184 120

Employed 196 128

Unemployed 83 106

Women, 20 years and over:

Civilian labor force 209 136

Employed 215 140

Unemployed 77 98

Both sexes, 16 to 19 years:

Civilian labor force 90 87

Employed 95 91

Unemployed 56 93

Black or African American

Total, 16 years and over:

Civilian labor force 113 73

Employed 121 79

Unemployed 64 81

Men, 20 years and over:

Civilian labor force 81 53

Employed 85 55

Unemployed 39 50

Women, 20 years and over:

Civilian labor force 72 47

Employed 77 50

Unemployed 40 50

Both sexes, 16 to 19 years:

Civilian labor force 42 40

Employed 39 38

Unemployed 28 46

Hispanic or Latino ethnicity

Total, 16 years and over:

Civilian labor force 90 59

Employed 100 65

Unemployed 54 69

Table 1-C. Approximate standard errors for unemployment rates

by major characteristics

(In percent)

Consecutive

Monthly month-to-

Characteristic rate month change

Total 0.09 0.12

Men .12 .16

Men, 20 years and over .12 .15

Women .13 .17

Women, 20 years and over .13 .16

Both sexes, 16 to 19 years .66 1.08

White .10 .12

Black or Africian American .39 .49

Hispanic or Latino ethnicity .37 .47

Married men, spouse present .12 .15

Married women, spouse present .14 .18

Women who maintain families .43 .54

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

standard errors for estimates of monthly levels

Factors

Consecu-

tive

Parameters month-to-

month

Characteristic a b change

Total or white

Total:

Civilian labor force, employed,

and not in labor force -0.0000077 1586.29 0.65

Unemployed – .0000174 3005.06 1.27

Men:

Civilian labor force, employed,

and not in labor force – .0000348 2927.43 .65

Unemployed – .0000348 2927.43 1.27

Women:

Civilian labor force, employed,

and not in labor force – .0000325 2693.27 .65

Unemployed – .0000325 2693.27 1.27

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force – .0002436 3005.06 .96

Unemployed – .0002436 3005.06 1.65

Black or Africian American

Total:

Civilian labor force, employed,

and not in labor force – .0001541 3295.99 .65

Unemployed – .0001541 3295.99 1.28

Men:

Civilian labor force, employed,

and not in labor force – .0003361 3332.28 .65

Unemployed – .0003361 3332.28 1.27

Women:

Civilian labor force, employed,

and not in labor force – .0002821 2944.26 .65

Unemployed – .0002821 2944.26 1.27

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force – .0015306 3295.99 .96

Unemployed – .0015306 3295.99 1.65

Hispanic or Latino ethnicity

Total:

Civilian labor force, employed,

and not in labor force – .0001260 3295.99 .65

Unemployed – .0001260 3295.99 1.28

Men:

Civilian labor force, employed,

and not in labor force – .0002570 3332.28 .65

Unemployed – .0002570 3332.28 1.29

Women:

Civilian labor force, employed,

and not in labor force – .0002140 2944.26 .65

Unemployed – .0002140 2944.26 1.27

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force – .0014250 3295.99 .96

Unemployed – .0014250 3295.99 1.65

Employment

Educational attainment -0.0000174 3005.06 0.65

Marital status, men – .0000348 2927.43 .65

Marital status, women – .0000325 2693.27 .65

Women who maintain families – .0000325 2693.27 .65

Nonagricultural industries:

Total – .0000174 3005.06 .65

Wage and salary workers – .0000174 3005.06 .65

Self-employed workers – .0000174 3005.06 .65

Unpaid family workers – .0000174 3005.06 .65

Full-time workers – .0000174 3005.06 .65

Part-time workers – .0000174 3005.06 .65

Multiple jobholders – .0000174 3005.06 1.27

At work

Total and nonagricultural

industries:

Total – .0000174 3005.06 .65

1 to 4 and 5 to 14 hours – .0000174 3005.06 1.65

15 to 29 hours – .0000174 3005.06 1.27

30 to 34 or 35 to 39 hours – .0000174 3005.06 1.65

1 to 34 or 40 hours – .0000174 3005.06 1.27

41 to 48 or 49 to 59 hours – .0000174 3005.06 1.65

35+, 41+, or 60+ hours – .0000174 3005.06 1.27

Part time for economic reasons – .0000174 3005.06 1.47

Part time for noneconomic

reasons – .0000174 3005.06 1.27

Unemployment

Educational attainment – .0000174 3005.06 1.27

Marital status, men – .0000348 2927.43 1.27

Marital status, women – .0000325 2693.27 1.27

Women who maintain families – .0000325 2693.27 1.27

Industries and occupations – .0000174 3005.06 1.27

Full-time workers – .0000174 3005.06 1.27

Part-time workers – .0000174 3005.06 1.65

Less than 5 weeks – .0000174 3005.06 1.27

5 to 14 weeks – .0000174 3005.06 1.65

15 to 26 weeks – .0000174 3005.06 1.65

15+ or 27+ weeks – .0000174 3005.06 1.27

All reasons for unemployment,

except temporary layoff – .0000174 3005.06 1.27

On temporary layoff – .0000174 3005.06 1.65

Not in the labor force

Total – .0000077 1586.29 .65

Persons who currently want

a job and discouraged

workers – .0000174 3005.06 1.65

Factors

Year-

to-year

change Change

of in conse-

monthly cutive

esti- Quarterly quarterly

Characteristic mates averages averages

Total or white

Total:

Civilian labor force, employed,

and not in labor force 1.22 0.87 0.77

Unemployed 1.38 .72 .91

Men:

Civilian labor force, employed,

and not in labor force 1.23 .86 .79

Unemployed 1.39 .72 .91

Women:

Civilian labor force, employed,

and not in labor force 1.22 .87 .78

Unemployed 1.39 .71 .90

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force 1.32 .81 .87

Unemployed 1.37 .68 .88

Black or Africian American

Total:

Civilian labor force, employed,

and not in labor force 1.22 .86 .78

Unemployed 1.38 .73 .90

Men:

Civilian labor force, employed,

and not in labor force 1.25 .84 .82

Unemployed 1.37 .73 .91

Women:

Civilian labor force, employed,

and not in labor force 1.27 .84 .80

Unemployed 1.39 .71 .90

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force 1.33 .80 .85

Unemployed 1.37 .68 .86

Hispanic or Latino ethnicity

Total:

Civilian labor force, employed,

and not in labor force 1.20 .86 .82

Unemployed 1.38 .71 .90

Men:

Civilian labor force, employed,

and not in labor force 1.26 .84 .82

Unemployed 1.38 .71 .90

Women:

Civilian labor force, employed,

and not in labor force 1.21 .86 .84

Unemployed 1.38 .71 .89

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force 1.34 .81 .84

Unemployed 1.42 .70 .89

Employment

Educational attainment 1.11 0.87 0.92

Marital status, men 1.15 .86 .93

Marital status, women 1.18 .85 .94

Women who maintain families 1.18 .85 .94

Nonagricultural industries:

Total 1.15 .88 .75

Wage and salary workers 1.13 .88 .84

Self-employed workers 1.15 .87 .96

Unpaid family workers 1.26 .81 .95

Full-time workers 1.17 .85 .92

Part-time workers 1.27 .81 .89

Multiple jobholders 1.29 .78 .91

At work

Total and nonagricultural

industries:

Total 1.21 .84 .77

1 to 4 and 5 to 14 hours 1.36 .67 .86

15 to 29 hours 1.33 .73 .88

30 to 34 or 35 to 39 hours 1.34 .67 .86

1 to 34 or 40 hours 1.30 .76 .87

41 to 48 or 49 to 59 hours 1.34 .71 .86

35+, 41+, or 60+ hours 1.25 .78 .86

Part time for economic reasons 1.37 .67 .87

Part time for noneconomic

reasons 1.29 .74 .85

Unemployment

Educational attainment 1.38 .72 .91

Marital status, men 1.39 .72 .91

Marital status, women 1.39 .71 .90

Women who maintain families 1.39 .71 .90

Industries and occupations 1.38 .72 .91

Full-time workers 1.38 .72 .91

Part-time workers 1.40 .69 .88

Less than 5 weeks 1.38 .72 .91

5 to 14 weeks 1.37 .66 .88

15 to 26 weeks 1.39 .67 .89

15+ or 27+ weeks 1.42 .75 .93

All reasons for unemployment,

except temporary layoff 1.38 .72 .91

On temporary layoff 1.35 .68 .87

Not in the labor force

Total 1.22 .87 .77

Persons who currently want

a job and discouraged

workers 1.41 .63 .83

Factors

Change in

consecutive

Yearly yearly

Characteristic averages averages

Total or white

Total:

Civilian labor force, employed,

and not in labor force 0.68 0.81

Unemployed .42 .57

Men:

Civilian labor force, employed,

and not in labor force .66 .80

Unemployed .43 .57

Women:

Civilian labor force, employed,

and not in labor force .67 .81

Unemployed .41 .55

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force .55 .71

Unemployed .40 .53

Black or Africian American

Total:

Civilian labor force, employed,

and not in labor force .66 .80

Unemployed .43 .58

Men:

Civilian labor force, employed,

and not in labor force .62 .76

Unemployed .43 .58

Women:

Civilian labor force, employed,

and not in labor force .64 .78

Unemployed .41 .56

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force .56 .70

Unemployed .41 .52

Hispanic or Latino ethnicity

Total:

Civilian labor force, employed,

and not in labor force .65 .78

Unemployed .42 .56

Men:

Civilian labor force, employed,

and not in labor force .62 .76

Unemployed .41 .55

Women:

Civilian labor force, employed,

and not in labor force .63 .76

Unemployed .41 .55

Both sexes, 16 to 19 years:

Civilian labor force, employed,

and not in labor force .58 .73

Unemployed .41 .55

Employment

Educational attainment 0.61 0.74

Marital status, men .59 .72

Marital status, women .57 .72

Women who maintain families .57 .72

Nonagricultural industries:

Total .71 .83

Wage and salary workers .67 .79

Self-employed workers .58 .71

Unpaid family workers .50 .65

Full-time workers .59 .72

Part-time workers .55 .69

Multiple jobholders .50 .64

At work

Total and nonagricultural

industries:

Total .66 .79

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

15 to 29 hours .45 .58

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

1 to 34 or 40 hours .51 .64

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

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

Part time for economic reasons .39 .52

Part time for noneconomic

reasons .49 .62

Unemployment

Educational attainment .42 .57

Marital status, men .43 .57

Marital status, women .41 .55

Women who maintain families .41 .55

Industries and occupations .42 .57

Full-time workers .42 .57

Part-time workers .40 .53

Less than 5 weeks .42 .57

5 to 14 weeks .35 .50

15 to 26 weeks .36 .50

15+ or 27+ weeks .44 .60

All reasons for unemployment,

except temporary layoff .42 .57

On temporary layoff .40 .53

Not in the labor force

Total .68 .81

Persons who currently want

a job and discouraged

workers .36 .48

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

employment, hours, and earnings estimates

Employment, Basic estimating cell

hours,and (industry, 6-digit

earnings published level)

All employees All-employee estimate for pre-

vious month multiplied by

weighted ratio of all employees in

current month to all employees in

previous month, for sample es-

tablishments that reported for both

months plus net birth/death model

estimate.

Production or nonsu- All-employee estimate for current

pervisory workers, month multiplied by (1) weighted

women employees ratio of production or non-

supervisory workers to all

employees in sample estab-

lishments for current month, (2)

estimated weighted ratio of

women employees to all employ-

ees.

Average weekly hours Production or nonsupervisory

worker hours divided by number

of production or nonsupervisory

workers.

Average weekly overtime Production worker overtime

hours hours divided by number of

production workers.

Average hourly earnings Total production or non-

supervisory worker payroll

divided by total production or

nonsupervisory worker hours.

Average weekly earnings Product of average weekly hours

and average hourly earnings.

Employment, Aggregate industry level

hours,and (supersector and, where

earnings stratified, industry)

All employees Sum of all-employee estimates

for component cells.

Production or nonsu- Sum of production or nonsuper-

pervisory workers, visory worker estimates, or esti-

women employees mates of women employees, for

component cells.

Average weekly hours Average, weighted by production

or nonsupervisory worker em-

ployment, of the average weekly

hours for component cells.

Average weekly overtime Average, weighted by production

hours worker employment, of the

average weekly overtime hours

for component cells.

Average hourly earnings Average, weighted by aggregate

hours, of the average hourly

earnings for component cells.

Average weekly earnings Product of average weekly hours

and average hourly earnings.

Employment,

hours,and Annual average data

earnings

All employees Sum of monthly estimates divided

by 12.

Production or nonsu- Sum of monthly estimates divided

pervisory workers, by 12.

women employees

Average weekly hours Annual total of aggregate hours

(production or nonsupervisory

worker employment multiplied by

average weekly hours) divided

by annual sum of production

worker employment.

Average weekly overtime Annual total of aggregate over-

hours time hours (production worker

employment multiplied by aver-

age weekly overtime hours)

divided by annual sum of produc-

tion worker employment.

Average hourly earnings Annual total of aggregate payrolls

(production or nonsupervisory

worker employment multiplied by

weekly hours and hourly earn-

ings) divided by annual aggre-

gate hours.

Average weekly earnings Product of average weekly hours

annual average and average

hourly earnings annual average.

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

post-benchmark 2002

(In thousands)

Natural Trade,

re- trans-

Year and month sources Con- Manu- portation,

and struction facturing and

mining utilities

2002:

April -1 22 -2 -31

May 1 37 6 21

June 1 29 5 20

July 0 -6 -22 -24

August 0 15 6 21

September 0 11 3 18

October 1 9 -3 30

November 0 -7 3 24

December -1 -9 3 23

2003:

January -4 -77 -29 -95

February 0 11 6 6

March 0 29 8 25

Cumulative total -3 64 -16 38

Profes-

sional Educa-

Year and month Infor- Financial and tion and

mation activities business health

services services

2002:

April 0 0 20 6

May 4 6 23 5

June 2 5 17 -6

July -1 -5 -16 -13

August 4 6 22 10

September 1 3 8 13

October 5 11 19 29

November 4 5 7 8

December 2 12 6 6

2003:

January -3 -25 -107 -7

February 6 10 33 14

March 2 7 31 6

Cumulative total 26 35 63 71

Total

Leisure monthly

Year and month and Other amount

hos- services con-

pitality tributed

2002:

April 29 1 44

May 67 6 176

June 78 5 156

July 37 -11 -61

August 18 4 106

September -36 2 23

October -34 1 68

November -20 1 25

December 8 3 53

2003:

January -32 -12 -391

February 28 5 119

March 37 6 151

Cumulative total 180 11 469

Table 2-C. Employment benchmarks and approximate coverage of BLS

employment and payrolls sample, March 2002

Sample coverage

Employment Unemployment

Industry benchmarks insurance

(thousands) counts

(UI) (1)

Total 129,672 126,923

Natural resources and mining 574 1,088

Construction 6,416 9,730

Manufacturing 15,375 16,885

Trade, transportation, and utilities 25,219 319,476

Information 3,448 2,286

Financial activities 7,793 6,120

Professional and business

services 15,845 16,690

Education and health services 16,197 13,334

Leisure and hospitality 11,622 13,153

Other services 5,347 6,681

Government 21,836 21,480

Sample coverage

Employees

Industry Number of

establishments (1) Number

(thousands) (2)

Total 328,016 37,879

Natural resources and mining 2,503 151

Construction 11,627 619

Manufacturing 25,935 5,014

Trade, transportation, and utili-

ties (3) 99,476 5,972

Information 11,181 833

Financial activities 47,249 1,631

Professional and business

services 39,904 2,948

Education and health services 42,829 4,681

Leisure and hospitality 33,207 1,888

Other services 13,929 429

Government 176 13,713

Employees

Industry

Percent of

employment

benchmarks

Total 29

Natural resources and mining 26

Construction 10

Manufacturing 33

Trade, transportation, and utilities 24

Information 24

Financial activities 21

Professional and business

services 19

Education and health services 29

Leisure and hospitality 16

Other services 8

Government 63

(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 2002.

(3) The Surface Transportation Board provides a ocmplete 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

Root- Mean percent

mean- revision

Industry square

error of

monthly Actual Ab-

level’ solute

Total 50,000 0 0

Total private 40,300 0 0

Government 26,200 0 0.1

Federal government 13,900 0.1 .4

Federal government, except

U.S. Postal Service 11,900 .3 .4

U.S. Postal Service 7,700 -.2 .5

State government 11,900 0 .2

State government

education 11,300 0 .5

State government, excluding

education 4,700 0 .1

Local government 18,300 0 .1

Local government

education 17,600 0 .2

Local government, excluding

education 8,700 .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 1998

through December 2002.

Table 2-E. Relative standard errors for estimates of

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

Relative standard error

All Average Average

emplo- hourly weekly

Industry yees earnings hours

Total nonfarm 0.2 (2) (2)

Total private 0.2 0.2 0.2

Goods-producing 0.4 0.4 0.4

Natural resources and mining 2.2 2.3 2.0

Logging 7.6 5.9 5.9

Mining 2.1 2.6 2.0

Oil and gas extraction 3.7 3.5 4.4

Mining, except oil and gas 2.4 1.4 1.8

Coal mining 3.1 2.5 3.5

Support activities for mining 3.5 6.2 4.6

Construction 0.9 0.7 0.8

Construction of buildings 1.6 1.3 1.4

Heavy and civil engineering

construction 2.0 1.5 2.2

Specialty trade contractors 1.2 0.9 1.0

Manufacturing 0.3 0.4 0.5

Durable goods 0.3 0.4 0.8

Wood products 1.3 1.1 1.6

Nonmetallic mineral products 1.5 1.5 2.6

Primary metals 1.3 1.2 1.8

Fabricated metal products 0.7 0.9 1.1

Machinery 0.9 1.1 1.5

Computer and electronic products 1.4 1.5 2.6

Computer and peripheral

equipment 3.8 7.6 9.1

Communications equipment 4.8 5.1 6.4

Semiconductors and electronic

components 2.3 2.6 5.2

Electronic instruments 1.1 2.6 1.8

Electrical equipment and

appliances 1.5 1.4 2.3

Transportation equipment 1.1 0.8 1.9

Furniture and related products 1.9 1.5 1.6

Miscellaneous manufacturing 1.3 1.3 2.3

Nondurable goods 0.6 0.9 0.8

Food manufacturing 1.1 1.3 1.6

Beverages and tobacco products 2.5 5.6 7.6

Textile mills 1.5 0.8 3.1

Textile product mills 3.3 3.2 4.0

Apparel 2.8 2.1 2.5

Leather and allied products 5.6 3.2 4.9

Paper and paper products 1.4 1.3 1.8

Printing and related support

activities 1.3 1.5 1.3

Petroleum and coal products 2.2 5.3 7.1

Chemicals 1.2 1.8 1.5

Plastics and rubber products 1.2 1.3 1.4

Private service-providing 0.2 0.3 0.3

Trade, transportation, and utilities 0.3 0.5 0.5

Wholesale trade 0.7 1.2 1.0

Durable goods 0.9 1.6 0.9

Nondurable goods 0.9 1.7 1.6

Electronic markets and agents

and brokers 2.4 4.1 3.4

Retail trade 0.4 0.7 0.5

Motor vehicle and parts dealers 0.7 2.8 1.3

Automobile dealers 0.8 3.8 1.6

Furniture and home furnishings stores 1.9 4.3 3.3

Electronics and appliance stores 2.0 6.8 3.8

Building material and garden

supply stores 1.1 1.4 1.9

Food and beverage stores 1.1 1.1 0.9

Health and personal care stores 1.2 3.0 3.8

Gasoline stations 1.6 1.6 1.6

Clothing and clothing accessories

stores 1.7 3.0 3.4

Sporting goods, hobby, book, and

music stores 2.3 2.1 3.7

General merchandise stores 1.2 0.9 0.9

Department stores 1.6 1.2 1.4

Miscellaneous store retailers 1.4 2.2 2.6

Nonstore retailers 4.6 2.6 3.7

Transportation and warehousing 0.7 0.9 1.4

Air transportation 1.0 5.2 4.5

Rail transportation 1.6 (3) (3)

Water transportation 5.7 5.5 8.7

Truck transportation 1.0 1.6 1.9

Transit and ground passenger

transportation 2.6 4.2 5.5

Pipeline transportation 6.3 2.5 5.2

Scenic and sightseeing transportation 31.7 11.6 41.1

Support activities for transportation 2.5 2.8 2.8

Couriers and messengers 1.2 2.3 4.7

Warehousing and storage 3.0 1.6 2.6

Utilities 0.9 2.2 2.3

Information 0.9 1.5 1.1

Publishing industries, except Internet 1.1 2.6 2.1

Motion picture and sound recording

industries 4.2 4.0 5.1

Broadcasting, except Internet 3.4 2.9 3.1

Internet publishing and broadcasting 9.5 7.5 9.5

Telecommunications 1.5 3.0 1.8

ISPs, search portals, and data

processing 2.2 5.7 3.5

Other information services 3.9 5.0 7.2

Financial activities 0.6 1.1 0.9

Finance and insurance 0.6 1.4 1.0

Monetary authorities–central bank 1.1 3.3 3.8

Credit intermediation and related

activities 0.9 2.2 1.7

Depository credit intermediation 0.6 1.8 2.1

Commercial banking 0.8 2.4 2.6

Securities, commodity contracts,

investments 1.6 3.6 2.0

Insurance carriers and related

activities 1.0 2.0 1.6

Funds, trusts, and other financial

vehicles 4.4 2.2 3.6

Real estate and rental and leasing 1.3 1.3 2.0

Real estate 1.7 1.8 2.5

Rental and leasing services 2.0 2.4 3.2

Lessors of nonfinancial intangible

assets 7.1 8.8 6.4

Professional and business services 0.7 0.7 0.7

Professional and technical services 0.7 1.1 1.2

Legal services 1.0 1.2 1.1

Accounting and bookkeeping

services 3.4 3.7 6.4

Architectural and engineering

services 1.6 1.6 1.4

Computer systems design and related

services 1.3 3.0 3.2

Management and technical consulting

services 2.3 2.6 2.5

Management of companies and enterprises 1.7 2.0 1.6

Administrative and waste services 1.4 1.5 1.4

Administrative and support services 1.4 1.5 1.4

Employment services 2.7 2.7 2.7

Temporary help services 3.1 2.9 1.8

Business support services 1.7 2.3 2.3

Services to buildings and dwellings 1.5 1.8 1.7

Waste management and remediation

services 2.6 3.2 3.6

Education and health services 0.3 0.5 0.8

Educational services 1.2 1.8 2.0

Health care and social assistance 0.3 0.4 0.8

Ambulatory health care services 0.4 0.9 1.8

Offices of physicians 0.6 1.3 1.7

Outpatient care centers 1.3 2.2 2.6

Home health care services 1.5 3.3 6.4

Hospitals 0.3 0.5 1.0

Nursing and residential care

facilities 0.5 0.7 1.1

Nursing care facilities 0.6 0.8 1.5

Social assistance 0.7 1.1 1.5

Child day care services 1.5 2.2 2.6

Leisure and hospitality 0.5 2.0 0.9

Arts, entertainment, and recreation 1.7 7.1 3.8

Performing arts and spectator sports 4.9 12.6 14.3

Museums, historical sites, zoos,

and parks 3.6 2.9 4.5

Amusements, gambling, and recreation 1.9 2.2 2.3

Accommodations and food services 0.4 0.9 0.9

Accommodations 1.0 1.2 1.4

Food services and drinking places 0.5 1.1 0.9

Other services 1.2 1.7 1.9

Repair and maintenance 1.0 1.9 1.5

Personal and laundry services 1.0 2.1 2.1

Membership associations and

organizations 2.1 3.1 3.5

(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.

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

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

Standard error

1-month change

All Average Average

em- weekly hourly

Industry ployees hours earnings

Total nonfarm 63,933 (2) (2)

Total private 60,368 0.03 $0.01

Goods-producing 22,170 0.06 0.02

Natural resources and mining 2,875 0.45 0.12

Logging 1,089 0.88 0.25

Mining 2,627 0.49 0.13

Oil and gas extraction 791 0.73 0.31

Mining, except oil and gas 1,223 0.38 0.11

Coal mining 583 0.65 0.24

Support activities for mining 2,135 1.12 0.24

Construction 15,443 0.12 0.04

Construction of buildings 7,932 0.23 0.09

Heavy and civil engineering

construction 4,843 0.35 0.09

Specialty trade contractors 12,630 0.16 0.06

Manufacturing 13,823 0.07 0.02

Durable goods 10,066 0.09 0.03

Wood products 2,414 0.27 0.06

Nonmetallic mineral products 2,279 0.33 0.08

Primary metals 2,039 0.28 0.08

Fabricated metal products 3,819 0.16 0.04

Machinery 3,619 0.21 0.06

Computer and electronic products 4,926 0.33 0.08

Computer and peripheral

equipment 840 1.10 0.29

Communications equipment 1,793 0.72 0.29

Semiconductors and electronic

components 1,588 0.60 0.11

Electronic instruments 1,759 0.34 0.12

Electrical equipment and

appliances 2,017 0.34 0.06

Transportation equipment 4,752 0.22 0.08

Furniture and related products 2,392 0.30 0.07

Miscellaneous manufacturing 2,484 0.30 0.07

Nondurable goods 8,678 0.11 0.03

Food manufacturing 5,483 0.23 0.06

Beverages and tobacco products 1,477 0.83 0.25

Textile mills 1,365 0.37 0.06

Textile product mills 1,719 0.53 0.06

Apparel 3,362 0.38 0.07

Leather and allied products 637 0.78 0.14

Paper and paper products 1,797 0.27 0.10

Printing and related support

activities 2,631 0.25 0.07

Petroleum and coal products 864 0.95 0.21

Chemicals 2,790 0.29 0.09

Plastics and rubber products 2,447 0.24 0.07

Private service-providing 54,189 0.04 0.02

Trade, transportation, and utilities 23,984 0.05 0.03

Wholesale trade 9,243 0.12 0.07

Durable goods 6,116 0.14 0.09

Nondurable goods 5,991 0.22 0.08

Electronic markets and agents

and brokers 3,051 0.32 0.24

Retail trade 16,169 0.06 0.03

Motor vehicle and parts dealers 4,203 0.20 0.13

Automobile dealers 3,073 0.24 0.20

Furniture and home furnishings

stores 3,239 0.36 0.16

Electronics and appliance stores 3,357 0.36 0.21

Building material and garden

supply stores 3,951 0.22 0.07

Food and beverage stores 5,891 0.11 0.05

Health and personal care stores 3,910 0.33 0.10

Gasoline stations 3,877 0.17 0.04

Clothing and clothing accessories

stores 7,123 0.26 0.10

Sporting goods, hobby, book, and

music stores 4,056 0.37 0.08

General merchandise stores 8,455 0.11 0.04

Department stores 6,778 0.15 0.06

Miscellaneous store retailers 4,669 0.27 0.10

Nonstore retailers 4,279 0.45 0.11

Transportation and warehousing 9,615 0.20 0.06

Air transportation 1,398 0.75 0.21

Rail transportation 1,728 (3) (3)

Water transportation 964 1.06 0.39

Truck transportation 4,573 0.30 0.10

Transit and ground passenger

transportation 2,588 0.50 0.17

Pipeline transportation 596 0.86 0.32

Scenic and sightseeing

transportation 2,986 3.60 0.65

Support activities for

transportation 4,074 0.40 0.11

Couriers and messengers 3,463 0.33 0.09

Warehousing and storage 3,402 0.34 0.11

Utilities 1,316 0.33 0.14

Information 8,699 0.17 0.10

Publishing industries, except

Internet 2,378 0.28 0.21

Motion picture and sound

recording industries 6,583 0.64 0.43

Broadcasting, except Internet 2,654 0.32 0.29

Internet publishing and

broadcasting 459 1.10 0.70

Telecommunications 3,427 0.24 0.14

ISPs, search portals, and data

processing 3,009 0.48 0.27

Other information services 603 0.76 0.14

Financial activities 10,242 0.11 0.05

Finance and insurance 7,450 0.14 0.06

Monetary authorities–central

bank 66 0.67 0.31

Credit intermediation and related

activities 5,290 0.20 0.11

Depository credit

intermediation 2,553 0.25 0.06

Commercial banking 1,833 0.31 0.07

Securities, commodity contracts,

investments 3,918 0.43 0.24

Insurance carriers and related

activities 4,672 0.15 0.06

Funds, trusts, and other

financial vehicles 450 0.59 0.12

Real estate and rental and leasing 6,603 0.18 0.07

Real estate 5,199 0.22 0.08

Rental and leasing services 3,770 0.35 0.12

Lessors of nonfinancial

intangible assets 449 1.03 0.52

Professional and business services 25,349 0.10 0.05

Professional and technical services 12,053 0.13 0.08

Legal services 3,022 0.16 0.12

Accounting and bookkeeping

services 7,314 0.67 0.15

Architectural and engineering

services 4,238 0.23 0.11

Computer systems design and

related services 5,326 0.26 0.22

Management and technical

consulting services 3,751 0.27 0.20

Management of companies and 4,962 0.20 0.10

enterprises

Administrative and waste services 24,264 0.14 0.06

Administrative and support

services 24,592 0.15 0.07

Employment services 22,197 0.24 0.12

Temporary help services 17,840 0.23 0.09

Business support services 3,694 0.33 0.08

Services to buildings and

dwellings 5,766 0.21 0.05

Waste management and remediation

services 2,572 0.48 0.15

Education and health services 16,082 0.07 0.03

Educational services 11,821 0.19 0.05

Health care and social assistance 10,083 0.06 0.03

Ambulatory health care services 6,300 0.11 0.06

Offices of physicians 3,857 0.21 0.10

Outpatient care centers 1,404 0.26 0.09

Home health care services 3,254 0.25 0.08

Hospitals 3,681 0.11 0.05

Nursing and residential care

facilities 4,046 0.12 0.03

Nursing care facilities 2,775 0.14 0.04

Social assistance 4,827 0.12 0.03

Child day care services 2,797 0.25 0.04

Leisure and hospitality 17,470 0.08 0.04

Arts, entertainment, and recreation 11,516 0.36 0.19

Performing arts and spectator

sports 5,460 1.58 0.57

Museums, historical sites, zoos,

and parks 933 0.48 0.15

Amusements, gambling, and

recreation 10,362 0.27 0.07

Accommodations and food services 13,883 0.07 0.02

Accommodations 6,809 0.18 0.05

Food services and drinking places 13,156 0.08 0.02

Other services 14,852 0.18 0.07

Repair and maintenance 4,653 0.19 0.07

Personal and laundry services 3,832 0.24 0.07

Membership associations and

organizations 13,636 0.30 0.11

Standard error

3-month change

All Average Average

em- weekly hourly

Industry ployees hours earnings

Total nonfarm 103,702 (2) (2)

Total private 95,968 0.05 $0.02

Goods-producing 38,329 0.08 0.03

Natural resources and mining 4,774 0.57 0.21

Logging 2,209 1.29 0.39

Mining 4,098 0.57 0.23

Oil and gas extraction 1,902 1.17 0.38

Mining, except oil and gas 2,045 0.47 0.16

Coal mining 833 0.90 0.35

Support activities for mining 3,093 1.49 0.45

Construction 26,732 0.16 0.06

Construction of buildings 13,240 0.30 0.14

Heavy and civil engineering

construction 8,420 0.52 0.15

Specialty trade contractors 22,424 0.21 0.09

Manufacturing 22,637 0.10 0.03

Durable goods 15,801 0.15 0.04

Wood products 4,059 0.45 0.08

Nonmetallic mineral products 3,864 0.53 0.11

Primary metals 3,234 0.40 0.13

Fabricated metal products 6,527 0.23 0.06

Machinery 6,142 0.31 0.09

Computer and electronic products 7,800 0.44 0.12

Computer and peripheral

equipment 2,483 1.62 0.63

Communications equipment 2,517 0.85 0.38

Semiconductors and electronic

components 5,034 0.88 0.15

Electronic instruments 2,334 0.42 0.21

Electrical equipment and

appliances 2,973 0.40 0.09

Transportation equipment 7,642 0.35 0.12

Furniture and related products 4,541 0.36 0.09

Miscellaneous manufacturing 3,887 0.42 0.10

Nondurable goods 16,575 0.14 0.04

Food manufacturing 10,654 0.31 0.07

Beverages and tobacco products 2,809 1.13 0.38

Textile mills 2,847 0.40 0.07

Textile product mills 2,966 0.87 0.16

Apparel 5,618 0.48 0.09

Leather and allied products 915 0.98 0.18

Paper and paper products 3,043 0.37 0.13

Printing and related support

activities 3,931 0.31 0.10

Petroleum and coal products 1,133 1.11 0.37

Chemicals 4,986 0.38 0.14

Plastics and rubber products 4,261 0.33 0.09

Private service-providing 89,054 0.06 0.03

Trade, transportation, and utilities 38,679 0.09 0.04

Wholesale trade 15,138 0.17 0.10

Durable goods 10,251 0.20 0.12

Nondurable goods 8,991 0.29 0.11

Electronic markets and agents

and brokers 4,922 0.44 0.44

Retail trade 29,834 0.09 0.04

Motor vehicle and parts dealers 7,112 0.27 0.20

Automobile dealers 4,817 0.30 0.29

Furniture and home furnishings

stores 4,949 0.52 0.21

Electronics and appliance stores 6,125 0.68 0.33

Building material and garden

supply stores 6,896 0.32 0.10

Food and beverage stores 10,484 0.16 0.06

Health and personal care stores 5,954 0.52 0.15

Gasoline stations 6,138 0.29 0.06

Clothing and clothing accessories

stores 10,756 0.37 0.14

Sporting goods, hobby, book, and

music stores 7,651 0.46 0.12

General merchandise stores 14,921 0.18 0.05

Department stores 13,101 0.23 0.06

Miscellaneous store retailers 7,460 0.36 0.15

Nonstore retailers 8,652 0.67 0.18

Transportation and warehousing 16,484 0.27 0.08

Air transportation 2,171 0.99 0.39

Rail transportation 4,155 (3) (3)

Water transportation 1,590 1.65 0.63

Truck transportation 7,362 0.39 0.15

Transit and ground passenger

transportation 4,846 0.83 0.23

Pipeline transportation 893 1.20 0.40

Scenic and sightseeing

transportation 6,189 4.20 1.00

Support activities for

transportation 5,687 0.52 0.15

Couriers and messengers 4,410 0.69 0.14

Warehousing and storage 5,623 0.57 0.15

Utilities 1,941 0.44 0.24

Information 14,829 0.21 0.19

Publishing industries, except

Internet 3,906 0.40 0.28

Motion picture and sound

recording industries 8,928 0.89 0.74

Broadcasting, except Internet 4,038 0.47 0.41

Internet publishing and

broadcasting 1,195 1.14 0.91

Telecommunications 7,312 0.37 0.25

ISPs, search portals, and data

processing 4,882 0.56 0.47

Other information services 793 0.93 0.26

Financial activities 17,926 0.14 0.08

Finance and insurance 12,728 0.18 0.10

Monetary authorities–central

bank 102 0.77 0.36

Credit intermediation and related

activities 9,308 0.27 0.17

Depository credit

intermediation 3,733 0.26 0.13

Commercial banking 3,143 0.35 0.14

Securities, commodity contracts,

investments 6,542 0.41 0.34

Insurance carriers and related

activities 9,304 0.27 0.12

Funds, trusts, and other

financial vehicles 807 0.67 0.24

Real estate and rental and leasing 11,562 0.29 0.09

Real estate 9,606 0.34 0.11

Rental and leasing services 5,289 0.46 0.18

Lessors of nonfinancial

intangible assets 757 1.03 0.81

Professional and business services 46,872 0.17 0.07

Professional and technical services 27,481 0.32 0.13

Legal services 4,560 0.21 0.14

Accounting and bookkeeping

services 23,427 1.69 0.35

Architectural and engineering

services 7,898 0.26 0.16

Computer systems design and

related services 7,491 0.46 0.37

Management and technical

consulting services 7,011 0.45 0.29

Management of companies and 9,574 0.30 0.15

enterprises

Administrative and waste services 42,382 0.23 0.09

Administrative and support

services 42,051 0.23 0.09

Employment services 37,723 0.39 0.17

Temporary help services 29,908 0.33 0.16

Business support services 6,937 0.49 0.11

Services to buildings and

dwellings 9,733 0.29 0.09

Waste management and remediation

services 4,563 0.67 0.23

Education and health services 26,028 0.09 0.04

Educational services 23,031 0.37 0.14

Health care and social assistance 15,937 0.08 0.03

Ambulatory health care services 10,010 0.16 0.07

Offices of physicians 5,990 0.25 0.10

Outpatient care centers 2,219 0.33 0.16

Home health care services 5,168 0.87 0.12

Hospitals 5,652 0.17 0.07

Nursing and residential care

facilities 6,045 0.17 0.04

Nursing care facilities 4,195 0.19 0.06

Social assistance 7,967 0.20 0.05

Child day care services 6,056 0.33 0.08

Leisure and hospitality 30,685 0.14 0.08

Arts, entertainment, and recreation 21,542 0.69 0.39

Performing arts and spectator

sports 8,493 2.36 1.08

Museums, historical sites, zoos,

and parks 1,770 0.73 0.21

Amusements, gambling, and

recreation 19,699 0.49 0.12

Accommodations and food services 22,392 0.11 0.03

Accommodations 11,954 0.21 0.06

Food services and drinking places 19,409 0.12 0.03

Other services 33,612 0.28 0.09

Repair and maintenance 6,473 0.29 0.11

Personal and laundry services 6,035 0.35 0.09

Membership associations and

organizations 31,718 0.48 0.16

Standard error

12-month change

All Average Average

em- weekly hourly

Industry ployees hours earnings

Total nonfarm 175,111 (2) (2)

Total private 158,128 0.07 $0.03

Goods-producing 75,023 0.14 0.05

Natural resources and mining 9,380 0.95 0.38

Logging 4,436 2.08 0.78

Mining 7,982 1.01 0.43

Oil and gas extraction 3,592 1.75 0.78

Mining, except oil and gas 4,019 0.74 0.28

Coal mining 1,844 1.46 0.54

Support activities for mining 5,248 2.36 0.82

Construction 55,934 0.27 0.12

Construction of buildings 22,647 0.44 0.22

Heavy and civil engineering

construction 14,970 0.80 0.26

Specialty trade contractors 44,083 0.34 0.15

Manufacturing 39,869 0.16 0.06

Durable goods 32,029 0.25 0.06

Wood products 6,892 0.63 0.13

Nonmetallic mineral products 7,093 0.93 0.20

Primary metals 6,190 0.76 0.19

Fabricated metal products 10,980 0.41 0.12

Machinery 10,873 0.51 0.17

Computer and electronic products 17,091 0.86 0.21

Computer and peripheral

equipment 6,871 3.14 1.40

Communications equipment 8,130 1.74 0.60

Semiconductors and electronic

components 10,591 1.69 0.32

Electronic instruments 4,277 0.73 0.40

Electrical equipment and

appliances 6,645 0.75 0.16

Transportation equipment 15,930 0.59 0.16

Furniture and related products 8,619 0.54 0.15

Miscellaneous manufacturing 7,852 0.82 0.15

Nondurable goods 27,154 0.25 0.10

Food manufacturing 13,704 0.52 0.14

Beverages and tobacco products 4,331 2.47 0.78

Textile mills 4,264 0.84 0.10

Textile product mills 6,141 1.45 0.36

Apparel 9,078 0.82 0.16

Leather and allied products 2,910 1.62 0.29

Paper and paper products 6,398 0.71 0.19

Printing and related support

activities 7,698 0.49 0.18

Petroleum and coal products 2,665 2.75 1.07

Chemicals 8,688 0.62 0.31

Plastics and rubber products 8,119 0.49 0.15

Private service-providing 139,364 0.09 0.04

Trade, transportation, and utilities 68,232 0.14 0.06

Wholesale trade 33,313 0.32 0.17

Durable goods 22,948 0.33 0.23

Nondurable goods 15,475 0.52 0.20

Electronic markets and agents

and brokers 11,991 1.14 0.82

Retail trade 52,952 0.15 0.07

Motor vehicle and parts dealers 11,035 0.47 0.37

Automobile dealers 8,210 0.56 0.55

Furniture and home furnishings

stores 9,927 0.86 0.41

Electronics and appliance stores 9,498 1.20 0.84

Building material and garden

supply stores 12,242 0.53 0.16

Food and beverage stores 23,665 0.25 0.10

Health and personal care stores 10,089 0.93 0.30

Gasoline stations 13,119 0.45 0.12

Clothing and clothing accessories

stores 18,560 0.63 0.28

Sporting goods, hobby, book, and

music stores 12,488 0.80 0.20

General merchandise stores 28,195 0.26 0.07

Department stores 25,974 0.39 0.09

Miscellaneous store retailers 13,218 0.63 0.25

Nonstore retailers 16,878 1.05 0.36

Transportation and warehousing 26,670 0.45 0.14

Air transportation 5,365 1.43 0.85

Rail transportation 3,368 (3) (3)

Water transportation 2,928 2.28 0.94

Truck transportation 13,274 0.67 0.21

Transit and ground passenger

transportation 8,495 1.37 0.47

Pipeline transportation 1,926 2.49 0.51

Scenic and sightseeing

transportation 9,831 11.31 1.52

Support activities for

transportation 12,594 0.89 0.33

Couriers and messengers 7,708 1.03 0.27

Warehousing and storage 10,742 1.05 0.26

Utilities 4,259 0.74 0.42

Information 28,380 0.38 0.26

Publishing industries, except

Internet 8,365 0.73 0.51

Motion picture and sound

recording industries 17,016 1.43 0.94

Broadcasting, except Internet 9,648 0.89 0.57

Internet publishing and

broadcasting 3,095 2.47 1.61

Telecommunications 14,217 0.65 0.46

ISPs, search portals, and data

processing 8,915 1.09 1.02

Other information services 1,366 1.49 0.45

Financial activities 37,745 0.28 0.16

Finance and insurance 28,349 0.32 0.22

Monetary authorities–central

bank 205 1.20 0.47

Credit intermediation and related

activities 18,120 0.52 0.32

Depository credit

intermediation 8,094 0.60 0.27

Commercial banking 7,564 0.76 0.35

Securities, commodity contracts,

investments 13,314 0.64 0.68

Insurance carriers and related

activities 19,141 0.56 0.30

Funds, trusts, and other

financial vehicles 2,304 1.38 0.44

Real estate and rental and leasing 20,742 0.55 0.15

Real estate 16,658 0.66 0.20

Rental and leasing services 12,127 0.90 0.27

Lessors of nonfinancial

intangible assets 1,613 1.82 1.52

Professional and business services 93,306 0.20 0.12

Professional and technical services 43,917 0.38 0.19

Legal services 8,846 0.34 0.24

Accounting and bookkeeping

services 27,208 2.15 0.49

Architectural and engineering

services 16,981 0.49 0.30

Computer systems design and

related services 13,671 1.01 0.68

Management and technical

consulting services 12,143 0.72 0.53

Management of companies and 25,379 0.54 0.37

enterprises

Administrative and waste services 85,064 0.39 0.17

Administrative and support

services 84,523 0.40 0.18

Employment services 76,777 0.70 0.33

Temporary help services 59,501 0.54 0.34

Business support services 10,791 0.68 0.23

Services to buildings and

dwellings 18,981 0.46 0.16

Waste management and remediation

services 7,382 1.23 0.45

Education and health services 37,976 0.19 0.06

Educational services 26,017 0.63 0.21

Health care and social assistance 27,506 0.21 0.06

Ambulatory health care services 17,307 0.44 0.11

Offices of physicians 9,590 0.43 0.19

Outpatient care centers 4,060 0.60 0.30

Home health care services 8,035 1.74 0.36

Hospitals 9,652 0.27 0.10

Nursing and residential care

facilities 10,522 0.30 0.07

Nursing care facilities 7,797 0.39 0.08

Social assistance 12,379 0.35 0.09

Child day care services 8,734 0.61 0.16

Leisure and hospitality 51,210 0.20 0.16

Arts, entertainment, and recreation 32,572 1.01 0.86

Performing arts and spectator

sports 15,391 4.68 2.25

Museums, historical sites, zoos,

and parks 2,900 1.10 0.32

Amusements, gambling, and

recreation 27,477 0.68 0.21

Accommodations and food services 37,459 0.16 0.06

Accommodations 17,882 0.37 0.10

Food services and drinking places 33,052 0.17 0.07

Other services 56,942 0.50 0.20

Repair and maintenance 10,145 0.47 0.23

Personal and laundry services 11,068 0.60 0.20

Membership associations and

organizations 54,351 0.89 0.38

(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

TDE 31%

EDI 19%

Fax/Tape/WWW 15%

Mail 13%

CATI 22%

COPYRIGHT 2003 U.S. Department of Labor

COPYRIGHT 2004 Gale Group