Alternative measures of supervisory employee hours and productivity growth

Monthly Labor Review

Volumn 127, Issue 4, 2004, Pages 9-28

  Eldridge, Lucy P ^a   Manser, Marilyn E ^a   Otto, Phyllis Flohr ^a  

^a BUREAU OF LABOR STATISTICS   (United States)

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EID: 14144251907     PISSN: 00981818     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (43)

1. Preliminary results using annual data and a draft paper were presented at the June 2001 meeting of the Federal Economic Statistics Advisory Committee.
2. BLS publishes quarterly productivity and related series for six major sectors. The output measures for the business sector, nonfarm business sector and nonfinancial corporations make use of Bureau of Economic Analysis data; those for manufacturing, durable goods manufacturing, and nondurable goods manufacturing utilize information from the Census Bureau's economic censuses and annual surveys, the Federal Reserve Board Index of Industrial Production, and BLS price programs.
3. CES employment data refer to persons who worked during, or received pay for, any part of the pay period that includes the 12th of the month. Workers on an establishment payroll who are on paid sick leave; on paid holiday or vacation; or who work during only a part of the specified pay period, even though they are unemployed or on strike during the rest of the pay period, are all counted as employed. Persons on the payroll of more than one establishment during the pay period are counted in each establishment which reports them. Persons are considered employed if they receive pay for any part of the specified pay period, but they are not considered employed if they receive no pay at all for the pay period. Proprietors other (unincorporated self-employed persons), and unpaid family workers are not included. Also excluded from the CES data are domestic workers in households; persons who are on layoff, on leave without pay, or on strike for the entire pay period; and persons who were hired, but have not yet started work during the pay period. See U. S. Department of Labor, Bureau of Labor Statistics, BLS Handbook of Methods, Bulletin 2490, April 1997.
4. BLS is planning to collect CES data on earnings and hours for all employees and publish estimates in 2006.
5. These studies provide data before 1978 on the regularly scheduled workweek of white-collar employees. See Employee Compensation in the Private Nonfarm Economy, 1977, Summary 80-5 (Bureau of Labor Statistics, April 1980).
6. Some hours at work may not be spent actively engaged in production. From the employer's perspective such downtime can arise for a variety of reasons. Although some types of downtime may be integral to the production process and thus increase output, others may have zero marginal productivity. Improving production processes to reduce machine downtime or organizing work to motivate workers and reduce shirking will increase hours actively engaged in production and increase output without changing hours at work. This will result in an increase in measured productivity, which is inherently a par of what we want to capture in a broad measure of productivity change. Similarly, effects due to reallocation of labor from a firm that is less productive as a result of more worker shirking to a firm with less shirking should be picked up in an aggregate measure.
7. It might be argued that to some extent paid leave can result in higher output. This effect is unlikely to be significant at the levels of time worked in the U.S. today. For instance, a BLS report based on a large number of case studies of productivity in U.S. manufacturing plants during World War II examined the impact on output and work injuries of patterns of work-days and workweeks that were dramatically longer than those typically observed in the United States; see, Hours of Work and Output, Bulletin no. 917 (Bureau of Labor Statistics 1947). (There were not enough observed workweeks shorter than 40 hours to examine the impact of shorter days and weeks.) Although the study did conclude that of the patterns examined the 8-hour day, 5-day week seemed optimal, increases in daily hours or workdays did yield very substantial output increases for all but extreme workweeks. There is, however, some research that concludes that vacations may be good for a person's health; for a summary,
8. Sue Shellenbarger, "Canceling a vacation could cost you dearly in the long run," Work and Family column, Wall Street Journal, Apr. 11, 2001.
9. BLS publishes data from the CPS on the labor force status of the civilian noninstitutional population, 16 years of age and older. The CPS is collected each month from a probability sample of approximately 60,000 occupied households. See the BLS Handbook of Methods.
10. Employment counts for employees in agricultural services, forestry and fishing come from the BLS 202 program, based on administrative records from the unemployment insurance system. These counts are moved forward in the current period using limited information on employment in agricultural services (part of SIC 07). The number of employees of government enterprises comes from the Bureau of Economic Analysis. These are moved forward using information from the CES. Average weekly hours for employees of government enterprises are from the CPS.
11. Off-the-clock hours affect output and ideally should be included for productivity measurement purposes. Such hours seem unlikely to occur for hourly paid workers. For salaried workers, the concept of hours paid or worked may not be clear in some instances. Different types of data will treat off-the-clock hours differently.
12. Wesley Mellow and Hal Sider, "Accuracy of response in labor market surveys: Evidence and implications," Journal of Labor Economics, 1983, vol. 1, no. 4, pp. 331-44.
13. John Bound, Charles Brown, Greg J. Duncan, and Willard L. Rodgers, "Measurement Error in Cross-Sectional and Longitudinal Labor Market Surveys: Results from Two Validation Studies," in J. Hartog, G. Ridder, and J. Teeuwes, eds., Panel Data and Labor Market Studies (Amsterdam, the Netherlands, Elsevier, 1990), pp. 1-19.
14. The study by John Bound and others focused on the bias due to measurement error when survey-based measures of earnings, hours, and hourly earnings are used in regression analyses.
15. Note that sample sizes are small. See for instance, Daniel S. Hamermesh, "Shirking or Productive Schmoozing: Wages and the Allocation of Time at Work," Industrial and Labor Relations Review, 1990, vol. 43, no. 3, pp. 121S-133S. Hamermesh uses Michigan time use diary data for 1975 and 1981
16. John P. Robinson, and Ann Bostrom, "The overestimated workweek? What time diary measures suggest," Monthly Labor Review, August 1994, pp. 11-23. Robinson and Bostrom use three separate studies for 1965, 1975, and 1985.
17. It is unclear if Robinson and Bostrom excluded time on breaks, which should not be excluded for productivity measurement, from their estimates of time use survey hours worked. Robinson and Bostrom, "The overestimated workweek?" 1994. It is clear from the data reported in Hamermesh that the general conclusion that labor force survey estimates of hours worked are higher than time use estimates of hours worked holds even when breaks are not excluded from the latter measures.
18. Hamermesh, "Shirking or Productive Schmoozing," 1990
19. He argues that Robinson and Bostrom's result that those who work longer hours tend to overreport hours more may be a statistical artifact of regression to the mean. See Jerry A. Jacobs, "Measuring time at work: are self-reports accurate?" Monthly Labor Review, December 1998, pp. 42-53.
20. Also see Robinson and Bostrom, "The overestimated workweek?" 1994.
21. Note this is for employees only, as data for the self-employed come from the CPS.
22. Katharine G. Abraham, James R. Spletzer, and Jay C. Stewart, "Divergent Trends in Alternative Wage Series," in John Haltiwanger, Marilyn E. Manser, and Robert Topel, Labor Statistics Measurement Issues, National Bureau of Economic Research, Studies in Income and Wealth, vol. 60 (Chicago, The University of Chicago Press, 1998, pp. 293-324).
23. This classification scheme is the same as that underlying the BLS Employer Cost Index (ECI) series for production and nonsupervisory workers. See Employee Cost Indexes, Bulletin 2532, appendix B. for service industries: CPS codes 003-037; for goods industries: CPS codes 003-389.
24. CPS codes 03-199, 243-263, 226, 229.
25. Abraham and others, 1998, p. 316.
26. The Abraham and others 1998 study focused on the private sector less agriculture and private households.
27. Multiple jobholders are employed persons who, during the reference week, had either two or more jobs as a wage and salary worker, or 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.
28. "Appendix A: Multiple Jobholder Adjustment," is available upon request to Lucy Eldridge via e-mail Eldridge.Lucyβls.gov or by telephone (202) 691-6598.
29. The Abraham and others, 1998, study did not adjust for unpaid absences.
30. Among those excluded from this category are persons in executive and managerial positions and persons engaged in activities such as accounting, sales, advertising, routine office work, professional and technical functions, and force-account construction. (Force-account construction is construction work performed by an establishment, primarily engaged in some business other than construction, for its own account and for use by its own employees.)
31. Excluded from this category are executive and managerial personnel, professional and technical employees, and workers in routine office jobs.
32. The full sample cps data, that is all four rotation groups, was used for alternatives A and B for all years and alternative C for 1982-2002. For 1979-81 only, the outgoing rotation group was used for alternative C because data on whether a respondent was paid by the hour was not in the full CPS sample data.
33. CPS codes 433-446, 456-469. We have decided not to include those with supervisory titles because the workers remaining in occupations 417-889 seem to be in professions where one envisions working supervisors who are involved in production.
34. According to the Standard Industrial Classification Manual, 1987, Executive Office of the President, Office of Management and Budget.
35. We chose to assess employment shares rather than employment levels because CPS employment growth over the 1990s fell short of CES employment growth. The employment share quarterly data are not seasonally adjusted.
36. According to the 1 -digit industry data, alternative A most closely captures the trends in nonsupervisory workers employment shares over the 1979-2002 period in all industries except finance, insurance, and real estate. In those industries, alternative C trends are the closest to trends in CES data, yet the levels are much lower than the CES data.
37. The OPT average weekly hours for production/nonsupervisory workers is constructed by multiplying the hours worked/ hours paid ratio for production workers to the CES average weekly hours paid production/ nonsupervisory workers. Thus, the new series represents a measure of hours worked to paid employment. (hours paid/employment paid)*(hours worked/ hours paid) = (hours worked/employment paid). Therefore, we construct the CPS-adjusted series as hours worked/ employment paid.
38. Appendix B: Analysis using alternative CPS-adjustments," presents the results using all three alternatives. Appendix B is available upon request to Lucy Eldridge via e-mail: Eldridge.Lucyβls.gov or via telephone: (202) 691-6598.
39. These studies provide data before 1978 on the regularly scheduled workweek of white-collar employees. See Employee Compensation in the Private Nonfarm Economy, 1977, Summary 80-5 (Bureau of Labor Statistics, April 1980).
40. OPT calculates the average weekly hours for nonproduction workers in durable and nondurable manufacturing by applying the 1977 ratio of average weekly hours of office workers to non-office workers for durable and nondurable manufacturing employees to a 23-month centered moving average of the average weekly hours of production workers in durable and nondurable manufacturing.
41. The 2001 and 2002 OPT average weekly hours for nonproduction workers are based on a 23-month moving average through January 2001 of the trends in average weekly hours for production workers. Therefore, the difference in the OPT series is not as close to zero as would be expected.
42. We also looked at the average absolute quarterly fluctuations in productivity and obtained the same results. (1979: quarter I-1990: quarter IV OPT: 2.39 percent, CPS-adjusted: 2.41 percent; 1990: quarter I-1995: quarter IV OPT: 2.45 percent, CPS-adjusted: 2.42 percent; 1995: quarter I-2000: quarter IV OPT: 2.45 percent, CPS-adjusted: 2.44 percent; 2000: quarter I-2002: quarter IV OPT 2.90 percent, CPS-adjusted 2.91 percent; 1979: quarter I-2002: quarter IV OPT: 2.56 percent, CPS-adjusted: 2.56 percent. The variance for both series over all time intervals prior to 2000 is 0.04 percent; for the 2000-02 period, the variance for both series is 0.09 percent.
43. See NBFR's Business Cycle Dating Committee Web site on the Internet at: www.nber.org/cycles.