HOURS MEASURES FOR PRODUCTIVITY MEASUREMENT AND NATIONAL ACCOUNTING

Transcription

1 HOURS MEASURES FOR PRODUCTIVITY MEASUREMENT AND NATIONAL ACCOUNTING Lucy P. Eldridge Office of Productivity and Technology Bureau of Labor Statistics U.S. Department of Labor September 14, 2004 Paper prepared for the meeting of the Paris Group, September 29 October 1, 2004.

2 The Bureau of Labor Statistics (BLS) collects data on labor hours in both household and establishment (business) surveys. These data are of interest for a variety of reasons, including comparing the situation of workers over time or in different countries. In addition, hours data are used together with measures of earnings in constructing average hourly earnings or other types of hourly compensation and unit labor costs measures. Another major use of hours data is in measuring productivity, which is a technical relationship between output and inputs in the production process. In the U.S. National Income and Product Accounts (NIPA) produced by the Bureau of Economic Analysis (BEA), hours measures are used to estimate components of current-dollar output and income, as well as to estimate components of real output. This paper discusses the hours concepts that are desired for productivity measurement and national accounting, and presents the impact of using alternative source data for measuring hours of work. In addition, the paper discusses the current International Conference of labor Statisticians (ICLS) resolution concerning statistics of hours of work and its consistency with the needs of measuring productivity. HOURS CONCEPT The most basic measure of productivity is labor productivity, which is an index of output divided by an index of hours worked. 1 For measuring productivity, it is appropriate to define hours as all hours spent at work. Thus, the BLS measure of hours worked used to construct productivity statistics excludes paid vacations and other forms of paid leave but include paid time for traveling between job sites, coffee breaks, and machine downtime. Paid leave is best viewed as a benefit rather than as time available for production. However, time spent resting, waiting or standing-by are considered part of the production process and typically take place at the work-site, but have no specified length. It would be very difficult to establish a maximum length of time which defines passive activities as work time since industries are quite heterogeneous. A 15-minute rest period may be sufficient in a service-producing occupation; however on a manufacturing production-line a longer break might be required. In addition, differences in work across countries would make the specification of such a length that is internationally acceptable virtually impossible. Off-the-clock hours affect output and ideally should be included. Such hours seem unlikely to occur for hourly-paid workers, yet for salaried workers the distinction between hours paid and hours worked may not be clear. Different types of data treat off-the-clock hours differently. In the national accounts, hours are used to estimate components of output and income when direct measures are not available. For current-dollar estimates of output and income, average hours paid data are combined with other data to develop indicator series to extrapolate NIPA component estimates. For real output estimates, hours paid data are used as quantity indicators when price data or direct valuation methods are not available. These hours-based quantity indicators are used to extrapolate reference period values. As a proxy for output, the desired hours measures would be similar to those used for productivity measurement and capture 1 A broader measure of productivity is multifactor productivity, which is an index of output divided by an index of combined inputs. 1

3 the hours used in production. However, for estimating component of income or as a deflator for income, the concept of hours paid may be more desirable. The current ICLS resolution defines hours actually worked in a way that is appropriate for productivity measurement. The ICLS resolution definition of hours actually worked includes hours actually worked during normal periods of work; time in addition to usual hours and usually paid at higher rate; time at workplace preparing, conducting maintenance or doing paperwork; time at workplace waiting or standing by for reasons of lack of work (downtime) or when no work is done but payment is guaranteed; short rest periods at the workplace. The definition excludes hours paid but not worked for reasons such as annual leave, sick leave, public holidays, meal breaks, commuting travel time. COVERAGE For productivity measurement it is necessary to construct hours measures for all persons engaged in production in the business sector. Thus, in addition to hours worked of wage and salaried employees it is necessary to measure hours worked for unpaid family workers and selfemployed workers. In the NIPA when using hours as a proxy for output, as well as in estimating income, it seems that it is also desirable to construct a measure of hours that cover all persons involved in production. The current ICLS resolution concerning statistics of hours of work applies only to wage and salaried employees. Specifically the resolution states that statistics of hours of work should always cover wage earners; at least once a year, similar data should be obtained for salaried employees. It would be desirable to have the ICLS expand coverage to all persons. BLS produces quarterly measures of labor productivity for major sectors of the U.S. economy, including business, nonfarm business, nonfinancial corporations, manufacturing, and durable and nondurable manufacturing. The business sector measure excludes from GDP the following sectors: non-profit institutions, paid employees of private households, general government, and the rental value of owner-occupied housing. 2 The real gross products of general government, of private households, and of nonprofit institutions are estimated primarily using data on hours worked and thus move with measures of input data and would bias labor productivity trends toward zero. The gross product of owner-occupied housing and the rental value of buildings and equipment owned and used by nonprofit institutions serving individuals are excluded because no adequate corresponding labor input measures can be developed. The business sector measure includes government enterprises. In constructing an hours series for use in measuring productivity, it is desirable to be able to remove these components from an economy-wide hours series, or to be able to aggregate hour components to exclude the above items. In addition, the BLS produces annual labor productivity measures for all 4-digit NAICS level manufacturing, wholesale and retail trade industries and a variety of industries in mining 2 Output measures are based on a variety of sources: the Bureau of Economic Analysis (BEA s) National Income and Product Accounts (business, nonfarm business, nonfinancial corporations), the Federal Reserve Board Index of Industrial Production and Census Bureau sources (total manufacturing, durable manufacturing, nondurable manufacturing), Census Bureau and other sources for detailed industry measures, and BLS s Producer Price Index and Consumer Price Index programs. 2

4 and the service-producing sector. For these productivity series, annual measures of hours worked are required at an industry-level of detail. In the national accounts, data on hours are used to fill in the gaps in output measurement and to construct wages and salaries for industry groups. Thus, there is a need for industry level hours data. The current ICLS resolution states that statistics of hours actually worked should be compiled at least quarterly for each major division of economic activity, excluding agriculture, and within each division for each important subdivision. It would be desirable to have the ICLS resolution encourage more industry detail where possible. CONSTRUCTING TOTAL HOURS FOR PRODUCTIVITY MEASUREMENT The BLS constructs a quarterly measure of hours worked for use in measuring major sector productivity by combining data available from various sources. 3 The primary source of hours information for the productivity program is the monthly establishment survey, the CES. The BLS prefers to use the CES for measuring hours for productivity statistics for a variety of reasons. 4 However, it is necessary to modify the CES data and supplement it with other information to arrive at an hours series appropriate for productivity measurement. As mentioned above, estimates of hours worked are preferable to estimates of hours paid for measuring productivity. Thus, the CES hours paid data must be converted to an hours worked basis. Information from the Employment Cost Index (ECI) of the BLS National Compensation Survey program 5 is used to construct a ratio of hours-worked to hours-paid. These ratios are currently constructed from the ECI data as the ratio of the value of paid work to the sum of the value of paid leave and paid work. For measuring the hours worked of all persons, the CES data must be supplemented with estimates of the hours of nonproduction and supervisory workers, as well as data on employees of farms, proprietors, and unpaid family workers that are not available from the CES. BLS introduced a new method of constructing estimates of hours for nonproduction and supervisory 3 BLS collects data on labor hours in two monthly surveys - the Current Employment Statistics survey (CES) of establishments and the Current Population Survey (CPS) of households. (See the BLS website for a detailed discussion of these surveys, 4 The CES has a larger sample than the CPS. In addition, the CES is benchmarked annually to levels based on administrative records of employees covered by state Unemployment Insurance tax records. There is no direct benchmark for the CPS employment data. Adjustments to the CPS underlying population base are made annually using intercensal estimates and every ten years using the decennial census. Also, establishment hours data are more consistent with the measures of output used to produce productivity measures; output data are based on data collected from establishments. In addition, establishment data provide reliable reporting and coding on industries and thus are well-suited for producing industry-level measures. Measures for industries based on household reports tend to produce industry estimates with considerable variance, even in a survey as large as the CPS. Thus, BLS s official measures by industry come from establishment surveys wherever possible. In addition, there have been studies that find that labor force surveys may overstate hours worked. 5 The conversion from hours paid to hours worked is carried out at approximately the three-digit NAICS level for manufacturing and the two-digit NAICS level for the rest of the economy. The annual Hours at Work Survey (HWS), which was terminated after 2000, is used for years prior to

5 workers in the August 10, 2004 Productivity and Costs News Release. 6 The new technique uses data from the CPS to construct a ratio of the average hours worked by nonproduction and supervisory workers relative to the average hours worked by production and nonsupervisory workers. This ratio is used with the CES information to arrive at a measure of total hours worked. The historical time series have been revised to incorporate this change. Data on employees of farms, proprietors, and unpaid family workers are taken directly from the Current Population Survey (CPS). 7 Government enterprise hours are developed from BEA estimates of government enterprise employment and CPS data on average weekly hours. Currently, continuous weekly survey data are not available in the United States. Therefore, monthly data must be estimated using data from a representative reporting period within each month that is surveyed. For the household survey the reference period is the week containing the 12 th of the month, and for the establishment survey the reference period is the pay period containing the 12 th of the month. The monthly estimates can then be averaged to arrive at quarterly and annual estimates of hours. IMPACT OF USING DIFFERENT DATA SOURCES The current ICLS resolution provides for hours of work to be collected by establishment surveys or by household sample surveys. This approach allows each country to elect its most preferred survey for these purposes rather than specifying which type of survey to use. Household surveys and establishment surveys each have their strengths and shortcomings and this varies widely across countries. Therefore, it seems imperative that the individual countries be allowed to assess the strengths of their available data sources and make an informed decision on what is the best source of hours data for each intended use. In the United States, for productivity measurement, the BLS feels the strengths of the establishment surveys outweigh its weaknesses. However, the recent divergence in the CES and CPS employment data have led some to speculate about the impact of using different sources of hours data to construct labor productivity. Therefore, for research purposes, we compared the BLS productivity program s hours series and two hypothetical hours series. For this exercise, we constructed two series using methods as similar as possible to those used to construct the hours series that underlie the official productivity series. Both data series are constructed using average weekly hours and employment for wage and salary employees by industry. The hours of nonprofit employees are removed and the data are then aggregated to the private nonfarm business sector, excluding government and private households. CPS hours for self-employed, unpaid family workers and general government are added to the aggregate to arrive at a series of hours for all persons in the nonfarm business sector. The first hypothetical hours series (CES-CPS) is constructed using CES employment and CPS average weekly hours. In order to combine the CES series based on jobs and the CPS series 6 An article discussing these new measures and the effects of this change was published in the April 2004 issue of the Monthly Labor Review. 7 Employment counts for employees in agricultural services, forestry and fishing are extrapolated in the current period using information on employment in logging. 4

6 based on persons, we made an adjustment for multiple job holders. The multiple job holder adjustment was constructed from our earlier research. The second hypothetical hours series (CPS) is constructed using CPS average weekly hours, as well as CPS employment. Because a consistent time series of CPS data that incorporates the updated Census population controls does not exist, we wedged-back the 2000 Census benchmark. In addition, there is also no adjustment for the 1994 CPS redesign. Therefore, we reiterate, that these series are strictly for research purposes and should be used with caution. From the data in Chart 1, we see that the two series that are constructed using CPS data produce higher levels of hours worked than the hours series that underlies the official productivity series, which as noted above is based primarily on the CES. This is consistent with the conjecture that average weekly hours from the household survey may be overstated. The difference in employment leads to the CES-CPS series being slightly greater than the adjusted all CPS hours series. Note that the CES-CPS series also includes the hours of nonemployees from the CPS. 235, , , , , , , , , , ,000 CHART 1: Nonfarm Business Sector, all person hours m illions CPS CES employment, CPS average hours Productivity The table below presents the annual average percent change in the three hours series, and their components. The trends show that the Productivity hours series grows more slowly than the two other series prior to This is a result of CPS average weekly hours remaining relatively constant while the productivity average weekly hours declined, and CPS employment growing faster than CES employment. This is reversed from , when the Productivity hours series grows faster than the two series containing CPS data. From the Productivity hours series again shows slower growth. During this period CPS average weekly hours remaining increase while the productivity average weekly hours decline; this is offset slightly by faster employment growth in the CES relative to the CPS From the Productivity hours and the CES-CPS hours are declining at a much faster rate than the CPS hours. This results primarily from the differences in employment trends over this period. 5

7 Total Hours for all persons Average Weekly Hours Employment, jobs CES- CPS Productivity CPS CES Trends CPS CPS Productivity % 1.22% 1.09% 0.01% -0.13% 1.34% 1.22% % 1.19% 1.26% 0.03% 0.10% 1.10% 1.16% % 2.59% 2.17% 0.28% -0.14% 1.97% 2.30% % -1.54% -1.72% -0.50% -0.68% -0.10% -1.00% Productivity trends using the three different hours series mirror those in the table above. Prior to 1990, the official productivity series grows faster than productivity series constructed using the hypothetical CPS and CES-CPS hours series. For the period , the official productivity series and a productivity measure constructed using the CES-CPS hours series grow significantly faster than a productivity measure constructed using the CPS hours series. Again, this is due to differences in the underlying trends in trends in employment. From this exercise, we see that using household survey (CPS) data on average weekly hours and employment to construct a total hours series provides a somewhat different picture of the growth of hours than does the hours series used to construct productivity measures, which is based primarily on the monthly establishment payroll survey (CES). In addition, we find that the choice of hours will impact measures of productivity. However, the exercise also reveals that the acceleration in productivity growth in recent years exists in all three series. RECENT FINDING FROM THE AMERICAN TIME USE SURVEY The American Time Use Survey (ATUS), which began collecting data in 2003, is a survey of how people living in the United States spend their time. The ATUS sample is drawn from households completing their final month of interviews for the CPS, which currently yields about 1,725 completed interviews per month. 8 ATUS collects a diary of the activities that a respondent was engaged in over the last 24 hours. These activities include information on hours worked such as time at work, time spent on work activities at home, and interruptions of 15 minutes or longer that take place during the work day. 9 The kinds of activities and the time spent on them are available by various demographic characteristics. BLS first published data for 2003 from the ATUS on September 14, Although the ATUS is not designed to collect detailed information on activities at work, it can provide insights on the reporting differences between the establishment and labor force 8 Beginning in January, 2004, the sample size will be reduced so that the number of completed interviews is expected to be approximately 1,100 per month. 9 ATUS interviewers are trained to ask for work breaks of 15 minutes or longer any time a respondent reports that he or she worked. Beginning in January 2004, an automated probe will be introduced into the survey instrument. If a respondent reports working for more than 4 hours at one time, the interviewer automatically will be prompted to ask Did you take any breaks of 15 minutes or longer? If the respondent reports taking a break, the interviewer records the start and stop time and what was done on that break; if no break, the solid work episode will be recorded. 6

8 survey estimates of hours. In the past, there has been evidence from household time use diaries that shows that respondents to labor force survey questions such as those in the CPS report higher hours worked compared to the estimates that result from time diary studies; see for instance Hamermesh (1990), who uses Michigan time use diary data for 1975 and 1981; and Robinson and Bostrom (1994), who use three separate studies for 1965, 1975, and Robinson and Bostrom also show that this reporting difference is greater for those who worked longer hours, and both papers show that it increased over time. Another study by Jacobs (1998), however, found that independent, self-reported measures of working time based on time of departures to and returns from work tend to corroborate labor force survey types of hours questions. Up until now, there have not been any recent studies comparing the post-redesign CPS questions with time use diaries, but presumably the discrepancy is less. 11 Although ATUS data has only been collected since January 2003, BLS has conducted a study to compare CPS reported hours and hours constructed from ATUS time-diaries. This study, What can time-use data tell us about hours of work? by Frazis and Stewart will be forthcoming in the October 2004 Monthly Labor Review. Using similar definitions of hours worked, the study finds that the CPS reported hours of work are very close to those constructed using ATUS time-diaries for the 12 CPS reference weeks in This could be due in part to efforts undertaken in the 1994 redesign of the CPS questionnaire. The revised questionnaire changed the way hours information is obtained in order to enhance respondent recall. However, the Frazis and Stewart study also finds that for weeks outside of the CPS reference weeks in 2003, the ATUS respondents worked fewer hours on average than were reported in the CPS. The ATUS data suggest that for weeks outside the CPS reference weeks, respondents actually work on average approximately 5% fewer hours per week. 13 Thus, this preliminary finding suggests that the use of the CPS data to extrapolate monthly, quarterly, or annual hours could be leading to an upward bias in the number of hours worked. Although it does not appear to be the case that respondents are over reporting hours to the CPS as suggested in the past, the average weekly hours reported in the CPS are greater than the average weekly hours worked outside the CPS reference weeks. This upward bias suggests that the reference week in not representative of the month. Using the 2003 ATUS results, we can construct a crude series of hours worked adjusting the CPS average weekly hours in non-reference weeks. This series is presented below for comparisons but should be used with caution. An upward bias adjustment is made to all nonreference weeks for all years using 2003 ATUS data. Thus the trends for the CPS and CPSadjusted series are the same. Given the limited amount of data, the adjustment is made at the annual level rather at the quarterly level. In addition, there is no information to suggest that this ratio is stable over time or whether there were significant differences before the 1994 redesign. 10 Note that the sample sizes in these studies are very small. 11 In the 1994 revised CPS, the question on usual hours is asked first, followed by questions about overtime and taking time off for reasons such as illness, slack work, vacation or holiday Polivka and Rothgeb (1993, p. 16) report that The mean of reported hours measured with the current [pre-1994] wording was 39.0 compared to 37.9 hours measured with the revised [1994- and later] wording. This is a combined survey effect of the employment and hours questions. 12 The CPS reference week is the week of each month that contains the 12 th. 13 Data have been compiled across all months due to the limited number of observations. 7

9 The CPS series that are presented in Chart 2 are for the nonfarm business sector and constructed to be comparable to the productivity hours series. CHART 2: Nonfarm Business Sector, all person hours millions 215, , , , , , , , , CPS Productivity CPS adjusted using 2003 ATUS ratio From the chart we can see that if the ATUS is used to adjust the CPS hours series, the level of total hours declines. As ATUS data accumulate over several years it will be possible to determine if the result that total hours constructed using the CPS are upward biased is robust. CONCLUDING REMARKS The BLS produces hours series based on data sources with different purposes. For purposes of productivity measurement, we combine data from different sources but have always preferred to use establishment data where possible. In this paper we discuss various concepts, and estimation procedures for measuring hours worked and their implications for productivity measures. Results reported above show that using household survey (CPS) data on average weekly hours to construct a total hours series provides a somewhat different picture of the growth of hours than does the hours series used to construct productivity measures, which is based primarily on the monthly establishment payroll survey (CES). In addition, we have shown that recent information from the ATUS suggests that respondents do not over-report hours to the CPS yet annual and monthly hours estimates constructed using CPS hours will tend to be overstated. If it is desired to construct an annual average number of hours worked, then the total annual hours must be divided by a measure of average employment for the year. The annual average employment figure could be constructed by averaging the monthly employment figures. The monthly estimates should be constructed using the data considered the most accurate and 8

10 reliable for a given country. Chart 3 below shows nonfarm business sector employment for all persons. The productivity series is based on jobs and two CPS series are presented, one based on persons and the other based on jobs. As the chart depicts, the choice of employment concept used in the denominator for an annual average hours series will significantly impact the series. 115, ,000 95,000 85,000 75,000 65,000 55,000 CHART 3: Nonfarm Business Sector Employment, all persons thousand CPS-persons CPS-jobs Productivity The current ICLS resolution defines hours actually worked to include short work breaks but exclude hours paid but not worked. This approach is appropriate for productivity measurement. The resolution does not specify a maximum length of time which defines passive activities as work time. As work patterns are quite heterogeneous across industries as well as countries, it seems that the specification of such a length that is internationally acceptable would be virtually impossible. In the U.S. national income and product accounts, hours data are used for estimating components of income or as a deflator for income. As such, the concept of hours paid may be more desirable. The current ICLS resolution concerning statistics of hours of work applies only to wage and salaried employees. It would be desirable to have the ICLS expand coverage to all persons. In addition, it would be helpful for productivity measurement and national accounting to have the ICLS resolution encourage more industry detail where possible. The current ICLS resolution provides for hours of work to be collected by establishment surveys or by household sample surveys. This approach allows each country to elect its most preferred survey for these purposes rather than specifying which type of survey to use. We support the continued reliance on a country s choice of preferred data. 9

11 REFERENCES Bound, John, 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, Elsevier, 1990, pp Eldridge, Lucy P., Marilyn E. Manser and Phyllis F. Otto, Alternative measures of supervisory employee hours and productivity growth, Monthly Labor Review, April 2004, pp Eldridge, Lucy P., Marilyn E. Manser, Phyllis F. Otto, and Brooks Robinson Hours Data in Productivity Measures. Paper presented at the Federal Economic Statistics Advisory Committee meeting, June 7-8, Frazis, Harley and Jay Stewart. What can time-use data tell us about hours of work? forthcoming, Monthly Labor Review, October Hamermesh, Daniel S. 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. Jacobs, Jerry A. Measuring Time at Work: Are Self-reports Accurate? Monthly Labor Review, December 1998, pp Mellow, Wesley, and Hal Sider. Accuracy of response in labor market surveys: Evidence and implications. Journal of Labor Economics, 1983, vol. 1, no. 4, pp National Research Council. Measurement and Interpretation of Productivity. Washington, D.C.: National Academy of Sciences, Polivka, Anne E. and Jennifer M. Rothgeb. Redesigning the CPS Questionnaire. Monthly Labor Review, September 1993, pp Roach, Stephen S. The Boom for Whom: Revisiting America s Technology Paradox. Morgan Stanley Dean Witter, Special Economic Study, January 9, Robinson, John, and Ann Bostrom. The Overestimated Workweek? What Time Diary Measures Suggest. Monthly Labor Review, 1994, vol. 117, no. 8, pp