Estimating Nonproduction and Supervisory Worker Hours. for Productivity Measurement

Transcription

1 Estimating Nonproduction and Supervisory Worker Hours for Productivity Measurement Lucy Eldridge U.S. Bureau of Labor Statistics Somayah Eltoweissy U.S. Bureau of Labor Statistics Sabrina Wulff Pabilonia U.S. Bureau of Labor Statistics Jay Stewart U.S. Bureau of Labor Statistics This Version: April 2017 Abstract This paper evaluates the BLS Current Employment Statistics (CES) all-employee hours series to determine if it can improve estimates of the underlying hours worked data used to measure productivity. In order to extend our time series beyond the short all-employee-hours series, we use Current Population Survey (CPS) data to simulate CES hours data, including the effect of standard-workweek reporting for salaried nonproduction workers in CES. We find that the growth in aggregate average weekly hours over long periods of time is about the same for the CES all-employee hours series and the current hours series used for the productivity estimates. However, the CES and simulated CES series miss quarter-to-quarter variation in actual hours worked, and there are moderate differences in quarterly growth rates between these series. Given that long-run growth rates are similar, these differences affect mainly the timing of hours growth, and hence productivity growth, across quarters. The current methodology for calculating hours worked for nonproduction workers makes use of a ratio of nonproduction and supervisory worker average weekly hours to production and nonsupervisory worker average weekly hours estimated using CPS data. Thus, we also use data from the American Time Use Survey (ATUS) to assess the reliability of CPS hours data for production and nonproduction workers on their main job. We find evidence that the accuracy in reporting differs by type of worker, with production workers underreporting hours to the CPS and nonproduction worker overreporting hours to the CPS. Thus, hours worked for nonproduction and supervisory workers may be slightly overstated and productivity growth may have been slower than reported during the Great Recession. Acknowledgements: The views expressed in this paper are those of the authors and not necessarily those of the U.S. Bureau of Labor Statistics. We thank Mark Dumas, Shawn Sprague, and Harley Frazis for helpful discussions about the data. We thank Jason Faberman, Daniel S. Hamermesh, Erik Hurst, Jennifer Ward-Batts, and participants at the 2012 NBER/CRIW Summer Institute Workshop, 2013 BLS Technical Advisory Committee, and BEA brown bag for useful comments

2 Introduction Labor is an important input to the production process, and the correct measurement of hours worked is critical for accurately measuring productivity. The main source of hours data for BLS productivity statistics is the BLS Current Employment Statistics (CES) survey, also known as the establishment survey. BLS prefers CES hours data for measuring productivity over data from the Current Population Survey (CPS), also known as the household survey,0f1 because: (1) output data come from establishments and therefore the reporting of hours from establishments is likely to be more consistent with the reporting of output data, (2) industry coding in the establishment survey is more accurate and more consistent with output data, (3) the larger sample provides better industry coverage and reduces the variability of industry-level estimates,1f2 and (4) employment estimates from the establishment survey are benchmarked to the employment universe each year, whereas the household survey employment estimates are not benchmarked.2f3 Historically, the CES did not collect hours data for nonproduction and supervisory workers (henceforth referred to as nonproduction workers). 3F4 To estimate hours for these workers, BLS uses data from CPS to estimate a ratio (henceforth referred to as the CPS ratio) of nonproduction and supervisory worker average weekly hours to production and nonsupervisory worker average weekly hours. This ratio is then applied to CES production and nonsupervisory worker (henceforth referred to as production workers) average weekly hours data that have been adjusted to an hours worked basis by removing paid leave (explained further below) to arrive at 1 Many countries only have reliable hours data from their household surveys. 2 The CPS sample is not stratified by industry, and the CES sample is six times larger than the CPS. 3 Although the CPS employment estimates are not benchmarked, the CPS weights are updated so that the population totals match those of the Decennial Census. The introduction of new population controls typically occurs about two years after the Decennial Census. 4 BLS refers to workers in the supersector of goods-producing industries as production or non-production workers and in the service-providing industries as nonsupervisory or supervisory workers. 1

3 an estimate of average weekly hours for nonproduction workers. Finally, these average weekly hours are multiplied by nonproduction worker employment from CES to obtain total nonproduction worker hours.4f5 In April 2006, CES started publishing hours paid data for all employees.5f6 Adjusting the new all-employee hours paid series to an hours worked concept would seem to be a more direct approach to constructing a measure of aggregate hours for all employees, rather than the current approach that uses the CPS ratio to estimate hours for nonproduction workers. However, it is not clear how accurately the CES can capture actual hours for nonproduction workers who are in the majority of cases paid a salary. The CES questionnaire explicitly instructs respondents to report the standard workweek for salaried workers. This type of reporting is problematic for productivity measures because standard workweeks do not necessarily capture the actual number of hours worked, and the hours-worked-to-hours-paid ratio from the National Compensation Survey (NCS) used to convert CES hours paid to hours worked only accounts for paid leave. If off-the-clock hours are important, then using the CES all-employee hours series could result in different levels and growth rates of average weekly hours, as well as miss cyclical movements in hours worked by salaried workers.6f7 Therefore, it is not clear that using the CES all-employee hours series would be an improvement over the current approach. The goal of this study is to evaluate the new CES all-employee hours series and determine whether BLS should use this new series for constructing productivity measures or continue to use the current methodology. Because the current methodology already uses CES 5 See Eldridge, Manser, and Otto (2004) for a description of the methodology. 6 Note that we will use the term workers and employees interchangeably throughout the paper. 7 Even though the stated concept for the CES hours measure is hours paid, we assume that the CES production worker hours series captures cyclical movements in hours worked because most production workers are paid hourly, which means that the two concepts are roughly equivalent. 2

4 production worker average weekly hours, we focus our analysis on hours for nonproduction workers to see if this simplified approach can produce reliable estimates for these workers. Our analysis covers the nonfarm sector with some additional analyses on major industry groups. First, we compare the levels and trends of the two series over the period. Then, to assess long-run cyclical behavior, we simulate the CES nonproduction-worker hours series using CPS data and compare that series to the series produced using the BLS Productivity program s current methodology and covering the period from 1994 to Finally, we look closely at the CPS data by worker type, using data from the American Time Use Survey (ATUS) from to assess its reliability. II. Hours for BLS Productivity Measures The primary source of data used to construct hours measures for BLS productivity statistics is CES, which is a monthly payroll survey that covers about 588,000 establishments. CES collects data on employment and total hours paid for all employees, as well as total hours paid for production workers. Employment includes all employees who worked or were on paid leave during the pay period that includes the 12 th of the month. Hours paid include hours paid that were actually worked during the pay period plus hours of paid leave, but will not include unpaid overtime or unpaid leave. The hours data are converted to weekly hours paid using conversion factors that vary depending on the number of days in the month. CES computes average weekly hours by dividing total weekly hours paid during the pay period by total employment. For productivity measures, BLS adjusts CES average weekly hours from an hours-paid basis to an hours-worked basis using a ratio of hours-worked to hours-paid that is estimated from 3

5 NCS. This adjustment ensures that changes in vacation, holiday, and sick leave policies do not affect the hours measure.7f8 Total annual hours worked by production workers are calculated as: HH PP = [AAAAAA PP CCCCCC HHHH HHHH ] NN PP 52 (1) where AWHP CES represents measured average weekly hours for production workers obtained from CES, (HW/HP) represents the hours-worked-to-hours-paid (HWHP) ratio from NCS8F9, and NP is production worker employment from CES.9F10 The total annual hours worked by nonproduction workers are estimated using CES production worker average weekly hours and the CPS ratio: HH NNNN = [AAAAHH CCCCCC PP HHHH HHHH ] AAAAHH CCCCCC NNNN CCCCCC AAAAHH NN NNNN 52 (2) PP CCCCCC CCCCCC where AAAAHH NNNN and AAAAHH PP are CPS estimates of average weekly hours worked for nonproduction and production workers, and NNP is the CES employment of nonproduction workers.10 F11 The BLS productivity program began using the CPS ratio to estimate nonproduction worker hours in F12 8 This adjustment captures variations in the amount of annual leave earned and sick leave used. 9 Prior to 2000, the annual Hours at Work Survey was used. 10 BLS does this at the industry level but here we first present estimates we calculate for the entire nonfarm sector. 11 To construct the nonfarm business sector measures, annual hours worked by production and nonproduction employees are constructed for 14 NAICS major industry groups, hours of employees of nonprofit institutions are removed, and then the data are aggregated. Total hours worked for all persons is the sum of production and nonproduction worker hours, and hours worked by the unincorporated self-employed, unpaid family workers and employees of government enterprises. Average weekly hours for the unincorporated self-employed, unpaid family workers and employees of government enterprises are obtained directly from the CPS; remaining data are obtained from various sources. Employment counts for employees in agricultural services, forestry and fishing come from the BLS QCEW program, which is based on administrative records from the unemployment insurance system. The number of employees of government enterprises comes from the BEA. 12 The new approach begins with 1979 data and is linked to the historical data. Prior to 2004, BLS adjusted the hours of production workers in the manufacturing sector using the ratio of office to non-office worker hours extrapolated from a survey that was last conducted in In the non-manufacturing sector, OPT assumed that supervisory workers worked the same hours as nonsupervisory workers. See Eldridge, Manser, and Otto (2004) for more details. 4

6 III. CES All-Employee Hours Data As noted above, the hours concept used by the CES is hours paid, which means that hours of paid leave are included in the reported hours. Therefore, for productivity measurement, the CES all-employee hours series must be adjusted to an hours-worked basis using an HWHP ratio from NCS, as is currently done for CES production worker hours data. This adjustment, as noted above, accounts for changes in paid leave benefits, but not actual leave taken (except sick leave). It also does not account for off-the-clock work. Figure 1 compares a series using current methodology (referred to as the OPT series) to an adjusted CES all-employee average weekly hours series for all workers. To make the CES series comparable to the OPT series, we calculate quarterly CES average weekly hours as a three-month employment-weighted average and seasonally adjust it using the Census Bureau s X-12 procedure.12 F13 The two series behave similarly. The main difference is that the OPT series is higher by of an hour per week. Because nonproduction workers only account for 20 percent of employment in the private nonfarm sector, changes in all-employee hours are mainly driven by changes in production worker hours. We now turn to nonproduction worker hours. IV. Hours Worked for Nonproduction Workers We use CES employment and hours data for production workers and all employees to back out the average weekly hours paid for nonproduction workers.13 F14 Figure 2 compares three estimates of average weekly hours series for nonproduction workers the series using OPT s current methodology, the unadjusted CES hours-paid (CESNP-hours paid) series, and the CES 13 In the official OPT series, the monthly hours data are seasonally adjusted prior to averaging the months across each quarter. 14 Nonproduction worker hours are calculated as the employment weighted difference between the all-employee average weekly hours and the production worker average weekly hours series. 5

7 series adjusted to an hours-worked basis (CESNP) for the period.14f15 The third series is constructed by multiplying CES hours paid by the NCS hours-worked-to-hours-paid ratio for nonproduction workers. Because the hours-worked-to-hours-paid ratios do not vary significantly over time, the two series based on all-employee CES data behave similarly; however, as expected, the average weekly hours paid (CESNP-hours paid) are higher than the average weekly hours worked (CESNP). Note that average weekly hours worked for nonproduction workers as measured by OPT are hours higher than those generated using the new CES series. The higher level of the OPT estimates for nonproduction workers indicates that the CPS ratio of nonproduction-toproduction worker hours is greater than the analogous ratio computed from CES data. It could reflect off-the-clock work by salaried workers or misreporting of hours worked in the CPS.15F16 We examine the reliability of CPS data in section VI. The short time period covered by the CES all-employee series spans the Great Recession, so it is possible to make only limited inferences about differences in the two series cyclical behavior. Between 2009Q2 and 2009Q3 as the economy slowly started to recover, the OPT hours series increases more gradually than the CES series (see Figure 1). This is because the OPT nonproduction worker hours decreased while the CESNP hours increased (Figure 2). Differences in growth rates are more relevant than levels for productivity measurement because the BLS labor productivity measure compares the growth in output with the growth in 15 This constructed OPT series will differ slightly from the data underlying the published series as a result of the seasonal adjustment techniques used because it is constructed from more aggregate CES data for production workers. 16 There are two other possible explanations. First, respondents could be misreporting hours worked by workers on monthly and semi-monthly payrolls, who are more likely to be salaried. There is evidence that employers report 80 hours for workers on semi-monthly payrolls and 160 hours for workers on monthly payrolls, although the correct numbers depend on the number of days in the month. This type of misreporting should result in slightly lower levels and some random month-to-month variation, but should not affect trends. Second, any turnover that occurs during the pay period will tend to increase reported employment, but not reported hours. 6

8 hours. The OPT nonproduction worker average weekly hours series grew by about 0.06 percent over this period while the CESNP nonproduction worker hours series grew by about 3.6 percent.1 6F17 Given that nonproduction workers are only 20 percent of CES employment, the 3.6 percentage point difference over the entire period translates into a 0.7 percentage point difference in the growth of all-employee hours. The implied difference in the annual growth rate of allemployee hours is only 0.07 percentage points. Thus, using the CESNP hours data instead of OPT production worker hours would have reduced the annual growth rate of labor productivity from about 1.2 percent to about 1.1 percent. But over shorter periods, the differences are larger, because there is some quarter-to-quarter variation and the two series do not move in lockstep. Figures 3 and 4 compare the growth rates of the two hours series. Figure 3 shows the annualized quarter-to-quarter growth rates of the CESNP average weekly hours worked series for nonproduction workers and the OPT series over the period. Figure 4 shows the difference in the growth rates of the two series. In most quarters, the difference in growth rates is 4.5 percent or less, suggesting that using the CES all-employee hours series would have a moderate effect on measured productivity growth at the major sector level. The difference in annualized aggregate hours growth for all employees is about 0.8 of a percentage point or about two-thirds of the average annualized quarterly productivity growth over this period. At the beginning of the Great Recession, the change in average weekly hours was much more pronounced in the CESNP series than the OPT series while the reverse was true immediately following the recession. Thus, the two series could produce very different pictures of productivity growth in a given quarter even though they tell the same story over longer periods of time.17f18 17 We assume that employment is the same for both series. 18 We find even bigger differences when the data are stratified by industry. 7

9 The data in Figures 2-4 provide some insight about the differences in the two nonproduction worker series, but the short time series limits our ability to make inferences about cyclical behavior. To examine this issue over a longer period of time, we use the CPS to construct an hours-paid series that simulates the CES nonproduction worker hours series and then compare that series to the OPT series for nonproduction workers. Simulating CES Nonproduction Worker Hours Data Using CPS data CPS collects data on employment and hours from a monthly sample of approximately 60,000 households. The reference period for CPS differs slightly from CES in that the CPS reference period is the week that includes the 12 th of the month rather than the pay period that includes the 12 th of the month.18f19 The CPS collects information about the usual and actual hours worked on the individual s main job and on other jobs that he or she may have. For the main job, the CPS collects class of worker, industry, occupation, and whether the individual is paid hourly. Information on the second job is more limited. Only class of worker, industry, and occupation are collected, and only for one-quarter of the sample. There is no information on whether workers are paid hourly on their second jobs. Because this detailed information about second jobs was only collected on a regular basis starting in 1994, we restrict our analysis to the period.19f20 About 10 percent of second jobs are classified as nonproduction. In constructing the CES simulated series using CPS data, our goal was to make the data as comparable to CES data as possible. The most significant difference between the two datasets is that CES collects information on jobs, while CPS collects information on people. The two 19 The length of the pay period varies across establishments, and changed rapidly in the 2000s. In 2002, 48 percent of establishments had weekly payrolls and another 46 percent had bi-weekly or semi-monthly payrolls. By 2007, those percentages were 32 percent and 65 percent. See Frazis and Stewart (2010). 20 For this study, we use CPS Outgoing Rotation Group (ORG) files. To create the CPS ratio for official estimates, OPT uses the full CPS for main jobs and the ORG data for second jobs (reweighting appropriately). 8

10 concepts would be the same if there were no multiple jobholders. To put CPS data on a jobs basis, we simply created duplicate observations for second jobs so that each observation is a job rather than a person. 20F21 For each job, we determined whether it was in the private nonfarm sector using industry and class of worker codes. Jobs that are not in the private sector are out of scope,21 F22 as are jobs in industries not covered by CES.22F23 The classification of jobs as production or nonproduction was based on industry and occupation codes using the Employer Cost Index (ECI) definitions, which are also used by the productivity program when constructing the CPS ratio.23f24 To simulate the CES hours-paid concept, we make several assumptions about how hours are reported in CES and incorporated those assumptions into the CPS definitions. As noted above, the CES questionnaire explicitly instructs respondents to report the standard workweek for salaried workers.24 F25 Over the period, about 31 percent of production workers and 71 percent of nonproduction workers were salaried.25f26 Thus, the use of standard workweeks for 21 A small fraction of multiple jobholders have more than two jobs. Because the fraction is so small and there is no information on third jobs, we assume that all multiple jobholders hold only two jobs. Note that even after making these adjustments, there remain known discrepancies between CES and CPS data (see Bowler and Morisi, 2006, and Frazis and Stewart, 2010). 22 Thus, for individuals with two jobs, it is possible for one job to be in scope while the other is out of scope. 23 For example, CES does not collect data for agricultural services. 24 We also experimented with two other approaches to classifying jobs as being production or nonproduction. The first includes first-line supervisors. It is not clear that this approach is correct, because these jobs should be classified as production if the supervisory function is secondary. The second alternative approach is to use the exempt/nonexempt classification for workers in services-providing industries. We used a modified version of the classification used in Abraham, Spletzer, and Stewart (1998, 1999). These alternative series exhibited almost exactly the same trend, but had different levels, with the ECI definition yielding the highest hours estimates and the exempt/nonexempt definition yielding the lowest. We opted for the ECI classification, because it most closely replicates the CES worker ratio. 25 For example, assuming a 40 hour workweek, an employer with a bi-weekly payroll and 10 full-time employees, should report 800 hours for the pay period. 26 We assume that the classification of workers as hourly or salaried (non-hourly to be more precise) is correct in the CPS. Hamermesh (2002) argues that, based on demographic trends, salaried workers should have grown as a fraction of employment more than it actually did. Our tabulations show that the fraction of salaried workers increased from 37 percent to 39 percent over the period, although we did not examine whether this is more or less than would be predicted for this time period. There does appear to be some cyclicality, with the fraction salaried increasing by about 1 percentage point during the Great Recession. 9

11 salaried workers could result in missing cyclical changes in actual hours worked by nonproduction workers and, to a lesser extent, production workers.26 F27 Therefore, we assume that hourly workers were paid for all of the hours they worked, and topcoded salaried workers paid hours at 40. We assumed that workers were paid for their usual hours (topcoded at 40) if they did not work during the reference week, but were paid for their time off.27f28 We also assumed that self-employed (incorporated) workers were salaried, and that all second jobs were hourly with no paid time off. To simulate CES employment, we included a job if the individual worked during the reference week or was paid for time off on the main job only. Including the latter, which primarily affects scaling, makes our employment counts conceptually more consistent with CES employment counts. Finally, we multiplied our simulated hours-paid series by the NCS HWHP ratio. Figure 5 shows that the simulated CESNP series differs fairly significantly in the first few years of the CESNP series, with the CESNP series being about 1.5 hours per week less than the simulated series. However, the CESPN series increases between 2006 and the end of 2011, and the two series track each other closely after that. Another difference between the two series is that there is less variation in the simulated CESNP series than in the actual CESNP series. It is worth noting that although the first CES all-employee data were published for March 2006, their data collection started in August The initial data were not published, because they were not 27 Aaronson and Figura (2010) examined how mismeasurement of salaried worker hours is likely to bias productivity statistics. They compared CPS average weekly hours to simulated CES hours and concluded that constant workweek reporting is a bad approximation of actual salaried workweek. In future work, we will examine whether it makes sense to use a ratio to also adjust production worker average weekly hours paid to account for this mismeasurement. 28 We are implicitly assuming that no salaried workers are paid for more than 40 hours. For individuals whose hours vary, we imputed usual hours as follows. If they reported that their hours varied on their main job, usual hours were set to 40 if they usually work full time and to 20 if they usually work part time. On second jobs, hours were set to 14 (the average on second jobs in the CPS). 10

12 thought to be reliable. Given the nature of the CES hours estimator, it is possible that the effects of the initial shakedown period lasted several years. Figure 6 compares the OPT hours worked series to the simulated CESNP series for the period. We can see that average weekly hours in the simulated CESNP series lies below the OPT nonproduction worker series, which partially reflects the CPS ratio adjustment capturing off-the-clock work by salaried workers as previously mentioned. Both series trend slightly downward, with the OPT and simulated CESNP series declining by 4.9 and 1.7 percent, respectively. The absolute difference is about 2 hours before 1999, but then the gap narrows to about 45 minutes in the mid-2000s and starts to widen again toward the end of the period. The gentle U shape of the OPT series reflects the trend in CES production worker hours. Figure 7 shows the annualized quarter-to-quarter growth in the two series. The OPT series is more volatile than the simulated CESNP series (see differences in growth rates in Figure 8). To quantify the differences, we computed standard deviations for the series in Figure 7. For the quarter-to-quarter changes, the standard deviation for the OPT hours-worked series is 0.03, while the standard deviation of the simulated CESNP series is As above, these differences in growth rates and the volatility of growth rates have implications for estimates of annualized quarterly productivity growth. Figure 9 shows the effect of using the simulated CESNP series for nonproduction workers on the growth in the combined total hours of production and nonproduction workers. Both series in Figure 9 use the OPT hours series for production workers, and use either the OPT series or the simulated CESNP series for nonproduction workers. It is clear that the two series track fairly closely (within 1 percentage point each quarter). 11

13 Figure 10 shows the difference in the growth rates between the two series. The lower volatility of the simulated CESNP series, relative to the OPT series, indicates that it does a good job of replicating the reporting of standard workweeks for salaried workers. However, in constructing the series, it was clear to us that the inclusion of paid time off did not make much difference, which suggests that the CPS does not capture this very well. A possible reason for this is that the CPS reference week (the week that includes the 12 th of the month) was chosen to avoid holidays. A paper by Frazis and Stewart (2004) comparing hours estimates from ATUS and CPS shows that average weekly hours are higher by about 2 hours per week during CPS reference weeks compared to non-reference weeks, and that this difference falls by about onethird when holiday diaries are excluded from non-reference weeks. This implies that much of paid time off surrounds holidays. It also suggests that the NCS hours-worked-to-hours-paid adjustment helps to make the CPS reference period, and to a lesser extent the CES reference period, more representative of the entire month. Thus, the jury is still out on how much difference using the CESNP series would make. Comparing the actual CESNP and OPT series for the period suggests that there could be moderate differences in annualized quarterly growth rates. But over the longer run, using the simulated CESNP series suggests that even quarter-to-quarter growth rates would not be that different. V. Industry Detail Our analysis so far has focused on the aggregate nonfarm private sector estimate for hours worked. However, industry detail is also important for two reasons: (1) BLS productivity program builds its aggregate estimates from industry estimates, and (2) publishes productivity 12

14 estimates at the industry level. Building the aggregate hours estimates up from industry estimates ensures that these measures are internally consistent. Figure 11 compares the OPT and CESNP average weekly hours series by industry. We see that the OPT average weekly hours series is greater than the CESNP series for nearly all industries and that the magnitudes of the differences also vary across industries (a notable exception is transportation where CESNP hours exceed OPT hours by 3 hours per week). Table 1 shows that Transportation and Warehousing, Information, Health and Education, and Leisure and Hospitality, the average annual growth rates differ by two percentage points or more. It seems likely that some of the differences could be due to increased variances that result from the smaller samples. However, the differences could also be due to industry misclassification in either CES or CPS. We measure industry misclassification as the sum of the absolute differences in industry employment shares. More intuitively, this measure is equal to twice the percentage of workers that would have to be reallocated to make the two industry distributions the same. Figure 12 indicates that between 6 and 8 percent of workers are misclassified in one of the surveys and that the extent of misclassification varies over time. Interestingly, the difference is the smallest in 2003, about the time when new Census industry codes were introduced. It also appears that there is more industry misclassification of production workers than of all employees. VI. Adjusting for Paid Time Off and Off-The-Clock Work The HWHP ratio adjustment does not capture off-the-clock work. This suggests that the CESNP series could be potentially improved upon by making a second adjustment using CPS data. We construct this new hours-worked-to-hours-paid ratio by dividing the CPS actual hours worked series by the simulated CESNP series (without the NCS HWHP adjustment). The NCS 13

15 HWHP ratio is less than one reflecting the removal of paid leave, while the CPS HWHP ratio is greater than one reflecting off-the-clock hours that are not paid. The paid leave that is captured by NCS appears to be taken predominantly during non-cps-reference weeks. By extension, it is likely that leave is taken mostly outside of the pay period that includes the 12 th of the month.28f29 Figure 13 shows the effect of using both the NCS and CPS hours-worked-hours-paid ratios for nonproduction workers. The new series (CESNP*) lies above the CESNP series (as expected) by 2-3 hours per week. It is closer to the OPT than the CESNP series, but is more volatile than the either of the other two series. VII. Evaluating the CPS hours data using ATUS Time-diary estimates are believed to be more accurate than summary questions about activities done over the previous week because of the shorter recall period and they are less subject to aggregation and social desirability bias. In this section, we assess the accuracy of CPS hours reports using the ATUS. We first compare average weekly hours reported by ATUS respondents in CPS reference weeks to the entire CPS sample over the period. We then adjust this difference for the difference in sample composition between the CPS and ATUS given the high non-response rate in the ATUS (Frazis and Stewart 2009). As a further robustness test, we compare CPS hours reports to estimates from ATUS for the same individuals, restricting the sample to those who likely had the same job and hours in the two surveys. We compared average weekly hours worked on the main job for private nonfarm workers aged F30 In addition, they had to be employed and at work in both surveys and have positive 29 However, it is important to keep in mind that a large fraction of pay periods are two weeks or longer. 30 We do not analyze hours on second jobs because we do not know class of worker, industry, or occupation for second jobs in ATUS and thus it is not possible to determine whether these are production or nonproduction jobs in the ATUS. 14

16 hours in CPS. The combined ATUS sample in reference weeks is 20,210 observations, and the combined CPS sample is 7,386,849 observations. For the ATUS sample, we defined hours worked as time spent in activities coded in the time diary as paid work on the main job plus breaks under 15 minutes and work-related travel (not commuting).30 F31 This definition of hours worked is most consistent with the concept applied for productivity measurement (Frazis and Stewart 2004). Responses were reweighted so that each day of the week received equal weight in each of our sub-samples. Daily time diary measures from ATUS were multiplied by 7/60 and averaged to estimate average weekly hours. For more details about the ATUS construction and why this survey may provide more accurate hours reports see Frazis and Stewart (2004). In Table 2, we find that after adjusting for the difference in sample composition that over the period, private nonfarm wage and salary workers underreported their hours by 0.4 hours. This is the combined effect of production workers underreporting hours by 0.6 hours and nonproduction workers overreporting hours by 0.2 hours.31f32 Thus, assuming ATUS hours reports are more accurate, the differential bias in reporting results in an upward bias in the CPS ratios by 0.02 and hence the current OPT methodology overstates nonproduction workers hours levels. However, what is more relevant for productivity measurement is whether the bias changes over time or over the course of the business cycle. Turning to the three sub-periods, the bias was 0.05 in the period, 0.02 in the period, and 0.02 in the Note that the ATUS does not determine whether work-related activities were done for the main job or for second jobs. We assumed that all work-related travel was done for the main job. 32 This latter finding is consist with Frazis and Stewart s (2004) findings for 2003 that college-educated workers significantly overreport hours while less educated workers underreport hours to CPS as nonproduction workers are, on average, more educated than production workers. In addition, Frazis and Stewart (2010) found that production workers underreport hours to the CPS while Eldridge and Pabilonia (2010) found that nonproduction workers are more likely to bring work home from their workplaces, which could possibly make it more difficult for them to accurately recall their hours worked. 15

17 2015 period. Note that over the period, the difference in the sample composition did not affect the hours reports as much as in the later sub-periods. To illustrate the effect of the change in bias, assume that production workers work 31.6 hours per week (the average weekly hours of production workers after making the hours-workedto-hours-paid adjustment) over the period and period. Then, using the ratios, the CPS ratio would show nonproduction worker hours increasing by 0.3 (= [ ] 31.6) hours instead of increasing by 1.3 (= [ ] 31.6) hours where we adjusted the ATUS ratios for the difference in the sample composition. This suggests that productivity growth may have been slower than reported during the recent recession. In Table 3, we investigate whether production workers underreported hours worked in the CPS due to the fact that 30 percent of production workers are salaried and establishments are requested to report standard workweeks for these workers. Surprisingly, we find the difference in hours is being driven by those production workers who are paid hourly. Hourly workers underreported their hours by 1.4 hours and salaried workers only overreported their hours by 0.5 hours. When we examine ATUS and CPS reports for the same individuals, we further restrict the sample to those who likely had the same job and hours at the times of their ATUS and final CPS interviews. Our sample includes workers who reported the same class of worker, major occupation and industry, and for whom ATUS and CPS usual work hours were within 5 hours of each other (exclusive). Those whose usual hours varied or who reported a change in employers were excluded from the sample. For some of the analyses, we further restricted the sample to ATUS respondents who were interviewed during CPS reference weeks to make the reference period the same as in CPS. However, this significantly reduces the sample size, so we present 16

18 both sets of estimates. For the all-weeks sample, we exclude those workers whose diary day was a major holiday in non-reference weeks, the day following Thanksgiving, or December 23- December 31. Our pooled sample covering the period includes 35,883 workers using all weeks and 9,872 workers using reference weeks only. For the all-weeks sample, there are 27,789 production workers and 8,094 nonproduction workers. For the reference-weeks sample, there are 7,651 production workers and 2,221 are nonproduction workers. In the all-weeks samples, private nonfarm workers overreported hours to CPS by only 0.5 hours (Table 4). Nonproduction workers report their hours correctly on average, but production workers underreport their hours by about 0.7 hours per week. When we restrict the sample to ATUS respondents whose diary day fell in a CPS reference week, we find that CPS respondents underreport their work hours by nearly an hour per week. This is the combined effect of production workers underreporting hours by 1.3 hours per week and nonproduction workers overreporting hours by 0.3 hours per week. In Table 5, we again show that the difference in hours is being driven by those production workers who are paid hourly. In Table 6, we present regression estimates for production workers of the difference in hours based on worker characteristics. For hourly workers, we find part-time workers underreport hours to CPS. In the reference week sample, we also find that proxy respondents underreport hours to CPS for salaried workers. VIII. Discussion and Conclusion Two conclusions that the reader should draw from our analysis are that: (1) the ideal data do not exist and (2) that it is very hard to properly measure hours worked. We found that both the OPT and CES all-employee series showed approximately the same average growth rate in aggregate average weekly hours over longer periods of time (several years). However, 17

19 annualized quarter-to-quarter growth rates often differed by quite a bit, and would lead to substantively different estimates of quarterly productivity growth. We also compared the two series by major industry (14 categories) and found that they were similar for some industries and quite different for others. The analyses presented here highlighted the differences but, in the end, did not answer the question of which series is better. The simulated CESNP series seemed to do a reasonable job of replicating the actual CESNP series, but it exhibited less quarter-to-quarter variation than the actual series, which reduces its usefulness in assessing the effect on quarterly productivity estimates. Perhaps more importantly, the timing of the variation differs, which could lead to different inferences about where we are in the business cycle. The differences by industry, which we did not discuss in detail, are also important because OPT aggregates industry data to get its topside numbers and there is a demand among users for industry detail. Many countries use estimates of hours worked from household surveys, primarily because they do not conduct establishment surveys. Time-use surveys are another source of hours data.3 2F33 Although individual reports are more accurate, the sample sizes are too small to produce reliable estimates for all series. As we noted above, BLS prefers to use the establishment survey as its main source of hours data because hours from an establishment survey are likely to be more consistent with output data, which also come from establishment surveys. Industry coding is also likely to be more accurate in establishment surveys, and hours data from payroll records are usually considered to be more reliable than reported work hours from household surveys. Using the CES all-employee hours series would greatly simplify the production of hours estimates. 33 See Burda, Hamermesh, and Stewart (2013) for an example of estimating productivity using time-diary data. 18

20 However, from the perspective of productivity measurement, there are several data issues to consider when estimating hours worked: 1. CES collects hours paid, not hours worked. For hourly-paid workers, hours paid and hours worked are the same, except for paid time off. But for salaried workers, CES collects only the standard workweek. This is the best that can be done in the CES data-collection environment, because employers have no business reason to keep track of actual hours worked by salaried employees. But hours worked and hours paid can be different for these workers because, in addition to paid leave, they may work off-the-clock. Thus, the standard workweek reporting may miss any secular or cyclical variation in off-the-clock work. The CPS ratio used by OPT addresses this issue for nonproduction workers, because this ratio is computed using actual hours worked (from the CPS). However, it is important to keep in mind that about 30 percent of production workers are salaried, which means that the standard workweek is reported for a large fraction of these workers as well. 2. The NCS HWHP ratio currently used by the productivity program to adjust CES hours data only accounts for paid time off.33f34 This adjustment ratio captures only changes in the amount of leave granted, not the actual amount of leave taken (except for sick leave). This ratio is not designed to measure off-the-clock work, which means that the CES hours-paid measure is not completely adjusted to an hours-worked basis. Adding a second, CPS-based, hoursworked-to-hours-paid ratio seems to improve OPT s estimate of hours worked. 3. The CPS ratio adjustment used in the current methodology assumes that we are able to accurately classify workers as either production or nonproduction. OPT currently uses the same classification definitions as BLS ECI program. While this definition does a pretty good 34 The NCS collects vacation time accrued and actual sick leave taken. 19

21 job of replicating the production worker ratio (the ratio of production workers to total employment), it does not do a good job at replicating trends in wages and hours. A CES record analysis survey (RAS) indicated that a significant fraction of establishments in service-providing industries used exempt/non-exempt status instead of the definition on the CES form. Abraham, Spletzer, and Stewart (1998, 1998) found that a hybrid series that uses the ECI definition in goods-producing industries and exempt/non-exempt status in serviceproviding industries did a better job of replicating trends in CES hourly wages. In this study, we considered two alternative definitions of production worker and nonproduction worker, and found that they differed only in levels.34f35 4. The CPS ratio adjustment also assumes any bias in CPS estimates of hours worked is similar for production and nonproduction workers. Our comparison of CPS hours reports to those from ATUS suggest differential bias. In particular, nonproduction workers overreport their work hours in the CPS while production workers underreport their hours. This combination of over-and underreporting results in a CPS ratio that is too large and higher hours worked levels. More importantly, the bias in the ratio varies over the business cycle. 35F36 5. We did not examine multiple jobholding in this ATUS-CPS comparison, but previous research has found that the reporting of multiple jobs in CPS differs considerably from that in ATUS. Frazis and Stewart (2004, 2009, 2010) found that the multiple jobholding rate in the ATUS is nearly double that in the CPS, and that average weekly hours on second jobs is 35 These are available from the authors on request. We considered a modified version of the ECI definition that includes first-line supervisors as nonproduction workers, and a modified version of the Abraham, Spletzer, and Stewart (1998, 1999) hybrid definition. The different levels of these series imply slightly different growth rates of average weekly hours. Given the small differences, OPT opted for the simplest definition. 36 There has been some concern among researchers about the accuracy of hours of work reported in household surveys. In particular, some researchers have found that respondents answering retrospective questions tend to overstate their work hours (Robinson and Bostrom, 1994; Otterbach and Sousa-Poza, 2010). Other researchers have compared CPS hours data to hours data from ATUS and found CPS data to be fairly accurate, although they find variation in reporting accuracy across demographic groups (Frazis and Stewart, 2004 and 2010). 20

22 about 35 percent lower. Thus, total hours worked on second jobs is underestimated in the CPS. 6. We found that the distribution of employment across industries is different in CPS and CES, which suggests that industry coding in CPS is not consistent with that in CES. Prior to 2003, the Census industry codes were not very compatible with NAICS codes. Coding became more consistent with NAICS when the 2000 Census industry codes were introduced in However, even with the new codes, industry coding in household surveys is more prone to error. Somewhat surprisingly, there is one dimension where industry coding in CPS may be better than in CES. The industry codes for contract workers may be more accurate (for the purpose of measuring productivity) in CPS, because these workers are more likely to report the industry of their worksite instead of their employer s industry. Coding industry based on the worksite is more appropriate for productivity measures, because it matches where the output was produced. In addition, although the topside CES nonproduction worker hours series appear to track the CPS nonproduction worker hours series reasonably well, this is not the case when looking at individual industries. In some industries, the CPS and CESNP series exhibit very different behavior. 21

23 References Aaronson, Stephanie and Andrew Figura How Biased Are Measures of Cyclical Movements in Productivity and Hours? Review of Income and Wealth 56(3), pp Abraham, Katharine G. and James R. Spletzer New Evidence on the Returns to Job Skills, American Economic Review: Papers and Proceedings 99(2), pp Bowler, Mary and Teresa L. Morisi Understanding the Employment Measures from the CPS and CES Survey, Monthly Labor Review 129(2), pp Burda, Michael, Daniel S. Hamermesh, and Jay Stewart Cyclical Variation in Labor Hours and Productivity Using the ATUS, American Economic Review 103(3), pp Eldridge, Lucy P., Marilyn E. Manser and Phyllis F. Otto Alternative Measures of Supervisory Employee Hours and Productivity Growth, Monthly Labor Review 127(4), pp Eldridge, Lucy P. and Sabrina Wulff Pabilonia Bringing Work Home: Implications for BLS Productivity Measures, Monthly Labor Review 133(12), pp Frazis, Harley and Jay Stewart What Can Time-Use Data Tell Us About Hours of Work? Monthly Labor Review 127(12), pp Frazis, Harley and Jay Stewart Where Does the Time Go? Concepts and Measurement in the American Time-Use Survey. in Hard to Measure Goods and Services: Essays in Memory of Zvi Griliches, ed. by Ernst Berndt and Charles Hulten. Frazis, Harley and Jay Stewart Comparing Hours Worked per Job in the Current Population Survey and the American Time Use Survey, Social Indicators Research 93(1), pp Frazis, Harley and Jay Stewart Why Do BLS Hours Series Tell Different Stories About Trends in Hours Worked? in Labor in the New Economy, Katharine G. Abraham, James R. Spletzer, and Michael J. Harper, eds., NBER Studies in Income and Wealth, University of Chicago Press. Hamermesh, Daniel S Million Salaried Workers Are Missing Industrial and Labor Relations Review 55(4), pp Mellow, Wesley and Hal Sider Accuracy of Response in Labor Market Surveys: Evidence and Implications, Journal of Labor Economics 1(4), pp Otterbach, Steffen and Alfonso Sousa-Poza How Accurate are German Work Time Data? A Comparison Between Time-diary Reports and Stylized Estimates, Social Indicators Research 97, pp Robinson, John and Ann Bostrom The Overestimated Workweek? What Time Diary Measures Suggest, Monthly Labor Review 117(1), pp