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1 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis llin_g hours ~ r r II r r U.S. Department of Labor Elaine L. Chao, Secretary Bureau of Labor Statistics Kathleen P. Utgoff, Commissioner The Monthly Labar Review (USPS 987-800) is publ ished monthl y by the Bureau of Labor Stati st ic s of the U.S . Departme nt of Labor. "J'he Review welcomes arti cles on the Jabor force , labor - m ana ge me nt relations , business co nditions , indu s tr y productivity, com p ensa ti o n , occupationa l safety and health. demographic trends. and other economic developments. Papers should be factua l a nd analytical, not polem ical in tone. 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Cover designed by Bruce Boyd https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis MONTHLY LABOR REVIEW _ _ _ _ _ _ _ __ Volume 127, Number 12 December 2004 What can time-use data tell us about hours of work? 3 CPS hours-worked estimates are very close to estimates from ATUS even though the CPS data are not representative of the whole month Harley Frazis and Jay Stewart OSHS: new recordkeeping requirements Comparing old OSHS data with those resulting from changes in recordkeeping rules in 2002 presents some challenges William J. Wiatrowski Hedonic regression models using two data sources 25 The two data sources-BLS and outside of BLS-used to create the models have their own distinct advantages and disadvantages Craig Brown Reports New and emerging occupations 39 Jerome Pikulinski Fatal occupational injuries at road construction sites 43 Stephen Pegula Departments Labor month in review Research summaries Workplace safety and health Precis Book review Current labor statistics Index to volume 127 2 39 43 48 49 51 129 Editor-in-Chief: William Parks II • Executive Editor: Richard M. Devens • Managing Editor: Anna Huffman Hill • Editors: Brian I. Baker, Kristy S. Christiansen, Richard Hamilton, Leslie Brown Joyner • Book Reviews: Richard Hamilton • Design and Layout: Catherine D. Bowman, Edith W. Peters • Contributor: Scott Berridge https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis The December Review Time-use studies in general, ar,d the American Time-Use Survey (Arns) in particular, are valued primarily for what they tell labor economists about the time workers spend away from the job. Harley Frazis and Jay Stewart remind us that time-use surveys do, in fact, contribute to our understanding of time on the job as well. Their analysis of working hours shows that Ams and the Current Population Survey ( C PS) produce similar estimates of hours at work for the weeks containing the 12th day of a month, but that the week containing the 12th is often the week in which people work the most hours. To a great extent, this reflects a deliberate choice: The week containing the 12th was selected as the government's standard reference week for surveys because it was the week in which the fewest holidays fall. William J. Wiatrowski explores the impact of recent changes in the Occupational Safety and Health Administration's recordkeeping requirements on the analysis of the Bureau's workplace safety and health statistics. Craig Brown reports on the advantages and disadvantages of using data from sources outside the Consumer Price Index (CPI) relative to using inhouse data to perform hedonic regressions for the purpose of quality adjustment. Jerome Pikulinski summarizes the new and emerging occupations identified in the 2001 Occupational Employment Survey. In a new department, "Workplace Safety and Health," Stephen Pegula reports on fatal work injuries at road construction sites. Factory compensation In the United States, hourly compensation costs for production workers in manufacturing increased to $21.97 in 2 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 2003. These costs were higher than those in all the economies covered outside Europe , but ten European countries had higher hourly compensation costs (expressed in U.S. dollars) than did the United States. (Hourly compensation costs include hourly direct pay, employer social insurance expenditures , and other labor taxes.) The strength of the European currencies in 2003 drove costs in those countries, as measured in U.S. dollars, up. Costs in the countries which use the euro as a currency rose the most, while costs in several of the European countries that do not use the euro (for example, the United Kingdom) rose less. The average for the 18 European countries in 2003 rose to more than $24 per hour. Costs in Germany nearly reached the $30 level. Compensation costs in U.S. dollars in Canada al so grew. In contrast , Mexican costs dropped. The drop was associated with a weakening peso and an increase in compensation costs on a national currency basis that was lower than average. For more information, see "International Comparisons of Hourly Compensation Costs for Production Workers in Manufacturing, 2003 ," news release USDL 04-2343. Car quality 2005 The value of quality changes in 2005 model-year cars averaged $283.12. This figure represents 73.8 percent of the average increase in manufacturers' invoice prices. In the light truck segment, $306.26 in quality changes accounted for 75.7 percent of average increases in invoice price. The retail equivalent value of the quality changes to 2005 model-year passenger cars averaged $310.50, or 7 4.3 percent of the over-the-year increase in manufacturers ' suggested list prices. The retail value of the quality changes broke down to $193 .11 for safety December 2004 improvements and $117 .39 for other quality change s, such as emission improvements, changes in audio systems , and changes in levels of standard or optional equipment. The retail equivalent value of quality changes for domestic light trucks averaged $345.38 , representing 75.2 percent of the average increase in manufacturers' suggested list prices. The quality changes broke down to $ 18.30 for federally mandated safety improvements, $120.43 for nonmandated safety improvements, and $206.65 for other quality changes such as powertrain improvements, theft protection, changes in audio systems, and changes in levels of standard or optional equipment. Estimates of the value of quality change are based on a review of data supplied by producers for similarly equipped 2004 and 2005 domestic vehicles priced in the Producer Price Index (PPI). For more information, see "Report on Quality Changes for 2005 Model Vehicles," news release UDSL 04-2351. Clarification In Jonathan A. Schwabish 's September article on wages and benefits, footnote 1 8 suggests that the distinction between defined benefit and defined contribution plans is unclear in the Employment Cost Index (ECI) survey. While that may have been true in the past, for the last decade, data used to compile the ECI has distinguished between defined benefit and defined contribution retirement plans using definitions consistent with other BLS surveys. Prior to 1995, ECI retirement costs used a different allocation scheme that classified retirement costs as "pensions" or "savings and thrift plans." Despite the change in definitions, the total cost of retirement benefits was not affected~ what changed was the distribution of the costs among the types of retirement benefits. D What can time-use data tell us about hours of work? Estimates of hours worked from the CPS are very close to estimates from the ATVS for CPS reference weeks; however, CPS reference weeks are not representative of the entire month Harley Frazis and Jay Stewart Harley Frazis and Jay Stewart are research economists on the Employment Research and Program Development Staff, Office of Employment and Unemployment Statistics, Bureau of Labor Statistics. E-mail: Frazis. Harley@bls.gov Stewart.Jay@bls.gov https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis T he number of hours people work for pay is an important economic measure. In addition to being a measure of labor utilization, it is a component of other economic statistics. For example, productivity measures are computed by dividing total output by total hours worked, and hourly wages are often computed by dividing usual weekly earnings by usual weekly hours worked. 1 There are two major sources of hours data for the United States-the BLS Current Population Survey (CPS) and the BLS Current Employment Statistics survey (CES)-and estimates of weekly hours from these two surveys behave differently for a variety of reasons. The goal of this article is to use data from the new American Time Use Survey (ATUS) to shed light on the accuracy of hours-worked reports in the CPS. Because the purpose of this study is to determine whether respondents report hours correctly in CPS, it does not examine other factors that could result in differences in estimates of hours worked from CPS and ATUS. In addition to differences in the reporting of hours, differences in estimates can be due to differences in sample composition and differences in the reporting of other variables. 2 We control for these other factors, but do not analyze their effects on differences in estimates. We examine the effect of these other factors on comparisons of weekly hours from CPS and ATUS in a forthcoming publication. 3 Previous studies Previous studies that assess the accuracy of hours data from establishment surveys either compare hours data for the same industries across surveys, or evaluate accuracy using cognitive methods such as focus groups and interviews with respondents. 4 The former approach allows researchers to document differences between surveys (after accounting for differences in concepts), while the latter provides information on how respondents compile their data. Studies that are directed at verifying hours measures from household surveys such as the CPS typically take one of two approaches: they compare weekly hours reports from a CPS-like question either to ( 1) records from the individual's employer or (2) data collected from the individual using a time diary. Studies by Wesley Mellow and Hal Sider5 and Willard L. Rodgers, Charles Brown, and Greg J. Duncan 6 took the first approach. Both studies assumed that employer-reported hours were correct, and that any difference between the two measures was due to respondent error. The Mellow and Sider study found that, compared with employer reports, respondents overreported hours by 3.9 percent on average, and that overreporting was greater for self respondents than when a proxy provided the information ( 4.3 percent versus 3 .4 percent). They also found that overreporting was greater among managerial and professional workers (11 percent). However, because these workers tend to be salaried, it seems unlikely that their employers kept records of their actual hours worked and instead reported the hours of a standard workweek. Monthly Labor Review December 2004 3 Time-Use Data In contrast, Rodgers, Brown, and Duncan found v !ry little measurement error on average. However, differences in the samples provide the most likely explanation for the different results. The sample in the Rodgers, Brown, and Duncan study was restricted to hourly-paid workers at a single firm. Further, all of the workers in their study were unionized, and most were full time. All of these characteristics would lead to more stable work schedules, which should reduce reporting errors. In contrast, the data in the Mellow and Sider study came from a special supplement to the CPS in which respondents' employers were contacted and asked to provide hours and earnings information. Thus, their sample is representative of the entire employed civilian population. The study that most closely resembles the one in this article was done by John Robinson and Ann Bostrom in 1994. 7 They compared time-diary estim~tes of hours worked from surveys conducted during 1965, 1975, and 1985, to estimates from CPS-like questions about hours worked last week asked during the same surveys. One drawback of using time-diary data from these surveys is that the data were collected only for a single day. To overcome this, they constructed synthetic weeks by combining diaries of demographically similar respondents. Another drawback is that the reference periods for the two measures of hours worked do not cover the same time period. The reference day for the time diary is the day prior to the interview, while the reference period for the CPSlike question is the week prior to the interview. Their results indicated that respondents overreport hours in the CPS-like question, that women tend to overreport more than men, and that the extent of this overreporting has increased over time. These authors also found that overreporting was greater among those who reported the longest hours in the CPS-like question. However, Jerry A. Jacobs in 1998 argued that the relationship between overreporting and reported hours worked is due to regression to the mean. 8 Regression to the mean arises because people who worked unusually long hours during the previous week (the reference period for the CPS-like question) were more Hkely to work more-normal hours during the week in which the time diary was collected. Jacobs's analysis indicates that estimates of time spent at work are very close to estimates of work'hours from a retrospective question, suggesting that self-reported hours are fairly accurate. 9 One possible explanation for the disagreement on the extent of overreporting between these two studies is that they used different measures of work. The Robinson and Bostrom study used actual work time as collected in the time diary, whereas Jacobs used time spent at work. If respondents take time off in the middle of the day to eat lunch or run errands, then the time-spent-at-work measure will overstate time spent working. On the other hand, work that is done at home after hours will be missed by this measure, but will be captured by the actual-time-worked measure. 4 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 The current study contributes to this literature by using ATUS data to examine the accuracy of reporting in the CPS. Because the ATUS sample is drawn from households that recently completed their participation in the CPS, it is possible to link ATUS respondents' interviews to their final CPS interviews. Thus, we can compare estimates of hours worked generated from ATUS time diaries to those generated from the actual CPS questions, rather than from a CPS-like question. One difference between ATUS and CPS survey methods turns out to be unexpectedly important in this comparison. CPS respondents report their labor force activity for the week containing the 12th of the month. This reference week was chosen to avoid holidays. In contrast, ATUS interviews are conducted over the entire month. We find that CPS hours reports are, on average, quite similar to those from ATUS for the CPS reference week, but the reference week is not representative of the entire month. About the data The data used in this study are from the new American TimeUse Survey. The ATUS sample is a stratified random sample that is drawn from households that have completed their participation in the CPS and is representative of the U.S. civilian population. The data cover the January-December 2003 period. Interviews were conducted every day during the year except for a few major holidays. Thus, the data cover the entire year, except for the days before these holidays. About 1,725 diaries were collected each month for a total sample size of 20,720. The response rate for 2003 was about 58 percent. Interviews with fewer than five activity spells or more than 3 hours of uncodeable activities were dropped from the sample. As in other time-use surveys, respondents are asked to sequentially report their activities on the previous day. The diary day starts at 4 a.m. and goes through 4 a.m. of the following day (the interview day), so each interview covers a 24hour period. The respondent describes each activity spell, which the interviewer either records verbatim or, for a limited set of common activities (such as sleeping or watching television), enters a numerical code. These responses are translated into 3-tier activity codes. 1 For each episode, the ATUS collects the start and stop times along with other information. 11 The ATUS does not collect information about secondary activities (for example, listening to the radio while driving) in the time diary. This lack of information on secondary activities should have only a minor impact on time spent in paid work, because most paid work is done as a primary activity. Th~ ATUS also contains labor force information about the respondent that was collected using a slightly modified version of the basic CPS questionnaire. These questions allow analysts to determine whether the respondent is employed, unemployed, or not in the labor force. 12 One notable differ- ° ence between ATVS and CPS employment questions is that the reference period in ATVS is the 7 days prior to the interviewthe last day being the diary day-instead of the previous calendar week as in CPS. The sample for this study is respondents 16 years and older who worked at a job during the 7 days prior to their ATVS interview and reported usual hours. The ATVS collects usual hours worked on respondents' main and other jobs, but does not collect actual hours. Having data on actual hours would be an advantage, because the time diary collects actual hours-and because using actual hours would make our results more comparable to those of other studies. But there is a potential problem with using time-diary estimates of actual hours collected during the ATVS interview: the procedure used for contacting respondents in ATVS could impart bias into estimates of actual hours for the previous 7 days. Each designated person is assigned an initial calling day. If he or she is not contacted on that day, the interviewer makes the next call I week later, thus preserving the assigned day of the week. Individuals who are unusually busy during a particular week (perhaps because they worked long hours) are less likely to be contacted during that week, making it more likely that they are contacted the following week (and asked to report hours for the busy week). Hence, long work weeks would tend to be oversampled, resulting in a correlation between hours worked during the previous week and the probability that that week is sampled. Definitions of hours worked For our comparisons, we consider three alternative measures of hours worked and one measure of time at work from the time diary data: • definition 1: Time spent in activities coded as paid work in the time diary. • definition 2: Definition 1 plus breaks of 15 minutes or less and work-related travel (travel between work sites). • definition 3: Definition 2 plus time spent in work-related activities. • definition 4: Total elapsed time between the start time of the first episode of paid work and the stop time of the last episode of paid work. 13 definitions were chosen for comparison because they represent possible ways that respondents might report hours of work, although, conceptually, one can make a strong argument for using any of definitions 1-3. Of these three definitions, definition 1 is the most restrictive and, based on the descriptions in John Robinson and Ann Bostrom and John Robinson and Geoffrey Godby, 14 is the one used in the earlier Tbl"'.c.P https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis studies to verify hours. This definition was also used by BLS in its recent ATVS press release. Definition 2 corresponds to the definition of hours used for productivity measurement. The inclusion of breaks is appropriate because, as Daniel Hamermesh 15 argues, breaks are productive. 16 On a more practical level, not all respondents report breaks as separate episodes, so definition 2 imposes more consistency across respondents. Definition 3 includes work-related activities-activities that are done for the respondent's job or business, but may have a leisure component and take place outside normal work hours (for example, dining or playing golf with clients or customers). Empirically, there is very little difference between definitions 2 and 3. Definition 4, which is similar to Jacobs 's 1998 time-at-work measure, 17 is potentially problematic because it could include time spent doing nonwork activities, such as running personal errands during work hours. Abraham, Spletzer, and Stewart 18 speculated that the pattern of overreporting found in Robinson and Bostrom 19-that hours are overreported in retrospective questions and that overreporting has increased over time-could be due to the increased flexibility and variability of work schedules. For example, a worker who arrives at work at 8 a.m. and leaves at 6 p.m. might report working a 50-hour week, even though he or she usually takes a 2-hour break each day to run personal errands. Under definition 4, these workdays would be 10 hours long. Jacobs's result ofv~ry little difference between the timeat-work measure and the CPS-like measure is consistent with Abraham, Spletzer, and Stewart's speculation. Comparing ATUS and CPS hours measures We compare the four definitions of time-diary measures of hours worked from the ATVS to retrospective measures from these respondents' final CPS interview. First, hours worked in the time diary are compared to usual hours worked per week from the employment section of the ATVS questionnaire. The advantages of this comparison are that the questions were asked in the same interview and that reference periods for the different measures are close in time. For each of the time-diary estimates, responses were reweighted so that each day of the week receives equal weight (1/7 of the total) and used these reweighted responses to compute the average number of hours worked per day for workers. In order to make the time diary measure-which records hours worked per day--comparable with hours worked per week, these estimates were multiplied by 7. Thus, sample averages should be an unbiased estimate of average hours worked per week for the population. Row (a) of table 1 shows the comparisons for our base sample. Usual hours worked per week reported in the ATVS (using a slightly modified version of the CPS question) are, on average, about 2-3 hours higher than the diary-based measures. The closest figure is 40.9 hours using definition 4-- Monthly Labor Review December 2004 5 Time-Use Data Comparison of time-diary estimates of average weekly hours to estimates from Arus definitions of paid work Respondent Definition 3: Work plus CPS questions cPs definitions of paid work Definition 1: Definition 2: Work only Work plus breaks plus work-related travel breaks plus work-related travel plus work-related activities Start time 37.6 38.0 38.2 40.9 40.3 ... ... (ATUS definition) Definition 4: minus Usual hours in ATUS Usual hours incPS Actual hours in CPS stop time (a) Worked last week, usual hours reported in ATUS (N = 11,988) ................. (b) (a) and worked during CPS reference week and reported usual hours reported in CPS (N = 10,036) ... ..... .. .... .... .. ...................... .. (c) (b) and usual hours in cPs and ATus were within 5 hours of each other (exclusive). (N = 6,268) ......................... 38.7 39.2 39.3 42.2 41 .3 40.0 39.4 37.3 37.8 37.9 40.7 39.3 39.3 38.6 (d) (c) and Arus diary day not during CPS reference week (N = 4,767) ............. 36.8 37.3 37.4 40.2 39.2 39.2 38.3 (e) (c) and ATUS diary day during CPS reference week (N = 1,501) .................... 38.8 39.3 39.5 42.3 39.7 39.7 39.3 (f) (d) and Arus diary day not a holiday 1 (N = 4,703) ........ .......... ........ ..... 37.4 37.9 38.0 40.8 39.3 39.2 38.4 1 Holidays include New Year's Day, Easter, Memorial Day, Independence Day, Labor Day, Thanksgiving, and Christmas. Interviews were collected for all except Thanksgiving and Christmas. the time work stopped minus the time work started. This definition yields an estimate 0.6 hours greater than the usual hours worked figure. Another interesting, and possibly more appropriate, comparison is to compare the time-diary measures with retrospective reports of actual hours worked per week. The ATUS does not collect such a measure. However, the CPS does; so, if a respondent worked during the period covered by his or her last CPS interview, his or her reported actual weekly hours from CPS can be compared with hours worked from the ATUS time diary. The results of this comparison are shown in row (b). Actual hours reported in the CPS are much closer to diary hours for definitions 1-3, but are about 3 hours less than the diary hours for definition 4. One problem with comparing actual CPS hours to ATUS hours from the time diary is that the CPS interviews occurred, on average, 3 months before the ATUS interview, and respondents' work schedules may have changed during that time. 20 Indeed, usual weekly hours reported in ATUS are on average 1.3 hours greater than usual hours reported in the last CPS interview. This change in usual hours presents problems in interpreting the comparison of CPS actual hours to the timediary measures, because one would expect that actual hours may have changed as well. Looking at the CPS as a whole (not just the sample matched with ATUS) , there is no evidence that usual (or actual) hours worked increased between October 2002 (the period that the January 2003 ATUS sample was drawn) and the end of 2003. 21 Together, these facts suggest either 6 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 that persons whose work hours increased were more likely to respond in ATUS, or that persons were more likely to report a high number of hours in ATUS than in CPS for the same jobs despite the fact that the questions are the same in both surveys.22 If the first explanation is correct, the two measures are not comparable because they were reported at different times. To control for changes in usual hours, the sample is further restricted to individuals whose reported usual hours in the CPS and in ATUS were within 5 hours (exclusive) of each other. 23 This comparison is shown in row (c) . The results show a greater difference between definitions 1-3 and CPS actual hours than in row (b). Diary hours corresponding to definition 1 are less than CPS actual hours by 1.3 hours, whereas they are only 0.8 and 0.7 hours less than CPS actual hours for definitions 2 and 3. All of these differences are statistically different from CPS actual hours at the 10-percent level using a 2-tailed test. One factor that could affect these comparisons is that the reference week for the CPS ( the week containing the 12th of the month, as mentioned above) was chosen to avoid holidays. Therefore, one might expect hours of work to be greater, on average, in CPS reference weeks than in nonreference weeks. Rows (d) and (e) show that this is indeed the case: for all measures, diary hours in reference weeks exceed diary hours in nonreference weeks by approximately 2 hours. Using the entire base sample (as in row (a)), rather than the restricted sample (as in row (c)), shows an even larger difference: about 2.6 hours for definitions 1-3 and 2.9 hours for definition 4. Thus, it is more appropriate to compare actual hours from CPS to time-diary estimates that include only diaries that are in the 12 CPS reference weeks. Comparing the CPS measure of actual hours to the timediary measures in row (e) indicates that they are quite close for definitions 1---3 when the diary day is in a CPS reference week. Except for definition 4, none of the diary measures are statistically different from the CPS measure. In contrast, when the diary day is not in a CPS reference week, CPS actual hours exceed time-diary hours for definitions 1-3 by 0.9 to 1.5 hours (all of these differences are statistically significant at the 5percent level using a 2-tailed test). To illustrate the effect of holidays, we recomputed the time-diary estimates for nonreference weeks excluding major holidays and reweighting the remaining diaries so that each day of the week is weighted the same. This exclusion reduced the difference between CPS actual hours and the diary measure by about half an hour, with only the definition I difference remaining significant. In summary, estimates of hours worked from time diaries are significantly lower than estimates of usual hours worked. However, when the sample is restricted to respondents whose usual hours did not change much between their final CPS interview and their ATVS interview, average time-diary hours are close to average actual hours as reported in CPS. These estimatPS are indistinguishable from each other when the ATVS diary day falls in a CPS reference week. When the diary day falls outside the CPS reference week, time-diary estimates are significantly lower than estimates of actual hours worked from CPS. The implications of these results are discussed later. Table 2 shows comparisons for individuals whose usual hours changed by less than 5 hours (the sample in rows (c) ■ r•••u--..- through (f) of table 1), tabulated by sex, education, and full- or part-time status. Men's diary hours are quite close to actual hours reported from CPS, with definition 2 hours being equal to CPS actual hours to one decimal place. Women report fewer hours in the time diary than in CPS for definitions 1-3 (all differences are statistically significant at the 5-percent level). This pattern of differential overreporting is also found in Robinson and Bostrom. 24 They argue that women may be more likely to work part time and have variable schedules, which would make it harder to report their work hours. Although we do not know why women's hours are overreported, we can rule out differences in reporting behavior between men and women. The difference between CPS hours and diary hours is virtually identical between women who self-reported hours in CPS and those whose hours were reported by proxy respondents (who are often spouses). Table 2 also shows comparisons between measures for different educational groups. The sample is further restricted to those ages 25 and older in order to minimize the influence of respondents who are still in school. The results show a consistent pattern, although the differences between CPS actual hours and diary hours are not very precisely estimated. More education is associated with more overreporting of hours in CPS relative to the diary. For high school dropouts, diary hours are slightly higher than CPS actual hours, although the difference is not significant. For high school graduates and those with some college, diary hours are quite close to CPS actual hours, at least for definitions 2 and 3. For college graduates, diary hours are less than CPS actual hours by 1.6 to 2.0 hours per week for definitions 1-3; these differences are statistically significant at the 5-percent level. Comparison of time-diary estimates of average weekly hours to estimates from demographic characteristics ATUS definitions of paid work CPS CPS questions, by selected definitions of paid work Definition 2: Definition 3: Definition 4: Work plus Work plus Usual hours Usual hours Actual hours Start time breaks plus breaks (ATIJS in ATUS incPS mn.is in CPS plus work- work-related definition) time stop travel plus related work-related travel - - - - -- - - - - - - - - + - - - - - - - + - - ----+-a_c_ti_v_iti_e s_----l--------l- ------+----- - + - - - - Respondent Definition 1: Work only Sex Men (N = 2,874) ... ... ... ........ ..... ........ ........ Women (N = 3,394) ................................. 40.4 34.2 40.9 34.6 41.0 34.7 43.9 37.4 41.6 37.1 41.5 37.1 40.9 36.2 38.5 37.5 37.8 37.8 39.1 38.2 38.3 38.1 39.2 38.2 38.4 38.2 41.9 40.7 41.2 41.7 39.6 39.4 39.7 41.0 39.6 39.4 39.6 41 .0 39.0 38.7 38.9 39.8 39.4 23.0 39.9 23.2 40.0 23.3 43.0 24.5 42.0 21.8 42.0 21.8 41.2 21.3 Education (age 25 and older) No high school diploma (N = 417) ... ..... .. High school diploma (N = 1,678) ............ Some college (N = 1,793) ...... ... ... .... ....... College graduates (N = 1,989) .............. .. Full• /Part-time status Full time (N = 5,408) .. .. .. ........................ .. Part time (N= 860) .... ... ........ .................... NOTE: The universe for this table is the restricted sample as defined in row (c) of table 1 (individuals who worked during the reference week in Arns and the reference week in cPs, and whose usual hours in cPs and ATUS were within 5 hours of each other (exclusive). For the education https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis comparisons, the sample was restricted to respondents age 25 and older. Full- / part-time status is determined using the response to usual hours worked in CPS. Respondents who usually work 35 or more hours per week are full time . Monthly Labor Review December 2004 7 Time-Use Data Finally, table 2 compares work hours by full-time and parttime status based on the usual-hours question in ATUS (full time is defined as 35 hours or more usually worked). Timediary hours for part-timers are above actual hours reported in CPS, while for full-timers they are below. Differences between ATUS hours and CPS actual hours are significant at least at the 10-percent level for definitions 1-3 for part-timers, and at the Ipercent level for full-timers. One obvious explanation for this is regression to the mean, as those with unusually high or low hours in CPS revert to more typical values. However, note that our procedure of limiting the sample to those with similar usual hours in CPS and ATUS should help limit this problem. By way of comparison, performing a similar procedure with CPS data by comparing actual hours 3 months apart for those whose usual hours have changed less than 5 hours shows no increase in hours for part-timers or decrease for full-timers. Implications. Our results indicate that, for ATUS respondents, estimates of actual hours worked from the CPS are very close to time-diary estimates for the CPS reference week. On the other hand, it also appears that the CPS reference week is not representative of the month as a whole, as there is a significant difference in hours between reference and nonreference weeks. The fact that CPS reference weeks avoid holidays simplifies the task of tracking employment and hours trends using CPS data. However, a measure of monthly hours worked constructed from CPS average weekly hours data would overstate actual hours worked during the month. The fact that hours for some groups (such as women and college graduates) are significantly overreported has implications for measuring differences in hourly wages between groups. Typically, studies that examine betweengroup differentials use usual hours worked in the denominator of their hourly earnings measure. 25 To illustrate the effect of overreporting by college graduates, if actual hours from the time diary (under definition 2) are used instead of usual hours from the CPS, the college-high school hourly earnings ratio would be 4.1 percent higher. Performing a similar experiment, the female-male hourly earnings ratio would increase by 5.4 percent. It is worth noting that, unless reporting patterns have changed over time, this differential overreporting should have a relatively small impact on trends. Various important economic indicators, including the BLS average hourly earnings series and productivity measures, use data on work hours from the BLS CES program. The CES collects data from establishments for the pay period that includes the 12th of the month; unlike the CPS, this period is longer than the week including the 12th. In addition, the CES measures hours paid rather than hours worked. Thus, the CES hours paid will be much more representative of hours paid over the entire month than the CPS is of hours worked over the entire month-both because the CES covers a longer period and because much of the time off for holidays is paid. 26 OUR COMPARISON OF HOURS WORKED IN ATUS AND CPS indicates that the CPS measure of actual hours is, on average, fairly close to all three of the time-diary definitions of hours worked when the diary day is in the CPS reference week (the week that includes the 12th of the month). However, for the other 3 weeks of each month, the CPS measure of actual hours is approximately 5 percent higher than the hours collected in the ATUS. There is variation in this correspondence between groups: for women and college graduates, reported hours of work are higher in CPS than in ATUS. Analysts should also keep in mind that judging by ATUS, workers work longer hours on CPS reference weeks than other weeks. Because we have only 1 full year of data, we are unable to report on trends in the reporting of hours worked. In the future, as ATUS data accumulate over several years, we will determine to what extent there are changes in hours reporting in CPS causing them to diverge from ATUS reports. D Notes 1 For a discussion of the importance of hours data for measuring real hourly wages, see 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, eds., Labor Statistics MeasuremenJ Issues, NBER Studies in Income and Wealth, Vol. 60, (Chicago, University of Chicago Press, 1998) pp. 293-324; Katharine G. Abraham, James R. Spletzer, and Jay C. Stewart, "Why Do Different Wage Series Tell Different Stories?" American Economic Review Papers and Proceedings, Vol. 89, No. 2, 1999, pp. 34-39; and Lucy P. Eldridge, Marilyn E. Manser, and Phyllis Aohr Otto, "Hours Data and Their Impact on Measures of Productivity Change." Paper presented to the NBER Productivity Program meeting, Boston, March 2004. 2 For example, the ATVS has a higher multiple jobholding rate than does CPS, which would tend to result in ATVS hours exceeding CPS hours. 8 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 3 Harley Frazis and Jay Stewart, "Where Does the Time Go? Concepts and Measurement in the American Time-Use Survey," in Ernst Berndt and Charles Hulten, eds., Hard to Measure Goods and Services: Essays in Memory of Zvi Griliches, NBER Studies in Income and Wealth (Chicago, University of Chicago Press, forthcoming). 4 Sylvia Fisher, Karen Goldenberg, Eileen O'Brian, Clyde Tucker, and Diane Willimack, "Measuring Employee Hours in Government Surveys." Paper presented to the Federal Economic Statistics Advisory Council, Washington, DC, June 2001; and Karen L. Goldenberg and Jay Stewart, "Earnings Concepts and Data Availability for the Current Employment Statistics Survey: Findings from Cognitive Interviews," in Proceedings of the Section on Survey Research Methods, American Statistical Association, 1999. 5 Wesley Mellow and Hal Sider, " Accuracy of Response in Labor Market Surveys: Evidence and Implications," Journal of Labor Economics, Vol. 1, No. 4, 1983, pp. 331-44. 6 Willard L. Rodgers, Charles Brown, and Greg J. Duncan, "Errors in Survey Reports of Earnings, Hours Worked , and Hourly Wages ," Journal of the American Statistical Association, December 1993, pp. 1208l 8. 7 John Robinson and Ann Bostrom, "The overestimated workweek? what time diary measures suggest," Monthly Labor Review, January 1994, pp . 11-23. 8 Jerry A . Jacobs, "Measuring time at work: are self-reports accurate?" Monthly Labor Review, December 1998, pp. 42-53. 9 The dataset that Jacobs used, the 1992 National Survey of the Changing Workforce, collected information on when respondents typically left for and returned from work, typical commute times, and number of days worked per week. 10 The verbatim responses are coded by coders, while the numerical codes are translated into activity codes during processing. See a forthcoming issue of the Monthly Labor Review for a description of coding procedures . 11 Frazis and Stewart, " Where does the time go?"; Michael Horrigan and Diane Herz, " Planning, designing, and executing the BLS American Time-Use Survey," Monthly Labor Review, October 2004, pp. 3-19. 12 ATUS distinguishes between " At Work" and " With Job But Absent From Work" for the employed, and between " Looking" and "On Lay off' for the unemployed. It does not distinguish between different reasons for being not in the labor force. 13 If the respondent reported that the first and last activities of the day are paid work (the first activity begins at 4 a.m. and the last activity ends 4 a.m. the following day) or if the respondent reported more than 4 hours of nonwork activities between the start and stop times, then we assume that the respondent is doing shift work and calculate hours worked using Definition 3 instead. 14 John P. Robinson and Geoffrey Godbey, Time for Life: The Surprising Ways Americans Use Their Time , 2nd edition (State College, PA, Pennsylvania State University Press, 1997). 15 Daniel Hamermesh, " Shirking or Productive Schmoozing: Wages and the Allocation of Time at Work," Industrial and Labor Relations Review, Vol. 43 , No. 3, 1990, pp. 121S-133S. 16 An episode is considered to be a break if it is less than 15 minutes in duration, occurs at the respondent ' s workplace, and the episodes immediately preceding and immediately following the break are coded as paid work. The two episodes of work that surround the break must also pertain to the same job (either main job or other jobs). 2003 (inclusive) . To replicate the effect of the 3-month interval between the final CPS interview and the ATUS interview, we examined a sample of individuals who reported hours in CPS interviews 3 months apart. This is possible because households are in the CPS for 4 consecutive months (Months-in-Sample 1 through 4), then out for 8 months, then in for another 4 months (Months-in-Sample 5 through 8) , so we can match responses from Month-in-Sample (MIS) 4 to MIS I and responses from MIS 8 to MIS 5. We found that, in this matched sample, both usual and actual hours worked declined by about half an hour. However, we need to account for rotation group effects, the wellknown phenomenon that responses to certain questions vary systematically with the length of time that the respondent has been in the survey. (For example, the unemployment rate is higher for respondents in their first month of the CPS than those in their second and subsequent months. See Barbara A. Bailar, ·'The Effects of Rotation Group Bias on Estimates from Panel Surveys," Journal of the American Statistical Association, Vol. 70, No. 349, 1975, pp. 23-30.) We estimate that the average difference in both usual and actual hours between MIS I and MIS 4, and between MIS 5 and MIS 8, within the same survey month is about half an hour-virtually identical to the observed decline in the matched sample. Thus, after adjusting for rotation group effects, the expected change in hours between CPS and ATUS is essentially zero. 22 One partial explanation for the change in usual hours is that CPS accepts proxy responses (responses from someone else in the house hold) , whereas ATUS is strictly self-response. Persons whose CPS response was by proxy show a slightly greater increase in reported usual hours worked than do CPS self-responders. 23 This restriction also helps control for differences between CPS and ATUS in the reporting of other variables that might affect estimates of hours worked. For example, this restriction controls for the difference in multiple jobholding rates that was noted in footnote 3. This restriction to respondents whose usual hours changed by fewer than 5 hours resulted in a 38-percent drop in the sample from 10,036 observations to 6,268. This drop was larger than expected, which led to further investigation. Several patterns emerged, although the decline was large for all groups. The sample restriction was more likely to exclude men and individuals whose MIS 8 CPS response was given by a proxy. Compared with individuals whose usual hours were in the 35-44 hour range in the MIS 8 CPS interview, part-time workers and those who usually worked 45 or more hours were more likely to be excluded. The greater the length of time between the final CPS interview and the ATUS interview the more likely it was that the observation was excluded. Finally, people who reported having more than one job in ATUS, but not in CPS, were more likely to be excluded. 24 Robinson and Bostrom, "The Overestimated Workweek ?" 25 Seasonality is not an issue because both hours measures cover an entire year. The ATUS data cover calendar year 2003 , while the CPS data approximately cover October 2002 through September 2003. See three recent studies: Steve G. Allen, "Technology and Wage Structure," Journal of Labor Economics, Vol. 19, No. 2, 2001, pp. 44083; John W. Budd and In -Gang Na, "The Union Membership Wage Premium for Employees Covered by Collective Bargaining Agreements," Journal of Labor Economics , Vol. 18, No. 4, 2000, pp. 783- 807; and Harley Frazis and Jay Stewart, "Tracking the Returns to Education in the Nineties: Bridging the Gap Between the New and Old CPS Education Items, " Journal of Human Resources, Vol. 34, No. 3, pp. 629- 41. 21 To determine the expected change in usual hours between the final CPS interview and the ATUS interview, we examined both usual and actual hours in the CPS data between October 2002 and December 26 In constructing hours measures for productivity estimates, BLS uses , where possible, information on the ratio of hours worked to hours paid from other BLS surveys to adjust CES hours paid data. 17 Jacobs , " Measuring Time at Work." 18 Abraham, Spletzer, and Stewart, " Divergent Trends in Alternative Wage Series." 19 Robinson and Bostrom , "The Overestimated Workweek?" 20 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 9 . Recordk eeping Requ1rements ' t~l6il ,:.~iill Occupational injury and illness: new recordkeeping requirements Changes to OSHA recordkeeping rules in 2002 resulted in new BLS data; comparing the old and new data series is challenging William J. Wiatrowski William J. Wiatrowski is an economist In the Office of Safety, Health, and Working Conditions, Bureau of Labor Statistics. Email: wiati0\,.,~•ki.william@bls.gov E 2002, the Occupational Safety and Health Administration (OSHA) implemented a number f changes in the definitions of injury and illness cases recorded by employers. The new definitions in tum resulted in changes in occupational injury and illness statistics provided by the Bureau of Labor Statistics (BLS). As an example, in one change, the old definition considered the application of a butterfly bandage to be medical treatment and a recordable case; the new definition considers such treatment to be first aid and not recordable. Using the new definitions, the BLS reported that there were 4.7 million nonfatal injuries and illnesses in private-industry workplaces in 2002, resulting in a rate of 5.3 cases per 100 equivalent full-time workers. 1 While these data follow the trend of declining cases and rates seen throughout the past decade, because of the change in definition they cannot be compared with data from prior years. When the first data from 2002 were released in late 2003 , the BLS cautioned readers of the differences between the 2002 data and data from previous years and discouraged year-to-year comparisons. Becaus~ employers were following the new rules when recording cases throughout 2002, there was no way that two sets of data (one maintained under the old rules, the other under the new rules) could be captured. Nonetheless, data users are interested in the relationship of 2002 data to data from past years. For example, among the questions they might want answered are, Did the 10-year trend of reduced injuries and illnesses continue in 2002? and What effect did the change in recordkeeping rules have on the data? This article provides background on the BLS survey and the change in the recordkeeping rule. Both 2002 data and data from earlier years are examined to determine what patterns might be uncovered. While it will never be possible to identify the rate of change in injuries and illnesses l 0 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 from 2001 to 2002, it may be possible to identify some patterns between the old and new data. These patterns may provide insight into how the change in recordkeeping affected estimates of occupational injuries and illnesses. With only 1 year of data under the new recordkeeping requirements , compared with 30 years under the old system, this analysis should be thought of as an initial attempt to identify patterns and trends. As more years of data collected under the new rules become available, patterns and trends are likely to become clearer. Background For more than 30 years, the BLS has been reporting on the number and rate of workplace injuries and illnesses, an activity that was mandated with the passage, in 1970, of the Occupational Safety and Health Act, according to which the Secretary [of Labor] shall compile accurate statistics on work injuries and illnesses which shall include all disabling, serious , or significant injuries and illnesses, whether or not involving loss of time from work, other than minor injuries requiring only first aid treatment and which do not involve medical treatment, loss of consciousnes s, restriction of work or motion, or transfer to another job. 2 injury and illness data are collected strictly for statistical reporting purposes and undergo the confidentiality and data security screening that apply to all of the Agency's programs. These data collection and reporting activities are independent of the regulatory and inspection activities of OSHA. The two agencies and their activities are linked in many ways, however, including the definitions they BLS use to identify injury and illness "cases"-that is, what counts as an occupational injury or illness. Employers covered under the Occupational Safety and Health Act are required to maintain records of injuries and illnesses that meet OSHA definitions. 1 his requirement is known as the "recordkeeping rule." Certain employers are required to maintain a recordkeeping log of injury and illness cases and, upon request, must make that log available to OSHA inspectors and supply the data contained in the log to the BLS. Other employers must maintain such a log only whe_n they are selected to be part of the BLS survey. In either case, the data the BLS gathers meet the most recent definitions as specified in the OSHA recordkeeping rule. When the rule changes, BLS data change. 3 The following introductory paragraph from the Federal Register notice regarding the change in recordkeeping provides the rationale for the change: The Occupational Safety and Health Administration is revising its rule addressing the recording and reporting of occupational injuries and illnesses (29 CFR, parts 1904 and 1952), including the forms employers use to record those injuries and illnesses. The revisions to the final rule will produce more useful injury and illness records, collect better information about the incidence of occupational injuries and illnesses on a national basis, promote improved employee awareness and involvement in the recording and reporting of job related injuries and illnesses, simplify the injury and illness recordkeeping system for employers, and permit increased use of computers and telecommunications technology for OSHA recordkeeping purposes. 4 (OSHA) The 2002 recordkeeping rule included many changes. For example, under the old rule, recurrences of injuries or illnesses after a 30-day period were recorded as separate cases. Under the new rule, a time frame is no longer specified. Accordingly, employers may now consider recurrences that are not brought on by a new event or exposure in the workplace to be the same case. In another example, the old rule considered the application of a butterfly bandage to be medical treatment and a recordable case; by contrast, the new rule considers such treatment to be first aid and not recordable. Intuitively, these two changes are likely to result in a decline in the number of recordable cases, but that is not the case for all the recordkeeping changes. For example, under the old rules, needle sticks were recorded only if they resulted in medical treatment; now needle sticks are recorded if there is the potential to be contaminated with another person's blood, regardless of whether the affected person is or is not treated. In its annual reports on occupational injuries and illnesses, the BLS has monitored the trend in rnjury and illness counts and rates. Both the actual number of cases and the rate of occupa- https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis tional injuries and illnesses generally have been declining over the past decade. 5 (See chart 1 and table 1.) The wide variety of changes to the recordkeeping rule made it impossible for the BLS to compare the 2002 data with data from previous years. Survey of Occupational Injuries and Illnesses Participation in the BLS Survey of Occupational Injuries and Illnesses is mandatory; indeed, the survey is the only Federally mandated one conducted by the BLS. 6 The survey covers private-sector employers, regardless of the number of workers, with a few exceptions.7 Data also are available for State and local government workers in a number of States. Each year, the BLS selects a sample of employers covered under OSHA regulations, including those which must maintain a log of workplace injuries and illnesses under the OSHA rules and those which do not have such requirements, typically because of their small employment. At the end of the year prior to which data are to be recorded, all sampled establishments are notified of their selection for the survey and are provided instructions for maintaining injury and illness records. A year later, these establishments are contacted again and are asked to provide the BLS with data from the records they maintained over the past year. Among the data to be provided are information on employment and hours, a summary of the number of recordable cases, and detailed characteristics of cases that involve days away from work. The BLS publishes two sets of national and State data based on information provided by employers. 8 The first release of data contains summary estimates of the number and rate of injuries and illnesses by industry, with some details provided on the type of case, such as that resulting in a job transfer or restricted work activity. The second release contains details on the demographics of the injured or ill worker and the circumstances surrounding the case. This detailed information is available only for those cases which involve days away from work-one of the types of cases recorded by employers. Number of occupational injuries and illnesses, private industry, 1992-2001 [In millions] Year 1992 ....... ... .. ........ ... ...... 1993 ....... ... ................. .. 1994 ... .. .. ... ..... ............ .. 1995 .... ... ........ ..... ....... .. 1996 ······· ······ ······· ········· 1997 ··· ······· ·· ·· ·· ···· ··· ······ 1998 ... .... ... .. .. .. ..... ........ 1999 ···· ····· ··· ··· ····· ··· ······ 2000 .. ....... .......... .... .. .. .. 2001 ..... ............. .... .. .... . Number of occupational injuries and illnesses Number of occupational Injuries 6.7994 6.7374 6.7669 6.5754 6.2389 6.1456 5.9228 5.7072 5.6501 5.2156 Monthly Labor Review 6.3420 6.2553 6.2522 6.0806 5.7999 5.7158 5.5309 5.3350 5.2876 4.8818 Injuries as a percent of total 93.3 92.8 92.4 92.5 93.0 93.0 93.4 93.5 93.6 93.6 December 2004 11 Recordkeepi ng Requirements lncidence1 of occupation al injuries and illnesses, private industry, 1976-2001 Rate Rate 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 0 1976 1 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 0 . . . . . . . . The incidence 1s the number of in Junes and illnesses per 100 full-time workers. The survey began in I 971 and has produced annual data since 1972, with a major revision in 1992. That revision resulted in the inauguration of a separate program to track workplace fatalities: the Census of Fatal Occupationa l Injuries. 9 The revision also introduced the current survey output of detailed characteristic s of cases involving days away from work. Prior to that time, there was no comprehensive nationwide study of the details of injury and illness cases. Instead, a number of special studies were conducted that explored certain industries or certain types of injuries. 10 The two Federal agencies The BLS and the OSHA play very different roles with regard to worker safety, as indicated in the mission statement of each agency: The Bureau of Labor Statistics (BLS) is the principal fact-finding agency for the Federal Government in the broad field of labor economics and statistics ... BLS data must satisfy a number of criteria, including relevance to current social and economic issues, timeliness in reflecting today's rapidly changing economic conditions, accuracy and consistently high statistical quality, and impartiality in 12 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 both subject matter and presentation. 11 OSHA 's mission is to assure the safety and health of America's workers by setting and enforcing standards; providing training, outreach, and education; establishing partnerships; and encouraging continual improvement in workplace safety and health. 12 The BLS is a nonpartisan statistical organization that provides data on a wide range of labor-related issues, including occupational safety and health. The agency does not have any regulatory or enforcement functions. OSHA uses BLS data in setting standards and identifying areas of emphasis for inspection. The rate of injuries and illnesses in a specific industry, as published by the BLS, is used as a standard for targeting reductions in workplace injuries and as a benchmark for individual employers. For example, OSHA has as one of its goals to "reduce the rate of lost workday injuries and illnesses by at least 5 percent annually." 13 Whether this goal has been met is determined with the use of BLS data. In addition, OSHA has established a number of cooperative programs to work with businesses and other organizations. Among these programs are the OSHA Voluntary Protection Programs, which use BLS data as a benchmark that participating employers must meet to be eligible for certain safe-worksite designations. 14 BLS data While the BLS has captured and reported on occupational injuries and illnesses since the early years of the 20th century, there were few standards in place regarding the reporting of occu-pational injury and illness data prior to the Occupational Safety and Health Act of 1970. The current BLS data series began soon after the Act was passed. Early revisions to the program reflected changes in industrial classifications and OSHA recordkeeping rules. The BLS survey was completely redesigned in 1992, the result of a detailed analysis of the existing program by the National Academy of Sciences. 15 The redesign resulted in the separate collection of fatalities 16 and the collection of detailed case characteristics. Despite these changes, the BLS has been able to produce a largely consistent data series showing the number of cases and the rate of occupational injuries and illnesses. That series ended with the 2001 data, although the rate of 5.3 injuries and illnesses per 100 full-time workers in 2002 is consistent with the trend seen in previous years. But the inability to track total cases and incidence rates before and after the recordkeeping change does not mean that certain patterns in the injury and illness data cannot be explored. Patterns involving the types of cases or events leading to injury, among other characteristics, may provide some indication of the effect the revised recordkeeping rules had on employer reporting. For example, about 6 percent of reported occupational injury and illness cases in 2002 were illnesses, nearly identical to the proportion reported over the previ0us several years. Injury and illness cases are divided into two broad categories: cases with days away from work, with a job transfer, or with a job restriction; and other recordable cases. Prior to 2002, cases were identified as either lost-workday cases or cases without lost workdays. Despite the change in case classification and definition, the division of cases between the two broad categories is generally consistent from 2000 to 2002, with about half of the cases falling into each of the categories. (In both 2000 and 2001 , 49 percent of all cases were lost-workday cases, while in 2002, 53 percent of all cases were cases with days ·away from work, with a job transfer, or with a job restriction.) In the past, data were recorded in such a way that information by type of case could be produced for injuries and illnesses combined or for each of those categories separately. The 2002 recordkeeping change eliminates the ability to produce separate case-type data either just for injuries or just for illnesses. Industry data Among most major industry groups, the number of cases involving days away from work exceeds the number involving a job transfer or job restriction, with the notable exception of ma11ufa8turing. In manufacturing in 2002, about 25 percent of cases involve days away from work, while 32 percent involve https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis a job transfer or job restriction. (The remaining cases generally involve medical treatment, but do not result in any time off, restricted duty, or transfer.) This difference specific to manufacturing continues a trend seen for the past several years, even before the change in recordkeeping rules. (See table 2.) In 2002, there were six industries 17 that recorded I 00,000 or more cases of occupational injuries. This figure compares with nine such industries in 2000 and eight in 2001. (See table 3.) The lists of industries in each of the 3 years are similar. Indeed, the six industries with the greatest number of injuries were the same for the last 3 years, although not in the same order. Hospitals became the industry with the greatest number of injuries in 2002, surpassing eating and drinking places, which had been the industry with the highest count nearly every year since the BLS began presenting data in this way in the late 1980s. Among the six industries listed, there were variations in the numbers of cases between 2001 and 2002 that could be the result of recordkeeping changes . For example, hospitals may report more cases due to changes in reporting requirements related to needle sticks. Of course, the many recordkeeping changes may have affected specific industries in a variety of, and perhaps offsetting, ways. As noted, illnesses as a proportion of total cases remained constant from 2001 to 2002, but the proportion in manufacturing dropped from 54 percent of all illness cases in 2001 to 44 percent in 2002. This change may be due to the recordkeeping changes that altered the types of illnesses reported. Prior to these changes, there were six specific types of illnesses, plus a category for "all other illnesses." In 2002 and beyond, there are three categories, plus "all other illnesses." 18 (See chart 2.) With the elimination of a separate category for disorders associated with repeated trauma, the proportion of cases recorded as "all other illnesses" became the predominant type of illness. 19 Shifts in employment and in hours worked in certain industries may influence the data on occupational injuries and illnesses. For example, hospitals had the greatest number of cases in 2002, surpassing eating and drinking places for the first time. The injury and illness rate in hospitals also was higher in 2002 than in 2001 , but did not increase as much as the number of cases. This reversal suggests that the increase in injuries and illnesses in hospitals was not strictly a function of changes in employment. The opposite may be true with the change in the proportion of illnesses in manufacturing: manufacturing employment and hours worked declined between 2001 and 2002, which may have affected the proportion of illness cases in the industry. Cases involving days away from work Detailed case and demographic data are available only for those cases involving days away from work. Once again, the definition of a case differed from 2001 to 2002, as did the method used to Monthly Labor Review December 2004 13 Recordke eping Requirem ents Incidence 1of occupational injuries and illnesses by industry and type of case, private industry, 2000-02 Industry and type of case 2000 2001 2002 Total .............. ................ ............ ......... Cases with days away from work 2 Cases with restriction 3 •••. .. ... . . . ........ 6.1 1.8 1.2 5.7 1.7 1.1 5.3 1.6 1.2 Agriculture, forestry, and fishing Total ... .. ...... ... ........ .... ... ..... .............. .. . Cases with days away from work 2 • • Cases with restriction 3 ••. •.. •. .... ••.•. . . . 7.1 2.5 1.1 7.3 2.7 .9 6.4 2.1 1.2 4.7 2.4 .6 4.0 1.8 .6 4.0 2.0 .7 8.3 3.2 .9 7.9 3.0 .9 7.1 2.8 1.1 Total ... ................... ............... ..... ......... Cases with days away from work 2 .• G;:is,=,s with restriction 3 . .. •.••.•.•.• • •• . ... 9.0 2.0 2.5 8.1 1.8 2.2 7.2 1.7 2.3 Transportation and public utilities Total ............................................. ... .. . Cases with days away from work 2 •• Cases with restriction 3 ••.•.... . •.......•.. 6.9 3.1 1.1 6.9 3.0 1.3 6.1 2.7 1.3 5.9 5.6 5.3 1.7 1.0 1.6 1.0 1.6 1.1 1.9 .6 .2 1.8 .6 .2 1.7 .5 .2 4.9 1.4 .9 4.6 1.3 .8 4.6 1.3 .9 Mining Total ...................... .. ... ... ............ ......... Cases with days away from work 2 •• Cases with restriction 3 ... . . • . .....•.••... . Construction Total ................. ....... ........................... Cases with days away from work 1 • • Cases with restriction 2 • • ••• ••••••.•••••••• Manufactur ing Wholesale and retail trade Total .... .............. .................. ............... Cases with days away from work 2 ••••• • ••••••• •• • ••.••• ••••••••••• ••.• Cases with restriction 3 •... •.•.... .. •. .. ... Finance, insurance, and real estate Total .. .... ........ ..................................... Cases with days away from work 2 •• Cases with restriction 3 . •............ .. . .. . Services Total ... ............... ............. ... ................. Cases with days away from work 2 •• Cases with restriction 3 •••... .•. . •. . •• . •..• 1 The incidence of Injuries and illnesses represents the number of injuries and illnesses per 100 full-time workers and is calculated by multiplying the number of injuries and illnesses by the total hours worked by all employees during the calendar year. The result of this calculation is then divided by 200,000 (100 workers , times 40 hours per week , times 50 weeks per year) to determine the incidence. 2 In 2000 and 2001 , includes cases involving days away from work with or without restricted work activity. In 2002 , includes cases involving days away from work with or without job transfer or restriction . 3 In 2000 and 2001 , defined as cases with restricted work activity. In 2002, defined as cases with job transfer or restriction . count the number of days away from work. Prior to 2002, days were counted as workdays away from work. In 2002 and subsequent years, the count is calendar days away from work. For those cases with days away from work, demographic characteristics that are captured by the survey include sex, age, occupation, and other items. Ch~1 racteristics of the injury or illness case include the nature of the injury or illness, the part of the body involved, the event that led to the injury or illness, and the source of the event. For example, an injury case with days 14 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Decembe r 2004 away from work involving a nurse who sprained her back while lifting a patient would have the following characteristics: • • • • Nature of disabling condition : sprain Part of body affected: back Event or exposure: lifting Source directly producing disability: patient In addition, these characteristics can be used to construct a count of musculoskeletal disorders, which are defined as injuries or disorders of the muscles, nerves, tendons, joints, cartilage, or spinal discs. Musculoskeletal disorders are determined by the nature of the condition and the event or exposure leading to that condition. 20 Both the rate and the number of injury and illness cases involving days away from work under the previous recordkeeping definition have been declining steadily since the data were first collected in 1992. (See charts 3 and 4.) The data for 2002-1. 4 million injury and illness cases involving at least 1 day away from work and a rate of 1.6 cases per 100 equivalent full-time workers are consistent with the declining numbers over the previous decade. Moreover, the distribution of these cases by sex follows the same pattern as in the past: in 2002, 65 percent of cases involving at least 1 day away from work affected men, a number nearly identical to that for the previous 2 years. Furthermore, as in the past, men had a greater proportion of injuries and illnesses than their proportion of hours worked. The distribution of cases by age also was consistent between 2002 and prior years, with about three-quarters of the cases occurring among those aged 25- 54 years. (See table 4.) The occupatio n with the greatest number of injuries and illnesses involving days away from work in 2002 was truckdrivers, as it has been since 1993.21 As table 5 indicates, many of the occupations with the highest number of cases were the same in 2002 as they were in the 2 previous years, although there were changes in the order. Two occupations are among the list of the 10 occupations with the greatest numbers of injuries and illnesses for the first time in 2002: supervisors of sales workers and other sales workers (those not in a specific sales occupation, such as auto sales or apparel sales). The greater prevalence of injuries and illnesses among these sales occupations may be due to the recordkeeping change. For example, as of 2002, incidents that occur on work property before or after work, such as assaults or falls in a parking lot, are recordable cases.22 Conversely, two occupations previously among the top 10, but which fell just below that threshold in 2002, are cashiers and stock handlers. Workers in these occupatio ns often suffer repetitive -motion injuries. The change in the recordkee ping requireme nt that eliminates the repeat recording of cases that recur after 30 days may have led to a decline in cases in these occupations. The characteristics of injuries and illnesses incurred in 2002 were nearly identical to those from 2001. The most prevalent kind of injury was a sprain or strain, affecting 43 percent of all cases. ■ r• 1 •"'---- Number of cases of nonfatal occupational injuries for industries with 100,000 or more cases, 2000-02 2002 Industry 2000 2001 Hospitals .. .......... ......... Eating and drinking places .. .... ................. . Nursing and personal care facilities ............. Grocery stores ............ Department stores .... .. Trucking and courier services, except c1.ir .. Air tram,portation , scheduled .. ... .... .. ...... . Motor vehicles and equipment .......... .. ... ... Hotels and motels ....... 259.5 265.7 296.1 285.3 283.7 247.5 199.0 180.1 150.7 192.9 175.1 143.3 180.4 154.5 138.9 129.1 134.9 104.0 127.2 116.3 124.6 101.0 102.7 NOTE : Industries are based on three-digit Standard Industrial Classification codes and are in order by the number of cases in 2002 . Dash indicates industry did not have 100,000 or more cases in year shown. prevalent involving floors, walkways, and ground surfaces; containers; and worker motion or position. Finally, the two events that were cited most often as leading to injury or illness were contacts with objects and equipment (such as being struck by an object) and overexertion (often due to lifting). The number of assaults and violent acts, and their percentage of all events, was slightly greater in 2002 than in 2001, a result that may be due to the recording of events which occur prior to and after work on employer property, such as incidents in parking lots. (Looking beyond work-related incidents, overall rates of violent crime dropped from 2001 to 2002, as did robbery and assault rates. 23 ) By contrast, repetitive-motion events and their proportion of all events were down slightly, due perhaps to the lack of a specific category to capture disorders associated with repeated trauma or to the change in rules for recording repeated occurrences of an injury or illness. Musculoskeletal disorders continue to account for about 1 in 3 injury and illness cases involving days away from work, as they have consistently over the past decade. (See chart 5.) Median days Body parts affected most frequently were the trunk (specifically, the back), followed by both the upper and lower extremities. Sources of injuries and illnesses were widespread, with the most One of the changes in the OSHA recordkeepin g requirements was the way in which employers were to count the number of Percent distribution of occupational illnesses by type, private industry, 2001--02 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis l Poisoning Respiratory 2001 ·soning / Respiratory 2002 Monthly Labor Review December 2004 15 Recordkeeplng Requirements Number of occupational injuries and illnesses by selected types of cases, private industry, 1992-2001 Millions Millions 12 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 12 11 ■ Cases of days away from work ■ Total cases 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 0 lncidence1 of occupational injuries and illnesses by selected types of cases, private industry, 1992-200 l Rate Rate 14 14 ■ Cases of days away from work ■ Total cases 12 12 10 10 8 8 6 6 4 4 2 2 0 1992 1 16 1993 1994 1995 1996 1997 1998 The incidence is the number of injuries and illnesses per 100 full-time workers. Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 1999 2000 2001 0 Percent distribution of occupational injuries and illnesses involving days away from work, by selected demographic characteristics, 2001 and 2002 Characteristic 2001 2002 Total .......... .. ..... ............ . 100.0 100.0 Men ......... ..... ... .. .... .. ......... ..... .... . Women .......... .......... ....... .. .. ....... . 65.7 33.6 64.8 .1 2.9 11 .2 25.3 28.5 20.5 {' ) 2.7 11 .1 25.0 27.9 21 .2 10.0 1.7 34.9 Age, years 14-15 .. ... .. .... ....... ... ....... ....... ..... . 16-19 .. .... .. .. ..... ... ........ ....... .. ...... 20-24 ... ..... ...... .... .... ....... ... ......... 25-34 ....... ..... .. .... .... .................. . 35-44 ··· ·· ·············· ···· ········ ·· ····· ··· 45-54 ······· ····· ········· ·········· ········ ·· 55-64 ............... .. .......... .............. 65 and older ... .... ....... .. ... ........... 1 8.8 1.6 Less than 0.1 percent. days away from work. Previously, the count pertained to workdays. Beginning in 2002, the count applies to calendar days, a change intended to "ensure that a measure of the length of disability is available, regardless of the employee's work schedule. •>2 4 This modification may have the effect of increasing the median number of days away from work recorded by the survey. For example, in the past, if an injury occurred on a Wednesday and the employee did not return to work until the following Tuesday, the employer would count 3 days away from work (Thursday, Friday, and Monday, assuming a standard 5day workweek). Under the new guidelines, the employer would count 5 days (Thursday through Monday). This change may especially affect those individuals or occupations which do not work a standard workweek. For example, those aged 14 or 15 years may work only a few days per week, perhaps after school or on weekends. In 2000 and 2001, such workers who suffered an injury or illness that required time off from work had a median of 2 days away from work. In 2002, that median was 7 days, perhaps reflecting the count of calendar days between their times at work. A closer look at occupations that are typically thought of as having irregular work hours or a large proportion of part-time workers shows that the change in recordkeeping rules regarding how days are counted may have affected different occupations in different ways. For example, waiters and waitresses who incurred injuries or illnesses involving days away from work were off the job for a median of 5 days in 2002, compared with 7 days in 2001. Cashiers, also a job with a large share of part-time workers, saw their median days away from work remain at 6 days from 2001 to 2002. These two examples suggest that other recordkeeping changes, aside from the method of counting days, are influencing the results. Overall, the median number of days away from work was 7 in 2002. Between 1995 and 2001 , the median was always 5 or 6 days. Chart 6 shows how the percent distribution of days has changed, https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis with the proportion at 31 days or more a few percentage points higher than in the past. Again, this effect may be the result of the change in recordkeeping rules. The distribution of days away from work for truckdrivers and registered nurses provides an example of how the data have changed. The median number of days away from work for truckdrivers rose from 10 in 2001 to 13 in 2002. For registered nurses, the median rose from 4 days to 6 days, and those with a median number of days away from work greater than 10 rose from 30 percent to nearly 40 percent of all cases. For injuries and illnesses requiring time off from work, the median number of days away from work increased between 2001 and 2002 for injuries and illnesses with a variety of characteristics. For example, cases of carpal tunnel syndrome led to a median 25 days away from work in 2001 and 30 days in 2002. Similarly, amputations led to a median 18 days away from work in 2001, compared with 26 days in 2002. In contrast, certain prevalent events leading to injuries or illnesses showed only slight increases in median days away from work: overexertion in lifting led to a median 8 days away from work in 2002, compared with 7 in 2001; and being struck by an object led to a median 5 days away from work in 2002, compared with 4 in 2001. The I-day increase in these more frequently occurring events reflects the overall 1-day increase in the median for all cases with days away from work. Finally, the data on musculoskeletal disorders show a slight increase in the median number of days away from work, from 8 days in 2001 to 9 in 2002. To compare or not to compare The BLS has stated that the change in occupational injury and 11•1• 11 =--- Occupations with the highest number of injury and illness cases involving days away from work, 2000-02 Number of cases (in thousands) Occupation Truckdrivers ........ ......... Nursing aides, orderlies, and attendants ...... .. .. Laborers, nonconstruction ... ... ......... Janitors and cleaners ... Construction laborers .. Assemblers .. ........ ... ..... Carpenters .. ......... ........ Supervisors and proprietors, sales ...... Cooks .. ......... ........... ... . Sales workers , other commodities ..... .... .... .. Cashiers .. ... .. ........... .. .. Registered nurses ... ... . Stock handlers and baggers .. ..... ....... .. .... . Nor E: 2002 2000 2001 136.1 129.1 112.2 74.2 71.0 79.0 87.0 40.7 45.4 38.9 38.3 68.9 38.6 31 .1 32.7 76.6 42.0 41.9 34.4 28.3 24.1 27.8 23.1 27.8 26.1 24.7 24.1 26.9 24.5 22.2 22.2 24.7 24.7 22.5 21 .9 23.8 25.7 21 .5 44.1 Occupations are in order by the number of cases in 2002 . Monthly Labor Review December 2004 17 Recordkee ping ReqJiremen ts Number of occupati onal injuries and illnesses involving days away from work and those resulting in musculoskeletal disorders, private industry, 1992-200 l Millions 3.5 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - -- Millions - - - , 3.5 ■ Musculosk eletal disorders ■ Days away from work 3.0 2.5 2.0 1.5 0.0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Percent distribution of days away from work, private industry, 2001 and 2002 Year 2002 2001 0 18 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 20 40 60 Percent December 2004 80 100 illness recordkeeping requirements in 2002 resulted in a discontinuity in the data series and that comparisons with previous years should not be made. Nonetheless, data users are eager to track trends and to determine the effect of the recordket>pif'lg changes. This article was written to provide some guidance for those users. Tracking trends will be difficult, because determining the exact effect of the recordkeeping changes is not possible. But with some careful analysis and some caveats, data users may be able to identify patterns. Specifically, users who are comparing the data for multiple years should keep the following suggestions in mind: • Consider proportions, as well as counts. • Consider multiple perspectives on the same data, such as industry and occupation. • Consider specific recordkeeping changes and how they might have affected the particular industry, occupation, worker, or injury/illness. • Consider the combination of recordkeeping changes; some modifications may counteract others. • Look for a _c ontinuation of long-run trends-patterns that developed for several years prior to and through 2001. • Look for future trends as additional years of data become available. The BLS Survey of Occupational Injuries and Illnesses will continue to report annually on the number and rate of incidents by type of case and industry, with detailed information on the characteristics of the workers and the incidents for cases involving days away from work. 25 As more years of data under the new recordkeeping requirements are accumulated, effects of the recordkeeping changes and trends may become more D apparent. Notes 1 See " Workplace Injuries and Illnesses in 2002," U.S. Department of Labor news release 03-913, Dec. 18, 2003. Injury and illness rates represent the number of injuries and illnesses per 100 full-time workers and are calculated by multiplying the number of injuries and illnesses by the total hours worked by all employees during the calendar· year. This result is then divided by 200,000 ( I 00 workers, times 40 hours per week, time s 50 weeks per year) to determine the rate per 100 eq uivale nt full-time workers. Occupational Safety and Health Act of 1970, Public Law 91-596, sec tion 24. 2 3 While the law does not actually specify that BLS data conform to the OSHA recordkeeping requirements, such a procedure allows for the efficient collection of data that in many cases are already being main tained by employers. In addition, by keeping the definitions consistent with OSHA requirements, the BLS guarantees that its data can be used by OSHA to monitor employers' progress in improving occupational safety and health. 4 Federal Register, Jan. 19, 2001, p. 5916. Changes to the program prior to 2002, including a major revision in 1992, affected the type and amount of data available, but did not change the basic definition of recordable cases of injuries and illnesses. Thus, data on the number and rate of occupational injuries and illnesses are consistent from 1972 through 2001 . 5 6 The BLS produces measures of employment, unemµloyment , compensation , price change, and productivity, among other things . Participation in some of these data collection efforts is mandatory in certain States, while participation in the Survey of Occupational Injuries and Illnesses is mandated by the Federal Occupational Safety and Health Act of 1970. 7 The BLS Survey of Occupational Injuries and Illnesses is designed to cover all private-industry employers, not just those required by the Occupational Safety and Health Administration to maintain records. Farms with fewer than 1 I workers are excluded. Data on railroads and on mecal and nonmetal mining are not collected directly by the survey. Rather, they are provided to the BLS by the Federal Railway Administration and the Mine Safety and Health Administration, respectivel y. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 8 The BLS occupational safety and health statistics programs are conducted in cooperation with the States, which jointly fund the programs . Those States participating in the survey-42 in 2002together with the District of Columbia and three U.S. territories, collect sufficient data to produce State estimates . No State data on occupational injuries and illnesses are available for nonparticipating States. 9 The Census of Fatal Occupational Injuries uses multiple source documents to identify and verify work-related fatalities. Data published annually include demographic details, as well as information on the circumstances surrounding the fatality and on the occupation, industry, and geographic location of the worker. (See "National Census of Fatal Occupational Injuries in 2003," U.S. Department of Labor news release 04-1830, Sept. 22, 2004. Additional data on occupational fatalities are available on the Internet at http://www.bls.gov/iif/home.htm, visited Sept. 30, 2004.) 1 ° For exa mple , earlier BLS studies known as Work Injury Reports used data captured from injured workers to identify the circumstances surrounding specific types of injuries, such as falls from ladders or scaffolds. Another program, the Supplementary Data System, compiled case and demographic data from workers' compensation reports in about 30 States. 11 The BLS mission statement is taken from the BLS Internet site , http://www.bls.gov/bls/blsmissn.ht m , visited Aug . 19, 2004. 12 The OSHA mission statement is taken from the OSHA Internet site, http://www.osha.gov, visited Aug. I 9, 2004. 13 See http://www.osha.gov/StratPlanP ublic/factsheet.pdf, visited Aug. 19, 2004. 14 See http://www.osha.gov/dcsp/vpp/index.html, visited Aug. 19, 2004. 15 Counting Injuries and Illnesses in the Workplace: Proposals for a Better System (Washington, DC, National Academy Press, 1987). (See also Katharine G. Abraham, William L. Weber, and Martin E. Personick, " Improvements in the BLS health and safety statistical system," Monthly Labor Review, April 1996, pp. 3- I 2.) Monthly Labor Review December 2004 19 Recordkeepi ng Requirements 16 The redesign ended the practice of reporting workplace fatalities collected in the Survey of Occupational Injuries and Illnesses. Because fatalities are rare events, collecting information on them through a sample survey did not provide reliable data. In place of the previous survey, the Census of Fatal Occupational Injuries was introduced to capture workplace fatalities. 17 Industry data are based on the three-digit Standard Industrial Classification code. 18 For 2004 and beyond, a fourth specific illness category-heari ng loss-was added to the recordkeeping requirement. 19 Despite the elimination of the specific illness category for disorders associated with repeated trauma, the BLS Survey of Occupational Injuries and Illnesses continues to provide some amount of data on similar conditions. For cases that involve days away from work, the survey records repetitive-moti on injuries and illnesses, as well as musculoskeletal disorders. 20 Work-related musculoskeletal disorders include cases in which the nature of the injury or illness is sprains, strains, tears; back pain, hurt back; soreness, pain, hurt, except the back; carpal tunnel syndrome; hernia; or musculoskeletal system and connective tissue diseases and disorders when the event or exposure leading to the injury or illness is bodily reaction/ bending. climbing, crawling, reaching, twisting; overexertion; or repetition. Cases of Raynaud 's phenomenon, tarsal tunnel syndrome, and herniated APPENDIX: spinal discs are not included: although these cases may be considered musculoskeletal disorders, the survey classifies them into categories that also include cases that do not involve musculoskeletal disorders. 21 In 1992, the first year that detailed occupation data were collected, nonconstruction laborers were the occupation with the greatest number of injuries and illnesses involving days away from work, with truckdrivers second. Since 1993, truckdrivers have had the greatest number of cases involving days away from work each year. 22 Prior to 2002, incidents in parking lots and recreation facilities were presumed not to be work related. Under the new rules, only motor vehicle accidents in parking lots are presumed not to be work related. 23 Information on overall crime statistics are from the U.S. Department of Justice, Bureau of Justice Statistics. (See http:// www.ojp.usdo j.gov/bjs/glanc e/viort.htm, visited Oct. 14, 2004. 24 Federal Register, Jan. 19, 2001, p. 5969. 25 Beginning with data for 2003, the survey will use the North American Industry Classification System to classify industries and the Standard Occupational Classification System to classify occupations. Prior to 2003, the survey used the Standard Industrial Classification System and the Bureau of the Census Occupational Classification System, respectively. This change will result in another break in series among specific industries and occupations, but not for the overall private-industr y data. Recordkeeping under the OSHA regulations Employer recordkeep ing requirements The Occupational Safety and Health Act of 1970 requires the Secretary of Labor to issue regulations requiring employers to maintain accurate records of, and make periodic reports on, workrelated deaths, injuries, and illnesses. The Occupational Safety and Health Administration (OSHA) maintains those regulations, known as the employer recordkeeping requirements. Employers not exempt from OSHA 's recordkeeping requirements must prepare and maintain records of work-related injuries and illnesses as follows: • Use the Log of Work-Related Injuries and Illnesses (Form 300) to list injuries and illnesses and to track days away from work, work or motion restrictions, and transfers to another job. • Use the Injury and Illness Report (Form 301) to record supplementar y information about recordable cases. A workers' compensation or insurance form may be used if it contains the same information. • Use the Summary (Form 300A) to show totals for the year in each category. The Summary is posted from February 1 to April 30 of each year. Recordkeeping is a critical part of an employer's safety and health efforts for several reasons: • Keeping track of work-related injuries and illnesses can help prevent them in the future. • Using injury and illness data helps identify problem areas. The more the employer knows, the better the employer can identify and correct hazardous workplace conditions. • The employer can better administer company safety and health 20 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis programs with accurate records. • As employee awareness about injuries, illnesses, and hazards in the workplace improves, workers are more likely to follow safe work practices and report workplace hazards. OSHA compliance officers can rely on the information thus reported to help them properly identify and focus on particular types of injuries and illnesses. The agency also asks about 80,000 establishments each year to report information directly to OSHA, which uses the information as part of its site-specific inspection-targeting program. The Bureau of Labor Statistics also uses injury and illness records as source data for its Annual Survey of Occupational Injuries and Illnesses, which shows nationwide and industrywide safety and health trends. 1 Changes to the recordkeep ing requirement Among the changes in the OSHA recordkeeping rule are the following: • changes in coverage • changes in the OSHA forms • changes in the recording criteria for determining the work relationship • the elimination of different recording criteria for injuries and illnesses Exhibit A-I offers a look at the old and new recordkeeping rules. This listing of an employer ' s obligations under OSHA 's recordkeeping rule is not comprehensive. (See 29 CFR Part 1904 and other parts of that instruction for details pertaining to all recordkeeping obligations.) Changes in types of cases December 2004 Exhibit A- 1. Changes to OSHA record keeping requirement from 2001 to 2002 New rule, 2002 and beyond Old rule, through 2001 Forms (§ 1904.29) 200, Log and Summary; OSHA 101, Supplemental Record OSHA OSHA OSHA 300, Log; OSHA 300 A: Summary; 301, Incident Report Work related (§1904.5) Any aggravation of a preexisting condition by a workplace event or exposure makes the case work related. Significant aggravation of a preexisting condition by a workplace event or exposure makes the case work related. Exceptions to the presumption of a work relationship: 1. Member of the general public 2. Symptoms arising on premises and due totally to outside factors 3. Parking lot/recreational facility Exceptions to the presumption of a work relationship: 1. Member of the general public 2. Symptoms arising on premises and due totally to outside factors 3. Voluntary participation in wellness program 4. Eating, drinking, and preparing one's own food 5. Personal tasks outside working hours 6. Personal grooming, self-medication, self-infliction 7. Motor vehicle accident in parking lot or access road during commute 8. Cold or flu 9. Mental illness, unless the employee voluntarily presents a medical opinion stating that he or she has a mental illness that is work related. New case(§ 1904.6) New event or exposure; new case 30-day rule for cumulative trauma disorders Aggravation of a case in which signs or symptoms have not resolved is a continuation of the original case. No such criteria General recording criteria(§ 1904.7) All work-related illnesses are recordable. Restricted work activity occurs if the employee 1. Cannot work a full shift 2. Cannot perform all of his or her normal job duties, defined as any duty the employee would be expected to perform throughout the calendar year Restricted work activity limited to the day of injury makes the case recordable. Day counts: Count workdays No cap on count https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Work-related illnesses are recordable if they meet the general recording criteria. Restricted work activity occurs if the employee 1. Cannot work a full shift 2. Cannot perform all of his or her routine job functions, defined as any duty the employee regularly performs at least once a week Restricted work activity limited to the day of injury does not make the case recordable. Day counts: Count calendar days 180-day cap on count Monthly Labor Review December 2004 21 Recordkeepi ng Requirements Exhibit A- 1. Continue d-chang es to OSHA recordke eping requirement from 2001 to 2002 Old rule, through 2001 New rule, 2002 and beyond Medical treatment does not include 1. Visits to a medical doctor for observation only 2. Diagnostic procedures 3. First aid First-aid list in Bluebook 1 is a list of examples and is not comprehensive. Two doses of a prescription medicine qualifies as medical treatment. Any dosage of over-the-counter medicine qualifies as first aid Two or more hot or cold treatments qualifies as medical treatment. Drilling a nail qualifies as medical treatment. Applying a butterfly bandage or an adhesive skin closure qualifies as medical treatment. Recordable nonminor injuries: 1. Fractures 2. Second- and third-degree burns Medical treatment does not include 1. Visits to a medical doctor for observation and counseling only 2. Diagnostic procedures (including the administration of prescription medication for diagnostic purposes) 3. First aid First-aid list in the regulation is comprehensive. Any other procedure is a medical treatment. One dose of a prescription medicine qualifies as medical treatment. An over-the-counter medicine at prescription strength qualifies as medical treatment. Any number of hot or cold treatments qualifies as first aid. Drilling a nail qualifies as first aid. Applying a butterfly bandage or an adhesive skin closure qualifies as first aid. Recordable significant diagnosed injuries or illnesses: 1. Fractures 2. Punctured eardrums 3. Cancer 4. A chronic irreversible disease Specific disorders Hearing loss: Federal enforcement for a 25-dB shift in hearing from original baseline Needle sticks and sharps injuries are recorded only if the case results in medical treatment, days away from work, days of restricted work, or seroconversion. All medical removal2 procedures that are under the provisions of other OSHA standards are recordable. A po"jtive skin test for tuberculosis is recordable when it is known to be a workplace exposure to active tuberculosis disease. In five industries, the presumption is of a work relationship. 22 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Beginning January 1, 2003, record all work-related cases of hearing loss that meet both of the following conditions on the same audiometric test for either ear: 1. The employee has experienced a standard threshold shift and 2. The employee's total hearing level is 25 dB or more above audiometric zero (averaged at 2,000, 3,000, and 4,000 Hz) in the same ear(s) affected by the standard threshold shift. Beginning January 1, 2004, a separate hearing-loss column appears on the OSHA 300 Log. Needle sticks and sharps injuries that may be contaminated with another person's blood or with infectious material are recorded. All medical removaF procedures that are under the provisions of other OSHA standards are recordable. A positive skin test for tuberculosis is recordable when it is known to be a workplace exposure to active tuberculosis disease. There is no presumption of a work relationship in any industry. Exhibit A- 1. Continued-changes to OSHA recordkeeping requirement from 2001 to 2002 Old rule, through 2001 New rule, 2002 and beyond Other issues The employer must enter the employee's name on all cases. Employees have access to the entire log, including names; they do not have access to supplementary form OSHA 101. The employer must enter "Privacy Cases," rather than the employee's name, and must keep a separate list of the case number and corresponding names. Employees and their authorized representatives have access to the entire log, including names. Employees have access to their own Incident Reports (OSHA 301). Authorized representatives have access to a portion of all OSHA301 's. Employers must report all work-related fatalities to OSHA. The employer, or the employee who supervised the preparation of the log and summary, can certify the annual summary. The employer must post the annual summary during the month of February. Employers need not inform employees regarding how they are to report an injury or illness. 1 Re co rdkeeping Guidelines for Occupational Injuries and Illn esses (0MB no. 1220-0229). 2 Medical removal is the required removal of an employee from a work location when certain criteria are met (for example, the Employers need not report fatalities resulting from motor vehicle accidents on public streets or highways that do not occur in a construction zone. A company executive must certify the annual summary. The employer must post the annual summary anytime from February 1 to April 30. Employers must inform each employee regarding how he or she is to report an injury or illness. amount of lead in the hood reaches a specific level). SOURCE : U.S. Department of Labor, Occupational Safety and Health Administration; on the Internet at http://www.osha.gov/ recordkeeping/RKside-by-side.html , visited Sept. 30, 2004. Exhibit A-2. Designation of types of occupational injury and illness cases under the OSHA record keeping rules, 2001 and 2002 2001 and prior years Total injury and illness cases Lost-workday cases Cases with days away from work 1 Cases with restricted work activity only Cases without lost workdays 2002 and future years Total injury and illness cases Cases with days away from work, with a job transfer, or with restricted work activity Cases with days away from work 2 Cases with a job transfer or restricted work activity Other recordable cases Total injuries Lost-workday cases Cases with days away from work 1 Cases with restricted work activity only Cases without lost workdays Total injuries Total illnesses Lost-workday cases Cases with days away from work 1 Cases with restricted work activity only Cases without lost workdays Total illnesses 1 May include days of restricted work activity, as well as days away from work. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 2 May include days with a job transfer or with restricted work activity, as well as days away from work. Monthly Labor Review December 2004 23 Recordkeeplng Requirements In 2001 and previous years, the type of occupational injury and illness case was recorded separately for total injuries and illnesses, for injuries only, and for illnesses only. This kind of separate calculation resulted in the ability to tabulate detailed case-type information for each of the three categories. Beginning in 2002, case-type data are recorded only once, which limits the amount of detail that can be tabulated. ExhibitA-2 indicates the available data by type of case before and after the recordkeeping change. Note to the appendix 1 The source of the preceding information is the Occupational Safety and Health Administration, U.S. Department of Labor. (See the OSHA notice in the Federal Register, Jan. liJ, 2001, p. 5916; and OSHA Fact 24 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Sheet: Highlights of OSHA 's Recordkeeping Rule. Links to these documents are available on the Internet at http://www.osha.gov/ recordkeeping/index.html, visited Aug. 19, 2004. Hedonic Regression Models ; "<~ _< >ii~ "X*N~l~ Hedonic regression models using in-house and out-of-house data Two data sources used by the Bureau of Labor Statistics to create hedonic regression models have their own distinct advantages and disadvantages; the Bureau performs research on each data source in an effort to meet its current and ever-evolving future needs Craig Brown Craig Brown Is an economist in the Office of Prices and Living Conditions, Bureau of Labor Statistics. E-mail: brown .cralg@bls.gov https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis T he research leading up to the publication of this article was conducted under the CPI initiative to expand the scope of developing hedonic regression models for quality adjustment purposes to more items within the CPI market basket. The primary focus of the article is to provide a detailed analysis of the hedonic modeling process and to illustrate the characteristics of two data sources the Bureau of Labor Statistics has chosen to utilize in its ongoing research on hedonic-based quality adjustment methods. Early research by BLS personnel and a significant portion of the current research done by the CPI staff in this area rely upon the existing sample of CPI data for the creation of hedonic regression models.' When it was recommended that the Bureau expand its use of hedonic models for quality adjustment purposes to more items within the CPI, situations arose in which the existing sample size of the items chosen were deemed insufficient to support the creation of hedonic models. To alleviate this problem, supplemental samples were designed and collected exclusively for hedonic modeling purposes.2 Despite the Bureau's having full control over this type of sample data, such an "in-house" prescription was not seen as a cure-all, because designing and collecting these data exhausts many BLS resources. Accordingly, the Bureau was led to investigate the use of hedonic models created with market data purchased from private firms that specialize in collecting point-of-sale observational data. 3 Purchased, or out-of-house, data offer many enhancements over in-house data, but are costly and have their own sets of limitations. With home-based telephones (corded or cordless), the Bureau has an opportunity to compare the process and results of using both in-house and out-of-house data in the creation of hedonic regression models. This article discusses the issues of data quality, the specification of a model, and the application of hedonic quality adjustments to substitutions in the CPI sample. Empirical evidence and quantitative data support the topics addressed. The next section examines the characteristics of the data. Following that section, the results of the models are presented, and a discussion illustrates how they could have been used in qualityadjusting substitutions in the CPI. The final section is a follow-up of what has changed with the data and presents a brief conclusion. The data Sources. The price data used in the analysis that follows were collected by BLS data collectors for the CPI and by the NPD Group, a private firm that collects and sells point-of-sale marketing information. The CPI sample consists of price data for home-based telephones from the official CPI sample and a specially designed supplemental research sample that was created and collected in order to increase the robustness of the existing sample to facilitate hedonic modeling research. CPI statisticians drew the supplemental sample Monthly Labor Review December 2004 25 Hedonic Regression Models on the basis of current CPI sampling procedures. The CPI sample is designed to be representative of consumer spending habits and is distributed across the country and across all types of retail outlets. The NPD Group sampie is a collection of point-ofsale data for home-based telephones from various partnered retail outlets from across the country. The Bureau purchased these data from NPD to explore the use of out-of-house data in developing hedonic regression models. NPD offers point-of-sale data for a wide range of consumer goods and services. Most of the company's clients are private firms that use the sales data to make marketing decisions. Sample design. All of the price data for this study were collected in August and September of 2000. It was during these months that the Bureau was able to collect its supplemental sample. The agency purchased roughly 2 years of telephone data from the NPD Group, but in order to make timely comparisons with the CPI sample, only data from the aforementioned months were used in the upcoming analysis. The CPI sample consists of price quotes, each of which represents a single item sold in a retail outlet. Each quote consists of the item's listed price at the time of collection, a description of the item's physical characteristics, and information about where the item is sold. Under current CPI sampling procedures, data collectors initiate price quotes by using a multistage probability selection technique. 'The probabilities of selecting items for pricing are proportional to the sales of the items. A slightly augmented item selection procedure was followed in pricing the supplemental sample. In this procedure, data collectors were told how many cellular, corded, and cordless telephones to price in each outlet. Respondents in the outlet were asked to rank unique model numbers according to their sales figures and the length of time they had been available for sale in the outlet. Unique models that were both good sellers and fairly recent arrivals to the outlet were chosen for the sample. The supplemental sample added 398 observations to the existing 115 observations from the official CPI sample. BLS data collectors were unable to collect 21 percent of the supplemental sample. The final sample consisted of 314 supplemental observations and the 115 observations from the official CPI sample, for a total of 429 observations. Telephones are included in the information and information processing CPI expenditure class (coded EE). Specifically, telephones represent one cluster in the entry-level item (ELI) category telephones, peripheral equipment, and accessories (EE041). This cluster includes both home-based telephones and cellular telephones. After observations for cellular telephones and observations with duplicate model numbers from within a unique retail outlet were excluded, the CPI sample for homebased telephones totaled 261 observations. The NPD sample is a collection of aggregated and averagepriced point-of-sale observations representing a group of unique 26 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 product model numbers. Specifically, data are collected in various cooperating retail chain outlets. NPD classifies these outlets by type, or "channel," and then aggregates the data across channels to arrive at a national estimate of the average price for each unique product model number for which data are collected. 4 Each observation includes an average price (total expenditures for a unique product model number, divided by total number of units of that model sold) and information on the item's physical characteristics. NPD has more than 400 participating retail partners that provide the market data it sells. This group of retailers includes department stores, mass merchants, specialty stores, and other venues. Because the data are collected from transactions, they represent actual consumer purchases in those retail outlets which cooperated with the survey. As stated earlier, the Bureau purchased approximately 2 years of monthly point-of-sale data for corded and cordless telephones from NPD, but only data from August and September of 2000 were used in this study. The combined sample of those 2 months for both corded and cordless telephones consisted of 669 observations. The NPD sample was further reduced by eliminating observations pertaining to unique product model numbers collected in August when data on those same numbers also were collected in September. Unique product model numbers with data for only August or only September were kept in the sample. After these reductions, the final data set totaled 371 NPD observations. Cleaning. Cleaning the data sets was a tedious exercise, but one that had to be regarded with importance. The CPI sample was cleaned by attempting to match the descriptions of all collected manufacturer model numbers with descriptions found on manufacturers' Internet Web sites. Corrections were made to the data when inconsistencies were found. Not all model numbers were capable of being verified through the manufacturers' sites, however. Because old models are discontinued and replaced by new models every few months, it becomes difficult to find data for older model numbers after a long time has passed since their discontinuation. For those model numbers for which primary data were not available, credible secondary Internet Web sites were used for verification. A unique characteristic of the CPI data is that they contain information about the type of business or retail outlet where the data were collected. The variable that gives this information is included in many hedonic regression models created by the CPI. Its primary use is as a control variable to account for the effects that varying business practices may have on the price of goods offered in an outlet. Each type of business category is assigned a code that BLS data collectors use to classify the retail outlet in which data are collected. Inconsistencies and coding errors between the classifications and actual retail outlet names were evaluated and corrected as needed. Cleaning the NPD data was surmised to be a task less tedious than cleaning the CPI data. The reasoning behind this assumption begins with the fact that describing the characteristics of an item correctly is difficult, given the diversity of work each CPI data collector is responsible for, the complexity of the data collection forms , the variety of the items themselves, and the paucity of knowledgeable respondents within outlets. The CPI data thus needed to be reviewed. By contrast, the NPD Group is a company whose business is collecting and selling market data. The accuracy of the NPD data is improved by electronic scanners in the retail outlets that supply data to the company. These scanners record price and quantity information, along with a minimal amount of characteristic information in the form of SKU numbers (unique identifying numbers manufacturers assign to their products). NPD maintains a library of SKU's and characteristic information on each one. Some degree of quality control is employed by NPD in packaging the company's data. Collecting and maintaining data in this way would seem to result in fewer errors in the measurement of characteristic variables. To test this theory, a random sample (5 percent of the total) was generated from each "precleaned" data set, and the item descriptions of those model numbers were checked against either manufacturers' information or information from credible secondary sources. The following tabulation of the quality of the samples before cleaning shows the results of this analysis (entries are percentages): Item descriptions Correct .................................................. . Missing or incorrect specifications ....................................... . Could not verify ......... .... ................. ....... CPI, NPD, 5 percent, n = 14 36 5 percent, n = 18 82 43 12 21 6 Because more than 80 percent of the NPD sample had correct descriptions, compared with roughly one-third of the CPI sample, verifying each model number in the NPD sample might have proven inefficient and unwise. Accordingly, another element of the NPD sample was selected for investigation: the description field. Found in the NPD sample purchased by the Bureau, the description field consists of a highly general description of each unique model number. The latter is likely to be a product identifier used by the manufacturer or retailer. A random selection of model numbers was cross-checked by comparing the information in the description field with the item's actual description. The consistency between the two was similar enough to justify simply verifying the item description by consulting the description field for all the model numbers in the sample. The NPD sample had to be further reduced after the initial cleaning phase. A total of 10 unique product model numbers had no price, rendering them unsuitable for modeling purposes. Seven https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis model numbers were for items that were outside the scope of the CPI and therefore were ineligible for pricing. 5 Finally, two model numbers had average prices that were well below average for the types of telephones they represented. After deletion of those 19 unique model numbers, the final NPD sample stood at 352. Market and product background. The market for home-based telephones is an established, mature market. Most consumers who have such telephones already have replaced their old ones with more technologically advanced models as they arrive in the marketplace (for example, corded to cordless or analog cordless to digital cordless phones). Consumers routinely accept new generations of advanced technology, and a few premium features are well known and valued by consumers. Manufacturers of home-based telephones bundle the different varieties of their product with features the consumer is thought to value. Corded telephones allow for a clear, static-free conversation, but lack the convenience and mobility of a cordless telephone. Still, many people choose to keep corded telephones in their homes because they are inexpensive and reliable. Consumers of cordless telephones are met with a wide variety from which to choose. The most basic and inexpensive type of cordless telephone is the 46- to 49-MHz analog phone. For more security, 900-MHz analog telephones are available, although they provide less clarity and security than their 900-MHz digital counterparts. Home-based telephones underwent a substantial technological improvement in 1995 with the introduction of digital spreadspectrum (oss) technology. This feature allows the signal to randomly jump channels, making it less susceptible to interference and eavesdropping. As the popularity of cordless telephones grew, the 900-MHz frequency range became overcrowded. In response, the Federal Communications Commission opened up the 2.4-GHz frequency to cordless telephones in 1998, whereupon the operating range of such telephones increased substantially. 6 However, even though the capability of a telephone to function at larger bandwidths is a valued characteristic, it is not as valuable as the ability to operate through oss technology. While there are fewer signals to interfere with in the higher frequencies and the operating range is increased, without oss technology interference and decreased security can still be problematic. 7 5.8-GHz technology was not available during the period covered by this study, but has recently become available in the U.S. market for cordless phones. Home-based telephone manufacturers will typically produce many models of a telephone, with each model including certain valued features. For example, a manufacturer may produce four types of 900-MHz digital phones: a basic model, a digital phone with caller ID, a digital speakerphone with caller ID, and a digital speakerphone with caller ID and a digital answering machine. It is presumed that the prices of these different telephones will become progressively higher as each additional feature is added to the basic model. Monthly Labor Review December 200'1 27 Hedonic Regression Models Comparing the samples. A closer look at the CPI and NPD data sets reveals a similar distribution in the number of observations of each type of telephone. Chart 1 shows the distribution of unique model numbers for each type of telephone by the percent of each sample. Four different samples will be analyzed in this section: the CPI, the unweighted NPD, the quantity-weighted NPD (NPDQ), and the value-weighted NPD (NPDV). 8 ln the August and September 2000 data set, most of the cordless telephones operate at 900 MHz. This type of telephone was, and still is, very popular. Corded telephones account for a large percentage of each sample, a fact that is interesting because the NPD sample for corded telephones is collected independently of the company's sample for cordless telephones and the NPD samples reflect transaction spending behaviors by consumers. In contrast, the CPI substitution procedure of pricing another item of similar quality when the original item is no longer available could result in a biased sample consisting of a disproportionate number of corded telephones or older generations of cordless telephones that are available for sale, but of which few are actually sold. However, because of the similarities in the percentages of each type of telephone in the CPI and NPD samples, it appears that the sample is unbiased toward any one type of telephone. Another point of interest is that the percentage of 2.4-GHz oss telephones in the CPI sample is more than twice as large as the percentage of those telephones in the NPD and NPDQ samples, but less than the percentage in the NPDV sample. The reason for these disp~rities could be that the CPI sample is disproportionately representing a new good. It is also important to note that the CPI sample had no 2.4-GHz analog telephones and the NPD sample had no 2.4-GHz digital telephones. Whether these omissions wili result in any specification bias is unclear. Overall, a priori expectations of the average prices of telephones were met. The newer, more sophisticated telephones clearly have higher average prices than the older telephones. Chart 2 shows the distribution of average prices by type of telephone for all samples. The average price of each type of telephone is usually higher in the CPI sample. This pattern is expected, because the Bureau collects list-price data for the CPI sample whereas NPD collects transaction prices, which usually are lower than list prices. The NPDV sample has a slightly higher average price than the NPDQ sample for each type of telephone. This difference has implications in the debate over whether to use quantity weighting or value weighting. Erwin Diewert suggests that quantity weighting tends to give too little weight to high-priced models and too much weight to less expensive models that have fewer newer characteristics.9 Another perspective from which to compare the similarities between the CPI and NPD samples is to examine the distribution of brand names and unique model numbers found in those samples. The final CPI sample of 261 observations contains 18 brand names and 156 unique model numbers. The final NPD sample of 352 observations contains 22 brand names and 352 28 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 unique model numbers. 10 In total, 13 brand names and 67 model numbers were common to both samples. Quality characteristics. Three unique value-adding characteristics of the CPI and NPD data sets are of particular interest to the analysis presented. The first is the vintage variable found in the NPD sample. The vintage represents the date each unique model number first appeared in the sample. It marks the introduction and prevalence of new, innovative technologies in the telephone market. The vintage variable is useful because it makes it possible to observe the changing prices of models over time. As stated earlier, only data for August and September of 2000 were used for this study, but approximately 2 years of telephone data were purchased from NPD. Looking at data over this longer period may reveal trends in the pricing behavior over the product life cycle. Chart 3 graphs the average vintage (or age) and the average (unweighted) price of each type of telephone. Vintage is measured by the number of months each model number has been in the sample. Newer model numbers will have lower vintage values. Model numbers that were introduced in September 2000 have a vintage value of unity. The vintage values were averaged for each type of telephone. In this small sample of data, it is apparent that those telephone models which are likely to have the lowest vintage values (the 2.4-GHz models) and the most advanced features (the oss models) also have the highest average prices. The vintage variable has many uses as a diagnostic tool and may indeed be valuable in modeling the NPD data. Another valuable feature of the NPD sample is the ability to weight each model number in it by either the quantity of units sold or the value of expenditures on that model number. As illustrated earlier in chart 2, the average prices of the various types of telephone can differ under all three weighting methods, a feature whose implications will be examined later in the article. It seems clear that, by the nature of the NPD data, a weighted sample would be preferred over an unweighted sample. Unweighted, the NPD sample is simply an equally weighted list of model numbers~ weighted, however, it becomes a more informative sample, indicating consumers' preferences for telephones. The importance of weighting is obvious in the case of the 46- to 49-MHz telephones: as shown in chart 1, this type of telephone represents a sizeable percentage of the unweighted NPD sample, but its presence in the weighted sample is minuscule. The opposite is true with the 900-MHz analog telephones, whose impact in the weighted sample is far greater than in the unweighted sample. The CPI sample does not weight quantity or expenditure data, but it is designed in a manner that gives more popular models a greater chance of being included in the sample. It could be argued that this is a satisfactory weighting method, but it is unlikely that it is as capable of tracking consumer spending preferences as accurately as the electronic scanners found in the checkout lines of NPD's retail partners. Percent of sample by type of telephone for each sample Percent Percent 55 55 ■ CPI ■ NPD □ NPDQ □ NPDV 50 50 45 45 40 40 35 35 30 30 25 25 20 20 15 15 10 10 5 5 0 Corded 46-49 MHz 0 900-MHz analog 900-MHz digital 2.4-GHz analog 900-MHz DSS 2.4-GHz digital 2.4-GHz DSS Type of telephone Average prices of telephones by type for each sample Dollars Dollars 180 180 ■ CPI ■ NPD 160 NPDQ [§1 NPDV 160 140 140 120 120 100 100 80 80 60 60 40 40 20 20 0 0 Corded https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 46-49 MHz 900-MHz analog 900-MHz digital 900-MHz DSS 2.4-GHz analog 2.4-GHz digital 2.4-GHz DSS Type of telephone Monthly Labor Review December 2004 29 Hedonlc Regression Models Average vintage and average price for types of telephone, NPD sample 1 Months or dollars 120 Months or dollars ~-------------------------------------------, 120 ■ Average vintage (in months) ■ Average price (in dollars) 100 100 80 80 60 60 40 40 20 20 0 Corded 46-49 MHz 900-MHz analog 900-MHz digital 900-MHz DSS 2.4-GHz analog 0 2.4-GHz DSS Type of telephone 1 There are no 2.4-GHz digital telephones in the NPD samples. The final quality characteristic worth mentioning is the set of control variables available in the CPI sample. The CPI uses the list price advertised for each type of telephone at the time the data are collected. CPI data collectors report that price and indicate whether it was classified as a sale price or a regular price. This information is used as a control variable in some hedonic models employed by the CPI to control for the negative effect sale price indicators have on prices, other things being equal. The CPI also includes information on the types of retail outlets in which the data are collected. This feature of the CPI sample is important, because it aids in controlling for the effects each type of retail outlet may have on the price of the items offered there. Usually, when these control variables are included in hedonic models, the resulting parameter estimates are logical and in accordance with a predicted order of relative value. The last control variable of note in the CPI sample is that indicating the region and city size for each observation. The CPI divides the United States into four regions, and the cities in which the Bureau collects data are assigned one of three size labels based on their populations. Variables for region and city size are used to control for the possible effects location may have on the price of goods sold. The usefulness of the geography variables is difficult to quantify, but they do aid in minimizing the unexplained variation in those price-determining variables which are being estimated. 30 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Data reservations. Despite the number of positive attributes associated with both data sets, four lingering reservations about the data deserve attention: • The representativeness of the samples • The absence of area, outlet (or channel), and type-of-price indicators from the NPD sample • The quality of the characteristic data • The age of the data The discussion of the representativeness of the data is best addressed by splitting it into two issues. The first is whether the data are representative of the market for home-based telephones. Mary Kokoski, Keith Waehrer, and Patricia Rozaklis note that, because the NPD sample is not collected under the probability sampling procedures that are used for the CPI sample, the relative degrees of representation of specific models in the respective samples are different. 11 The NPD sample is capable of reflecting changes in consumer purchasing habits, because it is a measure of actual telephone purchases. In this regard, the NPD sample could be more representative of the market for telephones than the CPI sample, which has the potential to be skewed toward older models due to the CPI 's procedure that instructs data collectors to substitute an item of similar quality when the one they were originally pricing becomes unavailable. 12 As shown in chart 1, however, such a concern over representativeness is not entirely warranted in this situation, because the CPI and NPD samples are fairly similar in their distribution of the various types of telephone. 'fhe second issue with regard to representativeness has to do with where the data are collected. The outlets from which the Bureau collects price quotes for the CPI differ from those sampled by NPD, thus affecting both the product mix and prices. The CPI data are collected in a wide variety of retail outlets across the country. These outlets, which represent answers by respondents of the Consumer Expenditure Survey to questions about where they make their purchases, are chosen through statistical sampling procedures. 13 NPD, by contrast, collects its data from retail outlets it has partnerships with. This approach introduces some bias into the NPD sample, because some types of retail outlets may not be represented in the national sales figures NPD produces. In particular, the Bureau is aware that certain major discount department stores do not contribute data to NPD. The exclusion of important retailers from the NPD sample is a critical shortcoming of the NPD data. The second reservation regarding the data-the absence of area, outlet, and type-of-price indicators from the NPD sampledeals with factors that contribute to the quality of the models. Each price quote in the CPI is accompanied by information stating where it was collected. This information consists of the specific outlet name, a type of business classification code, and the size and rf'gion category of the city in which the quote was collected. The information is converted into variables that control for the effects different types of business practices and geographic locations may have on the product mix and price. Such control usually helps minimize the variation in parameter estimates for price-determining characteristics in the model. The NPD sample does not have any control variables; therefore, even if the NPD sample happened to be collected in the same mix of retail outlets that the CPI sample used, there would still be no way of accounting for the effects of geography and outlet characteristics in regression models constructed from NPD data. The NPD sample also lacks a sale or markdown price indicator. Unlike the CPI sample, the NPD sample uses an aggregated average price of the units sold for a unique model number in the outlets that supply data to NPD. Such an average, however, is a national average and has no indication of whether the units were sold at a sale price or a regular price. Because items sold at a sale price generally are of a lower price than other items of similar quality, all other things being equal, it is appropriate to control for the effects of sale prices in the regression model through the use of a dummy variable. The third reservation about the data concerns the quality of characteristic data. Well-defined characteristic data are essential to ct"eating reliable models. CPI researchers spend considerable amounts of time and resources preparing and cleaning the data they use for modeling. The NPD characteristic data were defined more precisely than the CPI characteristic data. However, it may https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis be unwise to assume that NPD data for all other goods would be of the same level of quality as the telephone data. By the same token, while the CPI sample for telephones was not as well defined, CPI data for other goods may be of a more acceptable quality. More importantly, the collection of CPI data is under the control of the Bureau and is capable of being manipulated in order to increase the quality of the data. CPI data collectors can be directed as to what data to collect, and data coliection forms can be updated to capture innovations and other valuable quality characteristics. The Bureau forgoes this level of involvement with NPD data. Besides having this obvious concern about the accuracy of each data set, analysts may worry about the possibility that the definitions, categories, and quality characteristics identified by both data sets will differ from one another. Because hedonic models created with NPD data could eventually be used to make quality adjustments to CPI data, the variables denoting quality characteristics in those models would need to be similar enough to the specifications found in the CPI data for the desired quality adjustments to be made. For the most part, the CPI and NPD data are fairly similar, although there are a few differences wortl1 mentioning in the subsequent discussion of the hedonic mode1s. 14 The last major reservation about the data has to do with their age. Both samples represent data from August and September of 2000. The CPI sample was cleaned for this study during the first half of 2002, and the NPD sample was cleaned during 1he first quarter of 2003. Given the amount of time between collection and cleaning, difficulty arose in verifying the descriptions of each model number through Internet sites. Many model numbers were either no longer current or completely out of production by the time the data were cleaned. Also, the telephone market has changed considerably since 2000. 2.4-GHz telephones are far more prevalent now, and new technologies are available. Accordingly, using hedonic models created with old data to qualityadjust substitutions in current samples is questionable, especially because the market for telephones is so different now from what it was then. If the coefficients of the variables were created from current data, they would likely be quite different from what they were a number of years ago. Model specification The first step in building the hedonic model is creating variables from the telephone data. Dummy variables were constructed for all characteristic data, except for the vintage variable in the NPD models. Vintage is a continuous variable, and its parameter estimate must be multiplied by the age of the unique model number (in months) to be interpreted correctly. Once the variables are created, a functional form for the regression model is chosen and the model is specified on the basis of a priori assumptions about price-influencing characteristics. The most common functional form recommended in the Monthly Labor Review December 2004 31 Hedonic Regression Models hedonic literature is the semilogarithmic form 15 lnis form is preferred because it fits the data particularly well and because the coefficient estimates generated from the model can be interpreted as being the proportion of a good's price that is directly attributable to the respective characteristics of that good. The semilogarithmic form is given by the equation lnpi =Po+ plxli + p~2i + ... + pnxni + Ei, where 1n pi is the natural logarithm of the price of each good, P.J is the coefficient of the characteristic variable X.,J and E.I is the error term. The value of p0 is interpreted as the value of the base good without any of the quality characteristics that add value to or subtract value from that good. 16 The dependent variable is defined differently in both samples: for the CPI sample, it is the list price collected for each observation; the NPD sample uses the average transaction price of each unique model number. As stated earlier, the NPD sample is capable ofbeing weighted by the quantity of units sold or by the value of expenditures for each model number. Given this capability, regression models were calculated for the earlier mentioned four different data sets: the CPI sample, the unweighted NPD sample (NPD ) , the NPD sample with value weighting (NPDV), and the NPD sample with quantity weighting (NPDQ). Preliminary models. A set of preliminary models, each of which included only variables for the types of telephones, a variable controlling for prices collected on sale, and a continuous variable for vintage produced the results shown in table 1. The order of the relative values of the coefficients for the telephonetype variables matches a priori expectations. All of the cordless types of telephone are of greater value than the corded telephones. Cordless 46-to 49-MHz analog telephones are statistically insignificant in all models except the NPDQ. The other cordless types of telephone are in an expected order, with 2.4GHz oss types having the largest coefficient in all models. The CPI sample did not have any 2.4-GHz analog telephones, and the NPD, NPDV, and NPDQ samples did not have any 2.4-GHz digital telephones. The sign on the coefficient for the sale price variable in the CPI model is negative, meeting a priori expectations, but the variable is statistically insignificant. The sign on the coefficient for the vintage variable is negative, also meeting expectations, and is statistically significant in all three of the NPD models. Final models. Additional characteristic variables were included in the final models, along with a few brand-name variables. Control variables for the type of business, the region, and the size of the city in which the data are collected were included in the final CPI model. The final models yielded the results shown in table 2. Other than the variables for the type of telephone and the brand name, only the caller ID, handset keypad, and dual keypad characteristic variables appear in all four models. The caller ID 32 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 variable is statistically significant in all models, and its coefficient values in the CPI, NPDV, and NPDQ models are similar. The same is true for the keypad placement variables. Corded telephones with keypads only on the handset are typically featureless and inexpensive, which explains the negative sign on the coefficient of the associated variable. By contrast, cordless telephones with keypads on both the base unit and the handset are generally more sophisticated than those with keypads only on the handset, resulting in large positive coefficients for this variable. Other characteristic variables are similar, but not exactly the same, in all models. For example, the speakerphone feature variable applies only to corded telephones in the CPI model, but only to corded telephones without three or more line capabilities in the NPD models. Multiple line capability also is modeled differently in the various models. In the CPI model, the variable for multiple line capability does not distinguish between telephones with exactly two lines and telephones with three or more lines. By contrast, the NPD models make this distinction. Still, the variable for two line capabilities in the NPD models does not apply to telephones with conference call capability, and the variable for three or more lines applies only to corded telephones. Some characteristic variables are unique to one data set. For instance, the CPI model includes a variable for telephones with digital answering machines attached. Oddly, there are no variables for these types of telephones in the NPD sample. The NPD models, however, include variables for conference call and Preliminary models Variable name CPI Intercept ............ ... ... ............ 1 3.236 (-.0542) Base ... .157 (-.1829) 1 .697 (-.0827) 1 1.103 (-.1004) 1 1.392 (-.1614) (2) ... 1 1.492 (-.3074) 1 1.885 (-.1104) - .155 (-.0921) (2) ... .586 53.56 261 Corded .. .......... ..... ................ Cordless 46-49 MHz .... ...... Cordless 900-MHz analog .. Cordless 900-MHz digital ... Cordless 900-MHz DSS ...... . Cordless 2.4-GHz analog ... Cordless 2.4-GHz digital .... Cordless 2.4-GHz DSS ........ Sale price ...... ............. .. .... ... Vintage .... ...................... ...... Adjusted R 2 .. .. ... ... . ............. Fvalue .. .. ............................ Number of observations ..... 1 2 NPD 1 3.647 (-.0874) Base ... - .028 (-.0978) 1 .304 (-.0897) 1 .653 (-.1451) 1 .796 (-.1209) 1 .703 (-.3618) (2) ... 1 1.122 (-.1712) (2) ... 1 - .008 (-.0016) .344 27.35 352 Statistically significant at the p = .05 level. Not in model. NPDV NPDQ 1 3.839 (-.0845) 1 3.263 (-.0653) Base Base ... ... 1 - .087 .304 (-.1799) (-.1473) 1 .101 .418 (-.0783) (-.0623) 1 1.655 1.039 (-.1033) (-.1099) 1 1.602 1.011 (-.1253) (-.139) 1 .500 1.002 (-.2883) (-.3444) (2) (2) ... . .. 1 1 1.271 1.786 (-.1011) (-.1358) (2) (2) ... ... 1 1 - .012 - .008 (-.0018) (-.0013) .551 .546 62.43 61 .36 352 352 Final models Variable name Intercept ..... .. .. .. ....... ...... Corded ...... .... .. ............... Cordless 46-49 MHz .. ... .............. Cordless 900-M Hz analog .. ......... Corl.lies::: 900-MHz digital ... .. .. .... . Cordless 900-MHz DSS ..... . . .. . . .. . .. Cordless 2.4-GHz analog ...... ...... Cordless 2.4-GHz digital ............. Cordless 2.4-GHz DSS. ....... . .. .. .. .. Sale price .. .. ................. . Vintage ..... .. ...... ....... .... .. Digital answering machine .. .. ...... ........ ... .. Speakerphone (corded) .... .. ......... .. .... .. Speakerphone (not three-line capable) .. ... .. • 1 CPI 1 3.157 (.0725) Base .105 (.1236) 1 3.101 (.0997) Base 1 .213 (.0959) 1 3.014 (.0718) Base 1 .202 (.0940) 1 2.857 (.0615) Base .205 (.0830) Headset jack ... .. ... ... ...... (2) Brand B ... ...................... Base Brand C ......... ................ 1.083 (.1237) 1.207 (.1527) Brand F .... ...... ... .... .... .. ... 1 .861 (.1978) (2) (2) (2) Brand N .. ... ...... .... ..... ..... 1 1.413 (.0891) 1 1.211 (.1533) 1 1.420 (.0826) 1 1.466 (.0951) Audio/video store .. .... ... . Base - .070 (.0585) (2) (2) (2) (2) Full-price department store .. .... .. ... ..... ... ... ... ... 1 - .004 (.0012) -.001 (.0008) .0001 (.0007) Discount appliance store .... .. ....... ... ..... .. .... . (2) (2) (2) Discount department store ....... ...... ............... 1 .491 (.0711) 1 .488 (.0605) Brand 1 1 1 1 A ........ . ...... .. . . . ..... .642 .758 (.0881) .783 (.1334) .696 (.0796) (.0765) Brand M ........... .............. 1 1.044 (.1106) 1 1.060 (.1129) 1 .964 (.0808) 1 .949 (.0819) Brand H ... .... .... .... .... ...... Brand G ... ...................... (2) 1 1 .406 (.0549) 1 1 1 .235 (.0796) (2) Warehouse ............. .. .... . 1 .379 (.1054) 1 .334 (.0784) 1 .276 (.0681) .453 (.1129) .657 (.0723) Intercom (not dual keypad) ... ..... ........ ...... .. (2) Clock radio .. .. .. ... .... ....... (2) Caller ID ....... ... . ...... .. ... ... 1 .456 (.0449) .134 (.1223) 1 .677 (.1407) 1 .330 (.0554) .040 (.0512) 1 .565 (.0903) 1 .466 (.0233) .074 (.0900) 1 .587 (.0853) 1 .504 (.0259) 1 1 Midwest region .. .......... .. C-sized city ..... ......... ..... 1 Adjusted R 2 • • • •• ••• •• •• ••• • • • • Fvalue ... ..... ............... .. .. Number of observations ...... ......... .. 1 2 1 NPDV NPDQ 1 .905 (.1377) .075 (.0700) 1 .799 (.0674) 1 .105 (.0285) 1 .824 (.1051) Base Base Base - .154 (.0753) .142 (.0789) (2) -.036 (.0384) 1 .182 (.0421) (2) 1 .087 (.0396) 1 .294 (.0487) (2) 1 -.161 (.0724) 1 - .216 (.0815) 1 - .263 (.1045) 1 1.010 (.1823) 1 -.243 (.0314) 1 - .233 (.0386) 1 -.205 (.0345) 1 1.000 (.3014) 1 - .153 (.0303) 1 -.110 (.0450) 1 -.083 (.0377) 1 1.088 (.3524) (2) (2) (2) 1 1 .154 (.0336) 1 - .140 (.0743) 1 -.196 (.0735) 1 -.289 (.0551) 1 -.330 (.0679) 1 .104 (.0477) 1 -.414 (.1076) .852 60.62 261 Statistically significant at the p Not in model. = .05 (2) (2) (2) (2) (2) (2) .693 31.49 .940 210.94 .922 160.58 352 352 352 level. 1 .420 (.0811) Two-line capability (no conference calls) .. ....... (2) 1 .451 (.0868) 1 .509 (.0418) 1 .506 (.0616) Three-or-more-line capability (corded/ ... ... . (2) 1 1.860 (.1597) 1 1.759 (.0864) 1 1.783 (.1046) 1 -.312 (.0794) 1 - .317 (.1597) 1 -.424 (.0785) 1 -.414 (.0621) 1 .274 (.0665) 1 .224 (.0716) 1 .305 (.0308) 1 .341 (.0437) Corded handset on base (cordless) ............ (2) -.122 (.3171) - .133 (.0962) - .178 (.1417) Extra-handset capability (coraIessJ .... (2) .334 (.2374) - .055 (.0628) - .151 (.1223) https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis (2) 1.073 (.2595) 1 .507 (.0959) (2) Keypad on base and handset (cordless) .. .... . Headset style design.. .. ...... ... ......... .... .200 (.0595) 1 .189 (.0726) 1 - .851 (.1522) -.098 (.0752) -.092 (.0787) .021 (.1008) (2) 1 .545 (.0718) .706 (.0751) Keypad on handset only (corded) ... ....... .. .... NPD CPI 1 Conference call (-not three-line capable) .. ..... Two-or-more-line capability. ................ ..... Continued-Final models Variable name NPDQ NPDV NPD •t•ir~...- intercom capabilities, clock radio units, headset jacks, and headset-style designed telephones. Generally, the values of these variables are statistically significant and have the expected signs on the coefficients. Two variables unique to the NPD samples have values that are not statistically significant in the final NPD models, but that should be noted because of their significance in the current market for telephones. Three cordless telephone models include a corded handset on their base unit, and four cordless telephone models are capable of adding extra cordless handsets to their operation. Telephones with either of these features had average prices 2 to 3 times greater than prices for those without the Monthly Labor Review December 2004 33 Hedonic Regression Models features, yet, because relatively few of these telephones were sold, they were not statistically significant in the models. A comparison of the adjusted R 2 values from each model reveals some interesting results. First, the unweighted NPD model has a much lower adjusted R 2 value than do the value- and quantity-weighted models. This is expected, because, in general, weighted regressions produce much better fits. Of the two weighted models, it is unclear which is preferred. The argument by Diewert that quantity weighting ignores higher priced models with innovative features in favor of lower priced models with fewer advanced features 17 is not tully realized, because both models have the same number of characteristic variables with statistically significant values and the quantity-weighted model actually has one more brand-name variable with a value that is statistically significant. In fact, most of the variation in the coefficients of these two models appears to be found in the brand-name variables. Variations in the coefficients of other variables appear negligible. The other point of interest is the adjusted R 2 value of the CPI model. Comparing adjusted R 2 values from two different models is not econometrically sound, but if the values are compared in terms of how well each model explains the variation in its own data set, then it is fair to comment on the results. Under this analytical framework , it is interesting to note that the CPI model explains more of the variation in its data set than the unweighted NPD model does in its data set, while the weighted models explain more variation in price than the other two do. Experimental models. To compare the samples further, experimental models were run on modified data sets consisting of the observations for unique model numbers common to the CPI and NPD samples. 18 As stated earlier, there were 67 unique model numbers common to both samples. These model numbers represent 118 observations in the CPI sample and 67 observations in the NPD samples. Because the data sets consist of the common model numbers, the experimental models are specified with the same variables, which represent the quality characteristics present in the 67 common model numbers. These quality characteristics are found to be most consistently statistically significant in the final models in table 2. Variables for 12 of the 13 brand names common to all of the data sets also were included. 19 The experimental models yielded the results shown in table 3. The variables for the type of telephone and many of the feature variables remain very strong in these models. Two exceptions are the variables for handset placement, which are significant in the final models, but not in the experimental models. Also, although many of the telephone type and feature variables are significant, there are large differences in the parameter estimates for these variables. For example, the parameter estimates for cordless 900-MHz digital telephones range from 0.730 to 0.933, and the parameter estimates for the speakerphone feature range from 0.302 to 0.498. There is also much variation in the statistical 34 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis ['ecember 2004 ·••1•11=--- Experimental models Variable name Intercept ........................ Corded .......... ................. Cord less 46-49 MHz ..... Cordless 900-MHz analog ...... ..... Cordless 900-MHz digital ....... .. ... Cordless 900-MHz DSS . . .. ........ ... . Cordless 2.4-GHz oss .. .............. Caller ID ... ......... ........... .. Two-or-more-line capability ... ........ ........ .. . Speakerphone .... ..... .... .. Conference call capability ....... ...... .. ....... Keypad on handset only (corded) .... .. ... ....... Keypad on base and handset (cordless) ... .... Brand A .... ... .... .. ... ..... .... Brand B .... ..... ....... ......... Brand C ............ ... ... .... .. . Brand D ...... .... ...... .. ... .... Brand E .............. ....... .... Brand F .................. ........ Brand G .... ............... ..... . Brand H ..... ..... ............ ... Brand 1 •••••• •••• •••• •••• ••• •••••• Brand J ... ....... ....... ......... Brand K ..... ............. ... .... Brand L .... ... ...... .. .... ... .... Adjusted R 2 F Value .. ..... .... ... ........ .. . Number of observations ....... .......... 1 CPI NPO NPOV NPOQ 1 3. 124 (.1255) Base 1 3. 08 (.1823) Base 1 3.144 (.2086) Base 1 3.121 (.1635) Base .221 (.1944) 1 .528 (.2269) .463 (.2396) 1 .497 (.1908) 1 .645 (.1073) 1 .617 (.1646) 1 .569 (.2045) 1 .571 (.1550) 1 .920 (.1290) 1 .933 (.2287) 1 .760 (.2220) 1 .730 (.1947) 1 1.277 (. 1272) 1 1.084 (.1653) 1 .964 (.2149) 1 .981 (.1822) 1 1.460 (.1476) 1 1.611 (.2148) 1 1.403 (.2132) 1 1.407 (.1854) 1 .480 (.0585) 1 .377 (.0843) 1 .499 (.0437) 1 .506 (.0510) 1 .388 (.1147) 1 .302 (.1103) 1 .478 (.1648) 1 .414 (.1680) 1 .518 (.1 032) 1 .482 (.2209) (.1480) 1 .498 (.1695) .078 (.1588) .087 (.3543) .125 (.3751) .211 (.4098) -.158 (.1010) - .242 (.1737) - .221 (.2099) - .193 (.1543) -.180 (.1288) -.308 (.2036) - .253 (.2240) -.176 (.1868) .005 (.1083) Base .1040 (.1 374) Base .0750 (.0811) Base - .001 (.0948) Base -.023 (.1093) -.053 (.2783) -.155 (.2820) -.177 (.1282) 1 - .359 (.1137) 1 -.400 (.1204) ,-.499 (.2229) -.516 (.2901) ,-.547 (.1570) 1 - .753 (.1691) .8834 41.28 -.112 (.1441) - .074 (.3 121) -.050 (.2993) -.243 (.1785) 1 -.291 (.1427) ,- .373 (.1544) -.266 (.2609) - .130 (.2993) - .175 (.1893) 1 -.628 (.2232) .8447 17.31 - .144 (.0743) - .053 (.1629) -.189 (.1960) 1 -.368 (.0667) 1 - .405 (.0681) 1 -.583 (. 0630) ,-.500 (.1423) -.269 (.1509) 1 -.272 (.1062) 1 - .735 (.1656) .9688 94.24 -.141 (.0807) -.040 (.3325) - .174 (.2477) ,- .372 (.0709) 1 - .396 (.0809) ,- .595 (.0660) ,- .501 (.1114) -.254 (.1842) - .262 (.1368) ,- .738 (.1159) .9542 63.46 118 67 67 67 Statistically significant at the p = .05 level. '.45e significance of, and parameter estimates for, the brand-name variables. The adjusted R 2 values of the experimental models are quite high. As suggested earlier, they are highest in the weighted models, with the CPI model and the unweighted NPD model following after. Quality adjustment results To assess the usefulness of a hedonic model for home-based telephones, the model 's parameter estimates were applied to home-based telephone substitute price quotes (items selected by CPI data collectors to replace previously collected items that are no longer available) with quality changes. Substitutions from the months of May and June 2003 were chosen for this exercise. There were 18 home-based telephone substitutions during those 2 months. In the published index, 22 percent of the subsi.itutions were compared directly with the previous item. The price changes for the remaining noncomparable substitutions were imputed, or "linked," essentially by assuming them to be the same as the elementary item price change for the same geographic area that month. 20 The comparability decisions of the 18 substitutions were reassessed on the basis of the four hedonic models. Nearly 67 percent of the substitutions underwent quality changes that could be adjusted for with the use of one of the hedonic models. After the reassessment was complete, 39 percent of the substitutions were adjusted with the CPI model, 28 percent were adjusted with the NPD models, and 33 percent of the prices were directly compared under all four models. 2 1 The remaining substitutions (28 percent under the CPI model and 39 percent under the NPD models) were deemed noncomparable, and the price changes would have been imputed with the link method. The substitution comparability ratio (the ratio of directly compared and quality-adjusted substitute quotes to the total number of substitute quotes) jumped from 22 percent in the published index to 72 percent under the CPI model and 61 percent under the NPD models. Most of the quality adjustments were calculated for changes in the caller ID variable and brand-name changes, and most of the noncomparable substitutions had unadjustable quality changes, such as an expandable phone system capability and extra handsets included with the system. Table 4 summarizes the specification and quality change s that occurred in the substitutions. The hedonic models were analyzed further through a comparison of the unweighted mean price changes for the home-based telephone substitutions in the published index with the mean price changes for the quality-adjusted substitutions. The mean price change for directly compared substitutions increased dramatically after the reassessment. These substitutions were primarily those wherein only the https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis ■ 1•I•ir~•• Specification and quality changes in substitution price quotes Type of change Number of occurences Specification change Model number change or other, minor specification change ............ ........ .. .. ........ .. ..... Change in quality .. ... ...... ............. .. .... .. ........ .... .. 6 12 Quality Change 1 Type ... ...... ....... .. ....... ....... ................................... Brand .. .. ............. .... .................... ....................... . Digital answering machine ..... ... .. .. .... ... ........... ... Speakerphone ..... ..... ......... ..... .. ...... .. ............. ... . Keypad on base and handset (dual keypad) .. . Two-line capability .... ............ .......... .... .. ..... ..... .. . Caller ID ......... ............. ....................... ........ .. .... .. Expandable phone system ...... ....... ... ..... .... ..... . Extra handset(s) included .... ... .. ... ... ....... ...... .. .. . Headset included .... ..... ...... ... ..... .. ... .. .. .... ... ....... 1 For each specification listed , more than one could have changed for a given substitution . model number, with or without additional minor price factors, changed. The mean price changes for the quality-adjusted substitutions were mostly negative, with an average downward price change (across all models) of 4.7 percent. The overall mean price change for all substitutions with comparisons (directly compared and quality-adjusted) varied greatly, with the largest difference occurring between the unweighted NPD model and the weighted NPD models. Table 5 compares the mean price changes of the home-based te lephone substitutions under the published CPI with those calculated in the four hedonic models. On average, there were only nine home-based telephone substitutions each month . The small proportion of substitution price quotes for home-based telephones in the item index limits the impact of quality-adjusting the CPI for homebased telephones. Also, because cellular telephones account for roughly 40 percent of the entire telephone sample used in index calculations, the effect of quality-adjusting only homebased telephones is marginal. A side from the se points, however, applying any one of the home-based telephone models significantly decreases the number of noncom parable (imputed) substitution price quotes, the fewer of which exist in the index, the more likely it is that the index will measure true price change over time. What's changed? Since the data presented in this article were collected , new 5.8-GHz telephones have entered the market and the prices of older types of telephones have fallen . New innovations, accompanied by decreas ing prices, have nearly driven 46-49MHz telephones out of existence. Consumers seem to be accepting new technology, such as the 2.4-GHz models, and Monthly Labor Review December 2004 35 Hedonic Regression Models ....... -~·- Mean price changes of substitution price quotes Published cP1 Total substitutions(= 18) Number Substitutions with comparison (nonlink) Directly compared substitutions ....... Quality-adjusted substitutions .......... Noncomparable (linked) ........... .. .... .. .... .. 1 4 4 0 14 Mean price change (percent) -0.65 -.65 CPI qualityadjusted index Number Mean price Number change (percent) 13 6 7 5 are hc>nefiting from the lower prices. Clear evidence of this trend can be found by comparing the CPI sample in September 2000 with the CPI sample in June 2003. 22 Chart 4 compares the percentage of the sample each type of telephone accounts for, and chart 5 compares the average prices of each type of telephone. Expandable telephone systems and telephones sold with two or more handsets are becoming very popular, as evidenced by the number of substitute price quotes having those characteristics. The impact of the popularity of cellular telephones on the home-based telephone market is also of interest. Some consumers are now choosing cellular telephones as their primary communication device and abandoning their home-based telephone service altogether. With regard to changes in the data sources, the CPI sample has grown dramatically since September 2000. The size of the sample for telephones increased from 115 observations in September 2000 to 290 in June 2003, with the number ofhomebased telephones increasing from 84 to 167 observations. The 2003 figures for home-based telephones are large enough to support the creation of hedonic models without the aid of supplemental samples. Since September 2000, NPD's data on cordless telephones has changed to include 2.4-QHz digital models and 5.8-GHz models. This modification leads to the assumption that NPD's sample now includes model numbers of those types of telephone. NPD also has improved its control methods for its consumer electronics data and now offers a variety of data delivery options that may be of interest to the Bureau. 23 AN ARGUMENT CAN BE MADE FOR AND AGAINST the use of either of the two data sources examined in this article in creating hedonic regression models. The CPI sample is relatively small and is not the best item-oriented representation of consumer purchases. Moreover, its characteristic data are not as well defined as the NPD characteristic data. The CPI sample is, however, more representative of the entire market for telephones than are any of the NPD samples, becaus"'e it 36 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 NPD qualityadjusted index 1.48 5.74 -2.17 11 6 5 7 Mean price change (percent) 3.19 5.74 .14 NPDV qualityadjusted index Number 11 6 5 7 Mean price change (percent) -0.34 5.74 -7.62 NPDQ qualityindex adjusted Number 11 6 5 7 Mean price change (percent) -0.97 5.74 -9.02 includes many more retail outlets than those samples do and because the Bureau has direct control over data collection and maintenance of the CPI sample. 24 In addition, the results of the study presented indicate that the CPI model can be used to make quality adjustments more often than any of the NPD models can. In comparison, NPD data offer a large, representative sample of actual consumer preferences, contain accurate characteristic data, and have options for weighting the data, but suffer a bias in retail outlet representation. Further, the Bureau has little oversight in the upkeep of NPD data. An ideal research sample would meld the coverage and manageability of the CPI sample with the accuracy and robustness of the NPD sample. In this research, the age of the data examined is somewhat of a cause for concern, because applying the models discussed to quality-adjusting substitutions used in calculating the CPI may not now be appropriate. Doubtless, the parameter estimates of the telephone characteristics have changed as prices for telephones and the value consumers place on quality features have changed since the data were collected. Further research is recommended to determine whether using the by-now-outdated model for quality adjustment is preferable to simply linking substitutions with quality change. Aside from this issue, however, the challenging characteristics of the data sources still exist, and the benefits of each sample continue to define its appeal to researchers. Currently, BLS researchers still use NPD data in hedonic research studies, and new methods of model creation and application are being considered. 25 The Bureau continues to rely on CPI data to create hedonic models that are used to adjust the prices of apparel items. As the course ofBLS hedonic research continues to be charted, the future data needs of the Bureau will evolve, perhaps rendering one data source more suitable than another. Until a single source of satisfactory data is found for its research needs, the Bureau will likely further explore the use of other data providers, in addition to continuing to use its own in-house data. D Percent of CPI sample by type of telephone, September 2000 and June 2003 Dollars Dollars 45 ~ - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - ---, 45 ■ September 2000 ■ June 2003 40 40 35 30 25 20 15 10 5 0 Corded 900-MHz analog 46-49 MHz 900-MHz 900-MHz digital DSS 2.4-GHz analog 2.4-GHz digital 2.4-GHz 5.8-GHz DSS DSS Type of telephone Average price of CPI sample by type of telephone, September 2000 to June 2003 1 Dollars Dollars 200 200 ■ September 2000 ■ June 2003 180 180 160 160 140 140 120 120 100 100 80 80 60 60 40 40 20 20 0 0 Corded 46-49 MHz 900-MHz analog 900-MHz digital 900-MHz DSS 2.4-GHz analog 2.4-GHz digital 2.4-GHz 5.8-GHz DSS DSS Type of telephone 1 The June 2003 that model. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis CPI sample included one 2.4-GHz analog telephone; hence, the average price is the price of Monthly Labor Review December 2004 37 Hedonic Regression Models Notes ACKNOWLEDGMENT: The author thanks Nicole Rope, Paul Liegey, and Mary Kokoski for their guidance. The views and opinions expressed herein are solely those of the author and do not necessarily reflect the policies of the Bureau of Labor Statistics. 1 Most notably, the hedonic models for items of apparel and their continual upkeep. (For more information on early work with hedonic models for apparel items in the CPI, see P. Liegey, "Adjusting Apparel Indexes in the Consumer Price Index for Quality Differences," in Price Measurements and Their Uses, National Bureau of Economic Research Studies in Income and Wealth, 57 (Chicago, University of Chicago Press, 1993), pp. 209- 26, and "Apparel price indexes: effects of hedonic adjustment," Monthly Labor Re view, May 1994, pp. 38-45; and N. Shepler " Analysis of Hedonic Regression : Applied to Women's Apparel in the Consumer Price Index," manuscript (Bureau of Labor Statistics , 1994). 2 Between April and September in each of 1999 and 2000, supplemental research samples were collected for motor vehicle painting, film processing, refrigerators, microwave ovens, videocassette recorders (vCR's), digital video disk (DVD) players, camcorders, telephones, and dental restorations. 3 See M. Kokoski , K. Waehrer, and P. Rozaklis, " Using Hedonic Methods for Quality Adjustment in the CPI: The Consumer Audio Products Component," BLS working paper (Bureau of Labor Statistics, 2000), for an example of research conducted with this type of data. 4 A detailed explanation of NPD's aggregation procedure can be found in Kokoski , Waehrer, and Rozakli s, " Using Hedonic Methods," or on NPD's Web site at www.npd.com . 5 The seven model numbers were either conference call stations or extra handsets that are used with expandable tel ephone systems. 6 A complete history of cordless telephones is available on the Internet at www.affordablephone s.net/HistoryCordless. htm . Since the time of this report, the Commission has opened up the 5.8GHz frequency to cordless telephones as well. 7 More information on oss and other cordless telephon e technologies can be found on the Internet at www.howstuffworks. com/question326.htm . 8 The next section discusses the weighting methods of the NPD data in further detail. 9 E. Diewert, " Hedonic Regressions: A Review of Some Unresolved Issues," presentation given at the Seventh Ottawa Group Meeting on Price Indices, Paris, May 2003. 10 The NPD sample is designed so that each observation represents the average price of a unique model number, as calculated from I month's total expenditure and sales volume for that model number. 11 Kokoski, Waehrer, and Rozaklis , " Using Hedonic Methods. " 12 The CPI rotates approximately 25 percent of its sample each 38 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 year. It is during this process that the sample is most likely to incorporate the newest and most popular products in the market. IJ For a more detailed discussion of this survey, see BLS Handbook of Methods, Bulletin 2490 (Bureau of Labor Statistics, April 1997), chapter 16. 14 Guides to the characteristic data in each sample are available from the author. 15 Recommendations on functional forms in the hedonic model literature can be found in Z. Griliches, "Introduc tion : Hedonic Price Indexes Revisited," in Z. Griliches, ed., Price Indexes and Quality Change (Cambridge, MA, Harvard University Press, 1971 ); and J. Triplett, "The Theory of Hedonic Quality Measurement and Its Use in Price Indexes," BLS staff paper 6, 1971. 16 See E. V. Georges and P. Liegey, " An Examination Using Hedonic Regression Techniques to Measure the Effects of Quality Adjustment on Apparel Indexes," internal report (Bureau of Labor Statistics, 1988). 17 Diewert, " Hedonic Regressions." 18 The experimental models are intended to highlight the variation found in the datasets and are not suggested to be superior to the final models. Part of the reason for the latter claim is that the experimental models do not utilize the available data to their fullest extent. 19 One of the 13 common brand names did not appear in the 67 common model numbers. 20 The imputation or " link" methodology is explained in greater detail in the BLS Handbook of Methods, chapter 17, p. 186. 21 An additional benefit of employing hedonic models in evaluating substitutions is that the analyst is able to rely on statistical tools, rather than expert judgment alone, when making comparability de cisio ns . Because of this approach , th e percentage of directly compared sub stitu tions increased under the reevaluation. 22 This type of analysis was not possible with NPD data, because the Bureau does not have access to a current NPD sample. Also, the September 2000 CPI sample is from the official CPI sample used in calculating indexes. The su pplemental observations added to increase the sample size for modeling were excluded from the analysis in order to compare official CPI samples. 23 This information was reported to the Bureau by employees of NPD at a meeting on March 24, 2003. The meeting was scheduled to discuss the future relationship between the two organizations. 24 Specifically, the Bureau has the ability to find and correct erro· , in the data in a timely manner by communicating directly with the data collectors. It also has the capability of tailoring its data collection forms to fit the marketplace and its research needs. 25 Research projects using NPD data for televisions and stereo receivers are currently underway. Ariel Pakes 's new method of model estimation and application also is being studied. (See Ariel Pakes , " A Reconsideration of Hedonic Price Indexes with an Application to PC ' s," America n Economic Review, December 2003 , pp . I 578-96.) ,'x,,? Research Summary ',,Bl: New and emerging occupations Jerome Pikulinski A ccording to the OES survey, in ~001 , most new and emerging (N&E) occupations were in firms with fewer than 100 employees. No single industry dominated in the creation and growth of these occupations. (See chart 1.) More than one-half of these were distributed among human services, transportation, communications, business and personal services, and a wide Jerome Pikulinski is an economist formerly in the Division of Occupational Employment Statistics, Bureau of Labor Statistics. variety of wholesale and retail trade activities. Slightly more than half of all N&E occupations were paid in a range of $8.50 to $17. (See chart 2.) No single State or single occupational classification dominated in the creation of N&E occupations; however, healthcare, management, and production occupations were the three most frequent occupation classifications observed. (See chart 3.) Information on specific occupations that are new or emerging is presented below. 1 Construction field • Metal stud framer • Epoxy floor installers New building systems, particularly in commercial construction, and increased use of new materials explain the appearance of new occupations in the traditional construction industry. Educational services • School diagnosticians • Adaptive physical education specialist • Distance learning coordinators • Poison information specialist • Poison information technician • Home-school liaison • Technology infusion specialist • Director of technology • Technology coordinator • Athletic compliance coordinator Education continues to create N&E occupations. Some of these arise in connection with the objective of tailoring Percent 45 Percent 45 40 40 35 35 30 30 25 20 • N&E • National 25 20 15 15 10 10 5 5 0 1-49 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 50-99 100-249 250-999 1,000 and over 0 Employment size Monthly Labor Review December 2004 39 Research Summary Relative distribution of N&E occupations by survey wage range Percent Percent 18 ~ - - - - - - - -- - - - - - - - - - - - - - - - - - - - - - - - - - ~ 18 16 16 14 14 12 12 10 10 8 8 6 6 4 4 2 2 0 Under 6.75 6.75 to 8.49 8.50 to 10.74 10.75 to 13.49 13.50 to 16.99 0 17.00 to 21.49 21 .50 to 27 .24 27.25 to 34.49 34 .50 to 43.74 43.75 to 55.49 55.50 to 69.99 70.00 and over Survey wage range Regional distribution of N&E occupations and national employment Percent 30 Percent 30 - • N&E National 25 25 20 20 15 15 10 10 5 5 0 Western Southwestern New England Mid-Atlantic Midwest Southeastern 0 Region NOTE : Regions are defined as follows : Western: Alaska , Arizona, California, Guam , Hawaii , Idaho, Nevada, Oregon, and Washington ; Southwestern: Arkansas , Colorado, Kansas , Louisiana, Missouri, Montana, New Mexico, Oklahoma, Texas , Utah, and Wyoming ; Midwest: Illinois, Indiana, Iowa, Michigan , Minnesota, Nebraska, North Dakota, Ohio, South Dakota, and 40 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Wisconsin ; Southeastern :_Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina , South Carolina, and Tennessee ; MidAtlantic: Delaware, District of Columbia, Maryland , New Jersey, Pennsylvania , Virginia , and West Virginia ; New England : Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island , and Vermont. educational services to students' special needs. Others deal with the use of improved telecommunications applications to deliver education. Technology and its general uses in education explain the creation of other specialist occupations. Governmental regulations governing athletic and other physical education programs have contributed to other occupations in special education and the administration of athletic programs. Heal th services • • • • • • • • • • • • • • • • Monitor technicians Medical specimen couriers Patient-care technicians Urine sample collectors Polysomnographic technicians Tissue process technicians CRN anesthesiologist Tissue & eye bank technicians Spiritual care giver Tissue service coordinator Genetic counselor Sanitization technician Medical certification clerk Plasma processor Schedulers for surgical cases Night monitors In the health field , N&E occupations have addressed specialized patient care, continuing responses to advancing medical technologies, improved scheduling of surgical procedures, and alternative medical service delivery approaches. Increased attention has been directed toward management and care of tissue banks. In light of recent genome developments, genetic counselors are appearing upon the medical scene. Social service • • • • • • Bill review nurse Adult protective services Energy auditor HazMat drivers Weatherization director Director information management https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis • Cheer workers • Disaster preparedness staff There are several groups of occupations in social services - nurses and information management workers employed in new fields; workers helping seniors and others in their homes; and disaster preparedness staff. Nurses continue to be employed in areas other than those directly related to providing clinical care services, primarily in the control of medical costs. In addition, a new occupation for nurses was found in the legal field where they are employed as legal nurse staff specialists. Information management, like nursing, is not a new field, but one that continues to appear in a number of new industry settings. Senior and disabled persons are creating situations calling for cheer workers who provide opportunities for therapeutic interaction and others who investigate charges and complaints of mistreatment. Drivers qualified to deliver hazardous materials may provide oxygen to residences and other service locations. Providing services for the insulation and heating of residences has created occupations in weatherization and heating cost recovery. Finally, increased awareness of disaster preparedness is driving the creation of related positions. Transportation • Horsedrawn carriage drivers • Handicapped bus aides Clearly carriage drivers is not a new occupation; however, its appearance in connection with the development of urban entertainment districts makes this occupation noteworthy. Attention to the mobility needs of the handicapped has contributed to the creation of aide occupations to assist them on buses. Service • Surveillance person • Producer-Internet provider (ISP) • • • • • • • • • • • Psychic counselors Chief software architect Match-makers Web analyst Bar-proof checker Digital imagers and modelers Customer insight analyst Interactive media planner Sr. supply chain manager Televideo engineer Divers-underwater inspectors A variety of service occupations are appearing . Some deal with security needs. Others reflect cultural attitudes about future uncertainties or finding a mate. Behavioral science applications to marketing are creating other kinds of marketing research jobs. The continuing drive to improve the efficiency of manufacturing operations through improved material management has created specialist positions. Increased attention to docks and ports has created highly specialized underwater inspection jobs. Special attention is called to the Internet and telecommunications technologies. A variety of new specialized occupations continue to appear as a result of these, such as producers for Internet service provider sites, web analysts that study utilization patterns, and interactive media planners. The pattern in development of these N&E occupations appears to have its parallel in the development of new occupations that followed the introduction of automotive technology. The latter industry has continued to contribute to N&E occupations for more than I 00 years. The same pattern seems to be developing within the Internet and telecommunication industries. Engineering services and manufacturing • • • • • • Hazardous material engineer Neon glass benders Compliance engineer Cultured marble caster Laser engineer Glue mixer Monthly Labor Review December 2004 41 Research Summary • • • • • • • • Optical design engineer Perfumers Optical engineer Translators Roof truss designers Missile specialists Pharmacokineticist Truss layout and assembly workers New materials and processes have contributed to the creation of new occupations while the regulatory concern for the associated environmental and health impacts of these have created additional occupations. Lasers and various optical technologies continue to create new occupations. Manufactured housing components have created both design and production occupations. The drug industry has seen the creation of an occupation, pharmacokineticist, concerned with establishing dosage standards related to the drug availability of retained drug dosages in patients. Some occupations are not new but are once again emerging due to consumer preferences. The cultural resurgence of neon lighting has created the need for neon benders. Market demand for cast marble surfaces has resulted in more work for those who cast it. Finally, the growth of small-scale perfume distributors contributed to the appearance of perfume mixers. Other occupations that are not new but are emerging in engineering and sciences include translators as manufacturers' foreign markets and contacts increased. Maintenance and renewal of the national defense capabilities is associated with the expanded presence of missile specialists. □ Note 1 For information on the concepts and methodology of identifying new and emerging occupations, as well as detailed data on the findings of the Occupational Employment Statistics Survey, see Jerome Pikulinski, "New and Emerging Occupations," Occupational Employment and Wages , May 2003 (Bureau of Labor Statistics Bulletin 2567, September 2004). Where are you publishing your research? The Monthly Labor Review will consider for publication studies of the labor force, labor-management relations, business conditions, industry productivity, compensation, occupational safety and health, demographic trends, and other economic developments. Papers should be factual and analytical, not polemical in tone. We prefer (but do not require) submission in the form of an electronic file in Microsoft Word, either on a diskette or as an attachment to e-mail. Please use separate files for the text of the article; the tables; and charts. We also accept hard copies of manuscripts. Potential articles should be mailed to: Editor-in-Chief, Monthly Labor Review, Bureau of Labor Statistics, Washington, DC 20212, or by e-mail to mlr@bls.gov 42 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Workplace Safety and Health ,~~ , (?t! Fatal occupational injuries at road construction sites ries, it becomes even more important to determine the types of workers involved in road construction site fatalities and the events that precipitate the fatalities. 4 Stephen Pegula What is a road construction site? uring the 1995 to 2002 period, 844 workers were killed while working at a road construction site.' More than half of these fatalities were attributable to a worker being struck by a vehicle or mobile equipment. The range of these fatal occupational injuries was a low of 93 in 1996 and a high of 124 in 1999, as shown below: D 1995 1996 1997 1998 ········ 94 ........ 93 ........ 94 ....... 113 1999 ········ 124 2000 ........ 106 2001 ........ 118 2002 ........ 102 Fatal workplace injuries at road construction sites were first identified as a separate category in the Bureau of Labor Statistics Census of Fatal Occupational Injuries (cFot) in 1995. Since that time, overall workplace fatalities have generally declined, but fatalities at road construction sites have fluctuated, staying in the low 100's since 1998. Workplace fatalities that occur at a road construction site typically account for 1.5 percent to 2.0 percent of all workplace fatalities annually. A number of safety measures exist for road construction sites. For instance, the Federal Highway Administration's Manual on Uniform Traffic Control Devices provides guidance ranging from the types of signs to use at a road construction site to the proper use of rumble strips. 2 In addition, the Federal Highway Administration offers tips for motorists on traveling safely through road construction sites. 3 As fatal work injuries at road construction sites continue to account annually for a large number of fatal occupational injuStephen Pegula is an economist in the Office of Safety, Health, and Working Conditions, Bureau of Labor Statistics. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis There are various definitions of what constitutes a road construction site. According to the BLS Census of Fatal Occupational Injuries, a road construction site includes, " ... road construction workers and vehicle occupants fatally injured in work zones. Work zones include construction, maintenance, and utility work on a road, street, or highway." The Federal Highway Administration's Manual on Uniform Traffic Control Devices gives this definition, "A work zone is an area of a highway with construction, maintenance, or utility work activities. A work zone is typically marked by signs, channelizing devices, barriers, pavement markings, and/or work vehicles. It extends from the first warning sign or highintensity rotating, flashing, oscillating, or strobe lights on a vehicle to the END ROAD WORK sign or the last TIC [temporary traffic control] device." 5 In this report, only fatal work injuries that occurred at road construction sites as defined by CFO! are included in the analysis. Fatal work injuries at road construction sites were identified in two ways. First, all occupational fatalities that were coded as having occurred at a road construction site were included. 6 Next, the remaining CFO! record set was searched for key variables that might indicate that a fatal work injury did indeed occur at a road construction site, but was not coded as such. These variables include: • Keywords. Records with narratives containing variations on the following words were examinedzone, construction site, worksite, pedestrian, road construction, road site, flag, cone, road crew, high.way construction, street construction, barrel, manhole, road repair, painting line, pothole, and sewer. • Industry. All records in which the decedent was employed in Standard Industrial Classification (sic) 1611-Highway and Street Construction; or sic 1622Bridge, Tunnel, and Elevated Highway Construction; and where the fatality occurred on a roadway were examined. • Occupation. All records in which the decedent was employed, per the U.S. Census Bureau Occupation Codes, as a construction laborer (869), operating engineer (844), or paving, surfacing, and tamping equipment operator (594), and where the fatality occurred on a roadway were examined. • Worker activity. All records in which the decedent was, as classified by the CFOI worker activity codes, directing or flagging traffic ( 150); walking behind a vehicle (162); or resurfacing, blacktopping, etc. ( 140); and where the fatality occurred on a roadway were examined. • Source and secondary source. All records in which the source or secondary source of the fatal work injury, as classified by the Occupational Injury and Illnesses Classification System, was construction, logging, and mining machinery (codes 3200 to 3299) and where the fatality occurred on a roadway were examined. • Event. All records in which the decedent was killed, as classified in the Occupational Injury and Illnesses Classification System, by being struck by a vehicle or mobile equipment and where the fatality occurred on a roadway were examined. Records found through this key variable search deemed to have occurred at a road construction site (per the CFOI definition), but not coded as road con£truction, were recoded for this report. 7 Monthly Labor Review December 2004 43 Workplace Safety and Health Limitations of the data. The consistency of the application of the road construction site location code in CFOI could affect the data used for this analysis. An examination of the CFOI narratives shows that the road construction site location code was applied more rigorously later in the study period. 8 More cases in need of recoding were found in the early years of the study than in the latter years. These different applications of the code may skew the data; that is, the increase in fatal work injuries at road construction sites over time may be partly due to the more rigorous application of the location code in the latter years of the study period. country 's roads will mean that more road construction sites will be needed. To better protect workers, the Federal Government has taken steps to improve safety in work zones. For example, in 2001 , the National Institute for Occupational Safety and Health (N1osH) published "Building Safer Highway Workzones: Measures to Prevent Worker Injuries From Vehicles and Equipment." 12 In addition, the National Work Zone Safety Information Clearinghouse was created in February of 1998 to improve safety in highway work zones. 13 This clearinghouse provides access to data, training , and safety information for workers at road construction sites. Dangers at road construction sites Data analysis Few work environments present the multitude of risks as do road construction sites. For example, vehicles may pass by at high speeds, and the work conditions are constantly changing. Data from the National Highway Traffic Safety Administration show that injuries at road construction sites are a major concern. In 2001, 1,079 people were killed at a road construction site. 9 This figure includes people who were not at work at the time of their death, such as occupants of vehicles passing through road construction sites for nonwork-related reasons. Highway traffic is a concern for workers at a road construction site, but workers also face a similar danger from vehicles and mobile equipment being used at such sites. As shown later, fatally injured workers at road construction sites were more likely to be struck and killed by construction vehicles and equipment than by automobiles. To improve the country's roads, Congress passed the Transportation Equity Act for the 21st Century (TEA-21) in 1998. This act provided more than $200 billion dollars for transportation-related programs. 10 This legislation is in the process of being renewed. 11 Improving the 44 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December sites, and 10 percent and 12 percent of workplace fatalities to all workers. In terms of age, approximately 70 percent (594) of the decedents were between the ages of 25 and 54. Workers under age 25 made up 10 percent of fatal work injuries incurred at a road construction site and 11 percent of fatal work injuries overall. Workers age 55 and older accounted for 20 percent of the fatal work injuries incurred at a road construction site and 22 percent of workplace fatalities overall. Workers killed at a road construction site were largely working for wage and salary; approximately 96 percent (811) of the decedents were wage/salary workers, while only 4 percent were self-employed. For overall workplace fatalities from 1995 to 2002, 80 percent of the decedents were wage/salary workers and 20 percent were self-employed. Demographics. As mentioned earlier, over the 1995- 2002 period, 844 workers lost their lives due to fatal work injuries inWorker fatalities at road construction sites curred at a road construcover the 1995-2002 period, by selected demographic characteristics tion site. (See table 1.) The workplace fatality Characteristics Number of fatalities demographic breakTotal. .................... ....... ... ... ..... ...... down for this group was 844 Employee status: very similar to the workWage and salary workers 811 place fatality demoSelf-employed 33 Gender: graphic breakdown for Male .... ........... .................. ..... ... ... .... 787 workers in general. Female. ........ ... .................. ............ .. 57 Males accounted for 93 Age: 18 to 19 years .. ........ ....... ..... ........ .. 17 percent (787) of the 20 to 24 years .. .. .. ........ .... .............. 63 workplace fatalities at a 25 to 34 years ............. ......... .... ...... 185 35 to 44 years ................................ 213 road construction site , 45 to 54 years ...... ........ .. ..... . .... ... ... 196 compared with 92 per55 to 64 years .. .... ..... ..... ...... .. ........ 130 65 years and older ... .. ...... ........ ... ... 36 cent for all workplace Race or ethnic origin: fatalities. White workWhite .. ........... .. .. ............................. 613 Black or African American .... .... ...... 86 ers accounted for 73 perHispanic or Latino 118 cent (613) of the road May include volunteers and other workers rece iving construction site workcompensation. 2 Includes paid and unpaid family workers, and may include place fatalities and 73 owners of incorporated businesses, or members of partnerships. percent of fatally injured The categories "White" and "Black or African American" do not include "Hispanic or Latino" persons. workers overall. Black Persons identified as Hispanic may be of any race. workers and Hispanic NoTE: Totals for 2001 exclude fatalities resulting from the workers represented 10 September 11 terrorist attacks. Totals for major categories may percent and 14 percent, include subcategories not shown separately. respectively, of workSouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and place fatalities occurring Federal agencies, Census of Fatal Occupational Injuries. at road construction 1 2 • •••• •• • • • •• • • •• • • ••• •• •• •• • • • • •• • • • •• • • • •• • 3 4 1 3 4 2004 •• •• • •••• • • •• • • ••• • •• •• •• ...w:':':'"o-rk:-e-r':'fa"':t~a":li:tie_s_a~t_r_oa_d:--c-o-n-st'."'ru_c_t~io_n_s:it~e-s_o_v_e_r_t~h-e1 ..,, l!Bl!l'-::w::"o~r:'.':k':e':'r:fa:t~a:li:ti':'e~s~at=--roa"."':"d-,-c_o_n_s':'tr_u_c':'ti'."'o_n_s~it'."'e_s_o_v_e_r""'.t:""h_e,-~••l,,l.ullr_■ 1995-2002 period, by State of incident State of incident Number of fatalities Texas .. .... .............................. ..... .... ... .... ... . California ... ... ...... ........... .. ........................ . Florida ....... ........ ........ .. ........... ...... ...... ..... . Ohio ...... .. ....... ........ ...... ..... .......... ....... .. ... . Pennsylvania ... ... .... ..... ............................ . 71 51 46 46 44 New York ..... ... .................. ........ ................. Indiana ....... ........... ................................ .... Illinois ..... ..... .............. ..... ................. ......... . Virginia .. ............... ..................... ....... ....... .. Georgia ... .. ...... ... ..... .. ...... ......................... . 40 38 36 36 32 NOTE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks. SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries. ■ r• 1 •u~- Worker fatalities at road construction sites over the 1995-2002 period, by event or exposure Event or exposure Number of fatalities Transportation incidents .. .......... ...... ..... ......... . 693 Highway ........ ....... ............. ... ............... ..... Collision between vehicles, mobile equipment ... ... .. .. .... ... ... .... .. .............. . Moving in the same direction ..... ...... . Moving and standing vehicle, mobile equipment in roadway ..... .. Noncollision .... .................. .. .... ............. . Jack-knifed or overturned-no collision .... ...... .. ...... ... ..... ........ ....... . Nonhighway ............... ...... ......... .... .......... .. Noncollision accident ............ ..... ... ....... . Overturned .. ..................................... . Worker struck by vehicle, mobile equipment ......... .. ................ .. ..... ... ..... .. . Worker struck by vehicle, mobile equipment in roadway .... ...... ...... ...... . Worker struck by vehicle, mobile equipment on side of road ...... ... ..... .. 137 83 29 29 36 27 43 41 27 509 363 119 85 Contact with objects and equipment .. ..... ...... . Struck by object .. ... ......................... ... ...... . 44 Falls ........ .... .. ... .......................................... .... . 28 Exposure to harmful substances and environments ..... ... .. .................. ... ..... .. .... .... Contact with electric current .... .... ... ......... . Contact with overhead power lines ..... . 33 23 20 1995-2002 period, by industry and occupation Number of fatalities Characteristics Industry: Private industry ......................... ... .. ................... ...... Construction .... ...... ......... ... ..... .... ........ ..... ... ... ... .. . Heavy construction, except building ... .. .... ... .. Highway and street construction ............... .. Heavy construction, except highway .... ...... . Bridge, tunnel, and elevated highway ..... . Water, sewer, and utility lines .... ........... .. . Special trade contractors ........ ... .. .... ........ ..... . 688 566 467 340 125 70 34 90 Transportation and public utilities .... ........ .... ...... . Trucking and warehousing ... ........ .... ............ .. Trucking and courier services, except air .... ... ..... ... ..................... .... ......... . Trucking , except local ....... ....... .. ......... ..... . 52 44 Services ....... ... ......... .. ... ... ........ .... ................. .. .... 34 Government 1 • • • •• •••• • ••• • ••• • •• • • ••• •• •••••••• • •• ••••••••• • •••• • • • •••• State government ...... .. ....... .......... ..... ...... .......... . Construction ... ... .. ..... ....... ..... .... ......... ... ......... . Heavy construction , except building .... .... ... Highway and street construction ..... ...... ... Public administration ..................................... . 156 83 57 56 55 24 Local government .............. ................... .... .... ...... Construction .. .... .. .. ... ...................... .. ..... ..... .... Heavy construction, except building .. ........ . Highway and street construction ............. . Public administration ........ ................... .......... . 70 Occupation : Managerial and professional specialty ......... ............ . Precision production, craft, and repair .............. ....... . Construction trades ... ... ....... .. ... ... ..... .......... ... ..... . Supervisors, construction occupations ............ . Construction trades, except supervisors ......... . Paving, surfacing, and tamping equipment operators ............... ... .... ...... ... . 44 34 38 38 37 29 52 183 170 55 115 27 Operators, fabricators, and laborers ....... ....... ... ... .. . Transportation and material moving occupations .. ..................... .................... .... .... . Motor vehicle operators .. ....................... ... ..... . Truck drivers ..... ... ...... ... ... ....... ..... .......... ... .. . 558 Material moving equipment operators .......... . 101 Operating engineers ........ ...... ... .. ... ...... .... ... . Grader, dozer, and scraper operators .... ..... 54 27 Handlers, equipment cleaners , operators, and laborers .... ... ......... .. ..... ............... ....... ..... . Construction laborers .... ....... ...... .. ...... ............ 359 335 186 85 83 1 Includes fatalities to workers employed in governmental organizations regardless of industry. NoTE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks. Totals for major categories may include subcategories not shown separately. NoTE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks. Totals for major categories may include subcategories not shown separately. SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries. SouRcE : U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 45 Workplace Safety and Health Texas had the largest number of workplace fatalities at road construction sites; 8 percent (71) of the workplace fatalities occurred in this State. (See table 2, page 45 .) Other States with a large number of these types of occupational fatalities included California (6 percent), Florida (5 percent), Ohio (5 percent), Pennsylvania (5 percent), and New York (5 percent). operators (12 percent), and as truck drivers (10 percent), among other occupations.14 Struck by vehicle or mobile equipment incidents. Approximately 60 percent sector, 82 percent (566) of the road construction site decedents worked in construction. (See table 4, page 45.) Most of these construction fatalities (60 percent) were incurred by workers in highway and street construction. No other major industry group in the private sector accounted for more than 8 percent of the fatalities. Government workers accounted for 18 percent ( 156) of the workplace fatalities that occurred at a road construction site. These fatalities were incurred primarily by State and local government workers. As in the private sector, decedents working for a government entity were most likely to be working in highway and street construction. Among occupations, 40 percent (335) of the decedents worked as construction laborers. (See table 4, page 45.) The remaining decedents were employed in the construction trades (20 percent), as material moving equipment (509 fatalities) of the occupational fatalities that occurred at road construction sites were the result of workers being struck by vehicles or mobile equipment. Construction laborers incurred 49 percent (247) of these fatalities. In addition, 48 percent (242) of the decedents were working in the private highway and street construction industry. Geographically, these incidents were most likely to occur in Texas (9 percent, or 46 fatalities, of all struck by vehicle or mobile equipment workplace fatalities at road construction sites), Florida (7 percent), California (6 percent), Pennsylvania (6 percent) and Ohio (6 percent). (See table 5.) For fatalities for which the time of incident was available, 29 percent of the decedents who were struck by vehicles or mobile equipment at a road construction site were struck between the hours of 9:00 a.m. and 11:59 a.m., and 17 percent were struck between 6:00 a.m. and 8:59 a.m. These percentages were larger than those for all fatal occupational injuries, where 23 percent occurred between 9:00 a.m. and 11 :59 a.m., and 13 percent occurred between 6:00 a.m. and 8:59 a.m. Fatalities at road construction sites from being struck by a vehicle or mobile equipment also tend to be more clustered in the daylight hours (6:00 a.m. to 5:59 p.m.) than fatalities in general. Approximately 83 percent of the fatal work injuries incurred by workers at road construction sites from being struck by a vehicle or mobile equipment occurred in daylight hours, while 75 percent of all fatal work injuries occurred during these hours. In struck by vehicle or mobile equipment cases, the vehicle or mobile equipment that struck the worker is the source of the fatal injury. In 54 percent (274) of the cases, a truck struck the worker. Of these trucks, 36 percent were dump 46 2004 Event or exposure. More than four-fifths (693) of occupational fatalities that occur at a road construction site were caused by transportation incidents. Most prevalent were workers who were struck by 2 vehicle or mobile equipment, who accounted for approximately 60 percent (509) of all fatal work injuries that occurred at a road construction site. (See table 3, page 45 .) Other fatal events of note included highway collisions between vehicles or mobile equipment ( 10 percent of all fatal work injuries at a road construction site), being struck by an object (5 percent), and falls (3 percent). Industry and occupation. In the private Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December trucks, 21 percent were pickup trucks, and 19 percent were semitrailer, tractor trailer, or trailer trucks. Automobiles were the source in 28 percent ( 143) of all cases of struck by vehicle or mobile equipment at road construction sites. Finally, construction machinery, which includes backhoes, levelers, planers, scrapers, steamrollers, and road pavers, accounted for 11 percent (56) of the struck by vehicle or mobile equipment fatalities. (See table 6.) Note that workers at a road construction site faced a greater likelihood of being struck by a construction vehicle or construction equipment than of being struck by a car. While 28 percent of the workers who were killed in struck by vehicle or mobile equipment incidents at a road construction site were struck by automobiles, 31 percent were struck by dump trucks or construction machinery. With respect to the activity the decedent was performing when he or she was struck by a vehicle or mobile equipment, 29 percent (147) were constructing, repairing, or cleaning. Approximately 28 percent were walking in or near a roadway when they were struck, 11•1• 11 =--- Worker fatalities at road construction sites over the 1995- 2002 period, resulting from being struck by a vehicle or mobile equipment, by State of incident State of incident Number of fatalities Texas .......... ..... .... ............. . Florida .. .. ... ....... .... ............. California ...... .. .. ........ ... ..... . Pennsylvania ....... .......... ... . Ohio ............. ......... ........... . 46 37 33 30 29 Illinois ... ..... .- .. ............... ..... . Georgia ................. ....... .... . NewYork ............. ........ ...... Virginia ...................... .. .... .. North Carolina .. .. ...... ........ . 23 22 20 18 17 NOTE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks. SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries. Worker fatalities at road construction sites over the 1995-2002 period, resulting from being struck by a vehicle or mobile equipment, by worker activity Worker fatalities at road construction sites over the 1995-2000 period, resulting from being struck by a vehicle or mobile equipment, by source of the fatality Fatalities Fatalities Worker activity Source Number All struck by vehicle or mobile equipment fatalities 1 ••••••• ••••••••••• •• ••••••••••••••••••••••••••••••••••••• Vehicles .......... .... ... ... ................................... .... Highway vehicle-motorized .. .. ....................... . Automobile ....................... .............. ............. . Truck ................................................ ............ . Dump truck ........ .. ...................................... . Pickup truck ......... .............. .. .. ............... ..... . Semi-trailer, tractor trailer, or trailer truck .... .... ... ............................................. . Van ......................................... ..................... . Machinery ........................................... .............. . Construction, logging, and mining machinery Excavating machinery ............... .. ........... ..... . Backhoes ......... .. .................. ...................... . Bulldozers ..... ......... ........ .......... .. ..... .. ......... . Road grading and surfacing machinery ....... . Graders, levelers, planers, and scrapers .... Steam rollers and road pavers .................. . Percent 509 446 441 143 274 100 57 100 88 87 28 53 14 63 56 21 10 54 20 11 3 12 11 4 2 9 1 6 6 30 20 4 6 1 ' In struck by vehicle or mobile equipment fatalities, the source of the fatality is the vehicle or mobile equipment that struck the decedent. NorE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks. Totals for major categories may include subcategories not shown separately. SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries. slightly more than 18 percent were directing or flagging traffic, and 7 percent were resurfacing or blacktopping. (See □ table 7.) Notes The author thanks Dino Drudi , Samuel Meyer, Katharine Newman, St ep hanie Pratt , Scott Ri c hardson , Bill Wiatrowski , and Janice Windau for their assistance in the preparation of this article. A C KNOWLEDGMENT: Preliminary data for 2002 are used in thi s analysis . 1 2 For more information on the Manual on Uniform Traffic Control De vices, see http:// Number All struck by vehicle or mobile equipment fatalities ................................................... . Vehicular and transportation operation ...... . Resurfacing and blacktopping ................. . Directing or flagging traffic ...... ... ..... ... ....... Walking in or near roadway ..................... . Using or operating tools or machinery ...... . Constructing, repairing, or cleaning .. .. ....... Construction, assembling, or dismantling .... .. .... .... .... .. ............. ....... .. . Constructing or assembling ....... ..... .. ... . Installing .................. ............................ . Dismantling or removing ...................... . Repairs or maintenance ............ .............. . Repairing .................... ... ......... ... ...... ..... Maintenance ....................... ................. . Inspecting or checking ... .. ................. ....... . Painting , etc . ............................... ............ .. Material handling operations ...................... Physical activity, not elsewhere classified 1 • See http://safety.fltwa.dot.gov/roaduser/ wzs.htm 4 For an examination of worker fatalities in highway work zones from 1992 to 1998, see pages 5 and 6 of Building Safer Highway Work Zon es: M easures to Prevent Worker Injuries from Vehicles and Equipment, on the Internet at: http://www.cdc.gov/niosh/pdfs/O 1-128.pdf 5 See http://mutcd.fhwa.dot.gov/pdfs/ 2003rl/Ch6A-E.pdf, page 6C-2. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 8 The examination of the narratives should mitigate the problems ari sing from the application of the location code. The breakdown for added records is as follows: 1995 1996 1997 1998 ......... 63 ......... 47 ......... 36 ......... 37 66 10 14 8 30 13 2 3 2 6 3 2 4 2 2 9 17 9 18 11 12 46 SouRcE: U.S. Department of Labor, Bureau of Labor Statistics, in cooperation with State, New York City, District of Columbia, and Federal agencies, Census of Fatal Occupational Injuries. 7 Ascertaining whether a record should be recoded as a road construction fatality was sometimes complicated by vague and/or incomplete narratives. For the borderline cases, the determination as to whether a fatality occurred at a road construction site was made by examining the combination of the narrative, industry, occupation, and worker activity. Because there are various defini tions of what constitutes a road construction site, different people may make different determinations as to whether a fatal work injury occurred at a road construction site. For this analysis, the reclassifications were made by the author with input from CFOI staff. These reclassifications were based on a consistent set of requirements foffiluIated by the author and CFOI staff. Records added 100 55 7 18 28 3 29 NoTE: Totals for 2001 exclude fatalities resulting from the September 11 terrorist attacks. Totals for major categories may include subcategories not shown separately. 6 CFO I uses a location code of 65 to designate fatal work injuries that occur at road construction sites. Years 509 278 38 93 141 17 147 ' Includes walking, sitting, running, and climbing ladders or stairs. mu tcd. flt wa.dot.gov/ 3 Percent Years 1999 2000 2001 2002 Records added ........ ........ ........ ...... .. 53 43 45 24 In total , 496 records were included because the location code was 65-road construction. An additional 348 were added after examining records. 9 See Table 61 in http://www-nrd.nhtsa. dot.gov/pdf/nrd-30/NCSA/fSFAnn/fSF2001. pdf ° 1 Camille Villanova, "Looking for Safety Zones," Job Safety and Health Quarterly, Vol. 11, No . 3, p. 19. For more information on TEA-2I, see http://www.fltwa.dot.gov/tea2 l/ 11 For more information on the reauthorization on TEA-2I, see http://www.fltwa.dot.gov/ reauthorization/index.htm 12 See http://www.cdc.gov/niosh/pdfs/Ol- 128.pdf 13 The National Work Zone Safety Information Clearinghouse was the product of collaboration between the American Road & Transportation Builders Association (ARTBA) and the Federal Highway Administration. Now, it is run jointly by ARTBA and the Texas Transportation Institute. For more information, access http:// wzsafety.tamu.edu and http://wzsafety.tamu. edu/files/brochure.stm 14 Material moving equipment operators include occupations such as operating engineers; excavating and loading machine operators; and grader, dozer, and scraper operators. Monthly Labor Review · December 2004 47 The business cycle and earnings and incom e inequality electricity and increased mechani-zation of production that occurred in the early 20th century, and the widespre ad computerization that occurred later in the century. Individuals differ in their ability to incorpor ate the new technology. As a result, they differ in their ability to take advantage of itsome are able to benefit from the new technolog y relatively quickly, while others are slower to absorb it and thus lose ground, economically, relative to their peers. Eventually, however, these "laggards " begin to catch up as they embrace the new technolog y. Thus, technological innovations first tend to . increase earnings inequality, but in the longer term they tend to decrease it-at least until the next innovation comes along. In their empirica l analysis , the authors "begin with the one episode of declining earnings inequality during the past century," the period from the late 1920s to the early 1950s. They then examine two periods of increasin g earnings inequalit y-the early 1900s to the late 1920s and the late 1960s to "at least the end of the century." The empirica l findings support the predictio ns of their model, which attributes growth in earnings inequality to "technol ogical upheava ls." Inequality increased in the early 20th century due to the introduc tion of electricity and mechanized production. It decreased during the middle period, as the benefits of the new technology spread throughout the economy. Then, in the latter portion of the century, earnings inequality increased again with the widespread computerization of U.S. industry. Economists and other analysts generally agree that inequality in both earnings and income increased over the last 40 years. Moreover, such inequality tended to accelerate in periods of economic downturn during that span. Together, these facts have led to renewed interest in the extent to which earnings and income inequality are affected by the business cycle, especially recessions. In a recent study, "Earnings Inequality and the Business Cycle," NBER Working Paper 10469, economists Gadi Barlevy (Federal Reserve Bank of Chicago) and Daniel Tsiddon (Tel Aviv University) examine this issue. The authors develop a model in which 1ong-term trends in earnings and income inequality are amplified during recessio ns, regardle ss of whether inequalit y is generally increasin g or decreasing. In other words, the model predicts that during periods when inequality is growing, recessions will tend to exacerba te that trenu and inequality will increase more rapidly. However , during periods when inequality is decreasing, recessions will tend to accelerate that trend as well, and inequality will decrease more rapidly. To test their model empirically, the authors examine data from the entire 20th century. They find that, indeed, "downtur ns are associated with more rapid growth in inequality in [long-term] periods of rising inequality but with more rapid reductions in inequality in periods of falling inequality." Barlevy and Tsiddon provide some explanations for their findings. They Siblings and earnings argue, for example , that the U.S. inequality economy periodic ally experien ces "waves of drastic technolo gical In the Decembe r 2004 Chicago Fed innovation," such as the introduction of Letter, economist Bhashkar Mazumder 48 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Decembe r 2004 presents new research on siblings and earnings inequality. His essay, "What similariti es between siblings tell us about inequality in the U.S.," considers the question, "How important is family background in determining economic success in the United States?" To address this question, Mazumder analyzes data from the National Longitudinal Survey of Youth (NLSY). The dataset consists of a sample of more than 12,000 men and women who were between ages 14 and 22 in 1979. (This longitudi nal survey is ongoing; see http://www.bls.gov/nls/nlsy79.htm for more information). The sample includes more than 4,000 pairs of siblings. For men, Mazumder estimates that the correlation among siblings in annual earnings is 0.49 and the correlation for hourly wages is 0.54. For women, he notes that the results tend to be lower, which he explains is "not surprisin g given the more varied labor force participa tion patterns for younger women." Mazumder concludes that about half of earnings inequalit y in the United States is accounte d for by family background. He writes that his finding about the correlation among siblings in economi c outcome s "suggest s that inequalit ies between families persist strongly from generation to generation and that the U.S. is a less mobile society than is commonly believed. " We are interested in your feedback on this column. Please let us know what you have found most interesti ng and what essentia l readings we may have missed. Write to: Executive Editor, Monthly Labor Review, Bureau of Labor Statistics, Washington, oc 20212; fax: (202) 6915899, ore-mail: mlr@bls.gov Israel's economy The Israeli Economy, 1985-1998: From Government Intervention to Market Economics. Edited by Avi BenBassat. Cambridge, MA , Massachusetts Institute of Technology, 2002, 514 pp., $40/cloth. As editor, Avi Ben-Bassat presents a series of 14 essays based on a 1996 research project by variou s J,;;raeli academic and financial institutions as well as the International Monetary Fund, plus his own contribution in the form of an introductory essay discussing the obstacles Israel faced in its transformative efforts. These essays detail efforts to reform Israel from a poorly performing centralized command economy to a dynamic market economy. The Israeli Economy, 19851998 explores the obstacles faced and transformations experienced during this transition period. These essays describe the fiscal and monetary changes, changes in the markets , and external developments that allowed Israel to move from a situation where it faced 400 percent inflation to relative stability and growth . After the intensified conflicts of the 1967 Six Day War and the 1973 Yorn Kippur War, Israel reacted by centralizing decisionmaking and controlling resources. These conflicts resulted in the growth of the public sector and the public deficit. Lax fiscal discipline and the growth of government, due to increased defense expenditures and subsidies to businesses, resulted in severe economic decline. After a relaxation in the intensity of the Israeli-Arab conflict during the latter part of the 1970s and early 1980s, Israel was in a better position to address the economic difficulties it faced in the forms of high inflation and almost nonexistent growth of gross domestic product (GDP). To ~ddress these economic difficulties, Israeli policies needed some serious reform. How Israel addressed its https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis reform program is described in six major headings: fiscal policy; monetary policy; financial market reforms; reforms in the goods and services markets; immigration and the labor market; and changes in growth, branch structure, and income distribution. Changes in fiscal policy are explained in the first essay showing efforts at implementing tight fiscal discipline and a reduction in the size of government. Because of fiscal reform, three areas saw the greatest change in an effort to reduce expenditures: defense spending ; subsidies to the business sector and interest payments; and welfare transfers to households. Another part of these efforts was the drive to reduce public expenditures through the elimination of interest payments on external debts that approached 80 percent of GDP. Reform of monetary policy, as part of these stabilization program efforts, is addressed in three essays: reform of the financial system; di sinflation from 1985 to 1998 ; and the international view oflsraeli inflation. Israel's greate s t accompli s hment was to achieve disinflation. Liberalization is shown to be a key to enhancing competition in the financial markets in this accomplishment, as well as the Bank of Israel's (Bol) increased independence and use of interest rates as an instrument of monetary policy. These measures were fairly successful when combined with a reduction in the government budget deficit through fiscal discipline . The program did not work as well as it could have due to political pressures; changes in employment due to increased immigration from the former Soviet Union; and Israel's inability to reach a consensus about the process as a whole , shown by a conflict between the BoI and the Treasury. Achieving disinflation proved a difficult process when compared to nine other countries- all of which went from high inflation to lowered inflation levels with varying results. Despite set-back periods of double-digit inflation rates, Israel, by 1998, was experiencing mid single-digit rates. Liberalization of financial markets also saw mixed results, but in general the outcome has been positive. Liberalization of financial markets has allowed many projects to proceed when previously they would not have done so under government-provided funding, while others of less likely profit have been dropped. Because of more reliance for funding from the Tel Aviv Stock Exchange and less from bank debt or government-provided credits, credit crunches and reduced output volatility have been reduced over the business cycle , and results of this program are fairly solid. Pension reform, however, is a work in progress. Balancing social needs of pensioners against relieving the younger generation from the burden of a large share of income support is difficult. The solution is to accumulate more pension savings while workers are young, thus sharing the burden of risk between the generations. Fortunately for these workers, the unemployment rate dropped from 12 percent to 6 percent during the 1990s. This effect was produced by changes in immigration and liberalization of the goods and services market. These two issues are addressed separately, but are closely intertwined. While immigration increased during the 1990s due to the fall of the Soviet Union and an increase in migrants from Eastern Europe, the labor market grew as well. With the diminishment of economic centralization came growth in the labor market as industries increased efficiency efforts. In the first half of the 1990s, growth was greatest in exposed industries that were open to competition from imported items and required lower skills. In the second half, growth was seen most in more sophisticated industries that required skilled workers. Changes in capital accumulation and increased productivity resulted in changes to the labor market. It also re- Monthly Labor Review December 2004 49 Book Reviews sulted in challenging questions of income distribution. While Israel was growing in a dynamic market economy with immigration increases, income inequalities were also growing. The approach to these questions, and how Israe 1 is going to answer them, is addressed in the book's final chapters. A constant theme emerges as the authors describe the complex, and often contradictory, events and efforts to reform. Sectors and industries that are opened to competition thrive and be- 50 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis come more efficient, providing benefits to both labor and consumers. Efficiency aids efforts to globalize Israel's economy, though it also is more exposed to external crises. Another recurring issue of many essays is that success is not seen in every effort to liberalize, as is the case in some public utilities. Because this book is a collection of studies analyzing one country going through one process, there are some redundancies. This is unavoidable because this one process is complex and December 2004 has many levels that are affected by the same events. The essays provide a nonpartisan view of an economic system in transformation, and a good argument for market solutions versus dirigiste philosophies toward providing a healthy economic environment. -Scott Berridge Office of Publications and Special Studies, Bureau of Labor Statistics ',ii ',~~ Current Labor Statistics Notes on labor statistics .............................. Comparative indicators 52 30. Employment Cost Index, compensation.................... ......... 96 1. Labor market indicators .... .... .................... .... .................... 65 2. Annual and quarterly percent changes in compensation, prices, and productivity ....................... 66 3. Alternative measures of wages and compensation changes ................................................... 66 Labor force data 4. Employment status of the population, seasonally adjusted ....... ................................................ 5. Selected employment indicators, seasonally adjusted ....................................................... 6. Selected unemployment indicators, seasonally adjusted ....................................................... 7. Duration of unemployment, seasonally adjusted ....................................................... 8. Unemployed persons by reason for unemployment, seasonally adjusted ....................................................... 9. Unemployment rates by sex and age, seasonally adjusted ....................................................... l 0. Unemployment rates by States, seasonally adjusted ....................................................... 11. Employment of workers by States, seasonally adjusted ....................................................... 12. Employment of workers by industry, seasonally adjusted ....................................................... 13. Average weekly hours by industry, seasonally adjusted ....................................................... 14. Average hourly earnings by industry, seasonally adjusted........................................................ 15. Average hourly earnings by industry ................................ 16. Average weekly earnings by industry ............................... 17. Diffusion indexes of employment change, seasonally adjusted ....................................................... 18. Job openings levels and rates, by industry and regions, seasonally adjusted......................................................... 19. Hires levels and rates by industry and region, seasonally adjusted.......................................................... 20. Separations levels and rates by industry and region, seasonally adjusted.......................................................... 21. Quits levels and rates by industry and region, seasonally adjusted.......................................................... 22. Quarterly Census of Employment and Wages, IO largest counties......................................................... 23. Quarterly Census of Employment and Wages, by State 24. Annual data: Quarterly Census of Employment and Wages, by ownership ............................................. 25. Annual data: Quarterly Census of Employment and Wages, establishment size and employment, by supersector ... 26. Annual data: Quarterly Census of Employment and Wages, by metropolitan area ......................................... 27. Annual data: Employment status of the population ........ 28. Annual data: Employment levels by industry .................. 29. Annual data: Average hours and earnings level, by industry............................... ...................................... https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Labor compensation and collective bargaining data 31. Employment Cost Index, wages and salaries.................... 32. Employment Cost Index, benefits, private industry .. ...... 33. Employment Cost Index, private nonfarm workers, by bargaining status, region, and area size .................... 34. Participants in benefit plans, medium and large firms ...... 35. Participants in benefits plans, small firm s and government .. .. .... .. .. .. .. .... .. .. .... ... .. ..... .. .. .. ...... .... .. ..... 36. Work stoppages involving 1,000 workers or more ........... 67 Price data 68 37. Consumer Price Index: U.S. city average, by expenditure category and commodity and service groups ................ 38. Consumer Price Index: U.S. city average and local data, all items ........................................................ 39. Annual data: Consumer Price Index, all items and major groups........................................................... 40. Producer Price Indexes by stage of processing ................. 41. Producer Price Indexes for the net output of major industry groups ............. ... .... .... ........ ................ .... ......... 42. Annual data: Producer Price Indexes by stage of processing .... ............. ...... ..... ........ ..... ...... .... 43. U.S. export price indexes by Standard International Trade Classification .... ............. .. .. .. .... ..... .. .. .. .. .. ....... .. ... 44. U.S . import price indexes by Standard International Trade Classification ............. ........ .. ......... .... .... .. ..... ....... 45. U.S. export price indexes by end-use category ................. 46. U.S. import price indexes by end-use category ...... .......... 47. U.S. international price indexes for selected categories of services ..................................................... 69 69 70 71 72 73 74 77 78 79 80 98 99 l 00 101 l 02 l 03 104 I 07 I 08 I 09 110 111 112 113 114 115 116 Productivity data 81 82 82 83 83 84 86 87 48. Indexes of productivity, hourly compensation, and unit costs, data seasonally adjusted .... .... ............... 49. Annual indexes of multifactor productivity .... ........ .......... 50. Annual indexes of productivity, hourly compensation, unit costs, and prices ......... ................... ........................ 51. Annual indexes of output per hour for select industries........................ ....... ........................................ 117 118 1 19 120 International comparisons data 52. Unemployment rates in nine countries, 53. Annual data: Employment status of the civilian working-age population, 10 countries............ .............. .. 124 54. Annual indexes of productivity and related measures, 12 countries....... ................................ .... ........................ 125 88 89 94 94 95 Injury and Illness data 55. Annual data: Occupational injury and illness incidence rates ............. .................. ..... .......... .................. . 126 56. Fatal occupational injuries by event or exposure.. ........... 128 Monthly Labor Review December 2004 51 Notes on Current Labor Statistics This section of the Review presents the principal statistical series collected and calculated by the Bureau of Labor Statistics: series on labor force; employment; unemployment; labor compensation; consumer, producer, and international prices; productivity; international comparisons; and injury and illness statistics. In the notes that follow, the data in each group of tables are briefly described; key definitions are given; notes on the data are set forth; and sources of additional information are cited. General notes The following notes apply to several tables in this section: Seasonal adjustment. Certain monthly and quarterly data are adjusted to eliminate the effect on the data of such factors as climatic conditions, industry production schedules, opening and closing of schools, holiday buying periods , and vacation practices, which might prevent short-term evaluation of the statistical series. Tables containing data that have been adjusted are identified as "seasonally adjusted." (All other data are not seasonally adjusted.) Seasonal effects are estimated on the basis of current and past experiences. When new seasonal factors are computed each year, revisions may affect seasonally adjusted data for several preceding years. Seasonally adjusted data appear in tables 1-14, 17-21, 48, and 52. Seasonally adjusted labor force data in tables 1 and 4-9 were revised in the February 2004 issue of the Review. Seasonally adjusted establishment survey data shown in tables 1 12-14, and 17 were revised in the March 2004 Review. A brief explanation of the seasonal adjustment methodology appears in "Notes on the data." Revisions in the productivity data in table 54 are usually introduced in the September issue. Seasonally adjusted indexes and percent changes from month-to-month and quarter-to-quarter are published for numerous Consumer and Producer Price Index series. However, seasonally adjusted indexes are not published for the U.S. average All-Items CPI. Only seasonally adjusted percent changes are available for this series. Adjustments for price changes. Some data-such as the "real" earnings shown in table 14-are adjusted to eliminate the effect of changes in price. These adjustments are mt1cte by dividing current-dollar values by the Consumer Price Index or the appropriate component of the index, then multiplying by 100. For example, given a current hourly wage rate of $3 and a current price 52 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis index number of 150, where 1982 = 100, the hourly rate expressed in 1982 dollars is $2 ($3/150 x 100 = $2). The $2 (or any other resulting values) are described as " real," "constant," or " 1982" dollars. Sources of information Data that supplement the tables in this section are published by the Bureau in a variety of sources. Definitions of each series and notes on the data are contained in later sections of these Notes describing each set of data. For detailed descriptions of each data series, see BLS Handbook of Methods, Bulletin 2490. Users also may wish to consult Major Programs of the Bureau of Labor Statistics, Report 919. News releases provide the latest statistical information published by the Bureau; the major recurring releases are published according to the schedule appearing on the back cover of this issue. More information about labor force, employment, and unemployment data and the household and establishment surveys underlying the data are available in the Bureau 's monthly publication, Employment and Earnings. Historical unadjusted and seasonally adjusted data from the household survey are available on the Internet: http://www.bls.gov/cps/ Historically comparable unadjusted and seasonally adjusted data from the establishment survey also are available on the Internet: http ://www.bls.gov/ces/ Additional information on labor force data for areas below the national level are provided in the BLS annual report, Geographic Profile of Employment and Unemployment. For a comprehensive discussion of the Employment Cost Index, see Employment Cost Indexes and Levels, 1975- 95, BLS Bulletin 2466. The most recent data from the Employee Benefits Survey appear in the following Bureau of Labor Statistics bulletins: Employee Benefits in Medium and Large Firms; Employee Benefits in Small Private Establishments; and Employee Benefits in State and Local Governments. More detailed data on consumer and producer prices are published in the monthly periodicals, The CPI Detailed Report and Producer Price Indexes. For an overview of the 1998 revision of the CPI, see the December 1996 issue of the Monthly Labor Review. Additional data on international prices appear in monthly news releases. Listings of industries for which productivity indexes are available may be found on the Internet: http://www.bls.gov/lpd For additional information on interna- December 2004 tional comparisons data, see International Comparisons of Unemployment, Bulletin 1979. Detailed data on the occupational injury and illness series are published in Occupational Injuries and Illnesses in the United States, by Industry, a BLS annual bulletin. Finally, the Monthly Labor Review carries analytical articles on annual and longer term developments in labor force, employment, and unemployment; employee compensation and collective bargaining; prices; productivity; international comparisons; and injury and illness data. Symbols n.e.c. = not elsewhere classified. n.e.s. = not elsewhere specified. p = preliminary. To increase the timeliness of some series, preliminary figures are issued based on representative but incomplete returns. r revised. Generally, this revision reflects the availability of later data, but also may reflect other adjustments. Comparative Indicators (Tables 1- 3) Comparative indicators tables provide an overview and comparison of major BLS statistical series. Consequently, although many of the included series are available monthly, all measures in these comparative tables are presented quarterly and annually. Labor market indicators include employment measures from two major surveys and information on rates of change in compensation provided by the Employment Cost Index (ECI) program. The labor force participation rate, the employment-popu lation ratio, and unemployment rates for major demographic groups based on the Current Population (" household") Survey are presented, while measures of employment and average weekly hours by major industry sector are given using nonfarm payroll data. The Employment Cost Index (compensation), by major sector and by bargaining status, is chosen from a variety of BLS compensation and wage measures because it provides a comprehensive measure of employer costs for hiring labor, not just outlays for wages, and it is not affected by employment shifts among occupations and industries. Data on changes in compensation, prices, and productivity are presented in table 2. Measures of rates of change of compensation and wages from the Employment Cost Index program are provided for all civilian nonfarm workers (excluding Federal and household workers) and for all private nonfarm workers . Measures of changes in consumer prices for all urban consumers; producer prices by stage of processing; overall prices by stage of processing; and overall export and import price indexes are given. Measures of productivity (output per hour of all persons) are provided for major sectors. Alternative measures of wage and compensation rates of change, which reflect the overall trend in labor costs, are summarized in table 3. Differences in concepts and scope, related to the specific purposes of the series, contribute to the variation in changes among the individual measures. Notes on the data Definitions of each series and notes on the data are contained in later sections of these notes describing each set of data. Employment and Unemployment Data (Tables I; 4--29) Household survey data not work during the survey week, but were available for work except for temporary illness and had looked for jobs within the preceding 4 weeks. Persons who did not look for work because they were on layoff are also counted among the unemployed. The unemployment rate represents the number unemployed as a percent of the civilian labor force. The civilian labor force consists of all employed or unemployed persons in the civilian noninstitutional population. Persons not in the labor force are those not classified as employed or unemployed. This group includes discouraged workers , defined as persons who want and are available for a job and who have looked for work sometime in the past 12 months (or since the end of their last job if they held one within the past 12 months), but are not currently looking, because they believe there are no jobs available or there are none for which they would qualify. The civilian noninstitutional population comprises all persons 16 years of age and older who are not inmates of penal or mental institutions, sanitariums, or homes for the aged, infirm, or needy. The civilian labor force participation rate is the proportion of the civilian noninstitutional population that is in the labor force. The employment-population ratio is employment as a percent of the civilian noninstitutional population. Notes on the data Description of the series Employment data in this section are obtained from the Current Population Survey, a program of personal interviews conducted monthly by the Bureau of the Census for the Bureau of Labor Statistics. The sample consists of about 60,000 households selected to represent the U.S. population 16 years of age and older. Households are interviewed on a rotating basis, so that three-fourths of the sample is the same for any 2 consecutive months. Definitions From time to time, and especially after a decennial census, adjustments are made in the Current Population Survey figures to correct for estimating errors during the intercensal years. These adjustments affect the comparability of historical data. A description of these adjustments and their effect on the various data series appears in the Explanatory Notes of Employment and Earnings. For a discussion of changes introduced in January 2003, see "Revisions to the Current Population Survey Effective in January 2003" in the February 2003 issue of Employment and Earnings (available on the BLS Web site at: http://www.bls.gov/ ARIMA for seasonal adjustment of the labor force data and the effects that it had on the data. At the beginning of each calendar year, historical seasonally adjusted data usually are revised, and projected seasonal adjustment factors are calculated for use during the January-June period. The historical seasonally adjusted data usually are revised for only the most recent 5 years. In July, new seasonal adjustment factors, which incorporate the experience through June, are produced for the July-December period, but no revisions are made in the historical data. FOR ADDITIONAL INFORMATION on national household survey data, contact the Division of Labor Force Statistics: (202) 691-6378. X-12 Establishment survey data Description of the series Employment, hours, and earnings data in this section are compiled from payroll records reported monthly on a voluntary basis to the Bureau of Labor Statistics and its cooperating State agencies by about 160,000 businesses and government agencies, which represent approximately 400,000 individual worksites and represent all industries except agriculture. The active CES sample covers approximately one-third of all nonfarm payroll workers. Industries are classified in accordance with the 2002 North American Industry Classification System. In most industries, the sampling probabilities are based on the size of the establishment; most large establishments are therefore in the sample. (An establishment is not necessarily a firm; it may be a branch plant, for example, or warehouse.) Self-employed persons and others not on a regular civilian payroll are outside the scope of the survey because they are excluded from establishment records. This largely accounts for the difference in employment figures between the household and establishment surveys. Em_~>loyed persons include ( 1) all those cps/rvcps03.pdf). Definitions who worked for pay any time during the week which includes the 12th day of the month or who worked unpaid for 15 hours or more in a family-operated enterprise and (2) those who were temporarily absent from their regular jobs because of illness, vacation, industrial dispute, or similar reasons. A person working at more than onl': job is counted only in the job at which he or she worked the greatest number of hours. Unemployed persons are those who did Effective in January 2003, BLS began using the X-12 ARIMA seasonal adjustment program to seasonally adjust national labor force data. This program replaced the X-11 ARIMA program which had been used since January 1980. See "Revision of Seasonally Adjusted Labor Force Series in 2003," in the February 2003 issue of Employment and Earnings (available on the BLS Web site at http:www.bls.gov/cps/cpsrs.pdf) for a discussion of the introduction of the use of An establishment is an economic unit which produces goods or services (such as a factory or store) at a single location and is engaged in one type of economic activity. Employed persons are all persons who received pay (including holiday and sick pay) for any part of the payroll period including the 12th day of the month. Persons holding more than one job (about 5 percent of all persons in the labor force) are counted https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 53 Current Labor Statistics in each establishment which reports them. Production workers in the goods-prod uc ing industries cover employees, up through the level of working supervisors, who engage directly in the manufacture or construction of the establishment's product. In private service-providing industries, data are collected for nonsupervisory workers, which include most employees except those in executive, managerial, and supervisory positions. Those workers mentioned in tables 11-16 include production workers in manufacturing and natural resources and mining; construction workers in construction; and nonsupervisory workers in all private service-providing industries. Production and nonsupervisory workers account for about four-fifths of the total employment on private nonagricultural payrolls. Earnings are the payments production or nonsupervisory workers receive during the survey period, including premium pay for overtime or late-shift work but excluding irregular bonuses and other special payments. Real earnings are earnings adjusted to reflect the effects of changes in consumer prices. The deflator for this series is derived from the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W). Hours represent the average weekly hours of production or nonsupervisory workers for which pay was received, and are different from standard or scheduled hours. Overtime hours represent the portion of average weekly hours which was in excess of regular hours and for which overtime premiums were paid. The Diffusion Index represents the percent of industries in which employment was rising over the indicated period, plus onehalf of the industries with unchanged employment; 50 percent indicates an equal balance between industries with increasing and decreasing employment. In line with Bureau practice, data for the 1-, 3-, and 6-month spans are seasonally adjusted, while those for the 12-month span are unadjusted. Table 17 provides an index on private nonfarm employment based on 278 industries, and a manufacturing index based on 84 industries. These indexes are useful for measuring the dispersion of economic gains or losses and are also economic indicators. Notes on the data Establishment survey data are annually adjusted to comprehensive counts of employment (called "benchmarks"). The March 2003 benchmark was introduced in February 2004 with the release of data for January 2004, published in the March 2004 is- 54 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis sue of the Review. With the release in June 2003, CES completed a conversion from the Standard Industrial Classification (S IC) system to the North American Industry Classification System cNAICS) and completed the transition from its original quota sample design to a probability-based sample design. The industry-coding update included reconstruction of historical estimates in order to preserve time series for data users. Normally 5 years of seasonally adjusted data are revised with each benchmark revision. However, with this release, the entire new time series history for all CES data series were re-seasonally adjusted due to the NAICS conversion, which resulted in the revision of all CES time series. Also in June 2003, the CES program introduced concurrent seasonal adjustment for the national establishment data. Under this methodology, the first preliminary estimates for the current reference month and the revised estimates for the 2 prior months will be updated with concurrent factors with each new release of data. Concurrent seasonal adjustment incorporates all available data, including first preliminary estimates for the most current month, in the adjustment process. For additional information on all of the changes introduced in June 2003, see the June 2003 issue of Employment and Earnings and "Recent changes in the national Current Employment Statistics survey," Monthly Labor Review, June 2003, pp. 3-13. Revisions in State data (table 11) occurred with the publication of January 2003 data. For information on the revisions for the State data, see the March and May 2003 issues of Employment and Earnings, and "Recent changes in the State and Metropolitan Area CES survey," Monthly Labor Review, June 2003, pp. 14-19. Beginning in June 1996, the BLS uses the X-12-ARIMA methodology to seasonally adjust establishment survey data. This procedure , developed by the Bureau of the Census, controls for the effect of varying survey intervals (also known as the 4- versus 5-week effect), thereby providing improved measurement of over-the-month changes and underlying economic trends. Revisions of data, usually for the most recent 5-year period, are made once a year coincident with the benchmark revisions. In the establishment survey, estimates for the most recent 2 months are based on incomplete returns and are published as preliminary in the tables (12-17 in the Review). When all returns have been received, the estimates are revised and published as "final" (prior to any benchmark revisions) in the December 2004 third month of their appearance. Thus, December data are published as preliminary in January and February and as final in March. For the same reasons, quarterly establishment data (table 1) are preliminary for the first 2 months of publication and final in the third month. Fourth-quarter data are published as preliminary in January and February and as final in March. FOR ADDITIONAL INFORMATION on establishment survey data, contact the Division of Current Employment Statistics: (202) 691-6555. Unemployment data by State Description of the series Data presented in this section are obtained from the Local Area Unemployment Statistics (LAUS) program, which is conducted in cooperation with State employment security agencies. Monthly estimates of the labor force, employment, and unemployment for States and sub-State areas are a key indicator of local economic conditions, and form the basis for determining the eligibility of an area for benefits under Federal economic assistance programs such as the Job Training Partnership Act. Se~sonally adjusted unemployment rates are presented in table 10. Insofar as possible, the concepts and definitions underlying these data are those used in the national estimates obtained from the CPS. Notes on the data Data refer to State of residence. Monthly data for all States and the District of Columbia are derived using standardized procedures established by BLS. Once a year, estimates are revised to new population controls, usually with publication of January estimates, and benchmarked to annual average CPS levels. FOR ADDITIONAL INFORMATION on data in this series, call (202) 691-6392 (table 10) or (202) 691-6559 (table 11). Quarterly Census of Employment and Wages Description of the series Employment, wage, and establishment data in this section are derived from the quarterly tax reports submitted to State employment security agencies by private and State and local government employers sub- ject to State unemploymen t insurance (u1) laws and from Federal, agencies subject to the Unemployme nt Compensation for Federal Employees (uCFE) program. Each quarter, State agencies edit and process the data and send the information to the Bureau of Labor Statistics. The Quarterly Census of Employment and Wages (QCEW) data, also referred as ES202 data, are the most complete enumeration of employment and wage information by industry at the national, State, metropolitan area, and county levels. They have broad economic significance in evaluating labor market trends and major industry developments. Definitions In general, the Quarterly Census of Employment and Wages monthly employment data represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered private industry employment includes most corporate officials, executives, supervisory personnel , professionals, clerical workers, wage earners, piece workers, and part-time workers. It excludes proprietors, the unincorporate d self-employed, unpaid family members, and certain farm and domestic workers. Certain types of nonprofit employers, such as religious organizations, are given a choice of coverage or exclusion in a number of States. Workers in these organizations are, therefore, reported to a limited degree. Persons on paid sick leave, paid holiday, paid vacation, and the like, are included. Persons on the payroll of more than one firm during the period are counted by each u1subject employer if they meet the employment definition noted earlier. The employment count excludes workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness, or unpaid vacations. Federal employment data are based on reports of monthly employment and quarterly wages submitted each quarter to State agencies for all Federal installations with employees covered by the Unemploymen t Compensation for Federal Employees (ucFE) program, except for certain national security agencies, which are omitted for security reasons. Employment for all Federal agencies for any given month is based on the number of persons who worked during or receive<l pay for the pay period that included the 12th of the month. An establishment is an economic unit, such as a farm, mine, factory, or store, that produces goods or provides services. It is https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis typically at a single physical location and engaged in one, or predominantly one, type of economic activity for which a single industrial classification may be applied. Occasionally, a single physical location encompasses two or more distinct and significant activities. Each activity should be reported as a separate establishmen t if separate records are kept and the various activities are classified under different NAICS industries. Most employers have only one establishment; thus, the establishment is the predominant reporting unit or statistical entity for reporting employment and wages data. Most employers, including State and local governments who operate more than one establishment in a State, file a Multiple Worksite Report each quarter, in addition to their quarterly u1 report. The Multiple Worksite Report is used to collect separate employment and wage data for each of the employer's establishments, which are not detailed on the u1 report. Some very small multi-establishment employers do not file a Multiple Worksite Report. When the total employment in an employer's secondary establishments (all establishments other than the largest) is 10 or fewer, the employer generally will file a consolidated report for all establishments. Also, some employers either cannot or will not report at the establishment level and thus aggregate establishments into one consolidated unit, or possibly several units, though not at the establishment level. For the Federal Government, the reporting unit is the installation: a single location at which a department, agency, or other government body has civilian employees. Federal agencies follow slightly different criteria than do private employers when breaking down their reports by installation. They are permitted to combine as a single statewide unit: 1) all installations with IO or fewer workers, and 2) all installations that have a combined total in the State of fewer than 50 workers. Also, when there are fewer than 25 workers in all secondary installations in a State, the secondary installations may be combined and reported with the major installation. Last, if a Federal agency has fewer than five employees in a State, the agency headquarters office (regional office, district office) serving each State may consolidate the employment and wages data for that State with the data reported to the State in which the headquarters is located. As a result of these reporting rules , the number of reporting units is always larger than the number of employers (or government agencies) but smaller than the number of actual establishments (or installations). Data reported for the first quarter are tabulated into size categories ranging from worksites of very small size to those with 1,000 employees or more. The size category is determined by the establishment' s March employment level. It is important to note that each establishment of a multi-establish ment firm is tabulated separately into the appropriate size category. The total employment level of the reporting multi-establish ment firm is not used in the size tabulation. Covered employers in most States report total wages paid during the calendar quarter, regardless of when the services were performed. A few State laws, however, specify that wages be reported for, or based on the period during which services are performed rather than the period during which compensation is paid. Under most State laws or regulations, wages include bonuses, stock options, the cash value of meals and lodging, tips and other gratuities, and, in some States, employer contributions to certain deferred compensation plans such as 401 (k) plans. Covered employer contributions for oldage, survivors, and disability insurance (OASDI), health insurance, unemploymen t insurance, workers' compensation, and private pension and welfare funds are not reported as wages. Employee contributions for the same purposes, however, as well as money withheld for income taxes, union dues, and so forth, are reported even though they are deducted from the worker 's gross pay. Wages of covered Federal workers represent the gross amount of all payrolls for all pay periods ending within the quarter. This includes cash allowances, the cash equivalent of any type of remuneration, severance pay, withholding taxes, and retirement deductions. Federal employee remuneration generally covers the same types of services as for workers in private industry. Average annual wage per employee for any given industry are computed by dividing total annual wages by annual average employment. A further division by 52 yie lds average weekly wages per employee. Annual pay data only approximate annual earnings because an individual may not be employed by the same employer all year or may work for more than one employer at a time. Average weekly or annual wage is affected by the ratio of full-time to part-time workers as well as the number of individuals in high-paying and low-paying occupations. When average pay levels between States and industries are compared, these factors should be taken into consideration. For example, industries characterized by high proportions of part-time workers will Monthly Labor Review December 2004 55 Current Labor Statistics show average wage levels appreciably less than the weekly pay levels of regular fulltime employees in these industries. The opposite effect characterizes industries with low proportions of part-time workers, or industries that typically schedule heavy weekend and overtime work. Average wage data also may be influenced by work stoppages, labor turnover rates, retroactive payments, seasonal factors , bonus payments, and so on. Notes on the data Beginning with the release of data for 2001 , publications presenting data from the Covered Employment and Wages program have switched to the 2002 version of the North American Industry Classification System (NAICS) as the basis for the assignment and tabulation of economic data by industry. NAICS is the product of a cooperative effort on the part of the statistical agencies of the United States, Canada, and Mexico. Due to difference in NAICS and Standard Industrial Classification (SIC) structures, industry data for 2001 is not comparable to the SIC-based data for earlier years . Effective January 2001 , the program began assigning Indian Tribal Councils and related establishments to local government ownership. This BLS action was in response to a change in Federal law dealing with the way Indian Tribes are treated under the Federal Unemployment Tax Act. This law requires federally recognized Indian Tribes to be treated similarly to State and local governments. In the past, the Covered Employment and Wage (CEW) program coded Indian Tribal Councils and related establishments in the private sector. As a result of the new law, CEW data reflects significant shifts in employment and wages between the private sector and local government from 2000 to 2001. Data also reflect industry changes. Those accounts previously assigned to civic and social organizations were assigned to tribal governments. There were no required ind11 ~try changes for related establishments owned by these Tribal Councils. These tribal business establishments continued to be coded according to the economic activity of that entity. To insure the highest possible quality of data, State employment security agencies verify with employers and update, if necessary, the industry, location, and ownership classification of all establishments on a 3-year cycle. Changes in establishment classification codes resulting from the verification process are introduced with the data reported for the first quarter of the year. 56 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Changes resulting from improved employer reporting also are introduced in the first quarter. For these reasons, some data, especially at more detailed geographic levels, may not be strictly comparable with earlier years. County definitions are assigned according to Federal Information Processing Standards Publications as issued by the National Institute of Standards and Technology. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those areas designated by the Census Bureau where counties have not been created. County data also are presented for the New England States for comparative purposes, even though townships are the more common designation used in New England (and New Jersey). The Office of Management and Budget (0MB) defines metropolitan areas for use in Federal statistical activities and updates these definitions as needed. Data in this table use metropolitan area criteria established by 0MB in definitions issued June 30, 1999 (0MB Bulletin No. 99-04). These definitions reflect information obtained from the 1990 Decennial Census and the 1998 U.S. Census Bureau population estimate. A complete list of metropolitan area definitions is available from the National Technical Information Service (NTIS), Document Sales, 5205 Port Royal Road, Springfield, Va. 22161 , telephone 1-800-553-6847. 0MB defines metropolitan areas in terms of entire counties, except in the six New England States where they are defined in terms of cities and towns. New England data in this table, however, are based on a county concept defined by 0MB as New England County Metropolitan Areas (NECMA) because county-level data are the most detailed available from the Quarterly Census of Employment and Wages. The NECMA is a countybased alternative to the city- and town-based metropolitan areas in New England. The NECMA for a Metropolitan Statistical Area (MSA) include: ( 1) the county containing the first-named city in that MSA title (this county may include the first-named cities of other MSA, and (2) each additional county having at least half its population in the MSA in which first-named cities are in the county identified in step 1. The NECMA is officially defined areas that are meant to be used by statistical programs that cannot use the regular metropolitan area definitions in New England. FoR ADDITIONAL INFORMATION on the covered employment and wage data, contact the Division of Administrative Statistics and Labor Turnover at (202) 691 - 6567. December 2004 Job Openings and Labor Turnover Survey Description of the series Data for the Job Openings and Labor Turnover Survey (JOLTS) are collected and compiled from a sample of 16,000 business establishments. Each month, data are collected for total employment, job openings, hires, quits, layoffs and discharges, and other separations. The JOLTS program covers all private nonfarm establishments such as factories , offices, and stores, as well as Federal, State, and local government entities in the 50 States and the District of Columbia. The JOLTS sample design is a random sample drawn from a universe of more than eight million establishments compiled as part of the operations of the Quarterly Census of Employment and Wages, or QCEW, program. This program includes all employers subject to State unemployment insurance (UI) laws and Federal agencies subject to Unemployment Compensation for Federal Employees (UCFE). The sampling frame is stratified by ownership, region, industry sector, and size class. Large firms fall into the sample with virtual certainty. JOLTS total employment estimates are controlled to the employment estimates of the Current Employment Statistics (CES) survey. A ratio of CES to JOLTS employment is used to adjust the levels for all other JOLTS data elements. Rates then are computed from the adjusted levels. The monthly JOLTS data series begin with December 2000. Not seasonally adjusted data on job openings, hires, total separations, quits, layoffs and discharges, and other separations levels and rates are available for the total nonfarm sector, 16 private industry divisions and 2 government divisions based on the North American Industry Classification System (NAICS), and four geographic regions. Seasonally adjusted data on job openings, hires, total separations, and quits levels and rates are available for the total nonfarm sector, selected industry sectors, and four geographic regions. Definitions Establishments subm it job openings information for the last business day of the reference month. A job opening requires that ( 1) a specific position exists and there is work available for that position; and (2) work could start within 30 day s regardless of whether a suitable candidate is found; and (3) the employer is actively recruiting from outside the establishment to fill the position. Included are full-time, part-time, permanent, short-term, and seasonal openings. Active recruiting means that the establishment is taking steps to fill a position by advertising in newspapers or on the Internet, posting help-wanted signs, accepting applications, or using other similar methods. Jobs to be filled only by internal transfers, promotions, demotions, or recall from layoffs are excluded. Also excluded are jobs with start dates more than 30 days in the future, jobs for which employees have been hired but have not yet reported for work, and jobs to be filled by employees of temporary help agencies, employee leasing companies, outside contractors, or consultants. The job openings rate is computed by dividing the number of job openings by the sum of employment and job openings, and multiplying that quotient by 100. Hires are the total number of additions to the payroll occurring at any time during the reference month, including both new and rehired employees and full-time and part-time, permanent, short-term and seasonal employees, employees recalled to the location after a layoff lasting more than 7 days, oncall or intermittent employees who returned to work after having been formally separated, and transfers from other locations. The hires count does not include transfers or promotions within the reporting site, employees returning from strike, employees of temporary help agencies or employee leasing companies, outside contractors, or consultants. The hires rate is computed by dividing the number of hires by employment, and multiplying that quotient by I 00. Separations are the total number of terminations 0f employment occurring at any time during the reference month, and are reported by type of separation-quits, layoffs and discharges, and other separations. Quits are voluntary separations by employees (except for retirements, which are reported as other separations). Layoffs and discharges are involuntary separations initiated by the employer and include layoffs with no intent to rehire, formal layoffs lasting or expected to last more than 7 days, discharges resulting from mergers, downsizing, or closings, firings or other discharges for cause, terminations of permanent or short-term employees, and terminations of seasonal employees. Other separations include retirements, transfers to other locations, deaths, and separations due to disability. Separations do not include transfers within the same location or employees on strike. The separations rate is computed by dividing the number of separations by employment, and multiplying that quotient by 100. The quits, layoffs and discharges, and other separations rates are computed similarly, https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis dividing the number by employment and multiplying by 100. Notes on the data The JOLTS data series on job openings, hires, and separations are relatively new. The full sample is divided into panels, with one panel enrolled each month. A full complement of panels for the original data series based on the 1987 Standard Industrial Classification (SIC) system was not completely enrolled in the survey until January 2002. The supplemental panels of establishments needed to create NAICS estimates were not completely enrolled until May 2003. The data collected up until those points are from less than a full sample. Therefore, estimates from earlier months should be used with caution, as fewer sampled units were reporting data at that time. In March 2002, BLS procedures for collecting hires and separations data were revised to address possible underreporting. As a result, JOLTS hires and separations estimates for months prior to March 2002 may not be comparable with estimates for March 2002 and later. The Federal Government reorganization that involved transferring approximately 180,000 employees to the new Department of Homeland Security is not reflected in the JOLTS hires and separations estimates for the Federal Government. The Office of Personnel Management's record shows these transfers were completed in March 2003. The inclusion of transfers in the JOLTS definitions of hires and separations is intended to cover ongoing movements of workers between establishments. The Department of Homeland Security reorganization was a massive onetime event, and the inclusion of these intergovernmental transfers would distort the Federal Government time series. Data users should note that seasonal adjustment of the JOLTS series is conducted with fewer data observations than is customary. The historical data, therefore, may be subject to larger than normal revisions. Because the seasonal patterns in economic data series typically emerge over time, the standard use of moving averages as seasonal filters to capture these effects requires longer series than are currently available. As a result, the stable seasonal filter option is used in the seasonal adjustment of the JOLTS data. When calculating seasonal factors, this filter takes an average for each calendar month after detrending the series. The stable seasonal filter assumes that the seasonal factors are fixed; a necessary assumption until sufficient data are avail- able. When the stable seasonal filter is no longer needed, other program features also may be introduced, such as outlier adjustment and extended diagnostic testing. Additionally, it is expected that more series, such as layoffs and discharges and additional industries, may be seasonally adjusted when more data are available. JOLTS hires and separations estimates cannot be used to exactly explain net changes in payroll employment. Some reasons why it is problematic to compare changes in payroll employment with JOLTS hires and separations, especially on a monthly basis, are: (I) the reference period for payroll employment is the pay period including the 12th of the month, while the reference period for hires and separations is the calendar month; and (2) payroll employment can vary from month to month simply because part-time and oncall workers may not always work during the pay period that includes the 12th of the month. Additionally, research has found that some reporters systematically underreport separations relative to hires due to a num ber of factors, including the nature of their payroll systems and practices. The shortfall appears to be about 2 percent or less over a 12-month period. FOR ADDITIONAL INFORMATION on the Job Openings and Labor Turnover Survey, contact the Division of Administrative Statistics and Labor Turnover at (202) 961-5870. Compensation and Wage Data (Tables 1-3; 30--36) Compensation and waged data are gathered by the Bureau from business establishments, State and local governments, labor unions, collective bargaining agreements on file with the Bureau, and secondary sources. Employment Cost Index Description of the series The Employment Cost Index (ECI) is a quarterly measure of the rate of change in compensation per hour worked and includes wages, salaries, and employer costs of employee benefits. It uses a fixed market basket of labor-similar in concept to the Consumer Price Index's fixed market basket of goods and services-to measure change over time in employer costs of employing labor. Statistical series on total compensation Monthly Labor Review December 2004 57 Current Labor Statistics costs, on wages and salaries, and on benefit costs are available for private nonfarm wor!cers excluding proprietors, the self-employed, and household workers. The total compensation costs and wages and salaries series are also available for State and local government workers and for the civilian nonfarm economy, which consists of private industry and State and local government workers combined. Federal workers are excluded. The Employment Cost Index probability sample consists of about 4,400 private nonfarm establishments providing about 23,000 occupational observations and 1,000 State and local government establishments providing 6,000 occupational observations selected to represent total employment in each sector. On average, each reporting unit provides wage and compensation information on five well-specified occupations. Data are collected each quarter for the pay period including the 12th day of March, June, September, and December. Beginning with June 1986 data, fixed employment weights from the 1980 Census of Population are used each quarter to calculate the civilian and private indexes and the index for State and local governments. (Prior to June 1986, the employment weights are from the 1970 Census of Population.) These fixed weights, also used to derive all of the industry and occupation series indexes, ensure that changes in these indexes reflect only changes in compensation, not employment shifts among industries or occupations with different levels of wages and compensation. For the bargaining status, region, and metropolitan/nonmetropolitan area series, however, employment data by industry and occupation are not available from the census. Instead, the 1980 employment weights are reallocated within these series each quarter based on the current sample. Therefore, these indexes are not strictly comparable to those for the aggregate, industry, and occupation series. Definitions Total compensation costs include wages, salaries, and the employer's costs for employee benefits. Wages and salaries consist of earnings before payroll deductions, including production bonuses, incentive earnings, commissions, and cost-of-living adjustments. Benefits include the cost to employers for paid leave, supplemental pay (including nonproduction bonuses), insurance, retirement and savings plans, and legally required 58 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis benefits (such as Social Security, workers' compensation, and unemployment insurance). Excluded from wages and salaries and employee benefits are such items as payment-in-kind, free room and board, and tips. Notes on the data The Employment Cost Index for changes in wages and salaries in the private nonfarm economy was published beginning in 1975. Changes in total compensation cost-wages and salaries and benefits combined-were published beginning in 1980. The series of changes in wages and salaries and for total compensation in the State and local government sector and in the civilian nonfarm economy (excluding Federal employees) were published beginning in 1981. Historical indexes (June 1981=100) are available on the Internet: http://www.bls.gov/ect/ FOR ADDITIONAL INFORMATION on the Employment Cost Index, contact the Office of Compensation Levels and Trends: (202) 691-6199. Employee Benefits Survey Description of the series Employee benefits data are obtained from the Employee Benefits Survey, an annual survey of the incidence and provisions of selected benefits provided by employers. The survey collects data from a sample of approximately 9,000 private sector and State and local government establishments. The data are presented as a percentage of employees who participate in a certain benefit, or as an average benefit provision (for example, the average number of paid holidays provided to employees per year). Selected data from the survey are presented in table 34 for medium and large private establishments and in table 35 for small private establishments and State and local government. The survey covers paid le8.ve benefits such as holidays and vacations, and personal, funeral, jury duty, military, family, and sick leave; short-term disability, long-term disability, and life insurance; medical, dental, and vision care plans; defined benefit and defined contribution plans; flexible benefits plans; reimbursement accounts; and unpaid family leave. Also, data are tabulated on the incidence of several other benefits, such as severance pay, child-care assistance, wellness programs, and employee assistance programs. December 2004 Definitions Employer-provided benefits are benefits that are financed either wholly or partly by the employer. They may be sponsored by a union or other third party, as long as there is some employer financing. However, some benefits that are fully paid for by the employee also are included. For example, longterm care insurance and postretirement life insurance paid entirely by the employee are included because the guarantee of insurability and availability at group premium rates are considered a benefit. Participants are workers who are covered by a benefit, whether or not they use that benefit. If the benefit plan is financed wholly by employers and requires employees to complete a minimum length of service for eligibility, the workers are considered participants whether or not they have met the requirement. If workers are required to contribute towards the cost of a plan, they· are considered participants only if they elect the plan and agree to make the required contributions. Defined benefit pension plans use predetermined formulas to calculate a retirement benefit (if any), and obligate the employer to provide those benefits. Benefits are generally based on salary, years of service, or both. Defined contribution plans generally specify the level of employer and employee contributions to a plan, but not the formula for determining eventual benefits. Instead, individual accounts are set up for participants, and benefits are based on amounts credited to these accounts. Tax-deferred savings plans are a type of defined contribution plan that allow participants to contribute a portion of their salary to an employer-sponsored plan and defer income taxes until withdrawal. Flexible benefit plans allow employees to choose among several benefits, such as life insurance, medical care, and vacation days, and among several levels of coverage within a given benefit. Notes on the data Surveys of employees in medium and large establishments conducted over the 197986 period included establishments that employed at least 50, 100, or 250 workers, depending on the industry (most service industries were excluded). The survey conducted in 1987 covered only State and local governments with 50 or more employ- ees. The surveys conducted in 1988 and 1989 included medium and large establishments with 100 workers or more in private industries. All surveys conducted over the 1979-89 period excluded establishments in Alaska and Hawaii, as well as part-time employees. Beginning in 1990, surveys of State and local governments and small private establishments were conducted in even-numbered years, and surveys of medium and large establishments were conducted in oddnumbered years. The small establishment survey includes all private nonfarm establishments with fewer than 100 workers , while the State and local government survey includes all governments , regardless of the number of workers. All three surveys incl u(IP- full- and part-time workers , and workers in all 50 States and the District of Columbia. FOR ADDITIONAL INFORM ATION on the Employee Benefits Survey, contact the Office of Compensation Levels and Trends on the Internet: http://www.bls.gov/ebs/ Notes on the data This series is not comparable with the one terminated in 1981 that covered strikes involving six workers or more. FOR ADDITIONAL INFORMATION on work stoppages data, contact the Office of Compensation and Working Conditions: (202) 691-6282, or the Internet: http:/www.bls.gov/cba/ Price Data (Tables 2; 37-47) Price data are gathered by the Bureau of Labor Statistics from retail and primary markets in the United States. Price indexes are given in relation to a base periodDecember 2003 = 100 for many Producer Price Indexes (unless otherwise noted), 198284 = 100 for many Consumer Price Indexes (unless otherwise noted), and 1990 = 100 for International Price Indexes. Consumer Price Indexes Work stoppages Description of the series Description of the series The Consumer Price Index (CPI) is a measure of the average change in the prices paid by urban consumers for a fixed market basket of goods and services. The CPI is calculated monthly for two population groups, one consisting only of urban households whose primary source of income is derived from the employment of wage earners and clerical workers, and the other consisting of all urban households. The wage earner index (CPI-W) is a continuation of the historic index that was introduced well over a halfcentury ago for use in wage negotiations. As new uses we're developed for the CPI in recent years, the need for a broader and more representative index became apparent. The all-urban consumer index (CPI-U), introduced in 1978, is representative of the 1993-95 buying habits of about 87 percent of the noninstitutional population of the United States at that time, compared with 32 percent represented in the CPI-W. In addition to wage earners and clerical workers, the CPI-U covers professional , managerial, and technical workers, the self-employed , short-term workers, the unemployed, retirees, and others not in the labor force. The CPI is based on prices of food , clothing, shelter, fuel, drugs, transportation fares, doctors ' and dentists' fees, and other goods and services that people buy for day-to-day living. The quantity and quality of these items are kept essentially unch:rnged be- Data on work stoppages measure the number and duration of major strikes or lockouts (involving 1,000 workers or more) occurring during the month (or year), the number of workers involved, and the amount of work time lost because of stoppage. These data are presented in table 36. Data are largely from a variety of published sources and cover only establishments directly involved in a stoppage. They do not measure the indirect or secondary effect of stoppages on other establishments whose employees are idle owing to material shortages or lack of service. Definitions Number of stoppages: The number of strikes and lockouts involving 1,000 workers or more and lasting a full shift or longer. Workers involved: The number of workers directly involved in the stoppage. Number of days idle: The aggregate number of workdays lost by workers involved in the stoppages. Days ofidleness as a percent of estimated working time: Aggregate workdays lost as a percent of the aggregate number of standard workdays in the period multiplied by total employment in the period. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis tween major revisions so that only price changes will be measured. All taxes directly associated with the purchase and use of items are included in the index. Data collected from more than 23 ,000 retail establishments and 5,800 housing units in 87 urban areas across the country are used to develop the "U.S. city average." Separate estimates for 14 major urban centers are presented in table 38. The areas listed are as indicated in footnote 1 to the table. The area indexes measure only the average change in prices for each area since the base period, and do not indicate differences in the level of prices among cities. Notes on the data In January 1983, the Bureau changed the way in which homeownership costs are meaured for the CPI-U. A rental equivalence method replaced the asset-price approach to homeownership costs for that series. In January 1985, the same change was made in the CPI-W. The central purpose of the change was to separate shelter costs from the investment component of homeownership so that the index would reflect only the cost of shelter services provided by owner-occupied homes. An updated CPI -U and CPI -W were introduced with release of the January 1987 and January 1998 data. FOR ADDITIONAL IN FORMATION , contact the Division of Prices and Price Indexes: (202) 691-7000. Producer Price Indexes Description of the series Producer Price Indexes (PPI) measure average changes in prices received by domestic producers of commodities in all stages of processing. The sample used for calculating these indexes currently contains about 3,200 commodities and about 80,000 quotations per month, selected to represent the movement of prices of all commodilies produced in the manufacturing; agriculture, forestry, and fishing; mining; and gas and electricity and public utilities sectors. The stageof-processing structure of PPI organizes products by class of buyer and degree of fabrication (that is, finished goods, intermediate goods, and crude materials). The traditional commodity structure of PPI organizes products by similarity of end use or material composition. The industry and product structure of PPI organizes data in accordance with the 2002 North American Industry Classification System and product codes developed by the U.S. Census Bureau. Monthly Labor Review December 2004 59 Current Labor Statistics To the extent possible, prices used in calculating Producer Price Indexes apply to the first significant commercial transaction in the United States from the production or central marketing point. Price data are generally collected monthly, primarily by mail questionnaire. Most prices are obtained directly from producing companies on a voluntary and confidential basis. Prices generally are: reported for the Tuesday of the week containing the 13th day of the month. Since January 1992, price changes for the various commodities have been averaged together with implicit quantity weights representing their importance in the total net selling value of all commodities as of 1987. The detailed data are aggregated to obtain indexes for stage-of-processing groupings, commodity groupings, durability-of-product groupings, and a number of special composite groups. All Producer Price Index data are subject to revision 4 months after original publication. FOR ADDITIONAL INFORMATION, contact the Division of Industrial Prices and Price Indexes: (202) 691-7705. International Price Indexes Description of the series The International Price Program produces monthly and quarterly export and import price indexes for nonmilitary goods and services traded between the United States and the rest of the world. The export price index provides a measure of price change for all products sold by U.S. residents to foreign buyers. ("Residents" is defined as in the national income accounts; it includes corporations, businesses, and individuals, but does not require the organizations to be U.S. owned nor the individuals to have U.S. citizenship.) The import price index provides a measure of price change for goods purchased from other countries by U.S. residents. The product universe for both the import and export indexes includes raw materials, agricultural products, semifinished manufactures, and finished manufactures, including both capital and consumer goods. Price data for these items are collected primarily by mail questionnaire. In nearly all cases, the data are collected directly from the exporter or importer, although in a few cases, prices are obtained from other sources. To the extent possible, the data gathered refer to prices at the U.S. border for exports and at either the foreign border or the U.S. border for imports. For nearly all products, the prices refer to transactions com- 60 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis pleted during the first week of the month. Survey respondents are asked to indicate all discounts, allowances , and rebates applicable to the reported prices, so that the price used in the calculation of the indexes is the actual price for which the product was bought or sold. In addition to general indexes of prices for U.S. exports and imports, indexes are also published for detailed product categories of exports and imports. These categories are defined according to the five-digit level of detail for the Bureau of Economic Analysis End-use Classification, the three-digit level for the Standard International Trade Classification (SITC), and the four-digit level of detail for the Harmonized System. Aggregate import indexes by country or region of origin are also available. BLS publishes indexes for selected categories of internationally traded services, calculated on an international basis and on a balance-of-payments basis. Notes on the data The export and import price indexes are weighted indexes of the Laspeyres type. The trade weights currently used to compute both indexes relate to 2000. Because a price index depends on the same items being priced from period to period, it is necessary to recognize when a product's specifications or terms of transaction have been modified. For this reason, the Bureau's questionnaire requests detailed descriptions of the physical and functional characteristics of the products being priced, as well as information on the number of units bought or sold, discounts, credit terms, packaging, class of buyer or seller, and so forth. When there are changes in either the specifications or terms of transaction of a product, the dollar value of each change is deleted from the total price change to obtain the "pure" change. Once this value is determined, a linking procedure is employed which allows for the continued repricing of the item. FOR ADDITIONAL INFORMATION, contact the Division of International Prices: (202) 691-7155. Productivity Data (Tables 2; 48-51) Business and major sectors Description of the series The productivity measures relate real out- December 2004 put to real input. As such, they encompass a family of measures which include singlefactor input measures, such as output per hour, output per unit of labor input, or output per unit of capital input, as well as measures of multifactor productivity (output per unit of combined labor and capital inputs). The Bureau indexes show the change in output relative to changes in the various inputs. The measures cover the business, nonfarm business, manufacturing, and nonfinancial corporate sectors. Corresponding indexes of hourly compensation, unit labor costs, unit nonlabor payments, and prices are also provided. Definitions Output per hour of all persons (labor productivity) is the quantity of goods and services produced per hour of labor input. Output per unit of capital services (capital productivity) is the quantity of goods and services produced per unit of capital services input. Multi factor productivity is the quantity of goods and services produced per combined inputs. For private business and private nonfarm business, inputs include labor and capital units. For manufacturing, inputs include labor, capital, energy, nonenergy materials, and purchased business services. Compensation per hour is total compensation divided by hours at work. Total compensation equals the wages and salaries of employees plus employers' contributions for social insurance and private benefit plans, plus an estimate of these payments for the self-employed (except for nonfinancial corporations in which there are no self-employed). Real compensation per hour is compensation per hour deflated by the change in the Consumer Price Index for All Urban Consumers. Unit labor costs are the labor compensation costs expended in the production of a unit of output and are derived by dividing compensation by output. Unit nonlabor payments include profits, depreciation, interest, and indirect taxes per unit of output. They are computed by subtracting compensation of all persons from currentdollar value of output and dividing by output. Unit nonlabor costs contain all the components of unit nonlabor payments except unit profits. Unit profits include corporate profits with inventory valuation and capital consumption adjustments per unit of output. Hours of all persons are the total hours at work of payroll workers, self-employed persons, and unpaid family workers. Labor inputs are hours of all persons adjusted for the effects of changes in the education and experience of the labor force. Capital services are the flow of services from the capital stock used in production. It is developed from measures of the net stock of physical assets-equipment, structures, land, and inventories-weighted by rental prices for each type of asset. force; capital investment; level of output; changes in the utilization of capacity, energy, material, and research and development; the organization of production; managerial skill; and characteristics and efforts of the work force. FOR ADDITIONAL INFORMATION on this productivity series, contact the Division of Productivity Research: (202) 691-5606. Combined units of labor and capital inputs are derived by combining changes in labor and capital input with weights which represent each component's share of total cost. Combined units of labor, capital, energy, materials, and purchased business services are similarly derived by combining changes in each input with weights that represent each input's share of total costs. The indexes for each input and for combined units are based on changing weights which are averages of the shares in the current and preceding year (the Tornquist index-number formula). Notes on the data Business sector output is an annuallyweighted index constructed by excluding from real gross domestic product (GDP) the following outputs: general government, nonprofit institutions, paid employees of private households , and the rental value of owneroccupied dwellings. Nonfarm business also excludes farming. Private business and private nonfarm business further exclude government enterprises. The measures are supplied by the U.S. Department of Commerce's Bureau of Economic Analysis. Annual estimates of manufacturing sectoral output are produced by the Bureau of Labor Statistics. Quarterly manufacturing output indexes from the Federal Reserve Board are adjusted to these annual output measures by the BLS. Compensation data are developed from data of the Bureau of Economic Analysis and the Bureau of Labor Statistics. Hours data are developed from data of the Bureau of Labor Statistics. The productivity and associated cost measures in tables 48-51 describe the relationship between output in real terms and the labor and capital inputs involved in its production. They show the changes from period to period in the amount of goods and services produced per unit of input. Although these measures relate output to hours and capital services, they do not measure the contributions of labor, capital, or any other specific factor of production. Rather, they reflect the joint effect of many influences, including changes in tc>chnology; shifts in the composition of the labor https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Industry productivity measures ducing that output. Combined inputs include capital, labor, and intermediate purchases. The measure of capital input represents the flow of services from the capital stock used in production. It is developed from measures of the net stock of physical assets-equipment, structures, land, and inventories. The measure of intermediate purchases is a combination of purchased materials , services, fuels, and electricity. Notes on the data Description of the series The BLS industry productivity indexes measure the relationship between output and inputs for selected industries and industry groups, and thus reflect trends in industry efficiency over time. Industry measures include labor productivity, multifactor productivity, compensation, and unit labor costs. The industry measures differ in methodology and data sources from the productivity measures for the major sectors because the industry measures are developed independently of the National Income and Product Accounts framework used for the major sector measures. The industry measures are compiled from data produced by the Bureau of Labor Statistics and the Census Bureau, with additional data supplied by other government agencies, trade associations, and other sources. FOR ADDITIONAL INFORMATION on this series, contact the Division of Industry Productivity Studies: (202) 691-5618. International Comparisons (Tables 52-54) Labor force and unemployment Description of the series Definitions Output per hour is derived by dividing an index of industry output by an index of labor input. For most industries, output indexes are derived from data on the value of industry output adjusted for price change. For the remaining industries, output indexes are derived from data on the physical quantity of production. The labor input series is based on the hours of all workers or, in the case of some transportation industries, on the number of employees. For most industries, the series consists of the hours of all employees. For some trade and services industries, the series also includes the hours of partners, proprietors, and unpaid family workers. Unit labor costs represent the labor compensation costs per unit of output produced, and are derived by dividing an index of labor compensation by an index of output. Labor compensation includes payroll as well as supplemental payments, including both legally required expenditures and payments for voluntary programs. Multifactor productivity is derived by dividing an index of industry output by an index of combined inputs consumed in pro- Tables 52 and 53 present comparative measures of the labor force, employment, and unemployment approximating U.S. concepts for the United States, Canada, Australia, Japan, and six European count_ries. The labor force statistics published by other industrial countries are not, in most cases, comparable to U.S. concepts. Therefore, the Bureau adjusts the figures for selected countries, for all known major definitional diffe:r:ences, to the extent that data to prepare adjustments are available. Although precise comparability may not be achieved, these adjusted figures provide a better basis for international comparisons than the figures regularly published by each country. For further information on adjustments and comparability issues, see Constance Sorrentino, "International unemployment rates: how comparable are they?" Monthly Labor Review, June 2000, pp. 3-20 (available on the BLS Web site at http:// www.bls.gov/opu b/ml r/2000/06/ artlfull.pdf). Definitions For the principal U.S. definitions of the labor force, employment, and unemployment, see the Notes section on Employment and Monthly Labor Review December 2004 61 Current Labor Statistics Unemployment Data: Household survey data. Notes on the data The foreign country data are adjusted as closely as possible to U.S. concepts, with the exception of lower age limits and the treatment of layoffs. These adjustments include, but are not limited to: including older persons in the labor force by imposing no upper age limit, adding unemployed students to the unemployed, excluding the military and family workers working fewer than 15 hours from the employed, and excluding persons engaged in passive job search from the unemployed. Data for the United States relate to the popuiation 16 years of age and older. The U.S. concept of the working age population has no upper age limit. The adjusted to U.S. concepts statistics have been adapted, insofar as possible, to the age at which compulsory schooling ends in each country, and the Swedish statistics have been adjusted to include persons older than the Swedish upper age limit of 64 years. The adjusted statistics presented here relate to the population 16 years of age and older in France, Sweden, and the United Kingdom; 15 years of age and older in Australia, Japan, Germany, Italy, and the Netherlands. An exception to this rule is that the Canadian statistics are adjusted to cover the population 16 years of age and older, whereas the age at which compulsory schooling ends remains at 15 years. In the labor force participation rates and employmentpopulation ratios, the denominator is the civilian noninstitutionalized working age population, except that the institutionalized working age population is included in Japan and Germany. In the United States, the unemployed include persons who are not employed and who were actively seeking work during the reference period, as well as persons on layoff. Persons waiting to start a new job who were actively seeking work during the reference period are counted as unemployed under U.S . concepts; if they were not actively seeking work, they are not counted in the labor force. In some countries, persons on layoff are classified as employed due to their strong job attachment. No adjustment is made for the countries that classify those on layoff as employed. In the United States, as in Australia and Japan, passive job seekers are not in the labor force; job search must be active, such as placing or answering advertisements, contacting employers directly,or registering with an employment agency (simply reading ads is not enough to qualify as active search). Canada and the European countries classify 62 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis passive jobseekers as unemployed. An adjustment is made to exclude them in Canada, but not in the European countries where the phenomenon is less prevalent. Persons waiting to start a new job are counted among the unemployed for all other countries, whether or not they were actively seeking work. The figures for one or more recent years for France, Germany, and the Netherlands are calculated using adjustment factors based on labor force surveys for earlier years and are considered preliminary. The recent year measures for these countries are therefore subject to revision whenever more current labor force surveys become available. There are breaks in series for the United States(1994, 1997, 1998, 1999,2000,2003), Australia (2001), and Germany (1999). For the United States, beginning in 1994, data are not strictly comparable for prior years because of the introduction of a major redesign of the labor force survey questionnaire and collection methodology. The redesign effect has been estimated to increase the overall unemployment rate by 0.1 percentage point. Other breaks noted relate to changes in population controls that had virtually no effect on unemployment rates. For a description of all the changes in the U.S. labor force survey over time and their impact, see Historical Comparability in the "Household Data" section of the BLS publication Employment and Earnings (available on the BLS Web site at http://www.bls.gov/ cps/eetech_methods.pdf). For Australia, the 2001 break reflects the introduction in April 2001 of a redesigned labor force survey that allowed for a closer application of International Labor Office guidelines for the definitions of labor force statistics. The Australian Bureau of Statistics revised their data so there is no break in the employment series. However, the reclassification of persons who had not actively looked for work because they were waiting to begin a new job from "not in the labor force" to "unemployed" could only be incorporated for April 2001 forward. This reclassification diverges from the U.S. definition where persons waiting to start a new job but not actively seeking work are not counted in the labor force. The impact of the reclassification was an increase in the unemployment rate by 0.1 percentage point in 2001. For Germany, the 1999 break reflects the incorporation of an improved method of data calculation and a change in coverage to persons living in private households only. For further qualifications and historical data, see Comparative Civilian Labor Force Statistics, Ten Countries, on the BLS Web site at http ://www.bls.gov/fls/flslforc.pdf December 2004 FOR ADDITIONAL INFORMATION on this series, contact the Division of Foreign Labor Statistics: (202) 691-5654 or flshelp@bls.gov Manufacturing productivity and labor costs Description of the series Table 54 presents comparative indexes of manufacturing labor productivity (output per hour), output, total hours, compensation per hour, and unit labor costs for the United States, Canada, Japan, and nine European countries. These measures are trend comparisons-that is, series that measure changes over timerather than level comparisons. There are greater technical problems in comparing the levels of manufacturing output among countries. BLS constructs the comparative indexes from three basic aggregate measures--output, total labor hours, and total compensation. The hours and compensation measures refer to all employed persons (wage and salary earners plus self-employed persons and unpaid family workers) in the United States, Canada, Japan, France, Germany, Norway, and Sweden, and to all employees (wage and salary earners) in the other countries. Definitions Output, in general, refers to value added in manufacturing from the national accounts of each country. However, the output series for Japan prior to 1970 is an index of industrial production, and the national accounts measures for the United Kingdom are essentially identical to their indexes of industrial production. The 1977-97 output data for the United States are the gross product originating (value added) measures prepared by the Bureau of Economic Analysis of the U.S. Department of Commerce. Comparable manufacturing output data currently are not available prior to 1977. U.S. gross product originating is a chaintype annual-weighted series. (For more information on the U.S. measure, see Robert E. Yuskavage, "Improved Estimates of Gross Product by Industry, 1959-94," Survey of Current Business, August 1996, pp. 13355.) The Japanese value added series is based upon one set of fixed price weights for the years 1970 through 1997. Output series for the other foreign economies also employ fixed price weights, but the weights are updated periodically (for example, every 5 or 10 years). To preserve the comparability of the U.S. measures with those for other economies, BLS uses gross product originating in manufacturing for the United States for these comparative measures. The gross product originating series differs from the manufacturing output series that BLS publishes in its news releases on quarterly measures of U.S. productivity and costs (and that underlies the measures that appear in tables 48 and 50 in this section). The quarterly measures are on a '·sectoral output" basis, rather than a valueadded basis. Sectoral output is gross output less intrasector transactions. Total labor hours refers to hours worked in all countries. The measures are developed from statistics of manufacturing employment and average hours. The series used for France (from 1970 forward), Norway, and Sweden are official series published with the national accounts. Where official total hours series are not available, the measures are developed by BLS using employment figures published with the national accounts, or other comprehensive employment series, and estimates of annual hours worked. For Germany, BLS uses estimates of average hours worked developed by a research institute connected to the Ministry of Labor for use with the national accounts employment figures. For the other countries, BLS constructs its own estimates of average hours. An hours series is not available for Denmark after 1993; therefore, the BLS measure of labor input for Denmark ends in 1993. Total compensation (labor cost) includes all payments in cash or in-kind made directly to employees plus employer expenditures for legally required insurance programs and contractual and private benefit plans. The measures are from the national accounts of each country, except those for Belgium, which are developed by BLS using statistics on employment, average hours, and hourly compensation. For Canada, France, and Sweden, compensation is increased to account for other significant taxes on payroll or employment. For the United Kingdom, compensation is reduced between 1967 and 1991 to account for employment-related subsidies. Self-employed workers are included in the all-employed-persons measures by assuming that their hourly compensation is equal to the average for wage and salary employees. Notes on the data In general, the measures relate to total manufacturing as defined by the International https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Standard Industrial Classification. However, the measures for France (for all years) and Italy (beginning in 1970) refer to mining and manufacturing less energy-related products, and the measures for Denmark include mining and exclude manufacturing handicrafts from 1960 to 1966. The measures for recent years may be based on current indicators of manufacturing output (such as industrial production indexes), employment, average hours, and hourly compensation until national accounts and other statistics used for the long-term measures become available. FOR ADDITIONAL INFORMATION on this series, contact the Division of Foreign Labor Statistics: (202) 691-5654. Occupational Injury and Illness Data (Tables 55-56) Survey of Occupational Injuries and Illnesses Description of the series The Survey of Occupational Injuries and Illnesses collects data from employers about their workers' job-related nonfatal injuries and illnesses. The information that employers provide is based on records that they maintain under the Occupational Safety and Health Act of 1970. Self-employed individuals, fanns with fewer than 11 employees, employers regulated by other Federal safety and health laws, and Federal, State, and local government agencies are excluded from the survey. The survey is a Federal-State cooperative program with an independent sample selected for each participating State. A stratified random sample with a Neyman allocation is selected to represent all private industries in the State. The survey is stratified by Standard Industrial Classification and size of employment. Definitions Under the Occupational Safety and Health Act, employers maintain records of nonfatal work-related injuries and illnesses that involve one or more of the following: loss of consciousness, restriction of work or motion, transfer to another job, or medical treatment other than first aid. Occupational injury is any injury such as a cut, fracture, sprain, or amputation that results from a work-related event or a single, instantaneous exposure in the work environment. Occupational illness is an abnormal condition or disorder, other than one resulting from an occupational injury, caused by exposure to factors associated with employment. It includes acute and chronic illnesses or disease which may be i:aused by inhalation, absorption, ingestion, or direct contact. Lost workday injuries and illnesses are cases that involve days away from work , or days of restricted work activity, or both. Lost workdays include the number of workdays (consecutive or not) on which the employee was either away from work or at work in some restricted capacity, or both, because of an occupational injury or illness. BLS measures of the number and incidence rate of lost workdays were discontinued beginning with the 1993 survey. The number of days away from work or days of restricted work activity does not include the day of injury or onset of illness or any days on which the employee would not have worked, such as a Federal holiday, even though able to work. Incidence rates are computed as the number of injuries and/or illnesses or lost work days per 100 full-time workers. Notes on the data The definitions of occupational injuries and illnesses are from Recordkeeping Guidelines for Occupational Injuri es and Illnesses (U.S. Department of Labor, Bureau of Labor Statistics, September 1986). Estimates are made for industries and employment size classes for total recordable cases, lost workday cases, days away from work cases, and nonfatal cases without lost workdays. These data also are shown separately for injuries. Illness data are available for seven categories: occupational skin diseases or disorders, dust diseases of the lungs, respiratory conditions due to toxic agents, poisoning (systemic effects of toxic agents), disorders due to physical agents (other than toxic materials), disorders associated with repeated trauma, and all other occupational illnesses. The survey continues to measure the number of new work-related illness cases which are recognized, diagnosed, and reported during the year. Some conditions, for example, long-term latent illnesses caused by exposure to carcinogens, often are difficult to relate to the workplace and are not adequately recognized and reported. These long-term latent ill- Monthly Labor Review December 2004 63 Current Labor Statistics nesses are believed to be understated in the survey 's illness measure. In contrast, the overwhelming majority of the reported new illnesses are those which are easier to directly relate to workplace activity (for example, contact dermatitis and carpal tunnel syndrome). Most of the estimates are in the form of incidence rates, defined as the number of injuries and illnesses per 100 equivalent fulltime workers. For this purpose, 200,000 employee hours represent l 00 employee years (2,000 hours per employee). Full detail on the available measures is presented in the annual bulletin, Occupational Injuries and Illnesses: Counts, Rates, and Characteristics. Comparable data for more than 40 States and territories are available from the BLS Office of Safety, Health and Working Conditions. Many of these States publish data on State and local government employees in addition to private industry data. Mining and railroad data are furnished to BLS by the Mine Safety and Health Administration and the Federal Railroad Administration. Data from these organizations are included in both the national and State data published annually. With the 1992 survey, BLS began publishing details on serious, nonfatal incidents resulting in days away from work. Included are some major characteristics of the injured and ill workers, such as occupation, age, gender, race, and length of service, as well as the circumstances of their injuries and illnesses (nature of the disabling condition, part of body affected, event and exposure, and the source directly producing the condition). In general, these data are available nationwide for detailed 64 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis industries and for individual States at more aggregated industry levels. FOR ADDITIONAL INFORMATION on occupational injuries and illnesses, contact the Office of Occupational Safety, Health and Working Conditions at (202) 691-6180, or access the Internet at: http://www.bis.gov/ii fl Census of Fatal Occupational Injuries The Census of Fatal Occupational Injuries compiles a complete roster of fatal job-related injuries, including detailed data about the fatally injured workers and the fatal events. The program collects and cross checks fatality information from multiple sources, including death certificates, State and Federal workers' compensation reports, Occupational Safety and Health Administration and Mine Safety and Health Administration records, medical examiner and autopsy reports, media accounts, State motor vehicle fatality records, and follow-up questionnaires to employers. In addition to private wage and salary workers, the self-employed, family members , and Federal, State, and local government workers are covered by the program. To be included in the fatality census, the decedent must have been employed (that is working for pay, compensation, or profit) at the time of the event, engaged in a legal work activity, or present at the site of the incident as a requirement of his or her job. December 2004 Definition A fatal work injury is any intentional or unintentional wound or damage to the body resulting in death from acute exposure to energy, such as heat or electricity, or kinetic energy from a crash, or from the absence of such essentials as heat or oxygen caused by a specific event or incident or series of events within a single workday or shift. Fatalities that occur during a person's commute to or from work are excluded from the census, as well as workrelated illnesses, which can be difficult to identify due to long latency periods. Notes on the data Twenty-eight data elements are collected, coded, and tabulated in the fatality program, including information about the fatally injured worker, the fatal incident, and the machinery or equipment involved. Summary worker demographic data and event characteristics are included in a national news release that is available about 8 months after the end of the reference year. The Census of Fatal Occupational Injuries was initiated in 1992 as a joint Federal-State effort. Most States issue summary information at the time of the national news release. FOR ADDITIONAL INFORMATION on the Census of Fatal Occupational Injuries contact the BLS Office of Safety, Health, and Working Conditions at (202) 691-6175, or the Internet at: http://www.bls.gov/iif/ 1. Labor market indicators 2002 Selected Indicators II IV Ill 2004 2003 2002 2003 II IV Ill Ill Employment data Employment status of the civilian noninstitutional population (household survey): 1 Labor force participation rate ................................... ......... ..... Employment-population rat io .... .... ......................... ..... ......... . Unemployment rate ..... ............... .... ......... ... .... ... ...... ....... .. Men ... .... .... ....... ....... .... ... ........... ..... ... ....................... .. . 16 to 24 years ........ .......................... ................. .. ........ .. .......... . 25 years and older ............ .. .......... .. .. ..... ................ ................ . Women ...... .... .......... ... .... ....... ... ... . 16 to 24 years ..... .. .. . ... .................... ............ ... ..... .. .... .. 25 years and older ...................................................... .. Employment, nonfarm (payroll data) , in thousands: 66.6 62.7 5.8 66.2 62.3 6.0 66.6 62.8 5.8 66.5 62.5 5.9 66.3 62.4 5.8 66.4 62.3 6.1 66.2 62.1 6.1 66 .1 62 .3 5.9 66.0 62 .2 5.6 65.9 62 .2 5.6 66.0 62.4 5.5 5.9 12.8 4.7 6.3 13.4 5.0 5.9 13.1 4.7 6.1 12.5 4.9 6.1 12.6 6.1 13.1 4.9 5.7 12.5 4.5 5.7 12.9 4.5 5.6 12.5 4.4 5.7 11.4 4.6 5.6 10.9 4.6 5.7 11 .4 4.6 5.0 5.5 11.2 6.4 13.8 5.1 5.6 11.1 6.5 14.0 5.2 5.7 11 .8 5.8 11 .5 5.6 10.9 5.6 11 .1 4.5 4.6 4.7 4.6 4.5 5.4 10.9 4.4 5.4 11 .0 4.3 4.6 1 Total nonfarm ...... . Total private .. . Goods-producing ................. ..... . Manufacturing ........ ....... .. ...... ....... ....... ....... ... .... ... . Service-providing .. ... ........ ......... .... .. ... ... ......... .......... ..... . 130,341 108,828 129,931 108,356 130,287 108,736 130,248 108,654 130,047 108,428 129,878 108,309 129,820 108,260 130,002 108,453 130,367 108,827 131 ,125 109,577 131 ,52 1 109,897 22 ,557 21,817 22,252 14,979 21 ,848 21,718 14,775 14,570 14,410 21,676 14,340 21,719 14,326 21,869 14,385 21 ,927 21,817 22,466 15,197 22 ,025 22 ,557 107,789 108,114 107,821 107,995 108,022 108,030 108,102 108,326 108,648 109,256 109,595 14,403 Average hours: Total private .. Manufacturing .... Overtime .. Employment Cost lndex 3 ........ ............... . Service-providing3 ... State and local government workers Workers by bargaining status (private industry): Union ........ .. .............. .... ........................... ... ....... ......... .. . Nonunion .... ..... ... ........ ...... ....... ....................................... . 1 2 33.7 33.9 33.8 33.8 33.7 33.6 33.7 33.8 33.7 40.5 4.2 40.4 4.2 40.4 4.3 40.4 4.2 40.4 4.2 40.2 4.1 40.2 4.1 40.6 4.4 41 .0 4.6 40 .9 4.6 33.8 40.8 4.6 3.4 3.8 .9 .6 1.1 .5 1.4 .9 1.0 4.0 .6 .4 1.4 1.7 .8 3.2 .8 1.0 .4 1.5 .9 .8 3.7 4.0 .6 .9 1.8 .9 .7 .5 2.3 .9 .9 3.1 4.1 4.0 3.3 .6 2.2 .2 .9 1.5 .7 .8 .4 1.1 1.7 .5 .5 1.1 .7 1.0 .4 .8 1.7 4.2 3.2 4.6 3.9 1.2 .5 .9 .4 1.6 1.6 1.2 .8 1.0 1.0 .7 .4 2.8 1.3 1.5 .8 .8 .9 2 Percent change in the ECI , compensation : All workers (excluding farm , household and Federal workers) .. . Private industry workers.. ... . ... ...... .. ... ... .. ..... .. .. .... .... ....... .. Goods-producing 33. 9 Quarterly data seasonally adjusted. NOTE: Beginning in January 2003, household survey data reflect revised population Annual changes are December-to-December changes. Quarterly changes are calculated controls. Nonfarm data reflect the conversion to the 2002 version of the North American using the last month of each quarter. 3 Goods-producing industries include mining, construction, and manufacturing. Service- Industry Classification System (NAICS), replacing the Standard Industrial Classification (SIC) system . NAICS-based data by industry are not comparable with sic-based data. providing industries include all other private sector industries. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 65 Current Labor Statistics: Comparati ve Indicators 2. Annual and quarterly percent changes in compensation, prices, and productivity Selected measures 2002 2003 2002 Ill 2003 IV 2004 II Ill IV II Ill Compensation data 1' 2 Employment Cost Index-compens ation (wages, salaries, benefits) : Civilian nonfarm .......................... ........................................ Private nonfarm .............................................................. Employment Cost Index- wages and salaries: Civilian nonfarm ...... ................. .... ..... ... ......... .......... Private nonfarm................. ................. ..... ....................... 3.4 3.2 3.8 4.0 0.9 .6 0.6 .4 1.4 1.7 0.8 .8 1.1 1.0 0.5 .4 1.4 1.5 0.9 .9 1.0 .8 2.9 2.7 2.9 3.0 .7 .4 .4 .3 1.0 1.1 .6 .7 .9 .8 .3 .4 .6 .7 .6 .7 .9 .9 Consumer Price Index (All Urban Consumers) : All Items ...... 2.3 2.3 .6 -.1 1.8 - .3 - .2 -.2 1.2 1.2 .2 Producer Price Index: Finished goods .......................... .................... '. .. ... .......... .. ... Finished consumer goods ... ................................... .......... Capital equipment. .. .. .. ... ...... ... ..... .. .. ... ... ........ ..... .... Intermediate materials, supplies, and components .. .. .. ...... Crude materials ............... .......................... ........................... 3.2 4.2 .4 4.6 25.2 3.2 4.2 .4 4.6 25.2 .2 .0 - .7 1.1 1.9 -.1 -.3 .6 .1 6.5 3.7 2.4 .6 6.5 28.0 -.8 1.8 - .6 -2.1 -10 .6 .3 .3 - .1 - .1 3.4 .0 .0 .0 .0 14.4 1.2 1.5 .6 2.5 6.0 1.2 1.4 .5 3.0 7.6 .0 -1 .7 .4 1.9 -5.1 4.3 4.4 4.4 4.5 4.4 5.4 4.8 4.5 4.1 1.2 1.6 3.4 3.9 3.7 3.2 7.6 6.7 9.1 8.5 9.0 9.4 2.4 3.1 5.0 3.9 3.7 .1 1.5 3.9 2.7 2.3 1.9 Price data 1 Productivity data 3 Output per hour of all persons: Business sector .................................................................... Nonfarm business sector ......................... ............................. Nonfinancial cornorations 4 .. .... . 1 Annual changes are December-to-December changes. Quarterly changes are calculated using the last month of each quarter. Compensation and price data are not seasonally adjusted , and the price data are not compounded. 2 3 Annual rates of change are computed by comparing annual averages. Quarterly percent changes reflect annual rates of change in quarterly indexes. The data are seasonally adjusted. Excludes Federal and private household workers. 4 Output per hour of all employees. NOTE: Dash indicates data not available. 3. Alternative measures of wage and compensation changes Quarterly change Components 2003 Ill Average hourly compensation: 1 All persons, business sector ........................................................ . All persons, nonfarm business sector .............. .................... .. .. .. .. Four quarters ending- 2004 IV II 2003 Ill Ill 2004 IV II Ill 5.6 6.1 4.0 4.4 2.8 2.0 4.3 4.9 3.8 3.6 4.6 4.6 5.3 5.4 4.6 4.5 4.2 4.4 3.7 3.7 1.1 1.0 1.0 1.0 1.7 .5 .4 .7 .4 .5 1.4 1.5 2.8 1.3 .7 .9 .9 1.5 .8 .4 1.0 .8 .8 .9 1.7 3.9 4.0 4.8 3.8 3.6 3.8 4.0 4.6 3.9 3.3 3.8 3.9 5.7 3.6 3.3 3.9 4.0 6.0 3.5 3.4 3.8 3.7 5.8 3.4 3.4 .3 .4 .6 .2 .4 .6 .7 .6 .7 .4 .6 .7 1.0 .6 .2 .9 .9 .8 .8 1.0 2.9 3.0 2.6 3.1 2.3 2.9 3.0 2.4 3.1 2.1 2.5 2.6 2.5 2.6 2.1 2.5 2.6 2.9 2.5 1.9 2.4 2.6 3.0 2.5 2.0 Employment Cost Index-compens ation: Civilian nonfarm 2 ........... .. ......... .. ...... ..... ..... ..... . Private nonfarm ..................... ............. ...................................... . Union .... .. ... ... .. .............. ........................... ............................. .. Nonunion .... .. ... ..................................................................... .. State and local governments .................................................... . Employment Cost Index-wages and salaries: Civilian nonfarm 2 ............. .. ............. .... ... .... . ... .... ... .. .. .. ... ... ............ . Private nonfarm ........... .......... .................................................... Union ... ......... .. ........... .. .......................... ........ .... ..... .............. .. Nonunion ..... .. .................... ................................... .. ............... . State and local governments ........................ ........................... .. 1 2 .9 .8 .6 .9 1.0 I Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate. Excludes Federal and household workers. 66 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 4. Employment status of the populaHon, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted [Numbers in thousands] 2004 Annual average 2003 2003 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. 221,HW 146,510 66.2 137,736 222,039 146,892 66.2 138,095 222,279 147,187 66.2 138,533 222,509 146,878 66.0 138,479 222,161 146,863 66.1 138,566 222,357 146,471 65.9 138,301 222,550 146,650 65.9 138,298 222,757 146,741 65.9 138,576 222,967 146,974 65.9 138,772 223,196 147,279 66.0 139,031 223,422 147,856 66.2 139,660 223,677 147,704 66.0 139,681 223,941 147,483 65.9 139,480 224,192 147,850 65.9 139,778 62.3 8,774 6.0 74,658 62.2 8,797 6.0 75,147 62.3 8,653 5.9 75,093 62.2 8,398 5.7 75,631 62.4 8,297 5.6 75,298 62.2 8,170 5.6 75,886 62.1 8,352 5.7 75,900 62.2 8,164 5.6 76 ,016 62.2 8,203 5.6 75,993 62.3 8,248 5.6 75,916 62.5 8,196 5.5 75,565 62.4 8,022 5.4 75,973 62.3 8,003 5.4 76,458 62.3 8,072 5.5 76 ,342 96,439 73,630 76.3 69,734 98,272 74,623 75.9 70,415 98,696 74,942 75.9 70,726 98,814 75,188 76.1 70,964 98,927 75,044 75.9 71 ,099 98,866 75,171 76.0 71 ,329 98,966 74,797 75.6 70,969 99,065 75,018 75.7 71 ,128 99 ,170 74,871 75.5 71 ,118 99,279 75,048 75.6 71,162 99,396 75,372 75.8 71,570 99,512 75,577 75.9 71 ,847 99,642 75,639 75.9 71,870 99,776 75,443 75.6 71 ,677 99,904 75,622 75.7 71 ,882 72.3 3,896 5.3 22,809 71.7 4,209 5.6 23,649 71 .7 4,216 5.6 23,754 71.8 4,224 5.6 23,620 71 .9 3,945 5.3 23,882 72.1 3,842 5.1 23,694 71 .7 3,828 5.1 24,168 71 .8 3,890 5.2 24,047 71 .7 3,753 5.0 24,299 71 .7 3,886 5.2 24,231 72.0 3,802 5.0 24,023 72.2 3,730 4.9 23,935 72.1 3,768 5.0 24,003 71.8 3,766 5.0 24,332 72.0 3,740 4.9 24,282 1 105,136 populalion ······· ......... 63,648 Ci,iliar. labor force ............. 60.5 Participation rate ...... ... Employed ....................... 60,420 Employment-pop2 57.5 ulation ratio ... 3,228 Unemployed ................... 5.1 Unemployment rate .... 41,488 Not in the labor force .... ... 106,800 64,716 60.6 61,402 107,197 64,899 60.5 61 ,524 107,303 64,917 60.5 61 ,597 107,404 107,131 64,515 60.2 61,260 107,216 64,629 60.3 61,456 107,299 107,389 64,687 60.3 61,373 64,785 60.3 61,571 107,483 64 ,813 60.3 61 ,721 107,586 64,893 60.3 61,629 107,687 65,122 60.5 61 ,918 107,801 64,846 60.4 61,521 64,903 60.2 61,870 107,920 64,989 60.2 61,925 108,032 65,103 60.3 61,918 57.5 3,314 5.1 42,083 57.4 3,375 5.2 42,299 57.4 3,320 5.1 42,387 57.3 3,326 5.1 42,558 57.2 3,255 5.0 42,617 57.3 3,172 4.9 42,587 57.2 3,314 5.1 42,613 57.3 3,215 5.0 42,604 57.4 3,092 4.8 42 ,670 57.3 3,264 5.0 42,693 57.5 3,204 4.9 42,565 57.4 3,033 4.7 42,898 57.4 3,064 4.7 42,931 57.4 3,105 4.8 42,928 15,994 7,585 47.4 6,332 16,096 7,170 44.5 5,919 16,145 16,162 7,082 43.8 5,972 16,178 6,987 43.2 5,859 16,164 7,177 44.4 5,977 16,175 7,045 43.6 5,875 16,186 6,945 42.9 5,797 16,198 7,085 43.7 5,888 16,205 7,113 43.9 5,888 16,214 7,051 43.7 5,846 7,014 43.3 5,832 16,222 7,157 44.1 5,896 16,234 7,1 62 44.1 5,941 16,246 7,051 43.4 5,877 16,257 7,124 43.8 5,898 39.6 1,253 16.5 8,409 36.8 1,251 17.5 8,926 36 .2 1,205 17.1 9,094 37.0 1,109 15.7 9,080 36.2 1,128 16.1 9,191 37.0 1,200 16.7 8,987 36.3 1,170 16.6 9,130 35.8 1,148 16.5 9,240 36.3 1,197 16.9 9,113 36.3 1,225 17.2 9,092 36.0 1,181 16.8 9,200 36.3 1,262 17.6 9,065 36 .6 1,220 17.0 9,072 36.2 1,173 16.6 9,195 36.3 1,226 17.2 9,732 1 population .... .. ........ .......... 179,783 Civilian labor force ............. 120,150 66.8 Participation rate ......... Employed ...................... . 114,013 Employment-pop63.4 ulation ratio2 ........ 6,137 Unemployed ................... 5.1 Unemployment rate .... 59,633 Not in the labor force ....... 181 ,292 181 ,871 182,032 182,001 182,001 182,252 183,022 120,723 66.4 114,765 120,540 66.2 114,602 120,542 66.2 114,433 120,675 66.2 114,712 182,531 121,180 66.4 115,152 182,846 121,041 66.5 114,783 182,384 120,984 66.3 114,976 182,676 120,736 66 .4 114,535 182,185 120,751 66.3 114,678 181,879 120,546 66.5 114,235 121,428 66.5 115,623 121 ,300 66.3 115,547 121,016 66.1 115,323 183,188 121 ,240 66.2 115,572 63.0 6,311 5.2 60,746 63.0 6,200 5.1 61 ,135 63.1 6,258 5.2 60,991 62.9 6,073 5.0 61 ,434 63.1 5,958 4.9 61 ,156 63.0 5,938 4.9 61 ,460 62.8 6,109 5.1 61,579 62.9 5,963 4.9 61,577 63.0 6,008 5.0 61 ,400 63.1 6,028 5.0 61,351 63.3 5,805 4.8 61 ,248 63.2 5,753 4.7 61,546 63.0 5,693 4.7 62,006 63.1 5,668 4.7 61,948 25,578 16,565 64.8 14,872 25,686 16,526 64.3 14,739 25,825 16,589 64.2 14,696 25,860 16,524 63.9 14,812 25,894 16,365 63.2 14,679 25,867 16,602 64.2 14,886 25,900 16,404 63.3 14,804 25,932 16,595 64.0 14,909 25,967 16,485 63.5 14,878 26,002 16,442 63.2 14,818 26,040 16,506 63.4 14,833 26,078 16,755 64.3 14,926 26,120 16,724 64.0 14,983 26,163 16,703 63.8 14,981 26,204 16,839 64 .3 15,037 58.1 1,693 10.2 9,013 57.4 1,787 10.8 9,161 56.9 1,893 11.4 9,236 57.3 1,712 10.4 9,336 56.7 1,686 10.3 9,529 57.5 1,736 10.5 9,265 57.2 1,600 9.8 9,495 57.2 1,686 10.2 9,337 57.3 1,607 9.7 9,482 57.0 1,624 9.9 9,560 57.0 1,673 10.1 9,534 57.2 1,829 10.9 9,323 57.4 1,741 10.4 9,396 57.3 1,722 10.3 9,460 57.4 1,802 10.7 9,365 Employment status 2002 TOTAL Civilian noninstitutional 1 217,570 population ..... .... ... Civilian labor force ............. 144,863 66.6 Participation rate ...... ... Employed ....................... 136,485 Employment-pop2 62.7 ulation ratio ... 8,378 Unemployed ................... 5.8 Unemployment rate ... . Not in the labor force ..... ... 72,707 Men, 20 years and over Civilian noninstitutional 1 population . .. ............. . Civilian labor force ...... ....... Participation rate ....... .. Employed ................... .... Employment-pop2 ulation ratio ... Unemployed ................... Unemployment rate .... Not in the labor force .. ..... Women, 20 years and over Civilian noninstitutional Both sexes, 16 to 19 years Civilian noninstitutional 1 population .. Civilian labor force ............. Participation rate ......... Employed ....................... Employment-pop2 ulation ratio .. . Unemployed ...... .... ......... Unemployment rate .. .. Not in the labor force .. ... .. 3 Whlte Civilian noninstitutional Black or African Amertcan Civiii<in noninstitutional 3 1 population ... ......... Civilian labor force ............. Participation rate ......... Employed ......... .... .......... Employment-pop2 ulation ratio ... Unemployed ................... Unemployment rate .... Not in the labor force ... ... . See footnotes at end of table. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 67 Current Labor Statistics: Labor Force Data 4. Continued-Employment status of the ·population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted [Numbers in thousands] Annual average Employment status 2003 2004 2002 2003 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. 25,963 17,943 69.1 16,590 27,551 18,813 68.3 17,372 27,913 18,940 67.9 17,556 28,016 19,125 68.3 17,709 28,116 19,035 67.7 17,784 27,619 18,811 68.1 17,441 27,705 18,693 67.5 17,303 27,791 19,010 68.4 17,596 27,879 19,064 68.4 17,693 27,968 19,313 69.1 17,958 28,059 19,304 68.8 18,019 28,150 19,450 69.1 18,118 28,243 19,482 69.0 18,144 28,431 19,533 68.7 18,220 28,520 19,563 68.6 18,247 63.9 1,353 7.5 8,020 63.1 1,441 7.7 8,738 62.9 1,383 7.3 8,974 63.2 1,416 7.4 8,891 63.3 1,250 6.6 9,082 63.2 1,370 7.3 8,807 62.5 1,389 7.4 9,012 63.3 1,414 7.4 8,781 63.5 1,371 7.2 8,815 64.2 1,355 7.0 8,654 64.2 1,285 6.7 8,755 64.4 1,332 6.8 8,700 64.2 1,338 6.9 8,761 64.1 131 6.7 8,898 64.0 1,317 6.7 8,957 Hispanic or Latino ethnicity Civilian noninstitutional 1 oooulation ......................... Civilian labor force ............. Participation rate ...... ... Employed ....................... Employment-population ratio 2 ............. Unemployed ................... Unemployment rate .... Not in the labor force .......... 1 The population figures are not seasonally adjusted. 2 Civilian errployment as a percent of the civilian noninstitutional population. Beginning in 2003, persons who selected this race group only; persons who selected more than one race group are not included. Prior to 2003, persons who reported more than one race were included in the group they identified as the main race. 3 NOTE: Estimates for the above race groups (white and black or African American) do not sum to totals because data are not presented for all races. In addition, persons whose ethnicity is identified as Hispanic or Latino may be of any race and, therefore, are classified by ethnicity as well as by race. Beginning in January 2003, data reflect revised population controls used in the household survey. 5. Selected employment indic ators, monthly data seasonally a djusted [In thousands] Selected categories Characteristic Employed, 16 years and over .. Men .... ................. ................ Women ................................ Annual average 2003 2002 2003 Oct. Nov. 136,845 72,903 63,582 137,736 73 ,332 64,404 138,095 73 ,643 64,452 138,533 73,915 64,618 2004 Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct.. 138,479 74,085 64,394 138,566 74,343 64,223 138,301 73 ,901 64,400 138,298 74 ,006 64,292 138,576 74,053 64 ,523 138,772 74,035 64,737 139,031 74,476 64,555 139,660 74,822 64,838 139,681 74,860 64,822 139,480 74,601 64 ,879 139,778 74 ,837 64,941 Married men , spouse present. .............................. Married women, spouse 44,116 44,653 44,684 45,152 45,431 45,490 45,128 45,043 44,735 44,723 44,938 44,935 45,1 06 45,034 45,052 rrPSP'1I. .......................... . ... 34 ,155 34,695 34,993 35,076 35,034 34,585 34,502 34,256 34 ,339 34,522 34,461 34,599 34 ,448 34,601 34,798 Persons at work part tlme 1 All industries: Part time for economic reasons ...... ······· ··· ····· · Slack work or business condnions .... ........ ........ Could only find part-time work ......... .. ... .. ......... Part time for noneconomic reasons .............. ...... .... Nonagricultural industries: Part time for economic reasons .. .. ... .. ......... ...... Slack work or business cond~ions ............ ......... Could only find part-time work ........ .................. Part time for noneconomic reasons .......... .............. ... 4,213 4,701 4,800 4,880 4,788 4,714 4,437 4,733 4,574 4,665 4,513 4,490 4,504 4,452 4,732 2,788 3,118 3,030 3,226 3,205 2,996 2,865 3,011 2,819 2,853 2,803 2,660 2,812 2,808 3,053 1,124 1,279 1,356 1,350 1,295 1,380 1,347 1,427 1,439 1,467 1,404 1,500 1,461 1,312 1,371 18,843 19,014 18,935 19,1 10 18,561 18,905 18,900 19,006 19,000 19,621 19,531 19,741 19,680 19,386 19,710 4,119 4,596 4,690 4,782 4,727 4,613 4,328 4,622 4,471 4,605 4,442 4,400 4,391 4,379 4,627 2,726 3,052 2,964 3,153 3,144 2,911 2,778 2,927 2,756 2,812 2,762 2,605 2,714 2,753 2,995 1,114 1,264 1,349 1,353 1,279 1,399 1,340 1,414 1,431 1,476 1,387 1,496 1,442 1,315 1,357 18,487 18,658 18,628 18,752 18,367 18,636 18,691 18,693 18,664 19,220 19,072 19,290 19,213 19,025 19,305 ' Excludes persons "with a job but not at work" during the survey period for such reasons as vacation , illness, or industrial disputes. NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey. 68 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 6. Selected unemployment indicators, monthly data seasonally adjusted [Unemployment rates] 2002 2003 2004 2003 Annual average Selected categories Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Characteristic Tutal, 16 years and older. .......... ................ Both sexes, 16 to 19 years ...... .. ... .. .... ... Men. 20 years and older .................. ... ... Women. 20 years and older .. ................. 5.8 16.5 5.3 5.1 6.0 17.5 5.6 5.1 6.0 17.1 5.6 5.2 5.9 15.7 5.6 5.1 5.7 16.1 5.3 5.1 5.6 16.7 5.3 5.0 5.6 16.6 5.1 4.9 5.7 16.5 5.2 5.1 5.6 16.9 5.0 5.0 5.6 17.2 5.2 4.8 5.6 16.8 5.0 5.0 5.5 17.6 4.9 4.9 5.4 17.0 5.0 4.7 5.4 16.6 5.0 4.7 5.5 17.2 4.9 4.8 White, total' ........... ........ .... ...... .... ... Both sexes, 16 to 19 years ... ... .. ....... Men , 16 to 19 years ...... ... .............. Women, 16 to 19 years .................. Men, 20 years and older ........ .. ......... Women, 20 years and older ........... .. 5.1 14.5 15.9 13.1 4.7 4.4 5.2 15.2 17.1 13.3 5.0 4.4 5.1 14.3 15.9 12.6 4.9 4.4 5.2 14.3 16.8 11.5 5.0 4.4 5.0 14.8 16.3 13.1 4.7 4.3 4.9 14.1 14.0 14.2 4.5 4.4 4.9 15.2 15.5 14.9 4.5 4.2 5.1 14.8 16.2 13.3 4.7 4.4 4.9 15.7 17.9 13.3 4.5 4.2 5.0 15.7 18.6 12.7 4.7 4.1 5.0 14.8 16.4 13.2 4.5 4.4 4.8 14.9 15.5 14.3 4.3 4.2 4.7 15.3 15.8 14.8 4.4 4.0 4.7 14.7 15.8 13.6 4.3 4.0 4.7 15.2 17.4 12.7 4.3 4.0 Black or African American. total . . ...... . Both sexes, 16 to 19 years ............... Men. 16 to 19 years ... ...... .............. Women. 16 to 19 years .................. Men. 20 years and older ................... Women, 20 years and older .............. 10.2 29.8 31 .3 28.3 9.5 8.8 10.8 33.0 36.0 30 .3 10.3 9.2 11.4 37.3 40.9 33.2 10.5 9.8 10.4 28.9 32 .5 25.7 10.1 9.1 10.3 27.3 28.4 26.5 9.3 9.7 10.5 32 .5 42.1 25.8 9.6 9.1 9.8 25.1 29.6 21 .9 9.4 8.8 10.2 29.4 36.6 22.8 9.2 9.3 9.7 28.3 30.9 26.1 9.3 8.7 9.9 32 .5 30 .3 34.1 9.3 8.4 10.1 32.6 33.9 31.4 9.3 8.9 10.9 37.0 37.8 36.3 10.3 9.1 10.4 28.9 33.9 24.1 10.4 8.7 10.3 28.9 36.0 21.6 10.4 8.9 10.7 34.5 36.9 32.3 10.2 8.9 Hispanic or Latino ethnicity ...... ...... .... Married men. spouse present... .. ...... .. .. Married women, spouse present. .......... Full-time workers ........................ .......... Part-time workers ...................... .......... .. 7.5 3.6 3.7 5.9 5.2 7.7 3.8 3.7 6.1 5.5 7.3 3.8 3.8 6.1 5.5 7.4 3.7 3.8 6.1 5.1 6.6 3.3 3.9 5.8 5.3 7.3 3.3 3.7 5.7 5.4 7.4 3.4 3.6 5.6 5.2 7.4 3.2 3.7 5.8 5.4 7.2 3.1 3.7 5.6 5.3 7.0 3.1 3.3 5.7 5.2 6.7 3.2 3.7 5.6 5.5 6.8 3.2 3.5 5.6 5.2 6.9 3.1 3.5 5.5 5.2 7.1 3.0 3.2 5.6 5.0 6.7 3.0 3.1 5.4 5.5 Educational attalnment2 Less than a high school diploma .. .. ........... 8.4 8.8 8.8 8.5 8.1 8.8 8.5 8.8 8.7 8.8 8.8 8.3 8.1 8.8 8.2 3 High school graduates. no college ......... . Some college or associate degree ... .. ...... 5.3 4.5 5.5 4.8 5.5 4.8 5.4 4.8 5.5 4.5 4.9 4.5 5.0 4.4 5.3 4.7 5.2 4.1 5.0 4.0 5.1 4.2 5.1 4.2 4.9 4.0 4.8 4.0 4.9 4.1 Bachelor's degree and higher 4. ......... ...... 2.9 3.1 3.1 3.1 3.0 2.9 2.9 2.9 2.9 2.9 2.7 2.7 2.7 2.6 2.5 1 1 Beginning in 2003, persons who selected this race group only; persons who selected more than one race group are not included. Prior to 2003, persons who reported more than one race were included in the group they identified as the main race. 3 Includes high school diploma or equivalent. 4 Includes persons with bachelor's, master's, professional. and doctoral degrees. NOTE: Beginning in January 2003, data reflect revised population controls used in the ~ Data refer to persons 25 years and older. household survey. 7. Duration of unemployment, monthly data seasonally adjusted [Numbers in thousands] Weeks of unemployment 2002 2003 2004 2003 Annual average Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Less than 5 weeks .......................... 5 to 14 weeks ... ............................... 15 weeks and over .......................... 15 to 26 weeks ............................. 27 weeks and over .. ...... ............... 2,893 2,580 2,904 1,369 1,535 2,785 2,612 3,378 1,442 1,936 2,733 2,585 3,478 1,460 2,018 2,622 2,556 3,484 1,448 2,036 2,627 2,450 3,403 1,513 1,890 2,612 2,394 3,365 1,467 1,898 2,468 2,412 3,274 1,403 1,871 2,589 2,414 3,320 1,332 1,988 2,792 2,369 2,969 1,170 1,800 2,707 2,376 3,077 1,288 1,789 2,688 2,405 3,065 1,306 1,759 2,805 2,476 2,878 1,211 1,667 2,604 2,521 2,903 1,239 1,664 2,790 2,255 2,954 1,253 1,747 2,749 2,288 3,043 1,253 1,790 Mean duration, in weeks .......... ....... Median duration, in weeks .............. 16.6 9.1 19.2 10.1 19.4 10.3 20.0 10.4 19.6 10.4 19.8 10.7 20.3 10.3 20.1 10.3 19.7 9.5 20.0 10.0 19.9 10.8 18.6 8.9 19.0 9.4 19.6 9.5 19.6 9.5 NOTE: Beginning in January 2003, data reflect revised population controls used in. the household survey. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 69 Current Labor Statistics: Labor Force Data 8. Unemployed persons by reason for unemployment, monthly data seasonally adjusted [Numbers in thousands] Reason for unemployment 1 Job losers ......... . .... .... ..... ........ . . On temporary layoff.. .............. ..... Not on temporary layoff................ Job leavers...................................... Reentrants ...................... ................ New entrants .................................. Annual average 2002 2003 4,607 1,124 3,483 866 2,368 536 4,838 1,121 3,717 818 2,477 641 2003 2004 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. 4,877 1,097 3,780 789 2,518 653 4,719 1,055 3,664 931 2,440 619 4,618 1,060 3,558 783 2,366 694 4,382 1,028 3,353 804 2,509 681 4,323 1,064 3,258 827 2,424 676 4,607 1,040 3,567 836 2,424 627 4,399 994 3,405 822 2,314 645 4,211 926 3,286 846 2,438 713 4,099 1,011 3,088 902 2,435 636 4,181 1,065 3,116 895 2,330 680 3,936 982 2,955 884 2,447 694 3,984 917 3,068 827 2,424 692 4,064 944 3,120 824 2,419 748 50.8 51 .7 49.4 12.5 38.3 11 .2 30.2 7.9 13.2 38.5 11.1 28.8 8.4 12.3 37.1 11.1 30.7 8.7 50.3 11 .6 38.7 10.4 30.6 8.7 50.5 11.7 38.7 10.2 30.0 9.3 2.8 .6 1.7 .4 2.8 .6 1.6 .5 2.7 2.7 2.7 .6 1.7 .5 .6 1.6 .5 .6 1.6 .5 Percent of unemployed 1 Job losers .. .... .. ...... .... .. .......... ... On temporary layoff ........ ............. Not on temporary layoff ...... .......... Job leavers............................... ....... Reentrants ... .... .. .. .. ........... ........ .... .. New entrants ............................. ... .. 55.0 55.1 54.2 12.1 42.1 10.7 28.0 7.1 52 .3 52.4 54.2 53.8 12.8 42 .4 9.3 28.2 7.3 55.2 12.4 42 .8 8.9 28.5 7.4 54.6 13.4 41.6 10.3 28.3 6.4 12.5 42.0 9.3 28.0 8.2 12.3 40.0 9.6 30.0 8.1 12.9 39.8 10.0 29.4 8.2 12.2 42.0 9.8 28.5 7.4 12.1 41.6 10.1 28.3 7.9 51.3 11 .3 40.0 10.3 29.7 8.7 3.2 3.3 3.3 3.2 3.1 2.9 .5 1.7 .4 .6 1.7 .4 .5 1.6 .5 3.0 .6 1.7 .5 3.0 .6 1.7 .4 3.0 .5 1.7 .5 3.1 .6 1.6 .4 .6 1.7 .4 .6 1.6 .4 .6 1.7 .5 Percent of clvlllan labor force 1 Job losers .. .. ...... .... ... .. .............. Job leavers... ................................... Reentrants ...................................... New entrants ................................ .. 1 lndudes persons who completed temporary jobs. NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey. 70 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 9. Unemployment rates by sex and age, monthly data seasonally adjusted [Civilian workers] 2002 2003 2004 2003 Annual average Sex and age Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June Aug. July Sept. Oct. 5.6 11 .6 16.9 20 .2 14.7 9.2 4.5 4.6 3.8 5.6 12.1 17.2 21 .6 14.7 9.7 . 4.4 4.5 3.9 5.6 12.0 16.8 20.6 14.3 9.8 4.5 4.5 3.9 5.5 12.0 17.6 20.2 16.1 9.3 4.4 4.6 3.7 5.4 11 .6 17.0 20 .8 14.9 9.0 4.3 4.5 3.7 5.4 11 .8 16.6 19.6 14.9 9.5 4.3 4.4 3.7 5.5 12.2 17.2 20 .5 15.2 9.8 4.3 4.3 3.8 Total, 16 years and older ................. 16 to 24 years .............................. 16 to 19 years ... .. ...................... 16 to 17 years .................. .. .... 18 to 19 years ........................ 20 to 24 years ........... ...... .. ........ 25 years and older ....... ... ........... .. 25 to 54 years ........................ 55 years and older .............. .. . 5.8 12.0 16.5 18.8 15.1 9.7 4.6 4.8 3.8 6.0 12.4 17.5 19.1 16.4 10.0 4.8 5.0 4.1 6.0 12.3 17.1 20.2 15.2 10.1 4.9 5.1 3.8 5.9 12.1 15.7 17.5 14.7 10.4 4.8 5.0 3.9 5.7 11.7 16.1 18.3 14.7 9.6 4.7 4.9 3.9 5.6 12.0 16.7 18.2 15.7 9.8 4.5 4.7 3.7 5.6 11.8 16.6 17.6 15.7 9.5 4.5 4.7 3.8 5.7 11 .8 16.5 19.4 14.5 9.6 4.6 4.9 3.8 MP.n. 11:i years and older .......... .. .... 16 to 24 years .. ................... ....... 16 to 19 years ......................... 16 to 17 years ...................... 18 to 19 years ...................... 20 to 24 years .... ... ......... ......... 25 years and older ....... ............ .. 25 to 54 years ...................... 55 years and older. ....... ....... 5.9 12.8 18.1 21 .1 16.4 10.2 4.7 4.8 4.1 6.3 13.4 19.3 20.7 18.4 10.6 5.0 5.2 4.4 6.2 13.2 18.7 20.4 17.9 10.8 5.0 5.2 4.0 6.2 13.4 18.3 18.3 18.1 11 .2 5.0 5.2 4.1 5.8 12.6 17.4 18.4 16.9 10.4 4.7 4.9 4.0 5.7 12.7 17.5 19.3 16.2 10.5 4.5 4.7 3.6 5.7 12.2 17.2 19.4 15.7 10.0 4.5 4.7 3.7 5.8 12.6 18.3 22.3 15.8 10.1 4.6 4.8 3.8 5.7 12.8 19.1 23.4 16.5 10.0 4.4 4.5 3.9 5.8 13.0 19.1 23.3 16.6 10.3 4.6 4.7 4.1 5.6 12.8 18.1 22.8 15.8 10.4 4.4 4.4 4.3 5.5 12.2 17.7 21 .2 15.7 9.7 4.4 4.5 3.8 5.6 12.4 18.0 21.9 16.0 9.9 4.4 4.5 4.0 5.6 12.9 18.1 20.6 16.8 10.6 4.3 4.4 3.9 5.6 13.0 19.0 21 .7 17.7 10.3 4.3 4.3 4.1 Women, 16 years and older. .......... 16 to 24 years ........... ............... .. 16 to 19 years ......................... 16 to 17 years ....... ........ ... . 18 tO 19 years ........... ... ..... 20 to 24 years .... ................... .. 25 years and older ............. .. ...... 25 to 54 years ............... ....... 5.6 11 .1 14.9 16.6 13.8 9.1 4.6 4.8 5.7 11.4 15.6 17.5 14.2 9.3 4.6 4.8 5.7 11 .3 15.4 20 .1 12.5 9.3 4.7 4.9 5.5 10.7 13.0 16.6 11 .1 9.6 4.6 4.8 5.6 10.7 14.7 18.2 12.2 8.8 4.6 5.0 5.6 11 .3 15.9 17.1 15.2 8.9 4.6 4.8 5.5 11 .2 16.0 15.9 15.6 8.9 4.4 4.5 5.6 10.8 14.7 16.9 13.0 8.9 4.6 4.9 5.4 10.3 14.5 17.3 12.6 8.3 4.6 4.7 5.3 11 .1 15.3 20.1 12.7 9.0 4.2 4.4 5.6 11 .2 15.6 18.7 12.6 9.0 4.5 4.7 5.6 11 .7 17.5 19.4 16.5 8.8 4.5 4.7 5.3 10.7 16.1 19.7 13.6 8.0 4.3 4.4 5.2 10.6 15.2 18.6 12.9 8.3 4.3 4.4 5.3 11 .3 15.3 19.3 12.6 9.3 4.2 4.4 3.6 3.7 3.4 3.5 3.5 4.1 3.9 3.5 3.3 3.3 3.8 3.8 3.9 3.5 3.3 55 years and older 1 1 •• • •• • • • • • •• Data are not seasonally adjusted. NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 71 Current Labor Statistics: Labor Force Data 10. Unemploy ment rates by State, seasonally adjusted State Aug. Sept. 2003 2004P 2004P State Sept. Aug. Sept. 2003 2004P 2004P Alabama ....................... .. ............. ... ...... . Alaska ...................................................... . Arizona ........................... ................ ...... . Arkansas .......... ........... .......... .................... California ..................... .................. ....... . 5.8 8.0 5.5 6.6 6.7 6.0 7.6 4.4 5.4 5.9 5.7 7.6 4.8 5.5 5.9 Missouri Montana .. ... ............... ............... ... ........ ..... . Nebraska ....................................... ...... . . Nevada........................... ......................... . New Hampshire ... ............... ........ .. ......... . 5.7 4.8 4.1 5.3 4.3 5.5 4.8 3.6 4.0 3.7 5.6 5.1 3.7 3.9 3.5 Colorado ................................................ .. . Connecticut.. ....... ... .. ............ .......... .... ... . Delaware ... ......................................... ... .... District of Columbia ............................. .... . Florida............ .. ............. .. .. ....................... . 6.0 5.5 4.5 6.8 s.1 5.1 4.6 3.6 7.5 4.6 4.9 4.7 3.9 7.9 4.5 New Jersey ......................... .............. .. ..... . New Mexico .................................. ........ . New York ...................................... ........ .... North Carolina...................... ....... .... ... ... . North Dakota .... ................................... ..... . 5.8 6.6 6.4 6.6 4.0 4.8 5.4 5.6 5.4 3.3 4.8 5.3 5.5 5.3 3.6 Georgia............ ................. .... .... ..... ...... . Hawaii ........ ...................... .... ............ .. ... .... Idaho ..... .................... ... ................... .... . Illinois ........................................................ Indiana..................... .................... ... ... .. . 4.5 4.5 5.3 6.9 5.2 4.2 2.9 5.0 6.1 5.1 4.1 3.1 5.0 6.0 5.2 Ohio ........ .................... .... ......... .. ......... . Oklahoma........... .. ......... ... ... ... ... ... .. .......... Oregon ............................. ......... ..... ...... . Pennsylvania......................... ......... .......... Rhode Island .. .... ........ ................. ... .. ..... . 6.1 5.8 8.2 5.4 4.9 6.3 4.1 7.4 5.6 5.5 6.0 4.4 7.3 5.3 5.0 Iowa.......... .. ..................... ......... ... ....... . Kansas ...................... .......... ..................... . Kentucky .................................... ... ... .... . Louisiana .............. ............................. ...... . Maine ...................... ....... ..... .. .. .. .... .. ... . . 4.6 5.4 6.2 6.5 4.7 4.5 4.8 5.1 5.0 4.5 4. 7 4.7 4.6 5.3 5.2 South Carolina .......... ................. ...... ...... . South Dakota ....................... ........... ......... . Tennessee ................... ... ... ........ .......... . . Texas ........... ... ......... ................... ............. . Utah ................. .. .......... ........... .... .... .... . 7.0 3.7 6.1 6.8 5.5 6.4 3.2 4.9 5.7 4.7 6.9 3.4 5.1 5.5 4.8 Maryland ................... ........... ... ..... .. .. .... . Massachusetts .................. .. ..................... . Michigan ...... ............... ................... ....... . Minnesota ......................... .. ....... ........ ...... . Mississippi.. ......................... ........ ... .. .... . 4.5 5.9 7.6 5.1 6.1 4.3 5.4 6.7 4.8 5.9 4.1 4.6 6.8 4.6 6.0 Vermont.. .. ... ......... ... .......... .... .... .... ...... . Virginia ..... ............... ........... ....................... Washington .... ..... .............. ........ ..... ....... . West Virginia ................................ ............ . Wisconsin .. .................... ........ ...... ... ...... . Wyoming ...... ..................... ................... .... . 5.0 4.1 7.7 6.0 5.6 4.3 3.3 3.6 6.2 5.5 4.8 3.7 3.3 3.2 5.6 5.0 5.0 3.9 P 72 Sept. = preliminary Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Decembe r 2004 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 11. Employment of workers on nonfarm payrolls by State, seasonally adjusted Sept. State 2003 Aug. 2004P Sept. 2004P State Sept. Aug. Sept. 2003 2004P 2004P Alabama ............ ..... .... ... ........ .... . . Alaska ................... ..... .... ............... . Arizona .. .... .... ..... ... .......... . .... .. ... . . Arkansas ............................. ...... .. .. . California ................. ... ........... .... .. . 2,160,049 333,861 333,861 1,263,558 17,464,738 2,171 ,032 345,845 345,845 1,321 ,281 17,646,871 2,161 ,249 347,401 347,401 1,326,730 17,693,316 Missouri Montana .......... .. ........................... . Nebraska.............. ... ........... .... .... . Nevada ........... ... .. .... ......... .... .. ...... . New Hampshire .. ......... .. ... .... ... ..... . 3,029,677 477,507 979,224 1,146,920 722,716 3,048,875 483,962 990,212 1,185,851 730,469 3,038,268 483,831 991,725 1,184,592 729,402 Colorado ... ........... ..... .. ... .... ... .. ...... . Connecticut... .. ...... ..... ..... .... ........ . Delaware ...... ... ... ... ........... ... ... .. .... . District of Columbia ..... .............. ... . Florida ......... .............................. ... . 2,485,194 1,800,423 418,597 301 ,878 8,191 ,765 2,521,641 1,788,315 424,091 301 ,032 8,400,607 2,530 ,744 1,791 ,014 427,415 304,885 8,387,744 New Jersey ....... .. ......................... .. New Mexico ..................... .... ... .... . . New York ........ .. ... ... .. ... .. .......... ..... . North Carolina .. ... ....... .. ... ............ . . North Dakota .. .... .... .................... ... 4,379,131 900,779 9,603,949 4,253,937 347,271 4,425,145 910,889 9,308,448 4,183,628 350,563 4,409,429 911,138 9,327,402 4,156,918 351,278 Georgia .. ........ .. ....... ..... ... .. ...... ... . Hawaii ................. ................... .. ..... . Idaho ... ................... ....... . ..... . ...... . Illinois ........... .............. ... ... ....... ...... . Indiana .. ........................... .... .. .... . 4,440,823 624,018 693,057 6,340,126 3,188,438 4,439,453 630,197 710,466 6,388,300 3,147,244 4,417,986 630,197 708,364 6,426,479 3,152,373 Ohio .. ..... ........... .... ............ ... ... . .. . Oklahoma .... ... .................. ..... ... .... . Oregon ......................... .. ......... .. . . Pennsylvania .. ... ...... ........ ... .......... . Rhode Island ....... ..... .. ................ . . 5,923,631 1,695,200 1,845,115 6,142,682 573,558 5,875,960 1,698,816 1,850,802 6,275,025 568,893 5,867,088 1,704,629 1,831 ,288 6,289,636 567,061 Iowa ... ... .................. ... ... .. ... .... ... . . Kansas .. ... .................... ... ........ ... ... . Kentucky ... .............. ..... ..... ..... .. ... . Louisiana ............................ .......... . Maine .. .... .. ......... .... ... ....... ... ....... . 1,599,167 1,436,282 1,962,756 2,040,987 696,561 1,632,557 1,471 ,017 1,982,539 2,032,997 701 ,541 1,630 ,014 1,473,426 1,981,137 2,059,942 698,132 South Carolina ............. .......... ..... . . South Dakota........... .................. ... . Tennessee .. ... .... ... ... ...... ... ...... ... . . Texas .. ... ........ ....... .............. ... ..... .. . Utah .. .. ........ ............ ....... .. ........ .. . 2,019,922 425,674 2,606,564 10,935,870 1,190,270 2,068,869 424,034 2,931,130 10,963,157 1,211,405 2,082,012 425,250 2,941 ,877 10,976,345 1,214,025 Maryland .. ................... .... .... ...... .. . Massachusetts ...... .... ... ..... ... ...... ... . Michigan ... ... ...... ...... .. ................. . Minnesota ................. .................... . Mississippi. .. .. ................ ... ... ..... ... . 2,935,450 3,402,875 5,058,058 2,923,107 1,315,128 2,948,541 3,412,958 5,052,968 2,969,386 1,325,882 2,955,645 3,389,041 5,060,530 2,960 ,898 1,326,606 Vermont.. ... ... .... ................. ... .. . ... . Virginia ... ... ............ .................... .. .. . Washington .... ... ..... .. .. ........ .. .. .. ... . West Virginia .. .......... .... .... ..... ....... . Wisconsin ................ ..... ..... ... ... .... . Wyoming .. ............... .... .......... ... ..... . 351,934 3,782,841 3,142,922 784,831 3,089,324 279,960 354,281 3,846,077 3,211 ,058 803,717 3,115,623 279,926 352,558 3,828,941 3,209,744 802,974 3,1 16,331 279,926 P = preliminary. NOTE: some data in this table may differ from data published elsewhere because of the continual updating of the data base. Monthly Labor Review December 2004 73 Current Labor Statistics: Labor Force Data 12. Employment of workers on nonfarm payrolls by Industry, monthly data seasonally adjusted [In thousands] Annual average Industry 2002 TOT AL NONFARM ............... TOTAL PRIVATE...................... 2003 2003 Oct. 2004 Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.P Oct.P 130,341 129,931 129,944 130,027 130,035 130,194 130,277 130,630 130,954 131,162 131,258 131,343 131 ,541 131,680 132,017 108.828 22,557 108.356 21,817 108.384 21,674 108.483 21,686 108.491 21,668 108.667 21,696 108.738 21,684 109.077 21 ,778 109.382 21,822 109.618 21,894 109.730 21,891 109.771 21,906 109.912 21 ,939 110.007 21,935 110.303 22,000 583 70.4 512.2 121.9 571 68.5 502.3 122.9 569 67.9 501.5 124.1 571 67.6 503.4 123.9 570 65.9 504.3 124.6 570 65.1 505.1 126.9 572 64.2 508.1 128.9 581 65.9 514.9 130.0 585 66.7 518.5 131.0 589 65.6 523.2 132.3 587 64.5 522.7 132.0 592 64.5 527.5 132.2 591 64.6 526.6 132.7 592 65.0 527.1 132.9 591 64.1 526.9 133.1 Minina. exceot oil and aas •• ••• Coal minina ... ... .. ....... .. ...... Support activities for mining .... 210.6 74.4 179.8 202.7 70.4 176.8 202.1 69.6 175.3 202.4 69.5 177.1 202.0 69.8 177.7 200.0 69.6 178.2 200 .6 70.2 178.6 202.8 70.6 182.1 205.2 71 .8 182.3 207.8 72.9 183.1 207.9 73.5 182.8 211.2 75.0 184.1 209.2 74.6 184.7 208.8 74.4 185.4 208.5 74.2 185.3 Construction .............................. 6,716 1,574.8 930.6 4,210.4 15,259 6,722 6,791 6,853 6,872 6,909 6,930 6,945 7,016 1.593.3 928.0 4.290.2 14,314 1,590.9 924.0 4,276.5 14,321 1,607.6 926.8 4,318.9 14,344 1.609.8 924.7 4,337.3 14,365 1.622.9 924.3 4,362 .2 14,396 6,911 1,625.9 920.9 4,364.6 14,393 6,916 1,583.9 918.8 4.268.6 14,344 6,774 1,585.1 920.7 4,268.4 14,324 6,812 1,575.9 910.7 4 ,235.5 14.525 6,754 1,579.4 910.8 4,263.7 14,351 6,771 Construction of buildinas ......... Heaw and civil enaineerina .... Soecialitv trade contractors .. .... Manufacturing ............................ 1.629.7 920.2 4.365.6 14,398 1,635.5 921.9 4,378.9 14,412 1,645.3 921.0 4,378.6 14,398 Production workers ......... ..... Durable goods......................... 10.766 9,483 10.200 8,970 10.058 8,854 10.048 8,874 10.044 8,868 10.035 8,869 10.038 8,882 10.085 8,924 6.157 536.1 492.6 476.7 1.478.4 1.153.5 6.066 533.4 486.6 463.4 1.461 .3 1,137.0 6.089 536.3 489.7 464.1 1.468.1 1,142.5 6.079 536.6 487.5 464.6 1.471.2 1,140.4 6.081 536.3 492.7 432.2 1.471.8 1.138.7 6.088 538.4 490.5 462.2 1.476.6 1.141.2 6.126 540.0 497.8 462 .5 1.486.7 1,152.0 6.164 543.8 501.7 465.4 1.497.6 1,156.7 10.141 8,955 6,167 544.1 502.6 467.0 1,501.3 1.160.4 10.142 8,978 6.529 554.9 516.0 509.4 1,548.5 1,229.5 10.123 8,946 6,152 543.0 501.4 464.0 1.494.5 1,153.3 10.162 8,986 Production workers .............. Wood oroducts ...... ........ ......... . Nonmetallic mineral oroducts Primarv metals ... ..... ...... ........... Fabricated metal oroducts ....... Machinerv ...... ... ...... ............... Comouter and electronic 10.058 8,889 6,101 539.7 493.2 462 .0 1.478.5 1,145.1 6.195 545.9 501.6 465.4 1,504.7 1.163.3 6.181 544.8 502.0 464.2 1.505.6 1.160.8 1.656.2 926.5 4.432.8 14,393 10,134 8,983 6,180 549.1 500.4 464.5 1.507.0 1,160.5 1,507.2 1,360.9 1,332.8 1,334.4 1,332.2 1,333.2 1,333.9 1,338.0 1,339.7 1,345.8 1,346.2 1,351.9 1,353.0 1,351.2 1,349.9 250.0 185.8 225.7 157.0 219.3 1 53.9 219.1 154.4 217.8 153.0 219.4 154.8 219.0 154.8 218.6 155.0 218.1 155.1 218.8 155.9 217.7 157.1 217.2 158.2 217.9 158.5 217.2 157.8 215.6 157.2 524.5 450.0 461 .8 429.3 449.4 425.1 451.2 425.2 451 .3 425.3 450.2 423.7 451.4 423.3 452.1 426.8 453.4 427.5 455.8 430.1 458.0 429.8 460.7 432.4 460.2 433.0 460.0 433.3 459.3 435.1 496.5 1,828.9 459.9 1,775.4 450.8 1,765.5 450 .9 1,766.5 451 .2 1,762.7 449.8 1,760.6 448.6 1,766.5 446.8 1,769.1 446.5 1,768.8 447.3 1,764.4 448.6 1,765.1 449.2 1,745.9 449.6 1,774.4 449.3 1,773.1 448.7 1,776.3 604.1 688.3 573.5 662.8 568.2 655.2 568.9 652.7 569.3 651.9 571 .3 652.0 571 .2 653.0 573.4 653.0 577.6 654.4 575.0 654.6 576.7 655.5 574.6 653.6 574.1 653.0 574.1 652.6 Nondurable goods................... Production workers ..... ......... 5,775 4,239 5,555 4,043 5,497 3,992 5,470 3,959 5,445 3,954 5,439 3,950 5,445 3,957 5,450 3,971 5,426 3,967 5,420 3,961 5,410 3,954 1,525.7 1,518.7 1,528.2 1,508.3 1,500.7 1,502.4 1,504.5 1,502.7 1,507.0 5,438 3,964 1,502.8 5,443 3,974 rood manufacturing .......... ....... Beverages and tobacco products ... ... ..... ... .... ...... .... Textile mills ... ..... .. .. ... ... .. .. .. . Textile product mills ............ ..... Apparel ........ ... ... ........ ... ...... Leather and allied products ..... Paper and paper products .... ... Printing and related support activities .. ......... ...... .... .. .. ... Petroleum and coal products ... Chemicals ... ............... .. ........... . 5,456 3,965 1,506.3 576.5 653.0 5,441 3,959 1,508.0 1,499.6 1,497.5 1,497.3 207.4 290.9 194.6 359.7 50.2 546.6 200.6 260.3 179.8 312.7 45.2 519.0 201 .0 247.0 172.6 299.7 43.7 513.3 198.3 245.1 175.2 297.7 44.1 511 .7 198.3 241.0 174.3 297.7 44.3 510.3 197.7 239.2 176.9 296.1 44.6 509.8 195.9 237.3 176.6 297.1 44.8 508.0 197.2 237.1 179.7 294.3 44.8 508.8 197.8 235.8 180.1 292.7 44.6 507.0 197.5 236.1 181.4 290.8 45.1 508.1 197.6 235.0 179.7 286.8 44.7 506.7 198.4 235.6 179.3 284.8 45.3 509.0 197.2 234.4 179.4 284.2 44.8 509.8 198.7 233.8 180.0 282.1 45.2 508.5 197.0 232.9 180.6 278.4 45.3 507.5 706.6 118.1 927.5 848.0 680.0 ! 1 4.6 7.9 815.9 673.3 112.6 899.1 806.3 673.1 112.0 897.6 806.5 670.1 112.4 895.9 805.8 667.6 ·114.3 893.7 804.8 665.0 112.9 894.7 803.9 664.4 113.1 894.9 806.3 663.6 112.6 896.4 807.5 665.9 113.1 895.0 810 .2 667.0 113.8 895.2 808.6 663.8 113.6 894.2 811 .2 662.2 114.1 891.9 808.8 659.5 114.1 891 .5 809.0 658.0 114.3 889.9 808.5 SERVICE-PROVIDING .................. 107,784 108,114 108,270 108,341 108,367 108,498 108,593 108,852 109,132 109,268 109,367 109,437 109,602 109,745 110,017 PRIVATE SERVICEPROVIDING ............. .. .......... 86,271 86,538 86,710 86,797 86,823 86,971 87,054 87,299 87,560 87,724 87,839 87,865 87,973 88,072 88,303 25,497 5,652.3 3,007.9 2,015.0 25,275 5,605.0 2,949.2 2,002.1 25,272 5,581 .6 2,932.0 1,992.4 25,261 5,592.7 2,943.9 1,989.2 25,211 5,598.4 2,945.8 1,991 .8 25,312 5,611.4 2,954.9 1,993.7 25,331 5,612.2 2,953.8 1,994.5 25,415 5,623.5 2,963.4 1,995.3 25,448 5,632.5 2,967.5 1,996.3 25,477 5,636.7 2,969.7 1,997.2 25,497 5,639.5 2,975.6 1,994.3 25,499 5,649.6 2,986.0 1,994.3 25,516 5,652.8 2,989.6 1,992.1 25,530 5,662.9 2,992.9 1,992.5 25,566 5,668.5 2,997.5 1,997.2 GOODS-PRODUCING .......... .. .... Natural resources and mining .......... ......................... logging ... .... .... ... ... ................. Mining .... .. ..... ............. .. ....... ....... Oil and gas extraction .......... . , 1 1 oroducts •••••••••• • •• • •• ••••••• •• Comouter and oerioheral equipment. .. ....... .... .............. Communications equipment. . Semiconductors and electronic components .... ..... Electronic instruments ... ... .. .. Electrical equipment and appliances ........ .................. .... Transportation equipment... ..... Furniture and related products ...... .. ...... ... ......... ... Miscellaneous manufacturing Plastics and rubber products .. Trade, transportation, and utilities............................... Wholesale trade ....................... Durable goods ................. .. .. Nondurable goods ...... ... .. .... Electronic markets and agents and brokers ......... .. .. 629.4 Retail trade ............................ ... 15,025.1 Motor vehicles and parts 1 dealers • • •••• . •• . •••••••••••••• • • . Automobile dealers ....... ...... .. . Furniture and home furnishings stores ................. .. Electronics and appliance stores ... ....... ....... .... ................ 654.3 657.2 659.6 660 .8 662.8 663.9 664 .8 671 .5 668.7 669.8 669.6 670.7 674.0 673.8 14,911 .5 14,948.1 14.921.7 14,876.0 14,944.8 14.963.0 15,013.0 15,037.1 15,047.6 15,054.9 15,038.1 15.048.8 15,043.1 15,064.1 1,879.4 1,252.8 1,883.5 1,255.1 1,889.7 1,259.6 1,892.9 1,258.9 1,893.7 1,259.5 1,895.4 1,261 .3 1,900.9 1,262.9 1,906.9 1,263.9 1,910.9 1,264.7 1,911.4 1,263.6 1,908.5 1,262.3 1,908.1 1,259.2 1,904.9 1,256.8 1,904.9 1,253.3 1,904.8 1,251.8 538.7 542.9 540.2 544.8 547.2 546.4 544.5 544.8 544.5 545.7 546.3 546.4 548.7 548.5 549.4 525.3 511 .9 506.5 512.8 511 .9 509.3 508.2 511 .7 514.1 512.6 511 .5 510.7 511.6 512.7 519.7 See notes at end 0f table. 74 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 10.128 8,955 December 2004 12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted [In thousands] Annual average Industry 2003 2004 2002 2003 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. 1,176.5 2,881 .6 1,191.1 2,840.9 1,204.0 2,838.7 1,210.0 2,821 .4 1,209.5 2,813.9 1,221.4 2,826.3 1,231 .4 2,831 .3 1,243.5 2,838.9 1,247.3 2,839.9 1,248.7 2,845.3 1,245.8 2,839.7 1,246.9 2,834.5 1,251.7 2,832.9 1,256.2 2,834.0 1,260.5 2,836.7 938.8 895.9 943.1 879.9 948.3 873.8 951 .6 875.2 952.6 871 .1 954.1 875 ..1 954.9 871 .8 958.2 873.0 957.9 872.4 957 .1 871.6 957.2 870.3 956.7 869.9 956.4 870.3 956.6 873.5 954.8 871 .8 1,312.5 1,296.7 1,302.6 1,297.1 1,301 .0 1,304.3 1,311 .3 1,321.8 1,328.0 1,335.5 1,346.5 1,349.0 1,355.2 1,350.3 1,354.2 661 .3 2,812.0 1,684.0 959.5 443.7 645.0 2,815.2 1,618.8 934.1 427 .5 642.0 2,842.9 1,623.5 933.5 425.9 641 .6 2,826.4 1,612.6 930.9 417.3 633.2 2,793.4 1,601.3 924.4 424.1 635.9 2,822 .7 1,603.4 929.6 424.3 636.8 2,822.5 1,602 .7 924.6 424.8 636.5 2,824.4 1,604.9 926.9 427.4 635.8 2,831 .0 16.7 927.9 429.8 636.1 2,830 .5 1,610.9 925.7 427.4 635.7 2837.4 1,614.9 928.4 427 .6 635.5 2825.3 1,609.9 926.2 428.9 638.4 2832.8 1,607.9 927.1 427.8 638.1 2814.6 1,600.5 924.6 429.1 639.1 2815.5 1,599.7 927.7 429.9 4,223.6 563.5 217.8 52.6 1,339.3 4,176.7 527 .3 215.4 52.5 1,328.0 4,162.9 506.1 215.2 52.5 1,329.3 4,168.0 511 .5 215.5 50.9 1,335.7 4,157.0 512.9 215.5 50.0 1,338.7 4,175.9 510.2 215.4 50.6 1,343.6 4,175.8 511 .6 215.7 48.8 1,344.1 4,197.0 512.9 216.0 49.2 1,346.4 4,196.5 513.3 216.3 50.6 1,352.2 4,209.9 514.7 216.4 51 .1 1,353.9 4220.9 513.8 217.3 51 .7 1,353.9 4228.3 512.4 217.8 51 .7 1,361 .9 4232.5 511 .8 217.4 50.3 1,363.7 4240.3 512.3 217.9 50.3 1,368.7 4249.9 513.7 217.9 51 .0 1,368.0 380.8 41 .7 380.3 40.0 389.2 39.0 385.7 38.7 385.0 38.8 382.3 38.3 380.1 38.2 380.5 38.1 372.3 38.1 381.5 38.3 374.6 38.4 374.2 38.5 374.5 38.5 374.7 38.6 377.0 38.5 25.6 28.0 29.0 28.7 29.4 28.7 29.7 31.4 31 .1 30 .6 32.6 32.6 32 .7 32.9 32.9 524.7 560.9 516.7 516.3 566.6 522 .3 514.3 565.0 522.6 512 .4 564.7 524.2 511 .6 559.0 516.1 514.1 566.9 525.8 515.5 567.7 524.4 519.1 570.9 532.6 523.7 579.2 536.3 525.1 580.4 538.1 579.2 3,166 578.9 3,172 579.3 3,175 580.2 3,163 580.0 3,169 582.1 3,173 581 .7 3,182 582.6 3,173 582.0 3,166 525.3 581 .1 538.5 583.3 3,158 527 .9 580.8 542.2 580.8 3,198 519.5 572 .8 531 .1 582 .3 3,177 520.8 578.2 534.0 596.2 3,395 518.5 572.1 531 .9 581 .2 3,169 964.1 926.4 918.0 918.4 917.4 914.0 915.1 915.3 916.3 916.2 916.6 914.7 914.3 914.3 913.6 387 .9 334.1 376.1 327 .0 373.4 326.0 382.7 327.0 385.2 329.5 379.7 329.7 382.7 331 .8 381 .2 333.0 385.7 333.3 390.8 335.4 394.9 335.5 391 .0 336.4 388.0 336.6 388.7 336.9 395.9 338.1 33.7 1,186.5 30.0 1,082.6 29.9 1,065.2 30.4 1,062.2 30.4 1,061.2 30.8 1,061 .3 31 .9 1,058.2 31 .9 1,055.0 32.5 1,051 .9 32.5 1,047 .3 33.6 1,044.8 33.6 1,042.3 34.2 1,037.5 34.6 1,027.9 35.7 1,024.3 441 .0 47.3 407 .5 48.1 404.8 48.3 402.6 48.2 402.6 48.2 400.1 47.8 401 .1 48.0 403.7 48.6 404.0 49.6 405 .1 49.6 406.5 50.0 404.9 49.8 404.3 50.0 404.7 49.7 406.4 49.1 7,847 5,817.3 7,974 5,920.5 7,990 5,930.2 7,985 5,922.7 7,981 5,916.5 7,981 5,917.1 7,989 5,924.7 8,003 5,933.0 8,015 5,947.7 8,029 5,946 .0 8,049 5,960.4 8,044 5,951 .9 8,053 5,962.4 8,077 5,973.6 8,094 5,989.7 23.4 22.7 22.5 22 .5 22 .5 22.4 22.4 22 .3 22.3 21 .8 21 .9 21 .8 21 .8 2 1.8 21 .6 .. ... .. 2,686.0 2,785.6 2,801 .0 2,790.3 2,783.3 2,785.3 2,787 .2 2,793.8 2,802.1 2,800.8 2,809.9 2,804.1 2,807.3 2,815.4 2,823.5 intermediation' .. . . .. . .. . .. .... Commercial bankina .. Securities, commodity contracts, investments. ... .... Insurance carriers and related activities ........ ..... Funds, trusts, and other financial vehicles ..... ... .. . . . .. Real estate and rental and leasing .... .... .. ...... .. .... .. Real estate .. . . . .. .. . . . .. . . . . . .. .. Rental and leasing services .... Lessors of nonfinancial intangible assets ...... .... 1,733.0 1.278.1 1,752.1 1,281 .1 1,760.1 1,284.4 1,758.1 1.280.5 1,757.1 1.278.9 1,758.7 1.280.4 1,762.6 1.283.5 1,762.8 1.284.1 1,765.0 1.285.0 1,765.2 1.284.2 1,768.8 1.285.9 1,766.9 1.284.0 1,768.3 1.283.0 1,772.4 1.287.3 1,776.2 1.290.2 789.4 764.4 762.0 769.1 771 .9 773.8 778.2 780.8 781 .0 782.8 787.2 787.8 791 .6 793.0 800.9 2,233.2 2,266.1 2,264.7 2,261.2 2,258.1 2,255.8 2,257.4 2,257.1 2,259.5 2,262 .7 2,263.8 2,260.2 2,263.9 2,265.8 2,266.4 Building material and garden supply stores .... ... .... ........... ... Food and beverage stores ... Health and personal care stores .. ......... ...... ... .. ...... . Gasoline stations ....... ·· ··· ····· Clothing and clothing accessories stores ... .... ...... Sporting goods, hobby, book, and music stores .... .... General merchandise stores1. Department stores ·· ······"··· Miscellaneous store retailers .. Nonstore retailers .. ..... ..... .. Transportation and warehousing .......................... Air transportation ..... ....... .... .. Rail transportation .. ... . .... ..... Water transportation ...... .... ... Truck transportation ... ...... .... . Transit and ground passenger transportation ........ ........ .. .. Pipeline transportation .. .. ... .. Scenic and sightseeing transportation ... .. ... .. .. . .. . .... Support activities for transportation .. ... ..... .... ... ... Couriers and messengers .. .... Warehousing and storage Uiiilt1es .................... ................... Information ............ ... ............ Publishing industries, except Internet. ... ..... .......... ... .... .. Motion picture and sound recording industries. ..... .... Broadcasting, except Internet .. Internet publishing and broadcasting .... .... ... ... ······ · Telecommunications. .... .... .. ISPs, search portals, and data processing ........ ..... . . .. Other information services ... .. Flnanclal activities ... ... .... ... ...... Finance and insurance ......... .. . Monetary authoritiescentral bank ....... ... ........ .... . Sept.P Oct.P 583.5 3,163 Credit intermediation and related activities' .. .. . Deoositorv credit Professional and business services ..................... .. ..... ... 85.4 81 .7 80.0 79.6 80.7 79.8 79.5 79.0 78.8 77.9 77.6 78.0 77 .8 77.6 77.3 2,029.8 1,352.9 649.1 2,053.6 1,384.4 640.8 2,060.2 1,390.6 639.9 2,062.7 1,394.5 639.0 2,064.0 1,395.7 638.3 2,063.6 1,397.7 636.0 2,064.5 1,400.2 634.2 2,069.5 1,405.8 634.1 2,071 .6 1,409.2 633.2 2,083.1 1,418.7 635.4 2,088.1 1,418.8 640.5 2,092.0 1,422.1 641 .4 2,090.6 1,424.1 638.0 2,193.1 1,431 .1 643.7 2,104.0 1,433.4 642.5 27.6 28.4 29.7 29.2 30.0 29.9 30.1 29.6 29.2 29.0 28.8 28.5 28.5 28.3 28.1 15,976 15,999 16,070 16,114 16,159 16,172 16,196 16,237 16,363 16,432 16,457 16,490 16,518 16,562 16,659 6,675.6 1,115.3 6,623.5 1,136.8 6,624.1 1,140.4 6,647.9 1,142.9 6,669.3 1,140.5 6,657.9 1,138.7 6,658.1 1,139.2 6,679.8 1,138.4 6,701.4 1,141 .9 6,708.1 1,143.3 6,732.6 1,146.3 6,739.9 1,148.2 6,762.0 1,146.2 6,788.5 1,149.3 6,817.2 1,149.1 837.3 815.6 801 .5 810.6 826.6 815.2 813.3 812.8 818.5 806.3 811 .6 811 .9 815.3 817.7 824.2 1,246.1 1,228.0 1,230.9 1,233.9 1,235.2 1,230.9 1,240.0 1,246.4 1,254.1 1,258.3 1,261 .9 1,264.4 1,269.3 1,274.4 1,282.6 Professional and technical services' .. ... ..... .... .... .......... . Legal services ...... .... ........ . Accounting and bookkeeping services ... .. .. .. . . . .. . . . ..... ... . Architectural and engineering services .. ... ..... .. .. .... ... ... See notes at end of table. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 75 Current Labor Statistics: Labor Force Data 12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted [In thousands] Computer systems design and related services .......... Management and technical consulting services ............ Management of companies and enterprises .. .. ..... .... ... .. Administrative and waste services ........... ... ....... .... . .. Administrative and suooort services' .. .... .... ............... Employment services 1 •••• • . Temoorarv helo services. .. Business suooort services .. Services to buildinas and dwellinas ..... .. .... .... .. Waste management and remediation services ...... Educational and health services ... .. ............ ... .. ..... ... Educational services ............... Health care and social assistance ............ ....... .. ... 2004 2003 Annual average Industry 2002 2003 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. 1,152.8 1,108.3 1,107.0 1,105.7 1,105.7 1,104.6 1,099 .8 1,103.5 1,103.5 1,110.1 1,1 17.7 1,120.5 1,129.7 1,136.4 Oct.P 1,143.3 734.4 747.3 755.6 760.6 764.0 765.4 767.9 774.0 780.9 785.9 791.4 792 .2 794.3 795.9 798.6 1,705.4 1,675.5 1,669.1 1,671.6 1,670.2 1,675.1 1,675.6 1,676.6 1,679.7 1,683.3 1,684.5 1,685.9 1,682.5 1,677.2 1,677.9 7,595.2 7,698.3 7,776.3 7,794.5 7,819.2 7,838.5 7,862.4 7,880.1 7,982.3 8,040.1 8,040.0 8,064.3 8,073.0 8,096.1 8,162.2 7,276.8 73,764.0 7,456.0 7,473.7 7,496.3 7,517.5 7,539.6 7,556.8 7,657.0 7,715.6 7,713.0 7,738.1 7,746.6 7,770.2 7,837.3 3,246.5 3,336.2 3,402.0 3,427.6 3,461 .3 3,473.8 3,493.8 3,492.3 3,553.7 3,591 .5 3,573.4 3,606.8 3,607.8 3,641 .1 3,695.8 2.193.7 756.6 2.243.2 747.4 2.291 .7 753.2 2.319.4 746.7 2.355.3 745.1 2,344.3 739.0 2.370.4 739.8 2.380.3 746.0 2.423.8 748.6 2.451 .7 751 .2 2.449.4 754.0 2.460.2 749.9 2.474.7 751 .5 2.508.2 745.7 2.555.8 750.8 1.606.1 1.631 .7 1.639.6 1.639.4 1.635.9 1.637.1 1.639.5 1.646.2 1.674.5 1.686.0 1.694.1 1.691 .5 1.691 .6 1.690.4 1.690.4 318.3 321.9 320.3 320.8 322.9 321 322.8 323.3 325.3 324.5 327 326.2 326.4 325.9 326.9 16,199 2,642.8 16,577 2,688.5 16,678 2,707.7 16,705 2,723.1 16,731 2,728.0 16,746 2,729.3 16,764 2,727.4 16,813 2,736.0 16,854 2,740.8 16,871 2,731.1 16,897 2,727.4 16,901 2,731 .2 16,965 2,746.4 16,984 1,756.4 17,046 2,777.9 13,555.7 13,888.0 13,970.0 13,981 .5 14,003.2 14,077.1 14,113.1 14,140.1 14,169.8 14,169.3 14,218.3 14,227.9 14268.5 4,633.2 1,967.8 413.0 679.8 4,776.0 2,003.8 423.1 727.1 4,812.8 2,018.5 423.3 737.7 4,818.7 2,023.3 426.4 735.7 4,831 .0 2,030.0 425.0 739.9 4,840.3 2,032.3 427.8 740.2 4,855 .3 2,034.4 431 .1 741 .5 4,868.0 2,043.5 430.3 743.8 4,883.6 2,046.1 432.2 748.4 4,896.8 2,049.6 435.1 751 .7 4,909.6 2,053.9 436.0 754.2 4,920.8 2,057.5 437.6 756.8 4,935.1 2,062.1 438.0 760.1 4,939.3 2,068.5 437.0 760.7 4,961.4 2,076.6 437.5 765.4 4,159.6 4,252.5 4,268.9 4,278.1 4,283.9 4,287.8 4,284.1 4,298.0 4,305. 1 4,315.4 4,318.3 4,322.0 4,330.5 4,332.0 4,337.6 2,743.3 2,784.3 2,794.2 2,792.1 2,791.1 2,798.4 2,802.8 2,806.3 1.585.2 2,094.1 1.581 .7 2,095.3 1.580.3 2,096.9 1.578.7 2,106.3 1.582.1 2 ,1 12.7 1.584.0 2,121 .6 1.585.3 2,121 .6 2,809.0 1.586.5 2,132.9 2,812.0 1.582.8 2,075.2 760.5 12,128 2,792.8 1.584.1 2,091 .9 2,793.0 1.573.2 2,019.7 744.1 11 ,986 2,814.0 1.586.3 2,138.7 2,819.5 1.586.2 2,137.1 2,821.4 1.586.3 2,148.1 14,017.1 14,036.8 Ambulatorv health care services' .................... ..... Offices of physicians .......... . Outpatient care centers ..... .. Home health care services ... Hospitals ..... ....................... Sept.P Nursina and residential r:~rA f~r:il itiA~ 1 Nursina care facilities .......... 1 Social assistance ••••••••• ...... Child day care services .. 1.586.7 2,114.5 788.8 792.7 782.8 752.1 786 777.1 777.6 773.7 772.2 766.3 770 766.3 771 .6 12,364 12,351 12,341 12,344 12,339 12,331 12,271 12,303 12,229 12,218 12,178 12,192 12,147 Leisure and hospitality ...... ... .. Arts, entertainment, 1,786.3 1,792.7 1,785.6 1,791 .9 1,792.0 1,793.1 1,791 .1 1,798.7 1,796.7 1,801.4 1,795.2 1,799.4 1,796.9 1,801 .0 1,782.6 and recreation ......... Performing arts and 363.8 363.2 356.0 357.1 359.3 358.8 361 .4 364.6 366.5 369.4 368.8 371.7 369.6 370.2 363.7 spectator sports ...... ·· ···· ·· Museums, historical sites, 116.1 116.3 116.7 116.6 116.1 115.6 114.6 114.2 113.7 113.4 113.1 113.3 114.2 114.1 114.0 zoos, and parks .................. Amusements, gambling, and ........ 1,305.0 1,316.6 1,313.1 1,314.4 1,313.3 1,318.6 1,316.5 1,3 19.9 1,315.1 1,318.7 1,316.6 1,318.2 1,312.9 1,313.2 1,306.4 recreation .. .. ... .. ... Accommodations and food services .. ········· ...... .... 10,203.2 10,324.4 10,350.4 10,378.9 10,396.3 10,416.5 10,432.3 10,742.0 10,511 .8 105,837.9 10,546.7 10,551 .7 10,555.6 10,558.1 10,577.9 1,766.4 1,765.3 1,767.9 1,764.4 1,764.7 1,758.5 1,758.5 1,753.4 1,754.4 1,752.1 1,763.0 1,751 .7 1,733.7 1,765.2 1,778.6 Accommodations ·· ·· · · ······· · ··· Food services and drinking .5 8,811 .8 8,792 8,787.7 8,787.7 8,782.0 8,779.4 8,753.3 8,718.6 .9 8,677 8,664.4 8,633.3 8,627.2 8,616.7 8,559.2 8,424.6 places .......... . ... .. .. ....... 5,411 5,410 5,414 5,414 5,418 5,407 5,404 5,391 5,376 5,374 5,379 5,382 5,387 5,393 5,372 Other services ...... ....... ........... 1,238.4 1,236.8 1,235.2 1,236.3 1,235.1 1,237.7 1,238.2 1,239.4 1,230.5 1,233.5 1,228.5 1,234.4 1,237.6 1,236.2 Repair and maintenance ..... ... 1,246.9 1,253.8 1,254.1 1,259.9 1,262.1 1,268.4 1,265.5 1,260.5 1,255.9 1,247.6 1,251.2 1,250.2 1,254.1 1,254.6 1,257.2 1,258.2 Personal and laundry services Membership associations and 2,919.2 5,919.2 2,919.1 2,915.9 2,914.9 2,903.7 2,904.8 2,895.2 2,898.3 2,894.5 2,895.7 2,893.9 2,895.2 2,898.0 2,867.8 organizations .................. ... 21,714 21 ,673 21,629 21 ,572 21 ,528 21 ,544 21,572 21,553 21,539 21 ,544 21 ,527 21,544 21 ,560 21,575 21,513 Government. ............................... 2,704 2,710 2,712 2,710 2,716 2,712 2,727 2,710 2,716 2,715 2,720 2,736 2,723 2,767 2,756 Federal.. .. ..... ...... ....................... Federal , except U.S. Postal 1,920.9 1,926.3 1,926.3 1,922.5 1,930.5 1,925.7 1,939.5 1,921.1 1,923.8 1,921 .5 1,928.9 1,924.9 1,932.9 1,947.0 Service ......... ... ............. ....... 1,923.8 782 .9 784.0 785.3 787.2 785.4 786.5 787.3 789.1 791.7 793.1 791.4 798.1 803.3 809.1 842.4 U.S. Postal Service .. ... .. . .. .. . .. 5,065 5,052 5,035 5,019 5,004 5,004 5,019 5,023 5,018 5,027 5,007 5,031 5,023 5,017 5,029 State ......................................... 2,312.5 2,302.3 2,285.2 2,271 .1 2,257.8 2,261.4 2,278.3 2,283.2 2,279.6 2,268.0 2,285.7 2,282.5 2,290.4 2,266.4 Education ....... ................ .. ..... 2,242.8 2,752.2 1,749.2 2,749.4 2,747.8 2,746.1 2,742.8 2,740.6 2,739.7 2,738.4 2,738.9 2,740.9 2,740.0 2,740.4 2,750.7 2,786.3 Other State government ........ 13,945 13,911 13,882 13,843 13,808 13,828 13,826 13,820 13,805 13,805 13,797 13,798 13,793 13,802 Local. .. ............ ... ........ ..... .. ... ... .. 13,718 7,810.6 7,778.2 7,758.4 7,715.7 7,695.1 7,710.2 7,710.9 7,704.7 7,694.3 7,692.2 7,687.1 7,684.5 7,687.0 7,699.1 Education ......... ........ .. ... ... . ... . 7,654.4 6,133.9 6,132.7 6,123.2 6,116.8 6,113.3 6,117 .9 6,115.4 6,114.8 6,110.8 6,112 .7 6,113.1 6,109.7 6,105.9 6,104 0 6,063.2 Other local government... ..... • • • • • • • • • I • • 1 includes other industries not shown separately. pg preliminary. Classificat ion System (NAICS), replacing the Standard lndu~trial Classification (SIC) system . NAICS-based data by industry are not comparable with sic-based data. See "Notes on the NoTE: Data reflect the conversion to the 2002 version of the North American industry data" for a description of the most recent benchmark revision . Monthly Labor Review 76 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 13. Average weekly hours of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry, monthly data seasonally adjusted Industry 2003 Annual average 2002 2003 2004 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.P Oct.P TOTAL PRIVATE ............................. 33.9 33.7 33.7 33.8 33.6 33.8 33 .8 33.8 33.7 33.8 33.6 33.8 33.7 33.8 33.8 GOODS-PRODUCING .. ... .. ................... 39.9 39.8 39.9 40.1 39.9 40.2 40 .3 40.2 40.0 40.3 40.0 40.1 40.1 40.1 40.0 43.9 44.1 44.4 44.6 45.1 Natural resources and mining .... ........ 43.2 43.6 43.7 43.9 43.6 44.5 44.1 44.2 44.3 44.2 Construction ........................... .... ..... 38.4 38.4 38.4 38.5 38.1 38.5 38.5 38.6 38.2 38.3 38.1 38.4 38.1 38.3 38.3 Manufacturing .................................... Overtime hours ................................. 40.5 4.2 40.4 4.2 40.5 4.3 40.8 4.5 40.6 4.5 41.0 4.5 41.0 4.6 40.9 4.6 40.7 4.5 41.1 4.6 40.8 4.6 40.9 4.6 40.9 4.6 40.8 4.6 40.7 4.5 Durable goods .... ... ...... ..... .... ........ ... . Overtime hours .......................... ... .. .. Wood products ................................... Nonmetallic mineral products ... .......... Primary metals ............. .. ... ... ... ............ Fabricated metal products ............... ... Machinery ......... ..... .. .. .... .............. Computer and electronic products .. ... Electrical equipment and appliances .. Transportation equipment.. ................. Furniture and related products .......... Miscellaneous manufacturing ............. 40.8 4.2 39.9 42.0 42.4 40.6 40.5 39.7 40.1 42 .5 39.2 38.6 40.8 4.3 40.4 42.2 42.3 40 .7 40.8 40.4 40.6 41.9 38.9 38.4 40.9 4.4 40.6 42.1 42.3 40.8 40.9 40.7 40.9 41 .9 39.1 38.3 41.3 4.7 41.2 42 .4 42 .7 40 .9 41 .1 40.7 40.8 42.7 39.9 38.9 41 .2 4.7 41 .0 42.3 42.7 40.8 41 .1 40.4 40.7 42.7 39.7 38.5 41 .5 4.7 40.9 42.5 43.1 41 .2 41 .8 40.8 41.1 42.8 39.7 39.0 41 .5 4.8 41.1 42.5 43.0 41 .2 41 .8 41.2 40.7 42.9 39.4 38.7 41 .4 4.8 41.0 42.9 43.2 41.1 41.7 40.7 40.8 42 .8 39.6 38.7 41.2 4.7 41.0 42.3 43.1 41.0 41 .6 40.5 40.8 42.4 39.5 38.3 41.6 4.8 41.4 42.0 43.4 41.3 42.3 40.8 41.6 42.8 40 .0 38.9 41 .2 4.7 40.5 41 .8 43.5 41.0 42 .0 40.5 40.8 42.3 39.7 38.4 41 .3 4.7 40.7 42.1 43.3 41 .2 42 .0 40.9 40.8 42.4 39.4 38.5 41.3 4.7 40.9 42.3 43.3 41 .2 42 .1 40.5 41.0 42.5 39.5 38.5 41 .3 4.7 40.4 42.4 43.1 41 .2 42.3 40.4 40.7 42.4 39.3 38.3 41.2 4.7 40.3 42 .3 43.0 41 .1 42.2 40.3 40.8 42.3 39.0 38.3 Nondurable goods .............. .................. Overtime hours ......... ...................... .. Food manufacturing ................ ... ......... Beverage and tobacco products ....... .. Textile mills ........ ... .......... ............. Textile product mills .............. ... ... ... Apparel. ............. .. ......... .. ..................... Leather and allied products ....... .. ..... .. Paper and paper products ....... ....... . Printing and related support activities ...... ...... ........... ..................... Petroleum and coal products ... ...... ... Chemicals ... .. ... ...................... ... .. . Plastics and rubber products ... ..... ... . 40.1 4.2 39.6 39.4 40.6 39.2 36.7 37.5 41 .8 39.8 4.1 39.3 39.1 39.1 39.6 35.6 39.3 42.1 39.9 4.1 39.3 38.8 39.1 40.4 35.8 38.9 41.5 40.1 4.3 39.2 39.9 40.0 40.0 36.2 39.3 41 ..9 39.9 4.2 39.1 39.1 39.7 39.8 35.8 40.3 41 .8 40 .2 4.3 39.5 39.6 .40.0 39.4 35.7 39.8 41 .9 40 .3 4.3 39.4 40 .3 40 .0 39 .9 36 .2 39.5 42.0 40.1 4.3 39.3 39.4 40.2 38.8 36.3 39.4 41.8 40.0 4.3 39.1 39.6 39.5 38.3 35.9 39.1 41 .9 40.3 4.4 39.6 39.2 40.3 38.8 36.1 38.4 42.6 40 .1 4.4 39.4 38.7 40.3 38.9 35.9 38.0 42.0 40.1 4.4 39.3 39.2 40.5 38.5 36.1 37.2 42.4 40 .2 4.4 39.3 39.5 40.5 38.7 36.1 37.8 42 .5 40.1 4.4 39.5 39.1 40 .2 38.9 36.1 37.8 42.2 39.9 4.2 39.2 38.2 40.0 38.9 35.6 37.7 42.0 38.4 43.0 42.3 40.6 38.2 44.5 42 .4 40.4 38.5 44.9 42 .0 40.6 38.4 45.6 42.7 40.7 38.2 44.2 42.5 40.4 38.6 43.8 42.9 40.8 38.6 44.1 43.2 40.9 38.4 43.7 43.0 40.9 38.4 43.9 43.0 40.7 38.6 45.0 42.9 40.9 38.5 45.0 42 .6 40 .8 38.6 45.0 42.8 40.5 38.5 46.3 42.7 40.5 38.3 45.9 42.7 40.2 38.2 45.1 42.7 40.2 32.5 32 .4 32.3 32.4 32.2 32 .4 32 .4 32.4 32.3 32.4 32 .3 32.4 32.4 32.5 32.4 33.6 38.0 30.9 36.8 40.9 36.5 ~5.6 33.5 37.8 30 .9 36.9 41 .1 36.2 35.5 33.6 38.0 30.9 37.1 41 .0 36.1 35.5 33.6 38.0 30 .9 37.0 41.4 36.3 35.5 33.5 37.8 30.8 36.7 40.8 36.2 35.3 33.6 37.9 31 .0 36.9 40.8 36.2 35.7 33.7 38.0 30 .9 37 .2 41 .0 36.3 35.5 33.6 38.0 30.8 36.9 41.2 36.3 35.5 33.5 38.0 30.7 36.9 41.2 36.3 35.6 33.5 37.8 30.7 37.3 41.3 36.4 35.8 33.3 37.6 30.5 36.9 41.1 36.5 35.5 33.4 37.8 30.6 37.1 41.0 36.4 35.6 33.5 37.6 30 .7 37.2 40.9 36.4 35.5 33.6 37.8 30.8 37.3 41 .4 36.3 35.5 33.6 37.7 30 .8 37.3 40.7 36.2 35.6 34.2 32 .4 25.8 32 .0 34.1 32.3 25.6 31.4 34.0 32.3 25.6 31 .3 34.1 32.4 25.7 31.2 33.8 32.4 25.6 31 .0 34.1 32.4 25.7 31.1 34.2 32 .4 25.8 31.1 34.1 32.4 25.7 31 .2 34.1 32.4 25.7 31 .1 34.2 32.5 25.7 31 .2 33.9 32.5 25.7 31 .0 34.2 32.6 25.6 31 .1 34.2 32.5 25.5 31 .1 34.5 32 .5 25.5 31 .1 34.3 32.6 25.6 31 .1 PRIVATE SERVICEPROVIDING .................... ... .... ... .... Trade, transportation, and utilities .............................................. Wholesale trade ......... ....... .. .... ... ... ... .. Retail trade .................................. .. Transportation and warehousing .... .. .. Utilities .... ... ....... .. ... ........ ..... ... ... .. .. Information ... ... ........................ ......... Financial activities ............................ Professional and business services .. ...... ..... ...... ...................... Education and health services ............ Leisure and hospitality ...................... Other services ...................................... 1 Data relate to production workers in natural resources and mining and manu- NOTE: Data reflect the conversion to the 2002 version of the North American facturing , construction workers in construction, and nonsupervisory workers in the Industry Classification System (NAICS), replacing the Standard industrial Classification service-providing industries. (SIC) system . NAICS-based data by industry are not comparable with SIC-based data. See "Notes on the data" for a description of the most recent benchmark revision. p = preliminary. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 77 Current Labor Statistics: Labor Force Data 1 14. Average hourly earnings of production or nonsupervisory workers on private nonfarm payrolls, by industry, monthly data seasonally adjusted 2004 Annual average Industry 2002 2003 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.P Oct.P TOTAL PRIVATE Current dollars .. ... ....... ...... ....... Constant (1982) dollars .. ... ........ . $14.95 8.24 $15.35 8.27 $15.43 8.28 $15.46 $15 .45 8.30 $15.49 8.27 $15.52 8.27 $15.55 8.24 $15.59 8.25 $15.63 8.21 $15.66 8.20 $15.71 8.23 8.23 $15.76 8.26 $15.78 8.26 $15.83 8.25 GOODS-PRODUCING ............................. 16.33 16.80 16.90 16.94 16.97 17.00 17.06 17.08 17.13 17.13 17.16 17.19 17.24 17.30 17.31 Natural resources and mining ............. Construction ......................................... Manufacturing ....................................... Exduding overtime ............. .. ... ....... Durable goods .... .. ...... .. ... .... ....... ... Nondurable goods .. .... ......... ........ ... 17.19 18.52 15.29 14.54 16.02 14.15 17.58 18.95 15.74 14.96 16.46 14.63 17.72 19.06 15.83 15.03 16.54 14.72 17.79 19.06 15.89 15.06 16.58 14.79 17.91 19.04 15.93 15.09 16.64 14.81 17.95 19.11 15.94 15.11 16.63 14.85 18.01 19.18 15.99 15.14 16.68 14.89 18.10 19.17 16.01 15.16 16.69 14.93 18.08 19.20 16.08 15.24 16.75 15.00 18.10 19.20 16.08 15.23 16.75 15.02 18.24 19.19 16.13 15.27 16.78 15.08 18.15 19.22 16.16 15.30 16.81 15.12 18.12 19.25 16.23 15.37 16.90 15.15 18.09 19.28 16.30 15.43 16.99 15.19 18.20 19.33 16.37 15.42 16.98 15.12 PRIVATE SERVICE· PROVIDING ................... .... .. ........ .. ..... 14.56 14 .96 15.03 15.06 15.05 15.08 15.10 15.13 15.17 15.23 15.26 15.31 15.36 15.38 15.43 14.02 16.98 11 .67 15.76 23.96 20.20 16.17 14.34 17.36 11 .90 16.25 24 .76 21 .01 17.13 14.41 17.47 11 .95 16.32 25.17 21 .21 17.29 14.44 17.47 11.97 16.35 25.36 21 .10 17.30 14.41 17.46 11 .95 16.33 25 .13 20 .99 17.30 14.45 17.53 11 .95 16.46 25 .32 21 .15 17.35 14.49 17.54 11 .98 16.52 25.35 21.24 17.32 14.50 17.54 11 .99 16.53 25.38 21 .25 17.41 14.57 17.60 12.01 16.71 25.67 21 .29 17.46 14.61 17.63 12.06 16.75 25.46 21 .42 17.49 14.65 17.67 12.10 16.82 25.44 21 .30 17.50 14.70 17.71 12.12 16.89 25.57 21.45 17.55 14.73 17.70 12.16 16.99 25.54 21 .53 17.58 14.75 17.76 12.16 16.95 25.73 21 .61 17.62 14.76 17.82 12.16 17.01 25.75 21 .59 17.73 16.81 17.20 17.25 17.29 17.25 17.24 17.25 17.27 17.29 17.36 17.42 17.44 17.56 17.52 17.64 15.21 8.58 13.72 15.64 8.76 13.84 15.73 8.78 13.80 15.77 8.82 13.81 15.81 8.84 13.80 15.87 8.85 13.84 15.90 8.86 13.84 15.96 8.87 13.87 15.99 8.86 13.84 16.06 8.86 13.85 16.12 8.85 13.88 16.18 8.87 13.90 16.19 8.91 13.92 16.22 8.95 13.96 16.24 9.00 13.98 Trade,transportatlon, and utilities ........................................ Wholesale trade .................................. Retail trade ......................................... Transportation and warehousing ....... Utilities ..... ................. .................. . Information ............................................ Financial activities................................ Profe~slonal and business services............................................... Education and health services............................................... Leisure and hospitality........................ Other services....................................... 1 Data relate to production workers in natural resources and mining and manufac- NOTE: Data reflect the conversion to the 2002 version of the North American industry turing, construction workers in construction , and nonsupervisory workers in the Classification System (N AICS) , replacing the Standard Industrial Classification (SIC) system. NAICS service-providing industries. based data by industry are not comparable with SIC-based data. See "Notes on the data" for a p = preliminary. description of the most recent benchmark revision. 78 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 15. Average hourly earnings of production or nonsupervisory work~rs 1 on private nonfarm payrolls, by industry Industry TOTAL PRIVATE ........................... Seasonally adjusted ... ..... ... ....... Annual average 2002 2003 $14 .95 $15.35 15.18 15.47 2003 2004 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.P Oct.P $15.42 15.41 $15.52 15.43 $15.48 15.45 $15.56 15.49 $15.60 15.52 $15.55 15.55 $15.59 15.59 $15.63 15.63 $15.57 15.66 $15.59 15.71 $15.67 15.76 $15.80 15.78 $15.84 15.78 GOODS-PRODUCING ................... .......... 16.33 16.8 16.95 16.98 17.03 16.94 16.95 17.00 17.09 17.10 17.14 17.18 17.28 17.41 17 .37 Natural resources and mining ........... 17.19 17.58 17.69 17.15 17.97 18.00 18.05 18.17 18.14 18.06 18.18 18.07 18.01 18.03 18.18 Construction ................................... .. .. 18.52 18.95 19.13 19.08 19.19 19.01 19.07 19.07 19.15 19.15 19.12 19.25 19.33 19.42 19.46 Manufacturing .............................. .. . 15.29 15.74 15.81 15.92 16.05 15.98 15.99 16.01 16.07 16.05 16.09 16.04 16.17 16.37 16.25 Durable goods ... ···· ······ ···· ··· ··········· Wood products ............. .. ... ... ..... ... .. .. Nonmetallic mineral products .. .. . . . . Primary metals ....... .............. ...... ...... !" dbricated metal products .. ............. M.achinery ...... ............ ... ... ....... ... Computer and electronic products ... Electrical equipment and appliances Transportation equipment ............. .. . Furniture and related products ..... .... Miscellaneous manufacturing ... ..... .. 16.02 12.33 15.40 17.68 14.68 15.92 16.20 13.98 20.64 12.61 12.91 16.46 12.71 15.77 18.13 15.01 16.30 16.68 14.35 21 .25 12.98 13.30 16.55 12.82 15.95 18.25 15.03 16.35 16.77 14.37 21 .35 13.01 13.47 16.64 12.95 15.99 18.32 15.06 16.49 16.78 14.54 21 .48 13.08 13.53 16.78 12.93 15.98 18.39 15.23 16.62 16.85 14.68 21.74 13.08 13.60 16.66 12.90 16.03 18.39 15.20 16.53 16.81 14.50 21 .38 12.95 13.68 16.68 12.91 16.00 18.36 15.18 16.50 16.92 14.58 21.37 12.92 13.75 16.69 12.93 16.02 18.33 15.25 16.49 16.93 14.68 21 .34 12.96 13.78 16.72 13.00 16.19 18.52 15.21 16.53 17.01 14.80 21.36 13.09 13.70 16.71 13.03 16.18 18.48 15.20 16.53 17.11 14.83 21.29 13.04 13.76 16.75 12.98 16.24 18.51 15.23 16.56 17.21 14.88 21.36 13.10 13.81 16.61 13.03 16.38 18.66 15.26 16.68 17.29 14.88 20.77 13.11 13.89 16.85 1::i.01 16.29 18.58 15.27 16.72 17.37 14.98 21 .54 13.27 13.87 17.08 13.13 16.51 18.91 15.43 16.83 17.45 15.03 21 .98 13.37 13.97 16.99 13.03 16.33 18.68 15.4 1 16.82 17.46 14.93 21 .86 13.25 13.90 Nondurable goods ..... ... .. ......... ..... . Food manufacturing ... ... ......... ..... Beverages and tobacco products .... 14.15 12.55 1; 73 14.63 12.80 17.96 14.67 12.77 18.05 14.80 12.91 18.64 14.88 12.95 18.58 14.89 12.91 18.88 14.88 12.87 18.76 14.90 12.89 19.13 15.01 12.96 19.60 14.98 12.94 19.55 15.03 13.00 19.39 15.14 13.05 19.29 15.09 12.99 19.10 15.24 13.08 19.16 15.08 12.81 19.15 Textile mills ....... ... ..... .. ............ ..... .... Textile product mills ............ .... ....... .. Apparel .......... ......... ... ................ ... .. . Leather and allied products .. ..... Paper and paper products ... ... ... .... Printing and related support activitiei Petroleum and coal products ..... ... .. Chemicals ......... .... .. .. .... ...... ... ... . Plastics and rubber products ........... 11.73 10.96 9.10 11 .00 16.85 14.93 12.00 11 .24 9.56 11 .67 17.32 12.02 11 .37 9.69 11 .83 17.44 15.41 12.21 11 .44 9.80 11.90 17.60 15.56 12.11 11.45 9.74 11 .94 17.63 15.53 12.13 11 .40 9.58 11.76 17.55 15.57 12.09 11 .37 9.60 11 .64 17.59 15.61 12.23 11 .33 9.71 11 .65 17.84 15.54 12.24 11 .53 9.78 11 .55 18.20 15.97 12.17 11 .50 9.86 11 .75 17.98 15.99 24.00 18.77 14.27 24.06 18.79 14.47 24.13 18.83 14.43 24.32 18.85 14.45 24.82 18.87 14.45 24.48 19.02 14.58 12.15 11 .29 9.60 11 .59 17.86 15.54 24.24 19.20 14.59 12.08 11 .46 9.73 11 .68 17.84 15.86 23.63 18.66 14.19 12.08 11 .30 9.55 11.49 17.88 15.51 24.41 19.05 14.55 12.07 11.48 9.74 11 .68 17.91 15.71 23.04 17.97 13.55 15.37 23.64 18.52 14.18 12.08 11.35 9.71 11 .87 17.58 15.48 24.35 19.36 14.69 24.07 19.29 14.66 24.52 19.51 14.75 24.34 19.45 14.51 PRIVATE SERVICEPROVIDING ..... ....... ... ... ........ .......... 14.56 14.96 15.01 15.13 15.07 15.1 9 15.24 15.16 15.20 15.24 15.14 15.17 15.24 15.37 15.42 Trade, transportation, and utilities ...... .. ..... ........................ .......... Wholesale trade .... .... .. ........ ... ...... . Retail trade .. ... ............ ... ... ..... ..... . Transportation and warehousing ... ... Utilities ........... ......... .. ......... ..... .. ... 14.02 16.98 11.67 15.76 23.96 14.34 17.36 11 .90 16.25 24.76 14.38 17.42 11 .91 16.31 14.44 25.23 17.56 11 .92 16.40 25.50 14.31 17.46 11 .87 16.33 25.26 14.50 17.56 11.98 16.46 25.38 14.58 17.60 12.04 16.58 25.29 14.53 17.47 12.03 16.51 25.36 14.64 17.60 12.08 16.73 25.69 14.64 17.67 12.08 16.72 25.53 14.61 17.58 12.09 16.80 25.33 14.62 17.66 12.07 16.86 25.43 14.66 17.69 12.09 16.98 25.33 14.79 17.74 12.23 16.94 25.89 14.76 177.80 12.16 17.02 25.77 20.20 21.01 21 .25 21 .28 21.10 21 .21 21 .28 21 .17 21 .24 21.41 21 .18 21 .30 21 .44 21 .73 21 .69 16.17 17.13 17.25 17.42 17.26 17.35 17.47 17.37 17.45 17.62 17.38 17.44 17.58 17.60 17.72 16.81 17.20 17.13 17.41 17.29 17.38 17.47 17.28 17.26 17.45 17.28 17.31 17.46 17.43 17.55 16.23 Financial activities ...... .. ..... ......... ....... Professional and business services .............................. ......... . Education and health services .................................... ... 15.21 15.64 15.73 15.79 15.86 15.94 15.95 15.94 15.99 16.00 16.06 16.18 16.16 16.24 Leisure and hospitality .................... 8.58 8.76 8.78 8.83 8.94 8.89 8.92 8.89 8.84 8.85 8.78 8.78 8.80 8.94 9.03 Other services ................................... 13.72 13.84 13.78 13.85 13.88 13.89 13.90 13.85 13.87 13.90 13.82 13.78 13.84 13.98 13.97 1 Data relate to production workers in natural resources and mining and NOTE: Data reflect the conversion to the 2002 version of the North American Industry manufacturing, construction workers in constriJction, and nonsupervisory workers in Classification System (NAICS), replaci ng the Standard Industrial Classification (SIC) the service-providing industries. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis system. NAICS-based data by industry are not comparable with sic-based data. See "Notes on the data" for a description of the most recent benchmark revision . Monthly Labor Review December 2004 79 Current Labor Statistics: Labor Force Data 16. Average weekly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry Industry Annual average Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept.P Oct.P $517.36 - $519.65 519.99 $527.68 522.55 $520.13 519.12 $518.15 523.56 $527.28 524.58 $520.93 525.59 $522.27 525.38 $531 .42 528.29 $524.71 526.18 $528.50 531 .00 $535.91 531 .11 $530.88 533.36 $535.39 535.05 669.23 681 .39 684.29 682.90 674.21 674.61 681 .70 678.47 690.84 689.03 687.20 698.11 691 .18 698.27 711 .82 618.75 766.83 727.11 636.07 778.36 744.16 643.47 784.55 730.76 655.90 781 .70 714.34 662.87 784.80 712.88 650.39 786.98 711.31 652.39 797.66 732.29 653.21 794.53 721 .96 652.44 798.25 741.11 659.66 809.01 738.03 659.69 802.31 754.60 646.41 806.85 755.80 661.35 796.93 730.19 664.62 829.01 755.05 661 .38 652.97 492.00 646.91 749.32 596.38 645.55 671 .53 513.92 665. 11 767.63 610.33 664 .79 680.2 1 525.62 679.47 771 .98 616.23 667.08 692.22 537.43 681 .17 785.93 621 .98 682.69 703.08 531.42 669.56 799.97 635.09 696.38 688.06 517.29 663.64 796.29 626.24 689.30 688.88 521 .56 664.00 787.64 623.90 691 .35 690.97 524 .96 680.85 790.02 625.25 690.93 687.19 530.40 684.84 800.06 620.27 987.65 695.14 544.65 684.41 803.88 627.76 700.87 695.13 533.48 690.20 808.89 627.48 698.83 674.37 531 .62 694.51 791 .18 621 .08 692.22 695.91 538.61 700.47 798.94 627.60 697.22 698.91 521 .26 708.28 809.35 628.00 698.45 699.99 527.72 697.29 799.50 634.89 708.12 642.87 674.68 684.22 693.01 695.91 680.81 695.41 690.74 683.80 694 .67 698.73 696.79 700.01 701 .49 703.64 560.24 877.87 582.68 890.32 592.04 905.24 601 .96 925.79 616.56 950.04 594.50 915.06 591 .95 916.77 596.01 917.62 599.40 905.66 613.96 915.47 611 .57 912.07 599.66 841 .19 611 .21 911 .14 604.21 929.75 613.62 926.86 494 .01 505.23 508.69 523.20 528.43 510.23 505.17 510.62 517.06 517.69 521 .38 515.22 529.47 518.76 515.43 - 651 .61 Natural resources and mining ... ...... .. ..... .......... ... 741 .97 Construction .......................... Manufacturing ..................... ... Durable goods .. .. .... .... .... ....... . Wood products .............. .. ..... .... Nonmetallic mineral products ... . Primary metals ... ........ ... .... ... Fabricated metal products ... .... . Machinery ................... ... ..... . Computer and electronic products .. .............. ... ....... ........ Electrical equipment and appliances ................. .... .......... Transportation equipment.. ... ... Furniture and related products .. ..... ..... ........ ......... Miscellaneous manufacturing ........... .. .... .. .... .. 499.13 510.69 515.90 530.38 533.12 532.15 533.50 534.66 524.71 535.26 530.30 527.82 534.00 529.46 533.76 566.84 Food manufacturing .... .... ... .... ... 496.91 582.65 502.61 588.27 505.69 600.88 515.11 602.64 514.12 594.11 504.78 595.20 499.36 596.00 498.84 595.90 497.66 602.20 511.13 604.21 512.20 602.57 512.87 606.62 514 .40 611 .12 521 .89 603.20 507.28 698.39 476.52 429.01 333.66 412 .99 705.62 702.75 469.47 445.08 340.22 458.26 719.21 707.56 469.98 458.21 348.84 462.55 727.25 751 .19 485.62 456.27 356.36 465.30 743.63 722.76 490.84 464.46 352.80 485.52 751 .52 728.77 485.61 447.70 343.82 471 .63 738.70 737.27 486.41 450.30 345.84 464.52 731 .84 744.16 490 .85 441 .16 350.40 464.44 731 .74 780.08 484 .31 435.07 347.76 460.18 745.71 774.18 486.82 436.18 346.67 441 .22 756.32 760.09 490.86 444.83 348.48 442.74 748.33 760.03 481.59 435.09 348.69 422.82 750.43 762.09 489.24 443.50 353.20 441.50 754.63 764.48 487.15 445.06 346.21 429.66 773.50 725.79 484.37 448.50 350.03 442.98 756.96 573.05 587.42 597.91 603.72 602.17 593.25 597.89 600.99 593.63 594.03 593.63 600.12 610.61 611 .65 615.62 Nondurable goods...... ..... ......... ... Beverages and tobacco products .......... .............. ..... ... .. Textile mills ....... .. .... ··· ··· ·· ··· · Textile product mills .... ... ... .... . Apparel. .... .... .... .... ... ..... ..... .. Leather and allied products ...... . Paper and paper products . .. ... Printing and related support activities .. .. .... .. ....... . Petroleum and coal products ...... ... .... ....... ... .. ... . Ci,t:imicals ... ..... ......... .... ... .... Plastics and rubber products ... .. .... . . . . . . . . . . . .. . . . . . . 990.88 759.53 1,052.97 1,068.08 1,099.20 1,061 .05 1,068.96 1,074.94 1,079.67 1,062.43 1,091 .13 1,095.65 1,120.10 1,097.59 1,125.47 1,102.60 831 .13 824.68 811.41 814.06 815.34 819.84 816.99 823.68 808.99 806.09 804.04 816.21 784.56 785.59 549.85 572.23 578.95 586.50 596.16 585.86 588.12 589.56 594 .86 595.10 599.65 583.19 589.33 590.00 583.30 PRIVATE SERVICEPROVIDING ..... ........................... 472.88 484 .00 484.82 493.24 485.25 484.56 496.82 486.64 487.92 496.82 489.02 493.03 501 .40 496.45 499.61 Trade, transportation, and utilities ...... ........ .. .... ... ... . Wholesale trade .... .. .................. Retail trade ......... .... .... ... .... .... 471 .27 644 .38 360.81 481.10 657.12 367.28 483.17 661 .96 366.83 486.63 676.06 365.94 480.82 659.99 367.97 477.05 656.74 361 .80 488.43 670.56 368.42 482.40 658.62 365.71 486.05 665.28 367.23 493..37 674.99 372.06 489.44 661.01 372.37 494.46 665.78 376.58 498.44 672.22 378.42 496.94 667.02 377.91 494 .46 668.53 373.31 Transportation and warehousing ........ ... ....... .. ..... Utilities ........ ....... ... ... ... ....... ... 579.75 979.09 1 641 .84 628.47 634.85 627.00 621 .60 627.19 613.46 604 .27 610.65 603.47 615.00 602.58 597.50 597.79 1,016.94 1,039.48 1,068.45 1,028.08 1,032.97 1,039.42 1,039.76 1,053.29 1,054.39 1,046.13 1,032.46 1,030.93 1,074.44 1,053.99 Information ..... ...... ... ............ ... 738.17 761 .13 769.25 783.10 761 .71 763.56 776.72 760.00 764.64 777.18 775.19 773.19 788.99 788.80 785.18 Financial activities ........ .. ... ..... 575.51 608.87 608.93 628.86 607.55 612.10 630.67 611 .42 615.99 637.84 613.51 617.38 634.64 619.52 627.29 Professional and business services .................. 582.67 583.97 602.72 587.52 588.57 603.77 587.52 590.27 604.12 592.62 600.21 574.66 586.68 580.71 597.16 Education and health services ............. .. .... ... 492.74 505.76 506.51 516.33 512.28 514.86 519.97 513.27 516.48 521 .60 520.34 527.47 530.05 526.18 527.48 Leisure and hospitality ............ 221 .26 224 .35 223.89 226.05 223.29 221 .36 230.14 225.80 224 .81 229.22 227.40 230.91 234.08 226.18 230.27 Other services ......... ............... 439.76 434.49 431.31 434 .89 430.28 429.20 433.68 428.73 428.58 435.07 428.42 429.94 434.58 431 .98 434.47 Data relate to production workers in natural resources and mining and manufacturing, Industry Classification System (NAICS), replacing the Standard Industrial Classifification (SIC) construction workers in construction, and nonsupervisory workers in the service- system. NAICS-based data by industry are not comparable with sic-based data. See "Notes on providing industries. the data" for a description of the most recent benchmark revision . NOTE: 80 2004 Oct. TOTAL PRIVATE ...... ,. ........ .... $506.07 Seasonally adjusted.. .. .. .... GOODS-PRODUCING ... ........ ... .. 2003 2003 2002 Data reflect the conversion to the 2002 version of the North American Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Dash indicates data not available. p = preliminary. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 17. Diffusion indexes of employment change, seasonally adjusted [In percent] Timespan and year Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec. Private nonfarm payrolls, 278 industries Over 1-month span : 2000 .............. .. .. ....... ... ... .............. . 2001 ............ .. .. ...... .. ..................... . 2002 ........... ................ ............ ...... . 2003 ........ ........... .. ..................... .. . . 2004 ........ ... ... ... .......... ............ ...... . 61.9 52.2 40.1 41 .2 52.3 62.9 47.8 35.1 35.1 56.1 63.3 50.4 41.0 38.1 68.7 59.5 34.4 41.5 41.4 67.6 46.9 41.4 41.7 42.8 63.8 61.7 39.2 47.8 40.1 60.6 63.1 37.1 44.1 40.5 55.2 52.5 38.8 44.1 39.7 56.3 51.5 38.3 42.8 49.3 59.2 53.4 32.4 39.0 46.0 56.8 56.8 36.7 38.7 51 .1 53.8 34.9 34.5 49.1 69.2 52.7 34.0 66.2 50.4 37.4 67.8 50 .4 35.1 68.3 43.5 36.2 60.1 38.8 36.7 56.3 36.2 39.9 61.5 37.9 40.8 36.5 54.0 32.6 55.2 36.3 62.8 35.1 70.0 40 .5 74.5 58.1 34.9 39.4 42.6 68.7 37.4 64.6 35.4 57.2 56.5 34.7 38.7 40.1 61.0 53.2 35.3 37.1 45.5 58.1 52.9 30.8 34.4 50 .5 56.8 32.0 34.7 51.1 67.3 51 .8 29.5 69.1 50.0 30.0 75.2 51 .8 31 .1 72.5 47.3 31 .1 67.4 43.5 31 .7 33.6 48.9 31.1 54.1 31.7 59.6 31.7 64.7 33.5 67.8 67.8 41.5 37.1 37.8 71.2 66.7 38.1 37.2 36.2 68.3 60.8 35.4 39.0 36.5 71 .6 59.0 32.2 34.7 40.5 67.3 55.0 33.1 36.5 39.4 64.0 59.7 31.5 35.3 42.6 54.0 31.1 33.3 41.7 Over 12-month span : 2000 .............................................. 2001 .............................................. 2002 .............................................. 2003... ..... .. ............... .. ................... 2004. ............................................. 70.9 59.5 33.6 34.5 37.81 73.2 69.2 53.4 59.5 30.2 31.7 32.9 31 .5 43.2 1 47.3 71.0 49.3 30.4 33.5 50.7 69.8 48.6 30.2 36.2 54.9 70.3 70.3 39.9 43.9 31.3 30.0 37.6 33.1 65.3 63.8 industries 65.6 37.8 29.5 37.4 65.6 63.8 37.1 32.9 33.1 62.1 34.9 34.7 35.4 Over 1-month span: 2000........... ............. ...................... 2001 .............................................. 2002........ .. ..... .. ............................. 2003.................. .. ................. .. .. ..... 2004.............................................. 48.2 22 .6 21.4 26.2 42 .9 58.3 22.0 18.5 15.5 55.4 50.0 21.4 23.8 22.6 60.1 50.0 16.1 35.1 13.7 66.1 41 .1 15.5 29.8 26.2 64.9 57.1 23.2 60 .7 13.7 28.6 14.3 25.0 19.0 35.1 17.9 39.9 14.9 41 .1 10.1 32.7 25.0 54.2 40.5 28.0 57.1 28.0 26.2 48.2 31.0 27.4 42.3 11.9 28.6 42.3 15.5 51 .2 17.9 45.8 Over 3-month span : 2000............. .. ............................. .. 2001.. .......................................... .. 2002.............................................. 2003.... .. .. ...................................... 2004.......... .................................... 53.6 35.7 9.5 13.7 48.8 53.6 21 .4 10.1 13.1 51 .8 56.0 16.1 11.3 16.7 59.5 54.8 14.3 17.9 10.1 66.1 44.0 13.1 17.3 13.1 71.4 44.0 13.7 19.0 14.9 65.5 51 .2 11.9 28.0 16.1 65.5 47.6 8.9 22.0 16.1 51.8 32.7 8.3 23.8 16.1 53.0 25.0 13.1 15.5 24.4 42.3 23.2 8.9 6.5 27.4 38.7 10.1 41.7 Over 6-month span: 2000................................. ............. 2001 ........................ .. .... .. ............ .. 2002............. ......................... .. ...... 2003................ .. ........... ................. 2004......................................... .. ... 44.0 22.0 6.5 11 .3 28.6 52.4 23.8 8.9 9.5 36.9 55.4 22 .0 7.7 6.0 46.4 57.7 20.8 8.3 7.1 56.5 47.6 14.3 7.7 8.9 61.3 51.8 13.7 14.3 13.1 64.9 56.0 14.3 14.9 8.9 66.7 45.2 10.1 10.7 13.1 66.1 39.3 10.7 12.5 13.1 58.9 34.5 5.4 10.1 16.7 53.6 32.1 7.1 8.9 19.0 27.4 4.8 8.9 19.6 Over 12-month span: 2000 ....... .. ............. -......... ....... ...... 2001.. .... .. ......... ....................... .. .... 2002 .. .. .. .. ... ... ................................ 2003............ .. ............................ .. .. 2004.. .......................................... .. 41.7 29.8 7.1 10.7 9.5 39.3 32.1 6.0 6.0 19.0 47.0 20.8 6.0 6.5 16.7 50.0 19.0 6.5 5.4 26.2 46.4 13.1 7.1 8.3 29.8 52.4 12.5 3.6 9.5 40.5 51.8 10.7 4.8 9.5 50.0 49.4 11 .9 6.0 9.5 50.6 46.4 11 .9 4.8 10.7 53.6 40.5 10.1 7.1 11.9 56.0 35.1 8.3 4.8 9.5 33.3 6.0 8.3 11.3 Over 3-month span: 2000 .... .... .. ................................... . 2001 ............................................. . 2002 ........ ... ........ .. ........................ . 2003 ................... ....................... ... . 2004 ............................... .. .... .. ..... . . Over 6-month span: 2000 ..... ... .... .. ... ............................ . 2001 ....... .. .. ... ... ................. .. ... ...... . 2002 ..................................... .. ...... . 2003 ............................................. . 2004 ..................................... .. ... ... . NOTE: Figures are the percent of industries with employment increasing plus one-half of the industries with unchanged employment, where 50 percent indicates an equal balance between industries with increasing and decreasing employment. 70.0 71.0 43.3 45.0 32.0 29.1 34.7 34.4 64.0 60.3 Manufacturing payrolls, 84 4.8 See the "Definitions" in this section . See "Notes on the data" for a description of the most recent benchmark revision. Data for the two most recent months are preliminary. Monthly Labor Review December 2004 81 Current Labor Statistics: Labor Force Data 18. Job openings levels and rates by industry and region, seasonally adjusted Levels 1 (in thousands) Industry and region Rates 2004 Apr. Totai2 .... ... ......... ..... ......... .... ............. .. ... May June July 2004 Aug. Sept. Oct.P Apr. May June July Aug. Sept. Oct.P 3,135 3,105 3,022 3,237 3,195 3,294 3,330 2.3 2.3 2.3 2.4 2.4 2.4 2.5 Total private 2 ••• • ••••••••••••••••.• • •• . ••• • ••••••. • 2,778 2,746 2,640 2,894 2,859 2,934 2,950 2.5 2.4 2.3 2.6 2.5 2.6 2.6 Construction ..... ... ... ... ... ..... ... ... ....... 105 108 94 88 121 113 121 1.5 1.5 1.3 1.3 1.7 1.6 1.7 Manufacturing .... ..... .... ..... ..... ..... .. .. 251 244 247 240 234 251 259 1.7 1.7 1.7 1.6 1.6 1.7 1.8 Trade, transportation, and utilities ....... 531 521 503 567 551 591 592 2.0 2.0 1.9 2.2 2.1 2.3 2.3 Professional and business services .... 518 494 583 594 564 585 3.1 3.1 2.9 3.4 3.5 3.3 3.4 Education and health services ....... ... . 576 530 542 496 537 3.3 3.1 2.9 3.1 3.1 3.1 3.1 376 391 421 435 543 425 543 Leisure and hospitality .. .. ... ..... ..... .... 536 410 356 3.0 3.1 3.3 3.4 3.2 3.3 2.8 Government. .... ...... ........ ... ... .... .......... 354 360 380 343 337 350 382 1.6 1.6 1..7 1.6 1.5 1.6 1.7 Northeast. .... ............ .. ... ......... ... .... South .. ... ..... ........... .. ... .. ........... .... 560 1,191 526 1,164 546 1,164 545 1,280 540 562 1,245 599 1,311 2.2 2.0 2.1 2.5 2.5 2.4 2.1 2.7 2.1 2.6 2.2 2.6 2.3 1,259 Midwest. .......... ....... ......... ..... ..... ... 692 688 631 635 613 699 640 2.2 2.2 2.0 2.0 1.9 2.2 2.0 West. .. ... .... .......... ..... ....... .. .... ... .... 694 765 677 738 771 790 756 2.4 2.6 2.3 2.5 2.6 2.7 2.6 Industry Region' Detail will not necessarily add to totals because of the independent seasonal adjustment of the various series. 2.7 West Virginia; Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah Washington, Wyoming. NOTE: The job openings level is the number of job openings on the last business day 0 1 the month; the job openings rate is the number of job openings on the last business day 0 1 the month as a percent of total employment plus job openings. P = preliminary. Includes natural resources and mining, information, financial activities, and other services, not shown separately. 3 Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, 19. Hires levels and rates by industry and region, seasonally adjusted Levels 1 (in thousands) Industry and region Rates 2004 Apr. Total 2 • •• ••• • • ••• • • • • • •• • ••• • •••••• • •••••••••••••••••• • • • • May June July 2004 Sept. Aug. Oct. 1 Apr. May June July Aug Sept. Oct.P 4,398 4,206 4,433 4,229 4,375 4,253 4,317 3.4 3.2 3.4 3.2 3.3 3.3 3.3 4,090 421 3,938 406 4,110 3,930 3,987 383 323 3.7 6.1 2.5 3.6 5.3 3.7 5.8 3.6 5.0 945 957 356 984 388 379 3.7 6.3 354 1,032 368 352 3.6 5.9 Manufacturing ........ ..... ... ....... .... ..... Trade, transportation, and utilities ....... 436 370 4,058 401 3,906 Construction ... ........... ... ... .. ...... .... ... 864 951 4.1 Professional and business services ... .. Education and health services ........ ... 609 460 692 428 689 401 3.7 418 690 470 720 451 409 2.7 Leisure and hospitality ... ... ..... ........ ... 766 739 749 760 760 782 747 Government. ...... ................. ..... ...... ... . 300 272 328 310 322 337 Industry Total private 2 • ...•. •••.•••••••••••• . •••• . ••••• . .• . . Reglon 336 938 631 621 2.6 2.4 2.5 3.6 5.5 2.6 3.8 3.9 3.4 3.8 3.7 4.2 3.8 4.2 4.2 3.7 4.3 2.7 2.5 2.5 2.8 2.4 2.4 6.0 6.1 6.2 6.1 301 6.2 1.4 1.3 1.5 1.4 1.5 6.3 1.6 6.0 1.4 3.2 2.8 2.8 2.9 3.0 2.9 2.9 3.4 3.5 3.7 3.5 3.5 3.5 3.5 2.3 3.7 2.2 3 Northeast. .. ..... .......... .. ..... .. ... ...... .. 810 708 703 720 763 745 South ......... ...................... .......... ... 1,582 1,606 1,709 1,640 1,643 1,635 736 1,646 Midwest. ..... ... .... .. .. ... .... ......... .. .... .. 991 956 1,009 935 945 942 1,010 3.2 3.1 3.2 3.0 3.0 3.0 3.2 West. ............... ...... ........... ... .... ..... 1,093 951 1,023 685 1,018 942 893 3.8 3.3 3.6 3.0 3.5 3.3 3.1 Detail will not necessarily add to totals because of the independent seasonal adjustment of the various series. 2 Includes natural resources and mining, information, financial activities, and other services, not shown separately. Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming. 3 Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont; c;outh: Alabama, Arkansas, Delaware, District ot Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia; 82 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 NOTE: The hires level is the number of hires during the entire month; the hires rate is the number of hires during the entire month as a percent of total employment. P = preliminary. 20. Total separations levels and rates by industry and region, seasonally adjusted 1 Rates Levels (in thousands) Apr. Total 2 2004 2004 Industry and region • .... •.• ....•.•..•.. • . •.. .•. • ••. . ..•••.. • • . ......... Oct.P Sept. Aug. July June May 4,088 4,040 4,069 4,074 4,134 4,158 Apr Oct.P Sept. Aug. July June May 4,159 3.1 3.1 3.1 3.1 3.1 3.2 3.2 Industry 3,843 3,761 3,789 3,793 3,894 3,856 3,891 3.5 3.4 3.5 3.5 3.5 3.5 3.5 Construction ...... ... ........ ... .......... .... 391 367 382 364 391 350 469 5.7 5.3 5.5 5.3 5.6 5.0 6.7 Manufacturing ..... ..... .. .................... 353 377 343 367 379 381 352 2.5 2.6 2.4 2.5 2.6 2.6 2.4 Trade, transportation, and utilities ..... .. 1,013 917 927 972 951 909 935 4.0 3.6 3.6 3.8 3.7 3.6 3.7 Professional and business services .... 606 556 607 613 575 590 539 3.7 3.4 3.7 3.7 3.5 3.6 3.2 Education and health services ... .... .... 386 379 362 363 380 384 361 2.3 2.2 2.1 2.1 2.2 2.3 2.1 Leisure and hospitality ..................... 679 696 734 694 760 756 746 5.5 5.6 5.9 5.6 6.2 6.1 6.0 Government. ........................ .. ....... ..... 245 268 270 273 246 306 269 1.1 1.2 1.3 1.3 1.1 1.4 1.2 Total private 2 ...•. ...•.. ... ........•.. •. .. .. • ....•• Reglon 3 Northeast. .. ... ... .. ... .. ····· ········· ···· ··· 716 648 704 674 717 730 693 2.9 2.6 2.8 2.7 2.8 2.9 2.7 South .... ....... ......... ........... ....... .. ... 1,524 1,504 1,533 1,545 1,527 1,506 1,595 3.3 3.2 3.3 3.3 3.3 3.2 3.4 Midwest. ....... ........ ... ........ ...... ..... .. 877 833 853 935 831 931 894 2.8 2.7 2.7 3.0 2.7 3.0 2.9 West. ......... ... ... ... ........... ..... ... ... .... 959 1,008 979 945 1,087 978 952 3.4 3.5 3.4 3.3 3.8 3.5 3.3 Kansas, Michigan, Minnesota, Missouri , Nebraska, ' Dtltail will not necessarily add to totals because of the independent seasonal adjustment Midwest: Illinois, Indiana, Iowa, of the various series. North Dakota, Ohio , South Dakota, Wisconsin; West: Alaska, Arizona, California, Includes natural resources and mining, information, financial activities, and other 3 Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming. services, not shown separately. Northeast: Connecticut, Maine, Massachusetts, New Hampshire , New Jersey, New York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia; NOTE: The total separations level is the number of total separations during the entire month ; the total separations rate is the number of total separations during the entire month as a percent of total employment. P = preliminary. 21. Quits levels and rates by industry and region, seasonally adjusted 1 Rates Levels (in thousands) Industry and region Apr. Total 2 .. . .. . ..... . ··· ·· ·· ·· ··· ············ ······· ....... 2004 2004 May June July Aug. Sept. Oct.P June May Apr. Sept. Aug. July 1.7 1.7 Oct.P 1.7 1.7 1.7 1.7 2.0 1.9 2.0 2.0 1.9 1.9 1.9 2.2 2.1 2.3 1.5 2.1 2.0 2.8 2,278 2,173 2,284 2,265 2,252 2,248 2,259 1.7 . •... . •.•...•.•.... . •.. .• ••.. .. • .•.... 2,151 2,026 2,162 2,141 2,140 2,118 2,130 Construction ...... ......... .. ......... .. ...... 149 144 156 101 147 138 198 Industry Total private 2 Manufactu ring ....... .... ....... .. .... .. .. .... 189 171 171 174 165 183 173 1.3 1.2 1.2 1.2 1.1 1.3 1.2 Trade, transportation, and utiliti es ....... Professional and busi ness services .... 525 259 536 322 2.1 1.6 1.3 2.1 2.0 1.3 2.1 2.0 1.4 2.0 1.7 1.4 429 455 480 442 3.5 3.7 3.9 1.6 3.6 2.2 1.9 1.4 Leisure and hospitality ..... ... ...... .. ..... 239 476 2.2 2.0 1.5 2.2 2.0 225 536 325 240 520 284 223 559 322 271 552 308 Education and health services ........... 563 323 245 3.9 3.6 3.7 Government. ............ .......... ........... ·: ... 129 129 123 126 .6 .6 .5 .6 .6 Reglon 235 454 116 130 124 .6 .6 3 Northeast. ..................... .. .. .. .... .. .... 390 318 334 338 339 325 333 1.6 1.3 1.3 1.3 1.3 1.3 1.3 South .............. .................. ... ........ 888 857 910 901 897 903 888 1.9 1.8 2.0 1.9 1.9 1.9 1.9 Midwest. .... ... ..... ..... .... ....... ... ...... .. 479 479 485 505 447 472 480 1.5 1.5 1.6 1.6 1.4 1.5 1.5 524 521 573 519 566 546 561 1.8 1.8 2.0 1.8 2.0 1.9 1.9 West. ....... .... .... ......... ... .......... ...... 1 439 Detail will not necessarily add to totals becaL,se of the independent seasonal adjustment of the various series. Includes natural resources and mining, information, financial activities, and other services, not shown separately. 3 Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island , Vermont; South: Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Caroli na, Tennessee, Texas, Virginia, West Virginia; https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Kansas, Michigan, Minnesota, Missouri , Illinois, Indiana, Iowa, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming. Midwest: NOTE: The quits level is the number of quits during the entire month; the quits rate is the number of quits during the entire month as a percent of total employment. P = preliminary. Monthly Labor Review December 2004 83 Current Labor Statistics: Labor Force Data 22. Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003. County by NAICS supersector Average weekly wage 1 Employment December 2003 (thousands) Percent change, December 2002-03 2 Fourth quarter 2003 Percent change, fourth quarter 2002-03 2 United States3 .....................................................•.•.............•...••.•. Private industry .. .... ................ ..... ........... .. ........... .................... . Natural resources and mining .............................................. Construction ......................................................................... Manufacturing ............... ........... ............ ... ............................ . Trade, transportation, and utilities ....................................... . Information ....................................................... .. ................. . Financial activities .............. .... .. ......... ... ..................... ... .. ... .. . Professio;ial and business services ........ ...................... ...... . Education and health services .. ............................. .. .. ... .. .. ... Leisure and hospitality ............. .. .... .. .. .............. .............. ... .. . Other services .................. .. .. ............................................... . Government ................................ ................................ ... .. ........ 8,314.1 8,048.7 123.7 804.9 376.8 1,853.6 145.2 767.0 1,329.4 732.2 669.9 1,080.6 265.3 129,341.5 108,215.1 1,557.8 6,689.5 14,307.8 25,957.3 3,165.9 7,874.7 16,113.2 15,974.0 12,042.8 4,274.1 21,126.3 0.0 .0 .1 1.2 -4.2 -.3 -4.0 1.2 .6 2.1 1.7 -.1 -.2 $767 769 703 837 943 665 1,139 1,138 945 731 335 494 757 3.6 3.9 4.9 2.3 6.7 3.4 3.9 5.9 3.8 3.8 3.4 3.1 2.4 Los Angeles, CA ................................................ .......................... Private industry ....................................................................... . Natural resources and mining ............................................. . Construction ....... ... .... ....... ....................... .. .. ........................ . Manufacturing ....................................... ........................ ...... . Trade, transportation, and utilities ....................... ................ . Information ....... ................. ........... .... ........ .... .................. ... . Financial activities .. ................................................... ... .. ... .. . Professional and business services ....... .. .......... ................. . Education and health services ................... .... .. ... ................ . Leisure and hospitality ......................................................... Other services ........................ ... ... ..................................... .. . Government .......... ............................. .......................... .. ......... . 356.0 352.2 .6 12.9 17.8 53.9 9.2 23.0 40.1 26.6 25.6 142.1 3.8 4,075.3 3,486.3 11.0 133.9 485.2 794.6 194.9 237.9 575.0 456.5 375.9 220.7 589.0 -.5 -.2 .7 -1 .1 -7.1 -1.2 -2.0 .9 1.6 1.9 5.6 3.5 -2.3 903 898 955 883 900 735 1,627 1,258 1,043 820 766 422 930 4.2 4.2 16.9 1.7 6.5 2.7 5.2 7.0 3.7 3.9 6.5 5.0 3.3 Cook, IL ...................................................................................... .. Private industry ....................................................................... . Natural resources and mining ................ .............. ... ............ . Construction .......... ............................... ... ......... .... ........ .. ..... . Manufacturing ...................................................... ....... .. .... ... Trade, transportation, and utilities .................................. ...... Information ........... ... .... ... ... ............. ... .. ............ ........... .. ....... . Financial activities ................... ..................................... .. .. ... . Professional and business services .................................... . Education and health services ....................................... .. ... . Leisure and hospitality ........................................................ . Other services .............................................................. ....... . Government ............. ............................................................ ... . 126.7 125.5 .1 10.5 7.9 26.7 2.5 13.8 26.1 12.3 10.5 12.6 1.2 2,539.8 2,221.9 1.3 96.7 265.7 499.4 66.1 219.4 405.5 350.8 217.7 95.1 317.9 -1 .2 -.9 -3.6 .0 -5.1 -.8 -4.1 -.8 -1.3 1.0 2.8 -2.0 -3.1 922 929 1,037 1,169 975 753 1,164 1,471 1,206 791 375 655 871 3.0 3.2 3.2 -.8 6.3 .4 .1 8.1 4.1 3.7 -.3 3.0 .9 New York, NY .............................................................................. . Private industry ............................... ........................................ . Natural resources and mining .................... ......................... . Construction ........................................................................ . Manufacturing ........................ .. ..................... .. ................ .... . Trade, transportation, and utilities .......................... .... .. .... ... . Information ...... .................... ........ ............... ......................... . Financial activities ............................................................... . Professional and business services .. ......... ...... ............ .. ...... Education and health services ............ ................................ . Leisure and hospitality ..................... .. .. .... ........................... . Other services ................................... ................................... Government .............. ............. .......... ......... .............................. . 111.9 111.7 .0 2.2 3.5 22.1 4.3 16.7 22.6 7.8 10.1 16.0 .2 2,253.6 1,800.4 .1 30.0 46.6 247.6 130.6 352.0 439.7 273.8 188.2 82.9 453.2 -1.0 -.6 .0 -4.5 -4.9 -1.2 -5.1 -2.0 .5 2.4 .4 -2.2 1,480 1,623 1,197 1,567 1,290 1,164 1,751 3,034 1,702 918 787 871 912 7.2 8.1 -6.5 3.4 6.4 5.5 7.9 16.1 2.6 7.6 6.1 6.1 .1 Harris, TX .............................................................. ........... .. .... .... .. Private industry .............................................. ......................... . Natural resources and mining ....................................... ...... . Construction ................................. . ......................... ... ......... Manufacturing ..................................................................... . Trade, transportation, and utilities ....................................... . Information ........................................................................... Financial activities ..................................................... ... .. ..... . Professional and business services ........... ................ .. ....... . Education and health services .... ... ... .... ....... .. ........ ............. . Leisure and hospitality ................................ ........................ . Other services ........................................................... .. ........ . Government .................... ....... .. .......................... .. .. ................. . 89.4 89.0 1.2 6.3 4.7 21 .1 1.4 9.7 17.0 8.8 6.5 10.3 .4 1,841.5 1,595.2 62.5 135.5 164.0 403.2 33.8 113.1 279.0 188.3 155.2 56.3 246.3 -.9 -1.2 8.7 -5.0 -4.9 -2.1 -3.9 1.7 -1.7 1.5 .7 -3.1 1.1 906 929 2,185 919 1,106 821 1,098 1,181 1,073 812 335 539 759 2.1 2.1 -.9 2.6 2.3 1.0 .4 4.9 3.2 1.8 -.9 .4 3.1 Maricopa, AZ ................... ............................................ .. ............... Private industry .......... ................................. .................. ...... ... .. Natural resources and mining .................... ... ................... ... . Construction .. .... ........ ................ ......... ... ..... ............... .. ........ . Manufacturing ..................................................................... . Trade, transportation, and utilities ................. ..... ................. . Information .................................................... ............ ...... .. .. . Financial activities .............. ............... .. ....................... ......... . Professional and business services .............. ....................... Education and health services ........... ................................. . Leisure and hospitality ........................................................ . Other services .............. ... .......... ........ .. ................................ . Government ....................... .. ......... .......................................... . 80.9 80.5 .5 8.4 3.3 18.6 1.6 9.5 18.1 7.6 5.6 5.7 .5 1,621.2 1,401 .8 9.8 131.7 128.0 336.4 36.6 133.3 261.5 160.5 155.8 44.7 219.4 (4) 2.2 -2.6 5.9 -2.5 1.5 -4.1 1.5 4.2 5.6 .8 -2.6 1.6 757 755 545 779 1,050 712 872 933 776 842 364 500 766 4.0 3.9 4.4 2.1 8.2 3.2 .5 3.7 3.5 5.0 2.8 2.2 3.7 See footnotes at end of table. 84 Establishments, fourth quarter 2003 (thousands) tviur 1tr1ly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 -1.1 22. Continued-Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003. County by NAICS supersector Establlshments, fourth quarter 2003 (thousands) Average weekly wage 1 Employment December 2003 (thousands) Percent change, December Fourth quarter Percent change, fourth quarter 2002-03 2 2003 2002-03 2 Dallas. TX ................................................ .. ..... .. ........ ............ .. ... . .. Private industry ................. .......... ............ ............................ ... .. Natural resources and mining ........ ............................. .. ..... .. Construction .. ... .............. .................... ...................... ....... .... . Manufacturing ............................. ....... .. ...... .. ........... ............ . Trade. transportation. and utilities ........................ .. .... .... .. .. .. Information .. ... ... .. ... ........ .......................................... ... .. .. .... . Financial activities ............................ .... ................ ......... ..... .. Professional and business services ...................... .. .......... .. . Education and health services .......... .... .. ............ .. ........ ..... .. Leisure and hospitality ........................................................ . Other services ........... .... ... .. .............................. .................... Government ............................................. .... .. ........................ .. 68.6 68.2 .5 4.5 3.5 15.8 1.9 8.6 14.0 6.3 5.2 6.7 .4 1,450.8 1.294.6 6.8 73.0 144.9 326.1 64.0 140.0 237.7 131.4 127.5 40.5 156.2 -1 .4 -1.4 -20.5 -2.2 -3.1 -3.3 -5.1 1.2 .0 2.4 .0 -3.4 -1.8 $952 970 2.680 909 1.075 898 1.272 1.215 1.152 887 432 587 800 4.3 4.8 22.7 5.5 6.8 5.2 8.7 2.9 4.2 2.7 4.3 2.8 -.1 Orange. CA ......................... .. ......... .. ... .... ...... ... ....................... .... . Private industry ....................... .............. ............................ ... ... . Natural resources and mining .. .. ... .. .......... ... .. ......... ....... .. ... . Construction .............................. ... ............... ............ ... ... .... .. . Manufacturing .. .................................................................... Trade. transportation. and utilities .. ............... .... .. ............... .. Information .. ... ... ...... ......... ... ..... .. ............... .. ............... ... ... ... . Financial activities ......................... .. .......... ... ..... .. ... .. .. ... ... ... . Professional and business services .. .... .. ..... .. .. ... ....... ..... .. ... Education and health services ....... .. ....................... ............ . Leisure and hospitality ....... .. .... .. ................. ........................ . Other services ......... .. .............................................. .. ....... ... . Government .. ............. .............. ........... ......................... ... ........ . 88.8 87.4 .3 6.4 6.1 17.3 1.5 9.7 17.4 9.1 6.6 12.9 1.4 1,436.6 1.305.5 6.1 85.5 179.9 278.8 33.8 127.8 261 .0 126.6 159.9 46.0 131 .1 1.3 2.1 8.3 4.4 -3.0 .6 -4.4 9.9 1.0 6.1 2.5 6.3 -5.7 874 875 579 969 1.036 802 1.152 1.354 942 849 358 518 859 5.3 5.2 .2 5.9 11 .4 2.7 5.3 6.2 2.8 3.7 3.8 3.0 6.0 San Diego. CA ......... ........ .... .............. ..... ....... ..... ... ... ..... .... ... ...... . Private industry ....................................................................... . Natural resources and mining ..................... .. ...................... . Construction ............ .. .. .......................... .. ... ................... .. ... .. Manufacturing .. ..... ... .... .. .... ........ .. ...... ... .. ... ............... .......... . Trade. transportation. and utilities ... ......................... .. .. ... .... . Information ..... .. ................ ............. ... ...... ... ....................... .. . . Financial activities ................. .. ............................... ....... ...... . Professional and business services ....... ........... ..... .............. Education and health services ............ ........ ..... .................... Leisure and hospitality ................... ............. .. ............... ....... . Other services ..................... ...... ...... .. ... .... ... ... .. ............... ... . . Government ... .... ............. .. ..... ....... ..................................... .. .. .. 85.3 83.9 .9 6.4 3.6 14.2 1.4 8.8 14.9 7.6 6.5 19.5 1.3 1.278.2 1.060.2 11 .0 81.1 105.4 220.4 36.7 81 .6 208.1 122.6 141 .5 51.6 218.0 1.3 1.5 -5.4 4.7 -4.2 2.2 -4.5 4.8 1.5 1.6 3.5 1.8 .1 815 809 491 869 1.129 655 1.582 1.058 989 778 346 449 843 2.6 2.5 1.0 .7 11 .5 .9 -2.0 .4 2.8 5.7 2.4 2.7 2.9 King. WA ...... .............................. .... ..... .. ... ........ ............. ......... ... ... Private industry ........ .. ... ...... .................... .......... .. .................... . Natural resources and mining .. ............................. .. ...... .. .... . Construction ....................................................... ... .... .. ... ..... . Manufacturing . .. ... ... .. .......... ..... . ......... ..... .. ........... ... ....... . Trade. transportation. and utilities ...... .. ............. ........ .... ..... .. Information ..... ... .................................. ............... ..... .... ...... .. . Financial activities ........ ................................................ ........ Professional and business services .................................... . Education and health services ... ...................... ................... . Leisure and hospitality ........... ........ .......... .. ............. ............ . Other services .................................. ..................... ... .... ....... . Government .......................... .. ........ .. .. ... .. ................ ... .......... ... 81 .6 81 .0 .4 6.2 2.7 14.8 1.5 6.1 11.7 5.9 5.4 26.4 .6 1.100.6 945.5 2.8 53.4 101 .9 225.5 69.2 77.5 158.3 108.3 100.5 48.1 155.1 .2 .1 -11.3 -.4 -8.2 1.1 .8 2.4 .7 1.5 2.9 1.2 1.0 935 944 1.109 921 1.176 804 1.829 1.114 1.1 60 746 390 463 882 .2 -.3 .8 1.4 -2.1 2.6 -15.7 3.5 8.4 4.8 3.7 .4 3.6 Miami-Dade. FL ... ........... ... ....... ... ...... .... .. .... ....... .................... .. ... . Private industry ... ......... ........ ................ .... .. ........................... ... Natural resources and mining ............................................ .. Construction .. ....... ..... ........ ... ....... ................... ... .... ..... .... ... .. . Manufacturing ............................ ..... ........ .. ........... ... ............ . Trade. transportation. and utilities ..................... .................. . Information ........................................................................... Financial activities .. .. ... .. .... ................ .... ....................... ... .... . Professional and business services .. ...... ... ............. ........... .. Education and health services ....... ........ .. ... .................. .. .... . Leisure and hospitality .............................. ................. .. ...... . . Other services .... ................ ....................... ... ... ... .......... .. ..... . Government .............. .... ....... ............... .................................... . 80.2 79.9 .5 4.9 2.8 23.2 1.7 8.2 15.9 7.8 5.3 7.5 .3 980.8 827.5 9.9 40.7 49.4 247.2 28.5 65.5 132.0 123.4 92.8 34.5 153.3 -.5 -.7 -1.8 .3 -9.8 -1 .7 -3.2 .7 -.2 1.4 2.1 -1 .8 .5 765 742 421 788 695 689 990 1.062 948 748 432 450 886 3.5 3.6 4.0 2.7 5.8 4.2 1.7 -1 .1 5.2 2.3 9.9 3.0 2.8 1 Average weekly wages were calculated using unrounded data. 2 Perce nt changes were computed from quarterly employment and pay data adjusted for noneconomi c county reclassifications. See Notes on Current Labor Statisti cs. 3 Totals for the United States do not include data for Puerto Rico or the https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Virgin Islands. 4 Data do not meet BLS or State agency disclosure standards. NOTE: Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. Data are preliminary. Monthly Labor Review December 2004 85 Current Labor Statistics: Labor Force Data 23. Quarterly Census of Employment and Wages: by State, fourth quarter 2003. State Establishments, fourth quarter 2003 (thousands) Average weekly wage 1 Employment December 2003 Percent change, December Fourth quarter Percent change, fourth quarter (thousands) 2002-03 2003 2002-03 United States 2 .................................. . 8,314.1 129,341.5 0.0 $767 3.6 Alabama ............. .............................. . Alaska ................. ....... .. ....... .. .... ... .... . Arizona ........................................ .. ... . Arkansas ........... .. ............... .... .......... . California ........ ... .................... ... ........ . Colorado ..................... .............. ....... . Connecticut .. .......... ........... .... .... .. .... .. Delaware .. .... ..... .. ............................. . District of Columbia .................. .. ....... Florida ....... .... .. ........... ...................... . 111.8 20.0 126.9 75.2 1,190.8 160.0 109.1 27.1 30.0 504.1 1,838.1 282.7 2,352.1 1,133.6 14,922.3 2,134.6 1,648.9 408.4 654.8 7,424.5 -.1 -.7 .5 -.4 .8 657 746 710 587 869 784 992 825 1,238 685 4.0 1.1 3.8 4.1 3.8 2.0 3.8 5.0 3.9 3.8 Georgia .. ... ..................... .................. . Hawaii ................ .... .. ... .. ................... . Idaho .................................. ... .. .......... Illinois ........ ..... .. ................................ . Indiana ..................... ........................ . Iowa ... .. ........................ ................. ... . Kansas .............. .. ................. ... ......... . Kentucky .. ...... ............ .................. .... . Louisiana .................... ........ .............. . Maine ... .. ........................... ... ............ . 245.6 37.4 48.5 325.7 152.1 90.6 82.2 105.7 114.0 47.4 3,845.6 583.0 577.5 5,738.7 2,852.2 1,418.5 1,298.3 1,740.6 1,870.9 595.8 .2 1.3 .6 -1 .2 -.3 .0 -.9 .3 .5 .7 734 678 579 827 675 626 631 645 628 631 2.8 3.7 1.8 3.2 3.5 4.7 2.8 3.5 2.4 4.6 Maryland ....... ... .................... ............ . Massachusetts ................................. . Michigan ........ ........... ............... .. ....... . Minnesota .......................... ... ... ..... .... 2,466.4 3,154.6 4,365.8 2,591 .9 1,108.1 2,633.6 396.6 884.4 1,111.2 614.9 .7 -1.9 -1 .1 -.5 .4 -.7 Montana ... ........................................ . Nebraska ...... .. .................. ................ . Nevada ........................................ ..... . New Hampshire .............. .. ............... . 150.4 206.6 251 .3 159.0 65.6 165.4 42.0 55.3 60.3 47.0 .6 4.4 .6 831 954 806 777 559 676 549 613 721 788 3.6 5.2 3.9 3.2 3.7 2.4 4.0 3.2 5.1 4.0 New Jersey .................. .................... . New Mexico ............... ...................... . New York ....................... ... .. ... .......... . North Carolina ......... .......... ... .. .......... . North Dakota .............................. ... ... . Ohio ........ ... ............... .................... ... . Oklahoma ....................... .. .......... ...... . Oregon ........................ ... ...... .. ........ .. . Pennsylvania .. ........ .... .. ...... .. ............ . Rhode Island .... ..... .. .. .. ............ .......... 268.1 50.4 550.3 227.8 24.0 294.2 91 .6 118.8 326.9 34.7 3,912.8 757.1 8,379.2 3,759.6 317.6 5,322.4 1,423.4 1,579.8 5,524.5 480 .5 .1 1.4 -.4 -.1 .9 -.7 -1 .3 .2 -.2 1.2 945 612 959 679 563 713 597 694 750 738 3.4 4.1 5.2 4.5 4.3 3.8 4.2 3.3 4.7 5.1 South Carolina ........ ... ....... .. ............. . South Dakota ............... ................ .... . Tennessee .. .................. ................... . Texas ..................................... ..... ..... . Utah ........................ .. ....................... . Vermont ............. .............................. . Virginia ..... ............... ................ ...... ... . Washington ...... ................................ . 108.4 28.1 128.4 505.3 73.9 24.1 202.6 222.7 47.2 157.6 1,781.0 365.4 2,648.0 9,300.1 1,066.2 300.7 3,477.5 2,654.7 685.2 2,715.4 .3 .3 .4 -.3 1.2 .3 1.2 1.0 .1 .0 623 559 689 754 630 661 786 759 587 683 3.1 4.1 4.2 3.1 2.3 5.1 5.2 1.3 2.1 4.1 ~:::~~:f:.'..:::::::::::::::::::::::::::::: : ::::::::: ~~~~~~~~~'.~.:::::::::::::: ::::::::::::: :::::::::: 1.1 2.2 .5 .0 -1.1 1.1 Wyoming .. .......................... .. .. ..... .... . . 22.0 241 .6 1.7 616 4.1 Puerto Rico ........................ .......... ... . . Virgin Islands ..................... ............ .. . 50.2 3.2 1,074.1 42.5 3.5 -.2 450 629 4.7 2.4 1 Average weekly wages were calculated using unrounded data. 2 Totals for the United States do not include data for Puerto Rico or the Virgin Islands. 86 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 NOTE: Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. Data are preliminary. 24. Annual data: Quarterly Census of Employment and Wages, by ownership Year Average establishments Average annual employment Total annual wages (In thousands) Average annual wage per employee Average weekly wage Total covered (UI and UCFE) 1993 ................ .. .... .... .......... .. ... ....... .. 1994 ... ···· ···· ····· ··· ···· ············· ···· ···· ·· ··· 1995 ·· ······ ·· ········ ···· ··········· ··· ·· ··· ···· ·· ··· 1996 ....... ... .... ......... ..... ... ..... ............. . 1997 ·· ····· ·· ········································· 1998 ... ..... ............ ...... ... .. .................. . 1999 .. .... ... ...... .. .......... .... ... .... ... .... .... . 2000 ··················· ·············· ·· .. ·· ····· ······ 2001 ········ ··············· ·· ········ ····· ······· ···· · 200? ..................... ... ..... .. ... .... ... .. .... .. . 6,679 ,934 6,826,677 7,040 ,677 7,189,168 7,369,473 7,634,018 7,820,860 7,879 ,116 7,984,529 8,101 ,872 109,422 ,571 112,611,287 115,487,841 117,963,132 121,044,432 124,183,549 127,042,282 129,877,063 129,635,800 128,233,919 $2,884,472,282 3,033,676 ,678 3,215,921,236 3,414,514,808 3,674,031,718 3,967,072,423 4,235,579,204 4,587,708,584 4,695,225,123 4,714,374,741 $26,361 26,939 27,846 28,946 30,353 31 ,945 33,340 35,323 36,219 36 ,764 $507 518 536 557 584 614 641 679 697 707 $26,055 26 ,633 27,567 28,658 30 ,058 31 ,676 33,094 35,077 35,943 36 ,428 $501 512 530 551 578 609 636 675 691 701 $25,934 26,496 27 ,441 28,582 30,064 31 ,762 33,244 35,337 36 ,157 36 ,539 $499 510 528 550 578 611 639 680 695 703 $28,643 29,518 30,497 31 ,397 32,521 33,605 34,681 36 ,296 37 ,814 39 ,212 $551 568 586 604 625 646 667 698 727 754 $26,095 26,717 27,552 28.320 29,134 30 ,251 31 ,234 32,387 33,521 34, 605 $502 514 530 545 560 582 601 623 645 665 $36 ,940 38,038 38 ,523 40,414 42 ,732 43,688 44 ,287 46,228 48,940 52,050 $7 10 731 741 777 822 840 852 889 941 1,001 UI covered 1993 ········ ························ ... .. .. .......... . 1994 ··········· ···· ······· ···· ·· ···· ···· ········ ·· ·· ·· 1995 ... .... .. ..... ... ....... ... ... ............... .. .. . 1996 ................. ..... .. ... ...... ... ... ... ....... . 1997 ..... ...... .. ... ... .............. ................. 1998 .. ........... ........... ..... ... ...... ...... .... .. 1999 .... ... .. ......... .... .... .. ....... ... ...... .... .. 2000 ········ ······ ··· ·· ··· ·· ···· ···················· ·· 2001 ........... .. .... ......... .. .. ... ........... ... . 2002 .. .... .... .... ... .......... .... .................. . 6,632 ,221 6,778,300 6,990,594 7,137,644 7,317,363 7,586,767 7,771,198 7,828,861 7,933,536 8,051 ,117 106,351,431 109,588,189 112,539,795 115,081 ,246 118,233,942 121,400,660 124,255,714 127,005,574 126,883,182 125,475,293 $2,771,023,411 2,918,684 ,128 3,102,353,355 3,298,045,286 3,553,933,885 3,845,494,089 4,112,169,533 4,454,966,824 4,560,511,280 4,570,787,218 Private industry covered 1993 ......... ..... .. .............. .. ... ...... .. ... .. . . 1994 ........ .. ................... ................ .... . 1995 .............. ..... .... .. ...... .... ........ ... ... . 1996 ··· ·· ·· ···· ····· ·· ···· ·········· ···· ··· ········ ··· 1997 .... ..... ... ... .... ....... ....................... . 1998 ... ... ... .... .......... ... .. ........ ... ..... .. ... . 1999 2000 2001 2002 ··············· ···· ···························· ··· ········ ··· ··· ···· ·········· ···· ··· ·· ·· ··· ··· ····· ···· ··· ·· ·· ·· ···· ··········· ··········· ·· ··· ······ ..... ... ..... .... ... ....... ..... .. .... ...... .. ... . 6,454,381 6,596,158 6,803,454 6,946,858 7,121 ,182 7,381 ,518 7,560,567 7,622,274 7,724 ,965 7,839 ,903 91,202,971 94,146,344 96,894,844 99,268,446 102,175,161 105,082,368 107,619,457 110,015,333 109,304,802 107,577,281 $2,365,301 ,493 2,494,458,555 2,658,927,216 2,837,334,217 3,071 ,807,287 3,337,621,699 3,577,738,557 3,887,626,769 3,952,152,155 3,930,767,025 State government covered 1993 ............... .... .... ..................... ..... . 1994 .... .. .. ... ..... ... ... .. ... ... ... ......... ... .. .. . 1995 .. ................. .............. .. .... .... .. ... .. 1996 .. ... ..... ... .... ..... .... .... ............. .. .. .. . 1997 ............. ... .... ...................... ....... . 1998 .............. ... ... .... .. ... .... ... .. ... ........ . 1999 ······ ········ ··· ··· ······ ···· ········ ······ ······ 2000 ... ... ... ..................... ................... . 200 ·1 .... ... ....... ...... ... .......... ..... ... ........ . 2002 .. .... ... ..... ...... ........................ ..... . 59 ,185 60 ,686 60 ,763 62,146 65,352 67 ,347 70,538 65 ,096 64 ,583 64,447 4,088,075 4,162,944 4,201,836 4,191,726 4,214,451 4,240 ,779 4,296,673 4,370,160 4,452 ,237 4,485,071 $11 7,095,062 122,879,977 128,143,491 131,605,800 137,057,432 142,512,445 149,011 ,194 158,618,365 168,358 ,331 175,866 ,492 Local government covered 1993 ····· ·········· ··· ··· ·· ·· ···· ·· ····· .. ··· ....... . 1994 .. .. .... ....... .. .................. ... ............ 1995 ········ ···· ···· ········· ··· ···················· ·· 1996 ··· ·············· ·· ··· ·· ····· ··· ·· .. ··· ··········· 1997 .. ..... ....... .... ..... ...... ........ ........ .. ... 1998 ·· ····················· ···· ········· ··· ··· ··· ····· 1999 ...... ... .... .... .......... .................... . 2000 ·· ··· ··· ········· ······ ····· ······ ·· ········· ···· · 2001 ..... ..... ... ...... ... .......... ..... ..... ... .... . 2002 ........... ..... .. .. .... ... ....... ... ..... ....... . 118,626 121,425 126,342 128,640 130,829 137,902 140,093 141,491 143,989 146,767 11 ,059,500 11,278,080 11 ,442 ,238 11,621 ,074 11,844,330 12,077,513 12,339,584 12,620,081 13,126,143 13,412,941 $288,594,697 301 ,315,857 315,252 ,346 329,105,269 345,069,166 365,359,945 385,419,781 408,721,690 440,000,795 464,153,701 Federal Government covered (UCFE) 1993 ··· ···················· ····· ·· ····· ········· ··· ··· 1994 ············ ······· ······· · ..... .. ..... ......... . . 1995 .. .......... ..... ..... .......... ................. . 1996 ................ .... .................... ...... ... . 1997 ··· ···· ····· ···· ··· ············· ··· ··· ····· ··· ···· 1998 ...... .. .... ...... ... ... .. ....... .. ..... .... ... ... 1999 ...... ... ... .... .. ........... ... ....... ..... ..... . 2000 .. .... .................. .... ... ..... ... ... .. .. ... . 2001 ·········· ····· ·········· ······ ··· ··· ····· ········ 2002 ...... ....... ..... .... ...... ... ... ........ .. ..... . 47,714 48,377 50 ,083 51,524 52,110 47,252 49,661 50,256 50,993 50,755 3,071 ,140 3,023,098 2,948,046 2,881,887 2,810,489 2,782,888 2,786,567 2,871,489 2,752,619 2,758,627 $ 113,448,871 114,992,550 113,567,881 116,469,523 120,097,833 121 ,578,334 123,409 ,672 132,741 ,760 134,713,843 143,587,523 NOTE: Detail may not add to totals due to rounding. Data reflect the movement of Indian Tribal Council establ ishments from private industry to the public sector. See Notes on Current Labor Statistics. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 87 Current Labor Statistics: Labor Force Data 25. Annual data: Quarterly Census of Employment and Wages, establishment size and employment, private ownership, by supersector, first quarter 2003 Size of establishments Industry, estahllshments, and employment Total Fewer than 5 workers 1 5 to 9 workers 10 to 19 workers 20 to 49 workers 50 to 99 workers 100 to 249 workers 250 to 499 workers 500 to 999 workers 1,000 or more workers Total all industries 2 Establishments, first quarter .................. Employment, March ······························· 7,933,974 105,583,548 4,768,812 7,095,128 1,331,834 8,810,097 872,241 11,763,253 597,662 18,025,655 203,030 13,970,194 115,598 17,299,058 28,856 9,864,934 10,454 7,090,739 5,487 11 ,664,490 Natural resources and mining Establishments, first quarter ·················· Employment, March ······ ························· 124,527 1,526,176 72,088 110,155 23,248 153,629 14,773 198,895 9,226 275,811 2,893 198,122 1,593 241,559 501 171,063 161 108,563 44 68,379 Construction Establishments, first quarter ·················· Employment, March ............................... 795,029 6,285,841 523,747 746,296 129,201 846,521 76,215 1,021,722 46,096 1,371 ,071 12,837 872,274 5,604 823,846 1,006 338,107 262 172,944 61 93,060 381,159 14,606,928 148,469 252,443 65,027 436,028 57,354 788,581 54,261 1,685,563 25,927 1,815,385 19,813 3,043,444 6,506 2,245,183 2,565 1,732,368 1,237 2,607,933 Trade, transportation, and utilities Establishments, first quarter ·················· Employment, March ............................... 1,851,662 24,683,356 992,180 1,646 ,304 378,157 2,514,548 239,637 3,204,840 149,960 4,527,709 51 ,507 3,564,316 31 ,351 4,661 ,898 6,681 2,277,121 1,619 1,070,141 570 1,216,479 Information Establishments, first quarter .................. Employment, March ............................... 147,062 3,208,667 84,906 112,409 20,744 138,076 16,130 220,618 13,539 416,670 5,920 410 ,513 3,773 576,674 1,223 418,113 575 399,366 252 516,228 Financial activities Establishments, first quarter .................. Employment, March ······························· 753,064 7,753,717 480,485 788,607 135,759 892,451 76,733 1,017,662 39,003 1,162,498 11,743 801,1'10 6,195 934,618 1,794 620,183 883 601,549 469 935,009 Professional and business services Establishments, first quarter .................. Employment, March ······························· 1,307,697 15,648,435 887,875 1,230,208 180,458 1,184,745 111,532 1,501,470 73,599 2,232,506 28,471 1,969,466 17,856 2,707,203 5,153 1,762,251 1,919 1,307,870 834 1,752,716 Education and health services Establishments, fir::;t quarter ·················· E,·,1pl, ;yment, March ............................... 720,207 15,680,834 338,139 629,968 164,622 1,092,329 103,683 1,392,099 65,173 1,955,861 24,086 1,679,708 17,122 2,558,300 3,929 1,337,188 1,761 1,220,921 1,692 3,814,460 Leisure and hospitality Establishments, first quarter ·················· Employment, March ............................... 657,359 11,731,379 260,149 411,192 110,499 744,144 118,140 1,653,470 122,168 3,683,448 34,166 2,285,550 9,718 1,372,780 1,609 545,304 599 404,831 311 630,660 Other services Establishments, first quarter .................. Employment, March ............................... 1,057,236 4,243,633 851,231 1,037,360 116,940 761,518 56,238 740,752 24,235 703,957 5,451 371,774 2,561 376,832 454 150,421 109 71,453 17 29,566 Manufacturing Establishments, first quarter .................. Employment, March ······························· 1 Includes establishments that reported no workers in March 2003. 2 Includes data for unclassified establishments, not shown separately. 88 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Decen 1ber 2004 NOTE: Details may not add to totals due to rounding. Data are only produced for first quarter. Data are preliminary. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 26. Annual data: Quarterly Census of Employment and Wages, by metropolitan area, 2001-02 Average annual wage2 Metropolitan area 1 2001 2002 Percent change, 2001-02 Metropolitan areas3 ...... .. .......... ......... .. .. ... .... ... .... ........ .. .. .... . $37,908 $38,423 1.4 Abi lene, TX ............. .. .............................. ... ... .... .. ... ..... ......... ... Akron, OH .......... ... ... ............. ..................... ... .. ...... ................ .. Albany, GA ............................ .... .... ......... ......... ... ............ ....... . Albany-Schenectady-Troy, NY ... .. .. ...... .. ... ..................... .. .... .. Albuquerque, NM ... .. .... .. ............. ............. .. ........................... .. Alexandria, LA .. ... .... .. .... ..... .. ... .. ..... ............. .... .... .. .. .............. . Allentown-Bethlehem-Easton. PA .................. ... ..... ... .. .......... . Altoona. PA .. ..... ..... .... ... .. ............ ... .............. .... ..... ... ..... ...... .. . . Amarillo, TX .............. .................... .......... .... .... ... ............ ... ..... . Anchorage, AK .............. .......... ...... ..... .. .................. ......... .. .... . 25,141 32,930 28,877 35 ,355 31,667 26,296 33.569 26.869 27,422 37.998 25.517 34.037 29.913 35,994 32,475 27,300 34.789 27,360 28,274 39,112 1.5 3.4 3.6 1.8 2.6 3.8 3.6 1.8 3.1 2.9 Ann Arbor, Ml .. ... ........... .. ............. ............... ........ ... ........... ... .. Anniston, AL ............ ... ............... ........ ......... .. ................. .... .... . Appleton-Oshkosh-N eenah, WI .. ..... ..... ......... .............. .. ......... Asheville, NC ... ... .. .. ..... .. ... .. ... ... ..... .... ... ... ............... ... ..... .. ..... . Athens, GA ....................... .............................. .... .... .... .. ........ .. Atlanta, GA ..... .. .... ......... .... .... ..... ................ ...................... ... .. . Atlantic-Cape May, NJ .. ..... ............ .. ..... .. .... .... .............. .. ... .. .. . Auburn-Opelika, AL ................ ....... .. ... .... .. ........ .. .. ........ ... ..... .. Augusta-Aiken, GA-SC .. ..... ...................... ..... ........... .... .... ... ... Austin-San Marcos, TX .. ... ... ..... .... ... ... .. ... .............................. . 37.582 26,486 32.652 28,511 28,966 40 .559 31 .268 25,753 30 .626 40 ,831 39,220 27.547 33.020 28,771 29,942 41,123 32,201 26,405 31,743 39,540 4.4 4.0 1.1 3.4 1.4 3.0 2.5 3.6 -3.2 Bakersfield , CA ...... ..... ... .... .. ......... .. .. .. ... ............... ......... ........ . Baltimore, MD .......... ...... .. .. ............... .. ... ... ........................... .. . Bangor, ME ............ ..................... .. ........ .... ..... .... ... ... ............... Barnstable-Yarmouth, MA .. .. ............ .. ...... .. ........ .............. .. ... . Baton Rouge, LA .. ........... .................... .. ........... .... ................ .. Beaumont-Port Arthur, TX ................................................. .... . Bellingham , WA ... .. ..... ............. .. .. .. ......... ....... .. ...... .. .............. . Benton Harbor, Ml ................... ... ............ ...... ... ..................... .. Bergen-Passai c, NJ .. ..... .......... .. .... .. .... .. ..... ..... ... .. ................ .. Bi llings, MT .................. ... .............. .... .. .. .... .... ... ..................... .. 30 ,106 37,495 27,850 31 ,025 30 ,321 31,798 27,724 31 ,140 44 ,701 27,889 31 ,192 38,718 28,446 32.028 31 .366 32.577 28.284 32.627 45.185 28.553 3.6 3.3 2.1 3.2 3.4 2.4 2.0 4.8 1.1 2.4 Biloxi-Gulfport-Pascagoula, MS ... .. .. ... ... ............. .. ............ .. ... . Binghamton, NY .................. ..... ............ .................. .............. .. Birmingham, AL ............................. ... ... ...... .... .. ..... .................. Bismarck, ND ......... .. ........... ......... ... ... ............. ....................... . Bl oomington, IN ...... .. ....... .. .. ... .. ........... .. ... .. ... ........ ........... .. ... . Bloomington-Normal , IL .............................. .. ......................... . Boise City, ID ............. ...... .. ................................................... .. Boston-Worcester-Lawrence- Lowell-Brockton , MA-NH .... .. .. . Boulder-Longmont, CO ......................... .. ... ........................... .. Brazoria, TX ........................ ... .. ................................... ........... . 28,35 1 31,187 34 ,519 27,116 28.013 35.111 31 .624 45.766 44.310 35,655 28.515 31 .832 35.940 27.993 28.855 36.133 31 .955 45.685 44.037 36.253 Bremerton . WA ... ...... .. ... ... .. .......... .............. .. ... ... ... .... .... ... .. ... . Brownsville-Harlingen-San Benito. TX .... ............. ...... .. ... ...... . Bryan-College Station, TX ............. .............. ........... ......... ...... . Buffalo-Niagara Falls. NY .... .. ..... .. ... .... .. ........ ................ ....... .. Burlington , VT .................... ... ................. ........ .. .. .. .... .... .... .. ... .. Canton-Massillon. OH ..................... ...... ... ........... .... ............ .. . Casper, WY ... ..... ... .. .... ... .... ........... .... ... ....... ............... .... .. .. .... Cedar Rapids, IA ... ......... ... .. ............. ... ... ..... ... ........... .. .. .. ....... Champaign-Urbana. IL .... ................................... ... .. .. ... ... ..... .. Charleston-North Charleston. SC ............ .. ... .... .. .................. .. 31,525 22 ,142 25,755 32,054 34 ,363 29,020 28,264 34.649 30,488 28,887 33.775 22.892 26.05 1 32.777 35.169 29,689 28,886 34,730 31 ,995 29,993 Charleston, WV .. ... ..................... ........ .. ............ .................... .. Charlotte-Gastonia-Rock Hill . NC-SC ................ ................... .. Charlottesville. VA ..... .. .. .. .. ... .............................. .. .... .... .. ....... . Chattanooga, TN-GA .... .... ................. ... ............ .... ... .. ... ......... . Cheyenne. WY .. .... .. .... .. .. ........... .. .. ...... .. ... .. ... ......... ... .... ...... .. Chi cago, IL .. ... ...... .. .. .... ... ...... .. ... ... ..... .. .... ....... .. ... .. .... ... .. .... ... Chico-Paradise. CA ..... .. .. .. .......................... ... ... ........... .. ...... .. Cinci nnati, OH-KY-IN .................... .. ............. ... ................... ... .. Clarksville-Hopkinsville . TN-KY ..... .... .............................. .. ... .. Cleveland-Lorain-Elyria, OH .... ....... .. ... ..... ... .. ......... .. ..... .... .... . 31 ,530 37,267 32,427 29,981 27.579 42 ,685 26,499 36 ,050 25.567 35.514 32,136 38,413 33,328 30,631 28,827 43,239 27,190 37,168 26,940 36,102 Colorado Springs. CO ........... .. .. ......................... .. .. ...... ........ .. Columbia, MO ... ... .......... ... ... ... ... ... ..... ... ............... .............. .... . Columbia, SC .. .. .... .... .. ....... .. ......... .... .. .... ... .... ....... .. .............. . Columbus. GA-AL ................. ........ ........... .. .. ... ... ... .. ...... .. ... ... .. Colu mbus. OH ............... ... ... ............................................ .. ... . . Corpus Christi, TX ........ .. .. ......... .. ......... ................ ........... .. ... .. Corvallis, OR ..... ......... ... ...... ... ... ... ........ .. ....... .. ........... .. ... .. .. ... Cumberland , MD-WV ..... ....... .. .. .. .... .. .. ..... .. .. ............. ..... ... .... . Dallas. TX .... .. .. ................... ..... ... ...... .. ...... .......... .. .. ............... . Danville VA ............ .. .... .. ... ... .................... .. .. ........ ............... ... 34,391 28,490 29.904 28,412 35,028 29 .361 35.525 25.504 42,706 25.465 34,681 29,135 30,721 29,207 36,144 30,168 36,766 26,704 43,000 26,116 .9 .6 2.1 4.1 3.2 3.0 2.9 1.0 -.2 -.6 1.7 7.1 3.4 1.1 2.3 2.3 2.3 2.2 .2 4.9 3.8 1.9 3.1 2.8 2.2 4.5 1.3 2.6 3.1 5.4 1.7 .8 2.3 2.7 2.8 3.2 2.7 3.5 4.7 .7 2.6 See footnotes at end of tabl e. Monthly Labor Review December 2004 89 Current Labor Statistics: 90 Mrinthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Labor Force Data 26. Continued-Annual data: Quarterly Census of Employment and Wages, by metropolitan area, 2001-02 Average annual wage2 Metropolitan area 1 2001 2002 Percent change, 2001-02 Davenport-Moline-Rock Island, IA-IL ........... .. ..... ........... ........ . Dayton-Springfield, OH ................ ..................... ...... ... ......... .. .. Daytona Beach, FL ........................... ..................................... . Decatur, AL .................................................................... ....... .. Decatur, IL ........ ........................ ... ... .... ... ... .... .. .. ..................... . Denver, CO ................ .................... .... .................................... . Des Moines, IA ..... ............... .. ......... ... .................. .................. . Detroit, Ml .. .......................... ........ .......................................... . Dothan, AL .... .............................................. ............... ..... ... .... . Dover, DE .............................................. ... ... ..................... .... .. $31 ,275 33,619 25,953 30,891 33,354 42,351 34,303 42,704 28,026 27,754 $32,118 34,327 26,898 30,370 33,215 42,133 35,641 43,224 29,270 29,818 2.7 2.1 3.6 -1.7 -.4 Dubuque, IA .... ... .. .. ... .... ..... .... ............................ .............. .... .. . Duluth-Superior, MN-WI ... ..................................................... . Dutchess County, NY ... .. ........ .. .............................. .... .... ....... . Eau Claire, WI .. ..... .. .................. ..... ....... ................. ... ... ....... .. . El Paso, TX ... .... ........................... .. ........... .... .. ... .. ... ................ Elkhart-Goshen, IN .. .... ........... ..... ... ........................... ............. Elmira, NY ... ........................................... .... ......... .................. . Enid, o:< ................................................................................. Erie, PA ............................. .. ... .. .. ..... ........... ... .... ............. .. ..... . Eugene-Springfield, OR ................ ...... .. ................................. . 28,402 29,415 38,748 27,680 25,847 30,797 28,669 24,836 29,293 28,983 29,208 30,581 38,221 28,760 26,604 32,427 29,151 25,507 29,780 29,427 2.8 4.0 -1.4 3.9 2.9 5.3 1.7 2.7 1.7 1.5 Evansville-Henderson, IN-KY ... ..... ........................................ . Fargo-Moorhead, ND-MN ..................... ... .............................. . Fayetteville, NC .. ...... .... ... .. ..... ......................... .. .................... . Fayetteville-Springdale-Rogers, AR ... .......... ... .................. ... .. Flagstaff, AZ-UT .... ..................... ... .. ........................... ...... ..... . Flint, Ml ........... ....................................... .. ... .. .... ...... .. .... ......... . Florence, AL .... ... ............... ..................... ............................... . Florence, SC ...... .. .. .. .... ................. ... ............ .... ... ........... .... .... . Fort Collins-Loveland, CO .... ... ............................................ .. . Fort Lauderdale, FL ...... .. ........... .. .. ................ .. .... .... .. ............ . 31 ,042 27,899 26,981 29,940 25,890 35,995 25,639 28,800 33,248 33,966 31,977 29,053 28,298 31,090 26,846 36,507 26,591 29,563 34,215 34,475 3.0 4.1 4.9 3.8 3.7 1.4 3.7 2.6 2.9 1.5 Fort Myers-Cape Coral, FL .... .. .... ............................... ......... .. . Fort Pierce-Port St. Lucie, FL ............... ............... .... ............ .. . Fort Smith, AR-OK .. ... .......................................... ................... Fort Walton Beach, FL .. ............. .. ...................... .................... . Fort Wayne, IN .. .. ........... .................................................... .. . . Fort Worth-Arlington, TX .. .... ... ....... .. .. ... ... .............. .. .... .......... . Fresno, CA ......................... .... .. .. .. ... .. .... ... .. .. .... .. .... .... ........... . Gadsden, AL .. .... ................... .. ...... ... ............ .. ..... ... ................ . Gainesville, FL ...... .. .......... ..... .. ... .... .. ..... .. .. ................ ............. Galveston-Texas City, TX ... .... .............. .. .... ............ ..... ....... ... . 29,432 27,742 26,755 26,151 31,400 36,379 27,647 25,760 26,917 31 ,067 30,324 29,152 27,075 27,242 32,053 37,195 28,814 26,214 27,648 31,920 3.0 5.1 1.2 4.2 2.1 2.2 4.2 1.8 2.7 2.7 Gary, IN ... .............. ........ .... .. ... ................ ... ........... ... .............. . Glens Falls, NY ......... .... .... ... .. ......... .............. ... ............. .... ...... Goldsboro, NC .. ............... .. .... ... .... .. ............ .......... ................. . Grand Forks, ND-MN .. ...... ............ .. .. ..................................... . Grand Junction, CO ............... .............. ... ... .. ... .. ..................... . Grand Rapids-Muskegon-Holland, Ml ........ .. ......................... . Great Falls, MT ... ... ............ ........... ....................... ... .. .... .... ..... . Greeley, CO .... ........... ... ...... .. .... .............. ......... .......... .... ... .... .. Green Bay, WI .. ........................ .. ........... ... .. ..... ....................... Greensboro-Winston-Salem-Hig h Point, NC ... ..... ........... .. .. . 31 ,948 27,885 25,398 24,959 27,426 33,431 24,211 30,066 32,631 31,730 32,432 28,931 25,821 25,710 28,331 34,214 25,035 31 ,104 33,698 32,369 1.5 3.8 1.7 3.0 3.3 2.3 3.5 3.3 2.0 Greenville, NC ............. .... ........... .. .... .......... ................... .. ...... . Greenville-Spartanburg-Anderson, SC .. ......... ............ .... .. .... .. Hagerstown, MD ................... ................ .... .. ......................... ... Hamilton-Middletown, OH .... .. .. ............ .. ..... ................... ........ . Harrisburg-Lebanon-Carlisle, PA .......... ... .. ............................ . Hartford, CT .... .. ... ... .. .... .. .............. .. ... ..... .............. ..... ... .... ..... . Hattie~hurg, MS .. ................... ................. ... .... .. .. ........ ............ . Hickory-Morganton-Lenoir, NC ................ ............. ...... ........... . Honolulu , HI .. .. ... .... .. ... ............ .. ......... ... .... .... ........ ..... ..... .. ... .. . Houma, LA .. .......... ................................... ............. ................. . 28,289 30,940 29,020 32,325 33,408 43,880 25,145 27,305 32,531 30,343 29,055 31 ,726 30,034 32,985 34,497 44,387 26,051 27,996 33,978 30 ,758 2.7 2.5 3.5 2.0 3.3 1.2 3.6 2.5 4.4 1.4 Houston , TX ...................................... .............. .. ........... .... ...... . Huntington-Ashland, WV-KY-OH ......... ... .. ................. ... .... .... . . Huntsville, AL ............................... ... .................... .... ... ... .......... Indianapolis, IN ............ ..... .................. ..... ................. .. .......... .. Iowa City, IA .... ................... .................... .... ......................... ... Jackson, Ml .. ...... .. .. ... ... .. .. .. ........... ..... .................. ...... ..... ..... .. Jackson, MS ............................. ... ...... .............. .. .... ..... .......... .. Jackson, TN ........ .... .. .... ........ .. .... ...... .. ............... ... .. .... ......... ... Jacksonville, FL ................ ......... ... .... ........................... .. ..... .... Jacksonville, NC ..... ................ ... ... ......................... ............... . 42,784 27,478 36,727 35,989 31,663 32,454 29,813 29,414 32,367 21 ,395 42,712 28,321 38,571 36,608 32,567 33,251 30,537 30,443 33,722 22,269 3.1 5.0 1.7 2.9 2.5 2.4 3.5 4.2 4.1 See footnotes at end of table. December 2004 -.5 3.9 1.2 4 .4 7.4 3.4 -.2 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 26. Continued-A nnual data: Quarterly Census of Employment and Wages, by metropolitan area, 2001-02 Average annual wage2 Metropolitan area 1 Percent change, 2001-02 2001 2002 Jamestown, NY .. .............. ... .................. ................................ . Janesville-Beloit, WI .... ................... ................................ .... ... . Jersey City, NJ .... .. ............... .... ......... ................ .. ... .............. . . Johnsl)n City-Kingsport-Bristol, TN-VA ............... ............ ...... . Johnstown, PA ............................. ............ .......... .. ... ......... ...... . Jonesboro. AR ... ........... ...... .......... .. ... ....... ... ............. ... ......... . . Joplin, MO .......... .... .......... .... ................ ... ... ....... ... ... .............. . Kalamazoo-Battle Creek, Ml .. ................................................ . Kankakee, IL .............. ....... ........... ...................................... .... . Kansas City, MO-KS ............................. ................................. . $25,913 31 ,482 47,638 28,543 25,569 25,337 26,011 32,905 29,104 35,794 $26,430 32,837 49,562 29,076 26,161 26,165 26,594 34,237 30,015 36,731 2.0 4.3 4.0 1.9 2.3 3.3 2.2 4.0 3.1 2.6 Kenosha, WI ........................... .... .. .. ... .. .......................... ........ . Killeen-Temple, TX ... ... ..... ....................... .. ..... ... ... ................. . Knoxville, TN .... ......... ......... ... ..... .... .. .... .. ... ........... ....... ......... .. Kokomo, IN ...... .................................. ... .... ............................. . La Crosse, WI-MN ................ ............. ... .... .... .... ...................... Lafayette, LA ...... .. ............... ................... ...................... ........ .. Lafayette, IN ... .. ... ............................ ... .... .......... .. .. .. .............. .. Lake Charles, LA ............................................ .... .......... ... ... .. .. Lakeland-Winter Haven, FL ............................. ... ..... ... ........... . Lancaster, PA ..................... .............................. .... .. ....... .. .... ... 31,562 26,193 30,422 39,599 27,774 29,693 31,484 29,782 28,890 31,493 32,473 27,299 31,338 40,778 28,719 30,104 31 ,700 30,346 29,505 32,197 2.9 4.2 3.0 3.0 3.4 1.4 Lansing-East Lansing, Ml .... .... .... ......... ..................... .. ... ....... . Laredo, TX ....... ............. .. ... .......... ... ....... .............. .. ................ . Las Cruces, NM ............ .... ... ...................... ... ... ... ........... ...... .. . Las Vegas, NV-AZ ... .. ... ........... .............. ............. ................... . Lawrence, KS .... ......... ... ... ... ............ ............................ ..... .. ... . Lawton, OK ...... ... .. ....... .. ............................... .. ... .... ....... .... .... .. Lewiston-Auburn, ME ... ... ............................... ... .. ......... ..... .... . Lexington, KY ............. .. .......... ..... .. .............................. ........... Lima, OH ........... .... .. ..... ......... ...... .. ... ... ................ .. ................ . Lincoln, NE .......................... ....................... .. ......................... . 34,724 24,128 24,310 32,239 25,923 24,812 27,092 31 ,593 29,644 29,352 35,785 24,739 25,256 33,280 26,621 25,392 28,435 32,776 30,379 30,614 3.1 2.5 3.9 3.2 2.7 2.3 5.0 3.7 2.5 4.3 Little Rock-North Little Rock, AR ... ..... ... .......... .... ... .. ... .. ........ . Longview-Marshall, TX .................... .... ................ .. ......... ... .... . Los Angeles-Long Beach, CA .................. .... ...... ... .. ... ... ..... .... Louisville, KY-IN ...... ..... ..... .... ........ .... .. .... .... ... ... .. ...... ... .. ... ..... Lubbock, TX ...... ..... ... ..... .. ....... ........ ... ...... .... ..... ........ .. .......... . Lynchburg, VA ... .... .... .... ..... ............ .... ... .. .. ... ......... ................. Macon, GA .. ............................. .... .. .... ....... ... .. ... ... ................... Madison, WI ... .. .. ..................... .................... ..................... .... .. . Mansfield, OH ......... ... .. .. ..... .. ........... .......... .. ........ ...... .. .......... . McAllen-Edinburg-Mission, TX ... .................. ...... .......... ......... . 30,858 28,029 40,891 33,058 26,577 28,859 30,595 34,097 28,808 22,313 31 ,634 28,172 41 ,709 33,901 27,625 29,444 31,884 35,410 30,104 23,179 2.5 Medford-Ashland, OR ...... .................... ..................... ... .. .. ... ... . Melbourne-Titusville-Palm Bay, FL ... .. ... ........................ ..... ... . Memphis, TN-AR-MS .. .... ... .. ............................. ...... .. ............ . Merced, CA .... ... .. .. ... ........... ........... ... ... .............. .. .. .... .... .. .... .. . Miami , FL .... .......................... .. ........ ... .... ... .... ... ... .. .... .. ... ....... .. Middlesex-Somerset-Hunterdon. NJ ........ .. ........................ .. .. Milwaukee-Waukesha, WI .. ................................................... . Minneapolis-St. Paul, MN-WI ........, ...... ... .............................. . Missoula, MT .... .................. .. .... .... ... ... ................... ... .... ... ...... . Mobile , AL ············ ··································································· 27,224 32,798 34,603 25,479 34,524 49,950 35,617 40,868 26,181 28,129 28,098 33,913 35,922 26,771 35,694 50,457 36,523 41 ,722 27,249 28,742 3.2 3.4 3.8 5.1 3.4 1.0 2.5 2.1 4.1 2.2 Modesto, CA ...... .. .... .. .... ............. .. .. .......... ...... .... .................... Monmouth-Ocean, NJ .... ... ............. ................. .. ......... .. ......... . Monroe, LA ... .... ... ..... ... ..................... .. .... ............ ............ ....... . Montgomery, AL ................. ..... .... ......... ..... ... .. ... .......... ........... Muncie, IN ... .... .. .. ... .......... ..... .. ...... .. ..... .... .. ...... .. .... ............... . Myrtle Beach, SC ... .. ... .. .......... ... ... .... .............. ....................... . Naples, FL ...... ..... ...... ... ..... ... .... ............... .... ........ .. .. .. ... .... ..... . Nashville, TN ............................................ .. ....................... .... . Nassau-Suffolk, NY .. ...................................... ............. .... .... .. . New Haven-Bridgeport-Stamford-Waterbury-Danbury, CT ... . 29,591 37,056 26,578 29,150 28,374 24,029 30,839 33,989 39,662 52,198 30,769 37,710 27,614 30,525 29,017 24,672 31 ,507 35,036 40,396 51 ,170 4.0 1.8 3.9 4.7 2.3 2.7 2.2 3.1 1.9 -2.0 New London-Norwich, CT .. .. .... .... .. .............. .. .............. .. ....... . New Orleans, LA .......... ..... ............. ... .. .... ............... ... .. ... ....... . New York, NY .............. .... .. .... .............. ..... ...... ... ... .... .. ... ........ . Newark, NJ ...... .... ........... .. ........ ..... .................... ... .. ..... ........ .. . Newburgh, NY-PA ... ..... .... .. ............. .... ...... ..................... .... .. .. Norfolk-Virginia Beach-Newport News. VA-NC .. ................... . Oakland, CA .. .. .. .. ... ... ....... ............. ........... ........ .. ............. .... .. . Ocala, FL ................ ......... ... .... ... .... .... ... .. .. ................... ..... ..... . Odessa-Midland, TX .... .... ....... ... ...... .... ..... ..... ............ ... .. ... .... . Oklahoma City, OK .... ..... .. .. ... ......................... ........... ... ... ...... . 38,505 31 ,089 59,097 47,715 29,827 29,875 45,920 26,012 31,278 28,915 38,650 32,407 57,708 48,781 30,920 30 ,823 46,877 26,628 31 ,295 29,850 .4 4.2 -2.4 2.2 3.7 3.2 2.1 2.4 .1 3.2 .7 1.9 2.1 2.2 .5 2.0 2.6 3.9 2.0 4.2 3.9 4.5 3.9 See footnotes at end of table. Monthly Labor Review December 2004 91 Current Labor Statistics: 92 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Labor Force Data 26. Continued-Annual data: Quarterly Census of Employment and Wages, by metropolitan area, 2001-02 Average annual wage2 Metropolitan area1 2001 2002 Percent change, 2001-02 Olympia, WA .. .. ... ........... ... .... ..................... .. ..... .... .. ................ Omaha, NE-IA .. ... ................ .. ...................... ............ ...... ........ . Orange County, CA ..................................... .. .. ... ...... .... ..... ... .. Orlando, FL ............ .... .. ... ...... .... ......... ........................ ........ .... . Owensboro, KY ............. .... .. ... .. ...... .. .. ... .. .. .. ..... .................... .. Panama City, FL .. ....... ................................. .. .. .... .. .... .. ... .. .... .. Parkersburg-Marietta, WV-OH ... .... ................ .. ... ................. .. Pensacola, FL .............. ..... .. ... ... ........ .. .. .......... ....................... . Peoria-Pekin, IL ... .... ... .... .... ........................ ............. ...... ..... .. .. Philadelphia, PA-NJ ............... ............... ........ .. .... ........ ... ... ... . .. $32,772 31,856 40,252 31 ,276 27,306 26,433 27,920 28,059 33,293 40,231 $33,765 33,107 41,219 32,461 28,196 27,448 29,529 28,189 34,261 41,121 Phoenix-Mesa, AZ .......... .......... ............ ...... .. ... .................. ... .. Pine Bluff, AR ... .... ............................................ .................... .. Pittsburgh , PA .. .. ................ ... ... ......................... ............. ... ... ... Pittsfield, MA ..... .... .. .... ... ..... ........ .. ...... ........... .. ....... .... ... .. ...... . Pocatello, ID ... ... ... .... ...... ...... .................... ... ................. .. ....... . Portland, ME ... ...... .. .. ... .. .. ........ .......... .... ................. ....... ........ . Portland-Vancouver, OR-WA .... ......................................... .. .. Providence-Warwick-Pawtucket. RI .. ................ .. ... ............. ... Provo-Orem . UT .... ..... ... .......... ....... .. .. .. ... ...... .. ................ .... . .. Pueblo, CO ........................................ ............ .... ..... .... ..... .. .... . 35,514 27,561 35,024 31,561 24,621 32,327 37,285 33,403 28,266 27,097 36,045 28,698 35,625 32,707 25,219 33,309 37,650 34,610 28,416 27,763 Punta Gorda. FL .......... ............... ........... ... ... ........................... Racine, WI .............. ...... .. ... ............ ........... ... ... ... ...... .. ......... ... . Raleigh-Durham-Chapel Hill, NC ......... .... .. ... ................ ........ .. Rapid City, SD ............... .... ....... ........................................... ... Reading, PA .............................. ...... ............ .......................... . Redding, CA ... .... ......... .. ...................... .. ..... ... ........... ... ... ....... . Reno, NV ................ ..... .. ..................... ... .... ........................... . . Richland-Kennewick-Pasco. WA .. .... .. ..... .. .. .. ......... ............... . Richmond-Petersburg, VA ..... ... .... .... ............. ... .... ... ..... .... .. ... . RiversidP.-San Bernardino, CA ............................................... 25,404 33,319 38,691 25,508 32 ,807 28,129 34,231 33,370 35,879 30,510 26,119 34,368 39,056 26,434 33,912 28,961 34,744 35,174 36,751 31 ,591 Roanoke, VA .... .. .... ..... ... .................... ............ ................... ..... Rochester, MN .. ... .. ... .. ...... .. .. ... ...... .... .... ... .............. .... .... .... .. .. Rochester, NY ................ .. ............................ .. .................... .. .. Rockford, IL .......... .. ........ ......... ... .. .. ...... ... .... .. .... ................... .. Rocky Mount, NC .. ...................... .......... .. .... .... ...... ................ . Sacramento, CA .... .... ................... .. ... ... .... .. ... .. ..... ........... .... ... Saginaw-Bay City-Midland, Ml ................ ...... .. ...................... . St. Cloud, MN ........................................... ............................ .. St. Joseph, MO .... .. ... ..... ... ... ............... ................ .... ... ..... ... ... .. St. Louis, MO-IL ........ .......... .............. ... .. ..... ... .. .......... ...... .. .... . 30,330 37,753 34,327 32,104 28,770 38,016 35,429 28,263 27,734 35,928 31 ,775 39,036 34,827 32,827 28,893 39,354 35,444 29,535 28,507 36,71 2 Salem, OR .. .... .. ............... ............... ... ... ... ...... .. .. .......... .... .. .... . Salinas, CA .. .. .. .......... ....... .......... ......... ...... .. .. ....... .. ............... . Salt Lake City-Ogden , UT ... ................................................. .. . San Angelo, TX ..................................................................... . San Antonio, TX ................................................................... .. San Diego, CA ................ ............ ................ .... .. ........... ...... .... . San Francisco, CA ............................ .. ... .... .. ...... .. .... .. ... ... .. ... .. San Jose, CA .. .. ......... .. ............ ... ........... .. ... .................. .. ....... . San Luis Obispo-Atascadero-Paso Robles, CA .......... ......... .. Santa Barbara-Santa Maria-Lompoc, CA ........................ ...... . 28,336 31,735 31 ,965 26,147 30,650 38,418 59 ,654 65,931 29,092 33,626 29,210 32,463 32,600 26,321 31 ,336 39,305 56,602 63,056 29,981 34,382 2.2 2.3 -5.1 -4.4 3.1 2.2 Santa Cruz-Watsonville, CA ........................ ...... .. .... .............. . Santa Fe, NM ..................... .. .. ... ...... .. ... ................................. . Santa Rosa, CA .... .... .... .. .. ... ...... .. .. ............... ........................ .. Sarasota-Bradenton, FL ... ... ... ... ... ... .. .. ............................... ... . Savannah, GA ...................... .......... ............. .... ................... .. .. Scranton-Wilkes-Barre--Hazleton , PA .... ... ... ... ...... ............... . Seattle-Bellevue-Everett, WA .. .............................................. . Sharon, PA ............................... .. ........................... .. .... .... .... .. . Sheboygan, WI ......... ... ... .. .... .... ................... .. ..... .. ................. . Sherman-Denison, TX ........................................................... . 35,022 30,671 36 ,145 27,958 30,176 28,642 45,299 26,707 30,840 30,397 35,721 32,269 36,494 28,950 30,796 29,336 46,093 27,872 32,148 30,085 2.0 5.2 1.0 3.5 2.1 2.4 1.8 4.4 4.2 -1.0 Shreveport-Bossier City, LA ..................... .......... .................. .. Sioux City, IA-NE .............. ... ... ......................... .. ... .. ..... ... ....... . Sioux Falls, SD .............. .. ... ..... .......... .. .............. ............ ... ..... . South Bend , IN ........... .............. ..... .... .. .. ........... ..... .... .... .... .... . Spokane, WA ... ............. ..... ..... .. .................. ... ... .. ... ........... ..... . Springfield, IL .... .... ... ... .......... .... ..... .... .... ............... ... .............. . Springfield, MO ............... ... .... .... .. ......... .. ..... : ................ .. ... ... . . Springfield, MA ....... ....... .... .. ..... .. ....... .... ...... .. ....... .. ... ... ........ . . State College, PA ... ..... .. .... .. ... .. ...................... ... ... .. ... .... ... ... ... Steubenville-Weirton, OH-WV ... .. ................ .................. ..... .. .. 27,856 26,755 28,962 30 ,769 29 ,310 36 ,061 27,338 32,801 29 ,939 28,483 28,769 27,543 29,975 31 ,821 30,037 37,336 27,987 33,972 30,910 29,129 3.3 2.9 3.5 3.4 2.5 3.5 2.4 3.6 3.2 2.3 See footnotes at end of table. December 2004 3.0 3.9 2.4 3.8 3.3 3.8 5.8 .5 2.9 2.2 1.5 4.1 1.7 3.6 2.4 3.0 1.0 3.6 .5 2.5 2.8 3.1 .9 3.6 3.4 3.0 1.5 5.4 2.4 3.5 4.8 3.4 1.5 2.3 .4 3.5 .0 4.5 2.8 2.2 3.1 2.3 2.0 .7 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 26. Continued-Annual data: Quarterly Census of Employment and Wages, by metropolitan area, 2001-02 Average annual wage2 Metropolitan area , Percent change, 2001-02 2001 2002 Stockton-Lodi, CA ................ ... ..... .. .... ..... ... .. .... .. ........ ....... .... .. Sumter, SC ............................................................................ . Syracuse, NY .... .................................................. .... ... ...... ... ... . Tacoma, WA ............................ ... .. ........... ... ..... .. ..... .......... ..... . Tallahassee, FL ................... ..... .. ... ......... .... ...... ...... ........... ... .. Tampa-St. Petersburg-Clearwater, FL .... .. .... ............ ............ . Terre Haute, IN ............................................................... ....... . Texarkana, TX-Texarkana, AR ........................................ ...... . Toledo, OH ....... .... .... ......... ... .... ..... .............. .... ........... ... ...... ... Topeka, KS .. ....................................... ..... ...... .... ................ .... . $30,818 24,450 32,254 31 ,261 29,708 31,678 27,334 26 ,492 32,299 30,513 $31,958 24,982 33,752 32,507 30,895 32,458 28,415 27,717 33,513 31,707 3.7 2.2 4.6 4.0 4.0 2.5 4.0 4.6 3.8 3 .9 Trenton, NJ ... ... .................... .. ... ...... ... ... .......................... .... .. . . Tucson, AZ .. ...................... ................... ....... ..................... ..... . Tulsa, OK .... ... .... ... .. .... ..... .. .. ...... ..... .... .. .. ...... ... ... ............. ...... . Tuscaloosa, AL ..... ....................... ................... .................. ..... . Tyler, TX .. . ......................................... .. ............ ................... .. . Utica-Rome, NY .... .. ... ................. .... ............ .. ..... ...... ...... .... ... .. Vallejo-Fairfield-Napa, CA ......... .. ..... ..... ...... ......................... .. Ventura, CA ....................................... ... ............ ........ .. ........... . Victoria, TX ............. ............................................ .................. .. Vineland-Millville-Bridgeton, NJ ............................................ .. 46,831 30,690 31,904 29,972 30,551 27,777 33,903 37,783 29,068 32,571 47,969 31,673 32,241 30,745 31 ,050 28,500 34,543 38,195 29,168 33,625 2.4 3.2 1.1 2.6 1.6 2.6 1.9 1.1 Visalia-Tulare- Porterville, CA ............................ ................... .. Waco, TX .................................. ... ............. .. ....... .............. ..... .. Washington, DC-MD-VA-WV .............. .......... ............... ......... .. Waterloo-Cedar Falls, IA .... ............................................ ...... .. Wausau, WI ............. ...... ... ... ............................. ..................... . West Palm Beach-Boca Raton, FL ...................... .................. . Wheeling, WV-OH .. .............. .. ............................. ..... ..... ........ . Wichita, KS ....................... ... .. .. .. ..... .................. .... ............ .... . . Wichita Falls, TX ................... ................................ .. ..... .. ....... .. Williamsport, PA ......... .. ................................................ .. ...... .. 24,732 28,245 47,589 29,119 29,402 35,957 26,282 32,983 25,557 27,801 25,650 28,885 48,430 29,916 30,292 36,550 26,693 33,429 26,387 27,988 3.7 2.3 1.8 2.7 3.0 1.6 1.6 1.4 3.2 Wilmington-Newark, DE-MD ............ .. .... .. ... .. ... ................ ...... . Wilmington, NC ... ..... ........ .. ............ ........................................ . Yakima, WA .......... ... ... .. ............ ... ....... .. ........ .... .... ... ...... ........ . Yolo, CA .. ...................................... ................. .. .... ..... ..... ....... . York , PA .................................... ......... .......................... ........ .. Youngstown-Warren, OH ........ .. ................ ............. .............. .. Yuba City, CA ............... ...................... .. ............................. ... .. Yuma, AZ ..... .... ................... .... .... ................ ........................... . 42,177 2S,287 24,204 35,352 31,936 28,789 27,781 22,415 43,401 29,157 24,934 35,591 32,609 29,799 28,967 23,429 2.9 -.4 3.0 Aguadilla, PR ........................ ...... ...................................... ..... . Arecibo, PR ..................... .................. .... ..................... .. .... ...... Caguas, PR ............................. .... ... ...... ... ... ................. .... ... ... . Mayaguez, PR ..... .. ....... .... .... ... ...... ... ..... ....... ... .. ............. .... ... . Ponce, PR ............. ........ ......................... .... .......................... .. San Juan-Bayamon, PR ...................... .. .... ............................ . 18,061 16,600 18,655 17,101 17,397 20,948 19,283 18,063 19,706 17,500 18,187 21 ,930 6.8 8.8 5.6 2.3 4.5 4.7 .3 3.2 .7 .7 2.1 3.5 4.3 4.5 1 Includes data for Metropolitan Statistical Areas (MSA) and Primary Metropolitan Statistical Areas (PMSA) as defined by 0MB Bulleti n No. 99-04 . In the New England areas, the New England County Metropolitan Area (NECMA) definitions were used. 2 Each year's total is based on the MSA definition for the specific year. differences resulting from changes in MSA definitions. 3 Annual changes include Totals do not include the six MSAs within Puerto Rico. NOTE: Includes workers covered by Unemployment Insurance (UI) and Unemployment Compensation for Federal Employees (UCFE) programs. Monthly Labor Review December 2004 93 Current Labor Statistics: Labor Force Data 27. Annual data: Employment status of the population [Numbers in thousands] Employment status Civilian noninstitutional population ........... Civilian labor force ............................. ... Labor force participation rate .............. Employed ................................... .... Employment-population ratio ......... Unemployed ............... ... .. ..... ....... .. . Unemployment rate .. ...... ...... .......... Not in the labor force ..... ....................... . 1 1993 1994 1 1995 1996 199i 19981 1999 1 2000 1 2001 2002 2003 194,838 129,200 66.3 120,259 61.7 8,940 6.9 6<;,638 196,814 131 ,056 66.6 123,060 62 .5 7,996 6.1 65,758 198,584 132,304 66.6 124,900 62.9 7,404 5.6 66,280 200,591 133,943 66.8 126,708 63.2 7,236 5.4 66,647 203,133 136,297 67.1 129,558 63.8 6,739 4.9 66,836 205,220 137,673 67.1 131,463 64.1 6,210 4.5 67,547 207,753 139,368 67.1 133,488 64.3 5,880 4.2 212,577 142,583 67.1 136,891 64.4 5,692 4.0 69,994 215,092 143,734 66.8 136,933 63.7 6,801 4.7 71,359 217,570 144,863 66.6 136,485 62.7 8,378 5.8 72 ,707 221,168 146,510 66.2 137,736 62.3 8,774 6.0 74,658 68,385 Not strictly comparable with prior years. 28. Annual data: Employment levels by industry [In thousands] 1993 Industry 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Total private employment... .......................... 91 ,855 95,016 97,866 100,169 103,113 106,021 108,686 110,996 110,707 108,828 108,356 Total nonfarm employment.. ... ... .. .. .... ...... Goods-producing .............................. .... ... Natural resources and mining ................ Construction ............................. .... .. .. .... Manufacturing .. .................... .. ... ...... ...... 110,844 22,219 666 4,779 16,744 114,291 22 ,774 659 5,095 17,021 117,298 23,156 641 5,274 17,241 119,708 23,410 637 5,536 17,237 122,770 23,886 654 5,813 17,419 125,930 24,354 645 6,149 17,560 128,993 24,465 598 6,545 17,322 131 ,785 24,649 599 6,787 17,263 131,826 23,873 606 6,826 16,441 130,341 22 ,557 583 6,716 15,259 129,931 21,817 571 6,722 14,525 69,636 22 ,378 5,093.2 13,020 .5 3,553.8 710.7 2,668 6,709 11,495 12,303 9,732 4,350 72 ,242 23,128 5,247.3 13,490.8 3,701 .0 689.3 2,738 6,867 12,174 12,807 10,100 4,428 74,710 23,834 5,433.1 13,896.7 3,837.8 666.2 2,843 6,827 12,844 13,289 10,501 4,572 76,759 24,239 5,522.0 14,142.5 3,935.3 639.6 2,940 6,969 13,462 13,683 10,777 4,690 79,227 24,700 5,663.9 14,388.9 4,026.5 620.9 3,084 7,178 14,335 14,087 11 ,018 4,825 81,667 25,186 5,795.2 14,609.3 4,168.0 613.4 3,218 7,462 15,147 14,446 11,232 4,976 84,221 25,771 5,892.5 14,970.1 4,300.3 608.5 3,419 7,648 15,957 14,798 11,543 5,087 86,346 26,225 5,933.2 15,279.8 4,410.3 601.3 3,631 7,687 16,666 15,109 11,862 5,168 86,834 25,983 5,772.7 15,238.6 4,372.0 599.4 3,629 7,807 16,476 15,645 12,036 5,258 86,271 25,497 5,652.3 15,025.1 4,223.6 596.2 3,395 7,847 15,976 16,199 11,986 5,372 86,538 25,275 5,605.6 14,911.5 4,176.7 580 .8 3,198 7,974 15,997 16,577 12,125 5,393 18,989 19,275 19,432 19,539 19,664 19,909 20,307 20,790 21 ,118 21 ,513 21 ,575 Private service-providing ........... ..... ... ....... Trade, transportation, and utilities .......... Wholesale trade ..................... .. ........... Retail trade .... ................................... . Transportation and warehousing ........ Utilities ..... ........... .............. .... ........ ... Information ..... .. ........ .............. ... ... .... .. Financial activities ....... ... ........... ........... Professional and business services ... .. Education and health services .. ... ...... . Leisure and hospitality ........ ..... ......... Other services .. ..... ....... .... .. ..... .... ... . Government. ..... ..... ........ .. ..... ... ... ..... .... NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System (NAICS), replacing the Standard lndustrrial Classification (SIC) system. NAICS-based data by industry are not comparable with SIC-based data. See "Notes on the data" for a description of the most recent benchmark revision . 94 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 29. Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm payrolls, by industry 2001 2000 1999 1998 1996 1997 1995 1994 1993 Industry 2002 2003 Private sector: Average weekly hours .. ........................... .. ... ........... Average hourly earnings (in dollars) .......... .. .. .......... Average weekly earnings (in dollars) .... ............... .... 34.3 11.03 378.40 34.5 11.32 390.73 34.3 11 .64 399.53 34.3 12.03 412 .74 34.5 12.49 431.25 34.5 13.00 448.04 34.3 13.47 462.49 34.3 14.00 480.41 34.0 14.53 493.20 33.9 14.95 506.07 33.7 15.35 517.36 Goods-produclnQ: Average weekly hours .... ... ... ............ .......... ........... . Average hourly earnings (in dollars) .... .................. Average weekly earnings (in dollars) ........... ... ... .... 40 .6 12.28 498.82 41 .1 12.63 519.58 40.8 12.96 528.62 40.8 13.38 546.48 41 .1 13.82 568.43 40.8 14.23 580 .99 40.8 14.71 599.99 40.7 15.27 621.86 39.9 15.78 630.04 39.9 16.33 651 .61 39.8 16.80 669.23 Natural resources and mlnlnQ Average weekly hours ........... ....... ............. ............ Average hourly earnings (in dollars) .. .. ....... ..... .. ... Average weekly earnings (in dollars) ... .... .. .... .. ..... 44.9 14.12 634.77 45.3 14.41 653.14 45.3 14.78 670 .32 46.0 15.10 695.07 46.2 15.57 720.11 44.9 16.20 727.28 44.2 16.33 721 .74 44.4 16.55 734.92 44.6 17.00 757.92 43.2 17.19 741.97 43.6 17.58 766.83 Construction: Average weekly hours .... .. ........................ ... .... ...... Average hourly earnings (in dollars) ... ........ .......... Average weekly earnings (in dollars) ....... .. .... ... .. .. Manufacturing: Average weekly hours ... .... .... ... .. ................ ..... .... .. Average hourly earnings (in dollars) ..................... Average weekly earnings (in dollars) .................... 38.4 14.04 539.81 38.8 14.38 558.53 38.8 14.73 571.57 38.9 15.11 588.48 38.9 15.67 609.48 38.8 16.23 629.75 39.0 16.80 655.11 39.2 17.48 685.78 38.7 18.00 695.89 38.4 18.52 711.82 38.4 18.95 727.11 41 .1 11.70 480.80 41 .7 12.04 502 .12 41 .3 12.34 509.26 41 .3 12.75 526.55 41 .7 13.14 548.22 41.4 13.45 557.12 41.4 13.85 573.17 41.3 14.32 590.65 40.3 14.76 595.19 40.5 15.29 618.75 40.4 15.74 636.07 Private service-providing: Average weekly hours .. .... ........... ........ .. .............. . Average hourly earnings (in dollars) ... .. ... .............. Average weekly earnings (in dollars) ... ... .... .. .... .. ... 32.5 10.60 345.03 32 .7 10.87 354.97 32.6 11 .19 364.14 32 .6 11.57 376.72 32.8 12.05 394.77 32.8 12.59 412 .78 32.7 13.07 427.30 32 .7 13.60 445.00 32 .5 14.16 460.32 32.5 14.56 472.88 32.4 14.96 484.00 Trade, transportation, and utilities: Average weekly hours ....... .......... ....... ....... .. .... ....... Average hourly earnings (in dollars) ............. .... .. ... Average weekly earnings (in dollars) ..................... 34.1 10.55 359.33 34.3 10.80 370.38 34.1 11 .10 378.79 34.1 11 .46 390.64 34.3 11 .90 407.57 34.2 12.39 423.30 33.9 12.82 434.31 33.8 13.31 449.88 33.5 13.70 459.53 33.6 14.02 471 .27 33.6 14.34 481.10 38.5 12.57 484.46 38.8 12.93 501.17 38.6 13.34 515.14 38.6 13.80 533.29 38.8 14.41 559.39 38.6 15.07 582.21 38.6 15.62 602.77 38.8 16.28 631.40 38.4 16.77 643.45 38.0 16.98 644.38 37.8 17.36 657.12 30.7 8.36 484.46 30.9 8.61 501.17 30.8 8.85 515.14 30.7 9.21 533.29 30.9 9.59 559.39 30.9 10.05 582.21 30 .8 10.45 602 .77 30 .7 10.86 631.40 30 .7 11.29 643.45 30.9 11 .67 644.38 30.9 11.90 657.12 Transportation and warehousing: Average weekly hours ... ..................................... Average hourly earnings (in dollars) ................. . Average weekly earnings (in dollars) .. .... ... ...... .. 38.9 12.71 494.36 39.5 12.84 507.27 38.9 13.18 513.37 39.1 13.45 525.60 39.4 13.78 542.55 38.7 14.12 546.86 37.6 14.55 547.97 37.4 15.05 562 .31 36.7 15.33 562.70 36.8 15.76 579.75 36.8 16.25 597.79 Utilities: Average weekly hours .... ............... ... .......... ........ Average hourly earnings (in dollars) .... ............. . Average weekly earnings (in dollars) ................ . 42.1 17.95 756.35 42.3 18.66 789.98 42.3 19.19 811.52 42 .0 19.78 830.74 42.0 20.59 865.26 42.0 21.48 902.94 42.0 22.03 924.59 42.0 22.75 955.66 41 .4 23.58 977.18 40.9 23.96 979.09 41 .1 24.76 1,016.94 36.0 14.86 535.25 36.0 15.32 551.28 36.0 15.68 564.98 36.4 16.30 592 .68 36.3 17.14 622.40 36.6 17.67 646.52 36.7 18.40 675.32 36.8 19.07 700.89 36.9 19.80 731.11 36.5 20.20 738.17 36.2 21.01 761.13 35.5 11 .36 403.02 35.5 11.82 419.20 35.5 12.28 436.12 35.5 12.71 451.49 35.7 13.22 472.37 36.0 13.93 500 .95 35.8 14.47 517.57 35.9 14.98 537.37 35.8 15.59 558.02 35.6 16.17 575.51 35.5 17.13 608.87 34.0 11 .96 406.20 34.1 12.15 414.16 34.0 12.53 426.44 34.1 13.00 442.81 34.3 13.57 465.51 34.3 14.27 490.00 34.4 14.85 510.99 34.5 15.52 535.07 34.2 16.33 557 .84 34.2 16.81 574.66 34.1 17.20 586.68 Education and health services: Average weekly hours .... .. ...................... .......... .. Average hourly earnings (in dollars) ...... ... ... ...... Average weekly earnings (in dollars) ................. 32 .0 11.21 359.08 32 .0 11.50 368.14 32.0 11.80 377.73 31 .9 12.17 388.27 32.2 12.56 404.65 32.2 13.00 418.82 32 .1 13.44 431.35 32.2 13.95 449.29 32.3 14.64 473.39 32.4 15.21 492.74 32.3 15.64 505.76 Leisure and hospitality: Average weekly hours .. .... .. .. ..... .. ... .. .................. Average hourly earnings (in dollars) ................. . Average weekly earnings (in dollars) .............. ... 25.9 6.32 163.45 26.0 6.46 168.00 25.9 6.62 171 .43 25.9 6.82 176.48 26.0 7.13 185.81 26.2 7.48 195.82 26.1 7.76 202.87 26.1 8.11 211 .79 25.8 8.35 215.19 25.8 8.58 221 .26 25.6 8.76 224.25 Other services: Average weekly hours .............. .. ............ .......... .. Average hourly earnings (in dollars) .......... ........ Average weekly earnings (in dollars) ... .............. 32.6 9.90 322.69 32.7 10.18 332.44 32.6 10.51 342.36 32.5 10.85 352 .62 32 .7 11.29 368.63 32.6 11 .79 384.25 32.5 12.26 398.77 32.5 12.73 413.41 32.3 13.27 428.64 32.0 13.72 439.76 31 .4 13.84 434.49 Wholesale trade: Average weekly hours .. .................. ........ ............ Average hourly earnings (in dollars) ............. ... .. Average weekly earnings (in dollars) ......... ........ Retall trade: Average weekly hours ... .. ....................... .. .. ... ..... Average hourly earnings (in dollars) .. ................ Average weekly earnings (in dollars) .. .... .. .. .... .. . Information: Average weekly hours ................ .. ............ .......... Average hourly earnings (in dollars) ..... .. .... ...... . Average weekly earnings (in dollars) ... .. .. ... ........ Financial activities: Average weekly hours ...................... ... ...... .... ..... Average hourly earnings (in dollars) .... .............. Aver<1ge weekly earnings (in dollars) ............... .. Professional and business services: Average weekly hours .... ........ ....... ............ .... ..... Average hourly earnings (in dollars) ........... ....... Average weekly earnings (in dollars) .. ............... NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System (NAICS) , replacing the Standard Industrial Classification (SIC) system. NAICS-based data by industry are not comparable with SIC-based data. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 95 Current Labor Statistics: Compensation & Industrial Relations 30. Employment Cost Index, compensation, 1 by occupation and industry group [June 1989 = 100] 2002 Series Sept. 2003 Dec. Mar. June 2004 Sept. Dec. Mar. June Percent change Sept. 3 months 12 months ended ended Sept. 2004 Clvlllan workers2 ............................ ...... .................... ..... ...... .. 161 .3 162.2 164.5 165.8 167.6 168.4 170.7 172.2 173.9 1.0 3.8 163.5 161 .4 166.3 164.9 156.4 161.3 164.3 162.4 166.7 166.1 157.5 162.2 166.7 164.1 171 .1 168.3 159.8 164.1 167.9 165.0 172.0 170.0 161.4 165.0 169.9 167.0 174.0 171.7 162.9 166.8 170.7 168.0 174.9 172.5 163.7 167.9 172.7 170.2 175.8 175.3 166.9 169.7 174.0 171.2 177.1 177.2 168.8 170.9 175.8 173.6 178.2 178.7 170.1 172.7 1.0 1.4 .6 .8 .8 1.1 3.5 4.0 2.4 4.1 4.4 3.5 158.7 159.1 162.2 163.2 163.1 165. 7 161 .6 163.1 164.0 165.0 165.3 166.4 169.9 163.6 163.4 164.5 164.6 165.4 166.2 166.3 167.6 170.8 164.2 164.3 166.8 167.1 169.1 169.5 170.7 174.8 167.6 168.1 168.6 170.4 171.7 170.8 171.2 173.0 176.8 168.5 170.1 170.4 171.9 173.2 172.3 172.3 174.4 178.2 168.9 171.4 171.8 173.4 174.9 174.0 174.5 176.7 180.5 171.8 174.1 173.5 1.0 1.0 1.3 1.3 1.3 1.7 1.6 1.0 Wurkers, by occupational group: White-collar workers........................................................... Professional specialty and technical.... ............................ Executive, adminitrative, and managerial... ............. ...... Administrative support, including clerical........ .... ........... Blue-collar workers... ......................................................... Service occupations................................................. .......... Workers, by industry division: Goods-producing................................................................ Manufacturing. ........ ...................................... .................. Service-producing... ........................................................... Services................. .......................................................... Health services....... ........................................................ Hospitals............... .. ..................................................... Educational services...................................................... Public administration ...... .... ... ...•. ....• ..•.. ...•..•..• .......... ........ Nonmanufacturing... ........................................................... 160.2 161 .7 169.2 160.5 162.8 163.9 164.5 167.6 162.8 161.7 162.4 165.8 165.8 166.5 168.2 168.5 169.3 173.1 166.9 167.3 167.8 Private industry workers. .... ........... ......... ...... .... ....... .... Excluding sales occupations.. ........................................ 161.6 161.6 162.3 162.4 165.0 165.1 166.4 166.6 168.1 168.1 168.8 169.0 171.4 171.6 173.0 173.2 174.4 174.6 .8 .9 Workers, by occupational group: White-collar workers .......................... ..................... ... .. ..... Excluding sales occupations .......... ............................. . Professional specialty and technical occupations ......... . Executive, adminitrative, and managerial occupations .. Sales occupations ...... ... .. ............................................ . Administrative support occupations, including clerical. .. Blue-collar workers ......................................................... . Precision production, craft, and repair occupations ...... . Machine operators, assemblers, and inspectors ........... . Transportation and material moving occupations .......... . Handlers, equipment cleaners, helpers, and laborers .... 164.6 165.3 163.6 167.0 161 .6 165.6 156.3 156.9 155.4 151 .0 161.4 165.2 165.9 164.4 167.2 161.9 166.7 157.3 157.8 156.7 151.8 162.9 168.1 169.1 166.5 172.1 163.5 169.0 159.7 160.0 159.9 153.2 164.9 169.4 170.4 167.7 173.1 165.1 170.9 161.4 162.0 161.1 155.1 166.8 171.2 172.1 169.4 175.0 167.2 172.3 162.8 163.1 162.6 156.7 168.6 172.0 173.0 170.5 175.9 167.1 173.2 163.6 164.2 163.2 156.9 169.5 174.2 175.3 173.4 176.8 169.2 176.1 166.9 167.1 168.7 158.5 171.7 175.7 176.7 174.7 178.1 171.2 178.1 168.8 169.1 170.5 160.6 173.2 177.3 178.3 176.8 179.2 173.1 179.4 170.1 170.2 172.2 161 .8 174.3 .8 .9 .9 .7 .8 .7 1.0 .7 Service occupations ...................................................... . 159.0 159.8 161.7 162.6 163.8 164.3 166.9 168.2 168.9 .6 3.1 Production and nonsupervisory occupations 4 . . . .. . . .• •....... 159.7 160.5 162.6 164.1 165.7 166.6 169.3 171.0 172.4 .4 4.0 Workers, by industry division: Goods-producing ............................................................. . Excluding sales occupations .................................... . White-collar occupations .............................................. Excluding sales occupations ............................ ... ...... . Blue-collar occupations ................. ... ........................... . Construction ................................................... .. ............. . Manufacturing ........................................ .... ................... White-collar occupations ............................................. . Excluding sales occupations .......................... .. ...... .. . Blue-collar occupations .......................... .. .... .. ............. . Durables .. ......................................................... ............. . Nondurables .. .. ........................ ................. .................... . 158.6 157.9 162.9 161 .1 155.9 156.3 159.1 162.2 159.6 156.7 158.9 159.2 160.1 159.2 164.3 162.3 157.3 157.9 160.5 163.3 160.7 158.3 160.6 160.3 163.0 162.4 167.8 166.3 159.9 159.1 164.0 167.1 165.1 161 .6 164.4 163.1 164.5 163.8 169.2 167.5 161 .5 161 .1 165.4 168.7 166.4 162.8 165.5 164.9 165.7 165.0 170.1 168.5 162.9 162.3 166.5 169.5 167.4 164.1 166.6 166.0 166.5 165.9 170.5 169.2 163.9 163.3 167.1 169.6 167.8 165.1 167.3 166.6 170.3 169.8 173.5 172.2 168.1 164.6 171.7 173.2 171.3 170.4 172.4 170.4 171.8 171.2 174.7 173.3 169.8 165.9 173.2 174.6 172.6 172.0 174.0 171 .7 173.3 172.5 176.4 174.5 171.3 167.0 174.9 176.4 174.1 173.7 175.8 173.1 .8 .9 .8 1.0 .7 .9 .7 1.0 .9 1.0 1.0 .8 4.6 4.5 3.3 3.6 5.2 2.9 5.0 4.1 4.0 5.9 5.5 4.3 Service-producing .......................................... ... ... ....... .. .... Excluding sales occupations ..................................... White-collar occupations ............................................. . Excluding sales occupations ..................................... Blue-collar occupations ................................... .. ........... Service occupations .................................................... . Transportation and public utilities .. .................. ............. . Transportation .. ........................................................... . Public utilities ............................................... .. .............. . Communications ................ ....................................... . Electric, gas, and sanitary services .......................... . Wholesale and retail trade ................................... .. ....... . Excluding sales occupations ............... ..... ................ . Wholesale trade .......................................................... . Excluding sales occupations ...... ................... ........... . Retail trade ...................... .................... ..... ... ................ . General merchandise stores .. ................................... . Food stores .. .............................................................. 162.7 163.5 164.7 166.5 156.6 158.5 160.8 155.4 168.2 169.0 167.2 159.6 160.3 165.9 166.1 156.0 156.1 156.3 163.1 164.0 165.1 167.0 156.9 159.3 161.7 156.1 169.2 170.1 168.1 159.7 160.4 166.7 167.2 155.8 155.1 156.3 165.6 166.6 167.9 169.9 158.7 161.1 163.2 157.8 170.5 171.3 169.5 161.3 161.8 169.5 168.4 156.6 156.4 157.5 167.0 168.0 169.2 171 .3 160.8 162.0 165.4 158.9 174.2 175.5 172.6 162.5 162.7 171.3 169.9 157.4 159.2 158.6 168.8 169.7 171 .2 173.1 162.2 163.2 166.5 159.4 176.4 178.4 173.8 164.3 165.0 172.0 171.2 159.9 161.2 159.3 174.7 .8 175.6 .8 177.3 .9 179.4 .9 167.4 .6 168.1 .4 173.6 .6 166.2 .9 183.6 .3 183.6 .1 183.3 .5 169'. 1 .6 169.6 .6 177.8 1.1 175.3 .7 164.2 .3 168.8 1.6 163.5 . J . . _ - - _ j _ - - - - .0 3.5 3.5 3.6 3.6 3.2 3.0 4.3 4.3 4.1 3.0 5.5 2.9 2.8 3.4 2.4 2.7 4.7 2.6 --- 3 L __ _ See footnotes at end of table. 96 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 169.7 170.6 172.0 174.2 162.6 164.3 167.0 159.6 177.0 179.0 174.6 165.0 165.9 172.0 171 .3 161 .0 165.6 160.3 ..,___ __ j _ _ _- ' - - - - - - ' - - - - - - ' - - - _ _ 171 .6 172.5 174.1 176.2 164.1 166.1 169.8 162.0 180.4 182.2 178.2 166.3 167.4 173.8 173.7 162.1 165.8 162.1 _J'---- 173.3 174.2 175.7 177.8 166.4 167.4 172.5 164.7 183.1 183.6 182.4 168.1 168.6 175.9 174.0 163.7 166.2 163.5 -'----. .9 1.2 .6 1.1 - j ' - -- 4.6 5.0 3.4 3.6 4.4 4.3 2.9 4.1 3.4 3.7 3.9 3.6 3.6 4.4 2.4 3.5 4.1 4.5 4.4 5.9 3.3 3.4 1 30. Continued-Emp loyment Cost Index, compensation, by occupation and industry group [June 1989 = 100] Series Sept. Dec. Mar. June Percent change 2004 2003 2002 Sept. Dec. Mar. June Sept. 3 months 12 months ended ended Sept. 2004 Finance, insurance, and real estate ............ ..... .............. Excluding sales occupations ...... .......... ........... ...... .... Banking, savings and loan, and other credit agencies. Insurance ....................................... ....... ........................ Services ...................................... ......... .. ......................... Business services .......... .. ............................................ Health services ... ... ................ ............. ............... .. ... ...... Hospitals ................................................. .. ................. Educational services ........ ............ ....................... ....... .. Colleges and universities ... ....... .. .... ... ........................ 168.0 172.1 184.6 167.1 164.9 167.2 163.2 166.2 173.5 172.0 168.5 173.1 185.3 167.9 165.4 167.5 164.4 168.1 175.2 173.7 176.7 182.0 204.3 172.1 167.1 168.5 166.5 170.8 176.3 174.5 180.2 178.3 184.0 1,853.0 207.6 206.3 175.1 173.9 170.4 168.4 171.9 169.2 169.4 167.9 173.9 171.9 180.2 177.1 178.4 175.4 180.9 186.1 209.0 176.2 171.4 172.6 170.8 175.9 181 .3 179.4 182.5 186.6 207.2 177.8 173.5 174.8 173.3 178.1 183.1 181 .2 183.6 188.7 208.9 180.5 175.1 176.9 174.8 179.7 184.2 182.5 184.8 190.9 210.5 182.1 176.9 178.5 177.0 181.8 187.0 185.2 0.7 .7 .8 .9 1.0 .9 1.3 1.2 1.5 1.5 2.6 2.5 1.4 4.0 3.8 3.8 4.5 4.5 3.8 3.8 Nonmanufacturing ...................................... .................... White-collar workers ... ... ............. .. ............................. ... Excluding sales occupations ... ........ ......................... Blue-collar occupations ..................... ........................... Service occupations ...... ...... ....................................... 162.0 164.8 166.6 155.4 158.4 162.5 165.3 167.1 155.9 159.2 164.9 168.0 170.0 157:5 161.1 166.4 169.3 171 .4 159.7 162.0 168.1 171.2 173.2 161 .1 163.2 169.0 172.1 174.2 161.7 162.4 170.9 174.1 176.2 163.4 166.0 172.5 175.7 177.7 165.5 167.3 173.9 177.2 179.3 166.4 168.0 .8 .9 .9 .5 .4 3.5 3.5 3.5 3.3 2.9 State and local government workers ................................... 160.1 161 .5 162.6 163.2 165.9 166.8 168.0 168.7 171.5 1.7 3.4 159.3 158.1 162.3 161 .0 158.4 160.7 159.4 163.8 162.4 159.8 161 .7 160.2 165.3 163.8 161 .3 162.2 160.8 165.7 164.4 161 .7 164.9 163.4 168.0 167.9 163.6 165.7 164.1 169.1 168.5 165.2 166.8 165.1 170.1 170.4 166.7 167.5 165.6 171 .0 171 .8 167.5 170.0 168.4 172.1 174.3 169.9 1.5 1.7 .6 1.5 1.4 3.1 3.1 2.4 3.8 3.9 159.7 161.0 163.5 164.1 159.2 159.6 157.7 164.7 160.2 160.9 162.8 165.5 166.2 160.3 160.7 158.8 165.8 161 .7 161.8 164.0 166.4 167.0 161.1 161 .4 159.4 167.0 163.4 162.3 164.2 166.7 167.3 161 .7 162.0 160.0 167.5 164.3 164.9 166.8 169.5 170.3 164.3 164.7 163.0 169.2 167.3 165.7 168.2 171 .0 171.4 165.0 165.3 163.7 170.0 168.1 166.5 169.4 172.2 172.4 165.7 166.0 164.4 170.7 170.1 166.8 170.1 172.9 173.2 165.9 166.3 164.6 171 .0 171 .4 169.7 173.0 175.7 176.3 168.8 169.2 168.0 172.4 174.1 1.7 1.7 1.6 1.8 1.7 1.7 2. 1 .8 1.6 2.9 3.7 3.7 3.5 2.7 2.7 3.1 1.9 4.1 Workers, by occupational group: White-collar workers ...................................... .................. ... Professional specialty and technical. ............................... Executive, administrative, and managerial. .................... Administrative support, including clerical .. ...... ............... Blue-collar workers ... ........ .. ................................ ............... Workers, by industry division: Services .................................. .................. ........ ................ 5 Services excluding schools .... ... ....... .... .. ....... .. ............... Health services ....................................... ........... ......... .. Hospitals .......... ......... .............. ................................... Educational services ...................................... .. ............ Schools .... ...... .. ................. ..................... ... .............. ... Elementary and secondary ............. ........................ Colleges and universities ........................................ 3 Public administration .. ... ......... ... .. Cost (cents per hour worked) measured in the Employment Cost Index consists of wages, salaries, and employer cost of employee benefits. 2 Consists of private industry workers (excluding farm and household workers) and State and local government (excluding Federal Government) workers. 1 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 3 Consists of legislative, judicial, administrative, and regulatory activities. This series has the same industry and occupational coverage as the Hourly Earnings index, which was discontinued in January 1989. 4 5 Includes, for example, library, social, and health services. Monthly Labor Review December 2004 97 Current Labor Statistics: Compensation & Industrial Relations 31. Employment Cost Index, wages and salaries, by occupation and industry group [June 1989 = 100] 2002 2003 2004 Percent change Series Sept. Dec. Mar. June Sept. Dec. Mar. June Sept. 3 months 12 months ended ended Sept. 2004 1 Civilian workers .. 157.2 157.8 159.3 160.3 161.8 162.3 163.3 164.3 165.7 0.9 2.4 Workers, by occupational group: White-collar workers .. ............. .................. .......... ... .... .. ... .... Professional specialty and technical. ............... .. .. ........... . Executive, adminitrative, and managerial. .. ........ .. .. ... ... . Administrative support, including clerical. ..................... . Blue-collar workers ............. ............ .. ......... .... .. ................ . . Service occupations .......... ................. ............................... . 159.6 158.0 163.5 159.6 151.9 ·55.2 160.1 158.6 163.8 160.6 152.6 156.9 161.9 159.3 167.9 161.8 153.8 158.0 162.9 160.1 169.0 163.1 154.8 158.7 164.5 161.8 170.5 164.3 155.8 159.8 165.1 162.5 171.2 164.9 156.3 160.6 166.1 163.8 171.4 166.3 157.3 161.2 167.1 164.4 172.4 167.5 158.4 161.9 168.7 166.5 173.4 168.8 159.7 162.8 1.0 1.3 .6 .8 .8 .6 2.6 2.9 1.7 2.7 2.5 1.9 Workers, by industry division: Goods-producing .......... ............ ........ .. ............. ... ..... .. ........ . Manufacturing ................. ... ... ... .. ...... ....... .... .. .. ................. Service-producing ........................................................ ..... . Services ....... .. ............. .. ...... ............ ... ................. ... ... .... ... Health services ............ .. .......... .. .... ...... ... ....................... . Hospitals ............... ................. .. ................................... . Educational services ... ....... ... ..... .. ........... .. .................... . 153.9 155.4 156.4 160.7 159.6 160.3 159.3 155.1 156.5 158.8 161 .1 160.9 162.2 160.1 156.3 158.0 160.5 161.9 162.0 163.5 160.4 157.5 159.0 161.4 162.8 163.2 164.4 160.7 158.3 159.7 163.0 164.7 164.7 166.3 162.7 160.6 160.1 163.6 165.4 165.9 167.7 163.2 159.9 161 .3 164.6 166.5 167.7 169.0 163.6 161.0 162.4 165.5 167.4 168.6 169.9 163.8 162.3 163.8 167.0 167.3 170.8 171.8 166.0 .8 .9 .9 1.1 1.3 1.1 1.3 2.5 2.6 2.5 2.8 3.7 3.3 2.0 Public administration . ..... ... ..... •.. .....• .. •......... ... ................. Nonmanufacturing ............................................. .. .............. . 154.8 157.5 155.8 158.0 157.2 159.6 158.0 160.5 159.4 162.1 160.0 162.7 161.1 163.7 161 .4 164.6 162.6 166.0 .7 .9 2.0 2.4 Private Industry workers ... ...... ........ ........ .................... . Excluding sales occupations .... .. ...... ... ...... ......... ............ 157.0 157.0 157.5 157.9 159.3 159.4 160.4 160.5 161 .7 161 .7 162.3 162.4 163.4 163.5 164.5 164.5 165.9 165.8 .9 .8 2.6 2.5 Workers, by occupational group: White-collar workers ......... ....................... ......... ............... . Excluding sales occupations .... .. ... .. ...... .. .. ... .. .... ....... .. . Professional specialty and technical occupations .... .. ... . Executive, adminitrative, and managerial occupations .. Sales occupations .... ... ............. ... ................................ . Administrative support occupations, including clerical. .. Blue-collar workers ........ .............. .... ........... ... .... .. ........... . Precision production, craft, and repair occupations .... .. . Machine operators, assemblers. and inspectors ........... . Transportation and material moving occupations ... .. .. ... . Handlers, equipment cleaners, helpers, and laborers .... 160.0 169.8 158.2 164.3 156.9 160.3 151.7 151.8 152.0 146.3 156.0 160.4 160.8 158.5 164.5 156.8 161 .3 152.4 152.3 153.2 146.9 157.2 162.6 163.6 159.5 169.1 158.1 162.6 153.6 153.4 154.7 147.8 158.4 163.8 164.8 160.5 170.3 159.3 164.0 154.6 154.7 155.3 149.0 159.0 165.3 166.2 162.1 171.8 161.6 165.1 155.6 155.5 156.8 149.8 159.9 165.9 167.0 163.0 172.5 161 .1 165.7 156.1 156.2 156.9 149.8 160.6 167.1 168.1 164.7 172.7 162.6 167.2 157.2 157.1 158.6 150.4 161 .8 168.2 169.2 165.5 173.9 163.9 168.6 158.3 158.3 159.8 151 .8 162.7 169.7 170.6 167.6 174.9 165.9 169.7 159.5 159.3 161.6 152.9 163.6 .9 .8 1.3 .6 1.2 2.7 2.6 3.4 1.8 2.7 2.8 2.5 2.4 3.1 2.1 2.3 2 Service occupations ......... .... ... ...... ......... .... ... ... .... ... ... .. . . Production and nonsupervisory occupations 3 Workers, by industry division: Goods-producing ............................ ......... ....................... .. Excluding sales occupations .......................... .......... . White-collar occupations ............ .. ............... .. ....... ....... . Excluding sales occupations .................................... . Blue-collar occupations ....................... ........................ . C('\nstruction ... ...... .............................................. .. ... ... ... . Manufacturing .... .... ............... .... ... .. .. .. ..... .... .......... ... ... ... White-collar occupations .. ........................................... . Excluding sales occupations .. .......... ....... .. .... ......... .. . Blue-collar occupations ......... ......... .. .... .......... ..... ..... ... . Durables .. .................................... .. ..... .. .......... .. ............. Nondurables .......................................... .................... ... . .7 .8 .6 1.1 .7 .6 153.9 154.4 155.5 156.1 157.1 157.8 158.4 159.3 159.8 .3 1.7 154.7 155.2 156.4 157.4 158.8 159.4 160.7 161.7 163.1 .9 2.7 153.9 153.0 157.9 155.4 151.5 149.0 155.4 157.7 155.0 153.5 156.0 154.4 155.0 154.0 158.6 156.3 152.6 150.2 156.5 158.6 155.9 154.7 157.3 155.2 156.3 155.4 160.0 158.0 153.8 150.6 158.0 160.1 157.7 156.3 158.8 156.6 157.4 156.5 161.4 159.2 154.8 152.4 159.0 161.6 158.9 156.9 159.7 157.8 158.3 157.4 161.9 159.9 155.9 153.6 159.7 162.0 159.5 157.9 160.6 158.3 158.7 158.0 162.1 160.4 156.4 154.0 160.1 162.1 160.0 158.5 160.9 158.7 159.9 159.2 163.2 161.5 157.7 155.1 161 .3 163.3 161.2 159.8 161.9 160.4 160.9 160.2 164.5 162.7 158.6 155.9 162.4 164.7 162.5 160.6 162.9 161.6 162.3 161.2 166.0 163.6 159.8 157.1 163.8 166.1 163.5 162.1 164.5 162.8 .9 .6 .9 .6 .8 .8 .9 .9 .6 .9 1.0 .7 2.5 2.4 2.5 2.3 2.5 2.3 2.6 2.5 2.5 2.7 2.4 2.8 Service-producing .......................................... ... .. ........... ... 158.4 158.6 160.6 161.7 163.3 163.9 165.0 166.1 167.5 .8 2.6 Excluding sales occupations .................................... . 159.3 159.6 161.7 164.2 162.8 165.0 166.0 167.1 168.5 .8 2.6 White-collar occupations ................. .............. .............. . 160.5 160.7 163.0 164.1 166.0 166.6 167.8 168.9 170.4 .9 2.7 Excluding sales occupations .................. .................. . 162.5 162.8 165.3 166.5 168.2 169.0 170.2 171.2 172.8 .9 2.7 Blue-collar occupations .... ..... ............................... .... .. . . 151 .8 152.0 153.2 154.3 155.1 155.4 156.2 157.8 158.9 .7 2.5 Service occupations ........ ............................................ . 153.5 154.1 155.1 155.6 156.6 157.4 158.0 158.8 159.4 .4 1.8 Transportation and public utilities ............. ......... .... .. ..... . 153.4 154.1 154.8 155.6 156.0 156.5 157.6 159.1 160.4 .8 2.8 Transportation ............... .. ............. ......... .... .. ..... .. ... .. .... . 149.6 150.1 150.5 150.6 150.4 150.8 151.7 153.4 155.0 1.0 3.1 Public utilities ....... ... ... ........ .. .. .......... .. .................... ...... . 158.2 159.3 160.4 162.1 163.4 164.1 165.3 166.4 167.5 .7 2.5 Communications ........................................ ... .......... ... 159.6 160.7 161.9 163.4 165.4 165.9 167.0 167.5 168.8 .8 2.1 Electric, gas, and sanitary services ........ ......... .. ...... .. 156.5 157.4 158.6 160.4 161 .0 161.8 163.3 165.1 165.9 .5 3.0 Wholesale and retail trade ...................... ....... ... ....... ... .. . 155.5 155.5 156.7 157.5 159.2 159.5 161.6 160.3 162.5 .6 2.1 Wholesale trade .............. ................... ............... .......... . 160.4 161 .0 163.4 164.7 164.8 165.3 166.2 167.8 169.7 1.1 3.0 Excluding sales occupations ... .... .. .. ... .. ......... ... ........ . 162.6 163.7 163.9 165.2 165.7 166.3 167.8 167.6 168.6 .6 1.8 Retail trade .. ............... .. .... .... ... ........... .... ............... ..... . 152.9 152.7 153.1 153.8 156.3 156.5 158.4 157.3 158.7 .2 1.5 General merchandise stores ...... ... ........ .. ........... ... .... . 150.1 149.2 149.8 152.0 153.1 153.6 154.1 154.9 157.5 1.7 2.9 Food stores .... ................................................ ..... ..... .. 150.1 150.3 151.0 151 .6 152.2 152.8 153.8 154.3 154.5 .1 1.5 See footnotes at end of table. ' - - - - - ' - - - - - ~ - - - ' - - - ~ - - - - ' - - - - - ' - - - ' - - - - - ' - - - - -- ' - -- - - - ' - - - - - - 98 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 31. Continued-Employment Cost Index, wages and salaries, by occupation and industry group [June 1989 = 100] Percent change 2004 2003 2002 3 months 12 months ended ended Series Sept. Dec. Mar. June Sept. Dec. Mar. June Sept. Sept. 2004 Finance, insurance, and real estate ... .. ........... ... ... ... .. .... Excluding sales occupations .... ... ...... .. ...................... Banking, savings and loan, and other credit agencies. Insurance ...... .... ......... .. ........................... ........... .... .. ..... Services ..... ...... ............. .. ............................ ..... ... ... ......... Business services ...... ..................... ... ... ..... ... .... ... ........ Health services .. ......... .................................................. Hospitals ........................ ... ... ... ... .............. ... ............... Educational services .................. .. ..... ....... .. .................. Colleges and universities ..................... .... ...... .. ...... ... . 162.4 166.1 182.7 159.6 161 .5 164.6 159.9 160.2 165.2 163.1 162.6 167.3 183.9 159.1 161.7 164.8 160.7 162.1 166.5 164.3 171 .1 176.7 206.4 161 .6 162.8 165.6 161 .9 163.6 167.1 164.4 172.4 178.5 208.7 163.0 164.0 166.4 163.2 164.6 167.5 165.1 174.1 179.2 209.1 163.9 165.9 169.1 164.6 166.5 170.3 167.6 174.5 210.2 164.5 164.5 166.7 169.8 135.8 167.9 171 .0 168.4 175.2 179.2 206.7 165.1 168.1 171 .0 167.8 169.4 171 .9 169.5 175.3 180.5 207 .6 167.2 169.3 172.7 168.8 170.5 172.6 170.0 176.5 181 .8 209.5 168.9 171.1 174.3 170.9 172.4 175.5 172.9 0.7 .7 .9 1.0 1.1 .9 1.2 1.1 1.7 1.7 1.4 1.5 .2 3.1 3.1 3.1 3.8 3.5 3.1 3.2 Nonmanufacturing ............. .............................. ... ... ......... White-collar workers ..................................................... Excluding sales occupations ......... .. .... ... ............ .. .... Blue-collar occupations ................................................ Service occupations ..... ..... ..................... .. .... .. .. ........ .. 157.2 160.2 162.1 149.8 153.4 157.5 160.5 162.5 150.2 154.0 159.4 162.8 164.9 151.1 155.0 160.5 163.9 166.1 152.4 155.5 162.1 165.7 167.7 153.4 156.5 162.6 166.3 168.5 153.8 157.3 163.7 167.5 169.7 154.7 157.9 164.8 168.6 170.7 156.1 158.7 166.2 170.1 172.3 157.1 159.2 .8 .9 .9 .6 .3 2.5 2.7 2.7 2.4 1.7 State and local government workers ............... .. ..... .. ....... 160.1 161.5 162.6 163.2 165.9 166.8 168.0 168.7 171 .5 1.0 2.0 Workers, by occupational group: White-collar workers ....................... ................... ... ... .... ....... Professional specialty and technical. ....... .. ............ .......... Executive, administrative, and managerial. ... .... ............. Administrative support, including clerical. ... ... ......... ....... Blue-collar workers .. ............... .. ...... .......... .. ................... .... 157.4 157.5 159.0 155.1 154.5 158.4 158.4 160.1 156.0 155.1 158.9 158.8 160.9 156.9 156.2 159.2 159.1 161 .0 157.2 156.5 161 .0 161 .0 162.5 159.1 157.6 161.5 161.4 163.3 159.5 158.3 162.1 162.1 163.5 160.4 158.9 162.4 162.3 163.8 160.8 159.2 164.1 164.4 164.3 162.6 160.7 1.0 1.3 .3 1.1 .9 1.9 2.1 1.1 2.2 2.0 Workers, by industry division : Services ....................... .. ....... .. ............................. .......... .. . 158.4 159.2 159.5 159.8 161 .6 162.1 162.6 162.7 164.8 1.3 2.0 159.1 160.5 160.6 158.1 158.3 157.4 160.7 160.3 162.2 162.5 158.9 159.0 158.1 161 .6 161 .4 162.9 163.1 159.1 159.2 158.2 162.1 161 .8 163.5 163.8 159.3 159.5 158.5 162.1 163.2 165.1 165.5 161 .2 161 .4 160.6 163.5 164.5 166.7 166.7 161 .6 161 .8 160.9 164.0 165.1 167.4 167.4 162.0 162.1 161 .3 164.3 165.6 167.8 167.9 162.1 162.3 161 .5 164.4 167.5 169.6 169.9 164.2 164.3 163.8 165.4 1.1 1.1 1.2 1.3 1.2 1.4 .6 2.6 2.7 2.7 1.9 1.8 2.0 1.2 154.8 155.8 157.2 158.0 159.4 160.0 161 .1 161 .4 162.6 .7 2.0 4 Services excluding schools .. ··············· ··· ···· ······· ·· ···· ·· ·· Health services ........ .................. .......... ..................... .. .. Hospitals .. ............. .. ............................ ....................... Educational services ........ .. ........ .... .. ............................ Schools ....... ...... .. .. ................................. ................... . Elementary and secondary ... ...... ... ... ... ... ... ... .......... Colleges and universities ................. ...... .. .... ........... Public administration 2 ... Consists of private industry workers (excluding farm and household workers) and State and local government (excluding Federal Government) workers. 2 Consists of legislative, judicial, administrative, and regulatory activities. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 3 This series has the same industry and occupational coverage as the Hourly Earnings index, which was discontinued in January 1989. 4 Includes. for example, library, social. and health services. Monthly Labor Review December 2004 99 Current Labor Statistics: Compensation & Industrial Relations 33. Employment Cost Index, private nonfarm workers by bargaining status, region, and area size [June 1989 = 100] 2002 2003 2004 Percent change Series Sept. Dec. Mar. June Sept. Dec. Mar. June Sept. 3 months 12 months ended ended Sept. 2004 COMPENSATION Workers, by bargaining status 1 Union ........... ........ ........ .. ... ...................... ............. ......... .. ........ . Goods-producing .......... .. .. ... ..................... .. ........................ . Service-producing ......... ....... .......... ....... .... .. .... ... .. .. ............ . Manufacturing ......... ...... ....... ................... ... .. ... .. .... .... ... .... ... . Nonmanufacturing ..... .. .... ..... .............. ... ......... .. ... .............. . 158.1 156.2 159.9 155.9 158.8 159.5 157.8 161.1 157.9 159.9 162.1 161.4 162.6 162.3 161 .4 164.1 163.4 164.6 163.8 163.7 165.7 164.7 166.5 165.0 165.5 166.8 165.9 167.5 166.3 166.5 171 .4 172.3 170.2 175.0 168.8 173.9 174.6 172.9 177.0 171.6 175.3 176.0 174.4 178.4 173.0 0.8 .8 .9 .8 .8 5.8 6.9 4.7 8.1 4.5 Nonunion ................................ .. .... .. ........................................ . Goods-producing ... ...................... ........................... ............. Service-producing ... ... ......... .................................. .............. Manufacturing ................................................... .................. . Nonmanufacturing ............. ... ........................ ..................... . 162.5 159.5 162.9 160.1 162.4 162.8 160.8 163.3 161.3 162.9 165.4 163.6 165.9 164.5 165.4 166.8 164.9 167.2 165.8 166.7 168.4 166.1 169.0 166.9 168.5 169.1 166.7 169.8 167.3 139.3 171.3 169.7 171 .6 170.6 171.1 172.7 170.9 173.2 172.0 172.6 174.2 172.4 174.6 173.8 174.0 .9 .9 .8 1.0 .8 3.4 3.8 3.3 4.1 3.3 160.5 158.9 163.5 163.8 161.3 159.0 164.6 165.0 163.8 160.6 169.0 167.3 165.2 161.6 170.4 169.5 166.9 163.2 171.7 171 .4 167.9 163.9 172.5 172.2 170.2 166.4 174.7 175.3 172.3 167.9 176.2 176.8 173.7 169.5 177.6 178.1 .8 1.0 .8 .7 4.1 3.9 3.4 3.9 161.8 160.0 162.5 169.8 165.2 163.5 166.6 165.0 168.3 166.1 169.1 166.9 171.5 170.2 173.1 172.1 174.6 173.3 .9 .7 3.7 4.3 Union ........................ .................. ............ ....................... ... ...... . Goods-producing ................................................................ . Service-producing .............. .... ........ ... ........... ... ............ ....... . Manufacturing .... ...... .. ... .......... .......... ... ... ................ .... .. .... ... Nonmanufacturing ............................................................. . 151 .3 150.0 152.9 151.6 151 .1 152.5 151.2 154.1 153.1 152.1 153.3 152.4 154.6 154.6 152.5 154.3 153.9 155.1 155.9 153.5 155.3 154.8 156.3 156.7 154.6 156.2 155.4 157.3 157.1 155.6 157.2 156.3 158.5 158.1 156.6 158.7 157.5 160.3 159.2 158.4 160.0 158.7 161 .7 160.5 159.6 .8 .8 .9 .8 .8 3.0 2.5 3.5 2.4 3.2 Nonunion .................... .. .... .... .............. ... ... ... ... ................ ......... Goods-producing .... ...... ........... ... ... ... .............. ... .. ....... ......... Service-producing ... ..................................... .................. .. .. . Manufacturing ..... ............................ .... .... ....... .... ... ... ... ........ . Nonmanufacturing .................... .......................................... . 158.1 155.5 158.9 156.8 158.1 158.5 156.6 159.0 157.8 158.3 160.4 157.8 161 .2 159.3 160.4 161.5 158.9 162.3 160.2 161 .5 163.0 159.7 164.0 160.9 163.1 163.4 160.1 164.5 161 .3 163.7 164.6 161 .4 165.6 162.6 164.7 165.6 162.4 166.6 163.7 165.7 167.0 163.8 168.0 165.2 167.1 .8 .9 .8 .9 .8 2.5 2.6 2.4 2.7 2.5 155.1 154.7 159.2 159.3 155.7 154.6 160.2 160.1 157.3 155.3 164.1 161.3 158.4 156.1 165.0 163.1 160.0 157.4 166.1 164.7 160.9 157.9 166.5 165.2 162.0 159.1 166.9 166.8 163.6 160.1 167.7 167.9 164.9 161 .6 169.2 169.1 .8 .9 .9 .7 3.1 2.7 1.9 2.7 157.4 153.8 157.9 154.8 159.6 156.8 160.7 158.0 162.2 158.9 162.7 159.5 163.8 160.8 164.9 162.1 163.3 162.1 .8 .7 2.5 2.8 Workers, by region 1 Northeast. ..................................................... .. ....................... . South .. ........................................................ .... .. ...................... Midwest (formerly North Central) ............................ ... ..... ....... West. ...... ........ ........................ .. ... ...... .... .................. .... ........ ... Workers, by area size 1 Metropolitan areas ... ..... .. ... ....... .... .... ................................. ... .. Other areas ............................................... .. ... .. ... ............. .. ... . WAGES AND SALARIES Workers, by bargaining status 1 Workers, by region 1 Northeast.. ..... ... .... ... .. ..................... ........ .... .... .... ............ ....... . South .............. ... .. .... ............. ...................... ............... ............ . Midwest (formerly North Central) .. .. ...... .. ....... .. .... .. .... .... ... .. .. . West.. ....................... .... .................................. ... ... ........ ... ... ... . Workers, by area size 1 Metropolitan areas ......................................... .. ......... ... ... .... .... Other areas ......... .................................................................. . 1 The indexes are calculated differently from those for the occupation and industry groups. For a detailed description of the index calculation, see the Monthly Labor Review Technical Note, "Estimation procedures for the Employment Cost Index," May 1982. 100 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 34. Percent of full-Hme employees partlclpattng In employer-provided benefit plans, and In selected features within plans, medium and large private establishments, selected years, 198o-97 1995 1993 1991 1988 1989 1986 1984 1982 1980 Item Scope of survey (in OOO 's) .. .. .. ........... ..... . Number of employees (in OOO's) : With medical care ... .. ..... ... ... ... ..... ... ....... .... ..... . With life insurance .... ..... ... .... .. .... ... .. ... .... ..... ... . With defined benefit plan ..... ...... ..... . 1997 21,352 21,043 21 ,013 21,303 31 ,059 32,428 31 ,163 28,728 33,374 38,409 20,711 20,498 17,936 20,412 20,201 17,676 20,383 20,172 17,231 20,238 20,451 16,190 27,953 28,574 19,567 29,834 30,482 20,430 25,865 29,293 18,386 23,519 26 ,175 16,015 25,546 29,078 17,417 29,340 33,495 19,202 10 27 72 26 88 10 26 71 26 8 80 81 3.7 89 9.3 3.3 9 29 68 26 83 3.0 91 9.4 21 3.1 Time-off plans Participants with : Paid lunch time ... ... ... .. ........ ...... ......... .. . Average minutes per day ... ... .... .. .. ........ .... .. .... . Paid rest -time.. . . ... ..... ....... ... ....................... . Average minutes per day ...... .... ...................... . Paid funeral leave ......... .. .. ...... ........ ... .... ... . Average days per occurrence Paid holidays ..... .... ... .. ... .. ..... ... ... .. .. . . .d_vPrage days per year ...... . Paid personal leave ......... ..... ... .......... . Average days per year .. Paid vacations ... ... . ...... .......... ... .................... . 1 Paid sick leave ••••••• •• •• • • •• • • • • ••• • •• • • • • •• •• •• • •••• Unpaid maternity leave .. ... ......... ..... ...... .... ... .... . Unpaid paternity leave .. .. .. .......... ..... ....... ... ...... . Unpaid family leave .. . ... ...... ......... .. .... . Insurance plans Participants in medical care plans ........... .... .... . . Percent of participants with coverage for: Home health care ... ..... .... ....... ..... ... .......... .. .... .. . Extended care facilities ...... ... .... ..... .... . Physical exam ............................... . . Percent of participants with employee contribution required for: Self coverage ................. ....... ..... .. ......... .. . Average monthly contribution ..... ...... .. .. .. ... ... .. . Family coverage .... ............... .... . Average monthly contribution .. .. ... .. ... . Participants in life insurance plans ....... ... . Percent of participants with : Accidental death and dismemberment insurance ... ..... ...... .......... ................... ... .. .. ... .. . . Survivor income benefits .... ... .. ..... ... ....... .. ....... . Retiree protection available .... ...... .. ..... .. ...... .... . . Participants in long-term disability insurance plans .... ....... .......... .......... ... ............ . Participants in sickness and accident insurance plans ....... ... .... ............... ......... ...... .. ... . . 99 99 3.2 99 10.0 24 3.8 9.8 23 3.6 10.0 25 3.7 11 29 72 26 85 3.2 96 9.4 24 3.3 100 99 99 100 98 97 96 62 67 67 70 69 68 37 18 67 37 26 10 9 9 75 25 76 25 26 73 26 99 10.1 20 33 16 84 3.3 97 9.2 22 3.1 30 67 28 80 3.3 92 10.2 21 3.3 20 3.5 97 96 95 65 60 53 58 56 84 93 97 97 97 95 90 92 83 82 77 76 62 46 62 76 79 28 80 8 66 70 18 75 58 28 81 80 30 86 82 42 78 73 56 85 78 63 36 $11.93 58 $35.93 43 $12.80 63 $41.40 44 $19.29 64 $60.07 47 $25.31 66 $72.10 51 $26.60 69 $96.97 61 $31 .55 76 $107.42 67 $33.92 78 $118.33 69 $39.14 80 $130 .07 26 27 46 51 96 96 96 96 92 94 94 91 87 87 69 72 74 78 71 76 77 74 6 5 49 71 7 42 44 41 7 37 33 42 43 53 55 64 64 72 10 59 40 43 47 48 42 45 40 41 54 51 51 49 46 43 45 44 8 Participants in short-term disability plans ' ............. . Retirement plans Participants in defined benefit pension plans .. P"'"Al"!t of participants with : Normal retirement prior to age 65 ..... ....... .... .. . Early retirement available ... ... ... ... .... ... .. .......... . . Ad hoc pension increase in last 5 years ... ...... .. .. . Terminal earnings formula ....... ... .... .... . Benefit coordinated with Social Security ..... .. ... .. . . 3.3 89 9.1 22 6 84 84 82 76 63 63 59 56 52 50 55 58 97 98 7 56 52 96 6 4 54 61 48 58 62 62 97 22 64 63 52 95 52 45 64 98 35 57 62 59 98 26 53 45 63 97 47 54 56 55 98 51 52 95 10 56 49 60 45 48 48 49 55 57 33 36 41 44 43 54 55 2 5 10 12 12 13 12 23 36 52 38 32 7 Participants in defined contribution plans .. ... .. .. . Participants in plans with tax-deferred savings arrangements .. ... ........ .. .. .. ... ..... ..... .. .. 55 Other benefits Employees eligible for: Flexible benefits plans .... ...... .... ............ ... . 2 Reimbursement accounts ..• •• • . • • •••••••. . •••• .. . • •• .. . Premium conversion plans .... .. ... ... ....... ....... ..... . . ' Th e definitions for paid sick leave and short-term disability (previously sickness and 5 5 fits at less than full pay. accident insurance) were changed for the 1995 survey. Paid sick leave ·now includes only 2 plans that specify either a maximum number of days per year or unlimited days. Shortterms disability now includes all insured, self-insured, and State-mandated plans available specifically allow medical plan participants to pay required plan premiums with pretax on a per-disability basis, as well as the unfunded per-disability plans previously reported as tabulated separately. Prior to 1995, reimbursement accounts included premium conversion plans , which dollars. Also, reimbursement accounts that were part of flexible benefit plans were sick leave. Sickness and accident insurance, reported in years prior to this survey, included only insured, self-insured, and State-mandat ed plans providing per-disability bene- https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis NOTE: Dash indicates data not available. Monthly Labor Review December 2004 101 Current Labor Statistics: Compensation & Industrial Relations 35. Percent of full-time employees participating In employer-provided benefit plans, and In selected features within plans, small private establishments and State and local governments, 1987, 1990, 1992, 1994, and 1996 Small private establishments Item 1990 1992 1994 State and local governments 1987 1996 1992 1990 1994 Scope of survey (in 000 's) .. 32,466 34,360 35,910 39,816 10,321 12,972 12,466 12,907 Number of employees (in 000's) : With medical care ... .... .... .. . With life insurance .... ..... ... .. ... . .. ... ....... .. .... . With defined benefit plan .. ... .. ........ ....... ..... . 22,402 20,778 6,493 24,396 21,990 7,559 23,536 21,955 5,480 25,599 24,635 5,883 9,599 8,773 9,599 12 ,064 11,415 11,675 11 ,219 11,095 10,845 11,192 11,194 11 ,708 50 3.1 82 51 3.0 80 17 34 58 29 56 3.7 81 11 36 56 29 63 3.7 74 10 34 53 29 65 3 .7 75 62 3 .7 73 10.9 38 2 .7 72 13.6 39 2.9 67 14.2 38 2.9 67 11 .5 38 3.0 66 97 95 95 94 57 30 51 33 59 44 Time-off plans Participants with : Paid lunch time ...... ... . Average minutes per day Paid rest time ... ..... .. .... .. ...... ..... ....... .......... . . Average minutes per day .. ....... ... ... ... .. .. . Paid funeral leave ...... .. ......................... .... . Average days per occurrence .. ... ...... .. .. .... . . Paid holidays ........ .. ................................... . 8 9 37 48 27 47 2 .9 84 37 49 26 50 3.0 82 Averaqe days per year' .. ... .. .. .. ...... .. ............ . Paid personal leave .... ...... ... .. ........ .. ... .. ... .... . Average days per year .. .... ... .... .. .. .... .. .. ...... . Paid vacations.... . .. ... .... ........ .. .... . . 9 .5 11 2.8 88 9.2 12 2 .6 88 7.5 13 2 .6 88 7.6 14 3.0 86 47 53 50 50 17 18 8 7 Paid sick leave 2 ........ ... .. ............. ........ .. . ..... . . Unpaid leave ............. ........... .. ............ .... .. Unpaid paternity leave ............... . Unpaid family leave .... .. . 47 48 66 64 93 Insurance plans Participants in medical care plans .... .. ... .. . Percent of participants with coverage for: Home health care .................. .......... .. ........... .. . .. Extended care facilities ........... .... .... .... .... .. . Physical exam ................. ... ...... ....... . . Percent of participants with employee contribution required for: Sell coverage ... .... .................................... . .'.v.,,r;:,ge monthly contribution Family coverage ....... . ..... ......... ... .... . .. Average monthly contribut ion ..................... . Participants in life insurance plans ................ ... .... . Percent of participants with : Accidental death and dism emberm ent insurance .... ...................................... . Survivor income benefits .. ........ . Retiree protection available .. ... ... . Participants in long-term disability insurance plans .. .. ......................... .... ..... .. ... . Participants in sickness and accident insurance plans .. .... ............. . Part icipants in short-term disability plans 2 69 71 79 83 26 80 84 28 42 $25.13 67 47 $36.51 73 52 $40.97 76 $109 .34 $ 150.54 64 64 78 1 19 76 1 25 19 93 93 90 87 76 78 36 82 79 36 87 84 47 84 81 55 52 $42.63 75 35 $15.74 71 38 $25.53 65 43 $28.97 72 47 $30.20 71 $ 159.63 $ 181 .53 $71 .89 $ 117.59 $139 .23 $149.70 61 62 85 88 89 87 79 67 1 55 67 1 45 74 1 46 64 20 77 1 13 23 20 22 31 27 28 30 26 26 14 21 22 21 15 93 90 87 91 47 92 89 8 92 89 10 100 10 92 87 13 44 92 90 33 100 18 2 2 46 29 ....... Retirement plans Participants in defined benefit pension plans ..... Percent of participants with: Normal retirement prior to age 65 .. ... ......... ... . Early retirement available .... ........................... . Ad hoc pension increase in last 5 years ............. . Terminal earnings formula Benefit coordinated with Social Sec urity .... .. Participants in defined contribution plans ... .. . Participants in plans with tax-deferred savings arrangements ... ....................... ......... .. .. . 20 22 54 95 7 58 49 50 95 4 54 46 31 33 34 38 9 9 9 9 17 24 23 28 28 45 45 24 15 53 88 16 100 99 49 Other benefits Employees eligible for : Flexible benefits plans ... Reimbursement accounts 3 ..................... .. Premium conversion plans ........ ..... .... .. .... .. 1 1 2 3 4 5 5 5 5 8 14 19 12 5 31 50 64 7 Methods used to calculate the average number of paid holidays were revised Sickness and accident insurance, reported in years prior to this survey, in 1994 to count partial days more precisely. Average holidays for 1994 are included only insured, self-insured, and State-mandated plans providing per- no: cc,,nparable with those report ed in 1990 and 1992. disabil ity benefits at less than full pay. 2 3 The definitions for paid sick leave and short-term disability (previously sickness and accident insurance) were changed for the 1996 survey. Paid sick Prior to 1996, reimbursement accounts included premium conversion plans, which specifically allow medical plan participants to pay required plan leave now includes only plans that specify either a maximum number of days premiums with pretax dollars. Also, reimbursement accounts that were part of per year or unlimited days. Short-term disability now includes all in sured, self- flexible benefit plans were tabulated separately. insured , and State-mandated plan s available on a per-disability basi s, as well as the unfunded per-disability plans previously reported as sick leave. 102 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 NOTE: Dash indicates data not available . 36. Work stoppages involving 1,000 workers or more 2002 2003 2004P 2003 Annual totals Measure Dec. Nov. Oct. Mar. Feb. Jan. June May Apr. Oct. Sept. Aug. July Number of stoppages: Beginning in period ... .... ...................... In effect during period ..... ................... 19 20 14 15 5 5 0 3 0 2 0 1 1 2 1 1 0 1 2 2 3 4 0 1 2 2 2 3 - Workers involved: Beginning in period (in thousands) .... In effect during period (in thousands) . 46 47 129.2 130.5 82.2 82 .2 8.0 76.7 .0 70.5 .0 61.3 6.5 66.5 2.2 2.2 .0 2.2 103.0 103.0 27 .6 28.6 .0 1.6 3.7 3.7 6.0 8.0 - Days idle : Number (in thousands) .............. .... .. .. 6,596 4,091 .2 1,168.5 1,219.0 1,473.4 1,203.9 1,146.5 44.0 26.4 204 .0 94.0 3.2 52 .5 60.0 (2) .01 .04 .05 .05 .05 .05 .00 .00 .01 .00 .00 .00 .00 - 1 Percent of estimated workina time .. . . 1 Agricultural and government employees are included in the total employed and total working time; private household, forestry , and fishery employees are excluded. An - Monthly Labor Review , October 1968, pp.54-56. 2 Less than 0.005 . explanation of the measurement of idleness as a percentage of the total time worked is found in "Total economy measures of strike idleness," https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis NOTE: Dash indicates data not available. P = preliminary. Monthly Labor Review December 2004 103 Current Labor Statistics: Price Data 37. Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city average, by expenditure category and commodity or service group [1982-84 = 100 , unless otherwise ind icated] Annual average Series 2002 2003 2004 2003 Oct. Nov. Dec. Jan Feb. Mar. Apr. May June July Aug. Sept. Oct. CONSUMER PRICE INDEX FOR ALL URBAN CONSUMERS All items ....... ......... ... ..... . .. ............. .............. All items (1967 = 100) ..... ..... .. ..... .. ... ..... ········ .. ...... ········ ··· · •• ·· ·· ··· · ........... .......... .. ................ ······· ... ... . ... ..... ·· ··· ·" ..... at home .. ·· ··· ···· ···· ···· ····· ··· ··········· ·•· ..... .... .. .. Food and beverages ..... Food ... Food Cereals and bakery products ······· ··• · .... ...... . ...... . iv1 cais, riou ltry, fish. and eggs ... ....... ············· 1 Dairy and related prod ucts ······· ·· .... .... .. Fruits and vegetables ... .. ..... ····················· Nonalcoholic beverages· and beverage materials .... ............ ..... ....... .. ... ....... .. ... ..... Other foods at home .. ....... .. ... .. ..... ..... . ... .. .. Sugar and sweets ... .. ......... ..... . ............ ... ... .... Fats and oils ... ...... . ... ... ·························· Ot her foods ....... ...... . . . . . . .. . . . . . .. . . . . . . . . ........... ...... Oth er misce llaneous foods Food away from home 12 · .. . . .. . .........•... 1 12 Other food away from home · .. ............ ... .. Alcoholic beverages .. ... .. ... ..... . . .. .. . .. . .•• . . . •••. . ••• •.. Housing ... .. .......... .. .. ....... ... ... ... .... ... ... ..... .. 179.9 184 .0 538.8 551 .1 176.8 176.2 175.6 198.0 180.5 180.0 179.4 185.0 554 .3 185.2 554.9 186.2 187.4 188.0 189.1 189.7 189.4 189.5 557.9 561 .5 563.2 566.4 568.2 567.5 567.6 189.9 568.7 184.5 184.1 184.0 204.4 179.7 184.9 184.4 184.3 204 .8 185.0 184.5 184.1 205.5 186.5 186.1 186.6 186.8 186.3 186.8 202.9 181 .1 184.3 183.8 184.0 203.9 179.9 206.8 182.3 187.2 186.7 186.1 206.4 179.2 206.1 181 .1 187.3 186.8 186.7 207.2 179.5 187.2 186.8 187.1 207.2 183.7 184.5 184.3 552 .7 552 .1 182.9 182.4 182.4 202.5 184.7 180.0 184.1 179.3 190.9 571 .9 162.1 202.8 169.3 182.2 181 .7 181 .5 203.1 174.0 183.7 183.4 188.4 187.9 187.9 207.0 182.9 168.1 220.9 167.9 225.9 171 .8 226.3 171 .2 227.5 173.0 232.4 172.4 232.4 172.1 229.7 171.9 230.1 174.0 228.3 185.9 231 .7 188.8 226.7 187.7 224.5 184.9 224.0 181 .6 226.0 182.1 240.0 139.2 139.8 140.5 140.7 141.4 169.9 139.8 140.5 140.3 140.3 165.4 163.0 162.5 159.7 178.7 163.0 161 .0 157.7 179.6 162.8 163.0 160.7 178.0 163.7 163.9 162.3 178.9 140.8 165.1 163.3 166.2 180.4 139.7 162.6 162 .0 157.4 178.8 137.9 162.0 161 .7 157.3 177.9 139.3 160.8 159.0 155.4 177.1 165.0 162.6 166.2 180.4 165.4 163.5 169.4 180. 1 165.8 162.8 171 .3 180.5 166.0 163.8 171 .9 180.3 166.2 164.4 169.7 180.9 165.2 163.5 170.4 179.4 162.6 170.2 180.1 109.9 109.2 110.3 110.7 109.0 109.8 109.1 109.5 111 .7 110.5 110.8 110.9 109.4 111 .5 110.5 189.4 178.3 182 .1 183.3 183.8 184.3 184.9 185.5 185.8 186.2 186.7 187.0 187.8 188 .4 188 .9 126.8 117.7 183.6 121 .3 187.2 122.3 188.1 122.7 188.6 122.9 188.7 123.9 189.4 124.0 189.9 124.1 190.8 124.7 191 .8 124.8 191 .7 124.8 192.4 125.1 192.2 125.4 192.5 125.9 193.4 193.6 191 .0 180.3 184 .8 185.7 185.1 185.1 186.3 187.0 187.9 188.4 188.9 190.3 190.9 191.2 191 .0 220.6 208.1 213 .1 2 14.7 214.2 213.1 215.2 216.0 217.8 218.4 218.7 219.2 220.0 220.3 220 .2 212.8 199.7 205.5 206.9 207.5 205.5 208.3 208.8 209.2 209.7 210.2 210.7 2 11.2 21 1.9 212.4 212. 8 118.3 119.3 120.9 115.0 119.3 117.2 120.0 128.1 129.1 128.2 129.1 132.2 130.6 127 .0 128.0 214.7 219 .9 221 .4 221.9 219.9 222.6 222.9 223.3 223.9 224.3 224 .7 225.1 225.7 226.1 226.5 Tenants' and household insurance · Fuels and utilities .. .. ... .... ... . . ... . . ... . . . ..••....•. •• •.• . Fuels .... ..... .... .......... ... ................... ·· ··· ·•··· Fuel oil and other fuels .. ......................... Gas (piped) and electricity ·· ······················· ····· Household furnishings and operations .. .... ..... .. ..... . .... Apparel ... ........ .. ......... ... Men's and boys' apparel. .. .. ...... ....... ... ... .. .. 108.7 143.6 114 .8 154 .5 138.2 139.5 145.0 126.1 116.0 155.0 114.3 152.9 114.8 154.5 138.7 139.1 145.0 124.7 114.8 156.3 115.0 156.9 116.1 166.6 149.5 151 .1 156.9 125.2 150.5 157.4 157.6 124.8 149.3 161.6 156.0 125.0 116.3 162.8 144.9 125.4 116.2 165.5 148.5 150 .7 155.8 125.6 116.6 166.7 125.3 115.7 155.6 138.0 149.6 144.2 125.6 116.3 167.7 139.2 149.9 145.5 115.1 155.2 137.6 152.5 143.5 125.7 116.1 158.1 139.5 155.1 145.5 125.7 115.8 115.5 118.6 117.1 123.4 120.3 116.9 120.1 117.7 112.3 115.9 115.2 121 .2 116.2 114.4 124.1 11 8.3 Women's and girls' apparel ..... .... ......... .. .. .. ... 124.3 120.3 118.7 Shelter ... ... ........... .... .... ........... .. .......... .. ....... Rent of primary residence ........ ... ............... .. .... . . ....... .. lodgin g away from home .. Owners' equivalent rent of primary residence 3 .. 12 127.2 115.5 134.4 128.3 138.2 131 .4 145.6 135.7 134.8 142.6 125.1 124.9 124.8 120.8 123.1 121.4 140.4 150.4 146.8 177.3 150.0 126.1 115.8 120.9 118.0 113.1 118.8 115.7 119.0 118.0 110.9 105.7 110.3 123.5 119.8 117.6 106.1 116.5 113.8 107.5 Infants· and toddlers' apparel ...... .. . .... .... . Footwear .. ................... ...... ... ..... . ........... Transportation ... . ... . ..... .. ... .. . . . .. ........ ..... .... .. 126.4 122.1 125.2 123.0 119.2 117.7 119.3 121.9 120.5 11 8.1 116.2 114.5 115.0 119.5 120.6 121 .4 121 .8 157.1 121 .0 115.9 157.0 158.8 120.1 160.5 121 .0 161 .8 120.3 165.2 118.4 165.7 115. 1 164.0 117.3 162.9 121 .7 162.9 122.1 155.7 118.5 154.7 117.0 152.9 119.6 157.6 Private transportation ......................... ..• .. 148.8 153.6 153.0 151 .7 150.8 153.2 154.9 156.6 157.9 161 .5 161.9 160.0 159.1 159.4 162.9 1 . 124.0 121.7 119.2 166.4 99.2 96 .5 94.6 94.6 94.4 94 .3 94 .4 94 .2 94 .1 94.0 93.6 93.5 93.4 93 .9 94.3 140.0 137.9 136.5 137.5 138.0 138.0 138.3 137.9 137.6 137.4 137.2 135.9 134.9 134.9 135.9 ' 1scd r.a rs and trucks Motor fuel. ...... ......... . ........ .............. ··· ··· ·· ·· Gasoline (all types) . ......... ....... ..... .... .......... ...... . 152.0 116.6 135.1 136.6 131.0 127.8 127.2 131.2 150.5 149.8 130.6 173.3 172.7 164.5 133.8 162.0 161.2 136.5 161.2 155.3 131 .8 170.5 169.8 132.1 165.2 136.1 131.0 143.1 142.5 131 .3 155.9 136.0 132.0 131 .2 130.6 130.8 136.7 116.0 142.9 135.8 135.1 160.5 136.8 173.1 172.2 Motor vehicle parts and eq uipment.. .. ............ ..... Moto r vehicle maintenance and repair .. Public tra nsportation .... 106.9 190.2 207.4 107.8 195.6 209.3 107.9 196.9 211 .3 107.9 197.2 207.9 107.8 198.0 205.6 108.0 198.2 206.3 108.0 198.2 208.1 107 .8 198 .5 209.9 107.9 198.6 211.5 107.9 199.0 210.7 108.2 199.7 212.3 108.8 200.3 2 14.4 109.0 200.8 209.7 109.3 200.7 205.3 109.5 20 1.7 206.5 Medica l care ........... .... ... . . . ... . ..... .. ..• .......... . .. ...... Medical care commodities .... ..... ........ .. ....... Medical care services ... ..... . . .. . •• ••• ··········· ·· ···· Professional services ............ ............. .... ..... Hospital and related services .. ..... .. ···· ·· ··· ·· · ·· ·1 285.6 256.4 292.9 253.9 367.8 297 .1 262 .8 306 .0 261.2 394.8 299.9 264.7 309.1 263.0 400.7 300.8 264.0 310.6 263.0 405.6 302 .1 265 .0 311 .9 261 .2 407 .0 303.6 265.5 313.8 262.5 409.7 306.0 266.7 316.6 268.0 412.5 307 .5 267.3 318.4 269.7 413.8 308.3 268.5 319.2 270.6 413.6 309.0 269.1 319.8 270.9 414 .6 310.0 269 .6 321.0 271.6 416.9 311. 0 269.9 322.3 272.3 4 19.1 311.6 270.0 323. 1 273.3 418.8 312 .3 270 .9 323.7 273.3 420.3 3 13.3 271 .7 324.8 273.7 422.5 New and used motor vehicles2 New vehicles .............. .. ... . . .. . .. . . . . .... ..... . . 1 106.2 107.5 107.6 107.8 107.7 107.9 108.4 108.8 109.0 108.8 108.9 108.7 108.5 108.6 108.7 102.6 103.6 103.5 103.8 103.3 103.6 104.1 104.3 104.7 104.6 104.4 104.4 104.1 104 .0 104.2 107.9 109.8 110.9 110.8 110.9 111 .1 111 .2 111.1 110.9 110.6 110.8 110.9 111 .7 112.9 112.5 .... .... .. .. 126.0 317.6 134.4 335.4 139.1 339.7 139.0 336.0 139.4 342.8 140.1 345.4 140.4 348.6 140.6 348.9 140.7 349.5 140.9 349.6 141.6 350.6 142.1 349.5 145.1 353.3 147.9 352 .8 148.3 353.8 Tuition, other school fees. and child care .......... 362 .1 362.1 401 .1 401 .2 401 .7 403.6 404 .2 404 .7 404.9 405.6 407.6 409.4 428.2 89.7 88.4 88.2 88.2 88.1 88.1 87.7 87.4 86.9 86.8 86.5 418.3 86.1 427.4 92.3 86 .2 85.5 90.8 87.8 86.4 86 .2 86 .2 86.1 86.1 85 .7 85.4 84.8 84 .7 84 .5 84.0 84 .1 83.4 99.7 98.3 97.1 97.2 97 .2 97.0 97.1 96 .7 96.5 95.9 95.8 95.6 95.0 95.3 94.6 18.3 16.1 15.6 ' 15.4 15.3 15.3 15.2 15.2 15.0 14.9 14 .9 14.8 14.7 14.7 14.5 RAr.rn;,itinn 2 Vir1AO ;,inr1 ;,i11r1io 12 · 2 Education and communication 2 Education .. Ed ucational books and supplies .. ....... • ·· 12 Comm11nir.;,ition · 12 Information and information processinq · .. •· • 12 services · Telepho ne Information and information processing 14 nth Ar th;,in IAIAnhnnA ~Arvir.A~ · Personal computers and peripheral 12 eouipment · Other goods and services. ... . . . . . .. . . . .. .. . . . . . . . .. .. ... .. Tobacco and smoking products . . .. .. . . . .. ... .. . ... .. ... . . Personal care 1 Personal care products Personal care services 1 1 . . ······· ..... ... ... ........... 22.2 17.6 16.5 16.3 16.2 16.2 16.0 15.8 15.9 15.7 15.5 15.3 15. 1 15.0 14.6 293.2 461 .5 298 .7 469.0 300.2 469.5 300.0 469.1 300 .2 470.4 301.4 473.0 302 .3 472 .6 303 .1 473 .6 303.6 473.3 303.8 473.5 304 .1 476 .0 305.1 480.5 305.5 481 .6 306 .3 482 .9 306.8 482.3 174.7 178.0 179. 1 179.0 179.0 179.7 180.4 180.9 181.3 181.4 181.4 181 .7 181 .9 182.3 182.8 154.7 153.5 153.6 153.2 153.4 153.8 154.5 154.5 154.5 154.6 153.8 153.4 152.8 153.5 154.0 188.4 193.2 195.6 194.2 194.3 194.6 195.2 195.8 196.1 196.6 196.9 197.5 198.9 199.1 199.4 See footnotes at end of table . 104 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 37. Continue~onsumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city average, by expenditure category and commodity or service group (1982-84 = 100, unless otherwise indicated] -· Annual average Serles 2002 Miscellaneous personal services .... Commodity and service group: Commodities ... ..... ...... ........ ...............•. ········· Food and beverages .. .......... ... ........... .. ........ Commodities less food and beverages .. ... ....... Nondurables less food and beverages .. ......... Apparel ........................................... ........ . ... 2003 2004 2003 Oct. Nov. Dec. Jan. Mar. Feb. Apr. 184.5 153.7 184.9 134.1 151.2 123.1 148.4 115.8 134.2 151.4 124.8 146.7 119.0 145.1 124.0 149.7 120.9 154.2 187.3 154.9 187.2 156.7 115.9 135.6 156.1 136.7 157.8 123.4 160.5 120.1 188.4 139.4 162.6 116.5 121 .2 124.1 124.3 152.3 184.3 132.6 182.2 296.3 154.5 123.5 151.1 184.1 131.7 151 .4 180.5 134.5 Oct. 187.2 136.1 118.6 150.4 182.9 132.9 149.0 151.2 176.8 134.2 Sept. 186.8 138.2 136.0 155.3 150.9 149.7 Aug. 155.8 156.0 186.5 138.6 160.9 292.7 July 295.9 1 185.0 136.9 157.2 291 .6 287.0 295.2 154.3 290.4 285.8 294 .4 293.6 288.8 283.5 June 293.1 287.1 274 .4 May 157.1 I Nondurables less food, beverages, and apparel .. ..... ...... .. ....... ... .. ............... Durables ................. ....... ... ......... . ..... .•.... ...... 162.2 171.5 121 .4 117.5 171 .6 115.2 169.1 115.1 167.7 115.0 172.3 115.1 175.6 115.3 179.1 115.1 181 .7 115.0 188.2 114.8 189.5 114.5 185.8 114.1 184.4 113.7 184 .4 114.1 190.6 114.7 . .... ............. ............ ....... 209.8 216.5 218.4 217.9 217.9 219.1 219.9 221 .0 221 .5 221.9 223.3 224 .1 224.5 224 .5 224.5 Rent of shelter3 ... ............................... ... .. Transporatation services. ................ ..... ... .. .. .. 216.7 209.1 221 .9 216.3 254.4 222 .9 217.7 257.4 224.1 218.7 258.4 224.9 219.3 259.2 226.8 219.7 259.5 220.0 259.7 227.7 220.0 259.6 228.3 220.5 260.2 229.2 221 .6 260.5 229.4 246.4 223.0 218.6 257.3 227.4 ............... ························ ······ 223.5 218.9 257.2 220.8 261 .9 229.3 220.1 263.8 229.8 221.4 263.7 All items less food ............... ..................... All items less shelter ........... . . . .. . . . . . All items less medical care .. ....................... .... Commodities less food .. ... ... ... .. .. .. .. .. . . . . . . . ... . . 180.5 170.8 174.3 184.7 174.6 178.1 185.6 175.5 179.1 184.4 174.7 178.2 188.0 177.6 181 .3 188.6 178.2 181 .8 189.6 179.6 182.9 189.9 179.5 183.2 190.4 180.1 183.6 136.1 133.8 136.3 138.0 138.9 140.6 190.3 180.2 183.5 140.3 189.9 179.6 183.2 136.5 185.5 175.6 179.1 134.7 186.6 176.7 180.1 136.0 184.9 174.9 178.5 135.0 138.2 137.7 138.8 191 .4 181.4 184.6 141 .1 Nondurables less food . . ······ ················ ···· ........ Nondurables less food and apparel ... ···· ··· ·····• Nondurables ... .. ..... .. .... .. .. .. ......... ....... ........ 147.4 163.3 161.1 151 .9 172.1 165.3 153.3 172.2 166.8 151 .3 170.0 166.1 149.2 168.8 165.4 150.8 173.0 166.4 153.7 176.1 168.1 157.5 179.4 170.3 159.3 162.8 162.4 181.7 171.4 187.7 174.1 189.0 174.0 158.8 185.6 172.2 158.2 184.3 171 .9 159.9 184.4 172.8 190.0 175.8 Services less rent of shelter3. ............. ·•· .. Services less medical care services ........ ... .... Energy ........ ........... . . ... . .. .. .. . ... . . . . .. . . . ..•. . • .. ...... All items less energy . .. .... ....... ... ...... ..... .... All items less food and energy .... .. .. .. ........... 217.5 226.4 228.7 228.2 228.4 229.7 230.6 230.7 231.1 231 .7 234.2 235.0 235.6 235.9 235.1 202.5 121 .7 208.7 136.5 190.6 193.2 210.5 136.9 191 .7 194.3 209.9 133.1 191 .6 209.9 131 .8 191 .5 193.6 211 .7 140.6 192.7 194.9 212.7 143.1 193.7 196.1 140.9 136.7 140.4 137.0 140.3 151 .3 215.0 159.7 194.4 196.6 139.4 215.8 156.3 194.5 196.6 138.2 225.8 226.6 228.9 229.4 229.6 172.8 230.2 165.1 231 .0 138.1 162.5 231.4 139.4 162.0 231.6 216.0 157.7 196.0 198.2 140.5 174.2 223.8 139.3 144.6 227.5 213.6 154.1 194.3 196.5 140.2 170.1 216.1 154.3 195.2 197.4 143.7 117.1 217.5 213.2 145.9 194.1 196.5 140.5 156.3 216.2 155.3 194.7 196.8 Commodities less food and energy .. ........... Energy commodities ···························· ..... Services less energy ..................... .... ...... .. .. 211 .0 137.4 191 .9 194.0 138.5 Services .... .. Other services .. Special indexes: . . 187.7 190.5 193.9 139.9 132.1 225.6 139.0 129.0 225.5 138.2 164.2 232.1 CONSUMER PRICE INDEX FOR URBAN WAGE EARNERS AND CLERICAL WORKERS 175.9 179.8 180.7 180.9 538.7 181 .9 541 .7 182.9 544 .8 185.3 184.9 185.0 185.4 538.2 179.9 536.0 184.7 535.6 180.2 536.7 183.5 523.9 546.5 550.2 551.9 550.8 551 .0 552.4 186.5 555.7 176.1 176.5 175.1 179.9 179.4 178.5 181 .7 181 .2 180.7 182.4 181 .9 181 .6 183.6 183.1 183.3 183.8 183.3 183.2 184.0 183.5 183.2 184.4 183.8 183.5 184.5 183.9 183.3 186.0 185.6 185.8 186.4 185.9 186.1 186.8 186.3 186.3 186.9 186.4 186.1 186.8 186.2 185.5 187.9 187.4 187.1 198.0 202.8 202.4 179.2 202.4 181 .0 203.8 179.9 204.4 179.7 204.9 179.6 206.0 179.1 181 .1 206.7 182.4 207.2 169.2 203.2 173.8 205.5 162.0 183.7 207.0 183.7 206.3 183.4 206.9 183.0 Dairy and related products .. ............. ... Fruits and vegetables ........................ ... .... Nonalcoholic beverages and beverage 167.2 222.9 167.6 224 .3 171 .7 224 .9 171 .0 225.3 172.7 229.7 172.2 229.7 171 .7 227.5 171 .3 227.8 173.6 225.5 186.1 228.9 189.0 224.3 187.8 222.3 184.9 222.2 181 .4 223.9 181.8 238.0 materials ........... .. .. ... ....... Other foods at home .. ....... ... ... .. ....... .. ... .. . . ... . 138.6 139.1 137.3 161 .6 138.6 162.5 140.0 162.3 139.3 160.5 157.7 162.4 160.7 162.6 166.0 180.0 178.4 179.4 180.8 180.8 180.8 180.7 163.8 169.9 181 .4 139.7 164.8 163.1 170.3 178.3 165.1 162.9 169.4 180.5 139.6 165.8 161 .4 157.3 164.6 161 .9 166.1 139.3 165.5 162.2 171.4 139.8 163.3 163.2 162.2 140.1 164.7 139.1 162.2 161.6 157.4 179.2 139.8 162.5 162.1 159.6 179.0 140.8 160.4 158.8 155.3 177.6 179.7 140.0 165.0 162.2 170.0 180.5 12 · 109.7 110.8 111 .2 109.5 110.3 109.6 110.1 112.2 111 .0 111 .2 111 .4 109.7 112.0 111 .0 110.3 . ..................... •• .. . 178.2 182.0 183.3 183.7 184.2 184.8 185.3 185.6 186.1 186.6 186.8 187.6 188.2 188.8 189.3 118.1 183.3 121 .5 187.1 122.5 188.1 122.9 188.8 123.1 188.9 123.6 189.5 123.8 190.0 123.8 191 .2 124.3 192.1 124.6 192.0 124.7 192.7 124.9 192 .2 125.2 192.8 125.8 194 .0 126.8 193.9 175.7 201 .9 180.4 206.9 181 .3 208.3 180.9 208.2 181 .0 208.2 182.1 209.2 182.6 209.8 183.2 211 .0 183.6 211 .5 184.1 211 .8 185.6 186.2 213.0 186.6 186.5 186.2 212.2 213.4 213.4 213.8 199.0 118.4 204 .7 206.1 206.6 207.0 207.4 208.0 208.4 208.9 209.4 209.9 210.3 211 .0 211 .6 212.0 119.8 121.7 116.2 113.4 118.5 121 .1 128.8 129.8 128.2 128.8 133.0 131 .6 127.7 128.3 3 195.1 199.7 201 .0 201.4 201 .7 202.1 202.3 202.7 203.1 203.6 203.9 204.2 204.7 205.1 205.5 .... Tenants' and household insurance • Fuels and utilities .. ............. ........................... 108.7 142.9 126.1 114.7 153.9 137.0 116.0 154.3 137.0 114.4 152.3 114.4 153.0 114.9 155.6 115.1 156.2 115.2 154.7 121 .9 130.7 144.6 120.9 121 .0 148.9 143.5 121 .3 149.6 146.1 121 .1 149.8 155.1 121 .3 148.4 150.2 156.2 120.7 156.8 156.8 120.4 161 .1 155.3 120.6 177.2 149.1 121 .7 123.1 121 .7 120.0 117.5 112.1 123.9 120.0 118.2 122.6 121 .1 118.7 117.8 110.5 115.7 115.6 105.5 118.3 117.4 109.8 136.6 152.0 142.9 121.4 122.9 116.8 166.2 148.2 138.7 144.1 138.3 154.5 144.7 121 .4 116.5 167.2 149.3 115.0 133.4 124.4 138.0 149.6 144.7 116.5 165.0 147.4 116.5 161 .9 135.4 136.2 142.5 120.4 116.4 157.4 139.3 116.3 166.1 134.7 134.4 141 .9 116.0 155.1 137.0 123.8 122.8 120.3 119.6 117.8 115.6 115.2 115.9 113.3 120.6 115.6 123.5 117.8 127.7 121 .1 155.4 152.5 125.0 120.4 153.6 149.0 124.1 119.1 156.3 153.5 150.8 121.4 117.8 152.5 149.7 120.1 115.6 154.9 152.2 99.4 96.0 93.5 93.1 92 .8 92.7 All items ..... ·················· ................................ ..... .... ····················· All items (1967 = 100) .. ....... . Food and beverages .. . . . .. . . . . . . . .. ... ... ... . ....•.......... Food ..... ..... ...... .. ... .... .... ...• .. ········"·· ... ...... ...... Food at home ........................ ..... ........ . .. .... Cereals and bakery products ......... .. ... .. ...... Meats, poultry, fish , and eggs ..... .. .. ..... I 1 . Sugar and sweets ..... ......... ........... .. ........ .. . Fats and oils ···· ··· ··· ····· ··· ··· ····· ··· ..... .......... Other foods ................................ .. .... Other miscellaneous foods Food away from home 1 .. ...... ... 1 2 Other food away from home • .......... . ... Alcoholic beverages ........................ ...... ............. Housing .... ···························•·······--········--···--······-- Shelter .................................... ............ . .... .. .... Rent of primary residence .. ············ ··"············ Lodqinq away from home 2 Owners' equivalent rent of primary residence 12 Fuels .. . . . . . . . . . . . . . . . . ... ···· ···· ········· ... .... .. .. ... .. .. Fuel oil and other fuels .... .. ........................ Gas (piped) and electricity ... .. ...... .. ...... . ... . Household furnishings and operations .. . Apparel ............................... ... ........................... .... . Men's and boys' apparel ............ ...................... Women"s and girls' apparel ... .. .. ........ ...... ... . 1 Infants' and toddlers' apparel ....... . .. ... .. .. Footwear .... . . . . . . . . .. . . . . . . . . . . . . . . . . .. . ...•.... ...... Transportation ................... . ................ .•............ . Private transportation ... ........ .... ···· ··•·· ······ ···· . New and used motor vehicles2 114.6 128.6 121.2 151 .8 120.7 115.3 165.6 162.9 172.0 143.5 120.0 117.4 120.6 118.4 116.7 112.2 106.0 106.9 114.0 119.3 122.2 116.4 156.8 125.2 118.6 158.5 123.4 119.6 159.9 120.9 119.0 163.6 118.8 117.0 164.0 117.0 114.4 162.2 117.6 116.3 161.4 122.3 120.4 161 .6 123.3 120.6 165.3 154.0 155.7 157.1 160.9 161 .3 159.3 158.6 159.1 162.7 92.8 92.6 92.6 92.5 92.1 92.1 92.2 92.3 93.3 See footnotes at end of table. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 105 Current Labor Statistics: Price Data 37. Continue~onsu mer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city average, by expenditure category and commodity or service group [1982-84 = 100, unless otherwise indicated] Annual average Serles 2002 2003 Oct. Nov. 2004 Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. New vehicles .. .................... .... ....... .. .............. 141.1 139.0 137.8 138.7 139.2 139.2 139.5 139.0 138.7 138.5 138.2 137.0 136.0 136.0 136.9 1 152.8 117.0 116.4 106.1 191 .7 202.6 284.6 251 .1 292.5 256.0 363.2 104.6 102.0 107.6 125.9 318.5 354.8 93.7 92.7 99.9 143.7 136.1 135.5 107.3 197.3 206.0 296.3 257.4 305.9 263.4 391 .2 105.5 102.9 109.0 135.9 136.9 136.4 107.5 198.6 208.7 299.1 259.2 309.1 265.2 397.5 105.4 102.8 132.8 131.5 130.9 107.5 198.9 205.8 300.1 258.5 310.6 265.2 402.4 105.6 103.0 109.6 131 .7 128.1 127.6 107.3 199.8 203.6 301 .4 259.4 311 .9 266.5 403.4 105.5 102.5 109.7 138.0 343.8 390 .7 89.7 131.6 131 .7 143.6 143.0 107.6 200.1 206.2 305.4 260.9 316.8 270.6 408.7 106.2 103.2 110.0 132.6 171.1 170.4 107.5 200.8 208.8 308.4 263.3 320.0 273.5 410.7 106.6 103.9 109.2 139.9 350.4 394 .6 88 .. 4 88.2 97.2 87.0 96.1 131.4 173.8 173.2 107.8 201 .5 210.0 309.4 263.8 321 .2 274.1 413.0 106.7 103.7 109.4 140.6 351 .5 396.7 88.4 86.9 96.1 141.0 350.4 398.1 88.1 86.7 95.8 134.6 162.4 161 .7 108.4 202.7 208.0 311 .0 263.8 323.2 275.8 414.9 106.1 103.4 109.9 143.6 354.7 405.8 87.6 88.3 97.4 139.4 349.5 393.3 89.6 88.2 97.3 132.1 156.5 155.8 107.5 200.4 209.4 307.7 262.5 319.4 273.2 409.8 106.7 103.9 109.6 139.7 350.4 394.1 89.0 87.5 96.7 133.0 165.6 165.0 108.2 202.1 212.1 310.4 263.7 322.4 274.8 415.2 106.3 103.7 109.4 139.1 346.1 392.8 89.6 132.0 150.9 150.3 107.4 200.3 208.0 306.9 261 .5 318.6 272.3 409.9 106.5 103.5 109.8 139.6 349.9 393.8 89.3 87.9 96.9 86.2 95.2 137.3 161.7 161 .0 108.7 202.7 203.1 311 .7 264 .8 323.9 275.9 416.4 106.2 103.3 110.8 146.3 354.8 414 .0 87.8 86.3 95.5 137.6 173.6 172.9 108.9 203.8 204.2 312.7 265.4 325.0 276.3. 418.5 106.2 103.5 110.5 146.7 355.6 415.2 87.1 85.6 94.8 Used cars and trucks ··· ···•· • ·· ••····· ···· ·••·· Motor fuel .... .. .... ......... .. . ....... .. .. ... ... . ..... Gasoline (all types) .... . ..................... .. ........ Motor vehicle parts and equipment.. .. .......... ... Motor vehicle maintenance and repair .. .. .... .. . Public transportation .. ............ .. ........... .... ..... ... .. Medical care ......................... ...... ............ .. .... ... ..... Medical care commodities .... .. ...... ......... ... .. ..... .. Medical care services .. ... .. ........... .... ..... ............. Professional services .. .. .. .... .... ·············· ········ Hospital and related services ... .... •.. .... ..... .. .... RocrAalion 2 Vic1Ao and audio 1•2 Education and communicalion 2 .... .. ... Education 2 .. . .. ... . ·· ····· Educational books and supplies ................... 137.1 136.6 107.6 199.9 204 .6 302.8 259.8 313.8 267.8 405.9 105.6 102.7 109.8 89.9 98.5 109.7 138.1 340.6 390.1 89.9 88.5 97.3 138.0 337.5 390.2 89.8 88.4 97.4 19.0 16.7 16.2 15.9 15.8 15.8 15.8 15.7 15.5 15.4 15.4 15.3 15.3 15.2 15.0 21.8 302.0 463.2 17.3 307.0 470.5 174.1 155.5 189.1 274.0 177.0 154.2 193.9 283.3 16.2 308.2 470.7 178.0 154.1 196.3 285.6 16.0 307.7 470.2 177.7 153.8 194.8 286.7 15.9 308.1 471.5 177.8 154. 2 194.9 286.6 15.8 309.3 473.8 177.4 154.3 195.1 288.4 15.7 310.0 473.2 179.1 155.0 195.7 290.2 15.5 310 .8 474.2 179.7 155.0 196.3 291.6 15.6 311 .3 474.1 180.1 155.1 . 196.6 292.9 15.4 311 .5 474.4 180.2 155.1 197.1 293.1 15.2 311 .8 476.9 180.0 154.3 197.5 293.5 15.0 313.2 481.6 180.3 153.9 198.1 294.7 14.9 313.5 482.6 180.5 153.1 199.5 295.4 14 .8 314.4 483.9 180.9 154.0 199.7 296.2 14.3 314 .7 483.0 181.4 154.3 199.9 296.6 150.4 176.1 135.5 147.0 123.1 151.8 179.9 135.8 152.1 120.0 151 .9 181.7 135.2 153.6 123.9 151 .3 182.4 133.8 151.4 122.6 150.7 183.6 132.5 149.0 118.7 151 .5 183.8 133.5 151.0 115.7 152.7 184.0 135.2 154.3 118.3 154.1 184.4 137.0 158.4 122.9 154.8 184.5 138.0 160.5 123.8 156.7 186.0 140.0 164.7 122.8 156.6 186.4 139.6 164.4 119.6 155.2 186.8 137.5 160.4 115.6 154.9 186.9 137.1 159.5 115.9 155.7 186.8 138.2 161 .2 120.6 158.0 187.9 141 .0 166.5 123.5 165.3 121.8 205.9 175.6 117.4 212.6 175.7 114.7 214.4 216.0 187.0 113.9 217.1 194.5 113.9 217.6 196.0 113.5 219.0 191 .8 113.2 219.7 190.2 113.1 220.2 190.1 113.7 220.3 196.9 114.3 220.0 200.6 219.0 250.7 176.5 114.0 215.3 201.4 219.1 251.8 184.1 114.0 216.7 199.2 216.2 248.5 171 .6 114.0 214 .2 200.6 218.0 250 .9 180.2 1142.0 194.5 207.7 241.6 172.9 114.2 214.1 200.5 218.8 250.7 202.0 219.7 252.6 203.2 220.0 252.9 203.7 220.2 253.0 203.9 220 .3 252.7 204.4 220.7 253.3 205. 1 221.6 253.5 205.5 221.0 254 .4 205.5 220 .5 256.0 205.9 222.0 255.9 All items less food ............................... ........... All items less shelter .. . ····· ·····--···" ..... ... .. ....... All items less medical care ....... ... .. ...... ...... . Commodities less food .......... .. .. ... ......... ... .... ... Nondurables less food .. .. ............ .. .......... ....... ... ~J::nc:kuables less food and apparel ... .... .. ..... .. .. Nondurables ....... .. ...... ..................... .. ...... ........ 175.8 168.3 171.1 137.3 149.2 166.1 161.4 Services less rent of shelter3 .. ... Services less medical care services .. ............... Energy .................. ........ ........ ........ ..... All items less energy ................................. ... .. .. All items less food and energy ....... ........ ... ..... Commodities less food and energy . . .. ...... Energy commodities .... .................... .. ......... Services less energy .... ...... ..... . ···· ··· ··· ········ 193.1 198.9 120.9 183.6 185.6 144.4 17.3 213.9 179.7 171 .9 174.8 137.7 154.2 175.9 166.4 201 .3 205.2 135.9 186.1 187.9 141.1 136.8 220.2 180.4 172.6 175.6 137.0 155.7 176.1 168.1 203.2 206.9 136.3 187.0 188.6 140.3 137.2 222.1 179.7 171.9 175.0 135.8 153.7 173.6 167.3 202.7 206.5 132.4 187.0 188.4 139.7 132.1 222.1 179.2 171 .6 174.7 134.5 151.4 172.1 166.6 202.9 206.6 131 .1 186.9 188.0 141 .1 136.8 222.1 180.2 172.5 175.6 135.5 153.3 176.9 167.8 204.1 207.6 136.9 187.2 188.3 138.2 138.3 223.1 181.4 173.7 176.6 137.1 156.4 180.2 169.5 204 .9 208.2 140.2 187.9 189.1 139.0 144.7 223.9 182.6 174.7 177.6 138.9 160.4 184.0 171.8 204.9 208.8 143.0 188.7 190.1 140.0 151 .5 224 .9 183.2 175.3 178.2 139.9 162.4 186.6 173.0 205.2 209.2 146.0 189.0 190.4 140.1 156.7 225.3 184.4 176.8 179.4 141 .8 166.4 193.5 175.9 205.8 209.7 154.5 189.3 190.4 139.9 170.7 225.5 185.0 177.5 180.0 141 .5 166.2 194.8 175.9 208.2 211 .1 159.9 189.3 190.3 139.0 173.3 226.0 184.5 176.7 179.6 139.4 162.3 191 .0 174.0 208.9 211.8 156.2 189.3 190.3 138.0 165.5 226.7 184.5 176.6 179.6 139.0 161.5 189.6 173.6 209.3 212.2 155.1 189.5 190.5 138.0 162.8 2~7.1 185.1 177.3 180.0 140.2 163.2 189.7 174.5 209.5 212 .3 154.2 190.2 191.4 139.5 162.3 227.4 186.2 178.6 181.1 142 .2 168.2 195.6 177.7 208.6 212.0 157.8 191.0 192. 1 140.5 174.5 227.9 Tuition, other school fees, and child care .. r.omm1inir.atinn 1•2 Information and information processini{ 2 .. Telephone services 1 ·2 ... Information and information processing nthP.r than IAIAnhonA SArvir.As 1 .4 Personal computers and peripheral 1 equipment ' 2 .. .. Other goods and services ... ... .... ....... .. .... ... ..... Tobacco and smoking products.... .......... . ......... 1 Personal care ··· ··· ·· ·· ····· ·········· ······ ·· ·· ·•·· ···· · Personal care products 1 .. ..... .... ... . ..... 1 Personal care services .. . . . . . . . . . ... . Miscellaneous personal services.. ........ .......... Commodity and service group: Commodities .... .... ····· ····· .................... . . . . . .. .... .. . . Food and beverages .. ... .. ........ .. .......... .............. Commodities less food and beverages ..... ....... Nondurables less food and beverages ···· · Apparel .................................... ........... Nondurables less food, beverages, and apparel .. .. .... .. .... .......... Durables ··················· ...................... Services ........ .. ...... .. . ··········· ··· · ················· .. ······· .. .•.... .... Rent of shelter3 . . . . . . . . . . . . . . . . . . . . . . .. .. . .. ... . .... ... Transporatation services .... ....... .............. Other services. ····· ·· . . . .. .. .. . .. . ........ .. ... Special indexes: 1 Not seasonally adjusted. 2 Indexes on a December 1997 = 100 base. 3 2003 133.8 336.5 377.3 91.2 4 Indexes on a December 1988 = 100 base. Dash indicates data not available. NOTE: Index applied to a month as a whole, not to any specific date. Indexes on a December 1982 = 100 base. 106 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 38. Consumer Price Index: U.S. city average and available local area data: all items [1982-84 = 100, unless otherwise indicated] Pricing All Urban Consumers sched- 2004 ule U.S. city average .............. .. ..... ............. .... ........ .... Region and area slze 1 May June July 2004 Aug. Sept. May Oct June July Aug. Sept. Oct. 189.9 190.9 184.7 185.3 184.9 185.0 185.4 201.0 201 .2 202.5 196.4 197.5 197.3 197.2 197.7 199 203.1 203.2 204.5 197.1 198.3 198.0 198.1 198.4 199.7 189.1 189.7 189.4 189.5 M 199.9 201.1 201.0 M 202.0 203.3 203.0 M Urban Wage Earners 186.5 2 Northeast urban ···· ········ ····· ··········· ···· ·· ·· ·· ························ ··· SizP. A-More than 1,500,000 ... .. .. ........,........... ............... 3 M 118.3 118.7 119.2 118.9 119.2 120.1 118.4 118.8 119.1 118.7 119.2 120.1 . . . ........ .. .... ..... .... . . . . .......•.. •............ . ...•......... • M 182.9 183.3 183.2 183.3 183.6 184.5 177.8 178.2 178 178.2 178.6 179.5 Size A-More than 1,500,000 ....................................... ... M 185.0 185.3 185.4 185.6 189.5 186.8 179.4 179.4 179.5 179.8 180.2 181 .1 Size B/C-50,000 to 1,500,000 ........... .... .. .... ...... . ... ... .. . . Size D-Nonmetropolitan (less than 50.000) ......... .. ...... M 116.4 116.8 116.3 116.5 116.8 117.4 115.5 116.0 115.5 115.7 115.9 116.6 M 176.0 176.9 177.1 176.3 176.4 177.1 173.2 174.1 173.7 173.4 173.7 174.4 South urban .... ................................................................... M 182.0 182.9 182.6 182.6 182.8 183.7 178.9 179.7 179.3 179.4 179.7 180.6 Size A-More than 1,500,000 .. ........................................ Size B/C-50,000 to 1,500,000 ... .... .............. ............ .... Midwest urban 4 3 M 183.4 184.3 183.7 183.7 184.0 185.0 180.8 181.9 181 .2 181.2 181.4 182.5 Size B/C-50 ,000 to 1,500,000 . .... ............ .. .. ........ ......... Size D-Nonmetropolitan (less than 50,000) .. ... ... ... .... .. M 116.4 117.0 116.9 116.9 116.9 117.4 114.8 115.3 115.2 115.3 115.4 115.9 M 179.4 180.5 180.1 180.0 181 .2 179 193.4 193.3 192.9 193.0 193.8 M 195.9 195.9 195.4 195.5 196.4 197.5 189.7 188.0 188.9 180.7 188.8 Size A-More than 1,500,000 ............... ..... ...................... 188.6 189.6 179.5 188.0 182.3 M 180 188.6 179.4 West urban ........ ................................................................ 182.8 195.0 188.9 189.9 191 .0 M 118.2 117.9 117.9 118.1 118.4 119.2 117.8 117.6 117.4 117.6 117.8 118.7 M M M 172.9 117.0 180.9 173.4 117.3 181 .8 173.1 117.3 181.3 173.2 117.3 181.0 173.6 117.4 181 .8 174.6 118.1 182.9 171.2 116.0 178.8 171.7 116.4 179.7 171 .3 116.2 179.0 171 .4 116.2 178.8 171 .8 116.5 179.7 172.8 11 7.2 180.8 Chicago--Oary-Kenosha, IL-IN-WI. ........ ... .. .............. ... Los Angeles-Riverside-Orange County, CA ..... .. .... ... ... .. . M M 188.7 193.3 189.1 193.7 189.2 193.4 190.2 193.1 190.0 194.5 190.8 196.3 182.2 186.8 182.5 187.4 182.4 186.8 183.2 186.5 183.1 187.8 184.0 189.8 New York, NY-Northern NJ-Long Island, NY-NJ--CT-PA .. M 204.4 206.0 205.5 205.7 205.9 207.3 199.1 200.4 200.1 200.3 200.6 201 .9 Boston-Brockton-Nashua, MA- NH-M E--CT ........... .... .... 1 181 .3 208.9 - 209.8 - 207.9 - 179.1 181.7 183.8 172.6 1 118.9 - 172.8 Dallas-Ft Worth, TX ......... ......... ... ........ .. ........ ...... ...... - - 208.8 174.8 - 1 - 207.9 Cleveland-Akron, OH .... .... .. .......... .. .. ... ................ ... ... - - 185.7 184.1 183.9 185.1 - 198.0 - 199.1 - 200.2 199.0 - 198.7 - 200.3 195.3 - 194.6 - 196.5 3 3 Size B/C-50,000 to 1,500,000 ···· ···· .. .. .... ...... .......... ... Size classes: As. .............. .............................. .... .. ...... .... ..... 3 ······ B/C .. ... .. .. .... ... . ............... ..... . ..... ... . .. .. ... .... .. .. .. ....... .. .. ...... . 0 ... ............ .. ......... ..... .. ................................. ......... ...... 190.0 Selected local areas' 7 WashinQton-Baltimore, DC-MD-VA-WV ..... ... .... . ..... . ....... Atlanta, GA . ... .. . .... ..... ... .. .................... ..... ... .... ....... ... 1 118.9 2 Detroit-Ann Arbor-Flint, Ml. ............. .. .... ... .. ...... .... ..... .. 2 Houston-Galveston-Brazoria, TX ..... ... .. ....... .. ... .. .. ....... 2 Miami-Ft. Lauderdale, FL. ... .. ....... ...... .... .. .. ............ ..... 2 Ph1ladelphia-Wilmington-Atlantic City, PA-NJ-DE-MD ..... 2 San Francisco--Oakland--San Jose, CA ... ..... ..... ... .. ..... ... 2 Seattle-Tacoma-Bremerton, WA .. .. ... .. ..... .... ..... .. ... ...... 2 - 1 Foods, fuels, and several other items priced every month in all areas; most other goods and services priced as indicated: 185.8 169.3 185.6 179.1 120.2 186.8 169.1 179.7 120.8 - 179.5 118.4 187.6 171.8 187 - - 184.0 - 167.6 - 195.4 179.4 119.7 180.4 183.4 197.3 190.4 - - 180.0 120.4 - 182.5 - 181.7 181.5 - - 167.4 183.0 169.5 - 198.0 - 195.0 - 196.4 189.6 - 191 .6 182.9 185.1 199.8 Report: Anchorage, AK; Cincinnatti, OH- KY-IN; Kansas City, MO-KS; Milwaukee-Racine, WI ; Minneapolis-St. Paul, MN-WI; Pittsburgh, PA; Port-land-Salem , OR-WA; St Louis, M-Every month. MO-IL; San Diego, CA; Tampa-St. Petersburg-Clearwater, FL. 1-January, March, May, July, September, and November. 7 Indexes on a November 1996 = 100 base. 2-February, April, June, August, October, and December. 2 Regions defined as the four Census regions. NOTE: Local area CPI indexes are byproducts of the national CPI program. Each local 3 Indexes on a December 1996 = 100 base. index has a smaller sample size and is, therefore, subject to substantially more sampling and other measurement error. As a result, local area indexes show greater volatility than 4 The "North Central" region has been renamed the "Midwesf' region by the Census Bureau. It is composed of the same geographic entities. 5 6 Indexes on a December 1986 = 100 base. In addition, the following metropolitan areas are published semiannually and the national index, although their long-term trends are similar. Therefore , the Bureau of Labor Statistics strongly urges users to consider adopting the national average CPI for use in their escalator clauses. Index applies to a month as a whole, not to any specific date. appear in tables 34 and 39 of the January and July issues of the CPI Detailed https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Dash indicates data not available. Monthly Labor Review December 2004 107 Current Labor Statistics: Price Data 39. Annual data: Consumer Price Index, U.S. city average, all items and major groups (1982-84 = 100] 1993 Series Consumer Price Index for All Urban Consumers: All items: Index .................... .............................................-...... Percent change ................................ ... ......... .. ..... Food and beverages: Index .................. ... .................. ................................ Percent change ............................. .. ........... ....... .. Housing: Index ........ ..... .. ... .................................. ................ Percent change ..................................... .... ....... ... Apparel: Index .......................... .... .................................... ..... Percent change .............................. ... .. .. ..... ... ...... Transportation: Index ... ................................................................... Percent change ..................... ............ ......... .... ..... Medical care: Index ......... ......... ... .................................................. Percent change .............................. .. .. ........... ... ... Other goods and services: Index ............................................... ........................ Percent change .............................. ....... .. ...... ... ... Consumer Price Index for Urban Wage Earners and Clerical Workers: All items: Index .......................... .. ......................................... Percent change ................................ ..... ... .... ...... . l 08 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 144.5 3.0 148.2 2.6 152.4 2.8 156.9 3.0 160.5 2.3 163.0 1.6 166.6 2.2 172.2 3.4 177.1 2.8 179.9 1.6 184.0 2.3 141.6 2.1 144.9 2.3 148.9 2.8 153.7 3.2 157.7 2.6 161.1 2.2 164.6 2.2 168.4 2.3 173.6 3.1 176.8 1.8 180.5 2.1 141.2 2.7 144.8 2.5 148.5 2.6 152.8 2.9 156.8 2.6 160.4 2.3 163.9 2.2 169.6 3.5 176.4 4.0 180.3 2.2 184.8 2.5 133.7 1.4 133.4 - .2 132.0 -1.0 131 .7 - .2 132.9 .9 133.0 .1 131 .3 -1 .3 129.6 -1 .3 127.3 - 1.8 124.0 -2.6 120.9 -2.5 130.4 3.1 134.3 3.0 139.1 3.6 143.0 2.8 144.3 0.9 141 .6 -1.9 144.4 2.0 153.3 6.2 154.3 0.7 152.9 -.9 157.6 3.1 201.4 5.9 211 .0 4.8 220.5 4.5 228.2 3.5 234.6 2.8 242.1 3.2 250.6 3.5 260 .8 4.1 272.8 4.6 285.6 4.7 297.1 4.0 192.9 5.2 198.5 2.9 206.9 4.2 215.4 4.1 224.8 4.4 237.7 5.7 258.3 8.7 271.1 5.0 282.6 4.2 293.2 3.8 298.7 1.9 142.1 2.8 145.6 2.5 149.8 2.9 154.1 2.9 157.6 2.3 159.7 1.3 163.2 2.2 168.9 3.5 173.5 2.7 175.9 1.4 179.8 2.2 December 2004 40. Producer Price Indexes, by stage of processing (1982 = 100] Annual average Grouping 2002 2003 2004 2003 Oct. Nov. Dec. Jan. Feb. 1 Sept.P Oct.P 148.7 152.0 152.1 148.6 151.9 152.2 148.7 152.0 152.2 151 .9 155.5 154.7 Mar. Apr. May June July" 148.9 152.5 155.5 148.7 152.0 155.0 Aug. Finished goods.... ................................ Finished consumer goods ... ........ .. .... ....... Finish ed consumer foods ....................... 138.9 139.4 140.1 143.3 145.3 145.9 145.5 147.7 151 .0 144.5 146.5 150.1 144.5 146.7 150.3 145.4 147.8 148.1 145.3 147.8 148.4 146.3 149.0 150.7 147.3 150.4 152.7 Finshed consumer goods excluding foods .. .... ....... .... .......... .. .. ..... Nondurable goods less food ...... .......... Durable goods ............... ....................... Capital equipment.. ........ .... ... ....... ..... .. ... 138.8 139.8 133.0 139.1 144 .7 148.4 133.1 139.5 146.2 149.4 135.6 140.8 144.8 147.6 135.0 140.5 145.0 148.2 134.3 140.2 147.4 151 .7 134.3 140.5 147.3 151 .6 134.2 140.2 148.0 152.4 134.7 140.5 149.1 154.3 134.4 140.6 150.9 156.7 134.8 140.8 150.5 156.0 134.9 141 .1 151 .7 157.9 134.6 141 .2 151.4 158.0 133.7 141 .1 151 .5 158.1 133.8 141.3 155.5 162.0 137.7 143.5 Intermediate materials, supplies, and components............... .... 127.8 133.7 134.1 134.1 134.5 136.2 137.3 138.3 140.2 142.0 142.8 143.8 144.9 145.3 146.2 137.4 152.2 144.5 146.9 127.3 137.7 152.0 145.9 145.8 127.6 138.6 147.9 147.2 149.4 127.8 139.6 145.4 149.5 151 .0 128.1 140.8 144.2 152.1 153.3 128.0 141.2 144.2 153.5 152.8 128.2 Materials and components for manufacturing ..... ... .... ......................... Materials for food manufacturing ............. Materials for nondurable manufacturing .. Materials for durable manufacturing ..... ... Comrorients for manufacturing ........... .. ... 126.1 123.2 129.2 124.7 126.1 129.7 134.4 137.2 127.9 125.9 130.5 141 .8 137.5 129.5 125.8 130.7 141 .6 137.2 130.5 125.8 130.9 140.7 137.9 131 .2 125.8 131 .9 138.4 140.2 132.9 125.9 133.2 139.3 141 .0 137.3 126.2 134.3 141 .7 141.4 140.7 126.5 136.2 146.6 143.5 144.3 127.1 Materials and components for construction ......... ............ .... ....... ... ..... Processed fuels and lubricants ...... ........... . Containers ............... .. .................... .... ......... Supplies .......... ...... .......... .. ..... .... ...... ........ . 151.3 96.3 152.1 138.9 153.6 112.6 153.7 141 .5 155.2 111.5 153.2 141 .9 155.6 110.3 153.4 142.6 155.6 111 .7 153.5 142.8 156.2 116.8 153.9 143.2 159.0 116.8 153.7 143.8 161.9 116.5 154.1 144.8 164.7 118.4 154.9 146.4 166.9 122.3 156.7 147.2 166.9 124.9 158.9 147.3 167.8 126.5 159.5 148.1 170.0 128.5 161.4 147.o 171 .1 127.1 162.5 147.7 170.7 130.4 164.1 147.8 Crude materials for further processing .......................... ... ...... ...... .. Foodstuffs and feedstuffs .. ..... ................... Crude nonfood materials ......... ........... .... .... 108.1 99.5 111.4 135.3 113.5 148.2 138.3 128.1 141 .1 137.0 125.7 141 .4 141 .1 124.7 149.5 147.8 117.1 167.3 150.1 122.2 167.3 152.9 131 .7 164.8 155.7 135.4 166.6 161 .8 141 .1 172.9 163.0 137.4 178.0 162.0 131 .0 181 .3 160.7 124.7 183.9 153.8 121 .7 174.1 159.7 119.9 186.1 Special groupings: Finished goods, excluding foods ............ .... Finished energy goods ........... .. ... .. ............. Finished goods less energy ......... ...... ........ Finished consumer goods less energy ...... Finished goods less food and energy ........ 138.3 88.8 147.3 150.8 150.2 142.4 102.0 149.0 153.1 150.5 143.8 103.2 151.4 156.1 152.0 142.8 100.4 151.0 155.5 151.7 142.8 101.0 150.9 155.5 151.4 144.5 106.0 150.6 154.9 151.8 144.3 105.7 150.5 155.0 151 .7 144.9 107.0 151.3 156.1 152.0 145.7 109.5 151 .9 156.9 152.1 147.0 113.6 152.7 158.0 152.2 146.8 112.5 152.7 157.9 152.3 147.6 115.1 152.1 156.8 152.4 147.4 115.1 151.9 156.6 152.2 147.5 114.9 152.1 156.8 152.5 150.9 120.9 154.4 159.1 154.7 157.6 157.9 159.5 159.2 159.0 159.4 159.4 159.7 159.8 159.9 160.0 160.0 159.7 160.0 162.2 Finished consumer goods less food and energy ................. ............ ............ ... ... Consumer nondurable goods less food and energy .... .... ...... .............. ......... .. ...... 177.5 177.9 178.6 178.5 178.9 179.7 179.8 179.8 180.5 180.2 180.2 180.5 180.8 181 .3 181.6 Intermediate materials less foods and feeds .................................... ......... .... Intermediate foods and feeds .... ... ..... ...... .. Intermediate energy goods .... ... ................. Intermediate goods less energy .......... .. ..... 128.5 11 5.5 95.9 134.5 134.2 125.9 111 .9 137.7 134.4 131 .9 110.7 138.5 134.2 134.8 109.5 138.8 134.7 134.1 110.9 139.0 136.5 132.2 115.8 139.8 137.6 133.7 115.8 141.1 138.4 137.0 115.6 142.4 140.2 143.2 117.3 144.4 141 .9 147.7 121 .1 145.7 142.8 144.9 123.7 146.0 144.0 143.2 125.4 146.8 145.4 136.0 127.1 147.7 146.0 133.8 126.0 148.5 147.0 131.2 129.5 148.7 Intermediate materials less foods and energy ............ .................................. . 135.8 138.5 139.0 139.2 139.5 140.4 141 .7 142.9 144.6 145.7 146.2 147.1 148.5 149.5 149.9 Crude energy materials ........................ ..... . Crude materials less energy ..... .. ... ......... ... Crude nonfood materials less energy ... ..... 102.0 108.7 135.7 147.2 123.4 152.5 134.3 135.9 159.5 132.5 135.5 164.8 141 .8 136.2 170.1 163.5 133.2 179.3 158.9 139.8 189.9 153.0 148.0 195.2 158.8 148.7 187.6 172.1 150.1 177.9 180.0 147.0 176.3 178.3 146.5 191.6 178.1 144.5 200.9 166.3 140.9 195.4 179.5 142.0 204.6 December 2004 109 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review Current Labor Statistics: Price Data 41. Producer Price Indexes for the net output of major Industry groups [December 2003 = 100, unless otherwise indicated] NAICS 2004 Industry Jan. 211 212 213 - Total mining industries (December 1984:100)...................................... Oil and gas extraction(December 1985=100) .. ....... ...... .... ...... .... ..... ..... Mining, except oil and gas ....... ... ......... .. ......... .. ..... ....... .. ......... .... Mining support activities ....... ..... .... ...... .. .. ....... .. .. ... ... ... ...... ... ...... Feb. Apr. May June JulyP Aug.P Sept.P Oct.P 144.6 140.3 136.6 140.9 149.5 155.5 155.2 157.2 148.8 158.9 181.1 103.3 101.2 172.5 105.2 100.8 165.4 105.9 100.8 171 .7 108.5 101 .0 188.1 107.3 101 .3 198.0 108.1 102.2 196.9 108.5 103.5 198.7 110.2 105.5 182.8 111.6 107.5 199.9 112.3 110.1 138.9 139.3 101.4 100.4 99.9 139.3 140.4 101 .2 100.3 99.7 140.3 142.4 100.7 100.2 99.8 141 .8 146.1 101 .5 100.7 99.9 143.3 149.1 100.2 101 .1 100.0 142.9 148.6 101 .2 101.3 99.8 143.4 146.7 100.9 101 .6 99.6 143.7 144.4 101.4 101 .6 99.6 144.1 143.3 101 .0 101.2 99 .9 146.5 142.9 101.6 101 .7 100.1 Total manufacturing industries (December 1984:100) ........................ Food manufacturing (December 1984=100) ...... ..... ........ ....... ... .. .. .. Beverage and tobacco manufacturing ............................. ........... ........... Textile mills .............. ... .. .................................... ... ..... ....... ..... ................. Apparel manufacturing ................. ... .. ........ .... ...... ... ....... ............. Leather and allied product manufacturing (December 1984=100) ......... Wood products manufacturing ...... .. ..... ...... ...... ... ....... ..... .. ..... ... ... Paper manufacturing ...... ........................................................... ............. Printing and related support activities ........ ........................... ............. ... . Petroleum and coal products manufacturing (December 1984=100) .... Chemical manufacturing (December 1984=100) .. .. .... ...... .... ... .. ..... .. Plastics and rubber products manufacturing (December 1984=100) .. .. Primary metal manufacturing (December 1984= 100) ... ..... ..... ... ... .... . Fabricated metal product manufacturing (December 1984= 100) ....... .. Machinery manufacturing .... ... ..... .................... .... ... .... .... .......... ... Computer and electronic products manufacturino .. .. .. ...... .. .... ....... .. . Electrical equipment, appliance, and components manufacturing .... , .. . Transportation equipment manufacturing .... ....... .... ....... ... .............. Furniture and related product manufacturing(December 1984=100) ... . Miscellaneous manufacturing .... ......... ... ... .... ........ ....................... 143.3 99 .3 99 .3 100.2 143.6 102.7 99.4 100.2 143.8 105.9 99.5 100.4 143.5 108.1 100.1 100.8 143.4 110.2 101 .1 100.8 143.5 108.3 102.3 101.0 143.6 106.7 103.4 101 .3 143.7 109.9 104.2 101 .5 143.5 110.8 104.9 102.0 143.7 107.4 105.7 101.9 131.5 167.0 128.9 124.0 134.6 100.3 99 .8 100.2 100.2 147.4 100.5 130.7 167.9 129.4 128.5 135.7 100.6 99.5 100.7 100.1 148.7 100.9 134.3 168.8 129.6 132.3 137.5 100.9 99.3 101.8 100.4 149.0 100.8 141 .9 169.7 130.0 138.4 139.4 101 .3 99.5 102.7 100.2 149.7 101 .0 152.0 170.3 130.4 142.2 140.8 101.6 99.3 103.3 100.4 151.4 100.9 144.1 171 .6 130.8 142.3 141 .9 101.8 99.1 103.5 100.6 151 .7 101 .2 152.0 172.0 131.4 147.6 142.6 102.1 99.0 103.7 100.4 152.1 101 .3 155.6 173.2 131.8 149.1 143.7 102.2 98.9 103.8 99.9 152.7 101.0 158.9 175.6 132.5 150.9 144.2 102.5 98.9 104.1 99.9 152.7 101 .6 176.7 177.1 134.3 152.0 144.7 103.1 98.9 104.4 103.2 153.5 101 .6 442 443 446 447 454 Retail trade Motor vehicle and parts dealers .. ..... .. ... .... ....... ...... ... ..... .. .......... . Furniture and home furnishings stores .. .. ..... .. ............ ... .... ....... ... .. Electronics and appliance stores .... ... .... .. .. ............... ... ... ... ... ..... .. . Health and personal care stores .. .. .. .............. .. ... ... .. .... .. ........ ..... .. Gasoline stations (June 2001=100) ... ...... .. .... .... .. .... .......... .. .. ....... . Nonstore retailers ... ............. ... ... .. ...... .. .. .. .... ......... ... .... .. ... 101.6 99 .5 101.4 99.6 45.5 102.9 101 .7 100.8 99.7 99.9 46.6 105.4 103.2 101.8 99.9 96.9 55.4 113.2 103.8 102.0 101 .2 97.4 56.6 108.6 103.7 101 .4 101.2 97.5 53.2 107.0 103.7 102.8 98.8 98.7 59.3 108.7 104.0 102.5 99.9 99.5 46.0 106.1 103.4 103.0 98.8 101.5 47.0 103.6 103.5 103.6 101.6 107.3 45.8 107.5 104.2 104.0 100.6 106.8 42 .0 103.1 481 483 491 Transoortation and warehousino Air transportation (December 1992= 100) ....... ... .. .......... ... . Water transportation ... .. .. ... ......... ...... .... ... .. .. ..... ......... .. ... ....... .... . Postal service (June 1989=100) ... .. ......... .... ..... . ..................... ..... 163.3 99.0 155.0 163.6 98.9 155.0 162.0 99.4 155.0 162.3 100.1 155.0 162.2 100.3 155.0 162.8 100.3 155.0 163.4 100.4 155.0 165.1 100.5 155.0 160.6 103.0 155.0 161.6 103.6 155.0 221 Utilities Utilities ... .. ........... .. .. .. ... ... .. ... ... .... .. ·· ···· ···· ··· ··· ··· ······ ················ 101.7 102.5 101 .2 101 .8 103.1 106.9 107.1 107.5 105.1 104.0 Health care and social assistance Office of physicians (December 1996=100) .. ..... .... ...... .... .... .......... . Medical and diagnostic laboratories ... .. ... .. .. .... .. .. .......... .. ... ........... Home health care services (December 1996=100) .. ... .. .... .. ... .. ....... . Hospitals (December 1992=100) .. ....... ... .. ... . ·· ···· ·· ······· ··· ··· ····· ···· · Nursing care facilities ...... ........ .... ..... ........ ... .. ... .... ... .. .... .. ........... Residential mental retardation facilities .......... ... ......... ...... ... 114.1 100.3 119.5 139.5 101 .2 100.1 114.3 99.8 119.6 140.1 101.4 99.9 114.3 99.8 119.6 140.3 101.6 99.9 114.4 99.8 119.7 140.7 101.9 99.9 114.4 100.0 119.7 140.8 102.0 100.5 114.3 100.0 119.7 140.9 102.0 100.5 114.5 100.0 119.9 142.3 102.1 99 .9 114.5 100.0 119.8 142.1 102.9 100.6 114.5 100.1 119.7 142.4 103.1 100.6 114.4 100.1 119.9 142.9 103.5 100.9 100.9 97.8 100.4 99.9 101 .8 101 .3 99.1 100.0 98.9 102.0 101.3 100.3 100.2 98.4 101 .7 101.4 101 .6 100.1 98.5 102.3 101 .3 103.1 99 .9 98.9 102.4 101.4 102.7 99.9 99.0 102.7 101 .8 100.5 99.7 99 .0 102.5 101.2 100.1 100.0 99.0 102.3 101.0 101.9 99.5 98.8 103.2 101.5 103.2 99.2 98.9 104.0 99.1 100.0 100.1 107.9 131.4 100.8 99.4 100.2 100.6 109.8 131 .7 100.7 99.6 100.7 101 .1 107.4 131 .7 100.8 101 .0 100.8 101 .3 106.0 131 .8 101.1 102.6 100.8 101.9 104.5 131 .8 101 .2 102.1 101.0 98.5 105.6 131 .8 101.1 103.2 101.1 101.5 109.7 132.0 101 .3 105.2 101.1 102.7 111 .0 131.9 101.6 104.7 101 .1 100.7 108.2 132.3 101.8 104.1 99.5 98.5 108.0 132.5 102.0 125.7 99.6 112.1 99.0 100.3 100.8 122.2 125.9 99.6 112.5 98.7 100.3 101.3 123.6 126.5 99.8 113.2 98.7 100.4 100.8 124.9 126.6 99.9 113.1 98.7 100.5 101 .3 124.8 126.5 99.9 113.4 98.7 100.6 101 .5 124.4 126.6 99.9 113.8 97.4 101.0 101.5 125.6 126.9 100.3 114.0 96.1 100.8 101.3 128.6 126.9 100.7 114.8 95.4 101 .6 101 .3 128.6 127.2 100.4 11 4.8 94.8 100.9 101 .3 125.4 127.4 100.4 115.3 96.9 101 .5 101.4 125.4 311 312 313 315 316 321 322 323 324 325 326 331 332 333 334 335 336 337 339 441 6211 6215 6216 622 6231 62321 511 515 517 5182 523 53112 5312 5313 5321 5411 541211 5413 54181 5613 56151 56172 5621 721 Other services Industries Publishing industries, except Internet .... .. ....... ....... ...... ... .. .. .... ... .. Broadcasting, except Internet. ... ..... .... ... ... .. ..... ..... .... ..... .... ...... ... . Telecommunications .. ... ... ..... ... ... .... ..... .. ... .. ...... .... .... .. ............. .. Data processing and related services ............... ·· ·· ·· ····················· · Securitv. commodity contracts. and like activitv .... ... .. .... .... ..... ........ . Lessors or nonresidental buildings (except miniwarehouse) .. ... .... .. .... Offices of real estate agents and brokers .. .. ...... ..... ..... ... .. Real estate support activities ........ ... ..... .. ..... .. .. .. ... ... .. .... .... ......... . Automotive equipment rental and leasing (June 2001=100) ............... Legal services (December 1996=100) .. .. .. ... ....... .... ........ ... .. ... ....... Offices of certified public accountants ............. .... ..... ... ...... ..... . ..... . Architectural, engineering, and related services (December 1996= 100) ............ ... ... ..... ...... ........... ... .. ............... Advertising agencies ....... ... .... ..... ...... ..... .. .. ... .... ...... ..... ......... ..... Employment services (December 1996=100) ... .... ...... ...... ... ... ..... .... Travel agencies ..... .... ... ... .. ...................... ........ .. ...... ... ......... ..... Janitorial services ....... .. ........ ... .... ........... ... ...... ... . ·· ····· ············ ·· Waste collection ......... ....... .. .. ... .. .. ..... ... .. ... .. ...... ... .............. ..... .. Accommodation (December 1996-100\ .............. ..... .. .. .. .. ... ...... ..... NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System (NAICS), replacing the Standard Industrial Classification (SIC) system . 110 Mar. Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 42. Annual data: Producer Price Indexes, by stage of processing [1982 = 100) Index 1993 1994 1995 1996 1998 1997 1999 2000 2001 2002 2003 Finished goods Total. .. ....... ........ .. .... .... ... ... ........ ... ... ......... ....... ........ ...... . Foods ...................... ................... ...... ............ ... . ... . Energy ......... .... .. ... ..... ... ..... ............... ............ ..... . Other .............. ................ ..... ....... ... ........... ............ . 124.7 125.7 78.0 135.8 125.5 126.8 77.0 137.1 127.9 129.0 78.1 140.0 131 .3 133.6 83.2 142.0 131 .8 134.5 83.4 142.4 130.7 134.3 75.1 143.7 133.0 135.1 78.8 146.1 138.0 137.2 94.1 148.0 140.7 141.3 96.8 150.0 138.9 140.1 88.8 150.2 143.3 146.0 102.0 150.5 Intermediate materials, supplies, and components Total .... .... ............ .. ............. ................. ... .. ................. .. ... Foods ........................... .. ............ ..... .................. . Energy ... ..... ..... ... ......... .. .... ... ........... .. ............ ....... . Other .. ...... .... ... ..... .. .. .... ..... ... .... ... .... ... .. ... .... .... .. . .. . 116.2 115.6 84.6 123.8 118.5 118.5 83.0 127.1 124.9 119.5 84.1 135.2 125.7 125.3 89.8 134.0 125.6 123.2 89.0 134.2 123.0 123.2 80 .8 133.5 123.2 120.8 84.3 133.1 129.2 119.2 101 .7 136.6 129.7 124.3 104.1 136.4 127.8 123.3 95.9 135.8 133.7 134.4 111 .9 138.5 102.4 108.4 76.7 94.1 101 .8 106.5 72.1 97.0 102.7 105.8 69.4 105.8 113.8 121 .5 85.0 105.7 111 .1 112.2 87.3 103.5 96.8 103.9 68.6 84.5 98.2 98.7 78.5 91 .1 120.6 100.2 122.1 118.0 121 .3 106.2 122.8 101 .8 108.1 99.5 102.0 101 .0 135.3 113.5 147.5 116.8 Crude materials for further processing Total. ... .... .... ... ..... ..... ..... .... .. ... ................................ ...... . Foods .... .............. ..... .. ... . .... ............. ......... ........ . .. . Energy .................... .......... .. .. ..... ..... ........ ........... . Other .. ............... ............. .. ... ... ... . ..... .. . .... .... ... ..... .. . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 111 Current Labor Statistics: Price Data 43. U.S. export price Indexes by Standard International Trade Classlflcatlon [2000 = 100) SITC Rev.3 2003 Industry 2004 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Sept. Oct. 0 Food and live animals ......... .. .................. ................. Meat and meat preparations ............. ......... ..................... 01 Cereals and cereal preparations ..................................... 04 Vegetables. fruit, and nuts, prepared fresh or dry ........... 05 112.2 123.5 119.4 103.2 115.2 125.6 125.6 102.8 116.5 123.0 130.8 103.2 117.0 122.8 131 .6 103.1 119.9 125.0 135.2 108.4 122.7 127.1 139.6 110.1 126.1 127.6 147.7 109.5 126.7 127.7 146.0 113.3 123.9 127.3 141 .2 111 .1 119.8 123.0 128.0 110.0 116.5 126.1 120.6 113.2 117.8 124.6 122.0 120.2 118.5 125.9 115.5 131.2 2 Crude materials, Inedible, except fuels .......................... Oilseeds and oleaginous fruits ............................. ... ....... . 22 24 Cork and wood ..... ........................................................... Pulp and waste paper .... ... ............................... ............... 25 Textile fibers and their waste .......................................... 26 Metalliferous ores and metal scrap ................................. 28 111 .2 136.7 92.0 90.8 121 .4 121 .1 116.3 150.9 92.5 91.9 128.5 129.6 116.9 152.5 93.7 91 .7 121 .2 136.6 120.2 157.2 94.5 91 .7 123.7 148.9 122.3 160.9 95.6 92.5 122.2 156.8 129.0 181.6 96.5 94.2 121 .9 171.4 132.8 197.1 97.6 98.8 115.9 176.2 132.5 199.0 98.2 100.4 114.9 170.6 125.7 168.5 98.3 100.8 108.7 167.5 132.1 184.5 98.9 100.1 102.9 190.2 117.9 117.4 98.8 99.5 101.1 183.0 119.1 125.1 99.1 98.7 102.1 177.2 117.8 109.1 98.6 98.6 100.1 187.9 3 Mineral fuels, lubricants, and related products ............. Coal, coke, and briquettes .............................................. 32 Petroleum, petroleum products. and related materials ... 33 108.2 111 .6 104.1 106.3 111 .6 101.2 110.7 112.9 106.2 120.5 119.3 123.0 123.2 135.1 - 131 .8 - 137.5 139.3 116.8 - - 156.1 - - 141 .2 - 11 4.7 - 120.1 - 119.8 135.0 - 129.7 134.5 136.2 138.0 156.8 5 Chemicals and related products, n.e.s . ......................... Medicinal and pharmaceutical products ............ .............. 54 Essential oils; polishing and clean ing preparations ......... 55 Plastics in primary forms .. .............................................. 57 Plastics in non primary forms ................................. ......... . 58 Chemical materials and products, n.e.s......................... 59 100.7 105.9 98.9 95.5 98.3 102.4 100.9 106.5 99.4 95.8 97.1 102.5 101.4 105.8 100.1 96.5 97.2 102.6 102.9 105.4 104.3 98.3 96.8 105.0 104.0 105.3 104.2 100.9 97.2 105.2 104.9 105.5 104.3 102.1 97.4 104.8 105.5 105.7 104.1 102.2 96.9 104.8 105.6 105.7 104.4 102.9 96.7 104.8 105.8 105.8 104.3 103.2 96.5 104.9 107.0 107.9 104.1 104.8 97.2 104.6 108.7 108.1 105.0 107.5 97.2 106.3 109.6 108.0 105.5 109.6 97.5 105.5 111 .9 107.8 106.0 113.6 97.9 105.1 6 Manufactured goods classlfled chiefly by materials..... 62 Rubber manufactures. n.e.s ............... .. ...................... 64 Paoer. oaoerboard. and articles of oaoer. oulo. 100.3 100.7 100.8 101 .7 103.0 104.1 105.6 106.6 107.0 108.5 109.6 110.5 111 .1 109.2 109.5 109.9 110.4 110.9 110.4 110.9 110.8 111.2 111 .8 112.0 111 .2 111 .5 and oaoerboard ....... ..... ........... ... .. ... .. ..... ..... ..... ..... Nonmetallic mineral manufactures. n.e.s. ..... ......... ........ Nonferrous metals ............................. ......... ..................... 97.4 99.5 81.9 97.9 99.7 83.4 97.6 99.8 84.5 97.9 99.7 85.9 97.8 99.6 90.9 97.9 99.7 94.1 98.7 99.7 98.1 99.0 99.5 97.6 99.2 99.9 95.4 101 .2 99.9 95.4 101 .9 100.2 96.7 102.7 100.5 98.5 103.8 100.5 99.2 7 Machinery and transport equipment............................... 71 Power generating machinery and equipment... ..... ......... Machinery specialized for particular industries ............... 72 74 General industrial machines and parts, n.e.s., 97.7 107.9 103.1 97.7 108.5 103.3 97.8 108.7 103.4 97.9 109.3 103.9 98.1 109.4 104.0 98.2 109.4 104.2 98.4 108.7 105.1 98.4 108.7 105.4 98.2 108.7 105.4 98.2 108.9 105.7 98.2 109.0 105.9 98.3 109.0 106.1 98.6 109.6 107.2 and machine parts ............ .... .............. ............... ........... Computer equipment and office machines ...... .. ........... ... Telecommun ications and sound recording and reproducing apparatus and equipment... ...................... Electrical machinery and equipment... ........... ................. Road vehicles .................. .. ....... ... ............................... .... 102.6 87.9 102.8 88.0 102.8 88.6 103.3 87.7 103.5 88.2 104.0 88.4 104.5 88.8 104.8 88.6 104.9 87.2 105.2 86.6 105.3 86.4 105.3 86.2 106.3 85.9 92.8 88.6 101.5 92.2 88.2 101 .6 92.0 88.1 101 .5 92.6 88.0 101 .7 92.5 88.3 101 .9 92.4 88.6 101.9 92.2 88.5 102.3 92.0 88.6 102.3 91 .8 88.2 102.4 91 .5 88.3 102.5 90.7 88.2 102.5 90.7 88.2 102.8 90.5 88.6 102.8 102.1 102.3 102.3 102.2 102.3 102.3 102.2 102.1 102.0 101 .7 101.9 101 .8 102.2 66 68 75 76 77 78 Aug. 87 Professional, scientific, and controlling Instruments and apparatus ..................................... 112 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 44. U.S. Import price Indexes by Standard International Trade Classlflcatton [2000 = 100) SITC 2004 2003 Industry Oct. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Food and live animals ............................................. . Meat and meat preparations .. ....... ......... ....... .................. Fish and crustaceans, mollusks, and other aquatic invertebrates .... .... .. ... ........... .................... ... Vegetables, fruit , and nuts, prepared fresh or dry ........... Coffee, tea, cocoa, spices, and manufactures thereof. .. .. .. ..... .. .. .... ... ... ....... .... .. ... ... .... ... .. ..... ...... 100.3 115.2 100.0 117.2 101 .0 120.4 102.2 117.7 104.7 11 8.0 105.4 120.4 106.4 121.7 106.1 124.4 106.9 128.9 107.4 133.7 107.4 134.2 109.2 135.1 109.9 133.9 79.8 106.4 79.3 108.9 79.2 109.4 78.2 112.3 80.0 115.7 83.3 111 .3 85.1 109.5 84.1 106.1 84.1 105.9 86.1 102.1 86.9 100.6 86.1 109.2 85.4 110.2 95.5 93.1 96.0 100.1 101.9 101.7 103.6 102.4 107.0 102.7 103.3 105.6 104.5 1 Beverages and tobacco ............ ................ ....... ...... .. 11 Beverages ......... .. ... .................... ... .............................. . 104.3 104.2 104.4 104.2 104.4 104.3 104.7 104.9 105.0 105.2 105.3 105.5 105.3 105.5 105.4 105.7 105.3 105.6 105.9 106.4 106.1 106.6 106.2 106.7 106.5 106.9 2 Crude materials, Inedible, except fuels .......................... 24 Cork and wood ................................ ............ .................... Pulp and waste paper ........................ ............................. 25 Metalliferous ores and metal scrap .. ........... ......... ........... 28 Crude animal and vegetable materials, n.e.s ................ . 29 104.2 106.2 90.8 104.3 95.1 104.5 103.2 91 .9 108.7 94.8 107.9 108.0 92.8 115.3 99.6 109.5 108.9 93.3 124.2 98.9 114.1 115.7 91 .9 134.6 99.5 120.0 123.3 95.4 148.0 99.7 122.9 127.8 100.8 148.2 99.3 127.3 139.0 103.4 143.5 102.1 125.8 136.1 106.5 140.4 98.0 125.7 132.1 108.0 145.3 101 .2 134.1 149.0 107.7 160.8 97.6 135.1 151 .1 105.5 162.4 98.7 124.9 125.8 99.8 165.1 96.9 3 Mineral fuels, lubricants, and related products............. Petroleum, petroleum products, and related materials ... 33 Gas, natural and manufactured ............ ... ....... ................ 34 101 .3 100.1 106.2 103.3 102.3 106.6 108.2 106.9 113.9 117.3 114.0 138.0 117.7 114.5 137.1 120.8 120.0 122.9 121.1 120.3 123.3 131 .6 131 .5 129.5 131 .5 130.0 140.0 133.9 133.0 134.8 144.1 144.6 136.3 146.1 148.7 122.0 160.9 165.5 121 .1 5 Chemicals and related products, n.e.s. ......................... Inorganic chemicals .... ......................................... ........... 52 Dying, tanning, and coloring materials .... ........................ 53 Medicinal and pharmaceutical products .. ................ ........ Essential oils; polishing and cleaning preparations ..... .... Plastics in primary forms .... ................ ....................... ...... Plastics in nonprimary forms ... ........................................ Chem ical materials and products, n.e.s ................... ...... 100.2 108.8 98.1 102.3 91 .2 105.6 101.7 92.3 100.8 111 .9 99.0 103.4 91.6 105.6 101.7 93.1 101 .1 114.0 99.6 103.4 91.6 105.5 101 .8 93.3 103.0 119.3 99.9 107.2 92.7 104.4 102.1 94.3 103.4 120.6 99.7 107.7 93.3 105.2 102.4 94.9 103.8 120.5 99.5 108.1 93.7 106.9 102.9 95.8 103.5 115.9 100.6 107.7 93.5 105.5 102.9 95.4 103.5 117.5 100.8 107.3 93.4 105.8 102.9 95.1 103.8 119.8 100.3 107.1 93.5 104.6 102.3 95.2 104.6 122.2 98.3 107.3 93.5 107.8 103.0 94.7 105.1 124.0 98.4 107.0 96.4 108.4 103.3 94.1 105.7 124.4 98.4 106.5 93.4 109.2 103.6 94.5 106.3 125.1 98.5 106.4 93.3 109.7 104.0 94.9 6 Manufactured goods classified chiefly by materials..... Rubber manufactures, n.e.s.............. ....... ...................... 62 96.5 98.5 97.4 98.6 97.8 98.8 98.9 99.0 101.4 99.2 103.6 99.7 105.6 99.9 106.9 100.0 106.1 100.5 106.1 100.5 107.5 100.8 108.7 100.8 101.0 Paper, paperboard, and articles of paper, pulp, and paperboard ............ ....... ... ..... .......... .. .............. Nonmetallic mineral manufactures, n.e.s . ............... ....... Nonferrous metals ................... .................... .............. ...... Manufactures of metals, n.e.s . ....................... ... ....... .. .... 94.7 97.9 82.0 98.7 94.2 98.1 85.1 99.1 93.7 98.1 87.7 99.5 94.1 98.5 92.3 99.7 94.5 98.9 97.0 100.3 95.0 99.0 102.6 101 .1 94.8 99.3 105.8 102.3 95.5 99.4 106.1 102.4 95.5 99.4 101 .6 102.4 96.4 99.3 102.3 102.7 96.8 100.2 105.2 103.3 97.9 100.3 105.7 103.9 100.6 106.5 104.1 7 Machinery and transport equipment. .............................. Machinery specialized for particular industries ........ ....... 72 General industrial machines and parts, n.e.s. , 74 95.3 102.4 95.4 103.3 95.3 103.6 95.4 104.9 95.5 106.4 95.5 106.7 95.2 106.5 95.2 106.7 95.1 106.6 95.0 107.2 95.0 107.6 95.0 107.5 95.0 107.8 100.4 78.6 100.9 78.5 101 .2 78.2 101 .8 78.0 102.5 78.0 103.3 77.7 103.5 76.5 103.6 76.4 103.5 75.5 104.0 74.9 104.2 74.3 · 104.4 74.0 104.6 73.2 78 and machine parts ............................. ........................... Computer equipment and office machines ..................... Telecommunications and sound recording and reproducing apparatus and equipment... ....................... Electrical machinery and equipment... ...... ...... ................ Road vehicles ...... ....... .... ....... .... ................... ............. ..... 87.7 95.9 101 .3 87.5 96.0 101.4 86.7 95.3 101 .6 86.4 95.4 101 .9 85.4 95.7 102.0 85.1 95.6 102.0 84.9 94.9 102.2 84.9 94.8 102.3 84.7 94.7 102.4 84.3 94.6 102.6 84.0 94.7 102.8 83.8 94.6 103.0 83.5 94.6 103.5 85 Footwear ..... ..... ... ..•......... .... .............. . ························ 100.0 100.1 100.1 100.5 100.5 100.6 100.6 100.6 100.4 100.4 100.1 100.5 100.5 88 Photographic apparatus, equipment, and supplies, and ootical aoods, n.e.s . ... ................. ....... ...... ............ 99.3 99.8 99.9 99.9 100.3 100.0 99.4 99.0 98.2 98.2 98.2 98.2 Rev. 3 0 01 03 05 07 54 55 57 58 59 64 66 68 69 75 76 77 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 99.3. Monthly Labor Review Aug. Sept. December 2004 108.6 98.6 113 Current Labor Statistics: Price Data 45. U.S. export price indexes by end-use category (2000 = 100) 2003 Category 2004 Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June July Aug. Sept. ALL COMMODITIES .................................................. 100.0 100.5 100.8 101.5 102.2 103.0 103.7 104.1 103.4 103.9 103.4 103.8 104.5 Foods, feeds, and beverages .. .................. ...... .... . Agricultural foods, feeds, and beverages .. ............ .. Nonagricultural (fish , beverages) food products ..... 117.2 118.4 105.6 121.4 122.8 107.5 122.4 123.8 108.5 123.1 124.6 109.5 125.6 127.2 110.7 130.5 132.4 112.1 134.8 137.0 113.4 135.6 138.0 112.7 129.1 131 .1 110.7 128.0 129.9 110.1 116.5 117.0 111 .6 118.8 119.2 114.4 117.5 117.5 116.8 113.8 116.5 Oct. Industrial supplies and materials .. .. ........................ 101.0 101.7 102.5 105.1 106.4 108.1 109.1 110.2 109.9 112.0 113.1 Agricultural industrial supplies and materials .......... 113.3 119.0 117.5 118.6 116.6 117.2 114.8 113.7 110.7 109.0 108.4 109.4 108.7 Fuels and lubricants ....................................... .. .... . Nonagricultural supplies and materials, excluding fuel and building materials .... .............. Selected building materials ... ..... ........................... .. 97.5 96.4 99.0 106.1 106.5 108.9 109.6 117.5 114.9 118.6 120.4 120.8 131 .1 101 .1 98.8 101 .7 99.1 102.5 99.5 104.7 98.7 106.4 100.9 108.1 102.3 109.4 103.4 109.9 103.9 110.0 103.4 112.4 102.8 113.5 103.3 114.3 104.0 116.4 103.8 Capital goods .... ... ... ........................... ... ... ..... .. .. Electric and electrical generating equipment.. ........ Nonelectrical machinery ............................ .. .......... 97.3 101.7 93.9 97.3 101 .7 93.9 97.5 101.7 94.1 97.5 102.0 93.9 97.8 101.9 94.3 98.0 102.0 94.5 98.1 101.7 94.6 98.1 101.7 94.6 97.8 102.0 94.1 97.8 102.2 94.0 97.8 102.3 94.0 97.9 102.3 94.0 98.3 103.1 94.4 Automotive vehicles, parts, and engines ................. 101 .9 101 .9 101 .8 101.9 102.0 101.9 102.2 102.3 102.3 102.4 102.6 102.6 102.8 Consumer goods, excluding automotive ............. ..... Nondurables, manufactured .. ............... .................. Durables, manufactured ........ .. ......... ... .. ..... .. .... .. 99.8 99.0 100.3 100.0 99.4 100.3 99.9 99.2 100.3 100.2 99.9 100.1 100.1 99.9 100.0 100.2 99.9 100.1 100.4 100.1 100.5 100.5 100.1 100.6 100.4 100.0 100.7 100.9 100.8 100.8 101 .1 101.0 101.0 101 .0 101.0 100.9 100.9 101.0 100.6 Agricultural commodit:es ..... .. .. ... ....... ... ...... ....... .. . Nonagiic•Jltural commodities .. ... ....... .. ..... ... ........ ... 117.5 98.7 122.2 98.8 122.7 99.1 123.5 99.8 125.3 100.4 129.7 100.9 133.0 101.4 133.7 101 .7 127.4 101 .5 126.1 102.2 115.5 102.6 117.5 102.8 116.0 103.8 114 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 46. U.S. import price indexes by e nd-use category (2000 = 100] 2004 2003 Category Oct. ALL COMMODITIES ..... ... ... ... .................................... 96.3 Nov. 96.8 Dec. 97.5 Jan. 99.0 Feb. 99.4 July Aug. Sept. Oct. Mar. Apr. May June 100.2 100.4 101 .9 101 .7 102.1 103.5 104.0 105.6 107.3 114.0 92 .3 108.7 116.4 91.5 108.0 115.6 90.9 Foods, feeds , and beverages ..... .. .. ... ... ...... ...... .... Agricultural foods , feeds , and beverages .............. .. Nonagricultural (fish, beverages) food products ... .. 101 .9 109.0 86.3 102.4 109.7 86.0 103.2 110.9 86.0 103.7 112.0 85.1 105.3 113.4 87.2 105.9 113.0 90.1 107.2 114.2 91 .7 106.8 114.0 90.6 106.9 114.3 90 .3 107.5 114.5 91 .8 Industrial supplies and materials .. ..... .. .. ......... .. .... .. 99.5 100.7 103.6 108.5 110.0 112.7 113.9 119.7 119.3 120.6 126.4 128.1 134.4 Fuels and lubricants ....... ...................... ...... .... .. .... . Petroleum and petroleum products .. .... ... .. .... ... . 100.1 98.8 102.0 100.9 107.2 106.0 116.5 113.7 117.0 114.3 120.2 120.1 120.6 119.9 131 .0 131 .2 130.9 129.7 133.2 132.7 143.2 144.2 145.4 148.3 160.2 165.6 Paper and paper base stocks ... .. ........ ....... ...... .. ... .. Materials associated with nondurable supplies and materials .. ... .. ...... .. ... ...... .. ....... ..... ... Selected building materials .................. .. ................ . Unfinished metals associated with durable goods .. Nonmetals associated with durable goods ........ ... .. 94.0 93.9 93.9 94.1 94.2 95.6 96.8 98.2 99.0 100.0 100.4 101.2 100.8 103.4 109.5 94.4 97.7 104.2 108.1 96.4 98.1 104.4 108.0 99.2 98.2 104.7 106.8 104.5 98.5 104.8 113.7 109.5 99.2 105.4 118.4 114.9 99.3 105.1 120.2 121.7 99.3 105.4 123.6 126.2 99.1 106.0 120.5 124.4 98.7 106.5 117.6 126.1 98.5 107.7 124.0 129.2 98.5 107.9 125.6 132.3 98.8 108.5 115.2 133.4 98.6 Capital goods ....... .. .... ... ... ... .. ... ..... ...... ...... ... ..... Electric and electrical generating equipment. ......... Nonelectrical machinery .... ......... .................. ..... ..... 93.0 96.2 91 .4 93.3 96.5 91 .6 92.9 96.8 91 .1 93.1 97.4 91.2 93.1 97.9 91 .2 93.1 97.8 91.2 92.6 97.2 90.6 92.6 97.1 90 .5 92 .2 97.0 90.1 92 .2 Y7.5 90.0 92.1 97.5 89.9 92.0 97.4 89.8 91.8 97.2 89.6 Automotive vehicles, parts, and engines ... .. ....... ..... 101 .2 101 .2 101.4 101 .6 101 .7 101 .8 102.0 102.0 102.2 102.3 102.5 102.6 103.0 Consumer goods, excluding automotive .. ... ..... ... .... . Nondurables, manufactured .......... ... .. ... .. .... ........... Durables, manufactured ...... .... .. .. ...... ... ... ... .. ...... Nonmanufactured consumer goods ..... .. ..... .... ... .. 97.9 99.8 96.1 95.8 98.1 100.0 96.2 95.8 98.1 100.1 96.2 96.2 98.6 101.1 96.3 95.9 98.7 101.2 96.3 96.2 98.7 101.3 96.3 96.4 98.6 101.1 96.3 96.4 98.5 101 .0 96.0 97.3 98.5 100.9 96.1 96.8 98.5 101.0 95.9 97.4 98.4 100.9 95.9 97.9 98.4 100.8 95.9 97.9 98.4 100.8 96.0 97.9 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 115 Current Labor Statistics: Productivity Data 47. U.S. international price Indexes for selected categories of services (2000 = 100, unless indicated otherwise] 2002 Category Sept. 2003 Dec. Air freight (inbound) ........ ........... .. ..... ... .. ... ......... .. ... .... Air freight (outbound) ............. ... ..... ..... .... .... ........ ... 100.3 97.3 Inbound air passenger fares (Dec. 2003 = 100) ......... . Outbound air passenger fares (Dec. 2003 = 100)) ....... Ocean liner freight (inbound) ... ....... ........... .... ... ........ 93.5 2004 Sept. Dec. Mar. June Sept. 105.9 95.4 108.8 97.2 109.4 95.4 112.5 95.5 112.9 94.9 116.2 96.1 116.6 99.0 118.7 100.7 - - - - - - - - 93.3 94.0 116.1 116.2 100.0 100.0 117.7 105.1 99.3 119.1 106.1 114.2 121 .1 110.1 114.2 120.3 NOTE: Dash indicates data not available. 116 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June Mar. December 2004 48. Indexes of productivity, hourly compensation, and unit costs, quarterly data seasonally adjusted [1992 = 100] Ill IV I II 2004 2003 2002 2001 Item Ill I IV II Ill IV I II Ill Business Output per hour of all persons ............... ............... .... .. ... Compensation per hour .. ..... ............ ....... .. .. ... ....... . Real compensation per hour .. ... ....... ......... ..... .... ..... Unit labor costs ......... ........... ..................... .. ...... ..... ... Unit nonlabor payments ...... .......... .. ... ............ ... ...... . Implicit price deflator ........... .. .... ... ...... ... ...... ........ .. 118.8 140.4 113.2 118.2 110.2 115.2 120.9 141.5 114.2 117.0 113.1 115.6 122.7 143.2 115.2 116.7 113.4 115.5 123.2 144.4 115.2 117.2 113.6 115.9 124.7 145.0 115.0 116.3 115.7 116.1 125.0 145.5 114.8 116.3 116.8 116.5 126.2 147.4 115.3 116.8 117.7 117.1 128.6 149.6 116.8 116.4 119.0 117.3 131.2 151.7 117.7 115.6 120.8 117.5 132.0 153.2 118.7 116.0 120.7 117.8 133.3 154.2 118.4 115.7 122.9 118.4 134.2 155.9 118.3 116.1 124.8 119.4 135.0 157.3 118.9 116.6 124.8 119.6 Nonfarm business Output per hour of all persons ....................................... Compensation per hour ....... .... ......... .. ... ....... .. .. ..... Real compensation per hour ... ...... ...... ................. ... Unit labor costs ................. .................... ..... ... .... ........ Unit nonlabor payments ..... .. ............... ............ ...... ... Implicit price deflator ... ........ ...... ........................ .... 118.5 139.6 112.5 117.8 111 .9 115.6 120.4 140.7 113.5 116.8 114.7 116.0 122.4 142.6 114.7 116.4 115.1 116.0 122.8 143.8 114.7 117.1 115.4 116.5 124.1 144.3 114.4 116.2 117.7 116.8 124.6 144.7 114.3 116.1 118.9 117.2 125.8 146.6 114.7 116.6 119.6 117.7 127.8 148.7 116.1 116.3 120.4 117.8 130.6 150.9 117.1 115.5 122.3 118.0 131.7 152.5 118.2 115.9 121 .9 118.1 132.8 153.3 117.7 115.4 124.3 118.7 134.1 155.2 117.8 115.7 126.1 119.6 134.7 156.5 118.3 116.2 126.6 120.0 Nonflnanclal corporations Output per hour of all employees ... ... ............................ ('')mpensation per hour. ... ...... ... .. ........ ..... ... .... ...... Real compensation per hour ..... ......... .... ........ ... ...... Total unit costs ......... .. .... .............. ...... .. ..... .............. . Unit labor costs ........................... ................................. Unit nonlabor costs ....... ................ .......... ..... ... ...... .. ..... Unit profits .. .... .. ................ ... .. ............................ ...... .... .. Unit nonlabor payments ................. ........................ .. Implicit price deflator ....... ... .... ......... ........... ... ....... . 123.0 137.9 111 .1 112.8 112.1 114.7 79.4 105.2 109.8 126.3 123.9 139.9 139.3 112.6 112.5 111.6 113.4 112.4 1,110.8 114.0 116.2 89.1 75.8 107.4 105.4 109.6 110.1 127.9 141.3 112.7 111.2 110.5 112.9 94.7 108.1 109.7 129.2 131 .3 144.1 112.7 110.7 109.8 113.2 99.2 109.4 109.7 134.1 146.3 137.2 115.3 109.0 108.2 111 .1 118.7 113.1 109.9 138.9 150.9 115.9 108.8 108.6 109.5 128.1 114.5 110.6 141 .5 154.4 114.2 109.7 109.1 111 .4 111 .0 111.3 109.8 138.9 150.0 116.2 108.7 108.0 110.5 123.2 113.9 110.0 140.1 112.7 110.7 110.0 112.7 95.7 108.2 109.4 130.2 142.9 112.8 110.4 109.7 112.3 101.8 109.5 109.6 Manufacturing Output per hour of all persons ...................... ........... ...... Compensation per hour .. ..... ... ... .. ... ...... ....... .. ... .... . Real compensation per hour ..... ..... ... ... ........ .. ... ... ... Unit labor costs ........... .. ...................... ... .... .. ... ..... .... . 136.9 137.3 110.6 100.3 140.4 139.4 112.5 99.3 143.8 144.1 115.9 100.2 145.7 147.0 117.2 100.8 147.8 148.6 117.8 100.5 148.8 149.9 118.3 100.7 151.0 155.7 121.8 103.1 152.1 158.5 123.8 104.2 155.9 161 .6 125.4 103.6 157.2 163.9 127.0 104.2 158.3 162.2 124.5 102.5 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 142.1 148.5 Monthly Labor Review December 152.9 116.1 109.4 109.2 109.9 116.7 109.4 109.1 110.3 134.3 134.6 116.4 111 .6 116.8 111 .7 161.5 163.7 124.3 101 .4 163.2 165.5 125.0 101.4 2004 117 Current Labor Statistics: Productivity Data 49. Annual indexes of multifactor productivity and related measure s, selected years (1996 = 100) Item 1980 1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 Private business Productivity: Output per hour of all persons ..... .. ... .... .... ................ Output per unit of capital services ....... ..... .............. Multifactor productivity .... .. .... ......... ...... .... .. .......... Output. ................... ............... ........... .. .. .... ... .......... .. Inputs: Labor input.. ....................................... ......................... Capital services ................. .................. .. .... .... ....... Combined units of labor and capital input.. .... ..... ... .. Capital per hour of all persons ........... .............. .. ... .. .... 75.8 103.3 88.8 59.4 90.2 99.7 95.5 83.6 91 .3 96.5 94.5 82.6 94.8 98.0 96.7 85.7 95.4 98.7 97.1 88.5 96.6 100.4 98.2 92.8 97.3 99.8 98.4 95.8 102.2 100.3 101 .2 105.2 105.0 99.3 102.5 110.5 107.7 98.2 103.4 115.7 111.0 96.6 105.0 120.4 112.4 92.8 103.9 120.2 71 .9 57.6 67.0 73.4 89.4 83.8 87.5 90.4 88.3 85.7 87.4 94.6 89.3 87.5 88.7 96.8 91 .8 89.7 91 .1 96.6 95.6 92.5 94.6 96.2 98.0 96.0 97.3 97.5 103.5 104.9 104.0 101 .9 106.1 111 .3 107.9 105.8 109.0 117.9 110.9 109.7 110.1 124.5 114.7 114.8 109.5 129.6 115.7 121.1 77.3 107.6 91 .0 59.6 90 .3 100.4 95.8 83.5 91 .4 97.0 94.8 82.5 94.8 98.2 96.7 85.5 95.3 99.0 97.2 88.4 96.5 100.4 98.2 92.6 97.5 100.0 98.6 95.8 102.0 100.0 101.0 105.1 104.7 99.0 102.2 110.5 107.1 97.6 102.9 115.7 110.3 95.9 104.4 120.2 111 .6 92.0 103.3 120.1 70.7 55.4 65.5 71.8 89.2 83.2 87.2 89.9 87.9 85.1 87.0 94.3 89.0 87.0 88.4 96.5 91.8 89.4 91 .0 96.3 95.4 92.2 94.3 96.1 97.8 95.8 97.2 97.6 103.6 105.1 104.1 101.9 106.4 111.7 108.1 105.8 109.5 118.5 112.4 109.7 110.6 125.4 115.2 115.0 110.1 130.5 116.3 121.3 62.0 97.2 81.2 64.3 82.2 97.5 93.3 83.2 84.1 93.6 92.4 81 .5 88.6 95.9 94.0 85.5 90.2 96.9 95.1 88.3 93.0 99.7 97.3 92.9 96.5 100.6 99.2 96.9 103.8 101.4 103.1 105.6 108.9 101.7 105.7 110.5 114.0 101 .7 108.7 114.7 118.3 101 .0 111.3 117.4 119.7 95.1 110.3 112.1 103.7 66.1 86.1 63.9 65.8 79.2 101 .1 85.3 93.1 77.5 84.7 89.1 96.9 87.1 93.2 78.5 84.6 88.3 96.5 89.1 93.1 83.5 92.0 90.9 97.8 91.1 96.6 86.5 92.9 92.8 99.9 93.2 99.9 90.3 96.0 95.5 100.4 96.4 102.3 93.1 100.4 97.7 101 .7 104.1 97.5 101.9 103.9 102.4 101.5 108.7 100.6 107.5 103.1 104.6 100.7 112.8 102.9 107.9 105.4 105.5 99.2 116.2 104.3 106.9 106.5 105.5 99.6 117.9 98.9 105.5 97.7 101.6 Private nonfarm business Productivity: Output per hour of all persons ........ ..... .. .. ...... ......... Output per unit of capital services .............. ...... .. .... Multifactor productivity ... .... .. ..... ... .. ....... .... ... ........ Output. ................... ................ ... ..... ... .. ... ... ...... .. ... ... Inputs: Labor input.. ................................................................ Capital services ....... .. .. .................. ...... ..... .. ....... ... Combined units of labor and capital input.. .. .. ... .. ..... Capital per hour of all persons ....... ........ .. .......... .. ... . Manufacturing Productivity: Output per hour of all persons .... ..... ..... ... ...... ...... .. Output per unit of capital services .. ... .......... ..... ...... Multifactor productivity .......... ...... .... .. ...... .. .. ......... Output. ................................ .. ...... ... ... ...... ..... ..... .. ... . Inputs: Hours of all persons .... ................... .............................. Capital services .. ................. .. ... .......... .. .. .......... .... Energy .. ......... .. ... ...... .................. ................... ......... Nonenergy materials ....................................... ............ Purchased business services ...................................... Combined units of all factor inputs ................. ......... 118 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Decemb er 2004 5Q. Annual indexes of productivity, hourly compensation, unit costs, and prices, selected years (1992 = 100] Item 1960 1970 1980 1990 1995 1996 1997 1998 1999 2000 2001 2003 2002 Business Output per hour of all persons ......... ... ... ... .......... ... ... ..... Compensation per hour ...... .. ....... ..... ... ...... ... ... .... .. Real compensation per hour ...... ... ..... .. ..... .. ....... ..... Unit labor costs .............. .. ... ..... ... ......... ... ........ ...... ... . Unit nonlabor payments .... .. ................ ......... .. .... .. . Implicit price deflator .. .. ........ .... ... ......... ................. 48.7 13.8 60.5 28.4 24.9 27.1 66.0 23.5 78.4 35.6 31 .5 34.1 79.0 54.0 88.9 68.4 61 .3 65.8 94.4 90.5 96.1 95.9 93.9 95.1 101.7 106.0 98.9 104.3 108.2 105.7 104.5 109.5 99.5 104.8 111 .9 107.4 106.5 113.0 100.5 106.1 113.9 109.0 · 109.3 119.7 105.0 109.5 109.9 109.7 112.4 125.4 107.8 111 .6 109.2 110.7 115.7 134.2 111 .6 116.0 107.2 112.7 118.3 139.7 113.0 118.1 109.5 114.9 124.0 147.8 113.7 115.2 117.0 115.8 129.6 147.9 115.1 114.1 123.0 117.4 Nonfarm business Output per hour of all persons ... .......... ........... .... .... ... ... . Compensation per hour .. ................. ................. .. .. . Real compensation per hour ................ ...... .... .. ... ... . Unit labor costs ... ... ....... ... ........... .......... .... ... ............. Unit nonlabor payments ....... ......... .. ...... ... .... .. ... ... ... . Implicit price deflator .... ..... ..... ..... ....... .. ....... ... .... ... 51 .6 14.4 63.0 27.9 24.3 26.6 67.7 23.6 78.8 34.9 31 .1 33.5 80.3 54.2 89.2 67.5 60.4 64.9 94.4 90 .3 95.9 95.6 93.6 94.9 102.1 106.0 98.9 103.8 109.2 105.8 104.7 109.4 99.4 104.5 112.1 107.3 106.4 112.8 100.3 106.0 114.6 109.1 109.2 119.4 104.7 109.3 110.9 109.9 112.2 124.9 107.3 111 .3 110.8 111 .1 115.3 133.7 111 .2 116.0 108.8 113.3 117.8 138.9 112.4 118.0 111 .1 115.4 123.6 142.1 113.2 115.0 119.0 116.4 129.1 147.0 114.4 113.9 124.8 117.9 Nonflnanclal corporations Output per hour of all employees ...................... ............ Compensation per hour ..... ... ... .... .. ... .......... .......... . Real compensation per hour ..... ...... .. ... ... ........ .. ...... Total unit costs ......... .... .. ... .. ......................... .. .... ...... . Unit labor costs .. ............................... .................... ....... Unit nonlabor costs ............... ....... ... .. ...... ... ............. .... . Unit profits ............... .. ................ .. ..... .. ............ ............... Unit non labor payments .... ... ......... ...... ... .. ...... ... ... ... . Implicit price deflator ....... ... ..... ......... ·· ····· ········· ·· ·· 56.6 16.1 70 .3 26.9 28.4 23.0 49.5 30.1 28.9 70.4 25.6 85.3 35.1 36.3 31 .7 43.7 34.9 35.9 81 .0 57.0 93.8 68.8 70.4 64.5 66.5 65.1 68.6 95.5 91 .0 96.7 95.4 95.3 97.1 96.7 97.0 95.9 103.4 105.4 98.3 101 .8 102.() 101 .3 136.9 110.8 104.9 107.1 108.4 98.5 100.9 101.2 99.9 149.9 113.3 105.3 109.8 111 .7 99.3 101.2 101 .7 99.8 154.4 114.4 105.9 112.8 117.9 103.4 103.2 104.5 99.9 137.5 109.9 106.3 116.4 123.3 105.9 104.6 106.0 101 .0 129.8 108.7 106.9 120.6 131.7 109.5 108.0 109.2 104.8 109.3 106.1 108.1 122.7 137.0 110.8 111 .2 111 .6 110.2 91.4 105.2 109.5 128.9 140.1 111 .5 109.4 108.6 111 .5 111 .4 111 .5 109.6 136.3 145.9 113.5 107.4 107.0 108.4 134.2 115.3 109.8 Manufacturing Output per hour of all persons ...... ....... .... .......... .... ........ Compensation per hour ..... .... .. .... ..... ... .... ..... ...... .. . Real compensation per hour ....... .... ....... ..... .. .. .... .... Unit labor costs ................ ......................... ... ........ ..... Unit nonlabor payments ...... ... .... ..... .. ....... ......... ....... Implicit price deflator .. ......... .... ........ ... ........ ....... .. .. 41 .8 14.9 65.0 35.6 26.8 30.2 54.2 23.7 79 .2 43.8 29.3 35.0 70.1 55.6 91 .4 79.3 80.2 79.9 92 .9 90.1 95.7 97.0 101.1 99.5 110.1 107.7 100.5 97.8 107.6 103.9 113.9 109.9 99.8 96.5 110.4 105.2 117.9 112.0 99.7 95.0 110.5 104.6 123.5 118.8 104.2 96.2 104.1 101 .1 128.2 123.8 106.3 96.6 105.0 101 .8 134.2 135.0 112.3 100.6 107.0 104.6 137.1 138.3 111 .8 100.8 105.8 103.9 147.1 143. 8 114.5 97.8 154.6 151 .9 118.2 98.2 - - - - Dash indicates data not available. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 119 Current Labor Statistics: Productivity Data 51. Annual indexes of output per hour for selected NAICS industries, 1990-2002 [1997-100) NAICS Industry 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Mining 21 211 212 2121 2122 2123 Mining ....... ..... .... .. ... ... .. ..... ....... ... ..... ........... Oil and gas extraction ... ... .. ....... .... ..... .... ..... .... Mining, except oil and gas ...... ... .. .. ........ .. ........ Coal mining ..... ... ... .............. ..... ... .... ......... .... Metal ore mining .. .. .. ............ .... .... .... ...... ..... ... Nonmetallic mineral mining and quarrying .. ....... .. 2211 2212 Power generation and supply ... ........ .. .. .. . ......... Natural gas distribution ... ..... .. ... .. ..... .. ......... .... 86.0 78.4 79.3 68.1 79.9 92.3 86.8 78.8 80.0 69.3 82.7 89.5 95.2 81 .9 86.8 75.3 91 .7 96.1 96.2 85.1 89.9 79.9 102.2 93.6 99.6 90.3 93.0 83.9 104.1 96.9 101 .8 95.5 94.0 88.2 98.5 97.3 101 .7 98.9 96.0 94.9 95.3 97.1 100.0 100.0 100.0 100.0 100.0 100.0 103.4 101 .6 104.6 106.5 109.5 101.3 111 .1 107.9 105.9 110.3 112.7 101 .2 109.5 115.2 106.8 115.8 124.4 96.2 107.7 117.4 109.0 114.4 131 .8 99.3 112.3 119.3 111.7 112.2 143.9 103.8 71 .2 71.4 73.8 72.7 74.2 75.8 78.7 79.8 83.0 82.1 88.6 89.0 95.5 96.1 100.0 100.0 103.8 99.1 104.1 103.1 107.0 113.1 106.4 110.0 102.4 114.9 127.2 117.3 109.9 117.0 - Utilities Manufacturing 3111 3112 3113 3114 3115 Animal food ....... ....... ....... .... .......... ... ...... .. .. .. Grain and oilseed milling .............. .. .. .... .... ... .. .. Sugar and confectionery products ...... .. ... .. .. ...... Fruit and vegetable preserving and specialty ... .... Dairy products ..... .. ... .... . ... .... ... ... ... ... ...... .... ... 90.1 89.0 91 .0 86.4 90.8 89.3 91 .2 93.8 89.7 92.1 90.2 91 .1 90.5 90.7 95.4 90.2 93.8 92 .5 93.8 93.9 87.3 94.7 94.0 94.9 95.4 94.0 99.1 94.3 97.1 98.7 87.5 91.3 98.2 98.2 98.0 100.0 100.0 100.0 100.0 100.0 109.4 107.5 104.0 106.8 99.1 109.5 114.2 107.1 108.4 94.5 109.7 112.5 111 .9 109.8 96.0 ~116 Animal slaughtering and processing ...... .... ..... .. .. Seafood product preparation and packaging ....... . Bakeries and tortilla manufacturing ............... .. ... Other food products .................. ... ...... ......... .... Beverages .. ...... ......... .. ..... .. .. .. ... ... ... ..... ...... .. 94.5 117.5 92.6 91 .9 86.5 96.8 112.0 92.3 93.5 90.1 101 .5 115.3 95.6 95.9 93.8 100.9 113.9 96.0 102.8 93.2 97.4 114.1 96.7 100.3 97.7 98.5 108.4 99.7 101 .3 99.6 94.3 116.2 97.7 103.0 101.1 100.0 100.0 100.0 100.0 100.0 99.9 117.0 103.8 106.9 98.5 100.3 130.2 105.4 108.8 92.4 101.9 137.6 105.3 110.2 90.6 102.7 147.3 106.3 103.2 91 .7 3133 3141 Tobacco and tobacco products .... .. .... ..... .. ..... ... Fiber, yarn, and thread mills .. .... .. . ... .. . .... .. ........ Fabric mills .... ... ... ....... ....... ... ..... .... ... .... .. .... .. Textile and fabric finishing mills ...... .. .... .. ... .. .... .. Textile furnishings mills .. ... .. .... .. .... ...... .. .... .. .... 81.4 73.9 75.0 81.7 88.2 77.3 74.7 77.7 80.4 88.6 79.6 80.1 81.5 83.7 93.0 73.7 84.6 85.0 86.0 93.7 89.8 87.2 91 .9 87.8 90.1 97.5 92.0 95.8 84.5 92.5 99.4 98.7 98.0 85.0 93.3 100.0 100.0 100.0 100.0 100.0 98.1 102.2 103.9 100.6 99.9 92.1 104.6 109.8 101 .7 101 .2 98.0 102.6 110.2 104.0 106.8 100.0 110.5 109.1 109.7 106.9 3149 3151 3152 3159 3161 Other textile product millsv Apparel knitting mills .......... ..... ... .... .. .... ........... Cut and sew apparel. .. ........ .... ..... ............. .... .. Accessories and other apparel. .......... ... ..... ... .. .. Leather and hide tanning and finishing ...... ..... .... 91 .1 85.6 70.1 100.9 60.8 90.0 88.7 72 .0 97.3 56.6 92.0 93.2 73.1 98.7 76.7 90.3 102.5 76.6 99.0 83.1 94.5 104.3 80.5 104.6 75.9 95.9 109.5 85.5 112.4 78.6 96.3 121 .9 90.5 112.6 91.5 100.0 100.0 100.0 100.0 100.0 97.0 96.6 104.0 110.8 98.0 110.4 102.0 118.8 103.3 101 .6 - 110.4 110.2 127.7 104.9 110.0 105.0 108.4 131 .7 114.8 109.7 - - 3162 3169 3211 3212 3219 Footwear ..... ... .. .... ..... ..... ......... ... ... ....... ... ..... Other leather products .... .. ..... . .. ........... ........ ... Sawmills and wood preservation ... ... .... ........ .. ... Plywood and engineered wood products .. ... .... .... Other wood products .. ... ... .... ..... . ..... .. ...... ...... . 77.1 102.5 79.2 102.3 105.4 74.7 100.2 81 .6 107.4 104.7 83.1 97.0 86.1 114.7 104.0 81 .7 94.3 82.6 108.9 103.0 90.4 80.0 85.1 105.8 99.3 95.6 73.2 91 .0 101 .8 100.4 103.4 79.7 96.2 101 .2 100.8 100.0 100.0 100.0 100.0 100.0 100.9 109.2 100.8 105.6 101 .5 116.8 100.4 105.4 99.9 105.4 124.1 107.6 106.5 100.5 104.0 142.7 114.1 109.0 105.0 104.6 - 3221 3222 Pulp, paper, and paperboard mills ... ... .... .... ....... Converted paper products .. ..... ....... . .... .. ..... ..... Printing and related support activities ....... .. .. ...... Petroleum and coal products .......... .... .... .... ..... . Basic chemicals ...... .......... ... ..... .. ..... .... .. .. ... ... 88.5 90.5 96.6 76.7 91 .4 88.1 93.5 95.4 75.8 90.1 92.3 93.7 101 .3 78.9 89.4 92.9 96.3 100.1 84.5 89.9 97.6 97.6 98.3 85.6 95.1 102.0 97.2 98.8 90.1 92 .3 97.6 99.6 94.8 90.0 100.0 100.0 100.0 100.0 100.0 103.1 102.7 100.5 102.1 102.5 111.4 101.5 103.5 107.8 114.7 115.7 101 .9 104.9 113.2 118.4 117.5 101 .0 105.6 112.2 111 .0 - 75.8 84.6 91.4 85.1 83.2 74.7 81 .0 92.6 85.9 84.2 80.6 81.3 88.2 87.6 83.4 83.8 85.6 88.1 90.9 86.9 93.5 87.4 92.4 94.1 88.6 95.9 90.7 96.3 92.7 93.9 93.3 92.1 99.9 98.3 95.6 100.0 100.0 100.0 100.0 100.0 105.5 98.8 92.9 99.1 96.6 108.8 87.6 94.6 98.8 91 .1 108.1 91.4 3255 3256 Resin , rubber , and artificial fibers ....... .. ...... ... .. .. Agricultural chemicals .... ...... ... .. .... ..... ....... ... ... Pharmaceuticals and medicines ... ......... .. ... . ... ... Paints, coatings, and adhesives ... .... .. ... .. ...... .... Soap, cleaning compounds, and toiletries ........ ... 98.5 99.2 103.8 91.1 97.4 102.1 102.7 3253 3261 3262 3271 3272 Other chemical products and preparations ........ .. Plastics products ... .. .... ...... ..... .. ... .... ...... ....... .. Rubber products ... ... ... ....... .. .. ... . ...... .......... .. .. Clay products and refractories .... .... .... ...... .. ... ... Glass and glass products ...... .. ... .... .... ... ... .. ... .. 76.6 84.7 83.0 89.2 80.0 78.0 86.3 83.8 87.5 79.1 84.7 90.3 84.9 91 .5 84.3 90.6 91 .9 90.4 91 .9 86.1 92.6 94.4 90.3 96.6 87.5 94.4 94.5 92.8 97.4 88.8 94.2 97.0 94.4 102.6 96.5 100.0 100.0 100.0 100.0 100.0 99.4 103.5 100.5 101 .3 102.7 109.2 109.3 101 .4 103.5 108.6 120.0 111 .2 103.9 103.6 109.7 111.3 113.3 104.2 97.6 105.2 - 3273 94.8 96.5 84.1 79.8 69.6 83.8 93.7 82.7 81 .4 67.2 86.4 94.8 88.5 90.2 74.1 89.9 90.1 89.3 81.7 95.9 95.0 87.8 90.5 87.2 100.0 98.2 88.8 3312 Cement and concrete products .... ... ........ ..... .. .. . Lime and gypsum products ... .. . .. ..... .. .... ........... Other nonmetallic mineral products .. ..... ......... ... Iron and steel mills and ferroalloy production ....... Steel products from purchased steel. . ... ............ 91.7 89.7 100.5 100.6 92.4 96.5 94.1 100.5 100.0 100.0 100.0 100.0 100.0 103.5 113.1 98.8 101 .7 100.3 104.1 102.7 95.5 106.5 94.2 100.4 97.0 95.6 108.5 96.4 97.1 100.1 96.8 106.7 97.1 - 3313 3314 3315 3321 3322 Alumina and aluminum production .......... .... . ...... Other nonferrous metal production ......... ...... ..... Foundries .. .... ... ...... .......... .. ....... .. ............ .... . Forging and stamping .... ........ ....... ... ..... ....... ... Cutlery and hand tools ..... .. .. .. ... ...... .... ... .... ..... 91.9 95.6 85.3 88.6 85.1 93.3 95.8 84.5 86.5 85.4 96.8 98.8 85.8 91.7 87.2 96.0 101 .8 100.3 105.1 91 .4 93.7 94.4 96.8 102.9 93.1 94.2 97 .8 95.9 105.7 96.2 97.6 104.4 100.0 100.0 100.0 100.0 100.0 101.1 111 .2 101 .6 103.7 100.0 104.3 108.9 104.9 110.9 107.8 97.8 103.1 104.0 121 .3 105.8 100.5 109.3 121 .8 110.2 3323 Architectural and structural metals .. . .... ..... ......... Boilers, tanks, and shipping containers ......... ... ... Hardware ... ........ .... ... ..... .. ...... ... ........ ..... . ..... Spring and wire products ........ ....... ..... .. ..... ...... Machine shops and threaded products ........... ... . 87.8 90.4 84.4 85.2 78.8 89.1 92.6 83.8 88.4 79.8 92.5 95.3 86.9 90.9 87.2 95.1 100.5 95.7 91 .5 91.6 93.9 97.8 97 .3 99.5 98.7 94.2 100.7 102.6 102.8 100.0 100.0 100.0 100.0 100.0 100.0 101 .1 101 .3 101 .0 111 .6 99.3 101 .8 98.9 106.5 112.9 103.9 101 .0 97.7 115.8 114.6 107.2 100.7 98.2 114.6 110.6 107.2 3117 3118 3119 3121 3122 3131 3132 3231 3241 3251 3252 3253 3254 3274 3279 3311 3324 3325 3326 3327 120 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 89.8 94.6 91 .7 93.4 94.8 89.6 95.3 86.9 98.3 93.4 96.2 96.9 - - - - - 51 . Continued- Annual Indexes of output per hour for selected NAICS industries, 1990-2002 (1997=100) Industry NAICS 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 100.0 100.0 100.0 100.0 100.0 101 .7 102.3 104.2 94.4 107.5 101 .5 100.2 95.0 105.2 111 .2 105.9 100.8 101.0 129.7 101.4 105.1 98.2 99.5 104.6 94.4 2002 3328 3329 3331 3332 3333 Coating, engraving, and heat treating metals .. ..... Other fabricated metal products ... ... ......... ...... ... Agriculture , construction , and mining mach inery Industrial machinery .. ...... . ..... . .... .. .... ..... ... ...... Commercial and service industry machinery ... .. ... 81 .6 86 .7 82 .8 80.6 91 .4 78.1 85.9 77.2 81.1 89.6 86.9 90.6 79.6 79.5 96.5 91 .9 92 .1 84 .1 84.9 101 .7 96.5 95.0 91 .0 90.0 101 .2 102.8 97.1 95.6 97 .9 103.0 102.9 98.9 95.9 98.8 106.3 ..,..,..,... 3335 3336 3339 HVAC and commercial refrigeration equipment Metalworking machinery ...... ............. ....... .. .. .... Turbine and power transmission equipment.. ...... Other general purpose machinery ...... .... ... ........ 88.8 85.3 85.1 85.9 88.2 82.3 84.6 85.2 90.8 89.3 81 .2 85.1 93.8 89.3 84.8 89.8 97.3 94.0 93.3 91 .5 96.6 99.1 92.1 94.6 97.8 98.1 97.9 95.1 100.0 100.0 100.0 100.0 106.6 99.1 106.4 103.2 110.4 100.5 113.3 105.6 108.3 106.4 117.1 113.0 110.8 102.0 130.2 109.4 3341 Computer and peripheral equipment.. ....... ..... . ... 14.3 15.8 20.6 27.9 35.9 51 .3 72 .6 100.0 138.6 190.3 225.4 237.0 3342 3343 3344 3345 3346 Communications equipment. .... .... .... .... .. .... .. .. .. Audio and video equipment.. . .......... ... ..... .... ... .. Semiconductors and electron ic components ... ... .. Electronic instruments ........... .... ... ...... ... ......... . Magnetic media manufacturing and reproduction 47.3 75 .5 21.4 76.0 86 .6 49.3 82.8 24.5 80.5 91 .2 59.3 92.1 29.6 83.1 93.0 62.1 98.8 34.1 85.8 96.8 70.1 108.5 43.1 88.8 106.1 74 .6 140.0 63.4 96.8 106.7 84.3 104.7 81 .8 97.7 103.8 100.0 100.0 100.0 100.0 100.0 102.7 103.1 125.2 101 .3 105.4 134.0 116.2 174.5 105.1 106.8 165.5 123.3 233 .3 114.3 104.0 155.2 126.3 231.6 116.1 98.6 3351 3352 3353 3359 3361 Electric lighting equipment. .. .. ... . ...... ... .. ......... . . Household appliances ... .. ....... ......... .. ... ......... .. Electrical equipment. .... . .... .................... ... .. .... Other electrical equipment and components .... .. .. Motor vehicles ... ..... ..... .. ... ........ ... .... .. .. ....... .. . 87 .3 76.4 73.6 75 .3 86.0 88.5 76.4 72.7 74.2 82.4 93.6 82.4 78.9 81.6 91 .2 90.8 88.9 85.8 86.8 89.8 94.5 95.0 89.0 89.4 90.3 92 .2 92.7 98.1 92 .0 88.6 95.6 93.1 100.2 96.0 91 .0 100.0 100.0 100.0 100.0 100.0 103.8 105.1 99.8 105.5 113.3 102.5 104.3 98.9 114.8 123.3 101 .9 117.5 100.6 120.5 110.4 105.4 122.6 101 .0 113.5 108.7 3362 3363 3364 3365 3366 Motor vehicle bodies and trailers .. .... .. ......... ... . .. Motor vehicle parts ... .. .... ...... ..... . ... ... ....... ... .. .. Aerospace products and parts ... .... .. .... .. ......... .. Railroad rolling stock. ... ... .. .... ... . . . . . . . . . . . . . . . . . . . . Ship and boat building ..... .... ... ... ..... .... ... ......... 75 .8 75.7 87 .7 77.2 99.6 71.8 74.5 92.1 80.0 92.6 88.3 82.4 94.1 81 .1 98.5 96.3 88.5 98.2 82.3 101 .3 97.7 91 .8 93.8 83.1 99.0 97.3 92 .3 93 .7 82 .0 93.1 98.4 93.1 98.1 80.9 94.1 100.0 100.0 100.0 100.0 100.0 102.7 104.8 118.5 102.9 100.3 103.1 110.4 118.0 116.0 112.2 98 .4 112.7 101 .0 117.7 120.1 99.4 114.8 114.7 124.7 119.8 3369 3371 3372 3379 3391 3399 Other transportation equipment. ..... ... ... ........ . ... . Household and institutional furniture ............. ... .. Office furniture and fixtures .. ... ..... .. ......... .. .. .... . Other furniture-related products ...... ..... ... .. .. .... .. Medical equipment and supplies .... .. ... .. .. .... .... .. Other miscellaneous manufacturing .. . ..... . ...... .... 62 .6 87.6 80.8 88.1 81 .2 90.1 62.0 88.2 78.8 88.6 83.1 90.6 88.4 92.9 86.2 88.4 88.1 90.0 99.8 93.8 87.9 90.5 91 .1 92.3 93.4 94.1 83.4 93.6 90.8 93.0 93.1 97.1 84 .3 94.5 95.0 96.0 99.8 99.5 85.6 96.7 100.0 99.6 100.0 100.0 100.0 100.0 100.0 100.0 110.8 102.7 100.1 107.2 108.9 101 .9 113.3 103.7 98.5 102.5 109.6 105.2 130.9 102.5 100.2 100.1 114.2 112.9 146.9 106.1 97.1 105.3 119.0 110.9 42 423 4231 4232 4,:JJ Wholesale trade ... . ... .. ..... ..... ...... ... ... .. .... ..... .. Durable goods .. .... ...... .... .. .. .. .. .. ... ....... ... .. .. .. .. Motor vehicles and parts ... ... .. . .. .... ... . ··· ···· ··· ··· Furniture and furn ishings ..... ... ... ... . .. .... .. ... ...... . Lumber and construction supplies .. ..... .. ... ... .... .. 77.8 65.7 76.6 82 .4 115.0 79.1 66.1 73.3 87.2 113.2 86.2 75.0 82.2 92.0 119.6 89.5 80.5 88.0 95.8 113.9 91 .3 84.5 94.1 93.3 111 .9 93 .3 88 .9 93 .6 96.8 103.6 96.2 94.0 94.9 97.0 103.0 100.0 100.0 100.0 100.0 100.0 104.4 105.6 104.7 97.5 102.9 110.9 115.3 119.8 100.8 104.8 114.1 119.6 114.0 105.5 101.7 117.1 120.3 114.1 105.4 108.6 123.6 127.7 121 .7 101 .8 119.2 4234 4235 4236 4237 4238 Commercial equipment. ... .. .. . ....... .. ... .......... ... . Metals and minerals ..... ....... ... ..... .... .. .......... ... Electric goods .... .. ... ... ............ ... ..... ... . ...... .. ... Hardware and plumbing ......... ...... ..... .. ... .. ..... .. Machinery and supplies .. .. ..... ... ... .. .. ... .. ....... .... 33.8 101 .6 46.8 88.8 78.9 37.3 102.6 47.6 86.5 74.2 48.2 109.1 51.4 95.6 79.7 56.2 111 .7 59.1 94 .3 84.3 60.5 110.1 68.2 101 .3 85.4 74.7 101.2 79.3 98.0 89.7 88.4 102.7 87.8 99.1 93.9 100.0 100.0 100.0 100.0 100.0 118.2 102.4 105.9 103.5 104.2 14 1.1 96.0 126.2 107.8 101.4 148.9 99.2 151 .7 111 .1 104.1 164.9 102.2 148.1 102.6 102.7 189.4 102.2 161 .2 107.9 100.2 4239 424 4241 4242 4243 Miscellaneous durable goods ...... .... .. .... .. .... .... . Nondurable goods ... .... ...... .... .. . .. .... . .. . . .. . . .. .. . . Paper and paper products ... ..... . .......... .... ... .... Druggists' goods ..... ... .... ......... .. . ...... ........... ... Apparel and piece goods .. ... .... ..... . .. .... ..... .... ... 89.5 98.4 81.0 81 .8 103.9 96.6 99.8 85.5 86.6 103.3 112.1 103.2 96.5 91 .8 100.1 113.2 103.0 97 .2 89.3 97 .7 106.1 101.8 101 .5 92 .8 103.8 99.2 99.7 99.0 95.4 92.2 101 .0 99.2 96.5 98.3 99.0 100.0 100.0 100.0 100.0 100.0 101 .8 102.8 100.4 99.6 104.1 112.6 104.1 105.5 101 .7 103.5 116.7 103.5 105.5 96.8 102.7 116.1 106.9 109.0 101 .2 102.4 125.5 112.6 120.2 116.0 111 .5 4244 Grocery and related products .. .......... ....... ..... ... Farm product raw materials .... .... . .. .. ... ... ........... Chemicals .. .... ..... .... .... .. ... .. .... ........ .. .. ..... ... .. Petroleum ..... .. .... .. .... ... ... .... ... .. .... ..... ... ... .. .. .. Alcoholic beverages ..... . .. . ... ....... ..... ... ....... ..... 96 .4 80.6 107.3 97.3 109.4 98.2 85.9 106.6 107.0 111 .2 103.6 85.9 112.5 118.3 107.4 105.1 84 .0 110.0 119.1 105.6 103.3 80.4 110.5 115.8 105.9 103.0 87.7 102.1 108.7 102.5 99.8 90.6 100.0 105.9 104.5 100.0 100.0 100.0 100.0 100.0 101 .9 100.4 99.3 115.0 109.7 103.6 114.2 98.0 112.0 110.1 105.2 11 9.0 95.8 112.5 111 .0 109.4 120.0 93.6 116.5 111 .6 111 .8 4245 4246 4247 4248 4249 425 4251 1 42512 Miscellaneous nondurable goods .. .... ... ...... .. .... . Electronic markets and agents and brokers ... .... .. Business to business electronic markets ..... ..... ... Wholesale trade agents and brokers ........ .. .. ... .. . 107.3 70.7 70.4 70.8 98.2 73.6 72 6 74.0 93.9 81 .5 80.3 82.3 97.5 85.9 84 .8 86.8 94 .8 88.0 88.3 88.4 96 .2 91 .1 90 .5 91 .8 98.7 95.7 95.3 96.1 100.0 100.0 100.0 100.0 101 .7 104.6 103.5 104.8 99.6 114.4 121 .7 110.5 106.2 124.1 141 .3 115.7 104.2 131 .3 169.4 114.2 97.0 132.6 205.0 109.3 44-45 441 4411 4412 4413 Retail trade ... .......... ........ ... ········ ·· ··· ·· . . .. . . . . . . Motor vehicle and parts dealers ... .... ... ...... .... .... Automobile dealers ........... . ... ..... ..... ..... ... ... .... . Other motor vehicle dealers ....... .... .. ... ...... ....... Auto parts, accessories, and tire stores ..... ....... .. 83.2 89.7 92 .1 69.0 85.0 83.3 88.3 90.8 71 .7 84.0 86.8 92 .6 94.8 78.3 89.1 89.4 94.0 96.0 84.1 90.6 92 8 96.9 98.0 90.2 95.4 94.7 97.0 97.2 91 .0 97.9 97.7 98.8 98.9 97.7 98.3 100.0 100.0 100.0 100.0 100.0 104.3 102.7 102.7 105.9 105.7 110.3 106.4 106.4 113.0 110.0 114.2 107.2 106.6 108.6 112.0 117.4 110.0 109.1 112.6 109.3 122.7 109.7 106.0 11 6.4 115.8 442 4421 4422 443 Furniture and home furnishings stores ..... .... .. ... . Furniture stores ..... . ... ... .. ... ....... ... ...... ....... ..... Home furnish ings stores ... ... . .... ..... ..... .... ... .... . Electronics <Jnd appliance stores .. ... .... .... .. ...... .. Building material and garden supply stores ..... . ... 80.7 82 .1 78.5 46.0 81.8 81.1 83.5 77.6 49.2 80.2 88.1 89.0 86.8 56.9 84 .0 88.3 89.0 87.2 65.5 88.0 90.4 88.9 92.1 77 .6 93.7 94 .1 92 .5 95.9 89.2 93.7 99.4 97.8 101.3 95.0 97.5 100.0 100.0 100.0 100.0 100.0 101 .7 102.1 101 .3 122.9 106.7 109.6 108.2 111 .4 152.2 112.3 115.7 114.8 116.8 177.7 113.1 118.5 121 .1 115.6 199.1 115.8 125.1 128.6 121 .4 240.0 119.9 Wholesale trade Retail trade 4,14 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review December 2004 135.4 96.9 126.0 117.3 12 1 Current Labor Statistics: Internationa l Comparison 51. Continued - Annual indexes of output per hour for selected NAICS industries, 1990-2002 (1997=100] NAICS Industry 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 4441 4442 445 4451 4452 Building material and supplies dealers ... ....... ... .. Lawn and garden equipment and supplies stores Food and beverage stores ... ... ... .. ..... .... .... .... .. Grocery stores ... .... ... .... . .... .. ... ...... ......... .... .... Specialty food stores ........ ... .. .. . . .. . .. . ..... ... .. .... 83.2 74.5 107.1 106.5 122.9 80.7 77.5 106.6 106.6 115.0 84.7 80.2 106.9 106.7 111.4 89.1 81 .5 105.4 105.9 107.6 94.8 86.9 104.3 104.9 104.5 94.8 87.0 102.5 103.0 101.1 97.6 97.1 100.3 100.8 95.5 100.0 100.0 100.0 100.0 100.0 107.6 101 .2 99.9 100.3 95.0 113.7 103.5 103.7 104.3 99.6 113.8 108.2 105.1 104.9 105.6 115.3 119.4 107.6 107.5 110.8 119.8 121 .2 110.3 110.3 114.2 4453 Beer, wine and liquor stores ........ .. .. .. .... ... .. ... .. 100.1 100.2 101 .0 94.4 92 .9 96.2 103.1 100.0 105.8 99.8 111.1 110.4 446 447 448 111 .8 Health and personal care stores ....... ... .. .. .. ... ..... Gasoline stations ... .. ...... .... .. ... .. .. ............ .. .... . Clothing and clothing accessories stores .... ... ... .. . 92.0 84.8 69.5 91.6 85.7 70.5 90.7 88.5 75.3 91 .9 92 .8 78.9 91 .8 96 .8 83.3 93.0 99.7 91 .2 95.7 99.4 97.9 100.0 100.0 100.0 104.1 105.6 105.4 106.9 110.6 112.8 111.4 106.5 120.3 112.7 109.8 123.5 118.8 117.5 129.0 4481 Clothing stores ....... ....... . .. ..... .. ..... .. ......... .. .... 68.9 71.4 77.1 79.2 81 .9 90.1 97.1 100.0 106.7 113.3 120.9 125.2 132.7 4482 4483 451 4511 4512 Shoe stores .. .. ..... ..... ........ ......... ... ..... .... ... ... . Jewelry, luggage, and leather goods stores ...... .. . Sporting goods, hobby, book, and music stores ... Sporting goods and musical instrument stores .... Book, periodical, and music stores ..... ..... .... ...... . 73.7 68.6 80.8 77.1 89.0 73 .1 64 .5 85.6 82 .8 91.8 78.2 65.0 83.8 79.8 92.5 79.2 77.1 84.0 80.6 91 .6 88.3 85 .0 87.2 83.9 94.5 93.7 94 .1 93.0 92.3 94.5 102.4 97.3 94 .7 92.5 99.3 100.0 100.0 100.0 100.0 100.0 97.8 107.0 108.7 112.9 101.0 104.9 118.3 114.9 120.4 104.7 109.6 128.0 121.1 128.3 108.0 115.8 122.5 125.4 130.4 116.0 120.0 121.5 132.9 137.9 123.8 452 4521 4529 453 4531 General merchandise stores ............... .... ..... ..... Department stores .. ... ....... .. .. .... ... ... .. .... . ...... .. Other general merchandise stores ......... .. .... .. .... Miscellaneous store retailers .... ... .......... .. ... ... ... Florists .. ... ... .... ..... ... ..... .. .............. .... ..... ..... .. 75.3 84 .0 61.4 70.6 75.1 79.0 88.3 64.8 68.0 75.9 83.0 91.6 69.7 74.2 85.1 88.5 95.0 77.8 79.1 91 .4 90.6 95.1 82.6 87 .0 85.4 92 .2 94.7 87.6 89.5 83.5 96.9 98.4 94.3 95.0 96.1 100.0 100.0 100.0 100.0 100.0 105.0 100.6 113.4 108.3 101 .2 113.1 104.5 129.8 109.8 117.3 119.9 106.3 145.9 111 .3 116.0 124.2 104.0 162.1 108.4 108.6 130.5 104.7 177.5 115.6 120.7 4532 4533 454 4541 4542 4543 Office supplies, stationery and gift stores .. .... .... .. Used merchandise stores .. . ....... .. ... ... ..... ... . .... . Other miscellaneous store retailers ..... ... . .... ..... .. Non store retailers ..... ........... .. ..... ... ... ..... ... ... . Electronic shopping and mail-order houses ....... .. Vending machine operators .......... ... ... .... . .. . . Direct selling establishments .. ... ... . ...... .. ..... ... ... 64.6 84.9 79.6 54.4 43.5 97.1 70.0 66.3 83.1 69.2 55.0 46.7 95.4 67.6 71 .5 89.7 74.7 63.4 50.6 95.1 82.1 75.8 88.9 80.5 66.7 58.3 92.8 79.7 87.5 87 .3 89.7 73.8 62 .9 94 .1 89.2 90.9 90.2 90.5 80.9 71.9 89.3 94.7 91.8 97.4 98.0 91.6 84.4 96.9 102.2 100.0 100.0 100.0 100.0 100.0 100.0 100.0 113.0 113.5 105.0 111.3 118.2 114.1 96.2 118.0 109.8 101 .6 125.4 141 .5 118.1 96.3 124.1 115.7 99.6 142.8 159.8 127.1 104.3 125.1 115.0 93.2 146.9 177.5 110.4 98.7 481 482111 48412 491 Transportation and warehousing Air transportation .. ...... ... . ...... .. ..... .. ..... ...... .... .. Line-haul railroads .. .. ................... .... ........ ...... . General freight trucking, long-distance ........ ...... . U.S. Postal service .... ...... .. .. . ... .... .. .. .. .... .. ...... 140.3 121.4 92 .8 169.6 209.8 113.3 110.2 77.5 69.8 88.5 96.1 78.2 75.3 92.4 95.8 81.4 82.3 97.5 96.5 84.7 85.7 95.6 99.0 90.8 88.6 98.1 98 .5 95.3 92 .0 95.4 98.3 98.8 98.4 95.7 · 96.7 100.0 100.0 100.0 100.0 97.6 102.1 99.1 101.4 98.2 105.5 102.0 102.4 98.2 114.3 105.5 104.9 91 .9 121.9 104.2 106.1 103.2 131 .9 109.4 107.0 5111 5112 51213 5151 5152 5171 5172 5175 Information Newspaper, book, and directory publishers .. .. ..... Software publishers .... .... ............ . .... .. ... .. ..... ... Motion picture and video exhibition .......... ... .. .... . Radio and television broadcasting .. .... ... ..... ....... Cable and other subscription programming ......... Wired telecommunications carriers ............. ...... . Wireless telecommunications carriers .... .. ...... ... . Cable and other program distribution ....... ... .. . . . . 97.4 28.6 109.4 96.1 98.8 64.8 76.3 99.1 96.1 30.6 108.9 97.8 94.3 68.4 73.8 94 .3 95.8 42.7 104.1 102.8 96.0 74.5 85.6 95.9 95.3 51 .7 104.6 101.4 93.6 79.7 94.8 93.5 93.0 64.6 103.4 106.0 92.0 85.1 97 .1 91 .9 93.5 73.0 99.9 106.1 94.4 90.6 98.3 94.2 92 .7 88.0 100.0 104.1 93.7 97.5 103.0 93.5 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 104.5 115.9 99.9 99.1 129.3 105.5 114.2 95.7 108.5 113.0 102.0 99.4 133.2 112.7 134.3 94 .5 110.1 103.9 106.5 98.4 135.7 119.9 139.0 90.4 106.4 101.9 104.7 94.3 125.3 121.0 172.7 87.6 52211 Finance and Insurance Commercial banking ... ... .. .... .. ....... ....... . 108.1 106.7 104.4 100.4 131.4 130.6 192.0 93.5 4 r:- ")('\ oJ v..J ····· ···· 80.5 83 .2 83.3 90.3 92 .9 96.0 99.3 100.0 98.0 101.5 104.2 101.6 103.8 532111 53212 Real estate and rental and leasing Passenger car rental ..... ........ .. ..... ...... ...... ...... Truck, trailer and RV rental and leasing ...... ......... 89.8 70.7 97 .8 71.7 104.4 69.5 106.1 75.8 107.9 82.0 101 .1 90.3 108.9 96.7 100.0 100.0 101.2 93.7 113.1 97.8 112.0 95.9 112.1 93.6 113.3 91.4 541213 54181 Professional, scientific, and technical services Tax preparation services ........ ....... ....... .. .......... Advertising agencies ... .. . ... ........... ........ .... . ...... . 92.4 105.0 84 .7 99.7 99.5 111.9 119.1 111.3 119.9 106.8 96.2 101.4 92.1 102.1 100.0 100.0 105.1 95.8 99.2 110.1 91 .8 116.6 78.2 116.7 92 .1 123.9 7224 Accomodation and food services Traveler accommodations .. .. ...... .... .. ... .. ..... ..... . Food services and drinking places .......... ... .. .. . .. Full-service restaurants ...... ... .. ........ .. .... .... ... ... Limited-service eating places ....... ... .... .......... .. .. ' Special food services .... .... .. ..... .. .... .......... ... . .. Drinking places, alcoholic beverages ......... .. . .... .. 82 .9 102.9 99.1 103.3 107.2 125.7 85 .4 102.3 98.3 103.3 106.9 121.2 92 .9 101 .7 97.5 102.7 106.4 121 .5 93.0 102.3 97.7 105.6 103.8 112.7 97 .0 100.8 97.8 103.6 101 .1 102.6 99.2 100.6 96.6 104.7 99.3 104.4 100.1 99.2 96.3 102.2 97 .6 102.4 100.0 100.0 100.0 100.0 100.0 100.0 100.0 101 .2 100.0 102.4 102.1 100.0 103.6 101 .1 99.2 102.5 106.0 99.4 107.7 103.5 100.8 105.1 111 .7 100.4 102.0 103.7 100.8 106.6 108.4 98.2 104.1 104.9 102.0 107.1 108.1 107.2 8111 81211 81221 8123 81292 Other services (except public administration) Automotive repair and maintenance .. ... .. .... ........ Hair, nail and skin care services .. .......... ....... ..... Funeral homes and funeral services ....... ... ...... .. . Drycleaning and laundry services ... ............. .. .. Photofinishing .... .... ... ................... .. ... .... ........ 92 .8 81 .6 96.1 95.6 117.3 86.5 79.8 94.3 93.2 115.6 90.0 85.6 104.7 94.9 116.2 91 .2 84.3 100.4 93.8 123.6 96.7 88.7 103.6 95.9 124.9 102.9 92.4 100.4 98.8 114.7 98.9 97.1 97.9 101 .6 103.2 100.0 100.0 100.0 100.0 100.0 105.0 102.7 103.8 105.0 99.4 106.9 103.6 100.4 109.5 106.9 108.6 103.0 94.5 113.7 107.6 109.3 109.5 93.9 121 .1 115.0 103.7 104.2 90.9 120.2 133.6 7211 722 7221 7222 7223 NOTE: Dash indicates data are not available. 122 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 52. Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data seasonally adjusted Annual average Country 2002 2003 4.7 9.4 5.3 5.4 5.5 5.4 5.4 5.4 5.2 9.3 8.6 8.7 8.9 9.0 9.2 9.4 8.6 9.3 8.5 8.7 8.9 9.2 9.4 9.4 9.3 9.2 9.1 8.8 9.2 9.1 9.0 9.0 8.8 8.7 8.6 8.6 5.1 5.2 5.8 5.0 5.0 5.2 5.1 5.2 5.2 5.1 5.2 5.1 5.6 5.0 5.8 5.0 6.2 4.9 6.6 4.8 5.4 8.7 Germany ... ... ..... ... 1 5.0 9.4 6.1 6.9 6.2 France ....... .... ...... 2 5.1 9.4 5.8 6.7 6.2 Japan .... ....... ... .. .. Sweden • • • • ••• • • •••••• United Kinadom ..... 5.6 6.6 5.6 5.9 6.9 6.2 6.0 6.9 6.1 •••••••••• •• • •• • • •• 5.6 6.7 5.7 5.9 6.8 5.8 5.7 7.0 6.3 5.8 7.0 6.4 1 6.1 7.2 6.1 II I IV Ill II I IV Ill 5.8 6.9 6.4 United States ........ Canada .............. .. Australia ........ .... ... ltaly 2004 2003 2002 II Quarterly rates are for the first month of the quarter. Preliminary data for 2003. NOTE: Quarterly figures for France and Germany are calculated by applying annual adjustment factors to current published data. and therefore should be viewed as less precise indicators of unemployment under U.S. concepts than the annual figures. See 9.9 6.8 4.8 "Notes on the data" for information on breaks in series. For further qualifications and historical data, see Comparative Civilian Labor Force Statistics, Ten Countries, 1959-2003 (Bureau of Labor Statistics, June 23, 2004) , on the Internet at http://www.bls.gov/fls/home.htm. Monthly and quarterly unemployment rates , updated monthly, are also on this site. Monthly Labor Review December 2004 123 Current Labor Statistics: International Comparison 53. Annual data: employment status of the working-age populaHon, approxlmaHng U.S. concepts, 10 countries [Numbers in thousands] EmDlovment status and countrv Civilian labor force 1993 United States .. .... .......... .... .... .... .... ....... ............ . Canada ...... ............. .. ... ... ........ .. .............. ... .... . . 129,200 14,308 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 131,056 132,304 14,517 133,943 14,669 136,297 14,958 137,673 14,400 139,368 15,536 142,583 15,789 143,734 16,027 144,863 16,475 146,510 16,819 66,010 15,237 Australia ... ... ................. ..... .................... ......... . 8,613 8,770 8,995 9,115 9,204 9,339 9,414 Japan .......... ... ....... ... ...... ... ..... .. .... .. ..... .. ...... ... . France .. ..... ..... .. ... .. .. ... ............................... ..... . 9,590 9,752 65,470 24,480 65,780 24,670 65,990 67,200 25,130 67,240 67,090 9,907 66,240 Germany ... ..... .. ........ ... .. .... ........ ...... .. ..... ....... . . 39,102 39,074 38,980 39,142 39,415 25,460 39,754 25,790 39,375 66,990 26,070 66,870 24,760 66,450 25,010 Italy .. .. ...... ... ... .. ... ........ .. ...... ................... ..... .. . 39,302 26,350 39,459 39,413 26.730 39,276 22,570 22.450 22,460 22,570 22,680 22,960 23,130 Netherlands ......... ........... .. .......... ...... .. .. ........... . Sweden ..... ..... ...... ....... . .. .. .. ..... ................ ... ... . . 23,340 23,540 23,750 23,880 7,010 7,150 7,210 7,300 7,540 7,620 7,850 8,150 8,340 8,300 8,330 4,444 4,418 4,460 4,459 4.418 4,402 United Kingdom ....... .... ... .. .................... ....... .... . 4,430 4,489 4,530 4,544 4,567 28,165 28,149 28,157 28,260 28,417 28,479 28,769 28,930 29,053 29,288 29,490 26,590 Participation rate 1 10,092 United States ........................ ... ..................... .. . . Canada ...... ........ .......... .. .. ............ ........... .. .. .. . . 66.3 66.6 66.6 66.8 67.1 67 .1 67.1 67.1 66.8 66.6 66.2 65.5 64.9 64.7 65.0 Australia ...... .. ..... .. .... ... ........ .... .... .. .. ............... . 66.8 67.3 64.6 64.3 65.8 64.0 66.0 64.5 65.4 64.3 65.9 63.5 65.2 63.9 Japan ..... ... ... ... .. .. .. ..... ...... .. ........... ......... ....... . . 64.4 64.4 64.4 64.6 63.3 63.1 62.9 63.0 63.2 62.8 62.4 France .. .. ........ ... ........ ..... .. ... ..... ........ .. ....... .... . Germany ..... .. ...... ... ....... ............................. .... . 62.0 61 .6 60.8 60.3 55.4 57.8 55.5 57.4 55.4 55.6 55.5 55.9 56.3 56.6 56.8 57.0 57.0 Italy ............ ...... ......... ... ...... .. ....... ................ .. . Netherlands ............ .. ....... .... .. ... .. ... ..... .. ..... ... .. . . Sweden ........ .. .. ... ........ ...... ... ...... .. ... ............... . United Kingdom ... ... ... .... .. .. .. .......... ... ................ . 56.6 56.6 56.3 56.1 47.3 47.8 62.6 48.1 48.6 65.0 48.8 64.5 48.3 65.8 63.3 57 .7 47.6 61 .1 62 .8 56.8 47.9 57.9 62.8 63.8 63.7 64.0 64.6 64.0 64.5 58.6 63.7 62.7 United States ..... .... .......... .... ....... ..... ...... .. ..... ... . Canada .................................. .. ............ ... ... ..... . Australia ...... ................... ...... .. ..... .. .. ... ... ......... . 57.1 57.1 57.3 47.1 58.8 47.1 47.2 60.8 64.1 59.2 64.0 62.6 62.4 62.4 62.6 62.5 62.9 62.9 62.7 62.9 62.9 120,259 123,060 124,900 126,708 129,558 131,463 133,488 136,891 136,933 136,485 137,736 12,770 13,027 13,271 13,380 13,705 14,068 14,456 14,827 14,997 15,325 15,660 7,699 7,942 8,256 8,364 8.444 8,618 8,762 8,989 9,091 9,271 9,481 63,920 63,790 63,470 62,650 62,510 24,250 Employed Japan ... .... ..... ... ... ... .. ... ..... .. .. . .... .... ................. . 63,810 63,860 63,890 64,200 64,900 64,450 France ............. ........... .. ..... ... ....... ... .... .. ..... ..... . 21 ,710 21,750 21,960 22,040 22,170 22,600 23,050 Germany .... .... ....... .. .. .... .. .. .... ... ..... .. .. ............. . 23,690 24,140 24,280 35,989 35,756 35,780 35,637 35,508 36,061 36,042 Italy ............... .. .. ... .. .... .... .. .... ................ ......... . . 36,236 36,350 36,018 35,615 20,270 19,940 19,820 19,920 20,460 20,840 21 ,270 21 ,580 21,790 6,570 6,660 6,730 6,860 19,990 7,160 20,210 Netherlands .. .. ... .......... ... .. .. ........ ..... .......... .... .. . 7,320 7,600 Sweden ..... .. ..... ..... .................................. ...... . . 7,910 8,130 8,070 8,010 4,028 3,992 4,056 4,019 3,973 4,034 4,117 United Kingdom .............. ... ............................... . 4,229 4,303 4,310 4,303 25,242 25,429 25,718 25,964 26,433 26,696 27,048 27,350 27,570 27,768 28,011 61 .7 58.5 62.5 59.0 62.9 63.2 63.8 64.4 63.7 62.7 62.3 59.1 59.7 59.0 64 .1 60.4 64.3 59.4 61 .3 62.1 61 .9 60.1 62.4 63.0 60.7 Employment-population ratio 2 United Stai es ... .. ...... ... ... ... .. .. .... .. ........ ... .......... . Canada .............. .... ... ..... ... ........ ...... ... ... .......... . Australia .. ......... .. .... ........................... ... ... ....... . 56.8 57.8 59.2 59.3 Japan ... .... ..... ... .......... ................. .. .. ... ... .. .. .... . . France .. .. ....... ... ... ......... ....... .. ........ .... ... ........ .. . 61 .7 49.1 60.9 49.1 60.9 49.0 Germany ... .. ... ... ... .......................... ................ . 53.2 61 .3 49.0 52.6 52.0 Italy ... ... ..... .. .. ... .. ................ . ........ .... ... ... .... ..... . Netherlands ........... ..... ..... .. .. ... ......... ...... .... ... ... . 43.0 42.0 52.4 41 .5 61 .0 49.0 51.6 41.6 41 .6 54.2 54.6 54.9 55.7 57.8 59.3 59.6 60.3 60 .2 49.7 59.4 59.0 51.4 52.2 60.3 52.3 41 .9 50.3 52.0 42.3 42.9 58.4 52 .0 52.2 43.6 58.7 60.6 62.6 64.2 63.2 62.1 57.5 52.0 51.5 44.1 57.1 51 .7 50.9 44.6 Sweden ...... ...... ..... ..... .. .... .... .... .. .. ... ............... . 58.5 57.6 58.3 57.7 56.9 57.6 58.4 United Kingdom .. ..... ......... ... . ... ... ...... ....... ... . 60.1 60.5 60.7 60.3 56.2 56.5 57.0 57.4 58.2 58.6 59.1 59.4 59.5 59.6 59.8 United States .... ................... .... ... .... .. ...... .. ....... . Canada ..... .. ....... .... ........... ..... .. ........... ... ..... ... . 8,940 1,539 7,996 1,373 7,404 1,246 5,692 6,801 8,378 8,774 962 1,031 1,150 829 1,920 602 3,200 661 3,400 636 3,590 2,920 2,970 721 2.790 2,870 652 3,170 2,770 3,113 739 2,100 2,800 6,739 1,252 759 2,300 2,960 5,880 1,080 914 1,660 7,236 1,289 751 2,250 6,210 1,169 Australia ...... ... .... ... ..... .. ...... ....... .. ............. ...... . Japan ... .. ..... ... ............. .. ......... ........ .. ..... ... ...... . France .. ... .. .. .................... ... ....... .. .. ... .. ... .... ... .. . Germany ......... .. ... ... ... ....... ... ........... .... ..... ... ... . 2,740 2,380 2,310 3,318 3,200 3,505 3,907 3,693 3,333 Italy ............. .. .. ................... ... .... .... .. .... ... ........ . 3,065 2,210 3,110 1,159 611 3,500 2,480 3,396 3,661 2,300 2,510 2,640 2,690 2,750 2,670 Netherlands .. .. .. ... .... ... ... ... ...... ...................... ... . 2,500 2,270 2,160 2,100 440 490 480 2,650 440 300 Sweden ............................. .. ..... .. .... ... ............. . 210 230 320 404 440 368 United Kingdom ........ ... ..... ...... .......... .... .. ... .... ... . 260 227 2,916 426 2,716 250 313 240 416 370 445 2,439 2,297 1,985 1.783 1,721 1,580 1,483 234 1,520 264 1,479 United States .. ... ....... .. .... .......... .. .... ... .. .... ... ..... . Canada ..... .... .. ... ... ... .. ..... ... ............. ... .... .. ...... . Australia ... .... .................... ... ...... ............ ..... ... . . 6.9 10.8 6.1 9.5 5.4 8.8 4.9 4.5 4.2 5.8 7.0 6.0 6.9 8.2 7.7 7.7 4.0 6.1 9.4 8.4 8.3 7.0 10.6 5.6 8.6 8.2 Japan ... .. ....... ... ............ .. ... .... ............ ............. . 6.9 2.5 2.9 3.2 3.4 3.4 4.1 4.7 France .. ... ....... ...... ... .. ... ........... ... .............. ...... . 11.3 11 .8 11.3 11 .3 8.0 10.2 6.3 8.5 11 .2 6.9 8.2 11.9 9.0 11 .8 Germany ................. ............. .. ... .. ......... .. ........ . Italy .. ... ...... ....... .... ....... .... ...... ... .......... ... ....... .. . Netherlands ............ ... .. ........ .. ... ... ....... ... .... ...... . 9.9 9.3 11.7 6.0 11 .9 4.9 12.0 3.9 Unemployed Unemployment rate 11.8 6.7 4.7 6.3 6.4 6.8 6.4 6.1 4.8 5.1 5.4 5.3 10.6 9.1 8.5 8.4 7.9 11.5 7.8 10.7 3.2 2.9 9.6 2.5 8.7 9.3 8.6 9.1 9.3 2.8 8.8 3.8 Sweden ..... ..... .... .. ... .... .. .............. .. ... ... .. .... ... . . 9.4 9.6 9.1 9.9 10.1 8.4 Unitc<:i Kingdom ......... ..... .................... ............. . 7.1 5.8 5.0 5.1 5.8 10.4 9.6 8.7 8.1 7.0 6.3 6.0 5.5 5.1 5.2 5.0 ' Labor force as a percent of the working-age population . 2 Employment as a percent of the working-age population. NOTE: See "Notes on the data" for information on breaks in series. 124 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 For further qualifications and historical data, see Comparative Civilian Labor Force Statistics, Ten Countries, 1959-2003 (Bureau of Labor Statistics, June 23, 2004), on the Internet at: http://www.bls.gov/fls/home.htm. Table 54. Annual indexes of manufacturing productivity and related measures, 12 countries mm Item and country fHlQ? = 1960 1970 1980 Output per hour 70.5 0.0 United States ......... ... .. .. .. ... 72 .9 37.8 54.9 Canada ... .... .. ..... ... . .. ....... .. 37.7 63.6 13.9 Japan .... ....... ... .. ....... .. .. ... . 32.9 65.4 18.0 Belgium ...... .. .. ... ... ... .......... 83.2 46.3 25.2 Denmark ......... .. . ... ... ......... 61.6 19.9 39.0 France ...... ....... .. .. ............ . 77.2 ... 29.2 52.0 Germany .... ........... ... ...... 46.2 78.6 24.8 Italy .............. ..... ... . ... ... .... . 69.1 18.8 38.5 Netherlands .... .... ... .... .. . ... .. 77.9 37.6 59.1 Norway ... .. . ... .. . .. ......... ...... 52.2 73.1 Sweden ...... .... ... .. .............. 27.3 43.2 54 .3 30.0 United Kinadom .. ......... ..... .. Output 75.8 United States ............ ........ . 83.6 58.9 Canada ............... .. .... ..... ... 33.4 60.8 10.8 39.4 Japan .... ...... ...... ............. .. 78.2 57.6 Belgium ... .. .... ... .. ......... ... . .. 30.7 72.7 94 .3 42.0 Denmark .. ... ..... .... .. .. .. ....... 57.7 81.6 27.9 France ........ . ......... ....... .... . 85.3 41 .5 70.9 Germany ... ... .............. .... ... 84.4 48.1 . .... ... .. 23.0 ..... ... Italy ... .. .... ... .... 76.9 59.8 Netherlands ....... .. ...... .. ....... 31 .9 91 .0 104.9 57.7 Norway .... ... ........ ......... .. ... 90.7 80.7 Sweden .. ....... .... ... ..... ....... . 45.9 87.2 90.2 67.5 United Kingdom ........ ..... ..... Total hours 107.5 92.1 104.4 United States .. .. ..... ........... . 114.6 107.1 Canada ...... .. ... ... ... ... ... .. .... 88.3 104.3 95.5 77.8 Japan ........ ...... .... ... .. ..... ... 119.7 174.7 Belgium .. . .. .... ........ .... ... ... .. 170.7 157.1 113.4 Denmark .. .... ... ... ...... ......... 166.7 132.5 147.8 France .... ... .. .... .. .... . . . .. .. . .. 140.3 136.3 110.5 Germany ... ..... ........... .. .. .... 142.3 107.4 104.0 93.5 Italy ........ ... ... .. .......... ........ 111 .2 155.5 Netherlands ..... ... ...... .. .... ... 169.8 134.7 153.9 Norway ....... ....... .... .. ... . ... .. 153.6 124.0 154./ Sweden .... ... ....... ... ......... .. . 168.3 160.5 208.8 United Kingdom .. ....... ... .. .. .. 224.6 Hourly compensation oasIs1 \nauonaI currency 23.f ob.ti 14.\l United States ..... .. ........ ... .. . 17.1 47 .5 10.0 Canada ..... ..... ... .. .. ....... .. ... 16.4 58.6 4.3 Japan ......... ...... ..... ....... .. . . b2 .b 13./ b.4 Belaium .......... ...... ... .... .. .. .. 4!:J.1 .1 11 3.\l Denmark .... ..... ..... ..... ... ... .. 41.2 4.3 10.5 France .. .. ... .. ...... .. ...... ..... .. 20.7 53.6 8.1 Germany .. ....... .. ... ...... ... ... . 5.3 30.4 1.8 Italy .. .. ....... . ..... ..... ... .... .. ... tiU.b 1\l.4 ti.2 Netherlands .. ..... ... .. ... .... .... 3\l.U 11 .!S 4./ Norway ... ....... .. ..... .... .. .. ... . 37.3 10.7 4.1 Sweden .... ... ... .. ..... ... . ...... .. 32 .0 6.1 2.9 United Kinadom ... .. ... .......... Unit labor costs (national currencv basis) 78.8 United States ..... ...... .. ... .... .. 65.2 31 .1 26.4 Canada .... .... . .. . .... .... .. .... ... 92.1 .1 31 43.6 Japan .. ..... .. .... ... .. .. .. ..... .... 80.3 41 .7 Belgium .. . ... ... ...... ...... ..... . .. 30.1 54.2 23.9 15.3 Denmark ........... .... .. .. .. .... .. 67.0 26.8 21 .7 France .... .. ............ ...... ...... 69.4 39.8 27.8 Germany .... .... ... ......... .... ... 38.7 11 .4 7.2 Italy .. .. ....... ... ...... . ..... . ....... 87.6 50.4 32.9 Netherlands ......... .. . .... ..... .. 50.0 20.0 12.6 Norway ............. ...... ..... ... .. 51 .0 20.6 Sweden .. . ... ... ...... .... ... ... .... 15.0 14.1 59.0 9.8 United Kingdom .. ..... .. .... .. .. . Unit labor costs (U.S. dollar basis) 78.8 United States ... ................ .. ti/.4 3ti.U 32.\l Canada .. ........ .. ....... .......... .b bl lb.4 .U 11 Japan ...... ... ... .... ... ....... ..... 88.3 27.0 Belgium ...... ... ... .. .. ... ........ .. 19.4 58.1 19.3 13.4 Denmark ...... ......... ........ .... 2b . l !S3.\l 23.4 France .. ... . ... ... .. ... .. ... .. ... .. . 59.6 17.1 10.4 Germany ... ... ... .. ..... .......... . ob.I 22.3 14.3 Italy ............ ... .... ... ...... ..... . 77 .5 24 .5 15.3 Netherlands .... .... ... ..... .. .... . 62.9 11 .0 17.4 Norway .......... .. .. .... .... ..... .. /U.2 23.1 lti.\l Sweden .... .. ..... .... .... ...... .... 77.6 19.1 15.6 United Kinadom ... .. .... .... .... . NOTE: Data for Germany for years before 1991 are for the https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis - - - - - - - 1990 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 96.9 93.4 94.4 96.8 98.4 93.9 99.0 96.6 98.7 98.1 94.6 89.2 97.9 95.3 99.0 99.1 100.3 97.0 98.3 96.1 99.0 98.2 95.5 93 .9 102.1 105.8 101 .7 102.5 100.2 101 .0 101 .8 101 .2 102.0 99.6 107.3 103.8 107.3 110.8 103.3 108.4 112.6 108.9 109.6 104.8 113.1 99.6 117.8 108.0 113.8 112.4 111 .0 113.2 112.5 114.4 112.3 107.9 117.3 100.7 124.5 106.2 117.0 109.7 116.1 116.3 109.8 114.7 114.7 108.3 119.3 102.5 129.5 105.4 121 .3 113.5 121.0 125.5 118.0 121 .7 120.4 110.3 121.4 102.0 141 .0 106.9 126.5 115.5 121 .2 126.9 117.4 127.9 122.0 110.8 124.1 99.9 149.5 108.4 132.8 122.1 126.7 125.5 123.1 133.0 121 .4 110.6 127.0 103.6 162.7 113.6 143.5 129.3 135.9 130.8 126.6 142.5 127.0 113.5 132.7 106.6 175.5 121 .0 145.2 127.0 135.9 132.6 127.2 148.0 127.8 114.0 132.5 109.8 170.3 125.1 160.0 130.5 139.9 141.7 131 .3 155.1 131 .0 112.1 135.4 111.7 185.6 127.7 171 .0 132.1 146.2 146.2 136.9 158.0 134.4 110.9 101 .6 106.0 97.1 101 .0 101 .7 99.1 99.1 99.4 99.0 101.4 110.1 105.3 98.3 99.0 102.0 100.7 100.7 99.8 102.3 99.3 99.8 99.0 104.1 100.1 103.5 105.9 96.3 97.0 97.0 95.7 92.4 96.5 97.7 101 .7 101 .9 101 .5 111.1 114.1 94.9 101.4 107.3 100.3 95.1 102.4 104.5 104.6 117.0 106.2 118.4 119.6 98.9 104.2 112.6 104.9 95.2 107.2 108.2 107.3 131.9 107.8 121.3 119.6 103.0 105.9 107.7 104.6 92.5 105.4 108.9 110.3 136.4 108.6 127.9 127.7 106.5 112.7 115.9 109.7 95.7 108.8 111.6 114.2 146.5 110.7 133.1 133.9 100.2 114.4 116.7 115.0 97.7 110.7 114.9 113.7 158.3 111 .3 138.9 139.6 153.6 105.5 120.4 121 .6 128.0 99.9 113.0 121.9 112.3 183.1 113.4 142.9 101.9 114.4 117.9 118.7 95.8 110.3 117.6 113.6 172.5 112.1 147.6 159.2 109.2 119.9 121 .9 124.3 100.1 113.6 122.8 112.8 188.3 115.0 145.4 157.3 106.7 120.9 121.4 128.5 104.8 113.5 102.9 104.3 103.3 105.6 100.1 102.9 100.3 103.4 116.4 118.1 100.4 103.9 103.1 101 .5 100.5 102.9 104.1 103.3 100.8 100.8 109.0 106.6 101.4 100.1 94.7 94.7 96.7 94.7 90.8 95.4 95.8 102.1 94.9 97.7 103.6 103.0 91.9 93.6 95.2 92.1 86.8 97.7 92.4 105.0 99.4 98.4 104.0 106.4 89.1 92 .0 100.1 91.7 84 .8 99.4 92 .3 106.6 105.9 101 .5 103.6 109.0 88.7 91 .0 98.1 91.2 80.6 97.3 91 .2 107.6 105.3 103.1 105.4 112.4 88.0 89.8 98.2 90.2 79.5 98.6 91 .9 112.0 103.9 103.5 105.2 115.9 82.7 90.2 99.4 89.9 80.1 99.9 92.6 113.7 105.9 102.7 104.6 118.7 80.4 91 .2 95.8 89.2 78.9 99.8 92.6 109.6 106.0 98.7 102.9 123.1 80.3 91.7 96 .3 87.2 78 .8 100.1 92 .5 105.9 107.3 95 .0 \lU.!S 88.3 90.6 \lU.1 90.9 89.4 87.6 !S\l.!S \lb.ti 95.0 96.5 \j/ .3 \l/ .\j 96.4 91 .5 94.2 \!4.!S \!2.3 \!l .b 87.8 82.9 95.5 93.8 102./ 102.0 102.7 104.!S 102.4 103.1 106.4 105.7 104.o 101.b 97.4 104.5 10!:J.ti 103.7 104.7 1Uti.1 lUti.U 106.5 111.8 106.8 10\l.U 104.4 99.8 107.3 10/.\j 106.0 108.3 10\l.2 1U!S.1 110.4 11 7.6 111 .3 112.1 10\l.2 106.8 108.8 10\l.4 107.0 109.1 111.1 112.!S 112.2 123.3 119.0 114.4 113.ti 115.2 111 .4 111 .b 109.3 112.6 11!:J.2 11ti.ti 111 .8 125.7 123.0 11 / .2 1 l!S./ 121 .0 115.7 11 / .4 111 .7 115.4 11 / .U 11\l.ti 112.7 127.6 122.2 122.U 12!:J./ 125.6 123.0 122.U 115.8 114.8 11!S.b 12/.3 116.6 130.6 124.2 12ti.U 133.U 130.3 129.9 93.7 94.6 95.9 93.0 95.0 96.8 90.3 90.7 91.1 94.2 92.9 93.0 97.6 99.6 97.5 98.1 97.6 99.3 93.1 98.0 95.7 99.2 100.0 100.0 100.6 96.4 101 .0 102.3 102.2 102.0 104.5 104.5 102.4 101 .9 90.8 100.7 98.5 93.6 101 .4 97.9 94.2 97.8 102.0 101.9 96.4 104.8 84.7 99.4 94.8 94 .3 97.5 96.4 96.1 96.5 104.7 103.2 95.6 108.4 85.8 102.5 93.5 97.5 94 .0 95.5 102.8 97.8 107.5 109.8 95.9 110.8 89.0 105.7 91 .9 96.2 93.0 91 .8 98.8 91.9 104.5 111.4 96.5 116.4 85.8 108.2 92.8 96.7 95.2 92.2 101 .9 88.1 104.6 110.3 98.3 125.7 84 .0 113.5 93.7 \l!S.U !S3.\l 89.5 92.7 \!4.1 87.3 \!3.3 87.9 93.6 \ll .3 93.9 97.6 10!:J.1 \!1.!S 92 .3 92.0 \!3.1 87.5 \j/ .3 90.0 95.0 \lti.3 100.0 100.6 \lU.3 11!:J.3 95.1 95.1 \lo.3 98.7 !Sl.!S 96.9 89.2 ti/ .!S 85.6 98.5 !S2.!S 12!:J.!S 94.2 89.4 \!3.4 98.2 94.8 !S3.U 131 .ti 105.2 103.6 102.b 114.2 /!S.U 104.8 106.4 /U.U 91.6 93.5 !Sti.4 10\l.o 99.1 107.0 101 .2 111 .6 91 .9 !S4.U \j/.4 82.4 90.2 !S3.3 94.0 !SU.ti 8 7.0 102.1 tib.4 100.4 81 .6 91 .7 /\l.1 92.9 /!S.2 87.2 103.5 til .b 106.5 \!3.b II.\! !SI.I 100.0 106.6 //.3 93.4 144.9 158.0 103.4 121 .6 120.8 129.1 113.5 196.5 134.8 99.6 99.8 111.7 121 .0 111 .5 190.6 109.9 110.2 117.6 107.3 96.2 120.9 77.7 90.8 95.6 86.5 78.2 99.1 92.0 102.3 107.5 90.7 89.3 121.1 74.0 85.8 92.0 83.2 76.1 99.7 89.4 99.8 102.7 86.0 85.0 119.1 73.0 82.7 88.7 81.3 74.3 99.3 133.2 119.6 113.7 120.ti 130.2 122.8 137.4 127.8 132.U 140.b 136.8 137.6 13ti.3 123.7 114.6 12/.2 13ti.b 128.3 142.0 132.5 13!S.2 14!S.\l 143.8 144.3 14!:J.4 126.8 122.8 13ti.b 143.2 135.2 145.5 135.7 14/.3 lb/.\! 148.8 152.2 1!:J/.!S 131 .4 123.8 91 .9 94.9 90.6 94.4 103.4 87.6 107.6 112.3 99.1 128.4 80.1 114.3 92 .8 92 .5 83.6 92 .2 102.8 86.2 108.1 112.6 99.5 131 .9 77.9 113.7 93.9 97.4 84.4 95.9 107.3 86.6 111 .2 116.2 104.3 135.6 84.4 115.4 90.9 97.2 87.8 96.4 109.0 87.2 111 .1 121 .1 108.8 141 .3 80.2 119.2 92.3 99.4 84.7 92.8 /ts.ts 91 .9 92 .8 11 .2 lb .2 \!2 .2 101 .U 80.2 89.3 \l!S.4 6i' .8 76 .7 ti4.2 79.7 titi.2 73.3 93.0 4\l.b 97.6 93.9 /ti.U !S!S.U 68.4 77.8 ti2.ti 79.5 titi.2 74.5 93.7 4/.ti 94.0 90.9 /4.!S !S!S.\l 72.6 83.5 titi.b 83.9 lb.3 91.5 /ti.2 84.3 102.2 !>ti.4 104.7 93.2 92 .3 ti4.U 86.2 former West Germany. Data for 1991 onward are for unified Germany. Dash 1nd1cates data not available. Monthly Labor Review /2.\! 82 .1 110.0 4!S.1 101.4 December 2004 194.4 110.3 94.5 98.9 81 .9 - l!>U.U 139.1 148.9 140.0 - 1ti4.ti 154.3 160.3 109.6 88.0 110.8 126.2 112.6 144.9 78.6 118.9 92.3 !So.ts \!2.ti - 100.6 !SU.4 100.1 \lU.\l 101.7 127.2 !:>ti.ti 110.0 125 Current Labor Statistics: Injury and Illness 55. Occupational Injury and illness rates by Industry, 1 United States Incidence rates per 100 full-time workers 3 Industry and type of case2 1989 1 1990 1991 1992 1993 4 1994 4 1995 4 1996 4 1997 4 1998 4 1999 4 2000 4 2001 4 PRIVATE SECTORS Total cases ..... ............................ ..... ... .... ..... ..... .. .. .. ..... .. .... . Lost workday cases .................... ......... ..... ............................ ........ . Lost workdays .............. ... ...... .... ... ....... ............ ............................ . 8.6 4.0 78.7 8.8 4.1 84.0 8.4 3.9 86.5 8.9 3.9 93.8 8.5 3.8 8.4 3.8 8.1 3.6 7.4 3.4 7.1 3.3 6.7 3.1 6.3 3.0 6.1 3.0 5.7 2.8 Agriculture, forestry, and fishings Total cases .. ............................ .... ..... .... .... ...... .... ....... ......... . Lost workday cases .......... ....... ......... .. ..... ........... .. .......... ..... ....... ... Lost workdays ..... ........ .. ......... ... .... ...... ..... ............ ... ....... .. ....... ... .. 10.9 5.7 100.9 11 .6 5.9 112.2 10.8 5.4 108.3 11.6 5.4 126.9 11 .2 5.0 10.0 4.7 9.7 4.3 8.7 3.9 8.4 4.1 7.9 3.9 7.3 3.4 7.1 3.6 7.3 3.6 Mining Total cases ......................... ....... .. .... . Lost workday cases ..... .. ...................... ... .............. ................... ... .. . Los, workdays ....... ...... ... ................ ...... ................. ........ .......... ... . 8.5 4.8 137.2 8.3 5.0 119.5 7.4 4.5 129.6 7.3 4.1 204.7 6.8 3.9 6.3 3.9 6.2 3.9 5.4 3.2 5.9 3.7 4.9 2.9 4.4 2.7 4.7 3.0 4 .0 2.4 Construction Total cases .. .......... ........ .. .......... .. ..... .. ... .. ... .. .... .. ..... ...... .. ... . Lost workday cases ............... ............................. ... ..... .......... ....... .. Lost workdays ....... ... .... ..... ... ...................... ..... ... .. General building contractors: Total cases .......................... .... ... ..... ... ......... ... .. . Lost workday cases ........... ........ ............. .............. ........ ......... ....... . Lost workdays ....... .... .. ..... ............ ............. .... ..... ........................ . 14.3 6.8 143.3 14.2 6.7 147.9 13.0 6.1 148.1 13.1 5.8 161.9 12.2 5.5 11.8 5.5 10.6 4.9 9.9 4.5 9.5 4.4 8.8 4.0 8.6 4.2 8.3 4.1 7.9 4.0 13.9 6.5 137.3 13.4 6.4 137.6 12.0 5.5 132.0 12.2 5.4 142.7 11 .5 5.1 10.9 5.1 9.8 4.4 9.0 4.0 8.5 3.7 8.4 3.9 8.0 3.7 7.8 3.9 6.9 3.5 Heavv construction. exceot buildina: T atal cases ...... ......... ..... .. ..................... ....... .... . Lost workday cases .................... ......... ........................................ .. Lost workdays .. ......... .. .......... ... .............. ..................... ................. 13.8 6.5 147.1 13.8 6.3 144.6 12.8 6.0 160.1 12.1 5.4 165.8 11 .1 5.1 10.2 5.0 9.9 4.8 9.0 4.3 8.7 4.3 8.2 4.1 7.8 3.8 7.6 3.7 7.8 4.0 Soecial trades contractors: Total cases .. .. .............. ................ .... ..... ... .. ...... ... .... ........ ... . . Lost workday cases ....... ... ..... .................................. ...... ........ ....... . Lost workdays ............... ......... ...... ................... ........ ...... .. ............ . 14.6 6.9 144.9 14.7 6.9 153.1 13.5 6.3 151.3 13.8 6.1 168.3 12.8 5.8 12.5 5.8 11 .1 5.0 10.4 4.8 10.0 4.7 9.1 4.1 8.9 4.4 8.6 4 .3 8.2 4.1 Manufacturing Total cases ........ ........ .............. .. ... .... .. .. .. .. ...... ... .... ... .... ... ... . Lost workday cases ...... .............. ............................................. .... .. Lost workdays ............................. ........ ... ......... .................... ...... .. 13.1 5.8 113.0 13.2 5.8 120.7 12.7 5.6 121 .5 12.5 5.4 124.6 12.1 5.3 12.2 5.5 11 .6 5.3 10.6 4.9 10.3 4.8 9.7 4.7 9.2 4 .6 9.0 4.5 8.1 4.1 Durable goods: Total cases ................ .......... ....................... ... ... .. ... ... .... . . Lost workday cases ................. . Lost workdays .... .... .. ... ........ .... ... ........... .................... ................. . 14.1 6.0 116.5 14.2 6.0 123.3 13.6 5.7 122.9 13.4 5.5 126.7 13.1 5.4 13.5 5.7 12.8 5.6 11 .6 5.1 11.3 5.1 10.7 5.0 10.1 4.8 Lumber and wood products: Total cases ............................... ... ..... .. ....... .. ...... ........... ... . . Lost workday cases .. ........... .......................... ............. .... ........... . Lost workdays ........ ....... ........ .. ......................................... .... .... . 18.4 9.4 177.5 18.1 8.8 172.5 16.8 8.3 172.0 16.3 7.6 165.8 15.9 7.6 15.7 7.7 14.9 7.0 14.2 6.8 13.5 6.5 13.2 6.8 13.0 6.7 12.1 6.1 10.6 5.5 16.1 7.2 16.9 7.8 15.9 7.2 14.8 6.6 128.4 14.6 6.5 15.0 7.0 13.9 6.4 12.2 5.4 12.0 5.8 11.4 5.7 11.5 5.9 11.2 5.9 11.0 5.7 Furniture and fixtures : Total cases .................................... .. .... .. .. .. .. .... .. .... .. ...... ... . Lost workday cases .... ... ...... .................................... ...... ............ . Lost workdays ....... ........................... .... ...... .. .... ...................... ... 8.8 4.3 Stone. clav. and alass oroducts: :::ases ... ........ ............................. .... ..... ................. ..... . Lost workday cases ... ............. .. ..... ................ ................. - .... .... .. Lost workdays .. ........... .. .. ................... ............... ....... .. .............. . Primarv metal industries: Total cases ............ ............. ........... .... ... .. ... .. .... ............... .. . Lost workday cases .... ...... .... ... ...... .... ............ ............. .... .......... .. Lost workdays ....... ...... ........ ....................... ......... ............. .... ... . 15.5 7.4 149.8 15.4 7.3 160.5 14.8 6.8 156.0 13.6 6.1 152.2 13.8 6.3 13.2 6.5 12.3 5.7 12.4 6.0 11 .8 5.7 11 .8 6.0 10.7 5.4 10.4 5.5 10.1 5.1 18.7 8.1 168.3 19.0 8.1 180.2 . 17.7 7.4 169.1 17.5 7.1 175.5 17.0 7.3 16.8 7.2 16.5 7.2 15.0 6.8 15.0 7.2 14.0 7.0 12.9 6.3 12.6 6.3 10.7 5.3 11 .1 Fabricated metal oroducts: Total cases .............................. ... ... . ....... ... ............... ... .. ... . Lost workday cases. .. .......................... ................ ........ .. Lost workdays .. .......... ............... ... ..... .................. ........ .. ....... ... 18.5 7.9 147.6 18.7 7.9 155.7 17.4 7.1 146.6 16.8 6.6 144.0 16.2 6.7 16.4 6.7 15.8 6.9 14.4 6.2 14.2 6.4 13.9 6.5 12.6 6.0 11.9 5.5 11 .1 5.3 Industrial machinery and equipment: Total cases ................................ .... .. .. ... ........... ........ ... .. .... . Lost workday cases ........... ......... .......... ......... .......... ....... ...... ..... . Lost workdays .............. ....... ................ ............. ....................... . 12.1 4.8 86.8 12.0 4.7 88.9 11.2 4.4 86 .6 11.1 4.2 87.7 11.1 4.2 11.6 4.4 11.2 4.4 9.9 4.0 10.0 4.1 9.5 4.0 8.5 3.7 8.2 3.6 11 .0 6.0 Electronic and other electrical eauioment: Total cases .... ..... ........ ............. ....... ... ...... ........... .... ..... . Lost workday cases ......... .... ... ....... ........ .............. ... ................... . Lost workdays .. ............ ..... ....... ........................ ....... ........ .... .... . 9.1 3.9 77.5 9.1 3.8 79.4 8.6 3.7 83.0 8.4 3.6 81.2 8.3 3.5 8.3 3.6 7.6 3.3 6.8 3.1 6.6 3.1 5.9 2.8 5.7 2.8 5.7 2.9 5.0 2.5 Transoortation eauioment: Total cases ................ ......... ... ... ...... .. .. .... ... .. .............. ..... .. . Lost workday cases ...... ............ ............ ...... ... .... ........................ . Lost workdays .. ........ ... .................... ....... ...... ...... .... ...... ........... . 17.7 6.8 138.6 17.8 6.9 153.7 18.3 7.0 166.1 18.7 7.1 186.6 18.5 7.1 19.6 7.8 18.6 7.9 16.3 7.0 15.4 6.6 14.6 6.6 13.7 6.4 13.7 6.3 12.6 6.0 Instruments and related oroducts: Total cases ....... ..................... ........... ... ... ..... . Lost workday cases ................. .. .... ................. ........................... . Lost workdays .. ....... ... ... .. ........... .. .................... .. ...................... . 5.6 2.5 55.4 5.9 2.7 57.8 6.0 2.7 64.4 5.9 2.7 65.3 5.6 2.5 5.9 2.7 5.3 2.4 5.1 2.3 4.8 2.3 4.0 1.9 4.0 1.8 4.5 2.2 4.0 2.0 Miscellaneous manufacturina industries: Total cases ... .............. ..... .. ............. .. ...... ......... ... .... ... .... ... . Lost workday cases ............... .. ............ ......... ........... ...... ........... . Lost workdays ..... ................... . ...................... .......... .... ..... 11 .1 5.1 97.6 11 .3 5.1 113.1 11 .3 5.1 104.0 10.7 5.0 108.2 10.0 4.6 9.9 4.5 9.1 4.3 9.5 4.4 8.9 4.2 8.1 3.9 8.4 4.0 7.2 3.6 6.4 3.2 T"'"' ~----'---~--~-----'------'---~---'-----'-------'--------'--------'-----'---- See footnotes at end of table. 126 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 1 55. Continue~ccupational injury and illness rates by industry, United States 3 Incidence rates per 100 workers 2 Industry and type of case 1989 Nondurable goods: Total cases ....... .. ....... : ......... .... . . Lost workday cases .......... ....... .. ...... ...... ... ...... .... ..... .. .... . Lost workdays .............. .. ................................ .............. .. ...... .... . Food and kindred products: Total cases Lost workday cases Lost workdays ......................... . 1 11 .6 5.5 107.8 18.5 9.3 174.7 Tobacco oroducts: Total cases ......... .. ................. . . Lost workday cases ........ .. .... ...... .... .. .. ..... .... ... ..... .. . Lost workdays.............. 64.2 Textile mill oroducts: Total cases ...... ............ . . Lost workday cases ........ . Lost workdays ...... 10.3 4.2 81.4 8.7 3.4 Aooarel and other textile oroducts: Total cases .......... .. ...... .. ...... .. Lost workday cases ..................... .. Lost workdays .......... .... .. .... .. 1992 1993 4 1994 4 1995 4 1996 4 1997 4 2001. 2000. 1999. 1998. 11.7 5.6 116.9 11 .5 5.5 119.7 11 .3 5.3 121 .8 10.7 5.0 10.5 5.1 9.9 4.9 9.2 4.6 8.8 4.4 8.2 7.8 4.2 7.8 4.2 6.8 4.3 20.0 19.5 9.9 207.2 18.8 9.5 211 .9 17.6 8.9 17.1 16.3 8.7 15.0 8.0 14.5 8.0 13.6 7.5 12.7 7.3 12.4 7.3 10.9 9.2 6.4 6.0 5.3 2.4 5.9 2.7 6.4 3.4 6.2 2.6 6.7 2.8 5.5 2.4 42 .9 5.8 2.3 5.6 2.8 2.2 3.1 6.7 4.2 9.7 4.1 8.7 4.0 4.1 7.8 3.6 6.7 3.1 7.4 3.4 6.4 3.2 6.0 3.2 5.2 2.7 9.0 3.8 8.9 8.2 3.9 3.6 7.4 3.3 7.0 3.1 6.2 2.6 5.8 2.8 6.1 3.0 5.0 2.4 9.9 202.6 7.7 3.2 62 .3 52.0 10.1 9.9 85.1 4.4 88.3 4.2 87.1 8.8 3.9 92.1 9.2 4.2 99.9 9.5 3.8 80.5 12.7 12.1 5.8 5.5 11.2 5.0 122.7 125.9 8.6 Paoer and allied oroducts: Total cases ............................. .. Lost workday cases .......................... ... ... ... ..... . Lost workdays ... .. .. .. . 1991 1990 9.6 4.0 4.0 104.6 8.2 3.8 6.3 11 .0 9.9 9.6 4.6 4.5 8.5 4.2 7.9 3.8 7.3 3.7 7.1 3.7 7.0 3.7 6.5 5.0 3.4 6.0 3.2 6.9 3.1 6.7 3.0 6.4 3.0 6.0 2.8 5.7 2.7 5.4 2.8 5.0 2.6 5.1 2.6 4.6 2.4 4.8 2.4 4.8 2.3 4.2 2.1 4.4 2.3 4.2 4.0 2.2 2.1 132.9 124.8 Printina and oubl ishina: Total cases .... .. ... .. ...... . .. ............ ....... . Lost workday cases........ Lost workdays ........ ........ ... .... ........ . 6.9 3.3 63.8 6.9 3.3 69.8 6.7 3.2 74 .5 7.3 3.2 74.8 Chemicals and allied oroducts: Total cases ... .. Lost workday cases... .. .. ..... .. Lost workdays .. ......... . 7.0 3.2 63.4 6.5 3.1 61 .6 6.4 3.1 62.4 6.0 2.8 64.2 Petroleum and coal oroducts: Total cases .................. .. ........ . Lost workday cases .. .. ...................... ..... ... .. .. .... .. ... ....................... . Lost workdays. 6.6 3.3 68.1 6.6 3.1 77.3 6.2 2.9 68.2 Rubber and miscellaneous olastics oroducts: Total cases ............. ............. .. Lost workday cases Lost workdays ...... .. ....... .. 16.2 8.0 147.2 16.2 7.8 151 .3 15.1 7.2 150.9 14.5 153.3 Leather and leather oroducts: Total cases ................. . Lost workday cases .. ........... .. Lost workdays ............ .. ................ .. 13.6 6.5 130.4 12.1 5.9 152.3 12.5 5.9 140.8 9.2 5.3 121 .5 9.6 5.5 134.1 9.3 5.4 140.0 8.0 5.9 5.7 5.5 2.7 2.8 2 .7 5.9 5.2 4.8 2.4 4.3 3.9 4.1 3.7 2.9 2.5 4.7 2.3 4.6 2.8 71 .2 2.5 2.2 1.8 1.8 1.9 1.4 13.9 6.5 14.0 6.7 12.9 6.5 12.3 6.3 11 .9 11 .2 10.7 5.8 5.8 8.7 4.8 12.1 5.4 128.5 12.1 12.0 5.3 11 .4 4.8 10.7 4.5 10.6 4.3 9.8 4.5 10.3 9.0 5.5 5.0 4 .3 9.1 9.5 9.3 9.1 8.7 5.4 5.5 5.2 5.1 8.2 4 .8 7.3 4.3 7.3 4.4 6.9 4.3 6.9 5.1 144.0 6.5 6.1 2.7 5.9 6.6 2.8 2.7 2.5 6.5 3.3 6.3 3.3 5.8 5.3 3.1 2.8 6.8 1 10.1 1 5.5 5.8 Transportation and public utilities Total cases ........................... . Lost workday cases ...... ........ ..... ......... . Lost workdays ........................ .. ... ........... .. Wholesale and retail trade 8.7 4.4 4.3 Total cases ..... .. Lost workday cases .... .. .. Lost workdays .. ......... ...... .................... .......... .. 7.9 3.5 65.6 7.6 3.4 72.0 8.4 3.5 80.1 8.1 3.6 63.5 3.4 7.9 3.4 7.5 3.2 6.8 2.9 6.7 3.0 Wholesale trade : Total cases ......... ............... .. .. .......... .. .... . .. Lost workday cases ............ ............ ....... ....... ... . .. ........ .... .. ....... . Lost workdays 7.7 4.0 71 .9 7.4 3.7 71 .5 7.2 3.7 79.2 7.6 3.6 82.4 7.8 3.7 7.7 3.8 7.5 3.6 6.6 6.5 3.4 3.2 8.1 7.7 7.9 3.3 2.8 6.8 2.9 6.5 3.3 7.5 3.0 6.9 3.3 2.7 6.1 2.5 5.9 2.5 5.7 2.4 60.0 69.1 8.7 3.4 79.2 8.2 3.4 63.2 2.0 2.4 2.4 2.9 1.1 1.2 2.7 1.1 2.6 1.0 2.4 .9 2.2 .9 .7 .5 1.8 .8 1.8 1.1 2.9 1.2 1.9 .9 17.6 .8 .7 27.3 24.1 32 .9 5.5 6.0 6.2 6.0 2.6 4 .9 2.2 4.9 2.8 5.6 2.5 5.2 2.8 6.5 2.8 6.4 2.8 56.4 7.1 3.0 68.6 6.7 2.7 51 .2 4.6 2.2 Retail trade: Total cases .. Lost workday cases .... . Lost workdays .......... ... ............. . 8.1 3.J : Finance, insurance, and real estate .. . .. ..... ... .. . T otai cases .. .. .. .. .. .. .. . Lost workday cases ... Lost workdays ... Services Total cases Lost workday cases .. Lost workdays .......... . 1 Data for 1989 and subsequent years are based on the Standard Industrial Class- 60.0 2.8 2.4 2.2 N = number of injuries and illnesses or lost workdays ; EH = total hours worked by all employees during the calendar year; and ification Manual, 1987 Edition . For this reason, they are not strictly comparable with data f0r the years 1985-88, which were based on the Standard Industrial Classification 200,000 = base for 100 full-time equivalent workers (working 40 hours per week, 50 weeks Manual , 1972 Edition , 1977 Supplement. per year) . Beginning with the 1992 survey, the annual survey measures only nonfatal injuries and • Beginn ing with the 1993 survey, lost workday estimates will not be generated . As of 1992, illnesses, while past surveys covered both fatal and nonfatal incidents. To better address BLS began generating percent distributions and th e median number of days away from work fatalities, a basic element of workplace safety, BLS implemented the Census of Fatal by industry and for groups of workers sustaining similar work disabilities. 2 5 Occupational Injuries. 3 Excludes farms with fewer than 11 employees since 1976. The incidence rates represent the number of injuries and illnesses or lost workdays per 100 full-time workers https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis and were calculated as (N/E H) X 200,000, where: NOTE: Dash indicates data not available. Monthly Labor Review December 2004 127 Current Labor Statistics: Injury and Illness 56. Fatal occupational injuries by event or exposure, 1997-2002 Fatalities Event or exposure1 1997-2001 average 2001 2 2002 Number Number Percent Total. ........ . .... ................. .................... ... .. ... .. ............ ......... . 6,036 5,915 5,524 100 Transportation incidents............................................................... Highway incident. .. ..... .... .. .. .... ..................................................... . Collision between vehicles, mobile equipment.. .. ................... .. Moving in same direction .. ..... .... .. .................. .... .......... ... .... . . Moving in opposite directions, oncoming ..... .. .. .............. ...... . Moving in intersection .. .. ..... . ....................... ... .... ... .... ........... . Vehicle struck stationary object or equipment. .. .... .... ............ .. Noncollision incident. ........................ ........... .. .... .... ... ... ..... .... ... ... Jackknifed or overturned-no collision ....... . ... ... ................... Nonhighway (farm, industrial premises) incident... .. .................... .. Overturned . ... ... .... ....... ....... ........ ..... ..... ... .... ............... ... .. ........ . Aircraft. .......... ... .. ......... ............... .... ...... ... .... ... ... . ... . .... ... . Worker struck by a vehicle ... ........... .... .. .......... ... ........ .... .. .. . Water vehicle .. ......... .. ............ .... ......... ... .... ... ............. ..... .. ......... .. Rail vehicle .. .. ....... . .. ... .. .. . .. .. . ..... .. .... ...... ... .. ...... .............. . . 2,593 1,421 697 126 254 148 300 369 300 368 202 248 382 99 68 2,524 1,409 727 142 257 138 297 339 273 326 158 247 383 90 62 2,381 1,372 635 155 202 145 326 373 312 322 164 192 356 71 64 43 25 11 3 4 3 6 7 6 6 3 3 6 Assaults and violent acts ....................................... ....................... Homicides .... .......... .. ... .. ..... ........................... .................. .......... .. Shooting ........... ......... .. . .. .... .. .. .. .. .... .. ..... .. .... .. .. ... . .. ...... . Stabbing .. .... ... .. .. .... .. .. ... . ..... ..... ... ... ........ ...... ... ... . ..... ... . Other, including bombing ................ .. . ... ... ......... ... . ...... .. .. . Self-inflicted injuries ............. . ... ................. ... .............. ...... .. ...... .... 964 709 567 64 78 221 908 643 509 58 76 230 840 609 469 58 82 199 15 11 8 Contact with objects and equipment. ....................................... . Struck by object. ........ ...... ... ...... .... .. ........................................ .. .. Struck by falling object. ...... . .......................................... .......... . Struck by flying object. .. ........ .. . .. .... .. .. .............................. .... .. . Caught in or compressed by equipment or objects ............... ..... . Caught in running equipment or machinery ... ........................ .. Caught in or crushed in collapsing materials . . . ............. ............ . 995 562 352 58 290 156 126 962 553 343 60 266 16 9 5 122 873 506 303 38 23 1 110 116 Falls.... ............. .......................................................................... . Fall to lower level. ................. ..................... ............... .. ............... . Fall from ladder .. ... .. . .. ..... ............................. ....... .. .. .... ......... .. .. Fall from roof . ... ...... . .... .. .. ..... .... .... .... ......... ...... .. ...................... Fall from scaffold, staging .... ........ .. ..... ... ... ... .. ..... ........... .. .. .... . Fall on same level. .. ........... .......................... ............................. .. 737 111 155 91 61 810 700 123 159 91 84 714 634 126 143 87 63 13 11 2 3 2 Exposure to harmful substances or environments............... . . Contact with electric current. . ................ ... ................................ .. . Contact with overhead power lines . .... ... ................ ...... ......... .. . Contact with temperature extremes . ... .. ..... ... ...... .. ............ .. .... .... Exposure to caustic, noxious, or allergenic substances .. ... ......... . Inhalation of substances .... ... .... .......... .... .... .... .. ................ ...... . Oxygen deficiency .. .. .. .... .. .... ..... .. ....... ............. .... .............. ......... . Drowning, submersion . .... .. ............... ... ..... ... ..... ... .. .... .... ... .... ... . 529 291 134 41 106 52 89 71 499 285 124 35 96 49 83 59 538 289 122 60 98 49 90 60 10 5 2 Fires and explosions ................................... ........................... . 197 188 165 3 Other events or exposures3 . . .... .. .. ... .... .. .. . ...... .. . . .. . .... ..... . .... ....... . . . 21 24 13 1 Based on the 1992 BLS Occupational Injury and Illness Classification Structures. 3 4 2 2 1 2 1 2 Totals for 2001 exclude fatalities from the September 11 terrorist attacks. 2 The BLS news release issued Sept. 25, 2002, reported a total of 5,900 fatal wcrk injuries for calendar year 2001 . Since then, an additional 15 job-related fatalities were identified, bringing the total job-related fatality count for 2001 to 5,915. 128 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 654 144 1 4 December 2004 3 Includes the category "Bodily reaction and exertion. " NOTE: Totals for major categories may include subcategories not shown separately. Percentages may not add to totals because of rounding. Dash indicates less than 0.5 percent. Employment outlook 2002-12: Changes in State laws: Labor • Concepts and context • U.S. economy Workers' compensation Unemployment insurance • Labor force Labor market in 2003 Self-employed job fatalities International workplace injuries • Industries an_<;J ~mployment • Occupational employment January - Mdrch· February t of 9/11 on workers Workforce investments t(pnal Compen Size Self-e in Mexico b injuries and benefits Job demands among workers September American Time-Use Survey Work-related multiplefatalities Public sector employment https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis October Time-use data lnit,i l Oce'l. economy lndust ity trends Statistics o , althcare benefits Benefit replacement rates November OSHS recordkeeping requirements Hedonic regression models December Index to Volume 127 January 2004 through December 2004 Alaska Current Employment Statistics Alaska's ' brain drain': myth or reality? 2004 May 9-22. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. American Time-Use Survey Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19. What can time-use data tell us about hours of work? 2004 Dec. 3- 9. Benefits Accounting for wages and benefits using the EC!. 2004 Sept. 26-41. Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56. Incidence benefits measures in the National Compensation Survey. 2004 Aug. 21-28. Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36. National Compensation Survey, The: a wealth of benefits data. 2004 Aug. 3-5. New benefits data from the National Compensation Survey. 2004 Aug. 6-20. Trends in employer-provided prescription-drug coverage. 2004 Aug. 37-45. Business Employment Dynamics series Annual measures of gross job gains and gross job losses. 2004 Nov. 3-13. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Why size class methodology matters in analyses of net and gross job flows. 2004 July 3-12. Canada Current Population Survey Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Self-employment in the United States: an update. 2004 July 13-23. What can time-use data tell us about hours of work? 2004 Dec. 39. Displaced workers Worker displacement in 1999-2000. 2004 June 54--68. Earnings and wages 9/ 11 and the New York City economy: a borough-by-borough analysis. 2004 June 3-33. Accounting for wages and benefits using the EC!. 2004 Sept. 26-41. Alaska's 'brain drain': myth or reality? 2004 May 9-22. Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Employment and wage outcomes for North Carolina's high-tech workers. 2004 May 31-39. Employment and wages for the U.S. ocean and coastal economy. 2004 Nov. 24--30. Job mobility and hourly wages: is there a relationship? 2004 May 23-30. National Compensation Survey, The: a wealth of benefits data. 2004 Aug. 3-5. Using wage records in workforce investments in Ohio. 2004 May 40-43 . Worker displacement in 1999-2000. 2004 June 54--68. International analysis of workplace injuries, An. 2004 Mar. 41-51. Economic and social statistics Compensation costs 9/11 and the New York City economy: a borough-by-borough analysis. 2004 June 3-33. Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Employment and wages for the U.S. ocean and coastal economy. 2004 Nov. 24--30. National Compensation Survey, The: a wealth of benefits data. 2004 Aug. 3-5. New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50. Trends in employer-provided prescription-drug coverage. 2004 Aug. 37-45. Computers (See Technological change.) Construction Fatal occupational injuries at road construction sites. 2004 Dec. 43-47. Consumer Price Index Consumer prices during 2003. 2004 Apr. 3-8. Hedonic regression models using in-house and out-of-house data. 2004. Dec. 25-38. 130 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Economic development and growth Annual measures of gross job gains and gross job losses. 2004 Nov. 3-13. Employment in the public sector: two recessions' impact on jobs. 2004 Oct. 38-47. Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56. U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36. Education and training Alaska 's ' brain drain ' : myth or reality? 2004 May 9-22. Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Educational attainment of the labor force and jobless rates, 2003. 2004 July 57-59. Employer Cost Index (ECI) Accounting for wages and benefits using the ECI. 2004 Sept. 26-41. Employment (See also Unemployment; Labor force.) Alaska's ' brain drain ': myth or reality? 2004 May 9-22. Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Employment and wage outcomes for North Carolina's high-tech workers. 2004 May 31-39. Employment and wages for the U.S. ocean and coastal economy. 2004 Nov. 24--30. Employment in the information sector in March 2004. 2004 Sept. 42-47. Employment in the public sector: two recessions ' impact on jobs. 2004 Oct. 38-47. Employment projections to 2012: concepts and context. 2004 Feb. 3-22. Multiple jobholding in States, 2003 . 2004 July 60--61. Occupational employment projections to 2012. 2004 Feb. 80--105. Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56. Trends in job demands among older workers , 1992-2002. 2004 July 48-56. U.S. labor market in 2003 , The: signs of improvement by year 's end. 2004 Mar. 3-29. Why size class methodology matters in analyses of net and gross job flows. 2004 July 3-12. Exports Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56. Incidence benefits measures in the National Compensation Survey. 2004Aug. 21-28. Measuring defined benefit plan replacement rates with PenSync. 2004 Nov. 57-68. Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36. National Compensation Survey, The: a wealth of benefits data. 2004 Aug. 3-5. New benefits data from the National Compensation Survey. 2004 Aug. 6-20. New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50. Trends in employer-provided prescription-drug coverage. 2004 Aug. 37-45. Healthcare Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56. Measuring defined benefit plan replacement rates with PenSync. 2004 Nov. 57-68. Hispanics Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Hours of work Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Diurnal pattern of on-the-job injuries , The. 2004 Sept. 18-25. What can time-use data tell us about hours of work? 2004 Dec. 3-9. Immigration U.S . import and export prices in 2003. 2004 Sept. 3-9. Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Finland Imports International analysis of workplace injuries, An. 2004 Mar. 41-51. U.S. import and export prices in 2003. 2004 Sept. 3-9. Foreign-born workers Income (See Earnings and wages.) Foreign-born workers: trends in fatal occupational injuries, 19962001. 2004 June 42-53 . Industry Foreign trade Industry output and employment projections to 2012. 2004 Feb. 58-79. U.S. import and export prices in 2003. 2004 Sept. 3-9. Industry studies France Employment and wages for the U.S. ocean and coastal economy. 2004 Nov. 24--30. Employment in the information sector in March 2004. 2004 Sept. 42-47. Industry productivity trends under the North American Industry Classification System . 2004 Nov. 31-42. New and emerging occupations. 2004 Dec. 39-42. International analysis of workplace injuries, An. 2004 Mar. 41-51. Fringe benefits (See Benefits.) Gross domestic product U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36. Health and insurance plans https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Inflation Monthly Labor Review December 2004 131 Index to Volume 126 Consumer prices during 2003. 2004 Apr. 3-8. U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36. Information sector Employment in the information sector in March 2004. 2004 Sept. 42-47. International comparisons International analysis of workplace injuries, An. 2004 Mar. 41-51. Job creation Annual measures of gross job gains and gross job losses. 2004 Nov. 3-13. Employment in the public sector: two recessions' impact on jobs. 2004 Oct. 38-47. New and emerging occupations. 2004 Dec. 39-42. Job flow statistics (See Business Employment Dynamics series.) Job Openings and Labor Turnover Survey Job Openings and Labor Turnover Survey, The: what initial data show. 2004 Oct. 14-23. Job tenure Job mobility and hourly wages: is there a relationship? 2004 May 23-30. Industry output and employment projections to 2012. 2004 Feb. 58-79. Job mobility and hourly wages: is there a relationship? 2004 May 23-30. Job Openings and Labor Turnover Survey, The: what initial data show. 2004 Oct. 14-23. Measuring labor dynamics: the next generation in labor market information. 2004 May 3-8. New and emerging occupations. 2004 Dec. 39-42. Occupational employment projections to 2012. 2004 Feb. 80-105. Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56. Self-employment among older U.S. workers. 2004 July 24-47. Trends in job demands among older workers, 1992-2002. 2004 July 48-56. Using wage records in workforce investments in Ohio. 2004 May 40-43. U.S. labor market in 2003, The: signs of improvement by year's end. 2004 Mar. 3-29. Worker displacement in 1999-2000. 2004 June 54-68. Labor turnover Job Openings and Labor Turnover Survey, The: what initial data show. 2004 Oct. 14-23. Mexico Declining union density in Mexico, 1984-2000. 2004 Sept. 10-17. Job vacancies Migration Job Openings and Labor Turnover Survey, The: what initial data show. 2004 Oct. 14-23. Alaska's 'brain drain': myth or reality? 2004 May 9-22. Labor and economic history Employment in the public sector: two recessions' impact on jobs. 2004 Oct. 38-47. Labor force Alaska's 'brain drain' : myth or reality? 2004 May 9-22. Blacks,Asians, and Hispanics in the civilian labor force. 2004June 69-76. Educational attainment of the labor force and jobless rates, 2003. 2004 July 57-59. Labor force and unemployment, The: three generations of change. 2004 June 34-41. Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Using wage records in workforce investments in Ohio. 2004 May 40-43. Labor law Changes in State unemployment insurance legislation in 2003. 2004 Jan. 37-52. Changes in workers' compensation laws in 2003. 2004Jan. 30-36. State labor legislation enacted in 2003. 2004 Jan. 3-29. Labor market 9/11 and the New York City economy: a borough-by-borough analysis. 2004 June 3-33. Alaska's 'brain drain': myth or reality? 2004 May 9-22. Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Employment and wage outcomes for North Carolina's high-tech workers. 2004 May 31-39. 132 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Minnesota Job mobility and hourly wages: is there a relationship? 2004 May 23-30. Minorities Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Mobility Job mobility and hourly wages: is there a relationship? 2004 May 23-30. Multiple jobholders Multiple jobholding in States, 2003. 2004 July 60-61. National Compensation Survey National Compensation Survey. 2004 Aug. 3-50. National Compensation Survey, The: a wealth of benefits data. New benefits data from the National Compensation Survey. Incidence benefits measures in the National Compensation Survey. Medical and retirement plan coverage: exploring the decline in recent years. Trends in employer-provided prescription-drug coverage. New statistics for health insurance from the National Compensation Survey. New York City 9/ 11 and the New York City economy: a borough-by-borough analysis. 2004 June 3-33. North American Industry Classification System Industry productivity trends under the North American Industry Classification System. 2004 Nov. 31-42. North Carolina Employment and wage outcomes for North Carolina's high-tech workers. 2004 May 31-39. Occupations Employment projections to 2012: concepts and context. 2004 Feb. 3-22. New and emerging occupations. 2004 Dec. 39-42. Occupational employment projections to 2012. 2004 Feb. 80-105. Occupational safety and health Fatal occupational injuries at road construction sites. 2004 Dec. 43-47. Foreign-born workers: trends in fatal occupational injuries, 1996-200 I. 2004 June 42-53. International analysis of workplace injuries, An. 2004 Mar. 41-5 l. Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40. Occupational injury and illness: new recordkeeping requirements. 2004 Dec. l 0-24. Work-related multiple-fatality incidents. 2004 Oct. 20-37. Ohio Using wage records in workforce investments in Ohio. 2004 May 40-43. Employment projections to 2012: concepts and context. Industry output and employment projections to 2012. Labor force projections to 2012: the graying of the U.S. workforce. Occupational employment projections to 2012. U.S. economy to 2012, The: signs of growth. Recession Employment in the public sector: two recessions' impact on jobs. 2004 Oct. 38-47. Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56. Regional comparisons Educational attainment of the labor force and jobless rates , 2003. 2004 July 57-59. Employment in the information sector in March 2004. 2004 Sept. 42-47. Multiple jobholding in States, 2003. 2004 July 60-61. Retirement Incidence benefits measures in the National Compensation Survey. 2004Aug. 21-28. Measuring defined benefit plan replacement rates with PenSync. 2004 Nov. 57-68. Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36. National Compensation Survey, The: a wealth of benefits data. 2004 Aug. 3-5. New benefits data from the National Compensation Survey. 2004 Aug. 6--20. Older workers Salaries (See Earnings and wages.) Self-employment Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Self-employment among older U.S. workers. 2004 July 24-47. Trends in job demands among older workers, 1992-2002. 2004 July 48-56. Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40. Self-employment among older U.S. workers. 2004 July 24-47. Self-employment in the United States: an update. 2004 July 13-23. Pensions Measuring defined benefit plan replacement rates with PenSync. 2004 Nov. 57-68. Prices Consumer prices during 2003. 2004 Apr. 3-8. U.S. import and export prices in 2003. 2004 Sept. 3-9. Producer Price Index Consumer prices during 2003. 2004 Apr. 3-8. Productivity Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Industry productivity trends under the North American Industry Classification System. 2004 Nov. 31-42. U.S. economy to 2012, The: signs of growth. 2004 Feb. 23-36. Projections Employment outlook: 2002-12. 2004 Feb. 3-105. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Service sector Industry output and employment projections to 2012. 2004 Feb. 58-79. Small business Why size class methodology matters in analyses of net and gross job flows. 2004 July 3-12. State government Changes in State unemployment insurance legislation in 2003. 2004 Jan. 37-52. Changes in workers' compensation laws in 2003. 2004Jan. 30-36. State labor legislation enacted in 2003. 2004 Jan. 3-29. Statistical programs and methods Accounting for wages and benefits using the EC!. 2004 Sept. 26--41. Annual measures of gross job gains and gross job losses. 2004 Nov. 3-13. Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56. Hedonic regression models using in-house and out-of-house data. 2004 Dec. 25-38. Monthly Labor Review December 2004 133 Job Openings and Labor Turnover Survey, The: what initial data show. 2004 Oct. 14-23. Measuring defined benefit plan replacement rates with PenSync. 2004 Nov. 57-68. Measuring labor dynamics: the next generation in labor market information. 2004 May 3-8. National Compensation Survey, The: a wealth of benefits data. 2004 Aug. 3-5. New benefits data from the National Compensation Survey. 2004 Aug. 6-20. New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50. Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19. Using wage records in workforce investments in Ohio. 2004 May 40-43. What can time-use data tell us about hours of work? 2004 Dec. 39. Why size class methodology matters in analyses of net and gross job flows. 2004 July 3-12. Survey methods Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Hedonic regression models using in-house and out-of-house data. 2004 Dec. 25-38. Job Openings and Labor Turnover Survey, The: what initial data show. 2004 Oct. 14-23. Occupational injury and illness: new recordkeeping requirements. 2004 Dec. 10-24. Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19. What can time-use data tell us about hours of work? 2004 Dec. 38. Why size class methodology matters in analyses of net and gross job flows. 2004 July 3-12. Sweden Union membership and elections Declining union density in Mexico, 1984-2000. 2004 Sept. 10-17. Wages (See Earnings and wages.) Women Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Workers' compensation Changes in workers' compensation laws in 2003. 2004Jan. 30-36. Work injuries and illnesses Diurnal pattern of on-the-job injuries, The. 2004 Sept. 18-25. Fatal occupational injuries at road construction sites. 2004 Dec. 43-47. Foreign-born workers: trends in fatal occupational injuries, 19962001. 2004 June 42-53. International analysis of workplace injuries, An. 2004 Mar. 41-51. Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40. Occupational injury and illness: new recordkeeping requirements. 2004 Dec. 10-24. Work-related multiple-fatality incidents. 2004 Oct. 20-37. DEPARTMENTS Book reviews. Jan., Mar., Apr., May, June, July, Sept., Oct., and Dec. issues. Current labor statistics. Each issue. Labor month in review. Each issue. Workplace safety and health. Dec. issue. Precis. Each issue. Publications received. Feb., May, Aug., and Nov. issues. International analysis of workplace injuries, An. 2004 Mar. 41-51. Technological change Regional trends. July issue. Report from the regions. Sept. issue. Employment and wage outcomes for North Carolina's high-tech workers. 2004 May 31-39. Unemployment (See also Employment; Labor force.) Blacks, Asians, and Hispanics in the civilian labor force. 2004 June 69-76. Educational attainment of the labor force and jobless rates, 2003. 2004 July 57-59. Labor force and unemployment, The: three generations of change. 2004 June 34-41. Post-recession trends in non-farm employment and related economic indicators. 2004 Sept. 49-56. U.S. labor market in 2003, The: signs of improvement by year's end. 2004 Mar. 3-29. Worker displacement in 1999-2000. 2004 June 54-68. Unemployment insurance Changes in State unemployment insurance legislation in 2003. 2004 Jan. 37-52. Measuring labor dynamics: the next generation in labor market 134 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis information. 2004 May 3-8. December 2004 Research summary. Dec. issue. Visual essay. June and Sept. issues. BOOK REVIEWS (Listed by author of book.) Backhouse, Roger E. The Ordinary Business of Life: A History of Economics from the Ancient World to the Twenty-First Century. 2004 Oct. 49-50. Ben-Bassat, Avi, ed. The Israeli Economy, 1985-1998: From Government Intervention to Market Economics. 2004 Dec. 49-50. Brisbin, Richard, Jr. A Strike Like No Other Strike: Law & Resistance during the Pittston Coal Strike of 1989-1990. 2004 May 45-46. Costello, Cynthia B., Vanessa R. Wight, and Anne J. Stone, eds. The American Woman 2003-2004: Daughters ofa Revolution-Young Women Today. 2004 June 78. Darlington, Patricia S. E. and Becky Michele Mulvaney. Women, Power, and Ethnicity: Working Toward Reciprocal Empowerment. 2004 Apr. 44. Gornick, Janet C. and Marcia K. Meyers. Families that Work: Poli- cies for Reconciling Parenthood and Employment. 2004 Sept. 57-58. Holtz-Eakin, Douglas and Bruce D. Meyer, eds. Making Work Pay: The Earned Income Tax Credit and Its Impact on American Families. 2004 May 45. Houseman, Susan and Machiko Osawa, eds. Nonstandard Work in Developed Economies: Causes and Consequences. 2004 July 63-64. Kochan, Thomas A., Richard M. Locke, Michael Piore, and Paul Osterman. Working in America: A Blueprint for the New Labor Market. 2004 Mar. 53-54. Locke, Richard M., Michael Piore, Paul Osterman, and Thomas A. Kochan. Working in America: A Blueprint for the New Labor Market. 2004 Mar. 53-54. Meyer, Bruce D. and Douglas Holtz-Eakin, eds. Making Work Pay: The Earned Income Tax Credit and Its Impact on American Families. 2004 May 45. Meyers, Marcia K. and Janet C. Gornick. Families that Work: Policies for Reconciling Parenthood and Employment. 2004 Sept. 57-58. Mulvaney, Becky Michele and Pastricia S. E. Darlington. Women, Power, and Ethnicity: Working Toward Reciprocal Empowerment. 2004 Apr. 44. Osawa, Machiko and Susan Houseman, eds. Nonstandard Work in Developed Economies: Causes and Consequences. 2004 July 63-64. Osterman, Paul, Thomas A. Kochan, Richard M. Locke, and Michael Piore. Working in America: A Blueprint for the New Labor Market. 2004 Mar. 53-54. Piore, Michael, Paul Osterman, Thomas A. Kochan, and Richard M. Locke. Working in America: A Blueprint for the New Labor Market. 2004 Mar. 53-54. Stone, Anne J., Cynthia B. Costello, and Vanessa R. Wight, eds. The American Woman 2003-2004: Daughters ofa Revolution-Young Women Today. 2004 June 78. Wight, Vanessa R., Cynthia B. Costello, and Anne J. Stone, eds. The American Woman 2003-2004: Daughters ofa Revolution-Young Women Today. 2004 June 78. Zimbalist, Andrew. May the Best Team Win: Baseball Economics and Public Policy. 2004 Jan. 54. AUTHORS Barsky, Carl B. Incidence benefits measures in the National Compensation Survey. 2004 Aug. 21-28. Berman, Jay M. Industry output and employment projections to 2012. 2004 Feb. 58-79. Berridge, Scott. Book review. 2004 Dec. 49-50. Blostin, Allan P. The National Compensation Survey: a wealth of benefits data. 2004 Aug. 3-5. Bowles, Robert. Employment and wage outcomes for North Carolina's high-tech workers. 2004 May 31-39. Brown, Craig. Hedonic regression models using in-house and outof-house data. 2004 Dec. 25-38. Buckley, John E. and Robert W. Van Giezen. Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56. Campbell, Jim. Multiple jobholding in States. 2004 July 60--61. Clark, Kelly A. The Job Openings and Labor Turnover Survey: what initial data show. 2004 Nov. 14-23. Clayton, Richard L. and Jay A. Mousa. Measuring labor dynamics: the next generation in labor market information. 2004 May 3-8. Clayton, Richard L., R. Jason Faberman, Akbar Sadeghi, David M. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Talan, and James R. Spletzer. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Colgan, Charles S. Employment and wages for the U.S. ocean and coastal economy. 2004 Nov. 24-30. Dietz, Elizabeth. Trends in employer-provided prescription-drug coverage. 2004 Aug. 37-45. DiNatale, Marisa, Rachel Krantz, and Thomas J. Kralik. The U.S. labor market in 2003: signs of improvement by year's end. 2004 Mar. 3-29. · Dolfman, Michael L. and Solidelle F. Wasser. 9/11 and the New York City economy: a borough-by-borough analysi3. 2004 June 3-33. Drudi, Dino and Mark Zak. Work-related multiple-fatality incidents. 2004 Oct. 20--37. Eldridge, Lucy P., Marilyn E. Manser, and Phyllis Flohr Otto. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Faberman, R. Jason, Akbar Sadeghi, David M. Talan, Richard L. Clayton, and James R. Spletzer. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Fairris, David and Edward Levine. Declining union density in Mexico, 1984-2000. 2004 Sept. I 0--17. Fitzpatrick, John J., Jr. and Richard R. Nelson. State labor legislation enacted in 2003. 2004 Jan. 3-29. Fortson, Kenneth N. The diurnal pattern of on-the-job injuries. 2004 Sept. 18-25. Frazis, Harley and Jay Stewart. What can time-use data tell us about hours of work? 2004 Dec. 3-9. Gordon, Rich, Mark Schaff, and Greg Shaw. Using wage records in workforce investments in Ohio. 2004 May 40--43. Hadland, Jeff. Alaska's 'brain drain': myth or reality? 2004 May 9-22. Hammida, Mustapha. Job mobility and hourly wages: is there a relationship? 2004 May 23-30. Hatch, Julie. Employment in the public sector: two recessions' impact on jobs. 2004 Oct. 38-47. Hecker, Daniel E. Occupational employment projections to 2012. 2004 Feb. 80--105. Helwig, Ryan. Worker displacement in 1999-2000. 2004 June 54-68. Herz, Diane and Michael Horrigan. Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19. Hipple, Steven. Self-employment in the United States: an update. 2004 July 13-23. Horrigan, Michael and Diane Herz. Planning, designing, and executing the BLS American Time-Use Survey. 2004 Oct. 3-19. Horrigan, Michael W. Employment projections to 2012: concepts and context. 2004 Feb. 3-22. Johnson, Richard W. Trends in job demands among older workers, 1992-2002. 2004 July 48-56. Karoly, Lynn A. and Julie Zissimopoulos. Self-employment among older U.S. workers. 2004 July 24-47. Krantz, Rachel, David Langdon, and Michael Strople. Post-recession trends in nonfarm employment and related economic indicators. 2004 Sept. 49-56. Krantz, Rachel, Marisa DiNatale, and Thomas J. Kralik. The U.S. labor market in 2003: signs of improvement by year 's end. 2004 Mar. 3-29. Krolik, Thomas J. Educational attainment of the labor force and jobless rates, 2003. 2004 July 57-59. Kralik, Thomas J., Rachel Krantz, and Marisa DiNatale. The U.S. labor market in 2003: signs of improvement by year 's end. 2004 Mar. 3-29. Monthly Labor Review December 2004 135 Lancaster, Loryn. Changes in State unemployment insurance legislation in 2003. 2004 Jan. 37-52. Langdon, David, Rachel Krantz, and Michael Strople. Post-recession trends in nonfarrn employment and related economic indicators. 2004 Sept. 49-56. Lettau, Michael. New statistics for health insurance from the National Compensation Survey. 2004 Aug. 46-50. Levine, Edward and David Fairris. Declining union density in Mexico, 1984-2000. 2004 Sept. 10-17. Loh, Katherine and Scott Richardson. Foreign-born workers: trends in fatal occupational injuries, 1996-2001. 2004 June 42-53. Manser, Marilyn E., Lucy P. Eldridge, and Phyllis Flohr Otto. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Martin, Gary. Book review. 2004 Sept. 57-58. Messing, Ellen. Book review. 2004 Oct. 49-50. Mitchell, David. Book review. 2004 May 45. Moore, James H., Jr. Measuring defined benefit plan replacement rates with PenSync. 2004 Nov. 57-68. Mousa, Jay A. and Richard L. Clayton. Measuring labor dynamics: the next generation in labor market information. 2004 May 3-8. Nelson, Richard R. and John J. Fitzpatrick, Jr. State labor legislat10n enacted in 2003. 2004 Jan. 3-29. Okolie, Cordelia. Why size class methodology matters in analyses of net and gross job flows. 2004 July 3-12. Otto, Phyllis Flohr, Marilyn E. Manser, and Lucy P. Eldridge. Alternative measures of supervisory employee hours and productivity growth. 2004 Apr. 9-28. Pegula, Stephen. Fatal occupational injuries at road construction sites. 2004 Dec. 43-47. Pegula, Stephen M. Occupational fatalities: self-employed workers and wage and salary workers. 2004 Mar. 30-40. Perrins, Gerald. Employment in the information sector in March 2004. 2004 Sept. 42-47. Pfuntner, Jordan. New benefits data from the National Compensation Survey. 2004 Aug. 6-20. Pikulinski, Jerome. New and emerging occupations. 2004 Dec. 3942. Pinkston, Joshua C. and James R. Spletzer. Annual measures of gross job gains and gross job losses. 2004 Nov. 3-13. Reynolds, Joy K. Book review. 2004 Mar. 53-54. Richardson, Scott and Katherine Loh. Foreign-born workers: trends in fatal occupational injuries, 1996-2001. 2004 June 42-53. Russell, Matthew, Paul Takac, and Lisa Usher. Industry productivity trends under the North American Industry Classification System. 2004 Nov. 31-42. Sadeghi, Akbar, David M. Talan, Richard L. Clayton, James R. Spletzer, and R. Jason Faberman. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Schaff, Mark, Rich Gordon, and Greg Shaw. Using wage records in workforce investments in Ohio. 2004 May 40-43. Schwabish, Jonathan A. Accounting for wages and benefits using the ECI. 2004 Sept. 26-41. 136 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis December 2004 Schwartz, Melissa E. U.S. import and export prices in 2003. 2004 Sept. 3-9. Shaw, Greg, Mark Schaff, and Rich Gordon. Using wage records in workforce investments in Ohio. 2004 May 40-43. Sincavage, Jessica _R. The labor force and unemployment: three generations of change. 2004 June 34-41. Skelly, Kevin. Book review. 2004 Jan. 54. Sorrentino, Constance. Book review. 2004 July 63-64. Spletzer, James R. and Joshua C. Pinkston. Annual measures of gross job gains and gross job losses. 2004 Nov. 3-13. Spletzer, James R., R. Jason Faberman, Akbar Sadeghi, David M. Talan, and Richard L. Clayton. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Stewart, Jay and Harley Frazis. What can time-use data tell us about hours of work? 2004 Dec. 3-9. Strople, Michael, David Langdon, and Rachel Krantz. Post-recession trends in nonfarm employment and related economic indicators. 2004 Sept. 49-56. Su, Betty W. The U.S. economy to 2012: signs of growth. 2004 Feb. 23-36. Takac, Paul, Matthew Russell, and Lisa Usher. Industry productivity trends under the North American Industry Classification System. 2004 Nov. 31-42. Talan, David M., Richard L. Clayton, James R. Spletzer, R. Jason Faberman, and Akbar Sadeghi. Business employment dynamics: new data on gross job gains and losses. 2004 Apr. 29-42. Toossi, Mitra. Labor force projections to 2012: the graying of the U.S. workforce. 2004 Feb. 37-57. Usher, Lisa, Matthew Russell, and Paul Takac. Industry productivity trends under the North American Industry Classification System. 2004 Nov. 31-42. Ussif, Al-Amin. An international analysis of workplace injuries. 2004 Mar. 41-51. Van Giezen, Robert W. and John E. Buckley. Federal statistics on healthcare benefits and cost trends: an overview. 2004 Nov. 43-56. Wald, Michael. Book review. 2004 May 45-46. Wasser, Solidelle F. and Michael L. Dolfman. 9/11 and the New York City economy: a borough-by-borough analysis. 2004 June 3-33. Wasser, Solidelle Fortier. Book review. 2004 June 78. Whittington, Glenn. Changes in workers' compensation laws in 2003. 2004 Jan. 30-36. Wiatrowski, William J. Medical and retirement plan coverage: exploring the decline in recent years. 2004 Aug. 29-36. Wiatrowski, William J. Occupational injury and illness: new recordkeeping requirements. 2004 Dec. 10-24. Wilson, Todd. Consumer prices during 2003. 2004 Apr. 3-8. Yee, Charlotte. Book review. 2004 Apr. 44. Zak, Mark and Dino Drudi. Work-related multiple-fatality incidents. 2004 Oct. 20-37. Zissimopoulos, Julie and Lynn A. Karoly. Self-employment among older U.S. workers. 2004 July 24-47. Obtaining information from the Bureau of Labor Statistics Office or topic Bureau of Labor Statistics Information services Internet address http://www.bls.gov/ http://www.bls.gov/opub/ E-mail blsdata_staff@bls.gov Employment and unemployment Employment, hours , and earnings: National State and local Labor force statistics: National Local DI-covered employment, wages Occupational employment Mass layoffs Longitudinal data Job openings and labor turnover http://www.bls.gov/ces/ http://www.bls.gov/sae/ cesinfo@b ls.gov data_sa@bls.gov http://www.bls.gov/cpshome.htm http://www.bls.gov/lau/ http: //www.bls.gov/cew/ http://www.bls.gov/oes/ http://www.bls.gov/lau/ http://www.bls.gov/nls/ http://www.bls.gov/jlt/ cpsinfo@bls.gov lausinfo@bls.gov cewinfo@bls.gov oesinfo@bls.gov mlsinfo@bls.gov nls_ info@bls.gov Joltsinfo@bls.gov Consumer price indexes Producer price indexes) Import and export price indexes Consumer expenditures http://www.bls.gov/cpi/ http://www.bls.gov/ppi/ http://www.bls.gov/mxp/ http://www.bls.gov/cex/ Prices and living conditions cpi_info@bls.gov ppi-info@bls.gov mxpinfo@bls.gov cexinfo@bls.gov Compensation and working conditions National Compensation Survey: Employee benefits Employment cost trends Occupational compensation Occupational illnesses, injuries Fatal occupational injuries Collective bargaining http://www.bls.gov/ncs/ http://www.bls.gov/ebs/ http://www.bls.gov/ect/ http://www.bls.gov/ncs/ http://www.bls.gov/iif/ http://www.bls.gov/ iif/ http://www.bls.gov/cba/ oc ltinfo@b ls.gov ocltinfo@bls.gov ocltinfo@bls.gov ocltinfo@bls.gov oshstaff@bls .gov cfoistaff@bls.gov cbainfo@bls.gov Productivity Labor Industry Multi factor http://www.bls.gov/lpc/ http://www.bls.gov/lpc/ http://www.bis.gov/mfp/ dprweb@bls.gov dipsweb@bls.gov dprweb@bls.gov Projections Employment Occupation http://www.bls.gov/emp/ http://www.bls.gov/oco/ oohinfo@bls.gov oohinfo@bls.gov International http://www.bls.gov/fls/ flshelp@bls.gov Regional centers Atlanta Boston Chicago Dallas Kansas City New York Philadelphia San Francisco https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis http://www.bls.gov/ro4/ http://www.bls.gov/rol / http: //www.bls.gov/ro5/ http://www.bls.gov/ro6/ http://www.bls.gov/ro7/ http://www.bls.gov/ro2/ http://www.bls.gov/ro3/ http://www.bls.gov/ro9/ Other Federal statistical agencies http://www.fedstats.gov/ BLSinfoAtlanta@bls.gov BLSinfoBoston@bls.gov BLSinfoChicago@bls.gov BLSinfoDallas@bls.gov BLSinfoKansasCity@bls.gov BLSinfoNY@bls.gov BLSinfoPhiladelphia@bls.gov BLSinfoSF@bls.gov https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis