Full text of Monthly Labor Review : June 2005
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https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis U.S. Department of Labor Elaine L. Chao, Secretary U.S. Bureau of Labor Statistics Kathleen P. Utgoff, Commissioner The Mon1hly Labor Review (USPS 987- 800) is published monthly by the Bureau of Labor Statistics of the U.S . Department of Labor. The Review welcomes articles on the labor force , labor-manage me nt relations, bus ine s s co nditi o ns , industry productivity. co mpensat ion, occ upationa l safety and health, demographic trends , and other econom ic developments. Papers should be factual and analytical , not polemical in tone. Potential articles, as well as communications on editorial matters, should be submitted to: Editor-in-Ch ief Monihly Labor Review U.S. Bureau of Labor Statistics Washington, oc 20212 Telephone: (202) 691 - 5900 Fax: (202) 69 1-5899 E-mail: mlr@bls.gov Inquiries on subscriptions and circulation, including address changes, shou ld be sent to: Superintendent of Documents, Government Printing Office , Washington , DC 20402 . Telephone: (202) 512- 1800. Subscription price per year-$49 domestic; $68.60 foreign. Si ngle copy- $15 domestic; $2 1 foreign. Make checks payable to the Superintendent of Documents. Subscription prices and distribution policies for the Monthly Labor Revie w (ISS N 0098- 1818) and o ther governmen t publications are set by the Government Printing Office, an agency of the U.S. Congress. The Secretary of Labor has determined that the publication of this periodical is necessary in the transaction of the public business required by law of this Department. Periodicals postage paid at Washington, oc, and at addi ti onal mailing addresses. Unle ss stated othe rwi se, arti c les appeari ng in thi s publication are in the public domai n and may be reprinted without express permission from the Editor-in-Chief. Please cite the specific issue of the Monthly Labor Rel"iew as the source. Information is avai lable to sensory impaired individuals upon request : Voice phone: (202) 691 - 5200 Federal Relay Service: 1- 800--877- 8339. POSTMASTER: Send address changes 10 Monthly Labor Review, U.S. Government Printing Office, w ,,shington, oc 20402 -0001. Cover designed by Keith Tapscou https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis MONTHLY LABOR REVIEW _ _ _ _ __ _ __ Volume 128, Number 6 June 2005 American Time-Use Survey activity classification 3 Classifying what Americans do and how much time they spend doing it is an arduous task calling for addressing numerous coding issues Kristina J. Shelley A transaction price index for air travel 16 An experimental index is compared with the official CPI series and the consumer expenditure deflator series used in national accounts Janice Lent and Alan H. Dorfman Preliminary estimates of multifactor productivity growth 32 Final multifactor measures take more than a year to complete, however, estimates can be produced using a simplified methodology and preliminary data Peter B. Meyer and Michael J. Harper BLS and the Marshall Plan: the forgotten story 44 The Bureau's statistical technical assistance resulted in increased productivity inWestern European industry after World War II Solidelle F. Wasser and Michael L. Dolfman Report Reinserting labor into the Iraqi Ministry of Labor 53 Craig Davis Department Labor month in review International report Precis Book review Current labor statistics 2 53 62 63 65 Editor-in-Chief: William Parks II • Executive Editor: Richard M. Devens • Managing Editor: Anna Huffman Hill • Editors: Bri an I. Baker, Kristy S. Christiansen, Richard Hamilton, Leslie Brown Joyner • Book Reviews: Richard Hamilton • Design and Layout: Catherine D. Bowman, Edith W. Peters • Contributor: Constance Sorrentino https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Labor Month In Review ""(W:i-$ :;' ;%) The June Review The American Time Use Survey (ATVS) was developed to help researchers understand how people in the United States today are coping with the time demands of their jobs, childcare, their work commutes, their need to relax or exercise, and their religious, volunteer, and other commitments. If it is to accomplish these goals, ATVS must accurately classify peoples' daily activities. Kristina J. Shelley outlines the efforts that went into building a meaningful and easily understandable classification and coding system for the survey. Janice Lent and Alan H. Dorfman report the results of their research on using actual-transactions-based data to construct a price index for air travel, rather than using prices listed in the SABRE reservation and ticketing system, the primary method used to calculate the Consumer Price Index for airfares. As it turns out, the two measures are similar in longer term trends, but have differing seasonal patterns. Peter B. Meyer and Michael J. Harper announce the development of a procedure for making preliminary, but far more timely, estimates of multifactor productivity. Solidelle F. Wasser and Michael L. Dolfman dig into the history of the Bureau of Labor Statistics to chronicle the technical assistance the Bureau gave Western European industry in the immediate aftermath of World War II. Craig Davis contributes a report on the Iraqi Ministry of Labor and Social Affairs during the reconstruction period. Women's data book released A new book of data from the Current Population Survey on the condition of women in the labor force was released in May. As women have made substantial inroads into . the higher paying occupations, women 's earnings relative 2 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 to men's also have risen. In 2004, women made up half of all management, professional, and related workers. From 1979 through 2004, women 's earnings as a percent of men's have risen from 62 percent to 80 percent. The movement of women into higher paying occupations has gone hand in hand with their pursuit of higher education. In 1970, only 11 percent of women age 25 to 64 had finished four or more years of college. In 2004, nearly 33 percent held a college degree. In the latter year, female college graduates earned about 76 percent more than women with only a high school diploma. This difference in earnings by education has increased sharply since 1979, when female college graduates earned 43 percent more than female high school graduates. In addition to higher weekly pay, women's annual earnings have been affected by spending more weeks per year in the work force. Nearly 60 percent of wom~n who worked at some point in 2003 worked full-time year-round , compared with 41 percent in 1970. To learn more about the extensive data on women available from the Current Population Survey, see "Women in the Labor Force: A Databook," BLS Report 985. Foreign-born workers In 2004, there were 21.4 million foreignborn persons in the American labor force, 14.5 percent of the total. From 2002 to 2004, the number of foreign-born labor force participants grew by about 1.2 million and accounted for a little less than half of total labor force growth. Foreign-born men were more likely to be labor force participants than their native-born counterparts. In contrast, foreign-born women were less likely to be labor force participants than were native-born women. Overall, a little more than two-thirds-67 .5 percent-of foreign-born persons 16 years and older were in the labor force in 2004. The labor force participation rate for the native born was 65.7 percent. In 2004, the largest group of foreignborn workers was employed in management, professional, and related occupations (26.5 percent). This was also the case for native-born workers, with 36.3 percent employed in this occupational category. An additional 22.8 percent of foreign-born workers were employed in service occupations and 18.4 percent were in sales and office occupations, as were 15.2 and 26.7 percent, respectively, of the native-born workers. Reflecting the downward trend in manufacturing employment as a whole, the proportions both of foreign-born and native-born workers employed in production, transportation, and material moving occupations has declined. In 2000, 20.4 percent of foreign-born and 13 .8 percent of native-born workers were employed in these occupations. In 2004, the proportions were 17 .5 percent for the foreign born and 12.1 percent for the native born. Find more information in "Labor Force Characteristics ofForeignborn Workers in 2004," News Release USDL 05-834. Highest and lowest pay Healthcare practitioner and technical occupations accounted for 13 out of the 15 highest paying occupations in May 2004. The average hourly wages for surgeons were $87.31. Two other occupations, obstetricians and gynecologists and anesthesiologists, had average hourly wages greater than $80. The lowest paying occupation was fast food cooks, who earned $7.33 per hour, on average. The next three lowest paying occupations: combined food preparation and serving workers, including fast food; dining room and cafeteria attendants and bartender helpers; and dishwashers. In fact, seven of the ten occupations with average wages of $8 per hour or less were related to food preparation and serving. More data are in "Occupational Employment and Wages, May 2004," News Release USDL 05-877. 0 American Time Use Survey ·1ii <¼ Developing the American Time Use Survey activity classification system Classifying what Americans do during the day and how much time they spend doing those activities is an arduous task that calls for addressing numerous coding issues, but the data provide a broad source of information for various researchers Kristina J. Shelley Kristina J. Shelley is a supervisory economist in the Division of Labor Force Statistics, Bureau of Labor Statistics. E-mail : Shelley. Kristina@ bis.gov. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis T: e American Time Use Survey (ATUS) was officially added to the Federal Government's list of statistical surveys when it received approval and funding in December 2000. The roots of the survey had taken hold nearly 10 years earlier when a Congressional bill, the "Unremunerated Work Act of 1991," prompted the Bureau of Labor Statistics to investigate ways of measuring unpaid work. 1 This examination evolved into an interest in measuring time allocation of individuals, which is generally the starting point for estimating the value of nonmarket production. Thus, in 1998, a BLS working group was formed and tasked with examining the feasibility of collecting time-use data and then developing a detailed plan for doing so. By December 2000, significant progress had been made toward laying the groundwork for the survey, which was scheduled to be launched in January 2003. One of the most important undertakings in this process was the design of an activity classification scheme, or coding lexicon, for categorizing the activities that survey respondents report during the timediary portion of the interview. This article briefly discusses the processes that created both an ATUS activity coding lexicon and activity coding operations procedures. It also briefly describes the evolution of the major activity categories in the coding lexicon. Finally, it discusses how activities in the coding lexicon were combined so that BLS could produce analytically meaningful tables for publication. Development of the coding lexicon Background and research. Initial work on developing the ATUS coding lexicon was facilitated by a rich source of existing information on timeuse classification schemes. At least 11 countries had completed one or more national time-use surveys before ATUS was funded, and the Institute of Social Research at the University of Michigan and the Survey Research Center at the University of Maryland had, between them, fielded four timeuse surveys in the United States. Most of these earlier time-use classifications used a conceptual framework developed by Alexander Szalai for the Multinational Time Use project nearly 40 years ago. 2 Szalai recognized the need to standardize the classification of activities in a way that would allow time-use staff to code daily activities reported in respondents' everyday language in a meaningful way, and allow data users to analyze time-use information in systematic ways. His first classification scheme consisted of 96 activity codes that fell into 10 major categories of time use, and took into account the importance of social interaction (who was with the respondent during the activity) and location (where the activity took place) in describing and categorizing daily activities. Dagfinn Aas built on Szalai 's work by identifying four broad classifications, or typologies, of time into which time-use activity categories may be divided: 1) necessary time, 2) contracted time, 3) committed time, and 4) free time. 3 Monthly Labor Review June 2005 3 American Time Use Survey International comparability among time-use surveys usually is not possible at a detailed activity level because countries tend to adapt time-use classification schemes that reflect their own cultures and economies. However, broad comparisons are achievable for even differing classification systems when activities and categories are fit into Aas' four typologies. 4 Three sometimes competing concerns-international comparability, analytical relevance, and coder usabilityinfluenced the approach taken to create the ATUS lexicon. The ATUS coding team sought to build a system that would balance the desire for international comparability with the need for data that would be analytically meaningful to users in the United States. But the lexicon's usability (how understandable the activity categories are to the staff who assigns activity codes) was a primary concern as well; when activities cannot be coded accurately or consistently, the end result is poor data. After studying existing time-use classification systems used throughout the world-in particular, the coding schemes of Australia, New Zealand , Eurostat, Canada, and the University of Maryland's scheme used in surveys about the United States-the team decided to model the ATUS lexicon most closely after Australia's 1997 system. Its appeal, compared with other time-use survey classifications systems, lay in its high level of detail and the specific categories that appeared to describe well the types of activities done by persons in the United States. The greater level of detail did not prevent analysts from collapsing activities into the four-fold typologies of time for broad comparisons of other time-use surveys. Like most other countries' time-use surveys, the first ATUS classification system was designed using a three-tiered hierarchical structure, classifying reported activities into major categories, with two additional levels of detail in each category. In conjunction with researching and developing a first draft of the coding lexicon, the ATUS team researched coding operations issues that would have to be addressed prior to production. These issues included: l) how the activity data should be coded-"on the fly" by interviewers as they talked to respondents, after the interview by coding specialists, or some other way, 2) the kind of coding instrument (software application) that should be used, 3) what information, besides the activity verbatim, should be available to those coding the data, and 4) the best way to maintain quality control and ensure accurate and consistent coding. Again, the ATUS team started by examining coding operations used by other time-use survey administrators, and eventually leaned most heavily toward those used by the Australian Bureau of Statistics (ABS), but with modifications toward creating a system specific to ATUS needs. Two of the most important operational decisions made were to: 1) have interviewers also code activities (though not their own interviews with respondents), and 2) implement a coding 4 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 verification strategy to ensure quality control. Additionally, decided to use Blai se software 5 to build a coding application. Each of these decisions yielded positive results-most obviously during the dress rehearsal and prefielding, adding significant value to coding operations well into the second year of full production. 6 BLS Implem en tation, testing, and revisions. Although the decision was made early in the lexicon development process to use the Australian time-use activity classification scheme as a model for the ATUS, the classification system that was actually in place for coding AT US data in January 2003 was substantially different from the Australian system. First, BLS staff and reviewers of the initial ATUS lexicon concluded that adopting the four-fold typology as a central guideline for coding might prove problematic because of the number of exceptions to the rules governing how activities were to be classified within the typology. Instead, the classification syste m would be organized based on a widening sphere of social involvement as the underlying structure, beginning with activities done primarily by and for oneself, followed by activities done by and for one's household, and then followed by community activities. It was theorized that losing the typology as a coding guideline would not mean losing the ability to produce data comparable to other time-use survey s, as the ATUS coded data could be recoded into each typology of time either by BLS during postprocessing or by users of the data. For example, one could assume that all educational activities are contracted time and all shopping activities are committed time. And second, in another departure from the first draft "Australian model" lexicon, the final production lexicon contains significantly expanded categories at all levels to enable more detailed time-use analyses, thus enhancing the analytical flexibility for users. The final ATUS lexicon contains 17 major categories (compared with 9 in the Australian system), l 05 second-tier categories, and 438 third-tier categorie s. The coding team left room for up to 99 subcategories under each third tier. This break with the twodigit, nine subcategory convention used in other time-use systems occurred as the ATUS staff reasoned that a much larger sample size (up to 24,000 interviews per year) than any other time-use survey to date could support more detailed analyses, especially after pooling multiple years' data. Arriving at the final production lexicon took approximately 2 years. Over the course of this program development period, numerous revisions to the lexicon were implemented as a result of a series of coding tests, a dress rehearsal , and prefielding of the survey before data collection officially began in January 2003. Coding tests were used to evaluate the intuitive appeal of the lexicon's organizational structure, to assess coding speed and accuracy, to identify ambiguous or uncodable activities, and to test the usability of a prototype of the coding instrument. The first three tests were conducted at the Census Bureau's telephone center in Jeffersonville, Indiana, using Census Bureau staff, experienced in coding data from other surveys. The fourth test took place at Wes tat, a research corporation with facilities in Rockville, MD, which also used coders with experience on other surveys. The testing process was similar for each test: BLS staff discussed the purpose of the American Time Use Survey, introduced test participants to the lexicon, conducted coding training, and provided a set of coding rules to use during testing. Debriefings with test participants were held after each test, and further revisions were made to the lexicon based on their feedback and the measures of coding accuracy. Also, coding rules were added and more fully developed to address difficult-to-code activities. Then, the next test was conducted using the revised lexicon and coding rules, and so on. Coding issues and resolutions Numerous coding issues emerged during the testing period, dress rehearsal, and pre-fielding; the most difficult challenges were how to code work, childcare, adult care, and travel. Other significant issues emerged around coding consumer goods and services purchases, media use, and volunteer activities. The BLS coding team gave a great deal of attention to the best way to handle these issues, implementing a combination of lexicon revisions and coding rules, and also developing additional probes and summary questions to be asked during and after the diary portion of the interview to elicit information about the respondent's activity or travel purpose. A summary of these special challenges and the implemented solutions are described in more detail in the following sections. Work. Collecting and coding accurate measures of total time spent working was a BLS priority. Across occupations, work tasks are so varied that a coding system to handle them all would be prohibitively difficult to develop. Also, for most people, time spent working consists of numerous tasks, many of which are repetitive (such as "ringing up a customer's purchase"). Finally, a primary purpose of time-use surveys is to focus on examining how respondents balance work and other activities with family and leisure time, not specific occupational tasks. For these reasons, the ATUS team decided that "unpacking" the work day (collecting a detailed account of the respondent's activities) would unduly lengthen the interview, as well as create unnecessary coding difficulties. Early testing made clear, however, that although most work activities were clearly reported as such, the collected information did not always accurately capture work activities. Activities done outside the usual work environment or by self-employed persons or telecommuters were particularly https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis difficult to code. Consider a time diary with the following activities: 9:00 a.m. 9: 10 a.m. 9:25 a.m. 9:29 a.m. "I sorted laundry and started washing a load." ·'I composed and sent an e-mail to a coworker." ·'I put the clothes in the dryer." "I was working on the computer." Without additional information, these activities might be coded as doing laundry, sending e-mail, doing laundry, and computer use when, in fact, the respondent was doing work tasks at home in between household tasks. To address this issue, the ATUS questionnaire designers developed questions to be asked of all employed persons to identify work activities not clearly identified in the diary. Responses to these questions eliminated the guesswork about coding work activities. 7 The ATUS team also revised the working and work-related activities category to include select activities (eating and drinking, socializing, and playing sports) that respondents often identified as being done as part of their job. These activities were added at the second-tier level, thus allowing data users the flexibility to classify such activities as either the activity itself or as work-related. Childcare. The BLS coding team conceptually defined primary childcare as any activity done with a child that is interactive in nature-such as reading, playing, and talkingand correctly coding such activities posed few difficulties. However, other activities were considered primary childcare as well, but were not limited to this restrictive definition requiring interaction with a child. For example, an activity could be coded as childcare if a child was not present but the activity (such as "talking to my child's teacher") was clearly done in the child's interest or on the child's behalf. Further complicating coding were activities where a respondent reported doing something with a child, such as watching a movie; although not interactive, the presence of a child during the activity prompted coders to classify such an activity as childcare. These types of exceptions or ambiguities had to be addressed explicitly in a revised concept and related coding rules. Without such, coders would have trouble discerning that if a respondent reported "watching television" with a child in the room or "watching television with my child," the correct activity code would be the one associated with watching television under socializing, relaxing, and leisure. But, if the respondent reported "playing Monopoly with my child," the correct activity code would be "playing with children," under childcare. The ATUS coding team devised an approach to help coders deal with the difficulties coding childcare and helping activities-an approach that combined classroom training, written conceptual definitions, and lists of examples of Monthly Labor Review June 2005 5 American Time Use Survey activities that showed how and why a particular code should be assigned. The box (below) illustrates the types of examples used in the coding rules manual. These examples make it clear to coders that neither the presence of a child during an activity nor a child 's participation in the respondent's activity is sufficient alone to code an activity as childcare. Rather, the guiding rule is that when the respondent is directly watching or interacting with a child only or accompanying a child to an activity that has no clear purpose without the child's involvement , the activity should be coded as childcare. Also, coders were instructed to classify as childcare any activity during which the respondent reported doing something related to a child's health care or educational needs, even if the child was not present during the activity, such as "attending a parent-teacher conference." Caring for and helping adults. Beginning with the first coding tests, coders found that distinguishing household activities from helping activities was difficult. The first-tier household activities category included doing laundry, paperwork , pet care, and organizational tasks for the household. Categories also existed for helping adults who live in the household and those who do not live in the household. An activity such as packing a suitcase or feeding a pet for another adult arguably could be coded as either a household activity or a helping activity. The coding team developed guidelines, rules , and rationales similar to those in the box below to en sure consistent coding of activities done to help adults who live in the household. Coders were instructed to classify an activity under " helping household adults" only when an activity was done to benefit another household adult personally. So, the statement taken verbatim, " I helped my wife cook dinner," would be coded r.s a household activity (meal preparation) because cooking a meal benefits the entire household, whereas the statement taken verbatim, "I filled out my husband 's application form, " would be coded as a helping activity. Applying these same guidelines when respondents reported helping nonhousehold adults was not feasible , however, as "feeding my neighbor's cat" does not logically fit as an activity done for the respondent's household. In such cases, all reports of helping an adult who does not live in the re spondent's household were to be coded under the helping category in early versions of the lexicons. However, two coding activities-helping adults who do not live in the household and organizing and planning for these " nonhousehold adults"-were vague to coders. The BLS coding team sought a way to code activities done to "help" other adults while preserving the information about the actual helping activity. To accomplish this, the team significantly revised the second-tier lexicon category, helping nonhousehold adults, under caring for and helping nonhousehold members. This category was expanded to include eight categories that mirrored household activity categories. For example , the household section included "animal and pet care" and the new helping section included "animal and pet care assistance." This change meant that coders, when faced with a report such as "feeding my neighbor's cat," would need not struggle with deciding whether to classify the activity as a household activity or a helping activity, but rather would assign a code that clearly identified the activity as both a helping one and a household one under helping nonhousehold adults/animal and pet care assistance. The additional advantage to this restructuring was that data users who did their own tabulations would be able to choose to classify such activities as either household or helping (or both), depending on their research needs. Volunteering. Distinguishing volunteering activities from household or helping activities for nonhousehold members was problematic. Without clear rules, " reading to a blind neighbor" might reasonably be coded as helping a nonhousehold member, volunteering , or even socializing. "Feeding the neighbor's cat" might correctly be coded either as helping a nonhousehold member or as volunteering. Examples of how to code ch ildcare versus other activities Reported activity Correct lexicon category "Watching cartoons with my child" --shopping for school clothes with daughter" ·'Playing Monopoly with my wife and son" Talking to my neighbor and her children Playing Monopoly with my kids Relaxing/watching television Not an interactive activity Shopping Relaxing/playing games Respondent 's primary activity is shopping Interactive activity with child and adult; presence of adult trumps presence of child Interactive activity with children and adult; presence of adult trumps presence of children Interactive activity, child only Attending my child's school meeting Childcare 6 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Socializing and communicating Childcare PTA June 2005 Rationale Without the child, the respondent would not be attending the function During the development of the coding lexicon , BLS took several steps to define a "volunteering" concept and to ensure that the information collected on volunteering was consistent with that concept. The first step was to draw a clear line (in terms of the coding lexicon) between formal helping (volunteering) and informal helping (caring for and helping nonhousehold members) by separating these into two major categories. Next, to establish a standard definition or, at least, some distinguishing characteristics of volunteer activities, BLS contracted with the National Opinion Research Center (NORC) to provide a literature review on volunteering. BLS also drew on the definition of volunteering that was used in a special supplement to the Current Population Survey that collected information on volunteering activities. The final ATUS conceptual definition describes volunteering as an activity that one did for or through an organization, of one's own free will, and for no pay, except perhaps expenses. A question was added to the survey that asked respondents to identify which activities in their diary day were volunteering according to these criteria. Travel. Travel activities were the most challenging ones for coders to assign accurately. A general rule for coding travel in both time-use and travel surveys is to code trips according to the traveler's motivation or major purpose for each travel episode. For example, the verbatim " I drove my child to church" might reasonably be coded as travel related to religious activities by one coder and as travel related to childcare by another. Without clear-cut rules , assigning codes to travel episodes would be left up to each coder's interpretation of verbatim reports, because respondents are not asked to specify their travel purpose. 8 Initially, the main ATUS travel coding rule stipulated that travel episodes be coded to the travel destination, such as a school or store, the rationale being that destination implied purpose. However, the first draft coding lexicon associated travel with activities (for example, travel related to religious activities), not destinations or location s, so this rule could not be implemented successfully. To address this issue, the BLS coding team revamped the rules , instructing coders to associate the travel episode with the respondent's next activity at the travel destination. To illustrate, if "I drove my child to church" was followed by "I dropped my child off," then the travel episode would be coded as travel related to childcare. By contrast, if the next activity was "I attended worship service," then the travel episode would be coded as travel related to religious activities. Rules were also revised to clarify how to code waiting while traveling, multi-leg trips, and trips with several intervening activities and destinations. Despite these rule changes, travel activities were more complicated to code than any other category in subsequent https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis coding tests. As a result, "fixing" the travel coding rules and improving training became a top priority for the BLS coding team. The greatest challenges centered around two related issues: how to determine the purpose of the travel episode and how to code waiting activities during or after travel episodes. Determining the purpose of a travel episode involved looking ahead to the activity reported at the travel destination. Following this travel rule worked relatively well when coding a single-destination trip, but became increasingly complex when multiple stops were involved, some of which may only have been incidental to the primary purpose of the travel. To collect travel data that most closely reflected true travel purpose, the BLS coding team originally directed coders to code travel to a destination's activity during multiple-destination trips only if the duration of the intervening destination's activity was 10 minutes or longer. Thus, if someone drove 30 minutes to work, but stopped for 5 minutes along the way to purchase a cup of coffee , all the travel was to be coded as travel related to work. However, if the coffee purchase took IO minutes, the first leg of the trip was to be coded as travel related to consumer purchases and the second leg would be coded as travel related to work. Following this "IO-minute" travel rule proved confusing and difficult to implement on many occasions and accuracy rates remained low despite substantial training efforts. Ultimately, the BLS requirement to apply the I 0-minute travel rule when dealing with multi-stop trips was dropped. Instead , a rule was developed tu code travel according to the purpose of each leg of a multi-stop trip, no matter the length of the stops at each destination. Coding travel accurately was further complicated when the respondent reported waiting while traveling. 9 The difficulties can be demonstrated using a hypothetical example of a time-use diary: Travel leg 1: Activity: Travel leg 2: Activity: Activity: Driving to the train station (20 minutes) Waiting for the train ( 15 minutes) Taking the train to the city (30 minutes) Waiting for a table ( 15 minutes) Eating at a restaurant (2 hours) In this example, travel leg I would be coded as traveling related to waiting associated with traveling related to eating and drinking, whereas travel leg 2 would be coded as traveling related to waiting associated with eating and drinking. Because of these challenges, the confusing "waiting" categories were stripped from the travel categories, and coders were instructed to fold any waiting time while traveling directly into associated travel episodes. The decision to code multiple-destination travel according to the purpose of the activity at the next destination, regardless of the length of time of the stop, means that travel Monthly Labor Review June 2005 7 American Time Use Survey legs are often not actually coded to " main" purpose of the trip. Therefore, travel time related to certain activities may be under- or overreported when part of a multiple-destination trip. Analysts using travel data from the ATUS will probably want to examine the activity codes in detail and modify them according to their research interests. For example, those interested in measuring commuting time may want to make assumptions about trip purpose when the final destination is the workplace, but an intervening stop for another purpose took less than 10 minutes. Purchasing consumer goods and services. A common category in time-use survey coding systems is purchasing goods and services. The ATUS lexicon originally adopted this phrasing, which is meaningful to economists, as it included time spent in all purchasing activities, but it was not intuitive to coders. Coder feedback and the results of coding accuracy evaluation from the earliest coding tests immediately pointed to problems with understanding the original purchasing goods and services category. In particular, the coders did not relate medical, legal , or childcare services to the goods and services category, and did not know where to look when coding an activity such as "having a doctor's appointment." To facilitate coding, the BLS coding team decided to break the goods and services category into several categories. One category would cover purchases of consumer goods, and several others would cover purchases of various services: professional services (including financial , legal, and medical); household maintenance services; and government services. However, in published tables these categories would be recombined into one category covering all goods and services. Media use. In several other time-use surveys, activities such as reading books, magazines, and newspapers; watching television; listening to the radio; playing records, cos, or tapes; reading mail and writing letters; and using the telephone, are classified under a mass media category. But determining where to classify and how to code types of media use-including using a computer or the Internet-in the ATUS proved challenging. Tests showed that the distinctions between some of the major activity categories were blurry, and activities could reasonably be coded under more than one category, depending on one's interpretation of the category definitions. For example, classifying "reading the newspaper" under socializing and relaxing seemed to coders as logical as classifying it under media use, where other timeuse surveys included it. To ensure accuracy at the first tier, the BLS coding team decided to drop the "media use" language, which was sometimes confusing for coders, and to include watching television, listening to the radio, reading for personal interest, and computer and Internet use for personal interest as subcategories under the overarching 8 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 category called socializing, relaxing, and leisure. However, reading e-mail and writing e-mail were grouped in the major category household activities, where handling regular mail is classified. Other categories. Although the previously mentioned categories provided the most significant challenges, many other activities were important to clarify for coders as well. "Purchasing movie tickets" might be considered as making a consumer purchase or attending a movie. "Talking with a professor" might be coded as socializing and communicating or attending class. These and many more similarly ambiguous activities required BLS to make decisions about how conceptual definitions for each activity category should be refined and operationalized through coding rules. It was clear that any conceptual definitions and rules created for coding purposes might be at odds with the needs of individual data users because, ultimately, how an activity should be classified depends on the question being answered by analysts of time-use data. The need to build a coding lexicon that would allow consistent coding without losing analytical relevance and flexibility continued to be a challenge right up to the start of the survey. Full production coding operations Full production of the ATUS began in January 2003, with a 17-tier coding lexicon, desk aids, and an extensive coding rules manual. Although experienced in collecting data for other BLS surveys, Census Bureau employees at the Jeffersonville Telephone Center in Indiana faced new challenges in conducting and coding ATUS interviews. Collecting time-use data requires the use of conversational interviewing. That is, in addition to asking a series of structured, scripted questions to update household roster and employment status information, interviewers must guide respondents through their report about the prior day using active listening techniques and selective probing to keep respondents on task, filter out irrelevant information, and ensure adequate detail in order to code responses. ATUS also diverges from Census Bureau convention by requiring interviewers to code interview responses (although not from the interviews that they conducted) into activity categories-a job normally assigned to coding specialists. The ATUS coding team conducted debriefings of Census Bureau interviewers after the dress rehearsal and pre-fielding periods ended, and has continued to do so periodically since the survey entered full production. Over time, interviewers have become increasingly comfortable with conversational interviewing. More importantly, interviewers' reactions to their new dual job role as interviewers/coders have been consistently positive. When coding time diaries, interviewers become more aware of the difficulty of classifying activities and the consequences of improperly or vaguely recorded activities. Because of this perspective gained from coding, interviewers have become much more skilled at collecting and recording codable time diary information. Even the most carefully collected and recorded time diaries contain activities that are difficult to code. To achieve coding accuracy and consistency, the ATUS team focuses heavily on training and qualifying individuals before they are allowed to code real cases, and verifies all assigned codes in every case. This process is similar to the one implemented for the Australian time-use survey. After a coder completes a case, a second coder (the verifier) re-codes the same case without seeing the original codes. If both coder and verifier assign the same activity codes, the case is closed. If there is disagreement on any code, the case goes to an adjudicator who is an experienced supervisor or coach. The adjudicator assigns a correct code to the disputed activities, and then closes the case. The adjudicator also assigns an error to the coder or verifier (or both) who assigned the incorrect activity code. Information on errors is fed back to coders in the form of an error report and discussions with adjudicators as to why an activity code was reassigned. Thanks in part to this verification system, coding error rates dropped from 14.3 percent during the dress rehearsal in April 2002 to 5._5 percent in January 2004, 1 year into full production. The experiences from testing the coding process and conducting a dress rehearsal demonstrated that without substantial training, practice, a comprehensive set of coding rules, and a verification process, many reported activities are open to a wide range of interpretation. Training and practice are essential to first-time interviewers/coders, as they convey interviewing and probing techniques, explain the coding lexicon and rules for coding, and allow ample opportunity for questions and answers. Using the Blaise-designed computer coding application also contributes to accurate and consistent coding. Completed cases are loaded into the ATVS coding application, which has multiple windows so coders can simultaneously view the activity being coded, the coding categories, and the respondent's entire time diary. In the time diary window, the following information is included for each activity: start time, duration, who was in the room with or accompanied the respondent, location, and whether or not the respondent identified the activity as done as part of one's job, as another income-generating activity, or as volunteering for an organization. Using tabs at the top of the window, the coder can access additional information on the respondent's occupation and industry, the ages and relationships of household members, and any notes about the case that the interviewer ad~ed for assistance with coding. The coding software includes a search feature that helps coders find the https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis correct code for ambiguous activities and increases coding speed. Verification and adjudication systems are also built into the system. Since full production began, debriefings and the coding verification and adjudication systems have brought to light coding issues that required some changes to the coding lexicon and coding rules. These changes were implemented in January 2004, are few and relatively minor, and will have little or no impact on the continuity of the data between 2003 and 2004. Lexicon changes-mostly in the form of adding examples-largely help to disambiguate activity categories and provide a better understanding for the staff doing the coding. Unlike other survey classification systems-such as those relating to occupations or industries, which require periodic revisions to reflect changes in business practices or a restructuring of the economy-the time-use activity categories at the first-tier level in the coding lexicon are not likely to change significantly. Although relative time spent in various activity categories may grow or decline as a result of cultural, workplace, or technological changes, the major activity categories themselves will probably remain the same. After carefully reviewing and analyzing the first few years' timeuse estimates, second- and third-tier activity categories may be expanded to enable the collection of greater detail for activities that account for a lot of time, or collapsed to combine activities that show up infrequently. For example, if analyses show that computer use for personal interest accounts for a disproportionate amount of time spent in leisure activities, this category could be broken into two thirdtier categories: non-Internet computer use for personal interest and Internet use for personal interest to obtain measures of both "off-line" and "on-line" computer use. Structure of the classification system As mentioned earlier, the ATUS coding lexicon uses a hierarchical structure, classifying reported activities into major categories, with two additional levels of detail in each category. ATVS, however, has a much larger number of firsttier (major) categories than other time-use surveys: 17 as opposed to an average of 10. Also, ATVS coders assign a sixdigit classification code to each diary activity, rather than the three-digit code commonly used in other time-use surveys. The first two digits represent the major activity categories, the next two digits represent the second-tier level of detail, and the final two digits represent the third-the most detailed level of activity. The final code in every tier is 99, which represents activities classified in each tier's relevant activity, but which are not elsewhere classified. For example, the ATVS code for "making the bed" is 020101. "Making the bed" appears in the coding application as an Monthly Labor Review June 2005 9 American Time Use Survey Major analytical activity categories, 2003 Personal care Eating and drinking Household activities Purchasing goods and services Caring for and helping household members Caring for and help ing nonhousehold members Working and work-related act ivities Educational activities Organizationa l, civic, and religious activities Leisure and sports Telephone calls, mail, and e-mai l Other activities, not elsewhere classified (n .e.c.) example under the third-tier category, interior cleaning, which is part of the second tier category, housework, which falls under the household activities major category: 02 Household activities 0 I Housework 0 I Interior cleaning making the bed 02 Laundry 03 Sewing, repairing, and maintaining textiles 04 Storing interior household items, including food 99 Housework, n.e.c. The adoption of a 6-digit classification code has the advantage of enabling greater flexibility than 3-digit systems in addi ng new subcategorie s under major and second-tier categories. Although most categories have nine or fewer subc ategories, some, such as sports participation , have many more, taking advantage of thi s flexibility. The 99 options under each tier leave the door open for future revisions. An important note about the ATUS interview: only primary act1v1t1es are systematical ly collected and coded. Respondents are not sys tematica lly questioned about simultaneous activities; however, if they volunteer that two or more activities were done simultaneous ly, the interviewer probes for the main-or primary-acti vity, which is recorded first in the activity field. 10 The coding staff is instructed to assign an activity code only to the primary activity; in this way, each respondent's day adds up to no more than 24 hours. Coding versus publication activity categories The central concerns influencing the development of the coding lexicon were the need for coding consistency and the need for analytical flexibility. The lexicon categories are conceptuall y and operational ly di s tinct to enable consistency, but they are not necessarily the best categories for analytical reporting. In the first publication of ATUS data, composites of the original coding lexicon categories were developed into analytical categories to de scribe how people use their time. (See the box for the major analytical activity categories.) Appendix A provides definitions of the major categories used in the first published tables (as part of the September 2004 news release' 1) and appendix B "crosswalks" those categories to the lexicon categories described earlier. 12 IN SUMMARY, the ATUS classification system is characterized by its detail and flexibility. These characteristi cs, while important for maximizing the survey's use to analysts of the data, also increase the complexity for coders. Understandin g how ATUS data are collected and classified, as well as understandin g the special coding challenges, represent an important first step for researchers who wish to develop meaningful analyses, including comparisons of time-use data collected through other surveys. □ Notes 1 For a detailed description of th e evo luti on of ATUS, see Di ane Herz and Michael Horrigan, ··Planning, de signi ng, and executing the BLS American Time Use Survey " Monthly Labor Review, October 2004, pp. 3-19. 2 Alexander Szalai, Th e use of time: Daily acti vities in urban and suburban populations in twelve coun tr ies (T he Hague , Mouton, 1972). 1 Dagfinn Aas, ·'Studies of Time-Use: Problem s and Prospects, " Acta Sociologica, vol. 2, 1978, pp . 125- 141 ; Dagfi nn Aas, "'Designs for Large Scale Time-Use Studies of the 24-Hour Day," Its About Time (International Research Group on Time Budgets and Social Activities , 1982); and [iri s Niemi, Salme Kiiski , and Mirja Liikkane n, Use of Time in Finland 1979 (Helsinki. Central Statistical Office of Finland, 1986). 4 l0 Monthly Labor Review June 6 A .. dress rehearsal ," conducted during April-July of 2002, marked the first time all compone nt s (the collection instrument, the coding instrument, operations procedures, and so forth) of the ATUS were tested at one time, and was designed to mimic full production survey conditions, including live intervi ewi ng. Pre-fielding followed the dress rehearsal, and took place from August until full production began in January of 2003. Pre-fielding provided an opportunity to refine operations, interviewing and coding processes, and collect preliminary data for analysis. 7 See Herz and Horrigan, '"The 2004, for more information on the BLS American Time Use Survey," work summary questions. ATUS 8 In 2002, BLS contracted with the National Opinion Research Center to conduct cogni ti ve research on how respondents identified Szalai, Th e use of time , 1972. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 5 This software was developed by Statistic s Netherlands and is the standard for both survey and coding applications at the Census Bureau . 2005 the purpose of travel episodes . Research conclusions pointed to the difficulties in collecting accurate and consistent information on travel purposes . For example, respondents often reported on the purpose of their next activity, not the travel episode : The question, ··What was your purpose in driving to the gym?" might elicit a response of " Because I want to lose weight." For this reason, AT US interviewers are not instructed to probe for the main purpose for travel episodes, but rather deduce it from the nature of the activity reported following the travel episode. 9 The travel category had, like all other categories in the lexicon, APPENDIX A : 10 See Herz and Horrigan, '"The BLS American Time Use Survey," 2004, for more information on the decisions made about the collection and coding of simultaneous activities . 11 See ··Economic News Releases" on the AT US Web site at www.bls.gov/tus/home.htm for the September 2004 news release . 12 The complete 2003 ATUS Activity Coding Lexicon is available on the Internet at: www.bls.gov/tus/lexiconwex2003 .pdf. Activity categories and definitions Personal care activities. Personal care activities include sleeping, bathing, dressing, grooming, health-related self-care, and personal or private activities. Receiving unpaid personal care from others (for example, --my sister put polish on my nails") is also captured in this category. Respondents are not asked who they were with or where they were for personal activities, as such information can be sensitive. The following list illustrates sample activities that respondents report and the category into which the interviewer/coder placed those activities. Reported activity Lexicon category Tossing and turning in bed Blow-drying my hair My sister braided my hair Doing childbirth exercises Cuddling partner in bed Sleeplessness Washing, dressing, and grooming Washing, dressing, and grooming Health-related self-care Personal/private activities Household activities. Household activities are those done by respondents to maintain their households. These include housework; cooking; yard care; pet care; vehicle maintenance and repair; and home maintenance, repair, decoration, and renovation. Food preparation, whether or not reported as done specifically for another household member, is always classified as a household activity, unless the respondent identified it as a volunteer, work, or income-generating activity. For example , ·'making breakfast for my son" is coded as a household activity, not as childcare. Household management and organizational activities-such as filling out paperwork, ba lancing a checkbook, or planning a party-also are included in this category. Although all mail and e-mail activities are originally classified in the household activities category during coding, these activities are pulled out of the household activities and included in the composite category Telephone, Mail, and E-mail category in published tables. The following list is a sample of reported household activities and the categories into which they belong. Reported activity Putting away groceries Hemming a skirt Boiling water for tea Putting up bookshelves Loading software on PC Cleaning the pool Filling out tax forms https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis a ""waiting" category at the third tier for each second tier category. Lexicon category Storing interior items Sewing, repairing, and maintaining textiles Food and drink preparation Interior arrangement, decoration, and repair Appliance and tool set-up and repair Ponds, pools, and hot tubs Financial management Caring for and helping household members. Time spent doing activities to care for or help any child or adult in the respondent 's household, regardless of relationship to the respondent or the physical or mental health status of the person being helped, are classified here. Caring and helping activities for household children and adults are coded separately in subcategories. Household members are considered children if they are under 18. Primary childcare activities include physical care; playing with children; reading to children; assistance with homework; attending children's events; taking care of children 's health care needs ; and dropping off, picking up, and waiting for children. Passive childcare done as a primary activity (such as ·'keeping an eye on my son while he swam in the pool" ) also is included. A child 's presence during the respondent 's activity is not enough in itself to classify the activity as childcare. For example, --watching television with my child" is coded as a leisure activity, not as childcare. Secondary childcare is care for children that is done while doing something else. This information is collected by asking the respondent about times when ··a child was in your care" while doing something else as a primary activity, and is available in published ATUS tables and in the ATUS public use data files. It is not part of the ATUS coding lexicon. Caring for and helping household members also includes a range of activities done to benefit adult members of households, such as providing physical or medical care or obtaining medical services. Doing something as a favor for, or helping another household adult does not automatically result in classification as a helping activity. For example, a report of ··helping my wife cook dinner" is considered a household activity (food preparation), not a helping activity, because cooking dinner benefits the household as a whole. By contrast, doing paperwork for another person usually benefits the individual , so a report of .. filling out an insurance application for my husband" is considered a helping activity. For example, the following list shows the reported caring or helping activity on the left and the coded activity on the right. Reported activity Tucking my son in bed Riding bikes with my kids Waiting for the school bus with my child Talking to my child's teacher Lexicon category Household childcare: physical care Household childcare: playing sports Household childcare: waiting for or with household child Household childcare: meetings and school conferences (child's education) Monthly Labor Review June 2005 11 American Time Use Survey Meeting with my mother's adult care provider (mother is household member) Packing a suitcase for my wife Dropping my husband off at work Household adult care: obtaining medical and care services Helping household adults: organization and planning Helping household adults: picking up or dropping off Caring for and helping nonhousehold members. Activities done to care for or help any child or adult who is not part of the respondent 's household , regardless of the relationship to the respondent or the physical or mental health status of the person being helped, are classified in this category. Caring and helping activities for nonhousehold children and adults are coded separately in subcategories. Nonhousehold members are considered children if they are under 18. When done for or through an organization, time spent helping nonhousehold individu als is classified as volunteering rather than as helping nonhousehold members. Nonhousehold childcare, even done as a favor or a helping activity for another adult, is always classified as nonhousehold childcare, not as helping another adult. The activity classifications for this category parallel those for the caring for, and helping household members category, with one notable exception. The subcategory of helping nonhousehold adults is expanded to include more activities that the respondent identifies as '"helping;" this subcategory is further broken into broad shopping and household activity groupings. The following list show s examples of these activities and categories. Reported activity Lexicon category Attending my niece's Nonhousehold childcare: attending school play children 's events Dropping off my friend 's Nonhousehold childcare: dropping son at school off/picking up children Grocery shopping for Helping nonhousehold adult: my mother housework, cooking , and shopping assistance Filling out a form for Helping nonhousehold adult: my neighbor household management and paperwork assistance Waiting with my friend at Caring for nonhousehold adult: the emergency room waiting associated with caring Feeding my neighbor's cat Helping nonhousehold adults: animal and pet care assi stance Working and work-related activities. This category includes time spent working, doing activities as part of one 's job, engaging in income-generating activities (not as part of one 's job), and job search activities. "Working" includes hours spent doing the specific tasks required of one 's main or other job, regardless of location or time of day. Activities done outside of regular work hours are classified as work if identified by respondents as part of their jobs. ·'Workrelated activities" include activities that are not obviously work but are identified by the respondent as being done as part of one 's job, such as having a business lunch or playing golf with clients. "Other income-genera ting activities" are those done '"on the side" or under informal arrangement and are not part of the respondent's regular job. Such activities might include selling homemade crafts , babysitting, maintaining a rental property, or having a yard sale. Respondents identify these activities as ones they "are paid for or will be paid for." 12 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Work and work-related and income-genera ting activities are identified during data collection by the respondent and flagged as such with an M , 0 , or P in the instrument that coders use to assign activity codes. The following list shows examples of these reported work activities and the categories into which they belong (M = done as part of main job; 0 = done as part of other job; and P = done as income-genera ting activity). Reported activity Grading papers at home (M) Telephoning a coworker (M) Attending a conference (M) Using computer to write memos (0) Enrolling in work-related training (M) Having lunch with clients (0) Playing piano in a wedding (P) Mowing the neighbor 's lawn (P) Selling stuff at a yard sale (P) E-mailing resumes to employers Preparing for a job interview Lexicon category Working, main job Working, main job Working, main job Working, other job Working, main job Work-related: eating and drinking as part of job Income-genera ting activities: performances Income-genera ting activities: services Income-genera ting activities: other, n.e.c. Job search and interviewing: active job search Job search and interviewing: interviewing Educational activities. Educational activities include taking classes (including Internet or other distance learning courses); doing research and homework ; and taking care of administrative tasks, such as registering for classes or obtaining a school identification card. For high school students, before- and after-school extracurricula r activities (except sports) are also classified as educational activities. Activities are classified separately by whether the educational activity was for a degree or for personal interest. Educational activities do not include time spent for classes or training that respondents identified as part of their jobs. Time spent helping others with their education-rela ted activities is classified in the Caring for and helping categories. The following list shows examples of reported educational activities and the lexicon categories into which they are classified (Pl= personal interest and D =degree). Reported activity Attending a seminar (Pl) Taking an exam (D) Talking to a professor about a paper (D) Taking a parenting class (PI) Taking driving lesson s (Pl) Waiting for class to start (D) E-mailing homework to teacher (D) Meeting with the Science Club-DP is high school student (D) Lexicon category Taking class: for degree Taking class: for degree Taking class: for degree Taking class: for personal interest Taking class: for personal interest Waiting associated with taking classes Research/hom ework: for class for degree Extracurricular school activities: club activities Organizing class notes (D) Paying fees during registration (Pl) Research/homework : for class for degree Registration/ad mini stration activities: for class for personal interest Purchasing goods and services. Thi s category includes the purchase of consumer goods as well as the purchase or use of professional and personal care services, household services, and government services. Most purchases and rental s of consumer goods, regardless of mode or place of purchase or rental (in person , via telephone, over the Internet, at home , or in a store) are class ified in this category. Gaso line , grocery, other food purchases, and all other shopping are further broken out into s ubcategories. The followin g li st shows examples of respondents' reported activity and the lexicon category for purchasi ng goods and services. Reported activity Lexicon category Ordering groceries over the Internet Talking to the produce manager Pumping gas Paying for pizza delivery Buying fast food Browsi ng at the department store Renting a rug shampooer Returning videotapes to rental store Picking up film Comparison shopping Waiting in line at the grocery store Grocery shopping Grocery shopping Purchas ing gas Purchasing food (not groceries) Purchasing food (not groceries) Shopping, except groceries, food, and gas Shopping, except groceries, food, and gas Shopping, except groceries, food, and gas Shopping, except groceries, food , and gas Researching purchases Grocery shopping Time spent obtaining, receiving, and purchasing professional and personal care services provided by so meone else also is classified in thi s category. Professional services include childcare, financial services and banking, legal services, medical and adult care services, real estate services, and veterinary services. Personal care services include day spas, hair salon s and barbershops , nail salons, and tanning salons. Activities classified here include the time respondents spent paying , meeting with, or talking to service providers, as well as time spent receiving the service or waiting to receive the service. The following list shows examples of respondents' reported ac tivities regarding purchases of professional services and the lexicon category into which they are categorized. Reported activity Lexicon category Interviewing a nanny Paying for a child's day camp Checking out a daycare facility Using the bank ATM Meeting with a tax advisor Sitting in the doctor 's waiting room Using childcare services Using childcare services https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Using childcare services Banking Using financial services Using health and care services outside the home Looking at apartments to rent Talking to a real estate agent Paying for veterinary services Activities related to purchasing/selling real estate Activities related to purchasing/selling real estate Using veterinary services Time spent arranging for and purchasing household services provided by someone else also is classified in this category. Household services include housecleaning; cooking; lawn care and landscaping; pet care; tailoring, laundering, and dry cleaning; vehicle maintenance and repairs; and home repairs, maintenance, and construction. Some of the sample activities are included in the following list. Reported activity Lexicon category Paying the housecleaning Interior cleaning services service Hiring carpet cleaners Interior cleaning services Meeting with a caterer Meal preparation services Dropping clothes at the Clothing repair and cleaning dry cleaner services Hiring a building contractor Home maintenance, repair, decoration, and construction services Home maintenance, repair, Talking to the furniture decoration, and construction movers services Pet services Hiring a pet trainer Lawn and garden services Paying the landscaper Waiting while car oil Vehicle maintenance and repair services is changed This category also captures the time spent obtaining government services-such as applying for food stamps-and purchasing government-required licenses or paying fines or fees. Some other examples of these activities and categories are: Reported activity Talking to a police officer Waiting while the fire department detects for carbon monoxide Applying for food stamps Meeting a social worker Getting a passport Paying a speeding ticket Lexicon category Police and fire services Police and fire services Social services Social services Obtaining licenses and paying fines, fees, and taxes Obtaining licenses and paying fines, fees, and taxes Eating and drinking. All time spent eating and drinking (except when identified by the respondent as part of a work or volunteer activity), whether alone , with others, at home, at a place of purchase, in transit, or somewhere else, is classified in this category. Time spent purchasing or talking related to purchasing meals, snacks, and beverages is not counted as part of this category; time spent doing these activities is classified under Purchasing goods and services. The following list provides examples of eating and drinking activities and the categories into which they are classified. Monthly Labor Review June 2005 13 American Time Use Survey Reported activity Sipping tea Waiting for a restaurant table Snacking on pretzels Drinking some brews Eating a bite Waiting for pizza delivery Lexicon category Eating and drinking Waiting associated with eating and drinking Eating and drinking Eating and drinking Eating and drinking Waiting associated with eating and drinking Leisure and sports. The leisure and sports category includes sports, exercise, and recreation; socializing and com_mu~ica~ing; and other leisure activities. Socializing and commumcatmg mcludes face-to-face social communication and hosti ng or attending social functions. Time spent communicating with others using the telephone, mail, or e-mail is not part of this category. _ These activities are included in the separate Telephone calls , mail and email category. Leisure activities include watching television; reading; relaxing or thinking; playing computer,. board, or c~rd games; using a computer or the Intem~t_f?r personal interest;_ playing or listening to music; and other act1v1t1es, such as attendmg arts, cultural, or entertainment events. Participating in-as well as attending or watc~in_g -:-sports, exercise, and recreational activities, whether team or md1v1dual and competitive or noncompetitive, fall into this category. Some sample activities are in the following list. Reported activity Lexicon category Hanging out with the family Socializing and communicating with others Chatting with my neighbors Socializing and communicating with others Spending time with my Socializing and communicating friends with others Attending a friend 's Attending/ hosting parties, graduation receptions, ceremonies Attending a senior citizens Attending meetings for personal meeting interest Sunbathing Relaxing, thinking Daydreaming Relaxing, thinking Watching my wife garden Relaxing, thinking APPENDIX B: Organizing coin collection Attending the ballet Visiting an art gallery Horseback riding Watching a soccer game (not TY) Collecting as a hobby Arts and entertainment: performing arts Arts and entertainment: attending museums Participating in sports, exercise, or recreation: participating in equestrian sports Attending sporting, recreational events: watching soccer Organizational. civic, and religious activities. This category _is a composite of several coding lexicon categories and captu_res t~~e spent volunteering for or through an organization, ~~rforrnin~ ~1_v1c obligations , and participating in religious an~ spmtu~I act1v1t1es. Civic obligations include government-reqmred duties, such as serving jury duty or appearing in court, and a_ctivities that_assist or influence government processes, such as voting or attending ~own hall meetings. Religious activities include those no~ally a_ss?c,ated with membership in or identification with spec1f1c rehg1ons or denominations, such as attending religious services; participating in choirs, youth groups, orchestras, or unpaid teaching (unless identified as volunteer activities); and engaging in personal religious practices, such as praying. Reading the Bible or other holy text or scriptures is classified as reading under Leisure and sports. _The followino list shows sample reported activities and the lexicon category into which they belong (V = Volunteer activities). Reported activity Lexicon category Attending a church revival Attending religious services Praying alone Participating in religious practices Designing a Web site (V) Volunteer activities: administrative and support activities Participating in a Civic obligations and participation government survey Baking cookies for the Volunteer activities: social service PTA bake sale (V) and care activities Emceeing a charity Volunteer activities: Participating in function (V) performance and cultural activities Crosswalk between ATUS coding lexicon major categories and published tables major categories, 2003 Published tables: major categories Code Coding lexicon categories Personal care 01 1701 Personal care activities Travel related to personal care Eating and drinking 11 1711 Eating and drinking Travel related to eating and drinking Household activities All 02, except (020903 020904) 1702 14 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Household activities (Household and personal mail and messages Household and personal e-mail and messages) Travel related to household activities Appendix B: Continued-Crosswalk between ATUS coding lexicon major categories and published tables major categories, 2003 Published tables: major categories Purchasing goods and services Code 07 08 09 1001 100301 100302 100399 1004 1099 1707 1708 1709 171001 171002 171003 171099 Coding lexicon categories Con sumer purchases Profess ional and personal care services Household services Using government services Wai ting associated with using police/fire services Wa iting assoc iated with obtaini ng licenses Wai ting associate with using government services or civ ic obligations, n. e.c. Security procedures re lated to government services/civic obligations Government services, n.e.c. Trave l related to consumer purchases Trave l related to using professional and personal care services Trave l rel ated to using household services Travel rel ated to using police/fire services Trave l rel ated to using social services Trave l related to obtaining licenses and paying fines/fees Travel related to government services and civic obligations, n.e.c. Caring for and helping household members 03 1703 Caring for and helping househo ld members Travel rel ated to caring for and helping household members Caring for and helping nonhousehold members 04 1704 Caring for and helping nonhouseho ld members Trave l related to caring for and helping nonhousehold members Working and work-related activities 05 1705 Working and work-related activities Travel related to working and work-related activities Educational activities 06 1706 Education Travel rel ated to education Organizational, civic, and religious activities Leisure and sports Telephone calls, mail, and e-mail Other activities, not elsewhere classified https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 14 15 1002 100303 1714 1715 171004 12 13 1712 1713 16 1716 020903 020904 1717 1799 50 Relig ious and spiritual ac ti vities Volunteer ac ti vi ties Civic obligations and participation Waiting associated with ci vic obligations and participation Travel related to religious and spiritual activities Travel rel ated to volunteer activities Travel rel ated to civ ic obligations and participation Socializing, relaxing, and leisure Sports, exercise, and recreation Travel related to socializing, relaxi ng, and leis ure Travel related to sports, exercise, and recreation Telephone calls Travel related to telephone calls Household and personal mail and messages Household and personal e-mail and messages Security procedures related to traveling Traveling, n.e.c. Data codes Monthly Labor Review June 2005 15 Air-Travel Transaction Index , '¼fl :r ~ii "• A transaction price index for air travel Research on a price index estimator based on data from a U.S. Department of Transportation survey involves testing unique imputation and across-time matching procedures; the resulting experimental index is compared with the official CPI series and the consumer expenditure deflator series used in National Accounts computations Janice Lent and Alan H. Dorfman Janice Lent is a mathematical statistician with the U.S. Research and Innovative Technology Adm inistration , Washington . oc; Alan H. Dorfman is a mathematical statistician In the Office of Survey Methods Research, Bureau of Labor Statistics. The opinions expressed in this article are those of the authors and do not constitute policy of either the Research and Innovative Technology Administration or the Bureau of Labor Statistics. 16 S pecial discount airfares, facilitated by the Internet and "frequent-flyer" programs, complicate efforts to measure changes in the price of commercial air travel. Endeavoring to fill their flights, airlines offer a variety of discount fares through several media (credit card points, supermarketcoupons, and the like). The official Consumer Price Index (CPI) for commercial air travel, however, is based on prices listed by the airlines in SABRE, a reservation system used by many travel agencies. Thus, the CPI fails to reflect price changes that may be effected through special discounted prices and frequent-flyer awards. This article reports on a study ,,., hose aim was to produce an index series based on actual prices paid by consumers. The most promising data set currently available for that purpose is the Transportation Department's Data Bank I B, which contains data from the quarterly Passenger Origin and Destination (O&D) Survey, collected by the U.S. Government's Bureau of Transportation Statistics. These data are itinerary based: each observation consists of a fare (the actual fare paid, including tax), a sequence of airports and carriers, and other details of an itinerary traveled by a passenger or a group of passengers. The Department of Transportation is developing plans to improve and expand the O&D Survey. The additional data that the Department plans to collect will greatly enhance analysts' ability to compute detailed price indexes; among the new data is detailed information regarding the sale of the airline ticket, as well as transaction fares for flights Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 in the recorded itineraries. The Department also plans to improve the timeliness of the survey data. Currently, the data become available with a lag of 3 to 6 months-too late to be used in computing the airfare component of the CPI. This article examines research aimed at computing price indexes from the current O&D Survey data. The Bureau of Transportation Statistics will soon be publishing the new quarterly experimental Air Travel Price Index (ATPI) series, computed at a variety of aggregation levels. A secondary goal of the research is to test the feasibility of computing price indexes from nonmatched samples of customized items. The sample for the O&D Survey is selected independently each quarter and is a I 0-percent sample of airline tickets from reporting carriers, both foreign and domestic. Each ticket having a serial number that ends in "O" is selected for the sample. For the purpose of this research, the O&D sample is treated as a simple random sample. Because the quarterly samples are independently selected and airline itineraries are customized, matching the data across time is the primary challenge. Large data sets (containing, for example, scanned-in data) with the prices of other types of customized items may well become available in the future. The current research will provide insight into the potential usefulness and limitations of such data sets for price index computation. The next section compares the ATPI with two important airfare price indexes currently in use. Following that comparison, the methodological research undertaken in the development of the ATP! is discussed. Then, time plots of ATP! series, computed for research purposes, are presented. A discussion of possible directions for further investigation rounds out the text of the article. Most formulas and technical details are relegated to the appendix. Comparison of airfare indexes This section compares and contrasts the ATP! with two important airfare index series: 1. 2. the BLS Consumer Price Index (CPI) for airline fares the consumer expenditure deflator for airline fares, computed by the Bureau of Economic Analysis (BEA) and used in National Accounts estimation. Comparing the exp erimental ATP! with the CPI. The Bureau of Labor Statistics currently publishes several price indexes for airfares: (1) a Consumer Price Index (CPI), (2) a Producer Price Index, and (3) international import and export price indexes. Because the CP I is perhaps the best-known and most widely used of the BLS price indexes, this section focuses on a comparison between the ATP! and the airfare component of the CPI. The CPI measures changes in the prices paid by consumers for airline trips, including taxes and all distribution costs paid by the consumers. The experimental ATP! series are similar to the BLS CPI in that the prices they measure include taxes paid, as well as fares received by the airline. The ATP! prices, however, exclude any distribution costs that were not received by the carriers (for example, travel agents ' fees). The CPI includes trips purchased from foreign carriers, while the current ATP! series do not include data from foreign carriers. 1 CPI air-travel prices are gathered monthly from the SABRE system, while information on ATP! prices and quantities come from the O&D Survey. The sample for the CPI airfare component is drawn from a subset of the O&D Survey data. Conceptually, the CPI excludes business trips, but because such trips cannot be identified on the sampling frame (information on the purpose of a trip is not collected in the O&D Survey), they cannot be screened out of the sample. Thus, both the CPI and ATPI samples include personal trips as well as business trips. 2 Another important difference between the ATPI and the airfare CPI lies in the target index formulas used. The economics literature contains a wide variety of price index formulas that may be accepted as estimation targets. The "textbook" Laspeyres formula, for example, is given by https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis N L q i,l P; ,2 _:_l_ __ L=...:..i= N Iqi.lP;.1 i =l where N is the number of items in the target population and, for t E {1, 2} ,P;,, and q;, 1 denote the price and quantity purchased, respectively, of item i in period t, for i = I, 2, .. ., N. Note that the index represents a comparison between prices in two arbitrary, but discrete, periods 1 and 2 (for example, months or years). The classical index formulas also rely on the implicit assumption that the collection of N items remains the same for the two reference periods. Index estimators, in contrast, must allow for the continual flow of goods and services on and off the market, as well as for the fact that information on prices and quantities normally are available only for a sample of items in the population. 3 The Laspeyres formula, which measures changes in the price of a "fixed market basket" of items, is commonly used by government statistical agencies. Economic theory suggests, however, that other formulas may provide better approximations of changes in the cost of living, because consumers do not purchase the same set of items (a fixed market basket) in each survey period. Rather, they tend to alter their buying habits in response to changes in relative prices- for example, buying a particular brand of a product when that brand is on sale. Formulas such as the Jevons ( or geometric mean), Fisher, and Tornqvist indexes are often considered more appropriate, given a "dynamic" market basket. (See the appendix for definitions of these formulas.) The Fisher and Tornqvist indexes in particular are known as "superlative" indexes, because they approximate the change in the cost of living (that is, the cost of obtaining a fixed level of "utility") under relatively weak assumptions concerning consumer buying behavior.4 The Jevons and Laspeyres formulas are often more practical, however, because they require less information on consumer expenditures than do the superlative formulas. Since January 1999, the airfare CPI has been based on a weighted Jevons index formula within each sample geographic area, with sampling weights obtained from O&D Survey data. At the upper level of aggregation (aggregating across geographic areas), the C PI employs a modified version of the Laspeyres index, with weights estimated from Consumer Expenditure Survey data. The implementation of the Jevons index (replacing the Laspeyres index) at the lower level of aggregation in the CPI was motivated in part by empirical research. 5 In the course of the ATPI research, indexes based on the Jevons, Laspeyres, Fisher, and Tornqvist formulas were computed. The Jevons index estimates were severely biased downward relative to the Fisher and Tornqvist estimates. Moreover, the Fisher index series proved more robust to extreme fare values 6 than did the Tornqvist series. Accordingly, the 7 Fisher formula is the most desirable for the air-travel application and is thus the one presented in this article. The ATP! series also differ from the BLS CPI series in the definitions of their reference periods. From the current O&D Monthly Labor Review June 2005 17 Air-Travel Transaction Index Survey data, only quarterly indexes can be computed, and the reference quarter is the quarter in which the airline ticket was used for travel. 8 The BLS CPI is a monthly survey, and the Bureau collects prices at which tickets are being sold (not necessarily used) during the reference month. Moreover, the scope of the ATPI is slightly wider than that of the airfare CPI. The CPI covers only trips that originate in the United States, whereas the O&D Survey encompasses trips originating in foreign countries, provided that they include at least one stop within the United States. Indexes with more limited scope may, of course, be computed by aggregating selected subsets of the data. For 19982003, the ATPI series for itineraries of flights originating in the United States (see later) shows a trend similar to that of the airfare CPI, although the differing formulas and reference periods result in different seasonal patterns for the two series. Comparing the experimental ATP! with the BEA consumer expenditure deflator for airfares. The BEA computes chaintype price indexes for commodity categories for use in producing the National Income and Product Accounts estimates. For deflating consumer air-travel expenditure estimates, the BEA computes an index series based on both Department of Transportation data on total airline revenue per passenger mile flown and the BLS airfare CPI. Results presented later indicate that the BEA deflator, which relies on measures of average revenue per passenger mile, does not provide a good approximation to a price index when the airline industry is undergoing a period of structural change. Airline financial data collected by the Bureau of Transportation Statistics show that the length of the average airline trip has been increasing in recent years, 9 and longer trips generally cost less per mile than shorter ones. Moreover, the overall quality of air-travel service has decreased with the emergence oflow-cost carriers and the use of smaller, regional jets for cross-r.ountry flights. Both of these factors exert a downward pressur•' on the revenue that airlines collect per passenger mile, although they are not by themselves evidence of actual deflation. Estimation method and research results For the purpose of computing a price index, the peculiarity of the quarterly O&D Survey data is the absence of across-time matching of individual itineraries. In general, price index formulas are based on the direct comparison of prices of identical items in different periods. In the o&o Survey, the sample of tickets priced in time t is selected independentl y of the sample priced in time t - I. Moreover, some information that may affect the fares (for example, the time of day of the flight and the date the ticket was sold) is not collected through the survey. Thus, the survey cannot directly compare fares for identical air-service itineraries in different quarters. This section describes research on methods 18 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 of addressing this primary obstacle to the use of o&o Survey data for index estimation. First, two stages of record matching are outlined: itinerary- and segment-level matching. Because the o&o data provide only itinerary-level airfares, fares for segmentlevel matching must be estimated. Alternative imputation methods are therefore discussed and compared. Finally, the results of a test designed to compare unit-value indexes computed from imputed segment-leve l fares against those computed from itinerary-level fares collected in the o&o Survey are presented. Matching prices across time for index calculation. To circumvent the across-time matching problem, each quarterly sample can be divided into detailed categories, and a unit-value index (average price in time t, divided by average price in time t- 1) computed for each category. The unit-value indexes are treated as elementary aggregates, which may be further aggregated with the use of standard index formulas (for example, Laspeyres, Paasche, Fisher, and Tornqvist formulas). Unit-value indexes are appropriate only for aggregating prices of items that are similar (for instance, round-trip United Airlines coach service from Boston to San Francisco with one stop in Chicago). The first stage of matching is itinerary-level matching, in which the itineraries are cross-classifi ed by the fo11owing variables: ( 1a) sequence of origination and destination airports (that is, origination airport, first destination airport, second destination airport, and so on) (1 b) sequence of classes of service (that is, class of service for first segment, second segment, and so on) ( 1c) sequence of operating carriers Itineraries that are identical in characteristics 1a through I c form a.first-stage unit-value category. Note that trips within the firststage category must have exactly the same number of trip segments, or flights. 10 As the number of segments increases, the percentage of categories appearing in both of two consecutive quarterly databanks decreases. For trips with eight segments, less than 2 percent of the unit-value categories could be matched across consecutive quarters. As a result, the first-stage matching procedure was performed only for trips with eight or fewer segments. (Just 0.15 percent of the itineraries in the o&o Survey databanks comprise nine or more segments.) The second-stage matching procedure is segment-leve l matching. Itineraries not matched in the first stage are broken into individual segments. Because only itinerary-level fares are available in the databanks, the second-stage matching procedure involves imputing (that is, estimating) a fare for each segment. Two alternative methods of imputation are discussed in the next subsection. After the fares for second-stage match- ing are imputed, the trip segments are cross-classified by the following variables to form second-stage unit-value categories: (2a) segment-level origination and destination airports (2b) class of service (2c) round-trip itinerary or non-round-trip itinerary (2d) itinerary ofU.S. origin orof foreign origin (2e) operating carrier Unit-value indexes are computed for these segment-level categories and are then matched from quarter to quarter. The entire matching process, involving both first- and secondstage matching, is performed separately for each pair of consecutive quarters, to create a "rolling" sample. The extent to which the segment-level matching increases the percentage of trip segments that can be matched across quarters depends on the second-stage fare imputation method used. It is expected, however, that a small percentage of segments will always be omitted from the index computations due to incomplete matching. Second-stage fare imputation methods. Two methods of second-stage fare imputation were compared and designated the "single-segment matching method" and "proportionate distance method," respectively. Of the two methods, the singlesegment matching method clearly has the lower potential for introducing bias, but it results in a lower matching percentage. For the single-segment matching method, the proportion of the fare contributed by each segment is estimated on the basis of the relative values of fares for single-segment itineraries similar to those of the individual segments. Let M be the number of segments in an unmatchable itinerary i. Fo; each m = 1, ... ,M., segment m is matched to a set of single-segment itinerarie~ having the same origination airport, destination airport, and class of service. Let p;m denote the average fare, excluding fares with a value of zero, for single-segment itineraries that match segment m of itinerary i and pi denote the fare for itinerary i. Then, for this method, the imputed fare for segment m is Clearly, in order to impute a fare by the single-segment matching method, each of the segments in itinerary i must be able to be matched to at least one nonzero fare for a similar one-segment itinerary. The alternative second-stage imputation method examined assigns fares on the basis of the proportion of total mileage https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis represented by the individual segments within the itinerary. That is, the imputed fare for segment m in itinerary i is where pi is as before and dim is the distance 11 traveled in segment m of itinerary i (available in the databank) . Each of the methods described has its limitations. Because the proportionate distance method uses only relative distances to divide the fare among the segments, it can reasonably be applied only to itineraries in which all segments were flown in the same class of service. The restriction imposed by the singlesegment matching method, though, is even more severe: if just one segment in itinerary i has no comparable one-segment itineraries in the quarterly databank, the method cannot be used to impute fares for any of the segments in the itinerary. Both methods, however, allow for an implicit form of imputation within second-stage unit-value categories. Suppose, for example, that a particular segment does not qualify for singlesegment matching imputation. When this situation arises because another segment in the itinerary could not be matched to a similar single-segment itinerary, there may be fare values in the unit-value category into which the segment falls. The segment then implicitly receives an imputed value equal to the average imputed fare for that category. 12 That is, the segment's missing fare does not affect the average for the category, but the segment still contributes to the category's weight in the aggregate indexes. Similarly, a segment that fails to qualify for proportionate distance imputation because of disparate classof-service codes within the itinerary may fall into a unit-value category that contains fare values and be implicitly imputed. The clear disadvantage of the proportionate distance method relative to the single-segment matching method is that it fails to account for price pressures other than the distance of the flight (for example, airline "overhead" costs, and supply and demand). Note, however, that although this deficiency undoubtedly leads to biased fare estimates (generally speaking, assigning too large a proportion of the fare to longer flights) , it does not imply that the proportionate distance method yields unit-value indexes that are significantly biased relative to those computed by singlesegment matching. The initial thinking was that if the bias pattern were relatively constant across time, then the unit-value indexes computed by the proportionate distance method- and thus the aggregate indexes- would closely approximate those computed by single-segment matching. This hypothesis was tested with data from a four-quarter test period stretching from the third quarter of 1999 to the second quarter of 2000. Testing revealed that, within the itineraries not matched in the first stage, roughly 53 percent to 54 percent of the segments qualified for single-segment matching imputation, whereas the percentage qualifying for proportionate distance imputation Monthly Labor Review June 2005 19 Air-Travel Transaction Index hovered around 85 percent. As expected, proportionate distance imputation consistently allowed the imputation of a higher overall percentage of segment-level fares and the matching of more passenger flight segments across quarters, thus reducing the potential for index bias resulting from the omission of certain itineraries or segments. In general, roughly 84 percent of itineraries, representing about 7 5 percent of passenger flight segments, are matched in the first stage. (Because itineraries comprising large numbers of segments are less likely to be matched in this stage, the percentage of itineraries matched is expected to exceed the percentage of segments matched.) About 75 percent of the passenger segments not matched in the first stage are matched in the second stage under single-segment imputation. The newly matched segments include segments whose fares have been implicitly imputed, as described earlier. The matched segments represent approximately 18 percent of passenger flight segments in the databanks. For single-segment matching imputation, the resulting total percentage of segments matched is about 93 percent to 94 percent. Proportionate distance imputation provides a total matching percentage of roughly 98 percent. It is important to note, however, that under single-segment matching imputation, a larger percentage of segments receives implicit imputation: about 21 percent of second-stage segments (roughly 5.25 percent of all segments) are implicitly imputed under this method, compared with about 9 percent (2.25 percent of all segments) for proportionate distance imputation. Nonetheless, fares imputed by the proportionate distance method do indeed appear to be somewhat biased relative to those imputed by the single-segment matching method. On the one hand, results of t-tests indicated that, at low levels of aggregation (at which the indexes were subject to high variances), the differences between the unit-value indexes computed by the two methods were not significant. For !-statistics based on unit-value indexes within "city of origin" categories, for example, p values generally ran between 0.02 and 0.8. On the other hand, at higher levels of aggregation, significant differences were sufficiently common to raise concern, even given the magnitude of the sample sizes. Within "class of service" categories, p values below 0.05 appeared for three of the six major categories in one of the quarter-to-quarter test periods (the first to second quarter of2000). Moreover, even at low levels of aggregation, the t-statistics revealed that distance-based imputation yielded consistently higher unit-value indexes than did single-segment matching imputation. An examination of the differences between fares imputed by the two methods indicated that proportionate distance imputation generally overestimated fares for longer flights and underestimated fares for shorter flights. This was expected, because the method fails to account for airline overhead costs associated with individual flights. The majority of flights recorded in the databanks have distances less than the average 20 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 distance (that is, the mean flight distance exceeds the median distance), so we expect distance-based imputation to underestimate fares for the majority of flights. This tendency, along with the general one of the unit-value indexes to exceed unity, may account for the upward bias of the unit-value indexes computed through proportionate distance imputation. To see this relationship, let J;,11 andJ;,1~ represent the single-segment impuJed fares for periods I and 2, respectively, and suppose that J; ,1 < J; ,1~ ( in other words, fares increased between periods t 1 and t 2 ) . Let d represent the absolute value of the bias (assumed constant and additive) of the distance-based imputed fares relative to those computed ~by single-segment matching. (ThaJ is, for i E {I, 2} , let f 2.,; = J; ,,; -d.) Then, with d < J;,11 < Ii.,~, it follows that 1 J; ,lz J; ,lz -d Ii.,, > ----;:;--=-- , J; ,,1 fi.,1- d J; ,,1 giving an upward bias for the unit-value indexes computed by proportionate distance imputation. The assumption of a constant additive bias is, of course, a strong one. It is also possible that the upward direction of the bias of the unit-value index es computed by proportionate distance imputation indicates that the bias pattern of the fares imputed by this method is changing gradually over time. Specifically, the upward bias of the imputed fares for longdistance flights may be increasing, perhaps indicating that factors other than distance were exerting an increasing influence on the prices of airline flights over the period examined. It is therefore possible that, during other periods-especia lly those marked by rapidly increasing fuel costs- the direction of the bias of the unit-value indexes changes. In sum, the test results indicated cause for concern about the potential bias of unit-value indexes computed by the proportionate distance method, relative to those computed by single-segment matching imputation. Although the proportionate distance method yielded a higher overall matching percentage, the difference in matching percentages was not sufficient to warrant the use of that method in view of its evident deficiencies. Comparing first- and s econd-stage unit-value indexes. Under single-segment matching imputation, second-stage unit-value indexes were compared with unit-value indexes obtained from first-stage (itinerary-level) matching. Using only observations that matched in the first stage, the following indexes were computed for each first-stage category c: i. a first-stage unit-value index u c, i , 2 (as discussed earlier in the section; see the appendix for the formula) and ii. an index , u?;, 2 based on unit values computed through second-stage matching. (Again, see the appendix for the formula.) Note that u!? 2 reflects a price change for an itinerary-level (first-stage) category, but is computed by aggregating segment-level (second-stage) unit-value indexes for the various segment-level categories that correspond to the itinerary-level category. For example, the first-stage category comprising restricted coach itineraries for United Airlines round-trip service from Washington's Reagan National Airport to Chicago's O 'Hare Airport has two corresponding segment-level categories, one for each segment of the itinerary: ( 1) United restricted coach service from Washington Reagan to Chicago O'Hare within a round-trip itinerary and (2) United restricted coach service from Chicago O'Hare to Washington Reagan within a round-trip itinerary. To examine the effects of segment-level imputation and matching relative to those of itinerary-level matching, the distributions of ui~;,2 and uc,i.2 for the second through the fourth quarters of2000 were compared. Histograms 13 showed that the distributions were similarly shaped (slightly positivel(s skewed) and that the distribution of the differences uc.i, 2 - u/(, 2 was roughly symmetric about zero. For the three quarter-to-quarter changes tested, the numbers of first-stage categories, shown in table 1, hover around 300,000. In each case, the mean difference uc.i,2 -ui~;, 2 is statistically significant, due to the large number of categories. The Fisher indexes computed from the two sets of subindexes, however, differ only in the third decimal place, as indicated in the table. Chart 1 summarizes the current two-stage procedure in flowchart form. Note that the current experimental process does not include a "quality adjustment" step to account for changes in the real values of itineraries flown in different periods ( due, for example, to changes in food served or seating space). Quality adjustment is not practical here, because the data needed for such adjustment (for instance, by hedonic regression) are not collected in the current O&D Survey. More importantly, we have no reason to believe that the collection of itineraries matched in later quarters is qualitatively any better than the collections matched in earlier quarters. Rather, the unmatched flights and itineraries simply represent unusual travel routes flown in particular quarters. Thus, the systematic downward bias that the absence of quality adjustment may induce for items whose quality is generally improving with the introduction of new models 14 is unlikely to occur in the application presented here. Experimental index series This section examines some ATPI series, based on the Fisher index formula, for several class-of-service and point-of-origin https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Fisher indexes computed by aggregating firststage C uc,i ,z ) and second-stage C u~t value indexes Index period, 2000 First quarter to second quarter .. Second quarter to third quarter .... .... ............... ...... ... . Third quarter to fourth quarter ...... ....... ... ................. . ) unit- Secondstage Fisher index Number of categories 1.02679 1 02866 287,727 1 02468 1.02202 325,445 1 00613 1.01036 312,343 Firststage Fisher index categories. Indexes based only on first-stage matching are labeled "preliminary," while those based on both first- and second-stage matching are labeled "final." The index series to be presented were computed solely with data from U .S. carriers; that is, only itineraries flown entirely on U.S. carriers are in scope for these series. Except where otherwise stated, the index series shown are referenced to the first quarter of 1995. The discussion accompanying the charts that follow is intended to highlight interesting features of the index series. In interpreting the series, readers should bear in mind the scope of the O&D Survey, as well as the exclusion of foreign-carrier flights from the data. The survey covers all air itineraries having some U.S. component and being flown on all carriers reporting. Thus, the index series computed for foreign points of origin cover, not all itineraries originating from those points, but only the itineraries that include some U.S. destination or "stopover" points. The "class of service" variable for the O&D Survey underwent a standardization process in 1997- 98, and the change in reporting codes may be responsible for some of the movements observed in the index series. Accordingly, in the discussion that follows, special attention is given to the portion of the series between the fourth quarter of 1998 and the second quarter of 2003. Tables 2 and 3 summarize the percent changes over this period. Primary A TP! series compared with BL S and BEA airfare index series. Chart 2 compares the ATPI series with the BLS CPI series and the BEA Personal Consumption Expenditure Deflator for airfares. 15 The top panel shows all series referenced to the first quarter of 1995 , the bottom panel to the fourth quarter of 1998. The BLS index differs in its seasonal pattern from both the BEA index and the ATPI , due to its different definition of the reference period (the date of sale rather than the date of the flight). Consequently, just the long-term trends, and not the quarterly movements, of the different index series are comparable. The BLS CPI covers Monthly Labor Review June 2005 21 Air-Travel Transaction Index Two-state matching procedure for Passenger Origin and Destination (O&D) Survey airfare index computation All itineraries No Firststage matching Secondstage imputation No Yes No Secondstage matching INDEX CALCULATION 22 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Yes Yes DISCARD 0 >----N-- - - - - ( DISCARD) June 2005 11•1•n~- Percent change for major index series, fourth quarter 1998 to second quarter 2003 Series BLS c P1 for airline fares ....... ......... .... .. ........ .... ....... .. BEA personal consumption expenditure deflator for airfares .. ... .......... ........ .. ...... .... .. ........ .. ....... ... Full-scope ATP1 .... .... ..... ....... ..... ....... .... ........ .... ...... . U.S.-origin ATPI ............. .... ..... .. .. ... ..... .. ...... .. .. .. ... ... Foreign-origin ATP1 .... ...... ... .. .... .. .... .......... .... ... ..... .. Restricted coach class ATPI .. .... .. ........ .. .. .. .... .. .. .. . Unrestricted coach class ATP1 .. .... .... .. .... .. .... ...... .. Restricted first-class ATP1 .. ...... .. .. ....... .. .... .... .... .. .. Unrestricted first-class ATPI .... ... .......................... . Restricted business-class ATPI .... .. ............ .. .. .. .... . Unrestricted business-class ATP1 .. .. .. .. .. .. ...... .. .... .. Percent change 15.4 -11.7 6.6 6.8 4.4 9.8 -9.2 7.1 1.4 42 .1 11.4 only itineraries originating in the United States and is comparable, therefore, to the "U.S.-origin-only" ATPI. Before the third quarter of 1996, the BLS modified Laspeyres index suffered from an upward "formula bias." 16 Thus, we expect the BLS index to run above the U.S.-origin ATPI for the period from the first quarter of 1995 to the third quarter of 1996. For the period from the fourth quarter of 1998 to the second quarter of 2003, the BLS index is based on the hybrid Jevons/Modified Laspeyres formula. 17 The BLS index increased 15 .4 percent during this period, while the U.S.-origin ATPI increased 6.8 percent and the full-scope ATPI increased 6.6 percent. This difference is probably due mainly to (I) the different target formulas used (Fisher or Jevons/Modified Laspeyres) and (2) the ATPI 's inclusion of special discount fares that involve differential pricing (for example, frequent-flier awards and Internet specials), combined with consumers' increasing use of special discount tickets during the period. The U.S.-origin ATPI also shows a sharper drop in the last two quarters of 2001-a more pronounced "9/ l l effect"-than is seen in the airfare CPI. Chart 2 also compares the ATPI series with the quarterly BEA Personal Consumption Expenditure Deflator for airfares. In the top panel, which shows all series referenced to the first quarter of 1995, the BEA series runs above the U.S.-origin ATPI series for most of the period shown, but the two series cross in the fourth quarter of 2000, when the BEA series begins a steep decline. The bottom panel of chart 2 shows the BEA series running consistently below the ATPI. Research has revealed that the average distance flown per airline itinerary has been steadily increasing in recent years, which has naturally led to a decline in air carrier revenues per passenger mile. 18 Because the BEA index is driven largely by a measure of revenue per passenger mile, we expect the increase in distance, along with a corresponding increase in the percentage of passengers choosing "no-frills" air-travel service, to push the BEA series below the ATPI series during the 1999-2003 period. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Comparing final and primary ATP! series. The top panel of chart 3 shows the preliminary and final ATPI series for U.S . and foreign points of origin. As expected at this level of aggregation, the two series are virtually indistinguishable. The same holds for the series (not shown) for foreign and domestic points of origin combined. The remaining three panels of chart 3 show preliminary and final series by class of service for domestic points of origin. Index values for 1997-98 must be interpreted with caution, because the reporting codes were changed during this period. A variety of reporting codes previously used were standardized to produce the basic categories of first class, business class, and coach. Each of these categories is further divided into restricted and unrestricted tickets; the price for restricted tickets carried some restrictions for the purchasers. (For example, advance booking was required, and there was an added fee for a change in schedule.) Again, in general, little difference is found between the preliminary and final versions of the experimental series. Whatever differences there are are especially small for the largest category: restricted coach (second panel of chart 3). For unrestricted coach service, the preliminary and final series are similar, except that (I) the final series shows a less severe "break" (in this case, an upward jump) between the fourth quarter of 1997 and the first quarter of 1998, and (2) the final series shows a more pronounced drop from the terrorist attacks of 9/ l 1 in 2001. The restricted coach index is conceptually the closest substitute for a consumer price index that has been produced from the O&D Survey data: it reflects movements in fares paid by the most price-conscious buyers. The final restricted coach series increased by 2.6 percent from the first quarter of 1995 to the second quarter of 2003. From the fourth quarter of 1998 to the second quarter of 2003, however, it increased by 9.8 percent, closer to the increase indicated by the official airfare CPI (See chart 2.) The unrestricted coach series displays an unusual downward spike from the third quarter of 1995 to the second quarter of 1996; because a number of class-of-service code systems were in use during that period, the odd movement may be associated with variability in coding. Over the entire period from the first quarter of 1995 to the second quarter of 2003, the final unrestricted coach series increased 16.4 percent, while the restricted coach series increased by 2.6 percent, as just noted. Over the period from the fourth quarter of 1998 to the second quarter of 2003, however, the trend was reversed: the unrestricted coach series decreased 9.21 percent, while the restricted coach series increased the aforementioned 9 .8 percent. The series for business-class service appear in the third panel of chart 3. For these categories, the differences between the preliminary and final versions of the series are noticeable, but not extreme. Moreover, the final series runs slightly above the preliminary series for restricted business-class service and slightly below the preliminary series for unrestricted business- Monthly Labor Review June 2005 23 Air-Travel Transaction Index ■ 1•1•u~- Percent changes for point-of-origin ATPI series, fourth quarter 1998 to second quarter 2003 City or area Percent change Chicago ................ ........ ..... .. .... .. ... .... .... .. ... ..... .. Los Angeles ........ ..... .. ..... .... ..... ..... ...... ... .... ..... . New York .. .............. ......... ........... ..... .... ..... ..... ... . Montreal , Canada ...... .. ..... ...... ........... .............. . Toronto, Canada .. ... .... ............ ..... .. .................. . Vancouver, Canada .......... ........... ..... ... .. ....... ... . Canada .......................... .... ... ....... ...... ... ...... ..... . Frankfurt, Germany .... ... .... ... ..... ...... ........ ... ... ... London, England ...... ... ..... ..... ...... ..... .... .... .. ..... . Tokyo , Japan ..... ....... ...... ......... .. ...... ... ........... ... -0.78 3 .1 Houston .... ... ..... ....... .. ....... ........ ... ...... .... ....... ... . Minneapolis ..... .... .... ..... .... ... .... ... ...... .. .... ... .. .... .. Washington , oc .... ... .. .... ... .. ... ... .... .... .. ........ ...... Detroit .. ..... ..... .... ..... ........ .. ..... ... ....... ........ ..... ... . Charleston , sc ..... ............. ................. .............. . Colorado Springs ...... ........ ........................ .... .. .. Des Moines .... .... ....... ....... .... ... .... ... .... ..... ..... ... .. Albany ... .. ............... .. .... ... .. ............ ... .. ... .. .. ...... . Dayton .. ... ... .......................................... .... ....... . Tucson .. ..... .. .... ... ..... ........ ......... .......... .......... .... 8.8 17.1 14.1 18.6 23.5 7.0 -1.3 4.4 18.1 19.2 24.0 18.7 10.1 13.8 3 .2 10.8 7 .2 4 .0 class service , indicating that there is no systematic bias associated with the unit-value indexes produced through second-stage matching. The "big dipper" movement of the restricted business-class series during I 997-98 may be due in part to the earlier mentioned changes in reporting codes. Changes in frequent-flier upgrade behavior also may be partly responsible. The bottom panel of chart 3, showing the series for first-class service, reveals almost no difference between the preliminary and final versions of the series for restricted first-class service, except for a slight divergence during the I 997-98 break. The series for unrestricted first-class service are similar to those for unrestricted coach (second panel of the chart): the final series differs from the preliminary one only in that it suffers a milder 1997-98 break. Moreover, during the period from the fourth quarter of: )98 to the fourth quarter of 2000, the restricted firstclass series displays movements similar to those of the series for restricted coach service. This similarity may reflect a growing number of frequent-rlier passengers who upgraded and flew first class during the period, together with an increase in the number of coach service seats classified as first class by some carriers when reporting data for the O&D Survey. 19 The indexes shown in the last three panels of chart 3 generally indicate steeper price increases for unrestricted air service than for restricted service. Because special discount fares apply almost exclusively to restricted service, these indexes provide evidence that the divergence of the BLS and ATPI series (see chart 2) is due in part to the O&D Survey's inclusion of such discount fares. Index series by place of origin. This section examines O&D Survey index series computed for various cities of origin in 24 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 a passenger's itinerary. These series are local-area economic indicators reflecting changes in the airfare component of the cost of living for residents of the cities in question. Particular cities , representing a wide range of geographic areas and sizes, were selected to serve as examples. Note that, for these detailed itinerary-level points of origin, second-stage matching is not practical due to the small number of segments in most of the resulting second-stage categories. For these characteristics, the preliminary series are therefore final. The series in the top panel of chart 4 for the three largest U.S. cities indicate similar price movements for itineraries originating in these cities. The series run roughly parallel to, though slightly above, the U.S. Origin ATPI series shown in chart 2. Much more disparity appears in the movements of the series for Canadian20 cities of origin (middle panel of chart 4), with Toronto exhibiting the largest increase by far over the period shown. Except for the "9/11 effect," the Canadian city index series tend to gradually level off during the later years of the period. Interestingly, the Toronto series displays a much more pronounced 9/ l l effect than the series for the other Canadian cities. The most striking feature of the index series for large overseas cities of origin (bottom panel of chart 4) is the seasonal pattern. The third-quarter spikes indicate a predominance of vacation travelers paying peak-season fares. Price movements for overseas cities of origin are confounded with changes in currency exchange rates, which may account for some of the overall decrease in the series shown in the chart. Except for seasonality, these series, like those for U.S. and Canadian cities, tend to level off from the fourth quarter of 1998 to the second quarter of 2003. One possible exception, however, is the Frankfurt series, which shows an unusual increase in the final 2 years of the period. The Houston series (see top panel of chart 5) is similar to the series for Los Angeles (chart 4), except that it shows larger increases in the first quarters of 2001 and 2003. Similarly, the series for Detroit and Minneapolis (top panel of chart 5) track each other quite closely, perhaps due to geographic proximity and the dominance of the same air carriers in the two cities. Although these two series run well below those for the larger cities, they display the same "leveling" trend during the final years shown and a much less pronounced 9/ 11 effect. For the period from the fourth quarter of 1998 to the second quarter of 2003, the series for Detroit, Minneapolis, and Washington, DC (again, top panel of chart 5), show some of the larger increases among the "city of origin" series examined. The Washington index increased 14.1 percent over this period, while the Detroit and Minneapolis series increased 18.6 percent and 17 .1 percent, respectively. In the latter two series, however, the increases followed steady declines seen in the previous couple of years. The city index with the largest decrease (among those shown) for the period from the fourth quarter of 1998 to the second BLS hybrid airfare CPI and primary ATPI series, not seasonally adjusted, 1995-2003 Index (first quarter Index (first quarter 1995 = 100) 1995 = 100) 140 . . . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 140 130 130 120 120 110 110 100 - 100 90 90 80 80 70 ~ - - - - ~ - - - - - - - - - - - _ . __ _ _ _.___ _ __.__ _ _ _.....__ _ _______.._ _ _ _-'----' 70 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 Index (fourth quarter Index (fourth quarter 1998 = 100) 1998 = 100) 140 . . . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 140 BLS quarterly average 130 / 120 110 100 90 130 ~ - - -~ -- 120 ·····-·---- ~ ~---~ / ~ Full-scope ATPI 80 / / " .... - -- -- 110 100 ATPI for foreign origin only "---......------·--,,",,._ __ , ~------------- --- --------- ---- 90 80 BEA personal consumption expenditure deflator 70 ~ - - - - - - - - - - - - - - - - - - - - - ' - - - - - - - - ' - - ' - - - - - - - - - . . . . . __ _ ___, 70 Quarter 4 Quarter 4 Quarter 4 Quarter 4 Quarter 4 1998 1999 2000 2001 2002 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 25 Air-Travel Transaction Index Preliminary and final ATPI series for U.S. and foreign points of origin or for U.S. points of origin alone, 1995-2003 Index (first quarter 1995 = 100) 160 150 Index (first qudrter 1995 = 100) 160 150 U.S . and foreign points of origin, all classes of service combined 140 130 140 Preliminary ATPI, foreign origin 130 120 110 ::: ---·· ·.·:~------.., . --·--·7<-··--.~""m,z~=.__----~--~-~--.-----.. . . ..___. .. . ..-•,. . :: •... -··... : :~,-; :;,·,:~:,~:o~:~:··7 •·........ / ·••,...... / "•........: 100 90 ·-·-·- · · ·" ·•. ,. · ' , , Final ATPI , U.S. orlQin 60...__ _ _ _ _ __.__ _ _ _ _ _..___ _ _ _ _ _..___ _ _ _ _ _....__ _ _ _ ___,,_ _ _ _ _ ___.__ _ _ _ _ ___.__ _ _ _ _ __.__ _,60 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 Index (first quarter 1995 = 100) Index (first quarter 1995 = 100) 160 . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , 160 ;: U.S. points of origin . restricted and unrestricted coach-class service :: ,::: _,-,,,~.,:.:.:i.: ::_::~•<~:·:✓- ......... _ .................. •···-·..• · ___,..--·-. .......··· • •.• • • • •• • •• • : Fmal . Fmal • • .............. / .. _,,P,e1,mma::-:;~;~:::::~~:·-. ATPI. "~'.:'~'~'.'."~ • , • • • • • • • . • ATPI , " " " / 150 Preliminary A T P /Slri cted 140 ···•. 130 • • • •• •• • •• •• • •• • :~ 120 • :: 50 ~ - - - - - - - - - - - - - ~ - - - - - - - - - - - - - - - ~ - - - - - - - - - - - - - ~ - - - - - - - - - - - - - - - - - - - - - - - ' 60 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 Index Index (first quarter (first quarter 1995 = 100) 1995 = 100) 160 . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , 1 6 0 150 140 130 120 11 0 100 90 80 Preliminary ATPI , restricted 80 ro ro 5o u . . . - - - - - - - - ' - - - - - - - - - - ' - - - - - - - - ' - - - - - - - - ' - - - - - - - - - - - - - - - - - - - - - - - - - - - ' - - - - - - - - - - ' - - - ' 6 0 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 Index (first quarter 1995 = 100) 2003 Index (first quarter 1995 = 100) 160 . . - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - . 1 6 0 150 140 U.S. points of origin, restricted and unrestricted fi rst-class service ;: ---• ···········. 150 140 ~ Preliminary ~~.~'.:_LI~~·····-··················•.. ··························-······· ;: :~~-c=:::-:<~t;:;:;:;~;::: : : :· · ~ - - - - - - 1 ::~ 80 Preliminary ATPI , restri cted 80 ro ro 6 0 - - - - - - - ~ - - - - - - - - - - - - - - - . . _ ________....__ _ _ _ ___,,_ _ _ _ _ ___.__ _ _ _ _ __.____________.60 26 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 ATPI series for large cities of origin, all classes of service combined, 1995- 2003 Index (first quarter Index (first quarter 1995 = 100) 1995 = 100) 160 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 160 150 U.S. cities 150 140 140 New York 130 130 120 120 ---......... 110 110 100 100 90 90 Los Angeles 80 80 70 70 60 ~ - - - - - ~ - - - - - ~ - - - - - - - ' - - - - - - ~ - - - - - ~ - - - - ~ - - - - - - - - ' - - - - - - - - ' - - - - - ' 6 0 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 Index (first quarter Index (fi rst quarter 1995 = 100) 1995 = 100) 160 , - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 160 150 150 Canadian cities Toronto 140 140 -----------.. 130 _ 120 110 ~ 100 •• • ,,...,.....__ ,~----- .......- ~ - , -~ ____..,. All Canada------------- 130 / 120 ,,..,.. ___ ~•••••••••• • _-..;:/ ····-•. ~ •·•·················· · · ···· ··········· · ···· · ··· ......., - 110 100 ....- 90 Montreal 80 90 ' ~ 80 70 70 Vancouver 60 ._.__ _ _ _ __.___ _ _ _ _..___ _ _ ___.__ _ _ _ ___.__ _ _ _ __.___ _ _ ___._ _ _ _ ____.__ _ _ _ _....._____. 60 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 Index (first quarter Index (first quarter 1995 = 100) 1995 = 100) 160 , - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , 160 150 150 Overseas cities 140 140 London ~ ~ 130 130 :~ / / V ~ ;~ Tokyo_- 120 110 Frankl~ 100 ~ . '~ :: 5ou....._ _ _ _ _..J....__ _ _ _ _1-.._ _ _ ____J__ _ _ _ _---'-_ _ _ _ __.___ _ _ ____,_ _ _ _-=-......""'--------'=------'60 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 2003 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 27 Air-Travel Transaction Index ATPI series for selected U.S. cities, all classes of service combined, 1995-2003 Index (first quarter Index (first quarter 1995 = 100) 160 1995 = 100) 160 150 140 150 Large cities 140 130 130 Washington, DC 120 120 110 100 .....r.?!':':~~.:.;-,- ,:::»"N'-.~.~....K - ~ ~~· 90 80 '<-"\. . .• • • • • •• 110 ......___, , , , , ,._____, · ,. .., ... ··············· . · . . •· · • .. ·. ~~'._.... . . . .. Minneapolis 70 • ••-~ ~: • • • • • ' ~,-~..; ~ ' : : '~".'.'¾. •••• •.r ¾• ~ ; • • • - •"'., ,, -•~~•,..•11••••• . f ' r ~ ~ ·~. __,_ · · ;;;:'~W'i/, •••• •_. --._. 100 90 80 Detroit 70 6 0 ' - " - - - - - - - - - - - - - - - - - - - - - - - - - ~ - - - - - ~ - - - - - - - - - - - - - - - - - -........~ 5 0 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 Index (first quarter Quarter 1 2003 Index (first quarter 1995 = 100) 1995 = 100) 160 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , 160 150 150 Selected medium-size cities 140 140 130 130 Charleston. SC ...... 120 110 120 110 100 90 80 7o Colorado Springs Moines 70 60'-"------~------------------~-----~------------------~~50 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 Index (first quarter Quarter 1 2003 Index (first quarter 1995 = 100) 1995 = 100) 160 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , 160 150 150 Selected medium-size cities 140 140 130 130 120 120 ;:: ~~·~·······~~~······················ ···~1/'~-:::·:···~······ :: 90 ····-········· ~ 80 ~ Tucson 90 Dayton 80 70 70 60.....__ _ _ _ _...__ _ _ _ _..______________.....__ _ _ _ _...__ _ _ _ _..___ _ _ __...__ _ _ _ _........~60 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 Quarter 1 1995 1996 1997 1998 1999 2000 2001 2002 28 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Quarter 1 2003 quarter of 2003 is that of Des Moines, Iowa, with a drop of 1.3 percent. (See middle panel of chart 5.) The series for Charleston, South Carolina, however, gradua11y climbed 23.5 percent during the same period. The index series for Colorado Springs, Colorado (again, middle panel of chart 5), reflects the impact of Western Pacific, a low-cost airline that began offering discount service from Colorado Springs to Da11asFort Worth in the second quarter of 1995. In 1995-96, Western Pacific expanded its operations to other markets, including Seattle and Washington, DC. Larger airlines responded by lowering fares and expanding service in markets served by Western Pacific, which then was forced to curtail its operations, ultimately ceasing all operations in the early part of 1998. The series for Albany, New York, and Dayton, Ohio (bottom panel of chart 5), track each other fairly closely, except for the dip in the Albany series in the second and third quarters of 2000. Their similarity may reflect regional economic impacts and similar servicing carriers, as do the Detroit and Minneapolis series shown in the top panel of the chart. The Tucson, Arizo11d, series (bottom panel of chart 5) is atypical, displaying movements somewhat similar to those seen in the Colorado Springs series, though less dramatic. In the case of Tucson, however, there is no firm evidence of a "discount carrier" effect on the index series. (The presence of Reno Air in the Tucson market may have exerted a downward pressure on airfares from Tucson, but Reno did not exit the market until the second quarter of 1999, we11 after fares had begun to increase.) BLS employment and unemployment data21 indicate a general economic downturn in Tucson in 199697, characterized by increased unemployment levels and rates; this decline seems the most likely explanation for the contemporaneous dip in airfares. Additional developments The Bureau of Transportation Statistics' ATPI research project has involved numerous specific methodological studies. In one such study, an empirical investigation into alternative chaining intervals revealed no evidence of chain drift in the quarterly chained Fisher series presented in this article. 22 A study of sensitivity to extreme values showed the Fisher index estimator to be more robust than the Tomqvist for the airfare application. In the future, the expanded O&D Survey data wi11 offer the possibility of using shorter chaining intervals-for example, months or even weeks-and of producing timely monthly indexes. Other areas for future research include standard error estimation for the index series and the development of seasonal adjustment methods. □ Notes ACKNOWLEDGMENTS: Suggestions from Ed Wegman of George Mason University, Sylvia Leaver of the Bureau of Labor Statistics, and Othmar Winkler of Georgetown University have greatly enhanced the work presented in this article. We also thank the Monthly Labor Review referees for their helpful comments. 1 Although the ex perimental ATPI series were originally computed with data from both foreign and domesti c carriers, legal concerns from within the Department of Tran sportation required the suppression of the data from foreign carriers . Overall, the removal of those data resulted in minimal changes to the series presented. 2 For a detailed discu ss ion of CPI methods , see BLS Handbook of Methods (Bureau of Labor Stati stics, 1997) , Chapter 17 , '·The Consumer Price Index, " available on the Internet at ww~ .bis.gov/ opub/hom/home.htm . 3 For a discussion of alternative index formulas, see, for example, Irving Fisher, The Making of Index Numbers: A Study of Their Varieties, Tests , and Reliability (New York, Sentry Press, 1922) ; W. Erwin Diewert, " Index Numbers ," in John Eatwell, Murray Milgate, and Peter Newman, eds., The New Pa/gra ve: A Dictionary of Economics, Vol. II (London, MacMillan, 1987), pp. 767- 80; or Brent R. Moulton, "Basic components of the CPI: estimation of price changes," Monthly Labor Review, December 1993, pp. 13-24 (on the Internet at http:// www.hls.gov/opub/mir/1 993/12/art2full.pdf) . For more information on price index concepts and design, see Charles L. Schultze and Christopher Mackie, eds., At What Price? Conceptualizing and Measuring https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Cost-oFLi ving and Price Indexes (Washin gton , DC, National Academy Press, 2002). 4 See W. Erwin Diewe rt, '·Exact and Superlative Index Numbers, " Journal of Econometrics, May 1976, pp. 115-45. ~ See, for example, Ana M. Aizcorbe and Patrick C. Jackman, "The commodity substitution effect in CPI data, 1982- 9 I ," Monthly Labor Re view, December 1993 , pp. 25-33; and Matthew D. Shapiro and David Wilcox, " Alternative Strategies for Aggregating Prices in the CPI," Federal Reserve Bank of St. Louis Revie w, May/June 1997, pp. 113- 25. The method s a nd res ult s desc rib e d in the 5e articles have been called into question by Alan H . Dorfman , Sylvia G. Leaver, and Janice Lent, " Some Ob se r va tion s on Pri ce Index Estimators," Proceedings of the Federa l Committee on Statisti cal M e thodology Resea rch Conference, Monda y B Sess ions (Federal Committee on Statistical Methodolog y, 1999), pp . 56-65 . 6 The 0&D Survey fares include zero-va lue fares (for example, for frequent-flier awards), which are imputed as $0.01. These imputations often result in extreme values for the unit-value indexes that serve as the •·atoms" of the indexes presented in this article. (See later.) 7 For a discus sion of the performance of the index formulas with respect to outliers in air-travel application, see Janice Lent, "Effects of Extreme Values on Price Indexes: The Case of the Air Travel Price Index," Journal of Transportation and Statistics, vol. 7, no s. 2-3, 2004, pp. 41 -52. 8 If an itinerary straddles multiple quarters, it is counted in the Monthly Labor Review June 2005 29 Air-Travel Transaction Index quarter in which the first ticket in the itinerary is used. 9 See, for example, the findings of Steven Anderson and Richard Leonard, "Domestic Airline Industry Passenger Price Trends," internal document, Bureau of Transportation Statistics, April 26, 2004. 10 In the terminology used in this article, one segment involves exactly one aircraft takeoff and landing. Due to reporting deficiencies in the O&D Survey, some multiple-stop flights are currently being reported as nonstop flights, and the actual number of stops cannot always be determined . The Bureau of Transportation Statistics is working to correct this data-reporting problem. 11 Tests also were run that used the square root of the distance in place of the distance. The "square root of proportionate distance" method produced the same type of bias as the proportionate distance method, although the severi ty of the bias was somewhat reduced. 16 See, for example, Robert B. McClelland, "Evaluating Formula Bias in Various Indexes Using Simulations," 1996; on the Internet at http://www.bls.gov/ore/pdf/ec960140.pdf; or Brent R. Moulton, "Bias in the Consumer Price Index : What Is the Evidence?" BLS working paper no. 294, 1996; on the Internet at http://www.bls.gov/ ore/pdf/ec960170.pdf. 17 For a discussion of the change in index formulas, visit www.bls.gov/cpi/cpiadd.htm#4_1 on the Internet. 18 See Anderson and Leonard, "Passenger Price Trends." 19 For production purposes, the Bureau of Transportation Safety will define a new class of service comprising services provided by carriers that offer only one class of service. Thus, itineraries flown on carriers, such as Southwest, that report all their seats as first class will not be categorized as first class. 20 12 For formulas detailing the method of implicit imputation, see the appendix. 13 Copies are available from the authors upon request. 14 At least one researcher has identified such a bias. (See Jan De Haan, "Generalised Fisher Price Indexes and the Use of Scanner Data in the Consumer Price Index (CPI)," Journal of Official Statistics, March 2002, pp. 61-85 .) 15 A description of the BLS estimation method is available on the Agency's Web site at http://www.bls.gov/cpi/cpifacaf.htm. APPENDIX: Note that, for all points of origin outside the United States, the Survey indexes cover only itineraries incorporating some U.S. component. O&D 21 Visit http://l 46.142.4.24/servlet/urveyOutputServlet? series_id=lausm85200003 for the Tucson employment and unemployment figures . 22 For details, see Janice Lent, "Chain Drift in Experimental Index Series," Proceedings of the Section on Survey Research Methods, American Statistical Association, Alexandria VA, Joint Statistical Meetings, San Francisco, CA, Aug. 8-12, 2003 (published on CD only). Formulas for price index estimation A measure ofrelative change in the price of a particular item j between periods 1 and 2 is the price ratio, p 1_2 I p 1,i, where p 1_, represents the price of item j at time t e {1,2}. Because each quarterly O&D Survey sample is independently drawn, it is impossible to match each individual itinerary with an identical one in the following (or previous) quarter and compute individual price ratios. This article therefore presents a method for computing unit-value indexes for itineraries (or, in the second stage, segments) within each unit value category c e C, 2 , where C1, 2 is the collection of categories populated by sample un'its in quarters 1 and 2. (See text for definitions of categories.) For simplicity, it is assumed that prices are available for all observations in the data set. Let q1,, be the quantity of item} purchased in period t. For the O&D data, the item is an itinerary and q1,, is the number of passengers flying the same itinerary at the same fare. (The variable denoting the number of passengers is included in each O&D Survey itinerary record.) Because the O&D sample is self-weighting, we may directly apply the standard population price index formulas. Let Price index estimators. Once the unit-value index estimates are computed for all c e C, 2 , they are treated as price ratios in the standard index formulas. Forte{I,2}, let be the expenditure share for category c e c,,2 during period t. (Note that w e,, is dependent on C1, 2 and would be more clearly denoted by wq,, 2),,· For ease of notation, however, this dependence is left implicit; note also that all indexes described in this appendix indicate price changes between periods 1 and 2.) Then the following indexes may be estimated for all desired categories of aggregation c,. 2 : Laspeyres index: i = L wc,luc,1,2· ceC1•2 Je e The unit-value index estimator for category c is defined as Paasche index: p Fisher index: In words, the unit-value index is the average price paid for an item in category c during period 2, divided by the average price paid for an item in category c during period 1. 30 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 fr=&. Jevons (or geometric mean) index with weights from period I: G= TI u wc,I. stage category1 c is calculated as follows: Let K, denote the collection of second-stage (segment-level) categories k corresponding to category c. For a given quarter t , let c. l.2 CEC1,2 Tornqvist index: t = IT u("'c.l +wc,2 ) 12 _ c .1,2 cEC1,2 Implicit imputation through unit-value indexes. When some prices are missing from the data set, they may be implicitly imputed through the computation ofunit-value indexes. As noted in the text of this article, such imputation occurs in the computation of second-stage unit-value indexes. Let c' be the set of observations in category c with nonmissing price values, and let L jEc' q},IPJ.1 L jEc' q}, 1 be the average of the nonmissing prices in category c. Then the unit-value index for category c is defined as where qk.i is the number of passenger itinerary segments (possibly from itineraries in different first-stage categories) in second-stage category k for quarter t and, for l = 1, .. .,qk.1' P,.k.i is the imputed price of segment l in category k. Then L kEK, Pu L kEK, Pk.l As noted in the text, a second-stage category k may correspond to many first-stage categories c; that is, it may be that k EK, and k E Kc , where c1 -:t:- c 2 • Note also that is a Fisher i~dex indicat1ng price change from period I to period 2 for itineraries in category c, with the quantity associated with each Pk.i set equal to unity and the segment-level unit-value indexes serving as price relatives. That is, u~t. where The weight for category c in time t is u~~/. where qc.i is the total quantity of items in category c at time t (including those items with missing prices). The Laspeyres, Paasche, Fisher, Jevons, and Tomqvist indexes are then calculated from their given formulas, but with qc.i and w;_ , for t E {1,2}, 1 replacing u c, 1.i and w c. i, respectively. To compute the Fisher indexes shown in table I, the 2 were aggregated with the use of the Fisher formula, with expenditure share weights wc.i computed from itinerary-level data, as described in the text. Note to the appendix Using second-stage unit values to compute indexes [or first-stage categories. The second-stage unit-value index u~~1.2 for a first- https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 1 See text for a list of the variables that define a first-stage category. Monthly Labor Review June 2005 31 Multifactor Productivity · \~l: " ""~~»~ Preliminary estimates of multifactor productivity growth Final multifactor productivity measures take more than a year to complete; using a simplified methodology and preliminary data, it is estimated that private business multifactor productivity grew 3.1 percent in 2003 and 3.3 percent in 2004 Peter B. Meyer and Michael J. Harper Peter B. Meyer is a research economist, and Michael J. Harper Is the chief of the Division of Productivity Research and Program Development, Office of Productivity and Technology, Bureau of Labor Statistics. E-mail: Meyer. Peter@bls.gov Harper.Mike@bls.gov bor productivity growth supports longerm improvements in standards of living. roductivity can increase because of investments in equipment and structures, a more educated and experienced workforce, and improvements in technology. The BLS multifactor productivity (MFP) measures are designed to isolate the effects on labor productivity of capital growth and of the changing composition of the labor force. These input effects are reported separately, and multifactor productivity growth represents the unexplained portion of labor productivity growth. The multifactor productivity measures are designed along the lines of Solow's method of growth accounting. 1 Substantively, multifactor productivity change results from joint influences on economic output of technological change; efficiency improvements (for example, because of better transportation or communications); returns to scale; reallocation of resources (such as shifts of labor among industries); and other factors, after allowing for the effects of capital and labor growth. An example of a source of efficiency improvement is the construction of the interstate highway system. It has been argued that this raised multifactor productivity and, analogously, that the Internet and the World Wide Web have done so. Mulufactor productivity change is defined and measured as the growth rate of output minus the growth rate of measured inputs. Let Y be output, L be a measure of labor inputs, and K be a measure of capital services inputs. Define s to be the share of income paid to labor, and assume that U 32 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 the remaining fraction (1-s) is paid to capital. Delta (..:1) means the change since the previous year, so ..1YIY is the annual growth rate of output. BLS measures the quantities on the right side of the equation below to calculate the growth rate of multifactor productivity. l:lMFP MFP = l:lY Y _ s l:lL _ (1- s) l:lK (1) K L In the BLS approach, labor and capital inputs are divided further as discussed below. For example, labor input is a weighted combination of hours worked and can be divided into hours and changes in workforce composition. The notation we use later is that labor input L=H*LC, where H is a measure of hours worked and LC is an index of labor composition, adjusts for changes in the education and work experience of the employed population. Capital services can increase from growth in productive stocks of assets and from shifts within and across asset classes. A capital-income-weighted average of growth rates yields capital services. BLS publishes both index numbers and growth rates of multifactor productivity that averaged 0.96 percent from 1993 to 2002. BLS calculates the annual growth of multifactor productivity for the U.S. private business sector. This measure is generally released about 14 months after the end of the year being measured, often called the target year. 2 The lag occurs because the process of calcu- lating multifactor productivity requires detailed data from many sources. 3 Some users of productivity measures, including policy and budget organizations in the U.S. Government, have made their own preliminary estimates of multifactor productivity while awaiting the official BLS measures. For its frequent shortterm economic forecasts, the Federal Reserve routinely needs multifactor productivity growth figures before the BLS measure becomes available. Therefore, Oliner and Sichel of the Federal Reserve developed a method to make forecasts of the Bureau's estimates of multifactor productivity. 4 This article summarizes a simplified methodology that BLS plans to adopt to make preliminary estimates of private business sector multifactor productivity change available within a few months after the end of target year t. The simplified methodology involves making estimates of the growth rates of output, and of labor and capital inputs, and of the shares of each input. (See equation I). The simplified methodology works with fewer categories of capital and labor than the full methodology, as will be described below. The resulting simplified measure, called MFP5,, will later be supplanted by the full measure called MFPF1 when complete data become available. The simplified measure is usually based on information from the full calculation from the previous year and on up-to-date information about approximate rates of change in output, labor, and capital in the target year. The estimates of the rates of change use information from the National Income and Product Accounts (NIPA) and other sources that become available early in the year following the target year. The simplified methodology is designed to estimate multifactor productivity in a way that closely approximates that which is calculated by the full methodology, using the same basic structure and assumptions. For example, both methodologies estimate a productive capital stock for each of several kinds of assets. The productive stock is an aggregate of past investments weighted by estimates of their declining capacity to contribute to production because of deterioration and obsolescence. In the simplified method, such stocks are estimated for only a few summary asset categories instead of many detailed ones. In addition, rates of deterioration are determined from the recent average rate over all asset types in a class as developed in the full method. High-tech computer-related capital is still kept separate from other equipment in the simplified model because this category has grown substantially (representing half of nominal investment in the late 1990s) and has been influential on productivity trends in recent years. The simplified methodology is relatively transparent and robust. Simplicity will help make the estimate available as early as possible. The procedure is transparently related to the full measure, and has been designed to approximate the full measure with fairly modest degrees of random error and https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis bias. The computation is robust in that it is designed to work even when there are changes in accounting categories or procedures within the statistical agencies. For this reason, published data series were used wherever possible, not data series used only internally to the BLS or BEA. Although this may slightly lower the accuracy of the simplified measure, it reduces potential obstacles to producing the measure at an early date. The procedure is also meant to be relatively robust to structural change in the economy. A carefully tuned procedure might make better estimates for the 1990s data series than this one, but it might also be more sensitive to unexpected economic changes in the future. 5 There is a tradeoff between meeting these goals of simplicity, transparency, and robustness and the natural goal of reducing the discrepancy between the preliminary statistic and the full-method statistic. BLS expects to evaluate this methodology when there is a longer data series of simplified and full measure statistics with which to work. 6 The accuracy of the simplified measure should improve with experience. The purpose of this article is to describe the simplified method and the evaluation of its reliability. The article first reviews the estimation procedure for each component of multi factor productivity, providing summary statistics on the reliability of each estimate. After summarizing the simplified method and results for output, labor input, and eight components of capital, the article discusses the assembly of these estimates into the simplified measure of multifactor productivity. Contributions of errors in each component to this measure are discussed, and it is noted that these errors often offset. The resulting simplified multifactor productivity measure is fairly reliable. This article also reports and evaluates simplified estimates of productivity prepared for the second year ahead of the last year for which full model estimates are available. These "second-year" estimates are denoted MFP 52 1• The latest published BLS measures of multi factor productivity are for the year 2002. Finally, this article presents preliminary estimates of multifactor productivity for 2003 and 2004 using the simplified methodologies. The methodology is tested using annual data for each year since 1993. The simplified measures are estimated for each year, extrapolating from the previous year's full estimation. To evaluate the usefulness of this approximation, the simplified estimate for each year t, denoted MFP 51, is compared with the most recently published full measure for that same year, MFPF1• The evaluations in this article use the most recently available data for the full model, and therefore examine how well the simplified methodology replicates the full methodology for a given version of the data. In practice, when the BLS revises its simplified estimate to obtain a full estimate, the revision will reflect both the difference in methodologies and also any concurrent revisions to the underlying source data that will become available. 7 Monthly Labor Review June 2005 33 Multifactor Productivity Estimating output and labor inputs Background. The BLS private business multifactor productivity measures compare output to the combined inputs of labor and capital. The output measures used by BLS are derived from gross domestic product (GDP) and other data from the National Income and Product Accounts (NIPA) for BLS by the Bureau of Economic Analysis (BEA). The NIPA measures of "final product" exclude the value of intermediate inputs like the leather used to make shoes, and these output measures are appropriately compared to labor and capital inputs. 8 Productivity measures are meaningful only if outputs and inputs are measured independently. The NIPA measures of real output for general government, nonprofit institutions, private households and owner-occupied dwellings are excluded from published productivity measures in part because they depend on input measures to derive estimates of real output. 9 BLS publishes measures of labor productivity (output per hour worked) for the business sector on a quarterly and annual basis in its Productivity and Costs (P&C) news releases. 10 BLS publishes annual measures of multifactor productivity for the private business sector. The private business sector differs only slightly from the business sector in that it excludes the BEA estimate of the output of government enterprises. Government enterprises include the U.S. Postal Service and local government water and sewage services among other activities. 11 The private business sector accounts for about three-quarters of U.S. Gross Domestic Product. 12 The simplified method of measuring multifactor productivity estimates output growth and labor hours growth by applying the growth rates of output and hours in the business sector-from the published P&C measures-to the previous year's measures for the private business sector. The data for the simplified estimate are available soon after the conclusion of each year. Next, we describe the simplified approach and characterize how well the simplified estimate of each variable approximates the full computation. Exhibit 1 summarizes the inputs to the simplified multifactor productivity calculation. Components of simplified MFP calculation Component of multifactor productivity (MFP) calculation Sources and methods Structures and equipment investment (each of six categories) Apply growth rates of new investment from NIPA tables listed in exhibit A-1 in the appendix to BLS private business sector investment level from the previous year's "full-MFP" report Depreciation rates on existing capital assets Hold constant the depreciation rates in the most recent full-MFP report Structures and equipment productive capital stocks By perpetual inventory method; deduct estimated depreciation of the previous year's stock of each asset type and then add new investment Inventory capital stock The previous year's stock in the full-MFP report is extrapolated with the percentage change in the NIPA inventory series for the business sector (see exhibit A-1 in the appendix) Land capital stock Extrapolated using the structures capital stock Income shares of capital categories Detailed asset shares from the previous year's full-MFP report are aggregated into these eight categories and assumed constant Capital service inputs Chain index combining stocks of the eight categories of equipment, structures, inventories and land, weighted by capitalincome shares Labor hours Extrapolated from hours in the Productivity and Costs (P&C) news release Labor composition Computed from previous year's wage coefficients and current year hours from the Current Population Survey Labor share Previous year's full-MFP labor share is adjusted for change in labor share in P&C Output in private business Extrapolated from output measure in P&C 34 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Procedures fo r estimati ng each component are discussed below. For each component, table 1 presents estimates corre sponding to the full and simplified methodologies and the gap between these estimates, expressed by the average absolute value of the difference in the growth rates of the variables calculated from the full and simplified approaches. Errors in capital and labor figures are measured in growth rates because these are the form relevant to multifactor productivity calculation. The errors in growth rates are the ones directly relevant to the this calc ul ation, because multifactor productivity is defined to be the difference between the growth rates of output and of inputs. Errors in levels are shown in table A-1 in the appendix . Output. The simplified estimate of output, Y\ comes from the following computation. From the previous year's full multifactor productivity measures, we obtain the private business sector output level in year t-1, YF1_1 • From BLS 's Productivity and Costs (P&C) news releases, we obtain the percentage change in business sector output from year t-1 to year t. We make the assumption that the slightly smaller private business sector grew by the same percentage. This gives us an estimate of private business sector output in year t. On average, thi s assumption is reasonable because the two sectors cover nearly identical portions of the economy, although there are fluctuations in accuracy attributable to the use of preliminary data and the difference in scope. Over the 19932002 period, when output growth averaged 3.8 percent per year, the absolute value of the difference between annual growth rates of output (that is, IYF 1- Y51l !Y\ ) averaged 0.05 percent. Labor inputs. The simplified measure of hours worked, H51, comes from applying the percent change in hours worked in the business sector from the P&C report to the measure of private business hours in the previous year's multifactor productivity report, HF1_f' The hours measure is based mainly on the BLS Current Establishment Survey, but is supplemented by information from the Current Population Survey (CPS) . On average, the simplified estimate of the growth rate of hours worked differs from the full estimates in the most recent mul tifactor productivity data, HF1, by 0.04 perce nt. For the labor composition measure in the full methodology, the hours worked measure is adj usted for changes in the composition of the workforce. Rather than simply adding up hours worked, labor composition input is derived by aggregating the hours for groups of workers after weighting the hours of each group by shares in total compensation . 13 The Differences in growth rates of MFP components between the simplified and full methodologies [in percent] I Estimated component (capital stock, labor input, output, or MFP) Full model annual change, average (1993-2002) Simplified model annual change, average Average discrepancy in annual change between models Annual change in second year, average C1994-2002) I Average discrepancy in second-year change Capital services ...... .... ............. ... Structures stock ............... ......... Computer stock ........ .. ........ ....... Software stock ... ... ... ... ... ...... .. ... 4.38 1.74 30.4 13.6 4.30 1.76 29 .9 13.2 0.28 .09 3.4 3.1 4.1 1.9 31.8 13.9 0.46 .12 5.3 2.4 Other information technology stock ... ... .... ........ 7.2 7.0 .71 7.7 .4 All non-information technology equ ipment stock .. .... ..... ..... .. 3.2 3.2 .39 3.5 .5 .23 .34 1.3 1.3 4.0 .6 .4 .4 2.5 I Rental residence stock ............. Inventory stock .. .... ......... .. .. ....... Land stock .. .................... ... .. .... .. 1.1 3.8 .6 1.1 3.9 .5 Labor services ....... ... .. ... ... .... .. .... Labor hours ............... ... ........ .... . Labor composition ....... ............. Output .. ...... ... .... .. ... ... ... .. ... .......... 2.0 1.8 .5 3.8 1.6 1.8 .4 4.0 .24 .04 .23 .05 1.8 1.6 .4 3.9 .24 .07 .25 .05 MFP change ..... ..... ...... ........ ..... .... .96 .87 .22 1.07 .19 N OTE: "Dis~repancy" means absolute value of differences in growth rates, expressed in percentages, from the previous year to the target year. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 35 Multifactor Productivity groups are classified into about 1,000 types based on their education, experience, and gender. The labor composition index is the ratio of the labor input measure to the simple hours worked measure. The labor composition index reflects the effects on productivity of changes in the education and experience of the workforce. In the full methodology, the labor composition measure is constructed from data from the March Supplement to the CPS. Hours worked for each group are obtained from the survey data. The relationship between wage levels and education and work experience is estimated by a linear regression, from which it is possible to estimate wages for each group. 14 Then the shares of all labor income received by each group are estimated. Each group's income is its hours worked multiplied by its estimated wages. These shares are used to construct the measure of labor composition, which is a Tornqvist chain index of the groups. 15 After excluding the effects of hours growth, on average the labor composition index rose by 0.4 percent annually between 1973 and 2001 as the working population became more educated and more experienced. A simplified estimate of the labor composition index is developed here. An estimate of the distribution of hours worked, by education, work experience and gender, is constructed from the CPS for the middle month of each quarter of the target year. The information used to measure the work experience of each group of workers is also less complete than in the full method. It relies in part on more complete information from the previous year. Furthermore, in the simplified method, measures of hourly wages for each education-experience group are drawn from the previous year. Provided that the relative wages for each group have not changed substantially, these wage rates should provide a strong basis for constructing income share weights for each subgroup of the workforce. Shifts in hourly wage rates contribute to labor composition growth over long periods of time, but historically account for little of the year-to-year change in labor composition. Once hours and wage rates are estimated, a Tornqvist index of a simplified labor composition index is calculated. Again, subtracting hours growth, the average absolute value of the difference between the simplified and full estimates of labor composition from 1993-2002 is 0.25 percent. The labor input figure for the multifactor productivity calculation is then the labor composition index multiplied by hours worked. On average from I Q94 to 2002, the simplified aggregate labor input growth differs from the full procedure by an average absolute value of 0.24 percent. Because labor represents two-thirds of the input costs, this difference by itself would lead to approximately a 0.16-percent difference between the multi factor productivity estimated by the simplified method and the full method-although in some years, errors in other components (capital, labor share, or output) may be in the opposite direction, and therefore off- 36 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 setting in their effects of the multifactor productivity measure. Overall, roughly half of the discrepancy between the full model and simplified model multifactor productivity measures comes from variation in labor composition. The other half comes from capital estimation. Measures of capital inputs Background. The BLS multifactor productivity measures reflect the contributions of growth in capital service inputs, as well as labor inputs. The full procedures used to estimate capital are complex. Before describing the simplified procedures used to measure capital, it is helpful first to summarize how capital inputs are measured in the full procedure. Capital includes fixed reproducible business assets (equipment and structures), inventories, and land. The BLS capital input concept is designed to reflect the flow of services from these assets. These capital services measures are constructed through three stages of aggregation, two of which are reflected in the simplified methodology. The first stage involves vintage aggregation, where past investments in each of 7 4 types of asset are deflated, weighted and added together, resulting in productive capital stocks. This procedure is sometimes called the perpetual inventory method (PIM). In addition, capital stocks are measured, by methods other than PIM, for three types of inventories and for land, completing a set of 78 categories of assets. The second stage combines stocks for the 78 types of assets, using estimates of implicit rental prices to form an index of capital inputs, and the third stage involves aggregation of capital inputs across a set of industries. In the full methodology, the first two stages are repeated for each of 57 detailed industries. 16 The PIM is designed to adjust older capital goods for deterioration and obsolescence that reduce their productivity. The BLS specification of the PIM assumes that investments only slowly lose their effectiveness, like cars and light bulbs do. In the full methodology, we assume that the productivity of equipment declines as a function oflifetime (L)'7, age ( r), and that the fraction L-r L-.5r of the investment remains productive. 18 Similarly, structures are assumed to remain productive according to the slowermoving fraction L-r L-.75r The parameters of the efficiency formula (average service life and shape) represent the effects of obsolescence and deterio- ration of past investments. BLS has made efforts to fit them to evidence on declining equipment productivity. Chart 1 shows how an investment in structures with a 10-year life span would decline in productivity according to this relationship: The simplified calculation groups the 78 asset types into the following 8 asset classes: method multifactor productivity estimates exclude this. However, for most of the six categories, movements in the two investment series track one another closely. So the simplified method uses the percentage changes of the series that are available early to extrapolate the previous year's level of investment in each category. This provides an estimate of the level of investment in the target year. Then an estimate of the productive capital stock of that asset type is constructed as the sum of the new investment and of prior investments (weighted by remaining efficiency). Efficiency is assumed to decay at a rate derived from the full method for the previous year. Productive stocks of inventories and land are estimated without using a PIM calculation in both the simplified and full methods. However, the simplified estimates are constructed using different sources and simpler methods, which we will discuss below. Once stocks are prepared for each of the eight categories, the simplified procedure assigns cost shares to each and the eight are aggregated into a unified measure of capital service inputs. Category cost shares in the target year are assumed to be unchanged from the cost shares in the previous year, available from the full calculation. 19 Below we discuss the construction of each of the eight capital input components and assess the difference between the Structures Computers and peripherals Software Communication and other information technology Equipment other than the three information technology categories Rental residences Inventories Land For the first six of these categories, we calculate a productive capital stock by applying the PIM to data on investment published by the BEA during February following the target year. Exhibit A-1 in the appendix specifies the tables from which we have drawn source data. Investment by nonprofit institutions is included in the data that are available early, whereas the full- Assumed decline in productivity of an investment over time Remaining effective investment (in percent) Remaining effective investment (in percent) 120 120 100 100 80 80 60 60 40 40 20 20 0 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 0 3 4 5 6 7 8 9 10 Age of structures investment (in years) Monthly Labor Review June 2005 37 Multifactor Productivity simplified procedure and the full procedure in each recent year. The comparison is made using data available at the end of March 2005. 20 Early estimates for future years will have only preliminary information (for example, on investment) so subsequent revisions would reflect the incorporation of final data as well as the more complete methodology. In a later section, we list the components used to generate the major sector multifactor productivity estimates as published by the BLS and the components estimated by this procedure that uses data of the kind available shortly after the end of each target year. Details on the capital stock errors are shown in table 1 and table A-1 in the appendix. Structures. An early estimate of business investment in structures is published by the BEA in February of the year following the target year. This estimate includes nonprofits, whereas multifactor productivity calculations exclude them. For the target year t, the simplified procedure adjusts the investment figure from the full multifactor productivity calculation in year t-1 using the movement in BEA 's early estimate. Because structures investment is stable from year to year, this estimate for investment is reasonable. Over the 1993-2002 period thi s method produces, on average, a 1.8-percent discrepancy in the estimate of the percentage change of annual investment into structures compared to the later full estimate. The next step in converting investment figures to a capital stock requires two procedures. First, we apply a deterioration rate to the productive capital stock existing the previous year, year t-1. The deterioration rate for the simplified measure is based on the average deterioration rate for the asset class. We apply the last known rate to the stock in year t-1 , to produce an estimate of the remaining stock of used assets in year t. Second, we add the estimated new investment to get an estimate for structures in the private business sector in year t. Because deterioration of structures is slow, this produces accurate estimates for the stock of structures. Over the 19932002 period, the absolute value of the difference between the growth rate of the stock of structures measure by the two methods averaged 0.09 percent. The calculations for the other asset categories are analogous where possible, though they are less accurate than the structures estimate. Equipment deteriorates more quickly than structures, so differences in recent investment estimates have a greater effect on the total capital stocks for equipment than for structures. Equipment. We separate information processing equipment and software from other categories of equipment. This improves our estimate of multi factor productivity because hightech investment grew so much in the 1990s and has such a high rate of obsolescence. As in Oliner and Sichel 's work, 38 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 three categories of information processing investment are distinguished: computers and peripherals; software; and communications and other information technology equipment. All other equipment, taken together, makes up the fourth equipment category. For each of the equipment categories, investment estimates are calculated as they are for structures. Capital stocks are constructed in the same way as for structures. Capital stocks are reasonably well estimated for two of the categories but poorly estimated for computers and software. Because computer investment was booming and volatile with short life cycles and quickly evolving applications, our simple linear projections were not very close to the full measure in these categories. Much of this discrepancy is attributable to the differences between the early estimate of investment in computers and the later full estimate, an average absolute difference of 13.9 percent, as shown in table A-1 in the appendix . Another, smaller part of the discrepancy of 2.6 percent between the simplified and full estimates of the productive stock of computers is attributable to the depreciation rate that is inferred on previous computer stocks, which fluctuated widely in the 1990s and which was therefore not well estimated by the simplified procedure. These differences contribute substantially to the discrepancy in the final simplified measure of multifactor productivity. Rental residences. Investment figures for this category are not available early enough after the target year to be used in the simplified calculation. The simplified estimates simply assume investment was the same in year t as it was in year t-1. This estimate for investment is not very accurate, but new investment is small compared to the existing housing capital stock, so the absolute discrepancy between the two measures of the growth rates of the stock averages only 0.2 percent. Inventories. The full MFPcalculation defines inventory capital for each industry to be a weighted average of the values of private business inventory stocks in recent quarters. BEA 's aggregate inventory investment figures for the whole business sector taken together are available soon after the target year ends, and percentage changes from the previous year replicate the aggregate inventory stock in the full model well. Land. In the full calculations, land stocks are not calculated as an accumulation of past investments. Rather, nonfarm land stock is assumed to have one of three fixed proportions to the structures stocks depending on whether the land is used for residential structures, manufacturing structures, or other structures. The simplified calculation uses the overall ratio of the official capital stock of land to that of structures from year ti, and applies this ratio again to the estimated value of struc- tures in year t, which was estimated previously. This gives estimates of the growth rates of the productive stock of land that differ from the full estimates by 1.3 percent on average. The discrepancy is largely attributable to farmland , which in the full estimation is measured with data from the U.S. Department of Agriculture. In our simplified calculations, farmland is in effect estimated from farm structures. Capital services. Having computed simplified estimates of each type of productive capital stock, we proceed to estimate aggregate capital services provided in the target year. We assume that capital services are proportional to the productive stocks for each of the eight types of assets. 21 The productive stocks are combined into a measure of combined capital services inputs using implicit rental prices to determine weights for each type of capital. BLS uses BEA 's measures of property income and allocates a portion of this income to each type of asset. The resulting capital income shares do not vary much from year to year. To estimate the simplified measure of combined capital service inputs for year t, these asset shares are taken to be the same as in year t-1. Shares for categories of capital inputs and for labor input. Capital income is apportioned to various asset types by assuming the same distribution as in the previous year's full multifactor productivity estimation. For capital types aside from equipment, this introduces only small errors, but the computer and software categories grew a lot. Details of this are in tables 1 and 2. On average, rental residences accounted for 10 percent of capital income over the 1993-2002 period; inventories accounted for 7 percent; and land, 11 percent. Structures accounted for a declining share , averaging about 28 percent. Equipment of all kinds together rose from about 42 percent to 49 percent, because of growth in computer and software investment in this period. Capital and labor inputs are then combined using a Tornqvist index formula to create a single index of combi ned inputs. The capital and labor shares are estimated from changes in the corresponding figures from the BLS Productivity and Cost measures. In the full calculation, labor's share was in the 66-69 percent range. The absolute values of discrepancies from the fully-estimated figure in the simplified estimates of this share average 0.76 percent. Estimates of multifactor productivity All of the components discussed above are combined to make a simplified multifactor productivity estimate. The focus of this article is to assess the accuracy of the simplified method. During the 1993-2002 test period, the average of the absolute values of the annual errors between the percentage change in the preliminary (first year ahead) estimate and the published multifactor productivity was 0.22 percent. Table 2 prese nts an analysis of how much each component contributed to that error. Output errors contribute directly to multifactor productivity error, and input errors for specific input components can contribute in proportion to their weights in total input. In the final column of table 2, the input catego- Approximate magnitudes of error, by source, for 1993-2002 [in percent] Components ( 1) Capital services (31 .5 to 34 percent of inputs): Structures ...... .. ............. ... .. .. ...... .... ..... ... . Computers ............... .. ......... ..... .... ....... .... Software .... .... ... ..................................... . Other information and communications technology ............. ..... .. .... .... .... ....... .. ... Other equipment ........................ ... ... ..... . Rental residences .. .. ... ... ............... . Inventories ... .. ............... ..... ... .... .. .... ....... . Land ..... .... ..... ..... ... ... .... ... ... .. ... .. ............ . Range of share of capital income (2 ) Average absolute error in growth estimate, from table 1 (3 ) Approximate absolute error induced into MFP Product of averages of ( 1), (2), and (3) 25 .3 to 30 .3 3.6 to 6.1 4.4 to 7.6 0.09 3.4 3.1 0.01 .06 .06 8.3 to 9.3 25.0 to 27 .9 9.2 to 10.4 5.6 to 8.4 9.6 to 11 .5 .71 .39 .23 .34 1.3 .02 .03 .01 .01 .05 Labor services (66 to 68 .5 percent of inputs) : Hours worked ... ... .... ........ ............... .. ..... . Labor composition .. ... ... .... ..... ............ .... . All All .04 .23 .03 .15 Output ....... ..... ... .... ... .. .... .... ...... ... ... ....... ... . All .05 .05 Total ..... ......... ... ....... .... ...... ... .... ... .. ..... .. ... ... Net effect on MFP .. ........ ..... ..... .......... ..... .......... . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis .47 .22 Monthly Labor Review June 2005 39 Multifactor Productivity ries have been multiplied by their average cost share weights during the test period to assess their potential contribution to measurement error in multifactor productivity. For example, the growth of computer stock was estimated with an average absolute error of 3.4 percent, but their input cost share was small, averaging about 1. 7 percent of the value of all labor and capital inputs. We estimate they contribute only 0.06 percent to the multifactor productivity error. Not all sources of error can be identified in this share-weighted framework. Asset shares are assumed to be the same in year t as in year ti, and this is a source of some of the discrepancy between the simplified and full measures, especially for computers and software. However, in assembling the components into a multifactor productivity measure, the error contributions of the capital categories, of labor, and of output often offset. As a result, the total component contribution, of about 0.47 percent for the period, was reflected in a multifactor productivity error of only 0.22 percent. Table 3 presents annual time series for the simplified (columns 1) and full (column 2) estimates of multi factor productivity for recent years. The trends in these measures, presented near the bottom of the columns, are very similar. Annual errors (differences) in the simplified measure are presented in column 3. Because some errors are positive and some negative, the average of this column is very small: 0.09 percent. However, this represents only the difference in trends. To assess the effectiveness of the simplified method, we averaged the absolute values of column 3. That figure was 0.22 percent, as we mentioned earlier. Table 3 also presents the second-year-ahead estimates, MFP 52 1 (column 4). These were constructed by applying the simplified methodology for two consecutive years. The data presented are growth rates of multifactor productivity for the second year. The second-year simplified estimates (column 4) are compared to the published measures (column 2) in column 5. The average absolute error, during 1994-2002, for the second year estimates was 0.19 percent. By comparison, the average of published multifactor productivity growth rates is 0.96 percent, and they fluctuate substantially from one year to the next. The simplified estimates may serve as fairly reliable preliminary numbers . The accuracy of the second year estimates is comparable to that of first-year estimates, reflecting the stability of input shares and the similarity of the data used to estimate growth rates. While the simplified method can provide reasonable measures for a few years, it is not capable of replacing the full method. The simplified model draws heavily on the most recent full model for data on rental prices, cost shares, and deterioration rates. These values gradually change over Multifactor productivity (MFP) change estimates by simplified and full procedures [In percent] Full MFP change estimate (MFP") Discrepancy of 1year simplified estimate from full estimate ( 1)-(2) (2) (3) 0.16 .96 -.58 1.46 .84 1.18 1.11 1.18 -.08 2.43 3.10 0.40 1.00 -.20 1.70 0.90 1.10 1.30 1.40 .10 1.90 -0.24 -.04 -.38 -.24 -.06 .08 -.19 -.22 -.18 .53 .87 (1993-2002) .96 (1993-2002) Simplified estimate of MFP change (MFP5) Year ( 1) 1993 ........ ..... 1994 ............. 1995 ......... ... . 1996 ...... .. ..... 1997 ...... ...... . 1998 ······ ····· ·· 1999 ......... ... . 2000 ··· ···· ··· ··· 2001 ...... ....... 2002 ............. 2003 ............. 2004 ............. Average ........ Mean absolute error: ... .. ..... NOTE: Simplified estimate Discrepancy of MFP change 2nd between simplified year after last full 2nd-year and full estimates (4)-(2) model, MFP52 - .09 (1993-2002) .22 (1993-2002) Figures reflect percent changes from previous year's private business sector MFP. 40 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 (4) (5) 1.23 -.68 1.94 1.18 1.40 1.30 1.42 -.05 1.90 3.15 3.29 -0.23 .48 -.24 -.28 -.30 0.00 - .02 .15 0.00 1.07 (1994-2002) -.05 (1994-2002) .19 (1 994-2002) Best estimate of the MFP series, based on (1), (2), or (4) (6) 0.4 1.0 -.2 1.7 0.9 1.1 1.3 1.4 0.1 1.9 3.1 3.3 Productivity measures Change from previous year (in percent) 6.0 Change from previous year (in percent) 6.0 Business sector labor productivity (output p e \ . _. _ ... 5.0 5.0 -.. 4.0 3.0 Full methodology MFP - . -·-·-· ,·' . -.. -.. . , , 4.0 ·;·· 2.0 Simplified methodology 2 years ahead 1.0 3.0 2.0 1 .0 (MFP 52 ) 0.0 0 .0 Simplified methodology -1.0 (MFP5 ) -1.0 -2.0 -2.0 1993 94 95 96 97 98 99 2000 01 02 03 04 Year time, and so the simplified model will tend to become inaccurate unless data from the full model are available for a recent year. Table 3 also includes a series providing our best current estimate of a multi factor productivity time series (column 6). This column is the published full multifactor productivity measure (column 2) for 1993-2002, but then reflects the simplified estimate for 2003 (column 1), and the simplified estimate for the second year ahead for 2004 (column 4). Private business multifactor productivity grew 3.1 percent in 2003, and 3.3 percent in 2004. The last time this published series grew by more than 3 percent was in 1976. Rapid private business multifactor productivity growth in these recent years occurred at a time of high business sector labor productivity growth rates of 4.5 percent in 2003 and 4.0 percent in 2004--reported in the BLS Productivity and Costs news release. Capital growth and labor composition account for the difference between trends in labor productivity and multifactor productivity. The labor composition index grew 0.6 percent in 2003 and 0.2 percent in 2004, compared with a trend of 0.4 percent during the previous 10 years. In both 2003 and 2004, capital inputs grew 2.6 percent, less than their average of 4.5 percent per year during the previous 10 years. The annual rates of change in the full and simplified estimates are graphed in chart 2 along with growth in labor pro- https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis ductivity. While there are noticeable differences between the simplified and full estimates, the movements are very similar. BLS presents multifactor productivity measures in the context of a framework that explains changes in labor productivity. Aside from multifactor productivity, labor productivity growth reflects the contributions of capital and of labor composition. In chart 2, the simplified multifactor productivity measures account for about the same fraction of labor productivity growth as do the full measures. Conclusion. The simplified method uses preliminary information to estimate the components of multifactor productivity. The method is relatively transparent and avoids any kind of model that fits the 1990s but might not apply in the future. Based on the span of years for which we made the comparison, the largest sources of the discrepancy between this multi factor productivity estimate and the full measure come from differences in estimates of information technology capital and labor composition. In the future, BLS expects to makes these simplified method multifactor productivity measures available before the results from the full methodology can be ready. The results of the full methodology can be published as revisions to the preliminary statistics. □ Monthly Labor Review June 2005 41 Multifactor Productivity Notes ACKNOWLEDGMENT: We are indebted to Dan Sichel of the Federal Reserve Board, who described to us how Oliner and Sichel (2CXX>) forecasted multifactor productivity (MFP). We thank our colleagues Ryan Forshay, Randal Kinoshita, Marilyn Manser, Larry Rosenblum, Steve Rosenthal, and Leo Sveikauskas for their advice and assistance. The authors are responsible for any errors. 1 Robert Solow, ''Technical Change and the Aggregate Production Function," The Review of Economics and Statistics, August 1957, pp. 312-20. 2 The target year is sometimes called the reference year. Changes are measured between the target year and the previous year. In this study the present year is never measured, only past years. 3 Most of the data items are obtained shortly after the year is over from the National Income and Product Accounts (NIPA) published by the Bureau of Economic Analysis (BEA), and from BLS labor data sources. The MFPcalculation also requires information on investment expenditures and property income at the industry level from BEA and this takes longer to produce and obtain. 4 Their measures were reported by Oliner, Stephen D. and Daniel E. Sichel, ''The Resurgence of Growth in the Late 1990s: Is Information Technology the Story?" Journnl of Economic Perspectives, Fall 2CXX>, pp. 3---22. Our measure is similar, with less detail on equipment and structures than their 60 asset categories, but adding measures of inventories and land. The authors are indebted to Dan Sichel, who kindly discussed this work with us and who also provided valuable comments on an earlier draft of this article. 5 For example, in the 1990s, computer purchases rose dramatically as a fraction of all business investment. If particular categories of investment continue to grow rapidly, more accurate estimates would take recent trends into account. Instead, the calculation simply used the asset shares from the most recent year for which full calculation is available. 6 For example, the calculation could incorporate empirically observed relationships between the state of the business cycle and components of the calculation (such as the labor force composition and the shares of durable goods in investment) to make slightly more accurate estimates. 7 Revisions to the underlying data can be substantial. Edge, Laubach, and Williams (2004) discuss the significance of using real time data in evolving expectations about productivity trends; see Edge, Rochelle M., Thomas Laubach, and John C. Williams, "Learning and Shifts in Long-Run Productivity Growth," Working Paper No. 2004-04 (San Francisco, Federal Reserve Board of San Francisco, 2004). Orphanides (2001) demonstrates that monetary policy can look meaningfully different in retrospect when considered in the context of the economic data actually available to policymakers, not the best measures later available. (See Orphanides,Athanasios, "Monetary Policy Rules Based on Real-Tune Data," The American Economic Review, Sept. 2001, pp. 964-85.) Though we recognize the issue, this study does not measure how much this would have affected preliminary MFP measures in recent years. 8 Gullickson and Harper ( 1999) discussed why this is the appropriate concept of output to compare to capital and labor inputs at the aggregate level. See William Gullickson and Michael J. Harper, "Possible Measurement Bias in Aggregate Productivity Growth," MonJhly Labor Review, February 1999, pp. 47-67. 9 Output from these sectors is included in GDP, but the estimates for the value of output are largely based on inputs or input costs and assumptions about their productivity change. If these sectors were included in aggregate productivity measures, the assumptions about their productivity would affect the measure. 10 These are available on the Internet at http://www.bls.gov/schedule/archives/ prod_nr.htm. 11 Government enterprises are those activities of government that bring in approximately enough revenue to cover their variable costs. They generate approximately 1.3 percent of GDP. Government enterprises are excluded from MFP because of difficulties in estimating an income share for capital. Government enterprise capital is often heavily subsidized. Revenues often are sufficient to cover operating costs but insufficient to repay capital costs. 12 In recent years, nonprofits and households produced 11.5 percent of GDP, general government 11.3 percent, and government enterprises 1.3 percent. Sources 42 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 for those approximations are BEA 's online NIPA Table 1.3.5 on the Internet at http:/ /www.bea.gov/bea/dn/nipaweb and "Value Added by Industry in Current Dollars as a Percentage of Gross Domestic Product" table in the Industry Economic Accounts available on the Internet at http://www.bea.gov/bea/industry/ gpotables/gpo_action.cfm?anon=619&table_id=292l&format_type=O; (visited June 2004). BLS also publishes multifactor productivity growth estimates for subsets of private business, such as the following: private business excluding farms; manufacturing; durable manufacturing; nondurable manufacturing; and for selected industries. There are also "KLEMS" multifactor productivity growth that take more inputs into account capital, labor, materials, energy, and purchased business services. Access to these estimates is available on the Internet at http://www.bls.gov/ mfp'. This article does not consider preliminary estimates for these other statistics. 13 In theory, firms competing for workers and trying to make profits will minimize costs by paying each type of worker a wage that equals the worker's "marginal product" or labor productivity. 14 Other researchers, such as Jorgenson, Gollop and Fraumeni ( 1987) have used hourly wages directly instead of inferring them from a wage regression. See Jorgenson, Dale, Frank Gollop, and Barbara Fraumeni, Productivity and U.S. Economic Growth (Harvard University Press, 1987). 15 A chain index is a time series assembled by adjusting successive year 's observations by growth rates. The Tomqvist growth rate is an aggregate of growth rates of the hours worked by each group, weighted by their average shares in labor costs in successive years. For more on the index, see http://www.bls.gov/mfp/ mprlabor.pdf and Labor Composition and U.S. Productivity Growth, 1948-90, Bulletin 2426 (Bureau of Labor Statistics, Dec. 1993). 16 The full methodology also treats investments by corporations differently than other investments. For further infonnation on the construction of the capital stock for the multifactor calculation, see Handbook of Methods, Bulletin 2490 (Bureau of Labor Statistics, April 1997), p. 1ITT; see also Harper, Michael J., "Estimating Capital Inputs for Productivity Measurement An Overview of U.S. Concepts and Methods," lnJernntionnl Statistical Review Vol. 67, 1999, pp. 3T7-37. 17 Service lives of individual assets are assumed to have a normal distribution that is truncated at age zero and at twice the average service life. The average service lifetimes used in this calculation are consistent with the depreciation rates that BEA uses when estimating the net national product. In some cases, the service lifetime changes over calendar time. 18 The relationship of the productivity of a capital investment to its age and lifespan represented by these equations are sometimes called efficiency schedules. These particular efficiency schedules are hyperbolic functions of age. 19 In the full methodology, asset-type cost shares are detennined by allocating NIPA property income (the difference between revenues and labor cost) to the assets, under the assumption each asset type earns the same rate of return. Property income data comes from the BEA's GPO (Gross Product Originating) reports. The stock of each type, and structural rental price formulas for each type are used. For further details see Trends in Multifactor Productivity, Bulletin 2178 (Bureau of Labor Statistics, Sept. 1983), especially pp. 49-50. 20 In the comparison of the full method to the simplified method over a series of years, the investment data are drawn in slightly different categories from the ones used at the time. First, investment amounts for all years are taken in year 2CXX) dollars, based on chained-dollar adjustments between years which vary by the kind of investment good. Second, they are drawn in from NAICS (North American Industrial Classification System) category data whereas the figures historically used for the MFPcalculation had been in SIC (Standard Industrial Classification) categories. Third, investments for all years are also taken as restated by the BEA 's December 2003 comprehensive revisions. These changes introduce small differences between the multifactor productivity estimated by what is called the full methodology here and the multifactor productivity figures the BLS published for those years. 21 In the full procedure, the capital services from each of the eight components has been constructed from finer subcomponents. Our simplified procedure overlooks some composition effects that emerge from working with the greater detail. It might be possible to improve our simplified procedure by trying to estimate these composition effects within the components. We have not done so for these estimates. APPEN01x: Sources of data and average discrepancies in levels of investment and capital stocks Data sources for investment in the simplified multifactor productivity Component of MFP calculation Source for investment data calculation Structures investment .................... ................ . Computers investment ... .. .. .... .. .. .. ................. . Inventories stock .............. ........... ... ..... ........... Softw are .... ..... ..... ....... ................. .......... ........ . Other information process ing equipment ... .. . Res idential structures ..... ........ .... .... .... ... .... .. .. . Other equipment .. ....... .......... ... .......... ........ .. .. . Land stock ... ...... ..... .. ..... .. ........ ..... ...... .... .... ... . (MFP) Tables 5.4.6A and 5.4.6B Table 5.3.5 (deflated by price index privately sent from BEA) Tables 5.7.6A and 5.7.6B Table 5.3.6 or Table 5.5.6 Table 5.3.6 Table 5.3.6 Line 16 of Table 5 .3.6 Imputed from structures as di scussed in text NOTE: Investment data come from tables fro m the Bureau of Economic Analys is, available on the Internet at http://www.bea.gov/dn/nipaweb/. Figures in year-2000 doll ars are used in the simplified MFP calculation. Where possible, data without seasonal adjustments are used. -••••ir-..-•1 1' Differences in levels between simplified and full methods [in percent] Measured component of multifactor productivity ( MFP) Average discrepancy between full and simplified estimates, 1993-2002 Average discrepancy between full and 2-year simplified estimates, 1994-2002 (cumulative, in levels) Structures investment .. .... .. .................................... .......... ... .. ... .......... .... ..... . Productive stock of structures ...... ... ....... ....... ..... ... .. .. .............................. Computers and peripherals investment ... ... .... .......... .. .. .. ... .. ...................... . Productive stock ... ....... .... ........ ... .. ... .... .... .............. .. .... ..... .. ....... ... ... ...... .... .. Software investment ............. ... ...... .... .......................... .... .... ........... ...... ... .... Productive stock ... .. ......... .... ... .... ... ..... ... ................. ..... .. ... .................. .. ... . Communications and other IT equipment investment ...... ...... ... .. ... ..... ........ Productive stock ..... .... ..... .. .... .. .............. ........... ............... .. ...... .... ......... ... . Other equipment investment ..... ..... .... .... ................................ .. .... ...... ......... Productive stock .... .. ....... ............. ........... ................. ....... ... ....... .. .... .... .... .. Rental residences investment ... ........... .... ........... ..... ......... ........ .... ... .. .... .... . Productive stock ............... ............ .. ... ... .... ........................................ ..... .. . Inventories stock ..... ... ...... .... ... ....... ... ............................. .... ... .... ..... .. ........ ... . Land stock .... ... ......... .......... .. .. ... .... .... ... ........ ....... .. ............ ......... ..... ..... ... .... 1.8 .1 13.9 2.6 1.0 2.7 1.8 .7 1.0 .4 8.6 .2 .3 1.3 2.2 .2 24.1 3.9 1.1 5.1 1.9 1.0 1.8 .8 10.2 .7 .6 3.8 Labor hours (1994-2002) ..... .............. . .. .......... .. .. ......... .. ... .......... .. ..... ... .. .. Labor compensation index ....... ... ..... .. .... ........ ... ............... ........ ....... ..... ..... Labor input {the above two combined} , 1994-2002 ..... ..... ... ... .. ........... ...... . .04 .23 .24 .07 .25 .24 Share of income paid to labor ..... ... .. .. .... .............. ... ............ .... .. ..... .. ....... ... . Output estimates {Y F1 vs . Y5 ,) • •••••••••••• ••••••••••••• • •• . ••• . ..• • . • . •• . • . • • . • • ••• ••••••••••••• • • .76 .06 .67 .10 .22 .19 5 MFP estimates {MFP vs. Mf PF) ····· ···· ····· ··· ·· ··· ··· ······ ···· ······· ·· ·· ··· ···· · ······ ······ ·· N OTE: MFP discrepancies are annual averages of absolute differences in percentage changes from preceding years . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 43 The Marshall Plan ·;:: · · TH- " " BLS and the Marshall Plan: the forgotten story The statistical technical assistance of BLS increased productive efficiency and labor productivity in Western European industry after World War II; technological literature surveys and plan-organized plant visits supplemented instruction in statistical measurement Solidelle F. Wasser and Michael L. Dolfman Solidelle F. Wasser is a Senior Economist and Michael L. Dolfman is the Regional Commissioner for the Bureau of Labor Statistics in New York, New York. E-mail: Dolfman.Michael @bis.gov Wasser.Solidelle @bis.gov e European Recovery Program (Marshall Ian) has been recognized as the most successful foreign-aid program ever undertaken by the United States. The Bureau of Labor Statistics (sLs) role in the accomplishments of the Marshall Plan's Technical Assistance Program has largely been ignored. This article highlights the BLS achievements in the Marshall Plan. The Marshall Plan was named for then Secretary of State George C. Marshall, who, on June 5, 194 7, proposed his solution to war-devastated Europe. The proposal was enacted into law in April 1948 as the European Recovery Program, which created an Economic Cooperation Administration Agency to organize and administer the program. The Marshall Plan recognized that the economies of Western European countries had continued to deteriorate in the immediate postWorld War II period and that provisions of massive loans to individual countries had proven to be a failure. 1 Marshall's recovery plan proposal was revolutionary in that it required mutual cooperation among those 16 countries (a 17th, the German Federal Republic, joined in 1949) that responded to the invitation to participate. Recipients of American assistance under the Marshall Plan joined together to produce multilateral solutions to common economic problems. The result was a massive effort to improve the economic condition of 270 million people in Western Europe through increasing their domestic production by T: 44 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 collaborative effort. The participants proposed to do this by strengthening the economic superstructure of Western Europe. An important component of the Marshall Plan was the statistical technical assistance offered by BLS and directed at increasing productive efficiency and labor productivity in Western European industry. Because of the special circumstances caused by the war crises, BLS efforts widened to include foreign assistance. These efforts "reached almost every plant in every industry, marketing agency, and agricultural entity in Western Europe, introducing them to a technology more than a generation in advance of what they were using. " 2 Increases in industrial efficiency and productivity have been acknowledged as a major contributing factor to Western Europe's postwar economic recovery. Analysis by BLS of dislocations caused by the crises of war gave it good preparation to analyze post-war production problems. Therefore, BLS was not only capable ofusing its statistical measures to identify problems of inefficiency, but also could instruct Europeans in the most modern American industrial practices. Surveys discussed in technological literature and, more directly, plan-organized plant visits supplemented BLS instruction in statistical measurement. On June 7, 1940, Congress passed an act authorizing BLS "to make continuing studies oflabor productivity" and appropriated funds for the estab- lishment of a Productivity and Technological Development Division. The vehicle for the Marshall Plan's Technical Assistance Programs in each Western European country was a high priority national productivity drive, an area in which BLS had developed expertise through congressional mandate. Two basic methods of productivity calculation were advanced by BLS: (1) calculation from existing figures by dividing a time series on output by a time series on labor input; and (2) preparation of productivity reports by direct collection of comparable data for output and labor input in special studies. The latter approach examined the labor requirements per unit of output. The direct collection methods were effectively used during the European Recovery Program, and the funding for this approach was eventually transferred to the Marshall Plan's Agency, the Economic Cooperation Administration. 3 In retrospective comments on the productivity studies that BLS performed for the Marshall Plan, BLS Commissioner Ewan Clague remarked , "It would be a gross exaggeration to say that statistics did the trick, but it is fair to say that these studies played a significant role in the spectacular economic recovery of Western Europe." 4 It may have been a gross exaggeration to say that statistics did the trick , but this statement cannot be said of the BLS statisticians and economists who applied the statistics. Key roles Isador Lubin. To fully understand and appreciate the contribution of BLS staff to the success of the Marshall Plan, it is necessary to initially focus on Isador Lubin, Commissioner of BLS from I 933 until I 946. Sworn in during the depths of the Depression, "Lubin provided the impetus for the Bureau's development into a modern, professionally staffed organization equipped to deal with the many tasks assigned." 5 Prior to and during the Second World War, Lubin was assigned an office in the White House West Wing and served as special statistical adviser to President Franklin Roosevelt. Thus he expanded not only his own personal influence but also, by extension, that of BLS. Philosophically, Lubin was among the new breed of economists who postulated an increased role for government in the economic affairs of the Nation. In 1932, as adviser to Senator Robert LaFollette, he pioneered the notion of government responsibility for the national accounts. 6 He stimulated passage of the Senate resolution, which reads in part, "That the Secretary of Commerce is requested to report ... estimates of the total national income for each of the calendar years I 929, 1930, 1931 ... "7 Most importantly, Lubin recognized the importance of relevant data to the success of New Deal economic programs and worked to improve BLS statistical programs. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Not only must raw data be improved but the Bureau must be enabled more fully to analyze the data it now has, so that evidence may be available as to where the recovery program is having the greatest effect and where it is falling down. 8 Soon after assuming his position of leadership within BLS, Lubin, along with U.S. Secretary of Labor Frances Perkins, worked to implement President Roosevelt's Executive Order establishing a Central Statistical Board. The Board was soon legi slatively established for a 5-year period "to ensure consistency, avoid duplication, and promote economy in the work of government statistical agencies."9 Lubin 's professional career had begun during the First World War, when he was employed by the Food Administration to analyze governmental labor and price policy in order to increase production of foodstuffs needed by Allied Nations. He later joined the War Industries Board's Price Section where he studied the effect of price shifts on the petroleum and rubber industries' output. A most important period in his professional development was his work at the Brookings Institute. Founded in 1922 by Robert Brookings, who had served as chairman of the Price Fixing Committee of the War Industries Board, the Institute strived to develop adequate economic information that could be used in governmental policymaking. Lubin had a unique role at the Institute. He was hired as an instructor in its graduate program, that is, at that time the Institute was a Ph.D. granting institution; he was also awarded his own Ph.D. in I 926 with his book, Miners Wages and the Cost of Coal, accepted as fulfilling his dissertation requirements. 10 During his years at the Institute, he developed a national reputation for scholarly work in the field of industrial labor economics. Early in 1947, after having stepped down as BLS Commissioner, Lubin extolled the excellence of BLS in collecting and analyzing data. In his presidential address to the American Statistical Society in January of that year, Lubin emphasized both the place of statistics in modern economic society and the value to the free world of pertinent data. Even before the announcement of the Marshall Plan, he understood that the challenge facing America was to help Europe recover from the devastation of war. 11 He concluded his presidential address with the following: Our ability to meet this responsibility ... will to a large degree be determined by the availability and intelligent use of pertinent data. Never before have facts, figures and intelligent economic judgments been as important as they will be in the years immediately before us. Never before has adequacy of data and statistical integrity been so essential. For never before in history have the stakes been so high. 12 Monthly Labor Review June 2005 45 The Marshall Plan The Truman Administration. During the early days of the Truman Administration, in the postwar period, there had been some debate as to how best to seek a remedy to the devastation that had engulfed Western Europe. Two schools of thought emerged. 13 One, known as the "fundamentalist" approach, favored the granting of charity and loans to these countries and the continuing implementation of the efforts of the United Nations Relief and Rehabilitation Administration. A second approach, motivated by enlightened self-interest, was forwarded by American big business and gained influence within the Administration. Known as the "progressive" approach, it reasoned that if America could tutor Europe in the techniques of American productivity, the problem would be permanently solved. 14 The progressives also looked to a tariff-free and integrated European economy as a solution to postwar recovery. It was the belief ofU.S. Under Secretary of State William Clayton that Europe's interwar failure to keep pace with American economic growth had sprung from national rivalries, which had led to tariff restrictions throughout Europe and constraints on international trade. America viewed European markets as too local and advocated their integration and expansion. It was a belief shared by Lubin. 15 A key component of the Marshall Plan, put forward in 1947, called for cooperative meetings of the 16 European nations who would be its beneficiaries. These nations met in Paris in 194 7 and formed what came to be known as the Organization for European Economic Cooperation. It was the belief that this Organization would unanimously determine what Europe's economic needs would be and help give shape and substance to the Marshall Plan. Chief among the issues to be resolved would be the opening of tariff-free European markets to the products of American industry. As the Organization for European Economic Cooperation considered Europe's needs, other economic issues were drawn into focus. The report from the 194 7 meeting pointed out that "before World War II, the sixteen participating nations were ... highly efficient in trade, industry, and agriculture and derived a substantial income from international trade ... Trade, industry and agriculture had been twisted out of shape by the forces of war." 16 (However, BLS surveys of European productivity had revealed significant longer term deficiencies.) It became clear that if a meaningful recovery was to take place, problems associated with increasing industrial production throughout Western Europe would have to receive a high priority. BLS and productivity measures. During the prewar period and during World War II, BLS increased its capabilities, stature, and expertise. Although not a war agency itself, BLS "cooperated with and serviced practically every war agency that was established ... as well as the pertinent defense agencies, such as the Departments of War and Navy, and the 46 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Maritime Commission." 17 BLS responsibilities were directed at the collection and analysis of data for war agencies concerned with: Wages, prices, employment, factors affecting production with emphasis on wage stabilization, price control, rationing manpower, labor turnover, accident prevention, maximum hours of labor, extent and causes of strikes, productivity of labor, and labor conditions in the United States and other countries (especially countries that were or might be occupied by Allied forces). 18 As noted previously, the Productivity and Technological Development Division was established within BLS as the result of a congressional act passed in 1940. The function of the division was to provide government and private agencies: With current information on productivity, technological developments, and factors influencing productivity; and to maintain files and issue reports on technology and other topics relating to utilization of materials and human resources in peace or war. 19 The Division became operational in 1941, and by 1942 had organized itself into an administrative unit with two functioning divisions- the Productivity Statistics Section, which compiled indexes of output per person hour of labor and unit labor cost; and the Productivity Studies Section, which produced reports focusing on labor requirements per unit of output in specific industries and factors influencing the output trends in these industries. By 1944, three additional divisions had been added: the Absenteeism Studies Section, the Technological Relationships Section, and the Current Technological Development Section. A specific example of BLS importance to war procurement is its report on the air frame industry. Procurement for war materiel had created mass markets for previously specialized industries. One of the BLS most relevant direct productivity studies to address the adaptation to a mass market was that of the airframe industry. 20 The industry was, in a sense, new. The demand for airframes was expected to grow in the postwar period due to airplanes being manufactured for civilian use. The BLS study in the airframe industry found that there had been a phenomenal 200-percent increase in output between Pearl Harbor and 1944. This rise in productivity was made possible by a concentration of effort on standard designs produced in large volumes. Conversion of the industry to mass production was achieved through minute specialization of labor machinery and hand tools . Productivity data relating to individual plants and types of aircraft suggest that unit labor requirements in all plants tended to decline at fairly similar rates with production increasing 27 per- cent to 35 percent with every doubling of cumulative output. This study also demonstrated that one model does not fit all, that is, in one plant much of the work may be done on a single line, while in another producing identical planes, a series of subassemblies may be built first. Output per person hour may, nevertheless, be similar. The production technique actually adopted may depend on the nature of existing buildings and equipment or on the traditional methods of the company. The flexibility demonstrated in these analyses helped prepare BLS economists for the variety of situations they would encounter abroad. German reparations. President Franklin Roosevelt appointed Isador Lubin as Minister to the Allied Reparations Commission in 1945 after recognizing Lubin 's current service on the War Production Board, his experience with the War Industries Board during World War I, and his intimate knowledge of the mistakes that had led to hyperinflation. 2 1 The immediate issue facing Lubin, therefore, was an approach to the handling of German reparations in a way that would not further devastate Germany's industrial productive capacity. He knew that German industry was central to the recovery of Western Europe, but that its importance had to be measured in commodity terms in order to be effectively noninflationary. To tackle the problem, Lubin needed standardized measurements, that is, statistical data on the reparations Germany could afford, the state of German industrial capacity, and the living standards of the German population. For answers, he turned to BLS, of which he was still technically the Commissioner. He addressed the following query to A. Ford Himichs, the BLS Acting Commissioner during Lubin's assignment to the White House. In calculating Germany's capacity to pay reparations and in scheduling reparations details in kind, the United States Mission to the Reparations Conference will need a great deal of actual information on the input of resources and output of products in all various sectors of the German economy. Accordingly, I should greatly appreciate it if your Employment and Outlook Branch would prepare for us a study of the input and output relations in the German economy similar to studies that have been published for the American economy. It would be desirable to have as quickly as possible an initial report for some recent prewar year, say 1936. It would be desirable to have also a report on the postwar situation that would prevail under alternative plausible assumptions as to war damage, and possible capital removal and destruction in every industry. 22 Lubin was aware that the interindustry data and analysis that he had requested was already in the development process at https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis BLS. Lubin had authorized BLS to create a small research unit at Harvard University in 1941 ; the unit, under the direction of Wassili Leontief, constructed the first official input-output table. 23 Leontief's new technique employed a system of doubleentry bookkeeping that tabulated the transactions of any one transactor group industry with all other groups. It included the flow of intermediate as well as final output. The technique had proved useful to the Office of Strategic Services during the war, helping to pinpoint bombing targets of those German industries crucial to the war effort. Its earliest domestic application had been an estimate made in 1944 for the Planning Division of the War Production Board. 24 Within months, BLS had prepared a table of 27 industry groupings by applying the 1939 American coefficients to German industry, that is, the proportion of each industry's input to particular outputs. Detailed comment and analysis from German industrial experts accompanied the tables, thus modifying the methodology in light of what was known about German industry. Additionally, tables were prepared on consumer expenditures by German families. These data formed the basis for estimates on the effect on both industrial and household income of German reconversion to peacetime production. Lubin was named U .S. Representative to the Temporary Subcommittee on the Economic Reconstruction of Devasted Areas, which was created by the Economic and Employment Commission of the United Nations Economic and Social Council, serving from 1946 to 1949. He was one of the group of State Department officials who saw Germany as the key to the integration of Europe. They felt that German unity could not be achieved without the unity of Europe, and that the unity of Europe could best be approached "crabwise" through technical cooperation in economic matters. These ideas were the beginning of the concepts that led to the Marshall Plan proposal. 25 James Silb erman . Following the European Recovery Program's initiation, President Harry Truman signed in 1948 the act creating the Economic Cooperation Administration to administer the Marshall Plan. Paul G. Hoffman, C.E.O. of Studebaker Motors, was appointed its Administrator. He recognized immediately the backwardness of European production as a major problem that BLS would subsequently identify statistically. One enterprise Sir Stafford Cripps and I jointly inaugurated was the Anglo-American Council on Productivity. This turned out to be one of the most effective innovations introduced by the Marshall Program. Almost all European countries faced the necessity of a rapid increase in productivity. Their factories were filled with out-dated tools and they were employing old-fashioned methods. 26 Monthly Labor Review June 2005 47 The Marshall Plan W. Duane Evans, Chief of the BLS Office of Labor Economics, was appointed adviser to the Anglo-American Council on Productivity. Evans oversaw the work of James Silberman, Chief of Productivity and Technology Development, and his colleague Kenneth Van Auken. Silberman and Van Auken were sent to England and then to France in May 1948, shortly after passage of the European Recovery Program. Their assignment was to investigate industrial production in each country. After visiting 35 factories in 5 or 6 industries in England, Silberman pinpointed inefficiency in production management as the major problem.27 Countering claims by Europeans that the major problem was the war 's destruction, Silberman pointed out that in the prewar period, Europe had fallen so far behind the United States in output per person that trade relations had been seriously disrupted. His analysis prompted the rallying cry of "productivity" that swept over Europe. Many European economists eventually accused Americans of believing that they had been the discoverers of productivity. In actuality, it was the British economist Laszlo Rostas who that same year had noted, "British productivity was substantially below that of the United States, despite her having at one time been the industrial leader of the world." 28 Silbem1an's analysis of English as well as 16 French factories uncovered similar findings .29 Thus, BLS could be viewed as the logical entity to provide ground level measurement standards for productivity. BLS economists in the postwar period were experts in industrial organization both through training and experience. Many BLS economists, including Duane Evans, also held engineering degrees. By 1948, BLS had had many years of experience in the systematic collection and appraisal of productivity measures covering almost every type of industry in the United States. Each year, more than 3,000 American factories were visited, and BLS representatives conferred with plant managers, engineers, comptrollers, and cost accountants, among others. Detailed company output per person hour and production statistics were collected and factual information obtained regarding the numerous factors affecting operational efficiency. With this experience in the analysis of productivity data, BLS maintained a body of specialized knowledge relating to productivity measurement, which could be found nowhere else in the country. Additionally, the BLS technical abstract service, initiated in 1942, had served throughout the war as the official source for abstract information on factory equipment and methods. The Factory Performance Reports (discussed later) created for the Technical Assistance Program were rooted in this experience. 30 A number of personal plant visits led to additional funding in 1945 to develop a sizable project for the preparation of industrial productivity measures by an entirely new approach using cost accounting data. 48 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 These reports were detailed case studies of manufacturing operations in individual American plants, designed primarily for use in Europe. In this program, BLS agents collected detailed information which yields person hours per unit required to make a given product, for a plant as a whole, for each department, and for each important operation. The data were supplemented by a description of each plant's equipment, layout, manpower, materials handling methods, and other similar plant characteristics. Ewan Clague. Ewan Clague, Commissioner of Labor Statistics (1946-65), grasped the importance of the opportunities created by Silberman's productivity comparisons studies in England and France and brought them to the attention ofU.S. Secretary of Labor Maurice Tobin. In a memo written to Under Secretary of Labor John Gibson, Clague suggested: Either you, or the Secretary should make a report to Mr. Hoffman .. .I believe it is important to see Mr. Hoffman this week-before he attends the hearings on his budget which takes place this week. 31 Clague's intent was to have BLS "secure parallel data collection programs which will provide the basis for reasonably precise and accurate international comparisons." The architects of the Marshall Plan had assumed that financial aid, in the form of new investment, would quickly restore European productivity levels to U.S. levels, but BLS "techno-economic studies" had demonstrated otherwise. 32 Observations at 200 factories in 6 countries revealed dramatic differences between European and American productivity. Despite the fact that Europe was at least as advanced as the United States in terms of scientific and technical theory, BLS studies demonstrated that Europe had fallen behind America in applying this knowledge to industrial production. Western European managers and engineers were not aware of the productivity gap between them and their U.S. counterparts, and did not realize the need for substantial technology transfer until the Bureau of Labor Statistics' studies. 33 At the time (1949), Clague noted this distinction in remarks presented to a conference on productivity. It may not be generally realized that, in large measure, the high living standard in the United States is the direct result of higher productivity. Productivity levels in the United States are more than twice those in Great Britain, and recent figures indicate that our productivity is more than three times that of Belgium, France and other industrial countries ofEurope. 34 James Silberman, in a 1992 summary of the accomplishments of the Marshall Plan, stated it in a different way: The technical assistance program of the Marshall Plan was the largest and most comprehensive program of assistance to civilian industry ever undertaken. In a few years, and at low cost, those programs reached almost every plant in every industry, marketing agency, and agricultural entity in the war-devastated countries of Western Europe, introducing them to a technology more than a generation in advance of what they were doing. These programs accelerated the postwar economic recovery, raising the annual rate of increase in labor productivity of Western European industry from its historic level of about 1 percent per year to 4 percent or more. Within individual enterprises , productivity commonly increased by 25 to 50 percent within a year with little or no investment. 35 Formalizing the efforts The BLS studies indicating that net investment, by itself, was not the remedy placed an emphasis on increasing productivity through greater efficiency. Greater attention to operational efficiency had the advantage of being cost effective because it did not put pressure on the dollar scarcity which prevailed in these debtor countries of Western Europe. During the Marshall Plan period, $19 .4 billion were allocated for capital costs. The cost of the Technical Assistance Program was $300 million; only one-third was contributed by the United States . A means of realizing the potential in the Technical Assistance Program was noted by Sol Ozer, labor adviser to the Economic Cooperation Administration, who wrote the following memo to Ewan Clague: I was impressed by (the) thesis, namely that a few American labor production experts brought here to Europe-to France in particular- might make a few changes but would not correct the basic situation. However, if a few thousand of the brighter management and production people of France had the opportunity to see the operations in the United States in factories similar to theirs here, a revolution in technique might begin after they returned. If enough Frenchmen were involved they would stimulate each other to do in France what production planners and technical engineers have done in the States. 36 The idea behind the suggestion of Silberman to bring a few thousand management and productivity people to the United States was that European business practices were more traditional and less adaptable than were those of their American counterparts. The suggestion was an attempt to https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis introduce Europeans to the elusive quality of American "know how," a quality demonstrated by America's response to the war effort. The results achieved are shown in the following report: The technical assistance program has emerged as one of the Marshall Plan's most successful activities in France. To date, about 60 teams of700 specialists from nearly every French industry and profession have come to the United States to study productivity in specialized fields. Inside France, it has ... resulted in the first breakdown of the traditional iron-clad trade secrecies. 37 Team members now visit each others plants- usually for the first time in their lives- before going to the United States in order to have a rounded picture of their own industries. 38 Secretary of Labor Maurice Tobin foresaw that bringing people together from the same occupational culture could make a positive effect on European recovery and, thus, had moved to formalize these relationships. On August 20, 1948, he sent a memo to Paul Hoffman and several leaders of organized labor, who had been involved in the recovery program, with the four recommendations: 1. Department productivity personnel should participate in the technical staff for American-European Councils of productivity; 2. productivity targets, based on American performance standards, should be included as part of programs to increase productivity; 3. there should be a general exchange of information and the publication of info1mation; and 4. the technical abstract service should be used as the central clearing point for information. In forwarding these recommendations, Tobin was aware of the overall capabilities of BLS. Early in 1949, Paul Hoffman discussed these proposals with a delegation from the Department of Labor that included Secretary of Labor Tobin and BLS Commissioner Clague . BLS accepted responsibility for making statistical surveys of technology and labor productivity in American industry in order to provide guidelines for stimulating the productivity of Western European industry. European countries were encouraged to establish national productivity centers, which would both improve the productivity of their own workforces and make parallel studies for comparison with those made in the United States. These efforts were summed up in a report released by the International Cooperation Administration. While no complete accounting for TA (technical assistance) activities in Europe from 1948- 1957 is available, it Monthly Labor Review June 2005 49 The Marshall Plan may be readily estimated that about $60 million in direct U.S. aid was expended on TA projects over this period. These expenditures financed TA study trips of Europeans to the U.S., the use of American specialists in Europe and the provision of technical information and services. Reliable data indicate that through March 1957, nearly 19,000 European technicians, specialists and leaders of industry, labor, and government had visited the United States. Nearly 15,000 U.S. specialists had served abroad in the direct implementation of the national programs. Extensive technical services were provided including over 35,000 technical and scientific books, periodicals, and other literature; over 2,500 replies by mail to technical inquires, over 3,000 digests of articles from U.S. technical and trade magazines; some 48 Bureau of Labor Statistics' factory performance reports. 39 Factory Performanc e Reports/productivity As noted previously, a unique contribution ofBLS to the Technical Assistance Program was the preparation and issuance of Factory Performance Reports. These studies made use of a new technique in direct productivity analysis, that is, the utilization of the vast sum of information contained in industry cost accounting records. Never before had accounting data been used in the systematic study of productivity. Therefore, it was necessary to develop methodologies for adapting these accounting records to an application entirely different from that for which they were designed. Factory Performance Reports required direct observation in the field, and these field-based reports of actual productivity contributed substantially to European recovery. The reports were designed to present operational profiles of U.S. plants. Businessmen in other countries could then use these profiles to evaluate their own operations, isolate their areas of good or poor performance, and improve those areas that needed improvement. The case studies covered factories of similar size and products generally comparable with those in foreign companies. Extensive field-based research was conducted in order to adapt these records to the case study methodology. At each plant, BLS representatives discussed and analyzed cost accounting data to derive unit employee hours for each selected product. Also included in these examinations were classifications of labor accounts, scope of operations, parts and equipment purchased, the ratio of various indirect labor accounts to total direct employee hours per person, extent and type of hours paid for but not worked, and the basis for reporting capacity data. Use of these studies permitted the evaluation of similar plants in other countries and presented a standard for gauging 50 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 "good" or "poor" performance. The data were supplemented by an outline of each plant's equipment, layout, manpower, materials handling methods, production and work scheduling methods, and operating policies. BLS also organized two types of teams to close the productivity gap between the United States and Western Europe. In one, experts were sent to Europe to work closely with individual country productivity centers to provide information on turning statistical data into useful knowledge. The other program brought a total of 24,000 Europeans to the United States to see firsthand new approaches to organizing workplaces, new concepts of business and marketing organization, new products, new design and engineering functions, and new equipment. In this effort, teams ofbetween 12 and 17 Europeans, organized by industry and representing a cross-section of functions, visited their American counterparts. Each team prepared a comprehensive technical report that documented their findings . On their return, these reports were disseminated to plants within industries. The analyses provided by BLS F act01y Performance Reports and the "hands-on" approach of having European productivity teams visit their American counterparts challenged the institutional barriers to modernization in European industries. The effectiveness of these programs was based on the analytical and practical application of BLS data. Their use as tools in identifying organizational production deficiencies in European industry presented a rational basis for measuring success. BLS CONTRIBUTED SIGNIFICANTLY to the overall success of the Marshall Plan's Technical Assistance Program. As the Marshall Plan was coming to a close in 19 5 3, Aryness Joy Wickens, who had served as acting BLS Commissioner, made the following point in a presidential address to the American Statistical Association: In the past few years, statistics in the United States have come to be used as determinants of private and public actions affecting millions .. .Statistics have come to be one of the great descriptive and analytical tools of modem industrial society, comparable to the other new tools of science. 40 It is to the BLS credit that it was able to apply the new "tool of science" to help in the recovery of the postwar world. Still, however useful many of these statistical programs proved to be, the most remarkable achievement ofBLs was in the field of productivity. Its productivity achievement extended beyond just showing that productivity depended on many factors and also demonstrated the extent to which each factor influenced the entire result. □ Notes 1 See http://www.marshallfoundation.org (visited May 24, 2004). Summa ry of the Marshall Plan : "The idea of massive U.S. loans to individual countries had already been tried (nearly $20 billion - mainl y lon g-te rm, low interest loans - since the war's end) and had failed to make a ny headway against Europe's social and economic probl ems." labor Review, January 1948, p . 40. 2 James M . Silberman and Charles Weiss, Jr., Restructuring for Productivity: The Technical Assistance Program of the Marshall Plan as a Precedent for the Former Soviet Union (Washington, Global 2 Kenn eth A. Middleton, "Wartime Productivity C hanges in the Airframe Industry, " Mon thly labor Review, August I 945 , pp . 215- 25 . Tec hn o logy Management, Inc. , Under contract for the World Bank, 199 2) pp . vii - viii. 3 Joseph W. Duncan and William Shelton, Revolution in United Sta tes Government Statistics, 1926- 1976 (Washington, U .S . Depart- m e nt of Co mme rce , 1978), p . 97 . 4 Ewan C lag ue, The Bureau of labor Statistics (New York, Praeger Publish ers, In c., I 968), p. 158 . 5 Joseph P. Go ldb e rg and William T. Moye, Th e First Hundred Years of th e Bureau of labor Statistics, Bulletin 2235 (U.S . Bureau of Labor Sta ti stics, Septe mber 1985 ), p. 140. 6 Duncan and Shelton, Revolution in United States Government Statistics, p . 77. 7 Caro l S . Ca rson, "T he History of the United States National In co m e and Product Accounts: The Development of an Analytical Too l," Review of In come and Wealth I 975 , Vol. 21 , Issue 2, p . I 55 f. " In February, 1932, two gro ups interested in pursuing information on nation al in co m e were brought into contact. .. officials of the Co mmerce Department. .. and ... what was known as the LaFollette group." 8 Annual Report, 1933 (U .S. Department of Labor), p . 4 I. 9 Go ldberg an d Moye , First Hundred Years, p . 145 . 10 Lewis Lansky, Isador Lubin (Ph.D . diss . typescript, Case Weste rn Reserve Universi ty, C leve land , Ohio), p . 16. 11 Ri c hard D . McKin z ie , Oral History interview with Dr. Isador L ubin (Harry S. Truman Library, Independence, Missouri, January 1976), p . 31. McKinz ie: "A t the end of the war there was a view about the postwar world which econo mists had been nurturing during the war, particularly those who were close to Will C layton . It was that there would be a new order of things and it would be characterized by more integration of econom ies . .. But, a t least, the economic integration would create a mutual dependence and, therefore, stability. Did you share that view?" Lubin: "Ve ry defi nitely, yes." 12 Isador Lubin, "Social and Economic Adjustments in a Democratic World ," Journal of th e American Statistical Association, Marc h 194 7 , p. 19. 13 Ernie Engla nd er and Allen Kaufman, "The End of Managerial Id eo lo gy : From Corporate Social Res ponsibility to Corporate Social Indifference," Enterprise & Society, September 2004, pp. 407 - 08. Formed in I 942, the Co mmittee for Economic Deve lopment gave corporate management their first public voice . [Paul Hoffman was th e presiden t of the organization, which promoted the idea of the M a rshall Plan in order to create a receptive public opinion .] It gained promin ence when it help ed to coordinate the transition from war to p eace by estab li s hing reg ional offices that reported data on local business plans . 14 David Mclellan and C harles E. Woodhouse, "The Business Elite and Foreign Po li cy," The Western Political Quarterly, I 960, p. I 72. 15 McKinzie, Oral History, p . 23. 16 "Deve lo pm e nt of the E uropean Recovery Program," Monthly https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 17 "Activiti es of the Bureau of Labor Stati stics in World War II ," Historical Reports of War Administration, No . I, June 7, 1947, p. 9 . 18 Ibid., p . 10 . 19 ibid., p. 59. ° 21 McKin z ie, Ora l History, p. 29. McKinzie: " If Mr. Pauley didn't hav e any appre ciation of the problems which reparations caused after World War I, did the Department of State? "Lubin : " Definite ly, yes. The people in th e Economic Section were very conscious of what had happened in Germany as a resu lt of inflation and as a result of reparati ons. [Pres ident Roo seve lt] was very co n sc io us of what had happ e n ed to Germany as the result of reparations . He mad e it perfectly clear that we wou ld no t talk do ll ars . We would talk ph ys ic a l things that they ne eded to re build th e ir co untry and h e e mphas ize d that to m e." 22 Isador Lubin, Letter to A . Ford Hinri c hs, May I 9, 1946 (U.S. National Archiv es and Record s Admi ni stration (NARA ), Roosevelt Library, Lubin Papers). 23 Martin C. Ko hli , "The Leontief-BLS partne rship: a new framework for meas urement," Monthly labor Review, June 2001, p 3 1. 24 W. Duane Evans and M arv in Hoffenberg, "Th e Interi ndu s try Re lations Study for 1947," Review of Econom ics and Statistics, Ma y I 952, pp . 97 - 142 . 25 C harl es P Kindleberger, "C harl es P. Kindl eberger o n the Econ omic Backgrou nd (of the M ars hall Plan )" (U.S. Na ti ona l Archives and Record s Admin istra tion, State Department records, Record Group 59 [Ce ntral Decimal File 840 .50 Recovery/7- 2248]) . 26 Ora l Hi story interview with Paul G. Ho ffman (Tru man Mu se um and Library, October 25, I 964) on the Int e rn et at www. trumanlibrary.o rg/oralhist/hoffmanp.htm (vis ited De c. 26, 2004). 27 Memo from W. Duane Evan s to Ewan C lague , SG4 05 , June 28, 1948 (U.S . Nationa l Archives and Records Admini s tration) . 28 Ewan C lague: "A Britis h economi st, L. Rostas, estimates that o utput pe r worker in manufacturing in the United States was over two times that in the United Kingdom for th e years 1935- 1939. Accordin g to French sources, recent s tatistics indicate th a t output of s tee l pe r year is fo ur times that of France, and productivity in agr iculture is three times the French level. In Belgium, another highl y indu s trialized country, average production per h o ur according to recent estimates , is le ss than on e- third the leve ls for corresponding industri es in the U nited States ... In o rd er to exp la in these productivity differe ntials, it is nec essa ry to examin e the techniques of production . Bas ic scientific re searc h a nd technology are at least as far advanced in E urope as in the United States, but the app li cation of technology to indu stria l methods has not progressed so far. In short, America has m ore 'know how. "'See Ewan C lague, "Prod uctivity, Employment and Living Standards , "Conference o n productivity, held in Milwauk ee, Wisconsin) , p. 7. 29 James S il berman and Kenneth Yan A uken , "Fac to ry Vi si ts , May 29 to Jul y 11 , 1948, England a nd France," W. Duane Eva ns papers (Typescript report, W. Duane Eva ns Co ll ect io n , Co rn e ll U niver s ity archiv es), transmitted by Labor Advisor C linton Go lden : " I am very much impresse d by [the work of Jim Si lb erma n and Ke nn e th Yan Auken] and the content of the preliminary report, which in this case, was first submitte d to David Br uc e, C hief of th e ECA Mi ss ion in France ," in a Le tte r from Labor Advisor C linton Golden to Under Secretary of Labor John Gi b so n, Econom ic Coop eration Administration , RB 174 (U .S. National Archives and Records A dmini s tra ti o n ). Monthly Labor Review June 2005 51 The Marshall Plan 3 ° Foreign Operati ons Administration Techn ical Aids Branch in coopera tion with BL S Factory Pe1fo r111a nce Reports. Unda ted introducti on: Facto,y Pe,formance Rep orts are des igned to prese nt operati onal profil es of U.S. pl ants aga in st whi ch busin ess men in other co untri es can evalu ate their own operati ons, isolate th ei r areas of good or poor perfo rmance, and th en imp rove th ose areas whi ch may need improve ment. The reports are not engineerin g studi es, nor do they te ll a nov ice in the industry how to es tabli sh and operate a pla nt. They are des ign ed fo r practi cal use by fo reign manufac turers who are already fa mili ar with production tec hniques and prac ti ces in the industry. 31 Memo fro m Ewa n Clag ue to Joh n W. Gibso n, Dece mb er 27, 1948 (U. S. Nati onal Archi ves and Reco rds Admini strati on). 32 36 Me mo fr om Sol Oze r, Labor Advis er to the EC A, to Ewan C lague, Decemb er 23, 194 8 (U .S. National Archives and Records Administration) . 37 ~ames Silberman, " Survey of French Productivity" (Typescript report, W. Duane Evans Coll ection , Cornell University archives) p . 11 , " The unwillin gness of plant managements to visit other French pl ants , or be visited themsel ves (to guard their secrets of production), is wholly different and less effective than the free exchange of ideas found in American plants ." 38 Ibid., 3 years into the Marshall Plan. 39 European Productivity and Technical Assistance Programs, a s umming up (194 8- 1958) (Paris, International Cooperation Administration, Technical Cooperation Division, May 15, 1958), p. 7. Ibid. 33 Ibid. 34 Clague, op.c it. 35 James M. Silberman and Charl es We iss, Jr. , Res tru ctu ring fo r 52 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Producti vity, p. vii. June 2005 40 Aryn ess Joy Wickens, " Statistics and the Public Interest" Journal of th e American Statistical Association, March 1953, pp . 1- 14. International Report Reinserting labor into the Iraqi Ministry of Labor and Social Affairs Craig Davis The U.S. Department of Labor has been actively involved in the reconstruction of Iraq. During the summer of2003, Assistant Secretary for Policy Chris Spear served as Coalition Provisional Authority (CPA) senior advisor to the Iraqi Ministry of Labor and Social Affairs (MOLSA). Elissa Pruett acted as senior press officer in Strategic Communication at CPA. Later that summer, the Department's Bureau of International Labor Affairs (ILAB) assigned me to CPA, followed in the fall by James Rude, senior international program manager. Both of us acted as labor advisors to the Iraqi Ministry. In January 2004, the Department sent trial attorney Wade Green to Baghdad, where he served as Attorney-Commercial Law Reform Group, and where, among other things, he worked to revise the Iraqi Labor Code. In addition to personnel assignments, the ILAB also funded a $5-million project designed to demobilize, rehabilitate, and reintegrate former Iraqi soldiers within the framework of a larger workforce development program. This grant was the cornerstone of Iraqi labor reform beginning in August 2003. For most of 2004, up to nine U.S. Department of Labor-funded international consultants worked at the Ministry daily, providing technical assistance and building the capacity of the Labor Directorate within MOLSA. Craig Davis served as an international education program specialist in the Bureau of International Labor Affairs , U.S. Department of Labor from 2002 to 2005. The views expressed in this article are solely those of the author and do not reflect the official positions of the U.S . Department of Labor or the Coalition Provisional Authority. E-mail : Craigsd23@hotmail.com https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis r. ,-~ The Ba' athist legacy in the Ministry of Labor and Social Affairs. The "Labor" component within MOLSA-the entity of the Ministry with the mandate of securing workers' rights and training and preparing the workforce for the labor market-suffered severely during the last decades under the Ba' athist regime. The MOLSA was underfunded, received little political attention, and was afflicted by widespread corruption and lethargy. Low institutional capacity on the part of the staff and leadership was widespread.1 Skilled civil service workers and professional management gravitated toward the more prestigious, better-paid government jobs in the military and the foreign service. For example, a vocational trainer with 15 years experience in the Ministry of Military Industries2 earned approximately the equivalent of $100 per month, while an equally qualified counterpart at MOLSA would earn between $6 (Social Welfare) and $25 (Social ~ecurity). 3 Management consisted of little more than endless bureaucratic paperwork, while decision-making entailed top-down dictatorial orders barked by a high-level Ba'athist official intimidating his (all top management were men) staff into submission for fear of some type of punishment. 4 Initiative and critical thinking were not rewarded. As a result, through the spring of 2003, capacity, diligence, and resourcefulness were not plentiful at MOLSA. Historical background Social Welfare also was responsible for a number of social care institutions, including rehabilitation centers for the disabled and orphanages. Social Security operated a private pension system that paid retirement benefits to some 18,000 recipients. Within Social Security was a vocational training unit that had training facilities in 5 cities in the southern 15 governorates. The Prisons Department was responsible for the nation's prisons , including the infamous Abu Gharib prison outside of Baghdad. (This department was removed from MOLSA in the summer of 2003). Although each of the pillars housed separate offices , such as engineering, finance , legal, and auditing, the Administration Department was a redundant bureaucratic body comprising parallel offices that functioned as an overlapping and cumbersome oversight or coordination mechanism. In essence, due to corruption, apathy, incompetence, and bureaucracy, the Ministry was largely dysfunctional. Funding failed to reach the governorates; social welfare benefits were paid to but a fraction of those who qualified; malnourished and neglected children languished in orphanages while salaries were paid to ghost employees; contracts were diverted to Ministry engineers and other officers and their families; and corrupt officials took money targeted for, or demanded bribes from, the most vulnerable Iraqi populations: widows, orphans, and the disabled. Before the war in 2003, the Ministry of Labor and Social Affairs consisted of four departments or directorates (dawar): Social Welfare, Social Security, Prisons, and Administration (diwan). Social Welfare provided benefits to about 68,000 widows, orphans, and disabled Iraqis. This figurecapped by Saddam Hussein-limited the number of beneficiaries to a fraction of those who qualified while strategically discriminating against the needy, particularly those in the Shiite south. Employment centers. The 1987 Labor Code designated MOLSA as the mechanism to provide employment services and vocational training to unemployed Iraqis. 5 In theory, employment centers (marakaz al-tashghi[) had existed since 1971 seeking to fill government vacancies. At one point, Baghdad had three employment centers. The Labor Code expanded this mandate for MOLSA by extending its obligation to match jobseekers with private-sector jobs. MOLSA employment centers were legally Monthly Labor Review June 2005 53 International Report responsible for registering jobseekers. Employers were obligated to announce job vacancies through the employment centers. If the centers failed to respond within 15 days, the employer was free to hire workers outside the centers. 6 According to the law, jobseekers were to be selected in chronological order, not merit. 7 The centers were also responsible for providing work permits to foreign workers. 8 Employment servicessuch as employment training, career counseling, or other services for unemployed workers-were not articulated in the Labor Code as part of the employment center mandate. In practice, however, MOLSA 's employment centers during that period failed to match jobseekers with privateor public-sector vacancies. Government hiring was a product of nepotism and corruption. By the time the Labor Code became law in 1987, jobseekers had lost all faith in MOLSA 's employment centers and no longer made any effort to register for jobs there. By the early 1990s, just one of the original three MOLSA employment centers in Baghdad remained open-and only as a token display of government effort to assist the unemployed. None of the centers across the country any longer made an effort to secure employment for jobseekers; they merely made attempts to record government positions filled, a process that was also eventually abandoned. By 2003, the primary purpose of the centers was to authorize work permits to foreign workers. The entire Baghdad employment center consisted of five workers, while Ministry offices in most other governorates (or provinces) had only one or two officials, if any. While unemployment reached an estimated 50 percent after the 1991 GulfWar, 9 jobseekers had no official government office to which they could turn for assistance. The surest way to secure employment in Iraq was to rely on favors from friends, family, or tribe members (a process still popular today). 54 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Vocational training: Despite the mandates established in the Labor Code, during the last several years of Saddam Hussein's rule, the regime had little interest in providing vocational training through MOLSA as a means of job preparation for Iraqi jobseekers. MOLSA 's vocational training program had weakened over the years under the Social Security Department. The Ministry's compound on Palestine Street in Baghdad was commandeered by the Ministry of Military Industries. Instead of computer-skills training for the unemployed, Hussein's military machine taught chemical engineering. In place of air-conditioner repair instruction, the compound produced or assembled Rocket Propelled Grenade (RPG) cylinders. Rather than housing auto repair equipment, the center stored Scud missile nosecones. Emerging from the rubble. By the spring of 2003, the MOLSA was in shambles. Decades of Ba'athist oppression, an economy severely crippled by 8 years of the Iran-Iraq War (1980--88), the devastation of the Persian Gulf War in 1991, and 12 years of subsequent U.N. sanctions had brought the Ministry to its knees. Any semblance of a ministry functioning to provide protection and services for Iraqi workers was a thing of the past. The final blow to the labor function at the Ministry came after the regime's fall in April 2003, as looters took to the streets on a path of pillage and destruction. The Ministry's buildings were gutted; equipment hauled off; wiring stripped from the walls for copper; records burned (some by the staff itself); vehicles stolen; light fixtures and air conditioners removed; glass broken; and books and documents strewn about. All that remained of MOLSA 's vocational training center in Baghdad were shells of buildings; toolmaking equipment too heavy to haul away; pallets of Scud missile nosecones; barrels of gun powder; and crates of RPG cylinders. Implementing a new strategy Workforce development . Just as MOLSA 's infrastructure and facilities had to be rehabilitated in order to raise the physical structures from the rubble of Saddam Hussein's legacy, so too the human capacity of the Ministry needed revitalization and an injection of fresh ideas, approaches, and training. In the summer of 2003, the CPA advisors to MOLSA faced the daunting challenge of how best to invest in human capital at the Ministry. Because the CPA 's De-Ba'athification policy entailed releasing the top three layers of management from service, the Ministry was left with a staff with unproven leadership and inexperienced management. The first step for the CPA advisors was to assess the management, professional , and technical capacity of the Iraqi staff. The advisors quickly learned that the potential for capacity building from within the Ministry was poor. During the summer of 2003, for instance, the Ministry's top information technology director left a shipment of new computers donated by USAID in their boxes for 2 weeks because he was unable to solve the problem of how to adapt a European plug to a Middle Eastern outlet. (The solution was to purchase a $1 adapter readily available in the market.) The existing staff members that demonstrated the most initiative and promise were often women, who had been marginalized under the former regime. Although cultural and religious biases routinely stood in the way of women's careers, the CPA advisors made strong efforts to move a handful of talented women into strategic supervisory positions within the Ministry. Three newly promoted female MOLSAofficials visited Washington, DC, in October 2003 to attend workforce development training courses and visit one-stop employment centers. Nonetheless, there was simply not enough talent at MOLSA to undertake a multi-million dollar workforce development program across Iraq. Steps were taken to recruit young, strong, and skilled Iraqis-most of which were college graduates who had not been corrupted by the system-in both the private and public sectors. These dedicated Iraqis were hired and trained in Iraq and Jordan, and mentored by international consultants at the Ministry. The International Organization for Migration-implementing agency for the U.S. Labor Department grant-conducted training in Amman, Jordan, for some 130 MOLSA employees-both men and women-and two Iraqi staff members attended International Labor Organization (!LO) training in Turin , Italy, in the fall of 2003. Beginning in the winter of 2004, the CPA staff established an inhouse training program for MOLSA staff. A wide variety of management skills and ethics, computer skills, English as a foreign language, and labor-reporting systems courses were provided to labor officials in Baghdad and other governorates. By late spring of that year, nine U.S. Labor Departmentfunded international consultants had launched a program to build capacity of (and to mentor) Iraqis through the end of the year. Before the war, there was no labor department (or directorate) within MOLSA. Vocational training and employment centers (as well as a dysfunctional wage regulatory committee) fell under the Social Security Department. Key labor components of employment services-such as matching jobseekers with vacancies in both for the private and public sector, career counseling, and referral services-simply did not exist. Therefore, one major goal of the CPA advisory staff was to establish an independent Labor Department at MOLSA that would be responsible for labor-related issues. As a result, in the spring of 2004 the MOLSA Labor Department was formed with a separate revised Iraqi budget of $14 million to improve em- https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis ployment centers, vocational education, and support other labor programs. A key element in the CPA 's strategy for the Labor Department since the fall of 2003 had been the establishment of 28 employment service centers across the country. Unlike employment centers during the Saddam Hussein era, the new centers would provide valuable employment services to Iraqis, such as matching jobseekers with immediate employment opportunities; career counseling; and referrals for jobseekers to vocational and technical training, rehabilitation (for demobilized military and militia) , and other services, whenever possible. By the end of May 2004, MOLSA had opened centers in 18 cities-Amarah, Baghdad, Baqubah, Basrah , Diwaniyya, Fallujah, Irbil, Khanaqin , Kirkuk, Mosul, Najaf, Karbalah, Kut, Nasariyyah, Ramadi, Samawah, Sulaymaniyyah, and Tikritand had secured funding for the remaining 10 centers. 10 MOLSA also initiated a plan to rehabilitate, equip, staff, and provide training for Iraq 's six existing vocational training centers, and 26 additional training centers across the country. In addition to the traditional vocational training classes in electronics, household appliance and auto repair, welding, machine tool technology, and construction skills, MOLSA expanded its curriculum to include sewing, English as a foreign language, remedial and accelerated learning, and computer-skills training. By the end of May 2004, six training centers were operating, and funding for the remainder had been secured.'' In addition, CPA and MOLSA succeeded in establishing a number of other mechanisms to secure worker rights, training, and opportunities for workers before the transition. They opened the nation's first child labor unit, first onthe-job training program, first labor statistics office, first career counseling unit, and first veterans' services programall programs in their infancy and in need of technical assistance and capacity building. Security. In the best of times, the rebuilding of the Iraqi Ministry of Labor and establishing such an aggressive workforce development program would have been a challenge. Since the summer of 2003, however, Iraq has presented a most difficult implementing environment. The simplest of task s proved frustratingly complicated, difficult, and dangerous. For instance, restrictions imposed by UNSECOORD (the Office of the U.N. Security Coordinator)-as a result of the August I 9, 2003, bombing of the U .N. headquarters in Baghdad that killed 22 people- have severely impeded access to Iraq for U.N. international staff. The International Labor Organization and the International Organization for Migration, two of MOLSA 's most active partners , have been unable to send international experts to Iraq since September 2003. Training, the implementation of programs, and capacity building, therefore, must be conducted by " remote control" from Amman, Jordan, or halted entirely. Ironically, MOLSA, the Iraqi mechanism legally responsible for monitoring and enforcing labor laws, became a curious microcosm of workforce-related violence and threats that plagued the country on a larger scale. Beginning in the fall of 2003, a series of violent incidents struck MOLSA. In the presence of a CPA military lieutenant, a former Iraqi intelligence officer threatened to kill the director of the Baqubah Employment Center if the director failed to resolve an employment dispute. A few weeks later when no resolution was forthcoming, two of the director's brothers were attacked-and one was killed-allegedly by the same former intelligence officer. On October 26, 2003, two mortars directly hit the Al-Rashid Hotel room of !LAB 's two CPA Labor advisors to MOLSA-Jim Rude and myself. Both of us were injured in the attack. Rude Monthly Labor Review June 2005 55 International Report underwent emergency surgery for a serious injury to hi s left arm and was evacuated to Germany and then back to the United States. After months of warnings to blow up the Minister's residence at the Shaheen Hotel in Baghdad, terrorists finally made good on their threats in January 2004. The Minister survived a car bomb driven into the hotel , but one of his bodyguards and two other guests did not. Later that spring, a number of jobseekers raided the Al-Amarah employment center, broke into the director's home, and threatened his life if they were not given jobs. An employment center staff member in Fallujah was killed in April under unclear circumstances. A MOLSA official who was instrumental in opening 18 employment centers across the country was temporarily reassigned from Baghdad for his own protection after receiving a series of death threats. A number of attackers, presumably trying to flush the official out, assassinated his mother and one brother, wounding a second brother, while on their way to school. On a routine visit of an employment center in Mosul on March I 4, 2004, my convoy of three white Land Cruisers was ambushed by insurgents. A Kurdish liaison was killed, and his driver was injured. Only through the quick action taken by my Iraqi bodyguards and driver did I survive. Labor Code reform. In the fall of 2003, the CPA undertook an effort to revise the 1987 Labor Code, in order to encourage foreign investment and to protect worker rights. The final product was a CPA revision closely resembling U.S. labor law. 12 This version addressed most of the shortcomings of the 1987 Labor Code, such as concerns over child labor, freedom of association, and unionization in the public sector. Early in the spring of 2004, MOLSA in cooperation with the ILO began drafting a second revision based on the 1987 Labor Code. 56 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 The ILO final product was presented to in late spring. Because of time constraints, CPA was unable to secure an agreement by all parties, including the Iraqi Governing Council, on a final version before the June transition date. Therefore, upon transition, CPA handed to the Iraqi Interim Government a copy of the CPA revision as a legislative proposal with the hope that the newly elected government in 2005 would act upon and pass this version, or a very similar version. As a result, the I 987 Labor Code, with all its shortcomings, is still in effect. Fortunately, a number of measures taken by CPA and the Iraqi Governing Council have helped secure worker rights. For instance, Order 89 signed into law in May 2004 amends the 1987 Labor Code by securing the minimum age for working children at 15; forbidding hazardous types of work for children until 18 years of age; and prohibiting the worst forms of child labor. 13 Article 13 of the Interim Constitution, or the Transitional Administrative Law, secures the right of association and freedom to form unions. 14 CPA Unemployment. With estimates of unemployment rates up to 65 percent circulating about, the Ministry of Planning 's Central Statistics Organization undertook an unemployment study based on a household survey sample of 24,900 families in the last quarter of 2003. According to the resulting Report of the Employment and Unemployment Survey Results for 2003 published in January 2004, unemployment in Iraq for 2003 averaged 28.1 percent (not including Kurdistan) and underemployment 23.5 percent. The survey was not without shortcomings. For instance, the study did not include the three Kurdish governorates in the north, where unemployment is relatively low. Moreover, the unemployment rate included minors, ages 15 to 17 .'5 It is also unclear how the study measured some 750,000 Ira- qis who currently receive salaries or stipends, such as state-owned enterprise workers and former military. This group constitutes about 10 to 15 percent of the workforce. Despite these shortcomings, the report remains the only reliable source for unemployment statistics for the country through December 2003. Because this report was not widely circulated, other less reliable unemployment estimates, ranging from 20 percent to 65 percent, continue to surface in the media. The following estimates demonstrate the range of such reports: • Based on an eclectic analysis of various estimated figures, CPA 's Private Sector Development office estimated Iraqi unemployment at approx imately 20 percent. 16 • The United Nations /World Bank Joint Needs Assessment, published in October 2003, estimated 50 percent of the labor force to be unemployed or underemployed, the same figure as before the war. 17 • The International Labor Organization 's own needs assessment estimated unemployment at 60-65 percent ( 4.5-5.2 million) of the workforce. 18 Both of the latter assessments were based on gross estimates and failed to take into account the stipends and salaries being paid to some 500,000 state-owned enterprise workers and some 250,000 former military, many of whom are already working elsewhere. 19 In addition, these assessments did not include the new jobs created in reconstruction efforts, emerging private sector, new government hires, and the informal sector employment. MOLSA 's newly-formed Labor Statistics Office primarily reports on employment secured through its centers and does not conduct unemployment studies. However, it has undertaken its own survey of jobseekers who enter employment se rvice centers across the country. In thi s way, MOLSA has determined that approximately 44. 7 percent of jobseekers registering for job opportunities at the Baghdad Employment Services Center are already employed full time elsewhere . In fact, the Baghdad center has experienced an unexpected phenomenon: the center cannot fill all available job vacancies . In the absence of further scientific data, and taking into account the lower unempl oyme nt in Kurdi stan, it is not unre asonable to imagine that an accurate jobless rate of the Iraqi workforce ranges between 20 and 28 percent. In sum , while unemployment is high and a source of major concern , it may not be as extreme as commonly reported. Challenges in the evolving Iraqi labor market As is the case with unemployment estimates, the paucity of solid labor market data and information in the decade or so leading up to the 2003 war serves as an obstacle to adequate analysis in the current environment. During that period, most employment comprised government or state-owned enterpri se (SOE) workers, as well as family-owned small and medium-size enterprises , most of which were in the informal sector. The SOES are gove rnment- su bsidized industries, often referred to as mixed businesses. 20 The industriessuch as concrete, chemical , textile, carpet, and others-produced se rvices or goods primarily for gove rnment consumption. The potential of many to survive in the emerging private sector is questionable. In order to survive as independent profit-making establishments able to compete with foreign enterprises in foreign or domestic markets , most SOEs needed an infusion of capital; reconstruction and revitalization of equipment and https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis re sources due to looting or disrepair; and restructuring and modernization of management, staffing, and production techniques. But with bloated workforces (approximately 500,000), it became politically unpopular-if not untenable-to restructure the SOEs-including the privatization of some viable SOEs-in such a way that might threaten social stability by poss ible layoffs or firing of workers. Thus , throughout 2004, the CPA decided to pay SOE workers full salaries, whether or not they came to work. As a result, many SOEs today operate below capacity or not at all. Many enterpri ses produce no goods, while workers remain at home collecting salaries or, as is often the case , have taken sec ond jobs and earn additional salaries. Beginning in 1995, the United Nations developed a Public Distribution System-commonly referred to as the food basket-which essentially provided basic staples to all Iraqis each month. 21 The U.N. funded the food basket with Oil-for-Food revenues, and the former Ba 'athist government was only too happy to assume credit for feeding the entire Iraqi public. The food basket coupled with a reliance on government positions in the SOEs, public sector jobs, and armed services fed into a public perception that the socialist government was ultimately responsible for providing livelihoods for all Iraqis. The underlying public sentiment that government must provide sustenance to the entire Iraqi population has proven a large obstacle for MOLSA employment centers attempting to match jobseekers with vacancies in the private sector. The overwhelming majority of jobseekers have little or no interest in private-sector jobs. When filling out jobseeker forms, middle age Iraqis routinely refuse to include any previous work experience. For Iraqis, work experience only means government work experience. Most Iraqis see no value in listing experience in the private sector. When private companie s contact unemployed Iraqis through the MOLSA centers for potential interviews, the jobseekers often simply refuse. Unless thi s public perception that the government is ultimately responsible for the welfare of all Iraqis is overcome , the transition to an open, democratic market economy will face serious difficulties. Wages. Another issue contributing to the evolving labor market is the rapid increase in salaries. Despite reports to the contrary,22 salaries in Iraq, by and large , have spiked si nce the end of the war. 22 During the last years of Saddam Hussein 's rule , as mentioned above, some experienced and trained gove rn ment employees earned as little as $6 per month . Typical salarie s for unskilled laborers in the private sector ranged between $5 and $ IO per month , while professionals made as little as $30 per month. 23 The CPA, Iraqi sec urity forces , Iraqi government, and international contractors have increased wages substantially across the board. But while the purchasing power of many Iraqis is increasing rapidly, and spe nd ing on consumer goods-such as home appliances, clothes, ce ll phones, satellite di shes, and jewelry-may reach record high s, the new pay scales are resulting in many huge complications in the labor market. Unfilled positions. The dynamic s of unemployment and employment in Iraq are extremely complicated and deserve careful study. Increased wages, for instance, while a welcomed development for Iraqi workers, has created complications in the labor market. While there are simply not enough jobs to go around, the Iraqi officials at the Baghdad Employment Services Center have been increasingly fru strated by the inability to fill vacancies. The fact that 44.7 percent of the jobseekers are already employed full time elsewhere helps explain Monthly Labor Review June 2005 57 International Report why "unemployed" Iraqis routinely refuse to accept employment. But there are other factors. One huge obstacle is the predominate attitude-inculcated from the former Ba'athist regime's policiesthat public-sector jobs are superior to positions in the private sector, an attitude Iraqi Labor officials are struggling to change. 24 MOLSA officials explain that many Iraqi jobseekers believe it is the government's obligation to support them on the one hand, and the innate sentiment that government jobs are less demanding, secure, and permanent on the other. The security risks associated with some positions rendered the jobs unattractive for some Iraqis. The high-paying salaries, however, usually offset the risks. Skilled workers, ranging from untrained translators to engineers in the second half of 2003, began drawing salaries ranging from several hundred dollars to a few thousand dollars per month, previously unheard of for normal Iraqis. Similarly high wages were paid to unskilled workers. Salaries were so high, in fact, that-despite the overwhelming dangers-large numbers of Iraqis continue to seek and maintain high-risk jobs. For example, in January 2004, suicide bombers killed some 25 Iraqi workers at Assassins' Gate as they entered the checkpoint into Baghdad's protective compound known as the Green Zone. Despite this incident, few of the remaining Iraqi workforce were deterred. Within a few days, almost all Iraqis had resumed their duties under CPA. In April, May, :ind June, when insurgents began a new l'- ,gram of targeted assassinations of Iraqis working within the Green Zone, dozens of innocent Iraqis were followed home and executed. Still, the majority of Iraqi workers continued to show up for work. 25 Despite repeated suicide bombings and targeted assassinations of security forces and recruits, jobseekers 58 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 continue to run the risks of applying for jobs at recruitment centers. 26 A lack of qualified candidates to fill job vacancies-such as those for Englishspeaking accountants, sales clerks, or translators-has served as a great source of frustration for Iraqi MOLSA officials. Throughout 2004, the Baghdad employment center failed to fill a number of vacancies. Below are unfilled positions and reasons that candidates refused to accept the employment. • Experienced automobile painters: Salary $66 per month. Low wages. • Unskilled workers at tailoring shops: Terms of wages. • Doctors for private hospital: Salary of $100-$133 per month. Working conditions (double shifts) and low wages. • Administrative assistants: Salaries of $47 per month. Low wages and long office hours. • Experienced bricklayers: Salary of $150 per month. Low wages. 27 • Bank tellers: Salary of $33 per month. Low wages. • Pharmacists: Salaries of $166 per month. Low wages. • Engineers: Salary of $83 per month. Low wages. • • Unskilled workers in plastic bag production, vending, metalwork shops, and other fields: • Long working hours (8 a.m. to 5-7 p.m.). 28 • Low wages $50-$100 per month. 29 Sales managers, truck drivers, engineers, technicians: Salaries of $50-$66 per month. Low wages. • Maintenance: Salary of $66 per month. Low wages. • Metalworkers: Salary of $133 per month. Low wages, distance to work, and no transportation allowance. 30 • Unskilled workers for a newspaper: Cramped working conditions. • Experienced plumbers: Salary of $66 per month. Low wages and long hours: 8 a.m.-4 p.m. • Unskilled workers at candy factories: Salary of $60 per month. Low wages. • Beauty salon workers: Wages are 50 percent of customer receipts: Terms of wages. Conflict in the public sector. Increased wages also created conflicts in the public sector in a variety of ways. Government salaries were increased substantially in the summer of 2003 upon the introduction of a four-tier pay scale that paid government workers between $50 and $400 per month. The lowest paid government employees saw an immediate IO-fold increase in their salaries, from $5 to $50 per month. Others saw pay raises of 15 to 20 times their former salaries. 31 In the spring of 2004, for instance, inexperienced security guards with no high school education often earned as much as $200-$250 per month, including danger pay, which surpassed that of many college graduates, creating widespread animosity. 32 Iraqi government officials with bachelor's degrees complained that salaries should be based on education, not risk. By January 2004, in an effort to bring all ministries in line with a fair and equitable salary scale-established under Order 30: Reform of Salaries and Employment Conditions of the State Employees-the C PA and the Iraqi Ministry of Finance offered an incentive of further salary increases of 40 percent to each ministry willing to adopt the new system . Again, virtually every government worker received some type of a salary increase; the average increase was 40 percent. 33 Order 30. Unfortunately, the implementation of thi s order resulted , in some cases, in a near disaster. The motivation and intent behind the design of the Order 30 was sound. The drafters of the law had envisioned a system of fair hiring procedure s and an 11 -grade pay scale that established a fair and equitable sal ary structure for state employees, with salaries determined by position, years of service, and performance. (See table 1.) An employee's salary was based on his or her grade on the I I-scale tier, which was "determined by the classification of the employee 's position. " Within each grade, there were IO steps upon which an employee would advance according, in part, to "the employee's length of service" and in part to his or her performance. 34 Although the letter and spirit of the law were clear to the CPA ministerial advisors tasked with ensuring each ministry adhered to the law, their Iraqi counterparts in the ministries saw the lucrative salaries as an opportunity to cash in. Because the Ministry of Finance, not the Ministry of Labor, had enforcement responsibilities , MOLSA was not consulted on the law's implementation. The instructions for implementation drafted by Ministry of Finance officials were based not on the law itself, but on old Ba'athist policies that established salaries-not on position or performance, but simply on years of service and level of education. Worse, the translation of Order 30 from its original English into Arabic was sloppily ambiguous, confusing position, years of service, and performance. Worst of all , most of the Iraqi Ministry of Finance officials and other Ministry counterparts responsible for implementing the salary reform were veterans with 20 to 30 years of government service themselves; they had no incentive, interest, or desire to suddenly adopt a new wage scale based on position or performance. In the case of MOLSA, the Department of Labor officials who actively implemented the reform according to the letter and spirit of the law faced significant resistance. Because the labor department had been built from the ground up in 2004, 35 most of the management were young, strong, intelligent, and dedicated Iraqis. This new group successfully opened 18 functioning employment centers and six vocational training centers. But the higher salaries posed a potential windfall for older, retirement-aged Iraqi officials who lacked the capacity and/or desire to learn or run the new labor programs, many of whom had suffered under a couple decades of low pay. Now these older officialsapproaching or surpass ing retirement age-not only refused to retire, but demanded top salaries for themselves and their friends. Many threatened the lives of human resources officers , directors, and managers if they did not receive top salaries. MOF and MOLSA officials, who by now received top salaries themselves and feared a violent backlash, had everything to lose, and nothing to gain, by supporting the letter and spirit of Order 30. A case in point is the new generation of talented Iraqi managers at the nascent Department of Labor who established and ran the nation 's employment and training programs by communicating through modern technology: sending Email attachments across the globe ; mastering sophisticated reporting systems, Excel spread sheets , and PowerPoint Monthly salaries of state employees in Iraq [in thousands of Iraqi dinars (000)] Grade Step 1 Step 2 Step 3 Step 4 Steps Step 6 Step 7 Super A ... Super B ... 2,250 1,500 2,233 1,583 2,316 1,666 2,400 1,750 2,483 1,833 2,566 1,916 1 .... .......... 2 ....... ....... 3 ..... ......... 4 ········· ··· ·· 5 ............ .. 6 ............ .. 7 .............. 8 ...... ...... .. 9 ..... ......... 10 ·· ·········· 11 .. ... ... 740 574 444 342 264 204 157 125 102 83 69 760 589 456 352 271 209 162 128 105 86 71 780 605 468 361 278 215 166 132 107 88 73 800 620 480 370 285 220 170 135 110 90 75 820 636 492 379 292 226 174 138 113 92 840 651 504 389 299 231 179 142 116 95 79 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 77 Step 8 Step 9 2,650 1,999 2,733 2,083 2,81 7 2,166 3,000 2,249 860 667 516 398 306 237 183 145 118 97 81 880 682 528 407 314 242 187 149 121 99 83 900 698 540 416 321 248 191 152 124 101 84 920 713 552 426 328 253 196 155 127 104 86 Monthly Labor Review Step 10 June 2005 59 International Report presentations; and planning and establishing complicated systems for implementation and monitoring of employment and training centers. These Iraqis were the backbone of Iraq's labor ministry administration, and through June 2004 were paid according to Order 30. Months of death threats, chaos, complaining, rebellions, and sabotage of labor programs by older, lethargic, and incompetent Ministry workers who had been on the job for decades ensued. Occupational Health and Safety (OHS) officials boasted of 20 to 30 years of service. Some of the OHS workers, many of whom had been sitting idly at home over the past year, threatened to return to the Ministry with Rocket Propelled Grenades if the acting Labor Director General did not award them top salaries. As a result, in May 2004, the Minister of Labor capitulated to the demands of officials threatening violence and followed the lead taken by other ministries, falling in line with the old Ba'athist policy of awarding wages according to years of service. This decision has created an inverted pyramid, not only in terms of salary, but in terms of capacity, management skills, and leadership. Those earning the highest salaries are the least talented and the least capable of running the highly complex labor projects. Meanwhile, the most talented Iraqi Labor Ministry managers who run the employment and training programs, draw some of the lowest salaries in the Ministry. 36 These talented managers will likely be attracted to the emerging private sector, thus leaving the corrupt, incompetent MOLSA officials to sort out the multi-million dollar employment projects. Conclusion The MOLSA strategy for the reconstruction and rehabilitation of employment and training for 2004 and 2005 i~ laden with pitfalls. Security issues and counter-measures continue to hamper the implementation of even the simplest 60 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 tasks. Transportation, communication, electricity, fuel, and funding have proven to be daily obstacles that block progress at every step. Communication with the governorates, for instance, is still largely accomplished by one staff member traveling, often treacherous routes, between the capital and the governorate. Because of lengthy historical, political, and ethnic distrust, policy and implementation in Iraq essentially translates into dealing with three separate governments: KDP (Kurdish Democratic Party) and PUK (Patriotic Union of Kurdistan) in the north, and Baghdad Central in the south. Nonetheless, progress has been made at a respectable pace. The Ministry opened an entirely new department (directorate)-labor-within which the employment and training administration is housed. Eighteen employment centers and six vocational training centers have been opened, providing employment services and training to hundreds of thousands of Iraqis. Tens of thousands of jobs seekers have found employment through these offices. The Ministry continues to support the child labor unit, labor statistics office, on-thejob training office, veterans' services office, and career counseling office. KOICA (Korean International Cooperation Agency) has committed to a $7-million grant to construct, equip, and provide training for, a national vocational training center in Baghdad. In November 2003, the ILO and the former Iraqi Minister of Labor and Social Affairs signed a Memorandum of Understanding in Amman, Jordan. Under this agreement, the ILO has already begun to provide capacity building, labor law review, vocational training expertise, labor market survey, and assistance with the organization of labor unions. In October 2004, USAID awarded an $88-million contract to provide technical assistance to MOLSA 's employment and vocational training centers. MOLSA 's labor budget for 2004 alone was $14 million for employment, vocational training, child labor, occupational safety and health, and other labor programs, and $65 million for an expansion of Vocational Training. The most encouraging aspect of the reconstruction is the talent pool of young dedicated Iraqis willing to learn, which is quite extensive. During CPA 's tenure, extensive efforts were made to attract talented Iraqis to the Ministry. Through May 2004, the capacity at MOLSA had been increasing slowly, but consistently. In many ways, the dynamics of the Ministry are merely a microcosm of Iraq as a whole. Iraqis are proving that given half a chance-and sufficient encouragement, assistance, and guidance-they can and will succeed in taking their destiny in their own hands and reconstructing their nation. The challenges for MOLSA 's fledgling Labor Directorate over the next months or year are daunting. The Ministry's labor programs already in existence, in one form or another, were designed to mitigate violence through employment. If properly nurtured, the Labor Directorate can play a role in providing security to Iraq. However, every program requires support , staffing , capacity building, and international expertise. Many questions remain as the political commitment to labor programs were drafted and set in motion over the past 18 months-as well as the direction the Labor Department will take in the next year. Only time will tell. 37 □ Notes 1 The majority of the information for this article comes from my institutional knowledge of the Iraqi Ministry of Labor and Social Affairs earned during the reconstruction and rehabilitation of the Ministry in 2003-04 while serving as the Ministry ' s labor advisor. Most of the Ministry 's documentation was destroyed in the looting subsequent to the war against Saddam Hussein in 2003. 2 The Ministry of Military Industries (MMI), which provided weapons and military technological training and research for Saddam Hussein 's military machine, produced highly coveted jobs for Iraqis. Originally this Ministry was run by Hussein Kamel , Saddam 's son-i n-law; see also Khidhir Hamza , Saddam 's Bombmaker (New York, Touchstone, 2000), pp. 155- 56. 3 The base salaries were standardi zed across ministries, but monthly bonuses awarded to workers varied significantly. MOLS A 's vocati o na l trainers in Social Welfare trained di sabled Iraqis, while those in Social Security were responsible for training the re mainde r of the public. For another perspective on the di sparity o f wages, see Foote, Block, and others, ··Economic Policy and Prospects in Iraq ," Journal of Economic Perspecti ves. 2004, pp. 47-70. 4 Even today, punishment and the threat of punishment are routinely used at the Ministry as " management" tool s, surfaci ng in offic ial memos and staff meetings. One Iraqi supe rvi sor, who often used threats and on occasion had eve n bee n known to throw notebooks and penci ls, ex plained to me that these were effective management too ls. s Act No. 71 of 1987 Promulgating the Labour Code, 1987, Articles 15-28. 6 Ibid. , Articles 17-20. 7 Ibid ., Article 2 1. x Ibid., Article 23. 9 UNDP, " Iraq Country Offi ce 1999-2000 Report," 2000, p. 6. °Funding to open the re ma inin g centers was 1 a va ilabl e to MO LSA from a combi nation of sources: Commander 's Emergency Response Program (CERP) funds, MOLSA 's $ 14-m illi on 2004 revised budget, and an $88 milli on USA ID contract funded with U. S. suppleme ntal funding. 11 On May 15 , 2004, the CPA Program Review Board (PRB) unanimously approved $65 milli on for Vocational Trai ning . See CPA, Program Review Board (Prb) Minutes.May 15 , 2004 , on the Internet at http://iraqcoalition.or g/ regulations20030908_ CPAORD_30_Reform_of_ Salaries__and_Employment_Conditions_of_State_ Employees_with_Annex _A.pdf (vis ited Oct. 16, 2004); also available on the Internet at http:/ /w w w. i raq coa Ii t ion . o rg/bu d get/PR B/ Mayl5_PRB.html. 12 The CPA revi sion beca me known as the Bearing Point re vision, named after the Bearing Point contractor who coordinated the efforts of various CPA attorneys and spec iali sts who contributed to the re vision. 13 CPA, Coalition Provisional Authority Order Number 89: Amendments to the Labor Code-Law No. 71 of l 987, 2004, on the Internet at http:// w w w. i raq coalition .o rg/reg u I at ion s / 20040530 CPAORD89 Amendments to the Labor Code-Law No.pdf (v isi te d Oct. 16, 2004). -The Order actually overturned the earlier Ba'athist Revolutionary Command Council Resolution 368 that allowed children to work in hazardous and non-hazardous conditions at the age of 12. 14 For the entire TAL tex t, see CPA, Law of Administration fo r the State of Iraq fo r the Tran sitional Period- 8 March 2004, 2004, o n the Internet at http://www.cpa-iraq.org /government/fAL.html (vis ited Oct. 16, 2004). https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 15 Central Statistics Organization is part of the Mini stry of Pl annin g, and is also referred to as Central Stati sti cs Offi ce or Central Board of Statistics, ofte n depending on the translati on from Arabic. See the Central Board of Statistics, .. Report of the Empl oyment and Unempl oyment Survey Results: Year 2003," (Baghdad , Ministry of Pl annin g, 2004), p. 6; the U.S. Department of Labor uses the age of 16 as a startin g point for the c ivili an la bor force. See U.S. De partment of Labor-Bureau of Labor Stati sti cs, Geographic Profile of Employment and Unemployment, 2004, on th e Int e rn e t a t http://www.bl s.gov/gps/ gpsfaqs.htm#Q2 (visited Oct. 16, 2004). 16 Thi s analysis assumes as e mpl oyed all be neficiari es of stipend s program s. For example, fo rmer military officers curren tly rece iving stipends, w hether seeking e mpl oyment or not, are consi dered e mpl oyed . 17 United Nations and World Bank , ··united Na ti ons/World Bank Joint Iraq Needs Assessment," 2003, p. 18. See a lso UNDP, .. Iraq Country Offi ce 1999- 2000 Report ," p. 6. iR Internat ional Labor Organi zation, ·· eeds Assessment of the Employment Sector in Iraq," 2003, p. 5 . The Iraqi Central Stati sti cs Offi ce has conducted two additi ona l unemployme nt surveys in the first and second quarter of 2004, but the findin gs were not released at the time thi s article went to publication . 19 As early as August 2003, more than ha lf of the 2,300 civi lian workers from the former Ministry of Defense had already secured government jobs. A huge numbe r of officers who were skilled workers (doctors, e nginee rs, compu ter spec iali sts, and many others) were a lso working in other ministries, as well as collecting stipe nds. 20 Much has been written about the SOEs. For more information, see Foote, .. Economic Policy and Prospects in Iraq." See also CPA , State-Owned Ente rpris e Co mpany Profiles, 2004, on the Internet at http://www.cpa-iraq.org/businessfmdustries/index.html (v isi ted Oct. 17, 2004). 21 While all Iraqi s qualify for the food basket, it is estimated that about 90 pe rcent actually receive it, and some 60 percent are re liant on it for subsistence. 22 See, for example, John How ley, ··Toe Iraq Job Cri sis: Workers Seek Their O wn Vo ice," EPIC, Brie f No. I , 2004. Clarence Thomas and David Bacon, .. Re port from Iraq: Working Condi tions and Labor Ri ghts under the Occ upati on," Lahor Against the War , 2003. 23 For example, MOLSA hired a bilingual accountant from a bank in Baghdad in December 2003 for $50 per month. Her for mer salary after 3 years' ex perience was $30 pe r mo nth . See also Foote, ··Economi c Poli cy and Prospects in Iraq," p. 48. 24 Thi s attitude toward government jobs was reflected in a poll conducted in Dece mber 2003. Ibid., p. 68 . 25 Huge salaries no twi thstanding, it is important to recognize the dedi cation of many of these brave Iraqi s toward he lping the recons tru ction of their country. That the Iraqi s returned to work while fac in g such overwhelming threats on the ir lives and that of their families is a tribute to Iraq i resolve. 16 The fac t that jobseekers continue to apply for these dangerous jobs despite the high ri sks has been well documented . See for instance Rajiv Chandrasekaran, .. Police Rec ruit s Ta rge ted in Iraq: Bomb Kill s Scores near Headquarte rs," The Washinf{ton Post, 2004, p. A-22. 17 All wages are shown in U.S. dollars based o n a n exc hange rate of 1,500 Iraqi Din ars pe r dollar. In almos t every case , the sa laries were higher than wages before the war. In man y cases, 5 to 20 times hi gher. 18 Most of the jobseekers ex pect governme nt jobs , in which the working ho urs are offic iall y 8 a. m . through 3 p.m . Sunday throu gh Thursday. 19 Thi s amount for an un skilled worker is substanti ally hi gher (IO to 20 times) than for the sa me posi ti on before the war. Hi gh sa laries pa id by pub lic works job progra ms, CPA , internation al con tractors, and mini stri es have drive n up the price of wages. Dail y laborers generall y earn as much as $3 to $15 per day. For another perspective on wages for daily laborers, see Foote, .. Economic Policy and Prospects in Iraq ," p. 56. 10 In Iraq , it is customary for e mpl oye rs to pay for tran sportati on. 11 For exa mple, many vocati onal tra inin g in stru ctors earnin g $5 -$6 per mo nth in Marc h 2003 were likely in creased to $100 per month by late summer 2003. 11 · At MOLSA , for instance, eac h time security guard s received salary increases, other Ministry workers demanded hi gher sa laries as well, despite the fac t that their sa lari es were IO to 20 tim es higher than I year earlier. 33 For more information on Order 30, see CPA, Coalition Provisional Authority Order N umber 30: Reform of Salaries and Employment Co ndi tions of State Emp loyees, 2004, on the Internet at http://www. iraqcoalition.org/reg ulation s ~ CPAORD 30 Reform of Salaries and Employment_Conditions_of- State_ Employees _with_ Annex_ A.pdf (v isited Oct. 16, 2004). 1 -i See Ibid ., Section 3.3 The salary tab le can be found at Annex A of the sa me document. 35 Organizationally, the former e mploymen t centers and vocational training cente rs res ided in Social Security Depa rtme nt. The birth of the Labor Department did not take place officiall y until the spring of 2004. 36 For exa mpl e , the director of the Outreac h divi sion has the mos t important pos iti o n with regard to emp loyme nt in the na tion. He is res po nsibl e for sec uring job vaca nc ies wi th the publi c and priva te secto r, intervi e wing and providing career co un se ling fo r th e na ti o n 's un emp loyed, rei ntegra tin g demobilized so ldiers a nd militia, and refe rring th e jobseekers to potential jobs or vocat io na l trainin g. He has been moved from Gra de 2 to Grade 7 ove rnight, a reduction of approximately 30 pe rce nt of hi s former sa lary. Monthly Labor Review June 2005 61 ,·-j Pree is x Re-spacing work Technology, location, contractual arrangements, and time are the four substantive components to consider when defining "telework," according to an article by Leslie Haddon and Malcolm Brynin in the journal New Technology, Work and Employment. Students of the telework phenomenon have gone from leaving technology entirely out of the definition to focus on the knowledge content of the work itself to requiring at least some use of new information and communications technology to be considered any sort of telework at all. The authors acknowledge the crucial role of technology, but suggest that different technologies do more to define the specific type of telework one might be engaged in rather than to define telework itself. Similarly, on the factor of location, some definitions of telework refer exclusively as work in the home while other broaden out to other "remote" worksites. Again, the authors look at location as more a measure of how telework is being done , and would exclude only those who work only at a standard workplace from being engaged in some form of telework. The main distinction in the contractual arrangements argument for defining telework is between selfemployed and wage-and-salary workers, although some would distinguish between a self-employed teleworker who works for a single client and a selfemployed freelancer who works for several clients. Analysts incorporating time in their definitions of telework must take into account arrangements that stretch from an occasional hour of away-from-the-office work in the evening or on a weekend to working https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis )'h~ almost exclusively from a home or mobile work space. In any case, in the six countries Haddon and Brynin studied, working at the standard workplace is by far the most common arrangement, followed by what they call "mobile users"-workers including outside sale and transportation workers-who use a mobile phone but not any of the other advanced technologies. Old-fashioned home-based workers who do not use computers, the internet, or a mobile telephone come in third in Britain, Italy, Germany and Bulgaria, while personal-computer-using homeworkers are third in Israel and Norway. The oftdepicted internet-enabled homeworker is generally in the smallest definitional class. A case study by Susan Halford of the impacts of that more uncommon arrangement-working from home using a broadband-enabled personal computer for some part of the workweek-appears within the same issue of New Technology, Work and Employment. While she acknowledges that studies have found negative outcomes of homeworking by full-timers or the selfemployed, her own study concludes that having a hybrid home-workplace arrangement was generally evaluated positively by both management and employees. Global variety Many popular discussions of globalization revolve around jobs, while more academic debates about the benefits of international trade focus on the lower prices of existing goods. In a recent issue of Current Issues in Economics and Finance from the Federal Reserve Bank of New York, Christian Broda and David Weinstein summarize their research into another important gain from global trade: increased availability of a wider variety of goods. Their first finding is that the sheer number of goods available increased, on net, from not quite 8,000 in 1972 to just more than 16,000 in 2001. The total number of "varieties," each variety defined as a specific good imported from a particular country, was just under 75,000 in 1972 and almost 260,000 in 2001. As the arithmetic implies, there was a significant increase in the number of countries from which the United States imported goods. According to Broda and Weinstein, not only were there far more goods involved in the import trade, but in addition, "the median number of countries supplying each good doubled, rising from six countries at the start of the period to twelve at the end." As part of their calculation of the impact of increased import variety on economic well-being, Broda and Weinstein estimated the substitutability of the varieties of the thousands of goods being imported. The highest degree of substitutability of varieties was found in crude petroleum and shale oil and the lowest was in footwear. "In general," say the authors, "the degree of substitutability was higher for homogeneous products (petroleum is an apt example) than for highly differentiated products." Once the increase in varieties and the substitutability of one variety for another is taken into account, Broda and Weinstein estimate that an import price index would have a rate of change 1.2 percent per year lower than the conventionally calculated index. Such a drop in import prices, they argue, has raised economic well-being in the United □ States by some $260 billion. Monthly Labor Review June 2005 62 ¾'@ Book Review A new statistical annual Factbook 2005: Economic, Environmental and Social Statistics. Paris, Organization for Economic Cooperation and Development, 2005, 235 pp. , $63/paperback. OECD OECD F actbook 2005 is the first edition of a new statistical annual from the Organization for Economic Cooperation and Development, a Paris-based forum of 30 member countries that work together to address economic, environmental, and social challenges. In this volume, the OECD presents a set of more than 100 indicators organized according to 11 themes in an attractive, user-friendly volume. Each indicator is presented on two facing pages. On the first page, the usefulness of the indicator and its definition and cross-country comparability are briefly described. In addition, long-term trends are discussed, and other OECD sources of data and analysis are listed, often with Internet links to them. On the second page, the OECD presents a table and chart for each indicator, and they are easily downloadable. Putting these diverse OECD datasets under one roof is extremely helpful to the users of international data who previously had to hunt for them in various places or might not have known that they all existed. The OECD F actbook fills a unique niche among the volumes of similar international indicators presently available, such as the International Labor Office's (ILO) Key Indicators of the Labor Market and the World Bank's WorldDevelopment Indicators. Both the ILO and the World Bank indicators attempt to cover the entire world, while OECD's focus is on the industrialized countries of Europe, North America, Asia, and Oceania. Thus, the OECD Factbook covers 30 countries, while the ILO and World Bank attempt to cover 150 to 200 countries. OECD's narrower focus has several advantages. The major advantage is that the countries it covers have, for the most part, well-developed statistical systems that follow in- https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis ?11 ,, - ternational guidelines, allowing for better comparative data. The Foreword of the Factbook talks about the importance of comparable data. "Why this Factbook?" the Foreword asks. The answer is "Because governments pursue different economic, social, and environmental policies, and it is extremely valuable to policymakers and to the general public to compare cross-country data that they know to be comparable and reliable." In other words, we should be able to use the Factbook data as one way to evaluate public policies in a comparative context. Another advantage of the fewer number of countries covered in the Factbook is that it allows OECD to include all member countries for which data are available in each chart. It is valuable to users to be able to see the whole spectrum of OECD countries portrayed in rank order, often with the "OECD average" inserted as a convenient marker. The World Bank and ILO have to contend with many countries that have less developed statistical systems, leading to much missing data and many more comparability issues. The ILO and World Bank both have to make choices as to what countries are to be charted for each indicator. Oftentimes they chart only a few selected countries, or aggregates for world regions that involve estimates for missing data. For a few indicators, the OECD Factbook shows data for selected nonmember countries. For example, the steel production indicator includes data for China, India, Brazil, Russia, and the Ukraine. Nonmember countries appear to be selected for coverage where the indicators are relevant and where reasonably comparable data are available. This selectivity seems a good way to expand the OECD Factbook's horizon beyond developed nations, while not trying to cover the entire world like the ILO and World Bank Indicators. The nontechnical reader (such as a member of the media writing an article on deadline) will be well served by the succinct format of the OECD Factbook. It is unburdened by the voluminous number of footnotes and technical notes that usually accompany an international comparison. The comparability note gives broad guidance to this casual user, and as noted previously, the OECD member countries tend to follow international guidelines. Although international guidelines serve to draw countries toward a common conceptual framework, they still allow room for national variations that can affect cross-country comparability. In the absence of series that are fully comparable, it is important to have adequate documentation of the differences. The no-footnotes policy sometimes results in the omission of important country information that a technical user would want to know, but a good guide to technical sources is provided for experts to consult. Producing the F actbook involved many choices. The OECD has made reasonable compromises to satisfy the needs of a wide range of users of this publication. No one way can satisfy all. To include all the notes would make this an unwieldy encyclopedic volume and could put off the more casual data user. One future modification that could help bridge the gap would be to include more notes on the downloadable tables in the Internet version of the Factbook. I will provide two examples of why this is important, with reference to the indicators on annual hours worked and part-time work. The annual hours worked indicator is one of the most widely cited indicators provided by the OECD. The Factbook's comparability note says that "The data are intended for comparisons of trends over time and not yet suitable for intercountry comparisons." This warning is usually ignored. In its original form in the data annex to the annual OECD Employment Outlook, this table includes a warning about comparing levels as well as a great deal of country-by-country notes that assist the data user in assessing comparability among different countries. For example, data for the Netherlands exclude overtime hours-helping to explain the relatively low annual hours for this country. These notes could be attached to the Monthly Labor Review June 2005 63 Book Reviews tables in the Internet version of this table. An alternative to the chart for this indicator that is more consistent with the comparability note in the Factbook would be to chart the change in hours worked from 1990 to 2003 for each country rather than the 2003 level for each country. Another example where a footnote on the Internet version of the tables would prevent false conclusions is for the parttime worker indicator. Countries set the part-time cutoff at different levels of weekly hours. The European Union countries let the respondent define whether he or she works part time. Just one example of the important standardization efforts of the OECD is that it provides data users with a comparative measure by defining part-time work as work of less than 30 hours per week on the main job. The OECD standardizes data to this definition from special data runs submitted by member countries. The part-time employment data for Japan, however, remain at the 35-hour cutoff. Thus, Japan's proportion of parttime workers is among the highest on the OECD 's chart, but it is overstated for comparisons with other countries. This was noted in the original source, but such details are missing from the Factbook. The OECD Factbook warns on page 230 that "To avoid misunderstandings, the tables must be read in conjunction with the texts that accompany them." This argues for the inclusion of the notes on the downloadable tables. Otherwise, there is the danger that the Factbook's tables will be exported into articles and studies devoid of important country notes, such as the one on annual hours for the Netherlands and part-time workers for Japan. Hours worked is in the Quality of Life section of the F actbook, not in the Labor Market section. There does not seem to be a clear relationship between the hours measure in the F actbook and what most people consider as quality of life. The measure is annual hours per person employed, and the introduction to this section implies that reduction of working hours improves quality of life. If nonworking spouses enter the part-time labor force, Monthly Labor Review 64 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis the average hours worked measure would go down even though the family as a whole is putting in more time in the labor force. How does one interpret this trend in terms of quality oflife? Also, the hours indicator is in a subsection of Quality of Life entitled Work and Leisure that includes only one other indicator, arrivals of non-resident tourists staying in hotels and similar establishments. The United States has, by far, the highest rate. The fact that more tourists visit the United States, however, does not appear to translate ir.to anything clearly meaningful about work and leisure of Americans. The OECD may need to reconsider some of its indicator categorizations. The OECD F actbook comes in two forms: the printed version and an Internet version accessible from the OECD Web site (http://www.SourceOECD.or g/ factbook). Many consumers will most likely want to make use of both forms. Having the attractive printed volume at hand gives an immediate sense of the wide range of indicators available. The Internet version of this publication allows for easy downloads of tables with the click of a mouse. There is a charge for the printed volume, whereas the version on the OECD Web site is free of charge. The OECD deserves a great deal of praise for providing this free access to the consumers of international comparisons. Users of the ILO and World Bank indicators must subscribe to Internet access or purchase a CD-ROM in order to download tables. As an example of how the F actbook can be used to enrich one's perspective, let us look at some of the indicators for the United States. The United States ranks favorably among OECD countries with respect to indicators of the labor market that are familiar to BLS data users. Our employment-to-population ratios (employment rates) are relatively high, and we have a lower proportion of part-time workers than most other member countries. U.S. unemployment rates are comparatively low, and our percentage of persons in long-term unemployment is among the lowest in the OECD. The inflation rate June 2005 (growth in CPI and PP!) in the United States is well below the OECD average. U.S. business sector productivity growth (as measured by output per employee) is above the OECD average, and higher than in any other Group of Seven (G-7) major industrialized country. Beyond the labor market indicators, the United States fares well on some indicators and not so well on others. The Factbook charts show that the United States has the highest share of investment in information and communication technology, but the proportion of households with home computers and Internet access is just about average. Our high school students perform relatively poorly on international math tests, outranking only Portugal, Italy, Greece, Turkey, and Mexico. On the other hand, we are second only to Canada in percentage of the population attaining a college or university degree. The United States ranks highest on the obesity scale-percentage of the population with a Body Mass Index more than 30--and the U.S. proportion has more than doubled over the past 20 years. We also have the highest health expenditures per capita. Many other interesting comparisons can be made based on this Factbook that serve to highlight both a country's successes and problem areas. The OECD Factbook is a major contribution to international comparisons of statistics. It is a work that is designed to appeal to a wide audience. Limiting every indicator to two pages makes the volume attractive and easy to use, but it means that many things had to be left out. More information on comparability could be included on the Internet version of the tables in order to achieve the objective stated in the Foreword-to provide statistics that help evaluate public policies. The Factbook goes a long way in that direction already, and the OECD should be congratulated for this accomplishment. -Constance Sorrentino Division of Foreign Labor Statistics, Bureau of Labor Statistics i;' Current Labor Statistics Notes on labor statistics ....... .... ................. .. Comparative indicators I . Labor market indicators ........ .... ...... .. ............. ... .... .. ..... .. ... 2. Annual and quarterly percent changes in compensation, prices, and productivity .. .. ............... .. .. 3. Alternative measures of wages and compensation changes .. ...................... ..... ..... ......... ........ ~i{' 66 79 80 80 Labor force data 4. Employment status of the population, seasonally adjusted .. ............... ............ .. ......... ....... .... .. .. 81 5. Selected employment indicators, seasonally adjusted ........................ .... .. .. ... ............. ....... 82 6. Selected unemployment indicators, seasonally adjusted ......... .... ........... ... .... .. .... .. .... .. .. .. .. .... 83 7. Duration of unemployment, seasonally adjusted .................................. ..................... 83 8. Unemployed persons by reason for unemployment, seasonally adjusted .. ...................................... ......... .. .... 84 9. Unemployment rates by sex and age, seasonally adjusted ........................ ........... .. ... .. .. ........... 84 I 0. Unemployment rates by State, seasonally adjusted .... .... .... . ... . .... ...... .. .. ... . ... . .... ..... ... . ... 85 I I. Employment of workers by State, seasonally adjusted ... .. .................. ......... ..... .. .. .............. 85 12. Employment of workers by industry, seasonally adjusted ....................................................... 86 13. Average weekly hours by industry, seasonally adj usted .... ... ......... ... ...... .. ............ ............. ... 89 14. Average hourly earnings by industry, seasonally adjusted .. .. .... .... .. .. .. ........... ....... ............ .. .... .. 90 15 . Average hourly earnings by industry .. ........... ... .. .............. 91 16. Average weekly earnings by industry .................. ... .......... 92 17. Diffusion indexes of employment change, seasonally adjusted ....... .... .... ..... ....... ...... .. .. .. ...... .. ...... .. 93 18. Job openings levels and rates, by industry and regions, seasonally adjusted.................................. ... .................... 94 I 9. Hires levels and rates by industry and region, seasonally adjusted.................. ... ... .... .......................... .. .. 94 20. Separations levels and rates by industry and region, seasonally adjusted.......................................................... 95 21 . Quits levels and rates by industry and region, seasonally adjusted............................. .... ...... .. ....... .......... 95 22. Quarterly Census of Employment and Wages, 10 largest counties . ... . ... . .. .. . ... .. .. . ... . .. .. .. .. .. .. .. .. .. .. .. . ... . .. .. 96 23 . Quarterly Census of Employment and Wages, by State... 98 24. Annual data: Quarterly Census of Employment and Wages, by ownership .............................. ........ ....... 99 25 . Annual data: Quarterly Census of Employment and Wages, establishment size and employment, by supersector ... 100 26. Annual data: Quarterly Census of Employment and Wages, by metropolitan area .. .. .. .. .. .. . .... .. .. .. ... .. .. ... .. .. .. .. l O1 27. Annual data: Employment status of the population ........ I 06 28. Annual data: Employment levels by industry .................. I 06 29. Annu~I data; Average hours and earnings level , by industry.. .. .. .. ........ ...................................... ............... l 07 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Labor compensation and collective bargaining data 30. 31. 32. 33 . Employment Cost Index, compensation ......................... .. Employment Cost Index, wages and salaries ....... ............. Employment Cost Index, benefits, private industry ........ 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 firms and government .. .. .. .. .. .. .. .. . .. .. ... .. .. .. .. .. .. .. .. .. .. . .. .. ... .. .. . .. .. 36. Work stoppages involving 1,000 workers or more .......... . I 08 110 112 I 13 114 I 15 116 Price data 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, al l items and major groups .............. ............ ........ ......................... 40. Producer Price Ind exes 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 Class1ficat1on.. .. .... .. ....... .... .... .. .... .. .... .... ........ .... . . 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... ................ .. .......... .. .................... I 17 120 121 122 123 124 125 126 127 127 127 Productivity data 48. Indexes of productivity, hourly compensation, and unit costs, data seasonally adjusted .............. .. ....... 49. Annual indexes of multi factor producti vity .................... .. 50. Annual indexes of productivity, hourl y compensation, unit costs, and prices ......... ..... .. .............. .. ...... .......... .. .. 51. Annual indexes of output per hour for select industries .... .. .. . .. .. .. .. .. .. .. .. .. .. .. .. . .. . .. .. .. .. .. .. .. .. .. .. .. .. .. . . .. .. .. 128 129 130 131 International comparisons data 52. Unemployment rates in nine countries, seasonally adjusted ........ .................... ........................... 134 53 . Annual data : Employment status of the civilian working-age population, IO countries......... ............ .... ... 135 54. Annual indexes of productivity and related measures, 15 economies................ .................................... .. .. .. .. .. .... 136 Injury and Illness data 55. Annual data: Occupational injury and illness ................... 138 56. Fatal occupational injuries by event or exposure ....... ...... . 140 Monthly Labor Review June 2005 65 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 l and 4--9 were revised in the February 2005 issue of the Review. Seasonally adjusted establishment survey data shown in tables I, 12-14, and 17 were revised in the March 2005 Review. A brief explanation of the seasonal adjustment methodology appears in "Notes on the , ~ata." 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 made 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 66 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June index number of 150, where 1982 = I 00, the hourly rate expressed in 1982 dollars is $2 ($3/ l 50 x I00 = $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- 2005 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-population 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 com pensation and wages from the Employment Cost Index program are provided for all civilian nonfarm workers (exc ludi ng Federal and household workers) and for all private nonfarm workers. Measures of changes in consumer prices for all urban cons umers; 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 avai lable for work except for temporary illness and had looked for jobs within the preceding 4 weeks. Persons who did not look fo r 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. T he civilian labor force consists of all em ployed or unemployed persons in the civilian no n institutional population. Persons not in the labor force are those not class ified as employed or unemployed. This group includes di scouraged workers, defined as persons who want and are ava ilable for a job and who have looked for work someti me in the pas t 12 months (or s ince the end of the ir las t job if they held one within the past 12 months) , but are not currently looking, because they believe there are no jobs avai lable or there are none for which they wo ul d qu a li fy. The civilian noninstitutional population compri ses all persons 16 years of age and older who are not inmates of penal or mental institutions, sanitarium s, or ho mes for the aged, infirm, or need y. The civilian labor force participation rate is t h e p ro p o rtion of the civilian noninst itutiona l population that is in the labor force. The employment-population ratio is em ployment a s a percent of the civilian no ninsti tutional population. Notes on the data Description of the series Employment data in this section are o btained from the Current Population S urvey, 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 consec utive months. Definitions From time to time , and especially after a decennial census, adjustments are made in the C urrent Population Survey fi gures to correct fo r es timatin g error s durin g the intercensal years. These adjustments affect the comparability of hi storical data. A descripti on of these adjustments and their effect o n the various data series appears in the E x pl a natory Notes of Employment and Earnings. For a discussion of changes introduced in January 2003, see "Revisions to the C urrent Population Survey Effective in January 2003" in the February 2003 issue of Employment and Earnings (available o n the BLS We b site at: http://www.bls.gov/ X-12 ARI MA for seasonal adjustment of the labor forc e data and the effects that it had on the data. At the begi nn ing of each calendar year, hi storical seasonall y adj usted data usually are revised, and projected seasonal adjustment factors are calc ulated for use during the January-June period. T he 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 J une, are produced for the Jul y-December period, but no revisions are made in the historical data. FOR ADDITIONAL INFORMATION on nationa l househo ld survey data, contact the Di vis ion of La bor Force Statistics: (202) 691-6378. Establishment survey data Description of the series Employ ment, hours, and earnings data in thi s sec ti o n a re compiled from payroll records reported monthly on a vo luntary basis to the Bureau of Labor Statistics and its coop erat in g S tate agencies by about 160,000 businesses and government agencie s, w h ic h r epresent approximately 400,000 ind ividual worksites and represent all industries exce pt agriculture. The active CES sample covers approximate ly one-third of all nonfarm pay ro ll workers. Industries are class ified in accordance with the 2002 North American Industry Classification System. In most industries, the sampling probabilities are based on the size of the establi shment ; m os t large establishments are therefore in the sample. (An establishment is not necessaril y a firm; it may be a branch plant, for example, or warehouse.) Self-employed persons and others not on a regular civilian payroll are o utside the scope of the survey because they are exc luded from establishment records. This largely accounts for the difference in employment figures between the household and establishment surveys. Employed persons include (I) all those cps/rvcps03.pdf). Definitions who worked for pay any time duri ng the week which includes the 12th day of the month or who worked unpaid fo r 15 hours or more in a family-operated enterpr ise 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 one job is counted only in the job at which he or she worked the greatest number of hours. Unemployed persons are those who d id Effecti ve in January 2003 , BLS began using the X-12 ARIMA seasonal adjustment program to seasonally adjust national labor force data. Thjs program replaced the X- 11 ARIMA program wruch had been used since January 1980. See "Revision of Seasonally Adjusted Labor Force Series in 2003 ," in the Febru a r y 2 00 3 iss ue of Empl oym ent and Earnings (ava ilable on the BLS Web s ite at http:www.bls.gov/cps/cpsrs.pdf) for a disc ussion 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 activ ity. Employed persons are all persons who received pay (inc ludi ng ho liday 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 person s in the labor force) are counted https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 67 Current Labor Statistics in each establi shment which reports them. Production workers in the goods-prod uc i ng indu stries 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 nonsupervi sory workers account for about four-fifths of the total employment on private nonagricultural payrolls. Earnings are the payments production or nonsupervi sory 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 repre se nt 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 " benchmark s"). The March 2003 benchmark was introduced in February 2004 with the release of data for January 2004, published in the March 2004 is- 68 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 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 ( NAICS ) 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 an.d 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 iss ues 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 2005 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 I) 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. Seasonally adjusted unemployment rates are presented in table I 0. 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 I 0) 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 unemployment insurance ( u1) laws and from Federal, age ncies subject to the Unemployment Compensation for Federal Employees ( uC FE) program. Each quarter, State age ncies edit and process the data a nd send the information to the Bureau of Labor Statistics. The Quarterl y Census of Employment and Wages (QCEW) data, a lso referred as ES202 data, are the most comp lete enumeration of employment and wage information by industry at the national, State, metropolitan area, and county levels. They have broad economic significance in evaluati ng labor market trends and major industry deve lopments. 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 incl uded the 12th day of the month. Covered private industry employment includes most corporate officials , exec uti ves, supervi sory personnel , professiona ls, clerical workers, wage earners, piece workers , and part-time workers. It excludes proprietors, the unincorporated se lf-employed, unpaid fami ly members, and certain farm and domestic workers. Certain types of nonprofit employers, such as religious organizations, are gi ven a choice of coverage or exclusion in a number of States. Workers in these orga nization s are, therefore , reported to a limited degree. Persons on paid sic k leave, paid holiday, paid vacation, and the like, are included. Persons on the payro ll of more than one firm during the period are counted by each u,subject employer if they meet the employment defin ition noted earlier. The employment count excludes workers who earned no wages d uring the entire applicable pay period because of work stoppages, temporary layoffs, illness, or unpa id vacations. Federal employment data are based on reports of monthl y employment and quarterly wages subm itted each quarter to State agencies for all Federal installations with employees covered by the Unemployment 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 received 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 phy sical location and engaged in one, or predominantl y one, type of economic activity for which a single industrial classification may be applied. Occasionally, a single phys ical location encompasses two or more di stinct and significant activities. Each activity should be reported as a separate establishment if se parate records are kept and the various activitie s are classified under different NAICS indu strie s. Most employers have only one establishment; thus, the establishment is the predominant reporting unit or stati stical 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 quarterl y u, 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 IO or fewer, the employer generally will file a consolidated report for all establishments. Al so , some employers either cannot or will not report at the establishment leve l 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 hreaking down their reports by insta llation. They are permitted to combine as a single statewide unit: I) all installations with 10 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 install ations 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 establi shment 's March employment level. It is important to note that each establishment of a multi-establishment firm is tabulated separate ly into the appropriate size category. The total employment level of the reporting multi-establishment 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, spec ify 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 pl ans such as 40 1(k) plan s. Covered employer contributions for oldage, survivors, and di sability in s urance (OASDI), heal th insurance, unemployment insurance , workers ' compensation, and private pension and welfare fund s are not reported as wages. Employee contributions fo r 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 deduction s. 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 yields 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-ti me workers as well as the number of individuals in high-paying and low-paying occupations . When average pay leve ls between States and industries are compared , these factors should be taken into consideration. For example , industries characte rized by high proportions of part-time workers wi ll Monthly Labor Review June 2005 69 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 200 I, publications presenting data from the Covered Employment and Wages program have switched to the 2002 version of the North American Industry Classification Sys tem (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 (S IC) structures , industry data for 2001 is not comparable to the SIC-based data for earlier years. Effective January 200 I , 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 200 I. Data also reflect industry changes. Those accounts previously assigned to civic and social organizations were assigned to tribal governments. There were no required industry 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. Monthly Labor Review 70 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 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 coIT'parable 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: ( I) 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 I. 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 Divi sion of Administrative Statistics and Labor Turnover at (202) 691-6567. 2005 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 (u1) 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 submit job openings information for the last business day of the reference month. A job opening requires that (I) a specific position exists and there is work available for that position; and (2) work could start within 30 days 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 I00. 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 of 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 computeJ by dividing the number of separations by employment, and multiplying that quotient by I 00. 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 number 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 (EC I) 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 June 2005 71 Current Labor Statistics costs, on wages and salaries, and on benefit costs are available for private nonfarm workers 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 72 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 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 b0ard , 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 leave 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. 2005 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 I 00 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 estab1ishments 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 nonfann establi shments with fewer than I 00 workers, while the State and local government survey includes all governments, regardless of the number of workers. All three surveys include full- and part-time workers, and workers in all 50 States and the Di strict of Columbia. FOR ADDITIONAL INFORMATION 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 = I 00 for many Producer Price Indexes (unless otherwise noted), 198284 = I 00 for many Consumer Price Indexes (unless otherwise noted), and 1990 = I 00 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 were 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 unchanged 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 I 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 I 998 data. FOR ADDITIONAL INFORMATION , 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 commodities 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 PP! 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 June 2005 73 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 gro upings, 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- 74 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 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 forthe 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 -7 155. Productivity Data (Tables 2; 48-51) Business and major sectors Description of the series The productivity measures relate real out- 2005 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 multi factor 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. 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. Industry productivity measures Notes on the data 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 art 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 technology; shifts in the composition of the labor https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 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 countries. 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 differences, 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 adj us tmen ts 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.pdt). Definitions For the principal U.S. definitions of the labor force, employment, and unemployment, see the Notes section on Employment and Monthly Labor Review June 2005 75 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 Iimits and the treatment of layoffs. These adjustments include, but are not limited to: including older person~ 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 population 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 76 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 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 counte-d 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 survey s 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 O994, 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.pdt). For Australia, the 200 I break reflects the introduction in April 200 I 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 200 I 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 200 I . 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 2005 FOR ADDITIONAL INFORMATION on this series, contact the Divi s ion 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, Australia, Canada, Japan , Korea, Taiwan, and nine European countries. These measures are trend comparisons-that is, series that measure changes over time-rather than level comparisons. There are greater technical problems in comparing the levels of manufacturing output among economies. BLS constructs the comparative indexes from three basic aggregate meas ures--o utput, 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) with the exception ofBelguim and Taiwan, where only employees (wage and salary earners) are counted. 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 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. data from 1998 forward are based on the 1997 North American Industry Classification System (NAICS). Output is in real value-added terms using a chain-type annual-weighted method for price deflation. (For more information on the U.S. measure, see " Improved Estimates of Gross Product by Industry for 1947- 98 ," Survey of Current Business, June 2000, and " Improved Annual Industry Accounts for I 998-2003," Survey of Current Business, June 2004). Most of the other economies now also use annual moving price weights, but earlier years were estimated using fixed price weights, with the weights typically updated 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 economies. The measures are developed from statistics of manufacturing employment and average hours. The series used for Austra1ia, Canada, Demark, France (from 1970 forward), Norway, and Sweden are official series published with the national accounts. 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 United Kingdom from 1992, an official annual index of total manufacturing hours is used. Where official total hours series are not avai lable, 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. 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 economy, except those for Belgium , which are developed by BLS using statistics on employment, average hours, and hourly compensation. For Australia, 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 compensation is equal to the ~verage for wage and salary employees. mining as well. The measures for recent years may be based on current indicators of manufacturing output (s uch as industrial production indexes), employment, average hours, and hourl y compen sation until national acco unts and other statistic s used for the long-term meas ures become avail able. Official publi shed data for Australia are in fiscal years that begin on Jul y I. The Australian Bureau of Statistics has finished calendar-year data for recent years for output and hours. For earlier years and for compensation , data are BLS estimates using 2year moving averages of fi scal year data. FOR ADDrTIONAL INFORM ATION on this series , contact the Divi sion of Forei gn 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, farms 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 Notes on the data In general, the measures relate to total manufacturing as defined by the International Standard Industrial Classification. However, the measures for France include parts of https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Under the Occupational Safety and Health Act, employers ma intain records of nonfatal work-related injuries and illnesses that involve one or more of the following: loss of consciousness, restriction of work or motion , tran sfer 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 sing le, 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 em ployment. It includes acute and chronic illnesses or disease which may be caused 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 capac ity, or both, because of an occupational injury or illness. BLS measures of the number and incidence rate of lost workdays were discontinued begi nning 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 Federa l holid ay, even though able to work. Incidence rates are computed as the number of injuries and/or illnesses or lost work days per I 00 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 cl asses 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 diffic ul t to relate to the workplace and are not adequatel y recog- Monthly Labor Review June 2005 77 Current Labor Statistics nized and reported. These long-term latent illnesses 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 eas ier 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 I 00 equivalent fulltime workers. For this purpose, 200,000 employee hours represent I 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 rai lroad 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, 78 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June these data are available nationwide for detailed industries and for individual States at more aggregated industry levels. FOR ADDITIONAL INFORMATION on occupational injuries and illnesses, c0ntact the Office of Occupational Safety, Hea lth and Working Conditions at (202) 691-6180, or access the Internet at: http://www.bls.gov/iif/ 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 a nd 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 Hea lth Administration records , medical examiner and autopsy reports , medi a acco unts, State motor vehicle fatality records , and follow-up questionnaires to employers. In addition to private wage and sa lary workers , the self-employed, family members, and Federal, State , and local government workers are covered by the program. To be included in the fata lity census, the decedent must h ave 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. 2005 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 cen sus, as well as work-related illne sses, 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 -6 175 , or the Internet at: http://www.bls.gov/iif/ 1. Labor market indicators Selected indicators 2003 2003 2004 II 2004 Ill IV 2005 Ill II IV Employment data Employment status of the civilian noninstitutional population (household survey): 1 Labor force participation rate 66.2 66.0 66.3 66.4 66.2 66.1 66.0 66.0 66.0 66.0 6,508.0 Empl oyment-population ratio 62.3 62.3 62.4 62 .3 62.1 62.2 62.2 62 .3 62.4 62.4 62.3 6.0 5.5 5.8 6.1 6.1 5.9 5.6 5.6 5.5 5.4 5.3 6.3 5.6 6.1 6.5 6.4 6.1 5.7 5.7 5.6 5.6 5.4 13.4 12.6 12.8 13.9 13.7 13.0 12.6 12.9 12.5 12.6 13.2 5.0 4.4 5.0 5.2 5.1 4.9 4.5 4.5 4.4 4.3 4.1 5.7 5.4 5.5 5.7 5.8 5.6 5.6 5.4 5.3 5.2 5.1 11 .4 11 .0 11 .2 11 .8 11 .5 10.9 11 .1 10.9 10.9 10.9 10.4 4.6 4.4 4.5 4.6 4.7 4.6 4.5 4.4 4.3 4 .2 4.1 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: Total nonfarm I . 129,931 131,480 130,093 129,845 130,168 130,541 131 ,125 131 ,731 132,302 132,772 108,356 109,862 108,467 108,253 108,320 108,614 108,986 109,737 110,095 110,600 111 ,038 Goods-producing 21,817 2 1,884 22,036 21,828 21,700 21,684 21,725 2 1,868 2 1,932 22,000 220,471 Manufacturing 14,525 14,329 14,787 14,555 14,377 14,313 14,285 14,338 14,353 14,338 14,314 108,114 109,596 108,057 108,017 108,190 108,483 108,816 109,457 109,799 110,302 110,725 . . . . . . . . . . . . . . . . . . . . . . .. . . . .. . .. .. . .. .. . .. . . . .. . . . .. . . . Total private ... Service-providing . 129,890 Average hours: Total private Manufacturing .. Overtime .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . Employment Cost lndex . 33.7 33.7 33.8 33.6 33.6 33.7 33.8 33.7 33.7 33.7 33.7 40.4 40.8 40.3 40.2 40.3 40.7 41 .0 40.8 40.8 40.6 40.6 4.2 4.6 4.2 4.0 4.1 4.4 4.5 4.5 4.6 4.5 4 .5 2 Percent change in the ECI, compensation : All workers (excluding farm, household and Federal workers) .. Private industry workers ... Goods-producing 3 Service-providing 3 State and local government workers 3.8 3.7 1.4 .8 1.1 .5 1.4 .9 1.0 .5 1.1 4.0 3.8 1.7 .8 1.0 .4 1.5 .9 .8 .5 1.1 4.0 4.7 1.8 .9 .7 .5 2.3 .9 .9 .6 1.5 4.0 3.3 1.5 .8 1.1 .5 1.1 1.0 .8 .3 1.0 3.3 3.5 .7 .4 1.7 .5 .7 .4 1.7 .6 .9 Workers by bargaining status (private industry): Union ................ .. Nonunion .... 1 2 ........ . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... .... . ... . .... .. .. .... ... 4.6 5.6 1.6 1.2 1.0 .7 2.8 1.5 .8 .5 .7 3.9 3.4 1.6 .8 1.0 .4 1.3 .8 .9 .4 1.3 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 r eflect the conversion to the 2002 version of the North using the last month of each quarter. American Industry Classification System (NAICS), replacing the Standard Industrial 3 Classificati on (SIC) system. NAICS-based data by industry are not comparable with SIC- Goods-producing industries include mining, construction , and manufacturi ng. Service- providing industries include all other private sector industries. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis based data. Monthly Labor Review June 2005 79 Current Labor Statistics : Comparative Indicators 2. Annual and quarterly percent changes in c ompensation , prices, and productivity Selected measures 2003 2003 2004 II Compensation data 2004 IV Ill II 2005 IV Ill 12 • Employment Cost Index-compensation (wages, salaries. benefits) : Civilian nonfarm .......... .................. . Private nonfarm ............................................. . Employment Cost Index-wages and salaries: Civi lian nonfarm .. Private nonfarm Price data 3.8 4.0 3.7 3.8 1.4 1.7 0.8 .8 1.1 1.0 0.5 .4 1.4 1.5 0.9 .9 1.0 .8 0.5 .5 1.1 1.1 2.9 3.0 2.4 2.4 1.0 1.1 .6 .7 .9 .8 .3 .4 .6 .7 .6 .7 .9 .9 .3 .2 .7 .7 2.3 3.3 1.8 - .3 -.2 -2 1.2 1.2 .2 .2 1.0 3.2 4.2 .4 4.6 25.2 4.1 4.6 2.4 9.1 18.0 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 1.1 .9 1.6 .9 8.3 2.0 -2.6 2.1 3.5 9.7 4.5 4.4 4.2 4.0 4.1 3.9 4.1 4.0 2.2 7.6 6.7 7.7 8 .1 8.7 7.9 2.1 2.8 3.9 4.0 3.8 .9 2.9 3.9 3.3 2.0 1.3 4.9 3.7 2.1 5.3 2.1 2.6 1 Consumer Price Index (All Urban Consumers): All Items .. Producer Price Index: Finished goods .......................... ... .... ...... ... .......... . Finished consumer goods ............... .. ................. . Capital equipment. .. Intermediate materials, supplies , and components .... Crude materials Productivity data 3 Output per hour of all persons : Busin ess sector .. Nonfarm bu siness sector .. Nonfinancial coroorations 4 ' Annual changes are December-to-December changes. Quarterly ch anges 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 Four quarters ending- 2004 II 2005 Ill 2004 IV II 2005 Ill IV I Ave rage hourly compensation: All persons, business sector .. ... ...................................... All persons , nonfarm business sector .. ················"···· ··· · 2.9 2.1 5.3 5.9 5.8 5.4 4.9 3.8 4.3 1.4 1.5 2.8 1.3 .7 .9 .9 1.5 .8 .4 1.0 .8 .8 .9 1.7 .5 .5 .5 .4 .6 .5 .5 .5 .4 .6 .6 .7 .6 .7 .4 .6 .7 1.0 .6 .2 .9 .9 .8 .8 4.8 4.5 4.4 4.3 4.5 3.8 3.6 3.9 4.0 6.0 3.5 3.3 3.4 4.4 4.4 4.7 4.3 5.0 5.0 Employment Cost Index-compensation: 2 Civilian nonfarm Private nonfarm .... ................... Union .. . Nonun ion .. . State and local governments .. .................. ................... 3.9 5.7 3.8 3.7 5.8 3.4 3.4 3.7 3.5 3.8 3.4 5.6 3.6 3.4 3.4 3.5 3.6 2.4 2.4 2.8 2.4 2.1 2 .4 2 .4 2 .3 2 .4 2.3 Employment Cost Index-wages and salaries : 2 Civili an nonfarm Private nonfarm ..................... ........ ............ .. .. Union .. ................. ........... Nonunion .... . .. ................... ... . .... . ......... . .... State and local governments .. . 1.0 1 Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate. 2 Excludes Federal and household workers. 80 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 .3 .2 .3 .2 .4 .4 .2 .5 .2 .5 2 .5 2.5 2.6 2.6 2 .9 2 .5 2 .6 2 .1 2.5 1.9 2.4 2 .6 3.0 2.5 2.0 4. Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted (Numbers in thousands] 2005 2004 Annual average Employment status 2003 2004 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 221 .168 146,510 66.2 137,736 223,357 222,757 222,967 223,196 223,422 223,677 223,941 224 ,192 224,422 224,640 224,837 225,041 225,236 225,441 147,401 66.0 139,252 146,788 65.9 138,645 147,018 65.9 138,846 147,386 66.0 139,158 147,823 66.2 139,639 147,676 66.0 139,658 147,531 65.9 139,527 147,893 66.0 139,827 148,313 66.1 140,293 148,203 66.0 140,156 147,979 65.8 140,241 148,132 65.8 140,144 148,157 65.8 140,501 148,762 66.0 141 ,099 TOTAL Civil ian noninstitutional 1 population ... Civilian labor force ... Participation rate ... Employed ... Employment-pop2 .. ulation rat io Unemployed .. .. Unemployment rate .. . Not in the labor force ... 62 .3 62 .3 62 .3 62.5 62.4 62.3 62.4 62.5 62.4 62.4 62.3 62.4 62.6 8,149 5.5 75,956 62 .2 8,143 5.6 75,969 62.2 8,774 6.0 74,658 8,172 5.6 75,950 8,228 5.6 75,809 8,184 5.5 75,599 8,018 5.4 76,001 8,005 5.5 76,410 8,066 5.4 76,299 8,020 5.5 76,109 8,047 5.4 76,437 7,737 5.2 76,858 7,988 5.4 76,909 7,656 5.2 77, 079 7,663 5.2 76,679 98,272 99,476 99,170 99,279 99,396 99,512 99,642 99,776 99,904 100,017 99,4 76 100,2 19 100,32 1 100,4 19 100,520 74,623 75.9 70,4 15 75,364 75.8 71 ,572 74 ,908 75.5 71,158 75,095 75.6 71 ,226 75,631 75.8 71,575 75,567 75.9 7 1,830 75,615 75.9 71,847 75,462 75.6 71,70 1 75,632 75.7 71,895 75,866 75.9 71,134 75,754 75.7 72,020 75,594 75.4 72,029 75,81 6 75.6 72,131 75,92 1 75.6 72,429 76,173 75.8 72 ,817 Men, 20 years and over Civilian noninstitut ional 1 population Civilian labor force ... Participation rate ... Employed .. Employment-pop2 .. ulation ratio Unemployed ..... Unemployment rate .. . Not in th e labor force ... 71 .7 71 .9 71 .8 71 .7 720 72.2 72.1 71.9 72.0 72.1 71 .9 71 .9 71.9 72.1 72.4 4,209 5.6 23,649 3,791 5.0 24,113 3,751 5.0 24,261 3,869 5.2 24,184 3,786 5.0 24,035 3,737 4.9 23,945 3,768 5.0 24,026 3,761 5.0 24 ,314 3,736 4.9 24,272 3,733 4.9 24,151 3,733 4.9 24,372 3,565 4.7 24 ,625 3,685 4.9 24 ,505 3,492 4.6 24,498 3,356 4.4 24 ,34 7 106,800 107, 658 107, 389 107,483 107,586 107,687 107,801 107,920 108,032 108,129 107,658 108,316 108,403 108,486 108,573 64,716 60.6 61,402 64,923 60.3 61 ,773 64,776 60.3 61 ,591 64,803 60.3 61 ,723 64,989 60.4 6 1,731 65,085 60.4 61 ,902 64,909 60.2 61 ,877 65,008 60.2 61 ,939 65,126 60.3 62,024 65,244 60.3 62 ,145 65,260 60.3 62,208 65,318 60.3 62 ,295 65,270 60.2 62 ,202 65,051 60.0 62, 099 65,420 60.3 62 ,384 Women, 20 years and over Civil ian noninstitutional 1 population Civilian labor force .. . Participation rate .. . Employed ..... Employment-pop2 .. ulation rat io Unemployed .. . Unemployment rate .... Not in th e labor force . 57.5 57.4 57.4 57. 4 57.4 57.5 57.4 57.4 57.4 57.5 57.5 57.5 57.4 57.2 57.5 3,314 5.1 42,083 3,150 4.9 42,735 3,185 409.0 42,613 3,080 4.8 42 ,680 3,259 5.0 42,597 3,183 4.9 42,603 3,032 4.7 42,892 3,069 4.7 42, 912 3,102 4.8 42,906 3,099 4.7 42 ,885 3,051 4.7 42 ,96 1 3,023 4.6 42,998 3,068 4.7 43,133 2,952 4.5 43,435 3,036 4.6 43,153 16,096 16,222 16,198 16,205 16,214 16,222 16,234 16,246 16,257 16,293 16,222 16,302 16,317 16,332 16,347 7,170 44.5 5,919 7,114 43.9 5,907 7,104 43.9 5,897 7,120 43.9 5,896 7,036 43.4 5,853 7,172 44.2 5,907 7,152 44.1 5,934 7,062 43.5 5,887 7,165 43.9 5,908 7,202 44.2 6,014 7,189 44.1 5,927 7, 066 43.3 5,917 7,046 43.2 5,811 7,185 44.0 5,973 7,168 43.9 5,897 Both sexes, 16 to 19 years Civilian noninstituti onal 1 population Civilian labor force ... Participation rate ... Employed .. . Employment-pop2 .... ulation ratio Unemployed ... Unemployment rate ... Not in the labor force ... Whlte 36.8 36.4 36.4 36.4 36.1 36.4 36.6 36.2 36.3 36.9 36.4 36.3 35.6 36.6 36.1 1,251 17.5 8,926 1,208 17.0 9,108 1,207 17.0 9,094 1,223 17.2 9,086 1,184 16.8 9,178 1,265 17.6 9,051 1,217 17.0 9,082 1,175 16.6 9,184 1,227 17.2 9,122 1,188 16.5 9,074 1,262 17.6 9,104 1,150 16.3 9,235 1,235 17.5 9,271 1,212 16.9 9,147 1,271 17.7 9,179 181,292 182,643 121 ,686 66.3 115,239 182,252 120,713 66.2 114,779 182,384 120,997 66.3 115,006 182,531 182,676 121 ,383 66.4 115,610 182,846 121 ,278 66.3 115,526 183,022 121 ,553 66.2 116,158 183,767 121 ,621 66.2 116,022 183,888 121,606 66.3 115,966 183,483 121 ,509 66.2 115,910 183,640 120,995 66.1 115,318 183,188 121 ,273 66.2 115,618 183,340 121,212 66.4 115,199 184,015 121 ,961 66.3 116,574 63.1 63.2 3 Civili an noninstitutional 1 population Civilian labor force ... ... . Participation rate ..... Employed .... Employment-pop- 120,546 66.5 114,235 2 ulation ratio Unemployed .. ... .. Unemployment rate ... Not in the labor force .. Black or African Amerlcan 121,484 66.1 116,135 63.0 63.1 63.0 63.1 63.1 63.3 6,311 5.2 60,746 5,847 4.8 61 ,558 5,934 4.9 61 ,539 5,991 5.0 61 ,387 6,013 5.0 61,319 5,773 4.8 61 ,293 63.2 5,752 4.7 61 ,568 63.0 5,677 4.7 62,027 5,655 4.7 61 ,915 63.3 5,640 4.6 61,735 5,600 4.6 61 ,973 63.3 5,395 4.4 62,088 63.1 5,598 4.6 62,146 5,349 4.4 62,403 63.4 5,387 4.4 62 ,054 25,686 26,065 25,967 26,002 26,040 26,078 26,120 26,163 26,204 26,239 26,273 26,306 26,342 26,377 26,413 16,526 64.3 14,739 16,638 63.8 14,909 16,505 63.6 14,893 16,480 63.4 14,837 16,521 63.4 14,825 16,775 64.3 14,937 16,721 64.0 14,972 16,711 63.9 14,981 16,820 62.4 15,012 16,728 63.8 14,913 16,713 63.6 14,907 16,721 63.6 14,946 16,708 63.4 14,890 16,741 63.5 15,025 16,940 64.1 15,184 63.2 3 Civilian noninstitutional 1 population Civilian labor force ... Partici pation rate ... ... .. Employed .............. Employment-pop2 ulation ratio Unemployed ... Unemployment rate ... Not in the labor force .. 57.4 57.2 57.4 57.1 56.9 57.3 57 .3 57.3 57.3 56.8 56.7 56.8 56.5 57.0 57.5 1,787 10.8 9,161 1,729 10.4 9,428 1,612 9.8 9,462 1,642 100 9,523 1,696 10.3 9,520 1,838 11 .0 9,303 1,749 105 9,399 1,730 10.4 9,452 1,808 10.7 9,384 1,814 10.8 9,512 1,806 10.8 9,559 1,775 10.6 9,585 1,818 10.9 9,634 1,716 10.3 9,636 1,756 10.4 9,473 See footnotes at end of table. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 81 Current Labor Statistics: Labor Force Data 4. Continued-Empl oyment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted [Numbers in thousands] Annual average Employment status 2004 2005 2003 2004 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 27,55 1 18,813 68 .3 17,372 28,109 19,272 68.6 17,930 27,879 19,081 68.4 17,724 27,968 19,297 69.0 17,959 28,059 19,302 68.8 28,150 19,432 69.0 28,243 19,463 28,338 28,431 28,729 19,458 67.7 19,541 28,902 19,665 18,013 18,102 18,128 68.6 18,079 28,642 19,379 67.7 28,815 68.9 19,524 68.7 28,520 19,552 68.6 18,238 28,608 19,444 18,198 18,211 67.8 18,425 18,412 63 .1 1,441 7.7 8,738 63.8 1,342 7.0 8,837 63 .6 1,358 7.1 8,797 64.2 1,338 6.9 8,671 64.2 1,289 6.7 8,756 64.3 1,330 6.8 8,717 1,335 6.9 8,780 63.9 1,313 6.7 8,968 63.8 1,292 6.6 9,064 63.9 1,117 5.7 9,273 63.7 1,252 6.4 9,237 Hispanic or Latino ethnicity Civil ian noninstitutional 1 oooulation Civilian labor force ... Participation rat e .... Employed .. Employment-population ratio 2 Unemployed ... Unemployment rate ... Not in the labor force 1 The population figures are not seasonally adju sted. 2 Civilian employment as a percent of the ci vili an noninstitutional populaiion. 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 64.2 18,213 63.8 1,366 7.0 8,894 64.1 1,311 6.7 8,907 19,544 68.3 18,252 63.5 63.4 1,181 1,248 6.4 9,270 6.1 9,263 68.0 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 enµoyment indicators, monthly data seasordly adjusted [In thousands] Selected categories Annual average 2004 2005 2003 2004 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 137,736 73,332 64,404 139,252 74,524 64,728 138,645 74,104 64,541 138,846 74,118 64,728 139,158 74,501 64,658 139,639 74,811 64,828 139,658 74,824 64,834 139,527 74,629 64,898 139,827 74,852 64,975 140,293 75, 188 65,104 140,156 74,938 65,218 140,241 74,934 65,~7 140,144 74,964 65,180 140,501 75,375 65,127 141 ,099 75,735 65,364 Married rren, spouse present .. . 44,653 45,084 44,759 44,763 44,958 44,948 45,099 45,093 45,127 45,462 45,315 45,171 45,351 45,382 45,482 Married w:xren, spouse present ... 34,695 34,600 34,375 34,536 34,487 34,607 34,494 34,704 34,800 34,961 34,878 34,739 34,601 34,~7 34,539 4,701 4,567 4,557 4,634 4,504 4,488 4,500 4,476 4,762 4,533 4,474 4,395 4,269 4,344 4,293 3,118 2,841 2,813 2,845 2,801 2,642 2,816 2,805 3,052 2,761 2,735 2,768 2,629 2,643 2,613 1,279 1,409 1,431 1,449 1,400 1,472 1,403 1,312 1,385 1,420 1,440 1,329 1,296 1,419 1,363 19,014 19,380 19,1~ 19,570 19,564 19,737 19,657 19,410 19,704 19,499 19,502 19,089 19,555 19,458 19,584 Olaracteristic Errpoyed, 16 years and over .. Nlen ... v\lorren ... Persons at wor1< part tlrre1 All industries: Part tirre for a::oncrric reasons ........ . ...... Slack 'M'.)1-j( or business conditions .. Could ooly find part-tirre 'M'.)1-j(.,,. .. .............. Part tirre for nonocooaric nonecooonic reasoos ... Nooagricultural industries: Part tirre for a::oncrric reasons ............ ...... Slack 'M'.)1-j( or business conditions ... ............... Could ooly find part-tirre 'M'.)1-j(., , .. , 4,596 4,469 4,451 4,567 4,423 4,300 4,408 4,400 4,656 4,404 4,382 4,~ 4,153 4,268 4,186 3,052 2,773 2,747 2,801 2,753 2,580 2,722 2,750 2,971 2,685 2,682 2,702 2,572 2,592 2,540 1,264 1,399 1,425 1,458 1,382 1,484 1,388 1,320 1,363 1,396 1,397 1,m 1,268 1,411 1,351 18,658 19,026 18,844 19,145 19,123 19,327 19,204 19,061 19,288 19,141 19,176 18,765 19,254 19,182 19,226 Part tirre for nonocooaric reasons .. . 1 Eldudes persons "wth a job but not at oork'' during the survey period for such rea5005 as vacatioo, illness, or industrial cisputes. N'.:lTE: Begnning in January 2003, data refloct revised pqJUlatioo controls used in the household survey. 82 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 6. Selected unemployment indicators, monthly data seasonally adjusted [Unemployment rates] 2004 Annual average Selected categories 2003 2004 2005 Apr. May Ju ne July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Ap r. Characteristic Total , 16 years and older .. Both sexes, 16 to 19 years .... Men, 20 years and older .. . . . . .. . . .. . . Women , 20 years and older ....... . .... . .. . 6.0 17.5 5.6 5.1 5.5 17.0 5.0 4.9 5.5 17.0 5.0 4.9 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 5.4 16.5 4.9 4.7 5.4 17.6 4.9 4.7 5.2 16.3 4.7 4.6 5.4 17.5 4.9 4.7 5.2 16.9 4.6 4.5 5.2 17.7 4 .4 4.6 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 . . . . . . . . ' . . 5.2 15.2 17.1 13.3 5.0 4.4 4.8 15.0 16.3 13.6 4 .4 4.2 4.9 15.7 17.8 13.3 4.5 4.2 5.0 15.6 18.5 12.7 4.7 4 .1 5.0 14.8 16.2 13.3 4.5 4.4 4.8 14.9 15.5 14.2 4.3 4.2 4.7 15.4 15.8 15.0 4.4 4.0 4.7 14.7 15.9 13.5 4.3 4.0 4.7 15.1 17.4 12.6 4.2 4.0 4.6 14.4 15.5 13.2 4.2 4.1 4.6 15.7 17.9 13.4 4.2 3.9 4.4 14.0 16.3 11 .8 4.0 3.9 4.6 15.5 18.1 12 .9 4.1 3.9 4.4 14.5 17.7 11 .0 4.0 3.8 4.4 15.3 17.8 12.8 3.8 4.0 Black or African Ameri can , total' .. 10.8 33 .0 36 .0 30.3 10.3 9.2 10.4 31.7 35.6 28.2 9.9 8.9 9.8 28.4 30.7 26.4 9.3 8.6 10.0 32.3 30.4 33 .9 9.4 8.4 10.3 32.7 34.4 31.2 9.5 9.0 11.0 37.2 37.9 36.6 10.3 9.1 10.5 29.4 34 .9 24 .2 10.4 8.7 10.4 28.6 35 .9 21 .1 10.2 8.9 10.7 34.7 37 .1 32.4 10.2 8.9 108 32.7 38.1 27.0 10.5 9.0 10.8 30 .8 37 .7 24 .0 10.7 9.1 10.6 30.2 30.0 30 .5 10.4 8.9 10.9 31 .5 34 .1 28 .6 10.9 9.1 10.3 32 .6 35 .8 29.2 9.2 8.9 10.4 35.5 37.8 32 .8 9.3 8.8 7.7 7.0 3.1 3.5 5.6 5.3 7.1 3.1 3.7 5.6 5.3 6.9 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 3.8 3.7 6.1 5.5 7.0 3.0 3.1 5.5 5.0 6.7 3.0 3.1 5.4 5.5 6.7 3.1 3.4 5.4 5.4 6.6 3.1 3.4 5.4 5.4 6.1 3.1 3.2 5.2 5.3 6.4 3.0 3.2 5.4 5.4 5.7 3.0 3.0 5.1 5.4 6.4 2.7 3.3 5.1 5.3 8.8 8.5 8.7 8.7 8.7 8.3 8.2 8.9 8.2 8.0 8.3 7.5 7.8 7.8 8.4 5.5 4.8 5.0 4.2 5.2 4.1 5.0 4 .0 5.1 4.2 5.0 4.2 4.9 4.1 4.8 4.0 4.9 4.2 4.9 4 .3 4.9 4.3 4.7 4 .1 4.9 4 .2 4.7 4.0 4.4 3.9 3.1 2.7 2.9 2.9 2.7 2.7 2.7 2.6 2.5 2.5 2.5 2 .4 2 .4 2.4 2.5 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 .. Hispanic or Latino ethnicity .. Married men , spouse present .. Married women, spouse present .. Full-time workers .. Part-time workers .. 2 Educational attainment Less than a high school diploma .. ... .... 3 High school graduates , no college .. Some college or associate degree .. Bachelor's degree and higher' .. . . . .. ' Beginning in 2003, persons who selected this race group only; persons who Includes high school diploma or equivalent . 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 . 2 3.1 3.5 5.5 5.2 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 Less than 5 weeks ................ ... . 5 to 14 weeks ...... .. ...... .. ...... 15 weeks and over ... ..... . ... .. 15 to 26 weeks ······· ······ · .... .... 27 weeks and over . . . . . . . . . . . .. . Mean duration, in weeks .. Median duration, in weeks .. Annua l average 2003 2004 2005 2004 Apr. May J une July Aug. Sept. Oct. Nov. Dec . Jan. Feb. Mar. Apr. 2,785 2,612 3,378 1,442 1,936 2,696 2,382 3,072 1,293 1,779 2,772 2,370 2,956 1,165 1,791 2,731 2,376 3,059 1,277 1,783 2,715 2,397 3,051 1,294 1,757 2,803 2,458 2,885 1,198 1,686 2,605 2,521 2,924 1,243 1,681 2,796 2,251 2,971 1,227 1,744 2,753 2,290 3,032 1,261 1,771 2,611 2,361 3,012 1,294 1,718 2,865 2,264 2,961 1,325 1,636 2,599 2,343 2,824 1,201 1,623 2,755 2,317 2,888 1,255 1,633 2,531 2,319 2,817 1,165 1,652 2,666 2,268 2,698 1,093 1,615 19.2 10.1 19.6 9.8 19.7 9.4 19.8 9.9 19.8 10.8 18.5 8.9 19.2 9.5 19.6 9.5 19.7 9.5 19.8 9.8 19.3 9.5 19.3 9.4 19.1 9.3 19.5 9.3 19.6 8.9 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 June 2005 83 Current Labor Statistics: Labor Force Data 8. Unemployed persons by reason for unemployment, monthly data seasonally adjusted [Numbers in thousands] Annual average Reason for unemployment 2003 1 Job losers On tempora ry layoff ................. Not on temporary layoff .... ..... . .... Job leavers .. ................... ... . ... . Reentrants .. .. ............ ........ .. .... . . entrants New ......... 2004 4,838 1,121 3,717 818 2,477 641 4,197 998 3,199 858 2,408 686 2005 2004 Apr. May 4,322 993 3,329 835 2,310 650 4,190 920 3,270 855 2,437 723 June 4,117 1,009 3,108 909 2,426 642 July Aug. Sept. 4,228 1,068 3,160 896 2,333 686 3,!'.l78 971 3,007 885 2,440 699 4,014 919 3,094 830 2,417 697 Oct. Nov. Dec. Jan. Feb. Mar. Apr. 4,074 947 3,127 829 2,411 747 4,066 941 3,124 880 2,388 723 4,108 965 3,144 898 2,361 709 4,048 966 3,082 819 2,324 624 3,980 965 3,015 965 2,405 745 3,784 961 2,823 855 2,364 711 3,675 838 2,837 897 2,356 747 Percent of unemployed 1 Job losers On te mpora ry layoff ......... ........... Not on temporary layoff .. ....... ... Job leavers .............................. ..... . Reent rants .. ··········· ··· ·· ·· .. .. ....... New entrants . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 55.1 51 .5 12.8 42 .4 9.3 28.2 7.3 12.2 39.3 10.5 29.5 8.4 3.3 .6 1.7 .4 2.8 .6 1.6 .5 51 .8 53 .2 12.2 41 .0 10.3 28 .5 8.0 51 .1 11 .2 39.3 10.4 29 .7 8.8 50.9 12.5 38.4 11 .2 30.0 7.9 51.9 49.7 50.4 50.5 13.1 38.8 11.0 28.6 8.4 12.1 37.6 11 .1 30.5 8.7 11.6 38.9 10.4 30.4 8.8 11 .8 38.8 10.3 29.9 9.3 5.1 11 .7 38.8 10.9 29.6 9.0 50 .9 11 .9 38 .9 11 .1 29.2 8.8 12.4 39.4 10.5 29.7 8.0 49.2 11 .9 37.2 11 .9 29.7 9.2 12.5 36.6 11 .1 30.6 9.2 47.9 10.9 37 .0 11 .7 30.7 9.7 2.9 2.8 2.8 2.9 2.7 2.7 2.8 2.7 2.8 2.7 2.7 2.6 2.5 .6 1.6 .4 .6 1.7 .5 .6 1.6 .4 .6 1.6 .5 .6 1.7 .5 .6 1.6 .5 .6 1.6 .5 .6 1.6 .5 .6 1.6 .5 .6 1.6 .4 .7 1.6 .5 .6 1.6 .5 .6 1.6 .5 49.1 Percent of civilian labor force 1 Job lose rs Job leavers .............. ........ Reentrants .. New entrants .. 1 Includes persons wh o completed temporary jobs. NOTE: Beginning in January 2003, data refl ect revised populati on controls used in the household survey. 9. Unemployment rates by sex and age, monthly data seasonally adjusted [Civilian workers] Annual average 2003 2004 2005 2004 Sex and age Apr. May June July Aug . Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 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 ye ars and older ..... ..... 25 to 54 ye ars .. 55 years and older..... 6.0 12.4 17.5 19.1 16.4 10.0 4.8 5.0 4.1 5.5 11 .8 17.0 20.2 15.0 9.4 4.4 4.6 3.7 5.5 11 .7 17.0 20.5 14.7 9.2 4.5 4.6 3.8 5.6 12.1 17.2 21 .5 14.7 9.7 4.4 4.5 3.9 5.6 12.0 16.8 20.5 14.4 9.7 4.5 4.5 3.9 5.5 11.9 17.6 20.3 16.1 9.2 4.4 4.6 3.7 5.4 11.6 17.0 20.7 14.9 9.0 4.3 4.4 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 20.6 15.2 9.8 4.3 4.4 3.8 5.4 11 .5 16.5 21.2 13.5 9.2 4.3 4.4 3.7 5.4 11 .7 17.6 20.6 15.4 8.9 4.3 4.5 3.5 5.2 11 .7 16.3 19.3 14.4 9.5 4.1 4.2 3.5 5.4 12.4 17.5 20.6 15.5 10.0 4.2 4.3 3.6 5.2 11 .6 16.9 19.4 15.0 9.0 4.0 4.2 3.5 5.2 11 .8 17.7 19.9 16.9 8.9 4.0 4.1 3.5 Men, 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 ..... ...... .. . 6.3 13.4 19.3 20 .7 18.4 10.6 5.0 5.2 4.4 5.6 12.6 18.4 22.0 16.3 10.1 4.4 4.6 3.9 5.7 12.9 19.2 23 .3 16.6 10.0 4.4 4.5 3.9 5.8 13.0 19.0 23 .2 16.6 10.3 4.6 4.7 4.1 5.6 12.7 18.0 22 .3 15.9 10.4 4.4 4.4 4.3 5.5 12.2 17.8 21.2 15.9 9.7 4.4 4.5 3.8 5.6 12.5 18.1 21 .9 16.1 10.0 4 .4 4.5 4.0 5.6 12.9 18.2 20.6 16.8 10.5 4.3 4.4 3.9 5.6 13.0 19.2 22.1 17.7 10.2 4.3 4.4 4.1 5.5 12.4 18.2 23.0 14.8 9.8 4.3 4.4 3.7 5.6 12.5 20.3 24.3 17.8 9.0 4.4 4.6 3.5 5.3 12.7 18.2 22.0 16.1 10.2 4.0 4.1 3.9 5.6 14.1 20.4 25.0 17.7 11 .3 4.1 4.2 3.7 5.3 12.9 19.9 22 .9 17.5 9.7 4.0 4.1 3.6 5.1 13.0 20.4 22.2 19.9 9.5 3.8 3.9 3.5 Women, 16 years and ol der ... 16 to 24 years .. 16 to 19 years . . . . . . . . . . . . . . . ' . . 16 to 17 years .. .. .. ...... 18 t0 19 years ..... . ... .. .. 20 to 24 years .. . .. . . . ..•. . 25 years and older .. ...... ·•·· 25 to 54 years . . . . . . . . . . . . . .. .. 5.7 11.4 15.6 17.5 14.2 9.3 4.6 4.8 5.4 11 .0 15.5 18.5 13.5 8.7 4.4 4.6 5.4 10.4 14.7 17.9 12.5 8.3 4.5 4.7 5.3 11 .1 15.4 20.1 12.7 9.0 4.2 4.4 5.6 11 .2 15.6 18.9 12.7 9.0 4.5 4.7 5.5 11.6 17.5 19.5 16.4 8.7 4 .4 4.7 5.2 10.6 15.9 19.7 13.5 7.9 4.3 4.4 5.2 10.6 15.0 18.6 12.8 8.4 4.3 4.4 5.3 11 .3 15.1 19.0 12.5 9.4 4.2 4.4 5.2 10.5 14.6 19.3 12.1 8.5 4.3 4.4 5.2 10.8 14.8 17.2 12.9 8.9 4.2 4.4 5.1 10.5 14.3 16.8 12.7 8.7 4.1 4.4 5.2 10.6 14.6 16.5 13.2 8.6 4.2 4.4 5.0 10.1 13.7 15.8 12.2 8.3 4.0 4.2 5.2 10.4 14.9 17.5 13.9 8.2 4.2 4.4 55 years and older' .. 3.7 3.6 3.3 3.3 3.8 3.8 3.9 3.5 3.3 3.6 3.2 3.3 3.5 3.2 3.2 . 1 . Data are not sea sonall y adjusted. NOTE: Beginning in January 2003 , data reflect revised population controls used in the household survey. 84 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 10. Unemployment rates by S tote, seasondly cx:tjusted State Mar. Feb. Mar. 2004 2005 2005 State Mar. Feb. Mar. 2004 2005 2005 Alabama . . . . . . . . . . . . . . . .. . Alaska ........... ... .. .... ... .•.. Arizona ····· ·· ·• ··· ··········· Arkansas California 5.7 7.5 5.1 5.7 6.4 5.2 7.2 4.4 5.5 5.8 4.7 6.6 4.7 5. 2 5. 4 Missouri ····· ······· ····· · Montana Nebraska ... Nevada .. New Hampshire. 5.4 4.4 3.8 4.6 4.1 5.8 4.5 3.9 3.9 3.8 5.7 4.6 4.0 3.9 3.7 Colorado Connecticut .. Delaware ....... .... ....... ... ........... ...... ... ... Dist rict of Columbia Fl orida ................ .. ... ... .... ... ... ... ... ...... .... 5.6 5.1 4.0 7.7 4.8 4.9 4.8 4.1 8.1 4.6 5.1 4.9 3.9 7.8 4.4 New Jersey. New Mexico .. ... .... .. .... ... .. .. New Yo rk ... North Carol in a .. North Dakota . ........ ... ...... . 5.2 5.8 6.1 5.7 3.4 4.4 5.6 5.1 5.4 3.3 4.3 5.9 4.6 5.2 3.3 Georgia Hawaii ................... ... ... Idaho ····· ······ ·· ··· ··· ·· Illinois Indiana ............. ..... ....... .... 4.3 3.6 5.0 6.4 5.2 5.1 3. 0 4.1 5.9 5.7 5.0 2. 8 4.2 5.6 5.6 Ohio. Oklahoma Oregon. Pennsylvania Rhode Island 6.1 5.0 7.6 5.5 5.4 6.4 4.3 6.5 5.3 4.4 6.3 4.4 6.1 5.4 4.5 Iowa Kansas Kentucky Louisiana Maine . 4.7 5.6 5.7 5.5 4.6 5.1 5.4 5.2 6.0 4.6 5.1 5.4 5.3 5.3 4. 7 South Carolina. South Dakota .. Tenn essee Texas .. Utah 6.7 3.6 5.4 6.2 5.3 7.1 3.7 5.9 6.0 4.8 6.7 3.7 5.8 5.6 4.8 Maryland Massachusetts .. Michigan .... Minnesota Mississippi 4.1 5.4 7.1 5.0 5.2 4.2 4.9 7.4 4.2 6.8 4.3 4.9 6. 9 4. 4 7. 0 Vermont Virg ini a. ................. ...... ....... Washington West Virginia Wisconsin .. Wyoming ··· ·· ···· ····"· " •·••·· 4.0 3.7 6.5 5.4 5.3 3.6 3.5 3.3 5.5 5.0 4.9 2.9 3.4 3.3 5.2 5.2 4.6 3.1 p .......... ............ = preli minary 11. Employment of workers on nonfam oavrolls by State, seasondly oojusted State Alabama Alaska . . . . . . . . ... . . . . . . .. ... Ari zona ... . Arkansas Californ ia .. Mar. Feb. Mar. 2004 2005 2005 2,143,207 331 ,738 2, 755 ,301 1,296, 314 17,478, 356 2,161 ,746 336,367 2,803,959 1,325 ,679 17,742,274 2,153,150 336,833 2,810,730 1,327,837 17,656 ,815 Colorado .. Connecticut .... ... ...... ..... . Delaware .. District of Columbia Fl orida ········ ···· ········· · ·· ······ 2,505,450 1,801,209 422 ,289 298,624 8,335 ,053 2, 542, 845 1,776,732 426 ,313 306,282 8,564,633 Georgia ... Hawaii Idaho Illinois Indiana . ... .. . ... ... ... ... 4,361,479 614,769 699,913 6,380,895 3,169,863 Iowa .............. .... ...... .. .. ...... . Kansas Kentucky .. Louisiana. .......... .. .... ... ......... .. .. Maine ... ..... ... ... ..... Maryland .. Massachusetts .. Michigan ... Minnesota Mississippi .. . . . . . . . . . . . . . . . . . . .. . . . . State Mar. Feb. Mar. 2004 2005 2005 Missouri .. Montana ....... ... ... ... ... ... Nebraska .. Nevada ........... ....... .... ..... ...... New Hampshire . . . . . . . . . . . . . . . . . . . . 3,019 ,555 479,666 981 ,670 1,168,685 72 1,526 3,024 ,179 488,7 16 990,858 1,202 ,444 727,241 3,016,88 1 490,247 990, 127 1,207,926 729,623 2,543 ,820 1,789,618 426,866 303,350 8,560,910 New Jersey .. New Mexico ·· ····· ············ "' ··" ·· ··· ··· New Yo rk North Carol ina North Dakota 4,383,748 907 ,508 9,342,255 4,244,601 353,046 4,398,477 930 ,008 9,386,310 4,281 ,480 356,551 4,396 ,279 935, 178 9,33 1,794 4,286 ,131 356,230 4,448,731 627, 795 724 ,214 6,465 ,391 3,202 ,239 4,456 ,654 626,179 725 ,376 6,448 ,951 3,206,971 Oh io .. Oklah oma .. Oreg on ............... . .. . Pennsylvan ia Rhode Island ........... ............. . .. . .. 5,878,044 1,708, 650 1,853,158 6,244,806 562,739 5,9 18,703 1,723,722 1,866,51 1 6,333,48 1 56 1,746 5,923,898 1,720,072 1,863,090 6,336,022 564,027 1,62 1,332 146,094 1,979,803 2,049,645 696,056 1,636,426 146,353 1,980,779 2,094 ,263 701,394 1,643,096 1,465,613 1,983,259 2,081 ,643 701 ,658 South Caroli na South Dakota Tennessee Texas Utah ... 2,035,925 427,337 2,9 17, 190 10,995,767 1,199,198 2,076,128 430,258 2,924,0 13 11 ,164,843 1,219,979 2,070,732 429, 917 2,902,034 11,144,7 14 1,224,262 2,878,775 3,397, 787 5,073,535 2,938 ,851 1,319,520 2,896 ,321 3,377. 045 5,110,604 2,967,413 1,343,376 2,899,401 3,369 ,587 5,099,411 2,970,372 1,343,373 Vermont Virg inia .. Washington .. West Virginia Wisconsin Wyoming .. 353 ,313 3,798,642 3,2 17,080 789,355 3,075,803 279,264 353,340 3,856,856 3,260,27 1 790,579 3,07 1,111 283,157 352,673 3,86 1,448 3,253 ,606 797 ,866 3,05 1,571 283,436 NOTE: Some data in th is table may differ from data published elsewh ere because of the continu al updatin g of the data base. Monthly Labo r Review June 2005 85 Curre nt Labor Statist ics: Labor Force Data 12. Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted [In th ousands] Annu al average 2004 Industry 2003 TOTAL NONFAR M ............... TOTAL PRIVATE. ..................... 2004 May June July Aug. 2005 Sept. Oct. Nov. Dec. Jan. Feb Mar.P Apr.P 129,999 131.480 131,123 131,373 131.479 131,562 131 ,750 131,880 132,162 132,294 132.449 132,573 132,873 133,0 19 133,293 108.416 21 ,816 109.862 21,884 109.516 21,825 109.787 21,888 109.908 21,890 109.976 21,902 110.105 21,946 110.203 21,947 110.462 21,982 110.588 21,996 110.749 22,022 110.863 22,004 111 .140 22,066 111 .287 22,095 111 .543 22,140 mining ...... ............................. Logging .. Mining .. Oil and gas extraction . 572 69.4 502 .7 120.2 591 67.8 523.2 123.1 589 69.8 519.2 122.5 592 68 .9 523.3 123.7 591 67.6 523.8 123.2 596 67.4 528.9 123.2 595 67.5 527.8 123.8 597 68.0 528.5 124.0 595 67.0 527.7 123.6 599 66.9 532.5 124.4 602 67.9 534.4 124.1 607 68.0 538.7 123.4 602 67.3 545.0 122.5 619 69.2 550.1 123.5 623 64.7 558.2 124.0 Minina. exceot oil and aas 1 Coal minina ........ ........... Support activities for mining . 202.7 207.1 204.8 207.1 208.1 211 .8 209.1 208.5 208.4 210.7 211 .3 212.9 215.5 215.6 218.0 70.0 179.8 71 .7 193.1 70.4 191.9 71.3 192.5 72.0 192.5 73.5 193.9 73.1 194.9 72.9 196.0 72.7 195.7 73.7 197.4 73.9 199.0 75.4 202.4 76.1 207.0 76.1 211.0 76.7 216.2 GOODS-PRODUCING ........ ........ Natu ral resources and Constructi on .......... .................... 6,735 6,964 6,913 6,949 6,955 6,965 6,985 6,998 7,043 7,060 7,086 7,090 7,133 7,162 7,209 Construction of buildinas Heavv and civil enoineerino . Soecialitv trade cont ractors . Manufacturi ng ............................ 1.575.8 903.1 4.255.7 14,5 10 1.632.2 902.5 4.429.7 14,329 1,608 7 903.2 4.401 .5 14,323 1.623.1 903.0 4.423.3 14,347 1.6267 899 .8 4.428 .6 14,344 1.632.2 899 .7 4.433.1 14,341 1.6363 901 .1 4.447.6 14,366 1,6478 902.1 4.447.8 14,352 1.663.0 904.1 4.476.1 14,344 1.668.3 906.4 4.484.8 14,337 1.678.9 907.8 4.499.2 14,334 1.682.4 908.2 4.499 6 14,307 1.6892 911 .7 4.531 .8 14,321 1.694.3 916.6 4.550.7 14,314 1.693.4 924.9 4.591 .0 14,308 Production workers .. Durable goods .............. ....... .... 10,190 8,963 10,083 8,923 10,064 8,902 10,093 8,925 10,095 8,931 10,102 8,926 10.131 8,965 10,117 8,957 10, 111 8,960 10,104 8,954 10,097 8,957 10,082 8,942 10.085 8,962 10.085 8,957 10,076 8,959 Production workers . ...... Wood oroducts Nonmetallic mineral oroducts Primary metals .. Fabricated metal oroducts .. Machinery . Comouter and electronic 6,152 537.6 494 .2 477.4 1,5068 1,149.4 6,137 548.4 504.8 465.9 1.470.3 1,1415 6,114 544.9 5016 464.8 1.488 6 1,139.0 6,138 547.9 506.3 466.1 1.496.5 1,1 400 6,147 549 507.4 467.4 1.498 3 1,142 7 6,144 550 507.9 468.4 1,502 6 1,146.8 6.180 551 .7 507.6 467.4 1,506.8 1,151 .5 6,172 550.1 508.8 466.4 1,508.5 1,148 7 6.172 554.5 509 .1 466.0 1,511.5 1,147 3 6,166 553.3 507.9 465.8 1,5109 1,147.4 6,170 555.2 506.5 465.2 1,5128 1,1460 6,166 554.7 504.5 465.5 1,514.3 1,1459 6,178 553.6 504.0 466.9 1,5 141 1,148.0 6.781 555.3 502.5 467.1 1,5 168 1,151.2 6,184 552.7 505.8 467.7 1,517.3 1,153.2 1,355.2 1, 326.2 1,322.6 1,327.1 1,327.4 1, 332.8 1,334.0 1,332.5 1,329 .8 1,327.1 1,325.8 1,327.0 1,327.5 1,326.5 1,329.1 224.0 154.9 212.1 150.5 2 13.1 148.5 213.7 148.9 212.2 150.1 211.4 151 .3 212.4 151 .6 211 .9 151.0 209.7 150.7 209.3 152.7 210.4 153.7 210.2 155.1 211.2 154.5 211.2 153.7 212.1 153.8 461 .1 429.7 452.8 431 .8 451.2 429.1 453.3 431.1 455.2 431 .2 457.9 433.9 457.4 434.2 457.0 434.6 454.9 437.0 451 .9 435.6 448.0 435.7 447.4 436.4 447.1 436.4 447.1 436.4 446.9 437.6 459.6 1,774.1 446.8 1,763.5 445.8 1,765.1 446.1 1,763.6 446.8 1,762.2 447.3 1,739.1 447.7 1,769.5 447.0 1,768 .5 445.1 1,77 1.0 447.4 1,767.2 445.8 1,771 .9 445.1 1,760.1 445.3 1,781 .8 445 .3 1,776.1 446.3 1,778.7 572.9 663.3 572.7 655.5 574.1 655.6 574.5 656.4 573.6 656.4 574.0 656.8 573.3 655.2 572.1 654.5 571 .3 654.1 572.2 654.7 571 .7 656.4 570.3 654.3 567.5 653.5 565.5 650.9 559.9 648.9 Nondurable goods ................... Production workers 5,547 4,038 5.406 3,945 5.42 1 3, 950 5.422 3,955 5.413 3,948 5.415 3, 958 5.401 3,951 5,395 3,945 5,384 3,939 5,383 3,938 5,377 3,927 5,365 3,916 5,359 3,907 5,357 3,904 5,349 3,892 Food 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 ... 1,517.5 1.497.4 1,5005 1,501 .8 1.498.6 1,504.6 1.497.0 1.494.3 1.493.5 1.493.6 1.498.8 1.494.3 1.493.2 1.494.1 1.490.1 199.6 261.3 179.3 312.3 44.5 516.2 194.3 238.5 177.7 284.8 42.9 499.1 194.3 239.7 179.1 29 1.8 42.6 499 .0 194.0 239.7 180.2 289.1 42.8 498.9 194.4 239 .3 178.5 285.9 42.6 496.7 194.2 238.8 178.2 283.2 42.5 499.2 193.4 238.1 177.6 282.6 42.5 500.6 194.9 237.3 177.8 281.0 42.7 499.3 192.9 236.5 178.1 276.1 42.8 499.4 195.1 235.0 178.4 273.4 43.4 498.1 193.0 233.2 178.0 271 .9 43.1 497.9 192.2 231 .5 178.1 269.3 43.1 499.9 192.5 230.1 177.9 267.2 43.2 500.2 191.4 228.7 177.7 263.4 43.2 501.7 190.9 227.0 177.9 261.6 43.2 498.3 680.5 114.3 906.1 665.0 11 2.8 887.0 665.7 111.4 890.8 667.2 112.3 889.0 668.3 112.9 888.8 665.2 112.8 887.7 663.9 113.2 885.8 661.6 113.2 885.5 661.0 113.3 884.5 661 .3 113.6 882.4 660.8 113.8 880.5 659.6 114.5 877.1 659.2 115.1 876.4 659.1 114.8 876.7 659.5 116.2 877.5 1 oroducts Computer and oerioheral equipment.. Communications equipment .. Semiconductors and electronic components Electronic instruments Electrical equipment and appl iances. Transportation equ ipment. . Furniture and related products . Miscellaneous manufacturing Plastics and rubber products .. 815.4 806.6 805.9 807.3 807.1 808.9 806.6 807.1 806.3 808.6 806.2 804.9 804.1 806.5 806.4 SERVICE-PROVIDING ...... ............ 108,182 109,596 109,298 109.485 109,589 109,660 109,804 109,933 110,180 110,298 110.427 110,569 110,807 110,924 111,153 PRIVATE SERVICEPROVIDING ..... ..... ............... 86,599 87,978 87,691 87,899 88,018 88,074 88 ,159 88,256 88.480 88,592 88,727 88,859 89,074 89,192 89.403 25,287 5,607.5 2,940.6 2,004.6 25,510 5,654.9 2,949.1 2,007.1 25.481 5,648.2 2,941 3 2009.1 25,5 11 5,651.4 2,942.9 2010.6 25,536 5,653.4 2,948.4 2006.6 25,536 5,660.2 2,955.3 2004.0 25,537 5,662.9 2,957.8 2004 .0 25,555 5,672.4 2,960.2 2008.1 25,581 5,674 .7 2,962.3 2009 .1 25,621 5,680.0 2,960.4 2012.6 25,620 5,683.6 2,964.5 2009.9 25,652 5,679.9 2,965.6 2,005.4 25,7 14 5,688.7 2,968.7 2,006.9 25,735 5,702.9 2,974.4 2,013.0 25,774 5,707.7 2,974.6 2,014.2 698.8 697.8 697.9 698.4 700.9 701 .1 704.1 703.3 707.0 709.2 708.9 713.1 715.5 718.9 Trade, transportation, and utilities.............................. Wholesale trade ....... ................ Durable goods . ··· • Nondurable goods ... Electronic markets and agents and brokers .. 662.2 Retail trade ...................... ......... 14,917.3 Motor vehicles and parts dealers' ..... ....... .. Automobile dealers ... Furniture and home furnishings stores .. Electronics and appliance stores ... 15,034.7 15,038.0 15,052.3 15,060.5 15.048.2 15,043.3 15,037.7 15,0565 15.081.4 15,077.0 15.081 .2 15,125.4 15.123.3 15.147 7 1,882 9 1,254.4 1,901 .2 1,254.2 1,906.6 1,260.3 1,906.9 1258.5 1,904.1 1257.1 1,904.4 1254.1 1,899.8 1251.2 1,898.4 1247.3 1,896.4 1245.0 1,901 .2 1247.6 1,905.9 1249.1 547.3 560.2 558.1 558 .7 559.1 559.8 561 .6 561.9 562 .3 565.6 563.7 562.1 562.6 562.3 565.2 512.2 514.4 514.9 514.3 514.1 513.4 512.0 513.6 520.2 520.3 516.5 516.1 515.1 516.5 514.8 See notes at end of table . 86 Apr. Mo nthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 1,907.4 1247.9 1,911 .2 1248.8 1,913.4 1251.2 1,916.5 1254.2 12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted [In thousands] Industry 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 stores 1 Department stores ... Miscellaneous store retailers .. Nonstore retailers .... Transportation and warehousing ......... ................. Air transportation Rail transportation . Water transportation .. •· Truck transpo rtation ..... Transit and ground passenger transportation . Pipeline transportation . Scenic and sightseeing transportation . Suppo rt activities for transportation . Couriers and messengers .. Warehousing and storage Utilities ............. .. ..... .... ............... 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 .. Financial activities .. Finance and insurance .. Monetary authoritiescentral bank 2004 Annual average 2005 2003 2004 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.P Apr.P 1,185.0 2,383.4 1,226.0 2,826.3 1,224.7 2,830.8 1,227.9 2,835.8 1,223.8 2,832.6 1,224.7 2,828.5 1,228.1 2,826.2 1,232.5 2,827. 1 1,236.3 2,830.2 1,240.4 2,822.7 1,243.5 2,819.8 1,248.0 2,826.0 1,264.8 2,826.6 1,263.2 2,826.5 1,263.8 2,828.8 938.1 882.0 941 .7 8771 941 .6 879.3 941.2 879.1 941 .3 8775 941 .0 876.6 941 .0 876.5 942.1 878.0 941 .6 8770 944.5 873.7 946.6 871 .3 944.8 872.9 949.7 874.6 947.9 874.6 954.1 874.4 1,304.5 1,361 .8 1,352.1 1,357.5 1,367.6 1,369.5 1,374.4 1,371 .9 1,376.0 1,3779 1,381 .3 1,375.5 1,380.5 1,381 .8 1,384.4 646.5 2,822.4 1,620.6 930.7 427.3 639.2 2,843.5 1,61 2.5 918.6 424.8 639.8 2,847.7 1,613.6 916.8 425.6 639.7 2,848.4 1,614.2 917.0 425.8 639.4 2,856.4 1,618.0 919.2 425.4 638.9 2,848.0 1,616.1 918.8 424.6 639.0 2,842.5 1,611.4 918.9 423.3 638.7 2,832.9 1,603.3 917.0 423.6 638.0 2,835.2 1,604.2 920.5 422.8 639.0 2,854.9 1,619.1 917.4 423.8 635.8 2,852.9 1,619.3 918.2 421 .5 637.7 2,853.5 1,619.1 918.7 418.5 636.2 2,864.1 1,625.7 919.9 420.1 635.8 2,862.5 1,623.8 919.2 419.6 637.0 2,867.2 1,625.9 919.2 422.3 4,185.4 528.3 217.7 54.5 1,325.6 4,250.0 514.8 224.1 57.2 1,350.7 4,223.5 516.0 223.5 57.2 1,343.8 4,236.3 516.7 223.7 57.3 1,346.3 4,250.9 517.0 224.7 58.2 1,352.2 4 ,257.0 516.3 225.0 58.1 1,352.5 4,260.4 515.0 224. 6 56.7 1,352.5 4,274.1 513.8 225.5 57.2 1,358.5 4,279 .6 514.2 225.4 57.7 1,356.0 4,289.6 514.6 224.6 57.8 1,358.9 4,288.0 512.3 224.0 58.6 1,366.5 4,316.0 509.4 224.4 59.8 1,372.6 4,324.1 507.9 223.9 60.0 1,378.0 4,334.1 507.1 223.7 60.7 1,382.9 4 ,345.8 502.4 223.5 60.4 1,390.6 382.2 40.2 385.5 38.8 377.4 38.6 386.3 38.8 381 .6 38.9 383.2 39.0 386.2 38.9 388.3 39.0 389 .3 38.9 389.4 39.0 3910 38.7 391 .7 39.3 391 .0 39.4 388.5 39.5 392.7 39.7 26.6 26.7 26.8 27.0 27.4 26.3 27.7 27.8 25.6 26.1 26.6 24.2 24.9 26.5 27.0 520.3 561 .7 528.3 535.6 560.5 556.0 532.0 556.2 552.0 532.6 557.0 550.6 534.3 562.1 554.5 535.5 563.1 558.0 536.9 562.6 559.3 537.7 563.8 562.5 539.9 564.4 568.2 544.6 568.7 565.9 547.0 556.4 566.9 549.3 577.5 567.8 55 1.5 5776 569.9 554.2 580.0 571 .0 553.7 583.8 572.0 5770 570.2 571 .0 571 .1 570.8 570.9 570.1 571 .1 570.3 570.2 571 .3 574.7 576.0 575.0 573.1 3,188 3,138 3,142 3,146 3,151 3,144 3,135 3,127 3, 131 3,133 3,127 3,123 3,127 3, 135 3,147 924.8 909.8 911 .0 911 .1 911 .9 909.6 909.3 909.2 908.1 908.9 905.7 905.0 905.6 906.5 903.7 376.2 324.3 389.0 326.6 386.7 324.4 392.3 326.3 395.5 326.5 394.4 327.2 389.3 327. 8 389.7 328.1 395.3 329.5 390.6 329.7 384.8 329.7 380.3 331 .3 380.9 330.4 388.2 330.7 397.6 329.9 29.2 1,082.3 31 .3 1,042.5 30.0 1,050.9 30.6 1,046.6 31 .5 1,044.0 31.4 1,041 .9 31 .7 1,037.1 32.0 1,028.4 33.0 1,024.8 33.6 1,Q300 34.0 1,031 .5 34.8 1,030 8 34.6 1,032.2 34.8 1,031 .5 34.9 1,038.2 402.4 48.7 388.1 50.9 387.2 51 .3 388.2 51 .3 389.9 51 .6 388.6 51 .3 387.6 51 .7 387.6 51 .5 389.2 50.9 389.5 50.7 390.4 50.7 389.9 51 .0 392.6 50.9 392.8 50.7 392.0 50.3 7,977 5,922.6 8,052 5,965.6 8,021 5,948.4 8,037 5,956.0 8,051 5,965.6 8,043 5,958.6 8,058 5,970.2 8,083 5,982.1 8,093 5,994.1 8,107 6,001 .3 8,128 6,014.5 8,150 6,030.9 8,165 6,037.6 8,171 6,039.7 8,188 6,048.2 22.6 21 .6 22.1 21.6 21 .6 21 .5 21 .6 21 .5 21 .3 20.9 20.6 20.5 20.4 20.4 20.3 2,792.4 2,832.3 2,823.3 2,826.3 2,833.7 2,829.2 2,833.4 2,841 .0 2,847.9 2,859.2 2,871 .9 2,882.7 2,891 .0 2,896.9 2,901 .1 Credit intermediation and related activities' Deoositorv credit 1 intermediation Commercial bankino .. Securities, commodity contracts, investments ... Insurance carriers and related activities ...... Funds, trusts , and othe r financial vehicles .. Real estate and rental and leasing .. Real estate . Rental and leasing services ... Lessors of nonfinancial intangible assets .. Professlonal and business services ..... ..... ..................... 1,748.5 1,761 .2 1,756.5 1,758.2 1,762.1 1,760.6 1,763.0 1,765.1 1,768 .1 1,773.3 1,778.8 1,785.6 1,790.3 1,793.2 1,794.3 1,280.1 1,285.3 1,284.4 1.2846 1,2863 1,2839 1,2835 1,286.4 1,2883 1,2931 1,296.8 1,301 6 1,305.5 1,307.5 1,3071 757.7 766.8 759.2 761 .9 765.1 766.3 769.9 772.3 777.3 776.9 779.7 782.5 784.8 786.9 790.4 2,266.0 2,260.3 2,258.2 2,261 .6 2,260.9 2,257.0 2,261 .0 2,263.3 2,264.1 2,260.4 2,258.1 2,259.6 2,256.7 2,251 .0 2,252.7 83.9 84.7 85.6 84.6 84.3 84.6 84.3 84.0 83.5 83.9 84.2 85.6 84.7 84.5 83.7 2,053.9 1,383.6 643.1 2,086.2 1,417.0 643.9 2,072.2 1,406.2 640.6 2,081 .1 1,413.8 642.0 2,085.7 1,415.7 645.0 2,084.6 1,416.7 643.0 2,088.2 1,420.0 643.3 2, 101 .3 1,429.1 647.6 2,099.2 1,428.6 646.3 2,105.5 1,434.7 646.0 2,113.6 1,437.8 650.9 2,119.0 1,439.7 654.1 2,127.2 1,443.8 658.3 2,131 .2 1,446.2 660.0 2,140.0 1,450.1 664.1 27.3 25.4 25.4 25.3 25.0 24.9 24.9 24.6 24.3 24.8 24.9 25.2 25.1 25.0 25.8 15,987 16,414 16,305 16,384 16,415 16,453 16,470 16,514 16,614 16,611 16,674 16,694 16,775 16,807 16,843 6,629.5 1,142.1 6,762.0 1,161 .8 6,712.2 1,158.6 6,730.0 1,160.0 6,754.0 1,163.5 6,765.1 1,165.0 6,779 .7 1,1 63.6 6,805.4 1,166.8 6,835 .3 1,167.4 6,834.4 1,163.1 6,869.9 1,164.4 6,882.1 1,160.8 6,902.7 1,161 .2 6,913.7 1,161 .9 6,931 .5 1,162.9 815.3 816.0 811 .6 810.7 8105 813.9 814.2 816.1 821 .5 816.6 840.8 858.1 858.1 861.6 865.1 1,226.9 1,260.8 1,249.4 1,254.6 1,258.7 1,262.0 1,264.4 1,270.5 1,280.5 1,284.9 1,289.5 1,286.9 1,292.0 1,295.2 1,298.1 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 June 2005 87 Current Labor Statistics: Labor Force Data 12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted [In thousands] Annual average Industry Computer systems desi gn and related services .. Management and technical consulting services .. Management of companies and enterprises ..... Mministrative and waste services .. 2004 2003 2004 Apr. May June July Aug. 1,116.6 1,147.4 1,1 27.7 1,134.0 1,142.3 1,145.9 1,155.0 2005 I Sept Oct. Nov. Dec. Jan. Feb. Mar.P Apr.P 1,161 .1 1,167. 3 1,174.1 1,174.3 1,171 .8 1,174.2 1,176.0 1,177.1 744.9 779.0 772.9 778.2 783.6 784.7 786.9 787.9 790.5 787. 8 789.9 789.3 793.7 796.0 799.4 1,687.2 1,718.0 1,717.6 1,719.8 1,722.6 1,723.7 1,720.7 1,715.0 1,715.3 1,722.5 1,725.6 1,730.7 1,731 .3 1,732.4 1,735.6 7, 669.8 7,934.0 7, 875.5 7,934.1 7,938.3 7,964.0 7,969.7 7,993.2 8,063.1 8,054.3 8,078.0 8,081 .6 8,140.9 8,160.6 8,176.1 7,347.7 7,608.7 7,550.2 7,609.4 7,611.2 7,637.2 7,643.1 7,667.3 7,736.4 7,728.2 7,751 .4 7,755.2 7,813.6 7,835.8 7,853.1 3,299.5 3,470.3 3,422.4 3,461 .2 3,449.5 3,477.5 3,480.0 3,513.5 3,572.9 3,570.5 3,584.5 3,595.9 3,633.8 3,647.9 3,660.2 2,224.2 749.7 2.393.2 754.5 2.355.0 755.5 2.385.0 757.5 2,383.9 760.3 2.398.6 758.1 2.411 .8 757.9 2.438.7 752.6 2.486.5 755.9 2.484.7 754.6 2.479.4 757.0 2.479.1 752.8 2.508.0 755.7 2.507.9 754.5 2.518.4 755.3 1,636.1 1,694.2 1,688.5 1,700.1 1,707.7 1,705.2 1,706.6 1,706.4 1,708.6 1,707.2 1.706.1 1,701 .4 1,711 .2 1,712.9 1.716.9 322.1 325.3 325.3 324.7 327.1 326.8 326.6 325.9 326.7 326.1 326.6 326.4 327.1 324.8 323.0 16,588 2, 695.1 16,954 2,766.4 16,871 2,747.3 16,913 2,754.1 16,936 2,755.1 16,963 2,765.6 17,010 2,772.3 17,019 2,773.2 17,081 2,794.0 17,108 2,797.2 17,142 2,805.5 17,178 2,825.0 17,186 2,810.3 17,209 2,812.0 17,244 2,819.1 13,892.6 14,187.3 14,123.6 14,158.5 14,180.7 14,197.8 14,237.8 14,246.1 14,287.2 14,310.7 14,336.1 14,353.2 14,375.4 14,396.6 14,424.6 4,786.4 2, 002.5 426.8 732.6 4,946.4 2,053.9 446.2 773.2 4,916.1 2, 042.0 443.5 765.3 4,929.9 2,046.4 445.8 768.5 4,941 .9 2,051 .1 446.6 771 .7 4,956.2 2,054.5 448.4 775.4 4,969.2 2,059.1 449.7 778.0 4,975.0 2, 064.5 448.7 779.5 4,996.9 2,074.2 449.5 782.7 5,006.7 2,077.7 449.8 789.2 5,017.0 2,084.3 450.3 790.7 5,027.0 2,085.3 451 .5 796.6 5,035.0 2,090.9 451 .1 796.8 5,043.1 2,092.5 452.1 799.8 5,057.3 2,101 .5 453.0 799.2 4,244.6 4,293.6 4,279.7 4,290.0 4,292.2 4,296.2 4,305.0 4,306.0 4,311.2 4,319.7 4,323.5 4,329.6 4,337.8 4,346.3 4,356.0 2,786.2 2,814.8 2,808.7 2,811 .9 2,814.4 2,818.0 2,819.8 2,825.0 2,827.2 2,827.2 2,827.9 2,827.0 2,830.0 2,830.4 2,831.5 1,579.8 2,075.4 1,575.3 2,132.5 1,574.8 2,119.1 1,575.8 2,126.7 1.576.3 2,132.2 1,576.9 2,127.4 1,576.7 2,143.8 1,576.6 2,140.1 1,576.8 2,151 .9 1,576.4 2,157.1 1.574.5 2,167.7 1,571 .5 2,169.6 1,571 .6 2,172.6 1,572.7 2,176.8 1,570.7 2,179.8 755.3 12,173 767.1 12,479 760.3 12, 443 762 12,474 767.4 12,486 770.4 12,497 776.1 12,508 767.9 12,522 772. 8 12,546 775.3 12, 571 780.4 12,589 780.5 12,611 782.5 12,650 784.6 12,674 785.9 12,732 1,812.9 1,833.0 1,833.4 1,836.6 1,834.8 1,830.9 1,831.0 1,836.2 1,834.4 1,826.4 1,811 .0 1,805.4 1,808.4 1,811 .3 1,827.1 371 .7 364.8 365.1 362.8 363.6 359.2 358.4 363.6 364.4 362.5 357.9 355.6 357.0 358.1 362.7 114.5 113.6 115.5 116.1 1,335.3 1,337.8 1,337.7 1,348.3 10,805.1 10,841 .1 10,863.1 10,905.2 Mministrative 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 .. ··· •· · .Ambulatorv health care services' ..... .... ....... Offices of physicians. Outpatient care centers .. Home health care services .. Hospitals .. Nursina and residential r.~rA f::\r.iliti~-~ 1 Nursina care faciliti es .. Social assistance' Child day care services .. Leisure and hospitality ........... Arts, entertainment, and recreation . Performing arts and spectator sports .. Museums, historical sites, zoos, and parks .. .Amusements, gambling, and recreation .. kcommodations and food services ... . · · · · · • " " ' " " ' " kcommodations .. Food services and drinking places .. Other services ... .......... ......... .. Repair and maintenance .. Personal and laundry services Membership associations and organizations .. 1 114.7 117.1 117.0 117.8 117.8 118.6 118.8 118.3 118.2 116.9 114.8 1,326.5 1,351 .1 1,351 .3 1,356.0 1,353.4 1,353.1 1,353.8 1,354.3 1,351 .8 1,347.0 1,338.3 10,359.8 10,646.0 10,609.4 10,637.1 10,650.7 10,666.1 10,676.5 10,685.3 10,712.0 10,744.1 10,778.4 1,775.4 1,795.9 1,791 .6 1,792.2 1,798.0 1,797.3 1,801 .3 1,801 .5 1,800.6 1,814.7 1,824.6 1,825.9 1,830.3 1,831 .2 1,838.0 8,584.4 5,401 1,233.6 1,263.5 8,850.1 5,431 1,227.6 1,274.1 8,817.8 5,428 1,229.5 1,275.7 8,844.9 5,434 1,229.6 1,281 .6 8,852.7 5,443 1,226.5 1,283.4 8,868.8 5,438 1,227.4 1,278.0 8,875.2 5,441 1,225.9 1,276.9 8,883.8 5,436 1,226.9 1,271 .5 8,911.4 5,434 1,227.9 1,267.8 8,929.4 5,441 1,227.1 1,271 .6 8,953.8 5,447 1,229.9 1,276.8 8,979.2 5,451 1,229.4 1,280.4 9,010.8 5,457 1,233.7 1,280.5 9,031.9 5,461 1,234.4 1,282.6 9,067.2 5,475 1,237.7 1,287.5 2,903.6 2,929.1 2,922.3 2,922.3 2,932.7 2,932.8 2,937.9 2,937.9 2,938.1 2,942.3 2,940.6 2,941 .4 2,942.9 2,943.5 2,949.3 Government. ............................... Federal .......... ..... .............. 21,583 2,761 21,618 2,728 21 ,607 2,745 21,586 2,729 21,571 2,731 21 ,586 2,726 21 ,645 2,730 21,677 2,730 21,700 2,723 21 ,706 2,728 21 ,700 2,706 21,710 2,717 21,733 2,720 21,732 2,719 21,750 2,715 Federal , except U.S. Postal Service .. ... .' ............. U.S. Postal Service ... State. ...' ......' ...... ..' ..' ..' ... ' . ' . . . Education .... Other State government ..... Local ..... Education .. . Other local government .. . 1,952.4 808.6 5,002 2,254.7 2,747.6 13,820 7,709.4 6,110.2 1,943.4 784.1 4,985 2,249.2 2,736.2 13,905 7,762.5 6,143.0 1,957.2 787.3 4,975 2,243.3 2,731 .6 13,887 7,750.7 6,136.4 1,943.2 785.8 4,967 2,233.3 2,733.2 13,890 7,752.9 6,137.3 1,946.3 785.1 4,963 2,228.2 2,734.4 13,877 7,742.5 6,134.5 1,939.2 786.4 4,976 2,241.4 2,734.4 13,884 7,757.8 6,126.6 1,945.5 784.3 4,987 2,249.4 2,737.8 13,928 7,785.7 6,142.2 1,946.8 783.4 5,000 2,263.7 2,736.4 13,947 7,793.2 6,153.4 1,940.1 782.5 5,007 2,268.4 2,738.2 13,970 7,810.8 6,159.3 1,946.4 781 .4 5,015 2,271 .3 2,743.4 13,963 7,806.3 6,156.7 1,939.5 766.4 5,020 2,277.9 2,741.9 13,974 7,810.8 6,163.1 1,937.2 780.2 5,025 2,280.4 2,744.4 13,968 7,808.8 6,159.2 1,939.8 780.1 5,027 2,283.0 2,744.4 13,986 7,820.7 6,165.1 1,939.0 780.0 5,029 2,286.3 1,935.4 2,288.8 2,743.1 13,984 2,745.2 14,001 7,814.8 6,169.2 7,823.2 6,177.5 Includes other industries not shown separately. NOTE: See "Notes on the data" for a description of the most recent benchmark revision. p = preliminary. 88 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 779.5 5,034 13. Average weekly hours of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry, monthly data seasonally adjusted 2004 Annual average Industry 2003 2004 Apr. May June July Aug. 2005 Sept. Oct. Nov. Dec. Jan. Feb. Mar.P Apr.P TOTAL PRIVATE ... ........ ............ ...... 33.7 33.7 33 .7 33.8 33.6 33.7 33.7 33 .8 33 .8 33.7 33 .7 33.7 33.7 33 .7 33 .9 GOODS-PRODUCING ..... ......... ...... ...... 39.8 40.0 40 .0 40.2 39 .9 40.1 40.0 40.1 39.9 39.9 40.0 39 .8 39.9 39 .8 40 .2 45 .5 Natural resources and mining .. ...... ... . 43.6 44 .5 44 .3 44 .2 43.9 44 .2 44.4 44 .5 44 .8 45.0 45.4 45.5 45.1 45.2 Construction ... ..... ..... .. ....... ...... ...... .. 38.4 38 .3 38 .2 38.3 38.0 38.3 38.1 38 .1 38.2 38 .3 38 .4 37,6 38 .2 38 .3 39 .0 Manufacturing ..... ...... .. .... ....... ... ....... .. Overtime hours ... ... ... ..... .... ...... 40.4 4.2 40.8 4.6 40.8 4,5 41.0 4,6 40 .7 4.5 40 .8 4.6 40.9 4.6 40.8 4.6 40,7 4.5 40.5 4.5 40.5 4.5 40.7 4.5 40 .6 4,6 40.4 4.5 40 .5 4.5 Durable goods .. ........ . ..... Overtime hours ..... .... ..... ..... .... Wood products .. ......... ....... Nonmetallic mineral products ..... ... Primary metals ... Fabricated metal products ...... .... Machinery .. Computer and electron ic products .. Electrical equipment and appliances .. Transportation equipment.. Furnitu re and related products .. Miscellaneous manufactu ring .. 40,8 4.3 40.4 42 .2 42 .3 40.7 40.8 40.4 40 .6 41 .9 38 .9 38.4 41 .3 4.7 40.6 42 .3 43,1 41 .1 41 .9 40.4 40 .7 42 .5 39 .5 38.5 41 .3 4,7 40.9 42 .3 43.2 41 .0 41 .9 40.6 40 .9 42.4 39.5 38 .4 41 .5 4.8 41.3 42 .1 43.4 41 .2 42 .2 40.7 41 .5 42 .7 40.0 38.8 41.2 4,6 40.6 41 .8 43.4 41 .0 42 .0 40.4 40.8 42 .2 39.6 38.4 41 .3 4.7 40.7 42 .2 43.2 41 .2 42 .1 40.7 40.8 42.4 39.3 38.6 41 .3 4.7 40.8 42 .3 43 .2 41.2 42 .1 40.4 40.9 42 .5 39.3 38.5 41 .2 4.7 40.4 42.4 43.1 41.2 42 .3 40.3 40.6 42.4 39 .3 38.4 41 .2 4.7 40.3 42.4 43.0 41 .1 42 .2 40.1 40.6 42 .3 39.2 38.4 40.9 4.6 40,0 42 .1 42 .9 40.9 42 .0 39.6 40.1 42 .2 39 .2 38.2 41 .1 4.6 40,3 42 .3 42 .8 40.9 42 .0 39 .8 40.0 42 .4 39 .5 38.3 41.1 4.6 40.6 41 .9 43.1 40.9 42 .0 40.0 40.1 42.4 39.5 38.5 41 .0 4,7 39.9 42 .1 43.0 40.8 42 .0 39 .6 40.0 42.4 39.4 38 .6 40.8 4.5 39.6 41 ,7 42 .9 40,7 42 .0 39 .4 40.2 41 .9 39 .5 38 .9 40 .5 4.6 39 .5 41 ,9 42 .6 40.8 42.2 39.6 40.6 42 .1 39.3 38.9 Nondurable goods ....... . . . . . . . . . . . . . . . . . . . . .. . Overtime hours ......................... Food manufacturin g .. 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 ... 39.8 4.1 39 .3 39 .1 39.1 39 .6 35 .6 39.3 41 .5 40 ,0 4.4 39.3 39.2 40 .1 38 .9 36 .0 40 .3 4.4 39 .6 39.2 40.2 38 .7 36 .2 38 .4 42 .6 40.1 4.4 39.4 38.6 40.3 38.9 35.9 38.3 41 .9 40.1 4.4 39.3 38.9 40,5 38.6 36.0 37.8 42.4 40.2 4.5 39.3 39.4 40.5 38.8 36.2 38.1 42 .5 40 .1 4.4 39 .3 39 .2 40 .2 39.1 36.2 39 .9 4.3 39.0 38 .6 40.1 39.1 36 .0 38.4 42 .1 40 .0 4.3 39 .2 39 .8 39 .7 38.4 36 .0 38 .9 42 .0 38.2 42 .2 38.4 42. 1 39.8 4.3 39.1 39.0 40.0 39.1 35.7 38.2 42 .1 39.8 4.3 38 .8 39.6 39.8 39.0 35.9 37 .6 42 .0 40.0 4.4 39.0 40.5 40.2 39.5 35.9 37.1 42 .5 40.0 4,5 39 .3 40 .2 39.7 39 .5 35.9 37.2 42 .1 39 .7 4.4 38 .8 40 .6 40 .1 39 .6 36 .0 37.1 41 ,9 39.9 4,3 39.1 40.5 40.1 39.5 36.2 37.4 42 .0 38.2 44.5 42.4 40.4 38.4 44 .9 42 .8 40.4 38.4 44 .5 43.0 40.8 38 .6 45 .0 42 .9 40.9 38.5 44 .9 42 .6 40.8 38.6 45 ,0 42 .8 40 .5 38.5 45 .9 42 .9 40 .5 38.3 46,0 42 .8 40.3 38.3 45 .0 42 .7 40,1 38 .3 45 .5 42.4 39.4 38 .5 44 .6 42.6 39.8 38.6 44 .5 42 .8 40,0 38.5 44 .7 42. 3 40.1 38.3 45.1 ~2.2 39.8 38.4 46 .4 42 .4 39.7 32 .4 32 .3 32.4 32.4 32.2 32.4 32.4 32.5 32.4 32 .3 32.4 32.4 32.4 32.4 32 .5 33.5 37 ,7 , ,. PRIVATE SERVICEPROVIDING ....... ... ...... ... .. ........ ..... Trade, transportation, and utilities ... ..... ..... .... ....... .... ..... ..... .... .... . Wholesale trade ...... Retail trade .. Transportation and wareh ousing ... .... . Utilities .. Information ........ .. .... ................. ....... Financial activities ... ..... ......... ...... ... .. Professional and business services ... ........ .. ... .... ...... ..... .... ...... Education and health services .. ....... ... Leisure and hospitality .. ..... ......... .. ... . Other services .. ... .... .. ... ......... ...... .. .... ... 1 33 .6 33.5 33 .6 33.6 33.2 33.4 33.5 33 .6 33.6 33.5 33.6 33.6 33 .6 37 .9 30 .9 36.8 41 .1 37.8 30.7 37.2 40.9 38 .0 30.8 37 .1 37.6 30.4 36 .9 41 .1 37 .8 30 .6 37.2 40 ,9 37.7 30 .7 37,2 40 .9 36 .2 35.5 36 .3 35.5 41.2 36 .3 35 .6 37.8 30.8 37 .3 41 .3 36 .3 35.8 36.5 35.5 36 .3 35 .6 36.4 35.5 37 .8 30.8 37 .5 41.4 36 .3 37.7 30.8 37.5 40.8 36.3 35.7 37.7 30.6 37.5 40.4 36.2 35.6 37.6 30.8 37.4 40.7 36.4 35.7 37.7 30.7 37.5 41 .0 36.3 35.9 37 .8 30.8 37 .3 40.5 36.4 35.8 34.1 32 .3 34 .2 32.4 34 .2 32.4 34 .2 32.4 34 .0 32.4 34.2 32 .6 34.3 32 .5 34 .7 32 .5 34.3 34 .2 32.4 34 .2 32 .5 34.1 32 .6 32 .6 34 .1 32 .6 34 .2 32 .7 25.6 31 .4 25 .7 31.0 25 .7 31.1 25. 7 31.1 25.7 30.9 25 .6 31 .0 25 .6 31.0 25 .6 31.0 25.6 30.9 25.7 30.8 25.6 30.9 25 .7 30.9 25 .7 31.0 25.7 31 .1 Data relate to production workers in natural resou rces and mining and manufacturing, construction workers in construction , and nonsupervisory workers in revision . the service-providing industries. p = preliminary. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 35 .5 32 .5 25 .7 30 .9 34 .0 30 .7 37 .2 40.3 36.4 35.9 33.6 37 .8 30 .8 37.4 41 ,1 36.4 36.1 NOTE: See "Notes on the data" for a description of the most recent benchmark Monthly Labor Review June 2005 89 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 2005 2004 Annual average Industry 2003 2004 May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.P Apr.P Current dollars ................ .. .. .. . Constant (1982) dollars ····· ··· · ·· ·· $15.35 8.27 $15.67 8.23 $15.62 8.21 $15.64 8.20 $15.70 $15.74 S,15.77 $15 .81 $15.82 8.25 8.25 8.22 8.21 $15.85 8.23 $15.90 8.23 8.24 $15.91 8.22 $15.95 8.19 $16.00 8.16 GOODS-PRODUCING .............................. 16.80 17.19 17.13 17.16 17.19 17.24 17.30 17.32 17.33 17.36 17.35 17.43 17.44 17.50 Natural resources and mining ............. Construction .. .... ........... ... ...... .... .... .. .... . 17.56 18.95 18.08 19.23 18.02 19.19 18.16 19.19 18.08 19.21 18.05 19.25 18.06 18.10 19.34 18.22 Manufacturing .. ... .................................. 15.74 14.96 16.45 16.14 15.29 16.08 15.23 16.12 15.28 16.16 15.30 16.22 15.36 18.37 19.29 16.34 18.40 19.31 16.42 15.54 18.27 19.35 16.42 18.53 19.38 16.45 14.63 16.82 15.05 16.75 1502 16.77 15.07 16.83 15.09 16.90 15.14 15.42 16.98 15.18 18.43 19.24 16.37 15.51 17.10 15.18 17.18 15.19 15.55 17.16 15.21 15.58 17.21 15.21 14.96 15.26 15.21 15.24 15.30 15.34 Utilities .. Information ........... .. .... ........... .. .. ......... ... Financial activities .... ... ......................... 14.34 17.36 11 .90 16.25 24 .77 21 .01 17.14 14.59 17.66 12 08 16.53 25.62 21.42 17.53 14.54 17.60 12.04 16.51 25.51 21.43 17.47 14.59 17.66 12.07 16.54 25.48 2 1.28 17.49 14.63 17.71 12.10 16.58 25.60 21.4, Professional and business services ........ ....................................... 17.21 17.46 17.40 15.64 8.76 13.84 16.16 8.91 13.98 16 09 8.87 13.95 TOT AL PAIVA TE Excluding overtime .. .. ... , .. , ... .. ···· ·· Durable goods .................... .... ... .. Nondurable goods ... . ... .. . . .. ·· ····· ··· 16.27 15.42 19.31 16.29 15.43 16.97 15.15 16.99 15.16 15.48 17.06 15.16 15.36 15.40 15.42 15.45 15.51 15.51 15.56 15.60 14.66 17.73 12.16 16.53 25.82 21 .62 17.64 14.69 17.78 12.16 16.61 26 .00 21 .59 17.71 14.70 17.80 12.20 16.54 25 .77 21 .58 17.65 14.72 17.87 12.21 16.54 26.1 1 21 .70 17.71 14.82 17.91 12.32 16.58 26.23 21 .80 17.71 14.79 17.95 12.29 16.52 26.04 21 .67 14.84 14.87 18.04 12.34 16.63 26.32 21.82 17.55 14.65 17.69 12.13 16.65 25 .66 2 1.52 17.57 17.74 17.80 16.63 26.33 22.09 17.86 17.43 17.48 17.59 17.54 17.63 17.66 17.69 17.79 17.80 17.83 17.90 16.15 8.86 13.97 16.24 8.89 13.98 16.24 8.91 14.00 16.28 8.95 14.05 16.31 8.99 14.08 16.34 16.37 9.01 14.13 16.40 9.03 16.45 9.02 14.12 16.51 9.05 14.16 16.51 9.10 14.14 19.27 16.29 PRIVATE SERVICEPROVIDING ........................................ Trade,transportation , and utilities .. ...................................... Wholesale trade ............. .. ... . ... . .. ..... Retail trade .. Transportation and warehousing .... Education and health services ......... .... ............ ..... .. ... .... ... ... .. Leisure and hospitality ............ ..... ........ Other services .. ... .. ... .... .... ... ..... ... ..... ... .. 1 Data relate to producti on workers in natural res ources and min ing and manufacturing , construction workers in construction, and nonsupervisory workers in the se rvice-providing industries. Monthly Labor Review 90 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 14.15 9.05 14.17 17.99 12.31 NOTE : See "Notes on the data" for a description of the most recent benchmark revision . p = preliminary. 15. Average hourly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry Annual average Industry TOTAL PRIVATE .. .. .... ...... ... ..... .... . Seasonally adjusted .. 2003 2004 2004 $15.35 $15.67 15.47 - 2005 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.P Apr.P $15 .59 15.58 $ 15.63 15.62 $ 15.56 15.64 $ 15.59 15.70 $15 .66 15.74 $15 .79 15.77 $15 .82 15.81 $15.84 15.82 $15 .88 15.85 $16 .00 15.90 $15 .96 15.91 $15.95 15.95 $ 16.00 16.00 GOODS-PRODUCING ..... .... ...... ... ...... .. .... 16.80 17.19 17.08 17.10 17.14 17.18 17.28 17.40 17.39 17.37 17.43 17. 31 17.34 17.36 17.46 Natural resources and mining .... .... .. . 17.56 18.08 18 07 18.00 18.12 1802 17.95 17.97 18 07 18.21 18.46 18.53 18.45 18.36 18.64 Construction ... ...... ..... ......... .. ........ .. .... 18.95 19.23 19.15 19.15 19.12 19.24 19.33 19.42 19.47 19.35 19.31 19.12 19.20 19.25 19.33 Manufacturing ....... ......... ... ...... ... ... .. 15.74 16.14 16.06 16.04 16.08 16.03 16.1 6 16.35 16.26 16.32 16.46 16.42 16.43 16.40 16.43 Durable goods ... ........ ·· ····· Wood products . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . Nonmetal lic mineral products ····· ·· ·· Primary metals . ...... Fabricated metal products ........... ... Mach inery . ....... Computer and electronic products . Electrical equipment and appliances Transportat ion equipment . Furniture and related products . .... ... Miscellaneous manufacturing .. ...... .. 16.45 12.71 15.76 18.13 15.01 16.30 16.69 14.36 21 .23 12.98 13.30 16.82 13.03 16.25 18.57 15.31 16.68 17.28 14.90 21.49 13.16 13.85 16.71 13.00 16.17 18.51 15.21 16.54 17.02 14.84 16.73 12.99 16.22 18.50 15.23 16.56 17.22 14.92 21 .31 13.11 13.82 16.60 16.84 13.04 1302.00 16.37 16.28 18.65 18.57 15.27 15.27 16.68 16.72 17.30 17.38 14.92 15.04 20 .73 21.49 13.12 13.28 13.90 13.88 17 06 13.14 16.51 18.89 15.43 16.85 17.48 15.08 21 .91 13.39 13.97 16.98 13.03 16.38 18.73 15.38 16.84 17.52 1505 21 .78 13.27 13.92 17.04 13.13 16.45 18.66 15.43 16.85 17.65 15.10 21 .91 13.29 13.96 17.22 13.17 16.36 18.75 15.59 16.99 17.92 15.12 22 .17 13.46 14 .05 17.15 13.13 16.27 18.84 15.55 17.03 1804 15.07 21 .31 13.10 13.71 16.70 13.04 16.16 18.47 15.20 16.54 17.13 14.86 21 .25 13.05 13.76 21 .90 13.42 14.07 17.20 1304 16.20 18.78 15.67 17.02 18.04 15.15 21 .97 13.34 14 .04 17.15 13.10 16.30 18.73 15.63 17 06 17.95 15.12 21 .83 13.37 1402 17.18 13.14 16.73 18.74 15.61 17.07 18.13 15.12 21 .73 13.48 13.97 Nondurable goo ds ........ ... ........ .. ... Food manufacturing . Beverag es and tobacco products . 14.63 12.80 17. 96 15.05 12.98 19.12 15.00 12.98 19.57 14.97 12.96 19.51 15.03 13.01 19.37 15.13 13.07 19.26 1508 13.00 19 08 15.23 1309 19.17 15.11 12.94 19.18 15.16 12.99 18.80 15.21 1303 18.82 15.24 13.07 18.44 15.17 13.07 18.65 15.18 13.01 18.95 15.19 12.99 19.34 Textile mills . Textile product mi lls Apparel ..... ....................................... 12.13 11 .39 9.75 12.14 11 .27 9.60 12 06 11.45 9.73 12 08 11 .43 9.72 11 .48 17.93 15.52 24.39 19.00 14.54 11 .58 17.91 15.56 24.22 19.16 14.59 11 .67 17.96 15.73 24 .32 19.31 14.69 11 .67 17.89 15.88 24.05 19.24 14 .66 12.25 11.49 9.93 11 .56 18.21 15.96 24.44 19.44 14.75 12.11 11.42 9.97 11 .58 17.93 15.95 24 .33 19.42 14.55 12.09 11.44 10.00 11 .62 18.09 15.93 24 .71 19.44 14 .58 12.25 11.43 10.00 11 .51 18.07 15.80 24.48 19.59 14.76 12.33 11 .31 10.15 11 .60 18.00 15.77 24 .75 19.52 14.81 12.25 11.48 10.19 11 .63 17.90 15.72 24.38 19.16 14.58 12.22 11 .30 9.69 11 .64 17.89 15.55 24.45 18.96 14.58 12.07 11 .27 9.54 Leather and alli ed products · ···· ··· ·· · Paper and paper products . Printing and related support activitiei Petroleum and coal products . Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Plastics and rubber products . ...... 11 .99 11 .23 9.56 11 .66 17.33 15.37 23.63 18.50 14.18 15.79 24 .74 19.32 14.65 12.24 11 .56 10.06 11.48 17.92 15.70 24 .81 19.47 14.69 15.58 24 .11 19.58 14.75 PRIVATE SERVICEPROVIDING ...... .... ............. ........ ..... 14.96 15.26 15.19 15.23 15.13 15.16 15.22 15.35 15.40 15.43 15.46 15.66 15.60 15.59 15.62 utilities ... ...... ... .............. ... ..... ... .... .... .. Wholesale trade 14.34 17.36 14.59 17.66 14.58 14.55 17.57 14.56 17.65 14.69 17.75 14 .67 17.82 14.86 17.99 16.47 25 .72 12.07 16.53 25 .34 16.62 25 .36 12 .17 16.59 25.55 12.05 16.58 25 .45 25.89 26 .02 12 .16 16.56 26 01 14.87 17.92 12.35 16.62 14.92 18.05 12.08 16.53 25.62 14.61 17. 87 12.10 14.88 11 .90 16.25 24.77 14.58 17.68 12 .07 14.69 Retail trade . Tran sportat ion and warehousing . Uti lities .................................. ....... 14.57 17.59 12.07 21 01 21.42 21 .23 2 1.40 21 .16 21 .29 2 1.43 21 .73 21.69 17.14 17.53 17.46 17.64 17.40 17.46 17.59 17.62 11.42 17. 86 12.28 11 .52 10.06 11 .45 17.94 Trade, transportation , and Financial activities ....... ......... .. .... .. ..... 17.66 12.06 16.45 17.71 12.21 16.51 16.59 26 .00 18.03 12.34 16.59 26.14 12.35 16.57 25.98 26 .36 12.40 16.62 26 .39 2 1.70 21 .74 21 .83 21 .67 21.71 22 .04 17.68 17.61 17.67 17.83 17.73 17.75 17.87 Professional and business services .. .... ... .... .......... .... ... ... ..... .. 17.21 17.46 17.30 17.48 17.31 17.35 17.50 17.47 17.54 17.62 17.73 18.06 17.91 17.84 17.87 Education and health services .. .... ......... ..... ..... .... ... ... .... 15.64 16.16 16.04 16.05 16.10 16.23 16.20 16.30 16.30 16.33 16.44 16.47 16.46 16.50 16.51 Leisure and hospitality ........ ....... ..... 8.76 8.91 8.85 8.86 8.79 8.79 881 8.94 9.02 9.06 9.11 9.11 909 9.07 9.10 Other services .... ...... .... .... ... ... ... ........ 13.84 13.98 13.97 14.00 13.92 13.88 13.93 14.06 14.06 14.12 14.17 14.23 14.23 14.18 14.16 1 Data rel ate to production workers in natural resources and mining and manufacturing , constructi on workers in construction , and nonsupervisory workers in the service-providing industries. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis NOTE: See "Notes on the data" for a description of the most recent benchmark revision . p = prel iminary. Monthly Labor Review June 2005 91 Current Labor Statistics: Labor Force Data 16. Average weekly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry Annual average Industry 2003 TOTAL PRIVATE ........ ......... .. . $517.30 Seasonally adjusted .. . GOODS-PRODUCING ... .... ...... ... Natural resources and mining ... 2004 2005 2004 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.P Apr.P $528.56 $531.42 527.96 $524.37 525.50 $528.50 529.09 $535.57 530.44 $530.54 533.03 $534.72 534.38 $532.22 533. 13 $536.74 534. 15 $537.60 535.83 $534.66 536.17 $534. 33 537.52 $537.60 542.40 689.03 687.20 696.38 690.78 697.34 694.80 702.43 683.75 683.20 687.46 696.65 - - $522.27 525.05 669. 13 688.03 678. 08 689. 13 765.94 804.03 793.27 797.40 806.34 801 .89 804.16 796.07 820.38 824.91 836.24 833.85 822.87 822.53 842.53 726.83 735.70 721.96 741 .11 736.12 752.28 755.80 730.19 753.49 739.17 737.64 703.62 712.32 727.65 748.07 Manufacturing ......................... 635.99 • ·· Construction .......... .......... ... ... Durable goods ······· ·· · ·· ·· · · ·· Wood products Nonmetallic mineral products ... Primary metals .. Fabricated metal products ... Machinery .. Computer and electronic products .... Electrical equipment and appliances ... Transportati on equipment.. .... Furniture and related products .. Miscellaneous manufacturing .. . . . . . . . . . . . . . . 652.04 659.24 659.28 646.01 660.94 663.81 661.78 665.86 678.15 666.65 663.77 662.56 662.13 694.16 686.78 694.72 694.30 673.96 695.49 697.75 699.58 702.05 718.07 703. 15 703.48 699.72 699.23 514.10 664.92 767. 60 610.37 664.79 529.46 688.05 799.77 628.80 699.51 530. 40 683.99 799.63 620.57 688.06 545.07 683.57 803.45 627.76 699.64 535.19 689.35 808.45 627.48 698.83 532.03 694.09 788.90 621.49 692.22 539.03 700.04 796.65 627.60 697.22 521 .66 709.93 808.49 628.00 699.28 526.41 701.06 801 .64 633.66 707.28 526.51 694.19 802.38 634.17 7 11 .07 532.07 688.76 813.75 648.54 727. 17 527.83 665.44 815.77 637.55 718.67 511 .17 667.44 807.54 637.77 716.54 513.52 669.93 805.39 634.58 718.23 516.40 700.99 796.45 633.77 715.23 674.72 698.28 684.20 695.48 699.13 695.46 700.41 700.95 704.30 706.00 723.97 716.19 712.58 709.03 710.70 583.23 889.48 606.64 912.97 601.02 901 .41 615.20 911 .63 613.21 907.81 602.77 839.57 613.63 909.03 603.20 926.79 614.04 923.47 613.06 926.79 616.90 962.18 605.81 926.37 601 .46 933.73 604.80 919.04 609.34 910.49 505.30 519.78 517. 45 518.09 521.78 515.62 529.87 519.53 516.20 523.63 546.48 528.75 522.93 526.78 528.42 540.64 510.82 533.47 525.09 535.26 530.69 528.20 534.38 530.86 534.53 536.06 545.14 543.10 543.35 548.18 582.6 1 602.48 595.50 601 .79 604.21 602.17 606.22 610.72 602.89 607.92 612.96 608.08 600.73 601 .13 601 .52 502.92 509.66 498.43 511.92 512.59 513.65 514.80 520.98 508.54 515.70 513.38 505.81 505.81 496.98 498.82 702.45 469.33 444.70 340. 12 457.83 719.73 750.5 1 486.69 443.01 351.28 446.73 753.89 778.89 483.91 433.92 347.40 459.78 747.80 772. 60 486.42 433.90 346.30 440.83 758.44 759.30 490.46 444.04 348.48 442.36 750.43 758. 84 481.19 433.96 348.33 422.45 752.52 761.29 489.24 442.34 352.84 441 .13 756.75 762.97 488.78 444.66 352.52 430.03 772.10 734.59 481 .98 447.66 357.92 445.83 756.65 731.32 483.60 448.45 360.00 445.05 768.83 737.74 491 .23 451 .49 364.00 437. 38 775.20 735.76 498.13 445.61 361 .34 429.20 768.60 738.54 485.10 450.02 363.78 425.97 744.76 757.20 496.94 457.78 365.18 431.65 745.47 791 .01 491 .20 453.89 365.18 436.12 749.89 587.58 604.32 594.01 594.42 594.39 600.89 611.38 612.86 614.08 618.08 616.20 607.15 604.76 602.88 593.60 1,052.32 783.95 1,094.83 819.59 1,061.13 811.49 1,090.23 813.20 1,094.74 818.13 1,118.72 814.88 1,096.68 821 .55 1,119.35 830.09 1 097.28 825.35 1,131.72 830.09 1,099.15 844.33 1,096.43 835.46 1,100.93 817.24 1,106.53 821 .63 1,097. 01 826.28 872.26 589.70 594.86 594.69 599.65 583.19 590.80 591.48 583.46 578.83 596.30 592.40 586.00 584.66 585.58 483.89 493.67 487.60 496.50 488.70 492.70 499.22 495.81 498.96 496.85 500.90 507.38 502.32 502.00 504.53 481.14 488.58 485.18 491.35 487.43 492.13 495.72 493.58 492.12 488.51 490.90 494.02 493.35 493.68 496.84 657.29 367. 15 666.93 371.15 664.90 674.61 660.63 673.61 665.90 669.18 671 .81 670.13 681 .53 674.25 672.00 366.93 371.45 371.76 665.41 375.96 377.79 377.29 373.62 368.45 375. 10 372.67 374.21 374.21 678.68 378.20 Nondurable goods ... Food 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 . . . . . . . . . . . . . . . . . . ..... Plastics and rubber products . . . . . . . . . . . . . . . . . ··· •·• ·· 658.53 671.2 1 PRIVATE SERVICEPROVIDING .... .. ..... ..................... Trade, transportation, and utilities .. .. .... ..... ...... ...... . Wholesale trade .. ... . ... .. . . Retai l trade .. Transportation and 598.4 1 614.90 602.80 616.88 611 .61 616.78 628.24 617.47 622.13 622.66 625.44 620.47 608.12 611 .62 613.28 1,017.27 1,048.82 1,054.52 1,055.22 1,044.01 1,033.27 1,032.15 1,074.44 1,066.82 1,061.21 1,053.00 1,066.51 1,052.19 1,057.04 1,081 .99 Information .... ... .......... .. ......... 760.81 777.42 762. 16 776.82 774.46 772.83 788.62 786.63 787.35 787.71 791.34 798.98 786.62 783.73 793.44 Financial activities .. ........ ... ..... 609.08 622.99 616.34 636.80 614.22 618.08 635.00 620.22 627.64 625.16 627.29 649.01 632.96 631.90 639.75 Professional and business services .... .... ......... 587.02 596.96 589.93 604.81 590.27 591 .64 607.25 593.98 599.87 602.60 604.59 614.04 607.15 604.78 609.37 warehousing .. Utilities . .......... ... . .. 1 .. .. .. Education and health services .... .... ..... ......... 505.69 523.83 516.49 521 .63 520.03 529.10 531 .36 528.12 528.12 529.09 534.30 541.86 534.95 534.60 536.58 Leisure and hospitality .... ... ..... 224.30 228.63 224.79 229.47 227.66 231. 18 234.35 226.18 230.91 229.22 231 .39 230.48 231 .80 230.38 232.05 Other services .... ...... ......... ..... 434.41 433.04 430.28 436.80 430.13 431 .67 436.01 433.05 434.45 434.90 436.44 439.71 438.28 436.74 437.54 Data relate to producti on workers in natural resources and mining and manufacturing, NOTE: See "Notes on the data" for a description of the most recent benchmark revision. constructi on workers in construction, and nonsupervisory workers in the service- Dash indicates data not available. providing industries. p = preliminary. 92 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 17. Diffusion indexes of employment change, seasonally adjusted [In percent] Ti mespan and year Jan. Feb. Mar. Apr. May June July Aug . Sept. Oct. Nov. Dec. Private nonfarm payrolls, 278 industries Over 1-month span: 2001.. 2002 .. 2003 2004 2005 .. ······ ·· ······•··· Over 3-month span: 2001.. 2002 2003 2004 47 .7 41.0 44.4 50.9 54.1 35.6 38.7 53.4 61 .2 48.6 39.7 35.3 66 .0 55.8 32 .7 39.2 41.4 67 .3 61 .3 42.4 40 .5 39.4 64 .6 53.2 49 .8 37 .9 49.8 36.5 42 .3 38 .1 34.4 38.3 35.4 52 .5 58.5 53.8 60.3 33.3 56.7 65.1 33.5 69.4 64 .9 35.3 2005 Over 6-month span : 2001.. 2002 2003 2004 ... 2005 ..... Over 12-month span: 2001.. 2002 . . . . . . . . . . . . . . . . . . . . . . . 2003 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2004 . ... ... ... ... .. . ... ... . .. 2005 49.5 ···· ··· ·············•··· · 34.2 40.8 47 .7 36.7 42 .8 39 .0 43 .0 39.9 59.7 42 .1 55.4 39.4 53.8 34 .2 37 .8 40.6 37.6 42 .1 50.4 57.6 33.6 39.0 48 .9 58.6 44.1 34 .7 37 .8 35.4 37 .1 43.2 57.4 46.4 59.9 33 .5 34 .2 36 .0 37.6 36.5 39.4 41 .7 75.4 71 .2 63 .5 37.4 56.8 37 .8 53.1 50.9 29.9 52 .0 32 .0 45.5 31 .7 43.0 29.5 30.9 39 .7 37.4 38.5 37.1 33.6 38 .7 32 .7 47.3 60.3 32 .2 50.4 62 .8 31 .3 54 .9 63 .1 31 .3 62 .6 60 .3 33.1 64 .4 37 .6 69.6 33.6 67 .3 32 .2 68.9 59 .5 33 .6 34 .5 40.3 61 .2 59 .5 31.7 31 .5 53.4 30.2 32 .9 44 .8 49.3 30.4 33.5 48.7 63.7 65.1 42 .1 64 .7 48.6 45 .0 43 .3 30.2 34.2 52 .0 29 .1 32 .0 43 .9 31 .3 35 .1 32 .7 57.4 33 .1 57 .6 56.7 35 .3 40.3 64 .6 39.9 30.0 37 .1 60.3 43 .7 62 .2 37 .8 29.5 36 .7 62 .1 36.9 41 .5 50.0 54 .7 30.8 35.8 48 .6 37 .1 35 .1 50 .5 54 .3 32 .0 36 .7 59 .7 50.2 56.3 33.6 37.9 46.4 59 .7 35 .1 49 .3 55.9 37 .1 32.9 37.2 64 .6 30 .9 34.9 34 .7 39 .2 64 .0 Manufacturing payrolls, 84 indu stries Over 1-month span : 2001.. 2002 . 2003 .. 2004 . .. . . . . . . . .. . . . . . . . . 2005 .... .... . ... .... ...... ... ... Over 3-month span : 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2002 ... 2003 .. 2004 .... . ... ... ... .. .. .. .. 2005 .... Over 6-month span: 2001 .. 2002 2003 2004 2005 .. ············ ········· ·· Over 12-month span: 2001 . . . . . . . . . . . . . . . . . . . . . . . . . . 2002 2003 2004 .. 2005 22.0 17.3 22 .0 17.9 16.1 22 .6 13.1 15.5 18.5 17.3 14 .9 11 .9 19.0 35.1 39.3 42 .3 19.6 19.0 49.4 44.6 22.0 19.0 50.0 41 .1 32 .1 11 .9 65.5 50.0 26 .2 35.7 23 .2 24.4 48 .8 28 .6 32 .7 42 .9 15.5 19.6 60.1 31 .0 20.8 51.8 35.1 42 .3 18.5 39.9 46.4 16.7 42 .9 44.6 32 .7 10.7 16.1 20 .8 11 .9 14.3 16.7 14.3 14 .9 10.7 11 .9 9.5 20.2 10.7 7.7 20.2 69.0 69.6 15.5 53.6 18.5 52.4 12.5 13.7 27 .4 44 .6 11 .3 8.9 31 .5 45.2 9.5 9.5 35.1 35.7 45.2 43 .5 42 .9 25.6 14.3 62.5 23.8 42 .3 50.6 14.3 17.9 8.9 58.3 47.6 11 .9 11.3 22 .6 24 .4 6.0 12.5 27.4 43.5 8.3 10.1 29.8 44 .0 21 .4 8.3 7.1 33.3 43.5 19.6 9.5 8.3 47.0 38.7 14.3 7.1 11 .3 52 .4 11 .9 13.1 13.1 12.5 11 .3 11 .3 10.7 57.1 4.8 60.1 10.1 58 .9 10.7 14.3 13.1 58.9 7.1 8.3 16.7 50.6 7.7 8.3 19.6 45.2 5.4 7.7 26.8 42 .9 29.8 7.1 10.7 13.1 45.2 32 .1 6.0 6.0 14.3 45 .8 20.8 6.0 6.5 13.1 46.4 19.0 6.5 6.0 19.0 46 .4 13.1 7.1 8.3 25.6 12.5 3.6 10.7 4.8 11 .9 6.0 7.1 34.5 7.1 43.5 8.3 40 .5 11 .9 4.8 10.7 45.8 10.1 7.1 10.7 48.2 8.3 4.8 9.5 49.4 6.0 8. 3 10.7 46.4 12.5 42 .9 NOTE : Figures are the percent of industries with employment increasi ng plus one-half of the industries with unchanged employment, where 50 percent indicates an equal balance between industries with increasing and decreasing employment. 22 .6 60.7 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 June 2005 93 Current Labor Statistics: Labor Force Data 18. Job openings levels and rates by industry and region, seasonally adjuste d 1 Levels (in thousands) 2004 Industry and region Oct. TotaI 2 .. .... ... .. .... . . ..... . ... ... ... . Nov. Percent 2005 Dec. Jan. Feb. 2004 Mar. 3,300 3,277 3,507 3,385 3,569 2,924 Apr.P Oct. Nov. 2005 Dec Jan. Feb. Mar. Apr.P 3,598 3,664 2.4 2.4 2.6 2.5 2.6 2.6 2.7 Industry . Total private 2 ............ . ... . . .. .. ....... 2,910 3,106 3,020 3,160 3,212 3,267 2.6 2.6 2.7 2.7 2.8 2.8 2.8 Construction .. ........ ...... .. .... ······" Manufacturing .. 114 118 132 127 133 170 112 1.6 1.6 1.8 1.8 1.8 2.3 1.5 250 248 266 252 252 258 253 1.7 1.7 1.8 1.7 1.7 1.8 1.7 Trade, transportation, and utilities .... 559 554 561 564 668 624 644 2.1 2.1 2.1 2.2 2.5 2.4 2.4 Professional and business services .. 602 620 699 682 607 646 765 3.5 3.6 4.0 3.9 3.5 3.7 4.3 Education and health services ... 547 543 557 560 602 616 617 3.1 3.1 3.1 3.2 3.4 3.5 3.5 Leisure and hospitality .. 413 411 450 434 447 440 430 3.2 3.2 3.4 3.3 3.4 3.4 3.3 400 369 396 346 404 383 395 1.8 1.7 1.8 1.6 1.8 1.7 1.8 2.4 . . .. . .. ...... .. . . .. . . Government ... Region 3 .... ... ...... ........ 562 560 620 602 606 615 621 2.2 2.2 2.4 2.3 2.3 2.4 1,318 1,250 1,329 1,342 1,399 1,447 1,501 2.7 2.6 2.8 2.8 2.9 3.0 3.1 Midwest.. .... . ... ... .. .... .. . ·· · · ······ 688 726 740 716 745 737 716 2.1 2.3 2.3 2.2 2.3 2.3 2.2 West .. 742 759 792 718 823 806 818 2.5 2.6 2.7 2.4 2.8 2.7 2.7 Northeast.. . South . . . .. . . . .. . . .. .. .. .. . .. . .. . .. . .. . ... .. ....... . ... . ... .. .. West Virginia; Detail will not necessarily add to totals because of the independent seasonal Illinois, M idwest: Indiana, Iowa, Kansas, Michigan , Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin ; West: Alaska, Arizona, adjustment of the various series. California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico , Oregon, Utah , Includes natural resources and mining , information, financial activities, and other Washington , Wyoming. services, not shown separately. NOTE: The job openings level is the number of job openings on the last business day of Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New Yo rk, Pennsylvania, Rhode Island, Vermont; the month; the job openings rate is the number of job openings on the last business day of South: Alabama, Arkansas, the month as a percent of total employment plus job openings. Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, P Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, = preliminary. 19. Hires levels a nd rates by industry and region, seasonally adjusted 1 Levels (in thousands) 2004 Industry and reg ion Oct. Total 2 . ... .... .... . . .. .. . .... ······•·· Nov. Percent 2004 2005 Dec Jan. Mar. Feb. Apr.P Oct. Nov. 2005 Dec Jan. Feb. Mar. Apr.P 4,552 4,990 4,639 4,709 4,760 4,841 4,507 3.4 3.8 3.5 3.6 3.6 3.6 3.4 3.7 Industry Total private 2 .. . 4,216 4,652 4,337 4,374 4,430 4,497 4,174 3.8 4.2 3.9 3.9 4.0 4.0 Construction .. .. . ... . . .. . . ... .... ... . ..... 353 373 368 339 430 414 433 5.0 5.3 5.2 4.8 6.0 5.8 6.0 Manufacturing .. 353 386 324 307 336 334 318 2.5 2.7 2.3 2.1 2.3 2.3 2.2 Trade, transportation , and utilities .. 977 1,077 986 1,056 1,055 1,047 988 3.8 4.2 3.8 4.1 4.1 4.1 3.8 Professional and business services .. 812 935 878 882 853 895 815 4.9 5.6 5.3 5.3 5.1 5.3 4.8 Education and health services .. ... . . ... ... ... ... ... .. .. . Leisure and hospitality Government.. .. ....... Reg ion . 420 447 452 445 500 472 483 2.5 2.6 2.6 2.6 2.9 2.7 2.8 .. .. . ... ... ... .. 801 858 834 826 771 798 693 6.4 6.8 6.6 6.6 6.1 6.3 5.4 .... .. ..... ... ... .. 318 335 307 341 329 336 325 1.5 1.5 1.4 1.6 1.5 1.5 1.5 . 3 811 851 858 762 820 856 838 3.2 3.4 3.4 3.0 3.2 3.4 3.3 1,903 1,770 1,880 1,867 1,922 1,739 3.9 4.1 3.8 4.0 4.0 4.1 3.7 ..................... ....... ... ... 1,013 1,149 1,043 1,092 1,081 1,034 973 3.2 3.7 3.3 3.5 3.5 3.3 3.1 West ·· ······ ········ ···· ···· ······ ···· ··· ··· 916 1,014 970 959 1,069 1,036 1,030 3.2 3.5 3.4 3.3 3.7 3.6 3.5 Midwest: Illinois, Midwest . 1 .... .... .. 1,809 Northeast .. .. ... . .. ·· ·· ····· South .. .... ..... ·····•· ·· ········· Detai l will not necessarily add to totals because of the independent seasonal Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, adjustment of the various series. Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona, 2 California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon , Utah , Includes natural resources and mining, information, financial activities, and other Washington , Wyoming. services, not shown separately. 3 Northea st: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont; South : Alabama, Arkansas , Delaware, NOTE : The hires level is the number of hires during the entire month ; the hires rate is District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi , the number of hires during the eniire month as a percent of total employment. North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia; 94 Mo nthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 P = oreliminarv. 20. Total separations levels and rates by industry and region, seasonally adjusted 1 Levels (in thousands) Industry and region 2004 Oct. Tota1 2 .... .... ..... ............. · ·· ······"•"·" Nov. Percent 2005 Dec. Jan. Feb. 2004 Mar. Apr.P 4,215 4,266 4,435 4,352 4,295 4,502 4,588 3,957 3,996 4,146 4,091 4,035 4,237 4,331 425 351 355 417 3 303 416 Oct. Nov. 3.2 2005 Dec. Jan. Feb. Mar. Apr.P 3.2 3.3 3.6 3.6 3.7 3.7 3.6 3.8 3.9 6.0 5.0 5.0 5.9 5.7 4.2 5.8 2.6 3.3 3.2 3.4 3.4 In dustry Total private 2 .. .... .. ....... ..... ...... .. ... .. .. .. Construction . ... .. .... ... .. ... .................. .. . . .. . .. . ... Manufacturing .. 354 327 353 36 1 341 360 372 2.5 2.3 2.5 2.5 2.4 2.5 Trade, transportation , and utilities .. 889 943 1,062 882 940 980 984 3.5 3.7 4.1 3.4 3.7 3.8 3.8 Professional and business services .. 585 822 833 836 772 924 914 3.5 4.9 5.0 5.0 4.6 5.5 5.4 Education and health services ... Leisure and hospitality Government .. 376 408 375 356 389 445 424 2.2 2.4 2.2 2.1 2.3 2.6 2.5 .. ... . .. 767 727 758 832 790 743 667 6.1 5.8 6.0 6.6 6.3 5.9 5.2 ... . ... . . 263 275 274 258 260 267 256 1.2 1.3 1.3 1.2 1.2 1.2 1.2 3.2 ..... ... ... . ...... .... ........ . Region 3 Northeast .... ..... ... .. . .. ... South . .. . .. ... ... ... ....... .. .... ... .. ...... ... . . . . . . . . .. . . . . . . . . . Midwest .... ,, ... , West ·· ·· •······· ······· ·· · · ·· . . ... . ... 711 756 773 773 732 802 807 2.8 3.0 3.0 3.1 2.9 3.2 1,614 1,594 1,707 1,747 1,647 1,763 1,784 3.5 3.4 3.6 3.7 3.5 3.7 3.8 952 1,041 986 981 937 1,051 976 3.0 3.3 3.1 3.1 3.0 3.4 3.1 896 826 953 964 961 926 1,017 3.1 2.9 3.3 3.3 3.3 3.2 3.5 1 Detail will not necessarily add to totals because of the independent seasonal adjustment Midwest : Illinois, Indiana, Iowa, Kansas , Michi gan , Minn esota, Missouri , Nebraska, North Dakota, Ohio, South Dakota, Wisconsin ; West : Alaska, Arizona, Californ ia, Includes natural resources and mining, information , financial activities, and other Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon , Utah , Wash ington, services , not shown separately. Wyoming . of the various series. 3 Northeast : Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont; South : Alabama, Arkansas, Delaware, NOTE: The total separations level is the number of total separations during the en tire District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, month; the total separations rate is the number of total separations during the enti re North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia; month as a percent of total employment. p = preliminary. 21 . Quits levels and rates by industry and region, seasonally adjusted 1 Levels (in thousands) Industry and region 2004 Oct. Total 2 ······ · ·· ······· ··· · ······· ·· ···· ·· ·· · ··· ··· Nov. Percent 2005 Dec . Jan. Feb. 2004 Mar. Apr.P Oct. Nov. 2005 Dec. Jan. Feb. Mar. Apr.P 2,344 2,436 2,495 2,530 2,307 2,516 2,523 2,217 2,319 2,366 2,412 2,192 2,383 2,397 2.0 2.1 2.1 2.2 2.0 2.1 2.1 182 159 162 171 139 150 148 2.6 2.2 2.3 2.4 2.0 2.1 2.1 1.8 1.8 1.9 1.9 1.7 1.9 1.9 Industry Total private 2 .. ... ... ... .. .. . Construction ... .... .. , .. Manufacturing ... ....... Trade, transportation, and utilities .. 187 185 194 185 181 186 178 1.3 1.3 1.4 1.3 1.3 1.3 1.2 517 568 570 563 512 583 567 2.0 2.2 2.2 2.2 2.0 2.3 22 2.6 Professional and business services .. 281 401 415 417 410 424 439 1.7 2.4 2.5 2.5 2.4 2.5 Education and health services .. 239 250 232 230 259 280 285 1.4 1.5 1.4 1.3 1.5 1.6 1.7 Leisure and hospitality ..... 474 499 506 516 474 458 471 3.8 4.0 4.0 4.1 3.8 3.6 3.7 123 118 129 124 117 124 126 .6 .5 .6 .6 .5 .6 .6 Government . . .... . ... . .. . ... . ··· ·· ······ ····· Region 3 Northeast.. ....... ..... . .. . . 333 359 392 424 340 410 431 1.3 1.4 1.5 1.7 1.3 1.6 1.7 ... ......... .... ....... Midwest .... .. ... .. ... .. ..... .... .. .... ...... .. 943 1,014 1,021 1,053 914 1,003 1,003 2.0 2.2 2.2 2.2 1.9 2.1 2.1 500 551 544 539 509 561 513 1.6 1.8 1.7 1.7 1.6 1.8 1.6 West.. .... 550 492 536 530 550 562 598 1.9 1.7 1.9 1.8 1.9 1.9 2.0 South .. 1 .. .... ······ Detail will not necessarily add to totals because of the independent seasonal adjustment of the various series. Includes natural resources and mining, information, financial activities , and other services, not shown separately. Midwest: Illinois , Indiana, Iowa, Kansas, Mich igan, Minnesota, Missou Nebraska, North Dakota, Ohio, South Dakota, Wisconsin ; West : Alaska, Arizon California, Colorado , Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon , Uta Wash ington , Wyoming . 3 Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rh ode 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; https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis NOTE: Th e quits level is the number of qu its during the entire m.on th ; the quits rate the number of quits during the entire month as a percent of total employment. P = preliminary. Monthly Labor Review June 2005 95 Current Labor Statistics: Labor Force Data 22. Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003. County by NAICS supersector United States 3 ..................................... . Private industry ............. .... ......... .... . Natural resources and mining Construction ... ...................................... . Manufacturing Trade, transportation, and utiliti es Information .. Financial activities .. ..... ... ................... ...... . Professional and business services Education and health services Leisure and hospitality . Other services ...................... .. Government ............ ........ . Establishments, fourth quarter 2003 (thousands) Average weekly wage 1 Employment December 2003 (thousands) Percent change, December 2002-03 2 Fourth quarter 2003 Percent change, fourth quarter 2002-03 2 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 resou rces and mining Construction . Manufactu ring ................................... . 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 -1.1 -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.€\ 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. Monthly Labor Review 96 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 22. Continued-Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003. County by NAICS supersector Establishments, fourth quarter 2003 (thousands) Average weekly wage 1 Employment December 2003 (thousands) Percent change, December 2002-03 2 Fourth quarter 2003 Percent change, fourth quarter 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 Informat ion . 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 utili ties Information ... ................ Financial activities .. .. ... .. .. .. . . . . . . . . . . . . . . . . . . . . . . . Professi onal 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 Informat ion ·· ··················· Financial activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Professi onal 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 1,160 746 390 463 882 .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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Professi onal 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 Percent changes were computed from quarterly employment and pay data adjusted for noneconomic county reclassif icat ions . See Notes on Current Labor Statistics. 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 June 2005 97 Current Labor Statistics: Labor Force Data 23. Quarterly Census of Employment and Wages: by State, fourth quarter 2003. State Establishments, fourth quarter Average weekly wage 1 Employment December 2003 Percent change, December Fourth quarter Percent change, fourth quarter (thousands) 2002-03 2003 2002-03 8,314.1 129,34 1.5 0.0 $767 3.6 111 .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 1.1 2.2 .5 .0 -1.1 -.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 Georg ia Hawaii Idaho Ill inois 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 Maryl and . Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada . New Hampshire 150.4 206 .6 25 1.3 159.0 65.6 165.4 42.0 55.3 60.3 47.0 2,466 .4 3,154 .6 4,365 .8 2,59 1.9 1,108.1 2, 633.6 396.6 884.4 1,11 1.2 614.9 .7 -1 .9 -1 .1 -.5 .4 -.7 1.1 .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 Me xico New Yo rk North Caroli na North Dakota Ohi o Oklah oma .. Oregon Pennsylvania . Rh ode Island 268.1 50.4 550.3 227. 8 24.0 294 .2 9 1.6 11 8.8 326.9 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 ............ ... ... . Verm ont Virginia Wash ington West Virgin ia Wisconsin 108.4 28.1 128.4 505 .3 73.9 24.1 202 .6 222 .7 47.2 157 .6 1,78 1.0 365 .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 2003 (thousands) United States 2 Alabama Alaska . ... . .. .. . . . . . . . . . Arizona Arkansas California Colorado . Connecti cut ... Delaware . ... . . . . . . . . . . .. . . . . .. District of Columbia Fl orida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 calcu lated using unrounded data. 2 Totals for th e Un ited States do not include data for Pu ert o Rico or the Virgin Islands. 98 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 NOTE : Incl udes workers cove red by Unemployment Insurance (U I) and Unempl oyment Compensation for Federal Employees (UCFE) programs . Data are preliminary . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 24. Annual data: Quarterly Census of Employment and Wages, by ownership Average establishments Year 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 2002 ... ............. ..... ... ... . .. , ..... .. ......... ............. .. ....... .. 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 36,2 19 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 ,68 1 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 $710 731 741 777 822 840 852 889 941 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 200 1 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 2,837,334,21 7 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 $117,095,062 122,879,977 128,143,491 131 ,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 ,62 1,074 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 establishments from private industry to the publ ic sector. See Notes on Current Labor Statistics. Monthly Labor Review June 2005 99 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, establishments, and employment 100 Total Fewer than 5 workers ' 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 Manufacturing Establishments, first quarter .... . ... ..... .. . Employment, March ................... ...... .... 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 ,140 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 , first quarter . . . . . . . . . . . . . . . .. . Employment, 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 1,76 1 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 85 1,231 1,037,360 11 6,940 761,518 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 1 Includes establishments that reported no workers in March 2003. 2 Includes data for unclassified establishments, not shown separately. Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 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 Metropol itan area• 2001 2002 Percent change, 2001-02 Metropolitan areas3 . $37, 908 $38,423 1.4 Abilene , 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 34,03 7 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-Neenah, 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 28,77 1 29,942 41 ,123 32 ,201 26,405 31 ,743 39,540 4.4 4.0 1.1 .9 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-Passaic, NJ .... .................... . Billings, 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 . Bloomington , IN . Bloomington-Normal, IL Boise City, ID ........................................ ...... .. ....... ............... .. . Boston-Worcester-Lawrence-Lowell-Brockton , MA-NH Boulder-Longmont, CO . . . .................... . Brazoria, TX 28,351 31 ,187 34,5 19 27, 116 28,013 35,111 31 ,624 45 ,766 44 ,310 35,655 28,5 15 31 ,832 35,940 27,993 28,855 36,133 31,955 45,685 44 ,037 36,253 .6 2.1 4.1 3.2 3.0 2.9 1.0 -.2 -.6 1.7 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 34,6i 9 30,488 28,887 33 ,775 22 ,892 26 ,051 32,777 35,169 29 ,689 28,886 34,730 31,995 29,993 7.1 3.4 1.1 2.3 2.3 2.3 2.2 .2 4.9 3.8 Charleston, WV . Charlotte-Gastonia-Rock Hill , NC-SC Charlottesville, VA . Chattanooga, TN-GA . Cheyenne, WY .................. .. Chicago, IL ..... ... .. ............. .... ... ... Chico-Paradise, CA . Cincinnati , 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 ,5 14 32 ,136 38,413 33,328 30,631 28 ,827 43,239 27,190 37,168 26,940 36,102 1.9 3.1 2.8 2.2 4.5 1.3 2.6 3.1 5.4 1.7 Colorado Springs , CO Columbia, MO . Columbia, SC Columbus, GA-AL . Columbus, 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 .8 2.3 2.7 2.8 3.2 2.7 3.5 4.7 .7 2.6 See footnotes at end of table. Monthly Labor Review June 2005 101 Current Labor Statistics: Labor Force Data 102 Monthly Labor Review 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• 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 -.5 3.9 1.2 4.4 7.4 Dubuque, IA ..... . Duluth-Superior, MN-WI Dutchess County, NY Eau Claire, WI El Paso, TX Elkhart-Goshen, IN Elmira, NY . Enid, OK ............. . 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 Fayettevill e, 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 . ......................... .. Gm~Ba~WI ........ ...... ....... ... ...... ................. .... .. Greensboro--Winston-Salem--High 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 3.4 3.5 3.3 2.0 Greenville, NC ............... .. Greenville-Spartanburg-Anderson , SC Hagerstown, MD .. Hamilton-Middletown , OH .. ...... .. ........ . Harrisburg-Lebanon-Carlisle, PA Ha~m~CT . ... ................ . Hattiesburg, 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 -. 2 3.1 5.0 1.7 2.9 2.5 2.4 3.5 4.2 4.1 See footnotes at end of table. June 2005 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 1 2001 2002 Percent change, 2001-02 Jamestown , NY . Janesville-Beloit, WI . Jersey City, NJ Johnson 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 22 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 .7 1.9 2.1 2.2 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-N orth Little Rock, AR Longview-M arshall , TX Los Ange les-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 33,90 1 27,625 29,4 44 31 ,884 35,410 30,104 23,179 2.5 .5 2.0 2.6 3.9 2.0 4.2 3.9 4.5 3.9 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-Wat erbury-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 29,0 17 24 ,672 31 ,507 35 ,036 40,396 51,170 4.0 1.8 3.9 4.7 2.3 2.7 22 3.1 1.9 -2 .0 New London-Norwich , CT New Orleans, LA New York, NY ...... .. ........ .. .. Newa~,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 See footn otes at end of table . Monthly Labor Review June 2005 103 Current Labor Statistics: 104 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 $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 3.0 3.9 2.4 3.8 3.3 3.8 5.8 .5 2.9 2.2 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 1.5 4.1 1.7 3.6 2.4 3.0 1.0 3.6 .5 2.5 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 ... ........ ..... .. .. Riverside-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 2.8 3.1 .9 3.6 3.4 3.0 1.5 5.4 2.4 3.5 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 36,712 4.8 3.4 1.5 2.3 .4 3.5 .0 4.5 2.8 2.2 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 3.1 2.3 2.0 .7 2.2 2.3 -5.1 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 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 See footnotes at end of table. June 2005 -4.4 3.1 2.2 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• 2001 2002 Percent change, 2001-02 $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 ,7 17 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, Al. 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 .3 3.2 Visal ia-Tulare-Porterville, CA Waco, TX .. .. ............................... ...... . Wash ington, DC-MD-VA-WV Waterl oo-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 .7 Wilmington-Newark, DE-MD Wilmington , NC ..... .. .... ...... ....... ........ . Yakima, WA. Yolo, CA ... ... ............ ..... ... ....... . Yor~PA .......... ..... .. .... .. .. ... .... .. Youngstown-Warren, OH Yuba City, CA .. Yuma, Al. . 42,177 29,287 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 .7 2.1 3.5 4.3 4.5 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 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 ' Includes data for Metropolitan Statistical Areas (MSA) and Primary Metropolitan Statistical Areas (PMSA) as defined by 0MB Bulletin 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 June 2005 105 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 rati o ...... , . Unemployed.. ..... . ............. Unemployment rate .... . . . . .. .... . . Not in the labor force ... 1 1994 1 1995 1996 1997 1 1998 1 1999 1 20001 2001 2002 2003 2004 196,814 131,056 198,584 132,304 200,591 133,943 203 ,133 136,297 205 ,220 137,673 207,753 139,368 212,577 142,583 215,092 143,734 217,570 144,863 221,168 146,510 223,357 147,401 66.6 123,060 66.6 124,900 66.8 126,708 67.1 129,558 67.1 131,463 67.1 133,488 67.1 136,891 66.8 136,933 66.6 136,485 66.2 137,736 66.0 139,252 62.5 7,996 62.9 7,404 63.2 7,236 63.8 6,739 64.1 6,210 64.3 5,880 64.4 5,692 63.7 6,801 62 .7 8,378 62.3 8,774 62.3 8,149 6.1 65,758 5.6 66,280 5.4 66,647 4.9 66 ,836 4.5 67,547 4.2 68,385 4.0 69,994 4.7 71 ,359 5.8 72,707 6.0 74,658 5.5 75,956 Not strictly comparable with prior years. 28. Annual data: Employment levels by industry [In thousands] Industry 1994 Total private employment.. .. .. . . . . . . .. . . . . . ..... .. Total nonfarm employment . . . . . . . . .. .. . . Goods-producing ... Natural resources and mining .. Construction .. ............... ......... ... Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 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 . . . ...... .. ..... . . .. . . , . ... ..... .. 106 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 1995 1996 1997 1998 1999 2000 2001 2002 2003 95 ,016 97,866 100,169 103,113 106,021 108,686 110,996 110,707 108,828 108,416 109,862 114,291 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 15,259 129,999 21,816 572 6,735 14,510 131,480 21,884 591 6,964 14,329 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 86,599 25,287 5,607.5 14,917.3 4,185.4 577.0 3,188 7,977 15,987 16,588 12,173 5,401 87,978 25,510 5,654.9 15,034.7 4,250.0 570.2 3,138 8,052 16,414 16,954 12,479 5,431 19,275 19,432 19,539 19,664 19,909 20,307 20,790 21,118 21 ,513 21,583 21,618 June 2005 2004 29 . Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm payrolls, by industry 1994 Industry 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Private sector: Average weekly hours ... ....... . .......... ..... ... ... ... ... Average hourly earnings (in dollars) ...... .. ....... ... Average weekly earnings (in dollars) ....... ............ 34.5 11 .32 390 .73 34 .3 11 .64 399 .53 34 .3 12 03 41 2.74 34.5 12.49 431.2 5 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 .30 33.7 15.67 528 .56 Goods-produc ing: Average weekly hours .. ... .. . .. ... Average hourly earnings (in dollars) .... .... ... .... .. Average weekly earnings (in dollars) . ......... ...... 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 14.7 1 599. 99 40.7 15.27 621.86 39.9 15.78 630 04 39.9 16.33 651 .61 39 .8 16.80 669.13 40.0 17. 19 68803 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 72 1.74 44.4 16.55 734.92 44 .6 17. 00 757.92 43.2 17.19 741 .97 43 .6 17.56 765 .94 44 .5 18.08 804.03 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 18.95 726 .83 38.3 19.23 735.70 41 .7 1204 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 15.74 635 .99 40.8 16.14 658.53 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 1307 427 .30 32.7 13.60 445.00 32 .5 14.16 460.32 32 .5 14.56 472 .88 32.4 14.96 483.89 32 .3 15.26 493 .67 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 .14 33 .5 14.59 488.58 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 .9 17.36 657.29 37 .8 17.66 666 .93 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 10.05 582.2 1 30 .8 10.45 602.77 30.7 10. 86 63 1.40 30 .7 11 .29 643.45 30 .9 11 .67 644 .38 30.9 11 .90 657 .29 30 .7 12 08 666 .93 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 05 562.3 1 36. 7 15. 33 562.70 36 .8 15.76 579.75 36.8 16.25 598.41 37.2 16.53 614 .90 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 2 1.48 902. 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 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 .01 760.81 36 .3 21.42 777 .42 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 55802 35.6 16.17 575.51 35 .5 17.14 609 08 35.5 17.53 622 .99 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 17.21 587.02 34 .2 17.46 596 .96 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 .69 32.4 16.16 523 .83 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 2 11 .79 25.8 8.35 215.19 25 .8 8.58 221 .26 25.6 8.76 224 .30 25.7 8.91 228 .63 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 13.84 434.41 31 .0 13.98 433 04 Natu ra l resources and mining Average weekly hours .. ................ ... ....... ...... ... Average hourly earnings (in dollars) ... ........ .. .. Average weekly earnings (in dollars) .. ......... 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) .... ....... ... . Private service-providing : Average weekly hours ......................... ... ..... Average hourly earnings (in dollars) . . . . . . . . .. . . . . . . . Average weekly earnings (in dollars) ...... ...... ... Trade, transportat ion , and utilities: Average weekly hours .. ............ ..... ........... ... . Average hourly earnings (in dollars) . . . . . . . . . . . . .... Average weekly earnings (in dollars) .. ... . ... .. ...... Wholesale trade: Average weekly hours . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . .. . Average hourly earnings (in dollars) .. Average weekly earnings (in dollars) .. ............. Retail trade : Average weekly hours .. Average hourly earnings (in dollars) ........... Average weekly earnings (in dollars) ... ... .. ' ... .. Tra nsportation and warehou sing : Average weekly hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . Average hourly earnings (in dollars) .. Average weekly earnings (in dollars) ............ Util ities : Average weekly hours .. ......... . .. Average hourly earnings (in dollars) ......... ... . ... Average weekly earnings (in dollars) ...... ... . .... Informati on : Average weekly hours .. Average hourly earnings (in dollars) ........ ... Average weekly earnings (in dollars) . . . . . . . . . ... . Financial activities: Average weekly hours .. ·· ··········· ... ... Average hourly earnings (in dollars) .. Average weekly earnings (in dollars) .. Profess ional and bus iness services: Average weekly hours .. ... Average hourly earnings (in dollars) .. Average weekly earnings (in dollars) .. .. .... .. ..... Educat ion and health services: Average weekly hou rs .. . ..... ... ... ... ... ..... Average hourly earnings (in dollars) .. .. ..... Average weekly earnings (in dollars) ... Le isu re and hospital ity : Average weekly hours ... ... ....... .. ... .... ... ........... ... Average hourly earnings (in dollars) ... .............. Average weekly earnings (in dollars) ................ Other services : Average weekly hours .. ...... ......... ...... . .... Average hourly earnings (in dollars) .............. Average week ly earnings (in dollars) .. 40 .9 41 .1 24 .77 25 .62 1,017.27 1,048 .82 NOTE : Data ref lect the conversion to the 2002 version of the North American Industry Classification System (NAI CS), 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 June 2005 107 Current Labor Statistics: Compensation & Industrial Relations 30. Employment Cost Index, compensation, 1 by occupation and industry group [June 1989 = 100] 2003 Series Mar. June 2004 Sept. Dec. Mar. June 2005 Sept. Dec. Mar. Percent change 3 months 12 months ended ended Mar. 2005 Civilian workers 2 .................... ... ......... ... ... ...... ...... .. .... . 164.5 165.8 167.6 168.4 170.7 172.2 173.9 174.7 176.6 1.1 3.5 166.7 164.1 171 .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 .2 177.1 177.2 168.8 170.9 175.8 173.6 178.2 178.7 170.1 172.7 176.6 174.7 179.4 180.0 170.9 173.6 178.8 176.8 182.0 182.0 172.4 174.9 1.2 1.2 1.4 1.1 .7 3.5 3.9 3.5 3.8 3.3 3.1 163.1 164.0 165.0 165.3 166.4 169.9 163.6 163.4 164.5 164.6 165.4 166.2 166.3 167.6 170.8 164.2 164.3 165.8 165.8 166.5 168.2 168.5 169.3 173.1 166.9 167.3 167.8 166.8 167.1 169.1 169.5 170.7 174.8 167.6 168.1 168.6 170.4 171 .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 .8 173.4 174.9 174.0 174.5 176.7 180.5 171.8 174.1 173.5 174.4 175.4 174.7 175.5 177.7 181 .8 172.9 175.4 174.4 177.0 178.2 176.5 177.0 179.9 184.3 173.9 177.6 176.1 1.5 1.6 1.0 .9 1.2 1.4 .6 1.3 1.0 3.9 3.8 3.3 3.4 4 .0 4 .2 3.2 4.4 3.3 165.0 165.1 166.4 166.6 168.1 168.1 168.8 169.0 171 .4 171 .6 173.0 173.2 174.4 174.6 175.2 175.6 177.2 177.7 1.1 1.2 3.4 3.6 168.1 169.1 166.5 172.1 163.5 169.0 159.7 160.0 159.9 153.2 164.9 169.4 170.4 167.7 173.1 165.1 170.9 161.4 162.0 161 .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 .2 178.1 168.8 169.1 170.5 160.6 173.2 177.3 178.3 176.8 179.2 173.1 179.4 170.1 170.2 172.2 161.8 174.3 178.1 179.5 178.1 180.2 171 .4 180.7 170.8 171.2 172.5 162.3 175.3 180.4 182.0 180.8 183.0 173.1 182.8 172.3 173.1 173.3 163.7 176.9 1.3 1.4 1.5 1.6 1.0 1.2 .9 1.1 .5 .9 .9 3.6 3.8 4.3 3.5 2.3 3.8 3.2 3.6 2 .7 3.3 3.0 Workers, by occupational group: White-collar workers ... ...... . Professional specialty and technical.. Executive, adminitrative, and managerial. .. Administrative support, including cl erical .... .. ... .... Blue-collar workers .. Service occupations ..................... .. .......................... . .9 Workers, by industry division: Goods-producing ... ... ... ...... ... .... .... ... ...... ....... .. .... . .. .. . Manufacturing .. Service- produci ng ..... ........... .. ... .... ... .. ......... .... Servi ces ............. .. ...... .... .. .. .. .. .... .. ....... .. . .... .. ... .. Health servi ces ..... ...... ..... ........... ......... ............. ... . ... . Hospital s .............. ..... .... ..... .... ..... ........ ...... ....... . Educati onal services ........ ...... .......... .. .... .... .. ...... . Public administration Nonmanufacturing ... 3 .. Private industry workers ....... .... ..... .... ...... . ....... .. . Exc luding sales occupations ........ .. .. .... .. ... .... ... .. ... Workers , by occupati onal group: White-collar workers ................... ....... ...... ... . Excluding sales occupations .. . Professional specialty and technical occupations .... Executive, adminitrative, and managerial occupati ons .. Sales occupations .. Adm inistrative support occupations, includi ng clerical. . Blue-collar workers .... ................. ..... . .. ..... ... ... Precisi on production , craft , and repair occupati ons .. Machine operators, assemblers , and inspectors .. ....... . Tran sportati on and material moving occupations .. Handlers , equipment cleaners, helpers , and laborers ... Service occupations .. 161 .7 162.6 163.8 164.3 166.9 168.2 168.9 169.7 170.9 .7 2.4 162.6 164.1 165.7 166.6 169.3 171 .0 172.4 173.0 174.6 .9 3.1 Workers, by industry division : Goods-producing ... . ..... ... .... .. ..... .. .. ... .... ... ..... ... ... ...... . Excl uding sales occupations ..... .... .. .. .. ............ . Whit e-collar occupations.. . .. ... ....... .. .. ...... .. .. .... . Exc luding sales occupations .... .. .. .. .. .. .. ... . ... ..... . Blue-collar occupations . ............................... . Construction .. .. ..... .. ... ....... ..... .. ........ ........ .. ... ... ... Manufacturing ........ .... ......... .... ....... ..... .... .. .......... White-collar occupations .......... .. .. .. .... ............... ... . Excluding sales occupations .......... .... .. . .... ... ....... .. Blue-collar occupations ... .. .................. .. ......... ... ........ Durables . ......... .. .... ... .... ........... . .. .. .. .. ... .. .. .... Nondurables .. ... .... ... ... .. ... ... .......... ... ... ....... ... 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 .3 167.0 174.9 176.4 174.1 173.7 175.8 173.1 174.3 173.7 177.8 176.4 172.0 167.3 175.4 176.7 174.7 174.3 176.3 173.6 176.9 176.3 182.2 180.9 173.4 169.1 178.2 181.4 179.4 175.8 179.5 175.8 1.5 1.5 2.5 2.6 .8 1.1 1.6 2.7 2.7 .9 1.8 1.3 3.9 3.8 5.0 5.1 3.2 2.7 3.8 4.7 4.7 3.2 4.1 3.2 Service-producing ... ... .. .. . .... . ... ... .. ... .. .. .. .. . .. . ...... . Excluding sales occupations ....................... . White-collar occupations ... ... ... ... ..... .. ........ .... .. .. . Excluding sales occupations .. . ... .. ......... .. . Blue-collar occupations ... .. ........ ...... .. .. ... .. .. .... . Service occupations. .. . . ...... . ...... ........... . Transportation and publi c utilities .... .. .. .... ..... .. .. .... Transportation ............................ ...... ... ......... ... ..... . Publ ic 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 .. .... ... ...... .. . ........ ... ... .... ........ ...... . 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 .2 159.3 169.7 170.6 172.0 174.2 162.6 164.3 167.0 159.6 177.0 179.0 174.6 165.0 165.9 172.0 171.3 161 .0 165.6 160.3 171.6 172.5 174.1 176.2 164.1 166.1 169.8 162.0 180.4 182.2 178.2 166.3 167.4 173.8 173.7 162.1 165.8 162.1 173.3 174.2 175.7 177.8 166.4 167.4 172.5 164.7 183.1 183.6 182.4 168.1 168.6 175.9 174.0 163.7 166.2 163.5 174.7 175.6 177.3 179.4 167.4 168.1 173.6 166.2 183.6 183.6 183.3 169.1 169.6 177.8 175.3 164.2 168.8 163.5 175.3 176.5 177.8 180.4 168.1 168.9 173.5 166.2 183.4 183.5 183.3 169.1 170.4 176.6 176.3 164.7 169.5 164.0 177.1 178.4 179.7 182.4 169.9 170.1 174.5 165.5 186.9 186.0 188.0 170.9 172.3 179.1 179.2 166.2 172.3 165.0 1.0 1.1 1.1 1.1 1.1 .7 .6 .4 1.9 1.4 2.6 1.1 1.1 1.4 1.6 .9 1.7 .6 3.2 3.4 3.2 3.5 3.5 2 .4 2 .8 2.2 3.6 2.1 5.5 2 .8 2 .9 3.0 3.2 2.5 3.9 1.8 Production and nonsupervisory occupations 4 ........ ......... 1 See footnotes at end of table. 108 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 30. Continued-Employment Cost Index, compensation, 1 by occupation and industry group [J une 1989 = 100] 2003 Series Mar. June 2004 Sept. Dec. Mar. June Percent change 2005 Sept. Dec. Mar. 3 months 12 months ended ended Mar. 2005 Finance, insurance, and real estate . .. .. ...... 176.7 178.3 180.2 180.9 182.5 183.6 184.8 186.0 188.9 1.6 3.5 Excl uding sales occupations .. Banking, savings and loan , and other credit agencies. Insurance .. Services ..................... .. ............. . ..... .... ...... ....... ..... Business services .. .... .... . .. . . . . . . . . .... . .... ... .. . .. . ... Health services . . . . . . . . . . . . ···· ··· ··· ••·•············ . . . . . . . . . . .. . Hospitals .. . . ... . . .. . . . . . .. . . . . . . . .. .. . . . . . . . . . . . . . . . . . .. .. .... Educational services . .............. ..... ... .. ......... .... .. ....... Colleges and universities ······ ··· ······ ···· .... . .. .... ... 182 .0 204 .3 172 .1 167.1 168.5 166.5 170.8 176.3 174.5 184.0 206.3 173.9 168.4 169.2 167.9 171.9 177. 1 175.4 1,853 .0 207.6 175.1 170.4 171.9 169.4 173.9 180.2 178.4 186.1 209.0 176.2 171 .4 172.6 170.8 175.9 181 .3 179.4 186.6 207.2 177.8 173.5 174.8 173.3 178.1 183.1 181 .2 188.7 208.9 180.5 175.1 176.9 174.8 179.7 184.2 182.5 190.9 210.5 182.1 176.9 178.5 177.0 181 .8 187.0 185.2 191 .2 212.3 183.6 177.9 179.1 178.0 183.2 188.5 186.2 194 .3 213 .7 186.3 179.7 180.1 180.3 185.8 190.0 187.6 1.6 .7 1.5 1.0 .6 1.3 1.4 .8 .8 4.1 3. 1 4.8 3.6 3.0 4.0 4.3 3.8 3.5 Nonmanufactu ring ... 164 .9 166. 4 168.1 169.0 170.9 172.5 173.9 174.7 176.5 1.0 3.3 168.0 170.0 157.5 161.1 169.3 171.4 159.7 162.0 171 .2 173.2 161 .1 163.2 172.1 174.2 161.7 162.4 174.1 176.2 163.4 166.0 175.7 177.7 165.5 167.3 177.2 179.3 166.4 168.0 178.0 180.6 167.3 168.9 180.0 182.7 168.8 170.1 1.1 1.2 .9 .7 3.4 3.7 3.3 2.5 162 .6 163.2 165.9 166.8 168.0 168.7 171 .5 172.6 174 .1 .9 3.6 161.7 160.2 165.3 163.8 161.3 162.2 160.8 165.7 164.4 161 .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 .8 167.5 170.0 168.4 172.1 174.3 169.9 171.2 169.4 174.3 175.5 171.0 172.6 170.4 176.7 177.2 172.6 .8 .6 1.4 1.0 .9 3.5 3.2 3.9 4.0 3.5 161.8 164.0 162.3 164.2 164.9 166.8 165.7 168.2 166.5 169.4 166.8 170.1 169.7 173.0 170.8 173.8 171 .8 175.6 .6 1.0 3.2 3.7 166.4 167.0 161 .1 161 .4 159.4 167 .0 166.7 167.3 161 .7 162.0 160.0 167. 5 164.3 169.5 170.3 164.3 164.7 163.0 169.2 167.3 171 .0 171.4 165.0 165.3 163.7 170.0 172.2 172.4 165.7 166.0 164.4 170.7 170.1 172.9 173.2 165.9 166.3 164.6 171 .0 171.4 175.7 176.3 168.8 169.2 168.0 172.4 174.1 176.8 177.4 169.9 170.3 169.2 173.2 178.9 179.1 170.9 171.2 169.8 175.1 177.6 1.2 1.0 .6 .5 .4 1.1 1.3 3.9 3.9 3.1 3.1 3.3 2.6 4.4 . . ... . .. . .................... ....... ... Wh ite-collar workers ... ........ ... ........ ... ... ..... .. .... ..... Excluding sales occupations .. ., .. ······ ... Blue-collar occupations ... . . . .. . . . . . . . . . . .... . .... ......... ... Service occupati ons ·························•"''"''' ''' ' ''''' State and local government workers ................................. .. Workers , by occupational group: White-collar wo rkers .. ··· •·· • .. . ... ... ... ... Professional specialty and tech nical . . . . . . . . . . . . . . . .. . . . . Executive, administrative, and managerial ......... . ..... Adm inistrative 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 .... ... Publ ic administrat ion 3 ...... 163.4 1 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. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 168.1 3 175.4 Consists of legislative, judicial , administrative, and regulatory acti viti es. 4 This series has the same industry and occupation al coverage as the Hourly Earnings index, which was discontinued in January 1989. 5 Includes, for example, library, social , and health services. Monthly Labor Review June 2005 109 Current Labor Statistics: 31. Compensation & Industrial Relations Employment Cost Index, wages and salaries, by occupation and industry group [June 1989 = 100] 2003 2004 2005 Series Mar. June Sept. Dec. Mar. June Sept. Dec. Mar. Percent change 3 months 12 months ended ended Mar. 2005 Civilian workers 1 ...................... ...... ... . ........... . ...... . .. . .. . .. . . . Workers, by occupational group: White-collar workers .............. .. Professional specialty and technical Executive, adminitrative, and managerial. ..... .. Administrative support, including cler;cal ... .... . Blue-collar workers .. Service occupations .......................................... . Workers, by industry division : Goods-producing ..... .... .... .. ..... .... ..... ... .. ... ... Manufacturing ........ ... ... .. ....... ... Service-producing.. . .......... ... ... ..... . Services ..... Health services ....... .... ....... . Hospitals ...... . ..... ..... ... ... ... . .. .... .. ..... ..... Educational services ..................... ......... ...... 2 Public administration ....... •... . .. •..... . .. . .. . .. . .. ... Nonmanufacturing .... ... .. .. . ... .... ... ... ... ...... .. ... .......... .. ...... .. Private industry workers .. .. ... ... ....... . Excluding sales occupations . 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 .. . 159.3 160.3 161 .8 162.3 163.3 164.3 165.7 166.2 167.3 0.7 2.4 161 .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 .9 168.7 166.5 173.4 168.8 159.7 162.8 169.1 167.0 174.4 169.7 160.0 163.6 170.3 168.1 175.9 170.9 161 .0 164.4 .7 .7 2.5 2 .6 2 .6 2.8 2 .4 2.0 156.3 158.0 160.5 161.9 162.0 163.5 160.4 157.5 159.0 161.4 162.8 163.2 164.4 160.7 158.3 159.7 163.0 164.7 164.7 166.3 162.7 160.6 160.1 163.6 165.4 165.9 167.7 163.2 159.9 161 .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 .8 166.0 162.4 164.0 167.5 170.1 171.7 173.2 166.8 163.8 165.3 168.6 171 .2 173.2 174 .7 167.5 2.3 2 .5 2.4 2.8 3.3 3.4 2.4 157.2 159.6 158.0 160.5 159.4 162.1 160.0 162.7 161 .1 163.7 161.4 164.6 162.6 166.0 163.5 166.5 165.0 167.6 2.4 2 .4 159.3 159.4 160.4 160.5 161 .7 161 .7 162.3 162.4 163.4 163.5 164.5 164.5 165.9 165.8 166.2 166.5 167.4 167.6 162.6 163.6 159.5 169.1 158.1 162.6 153.6 153.4 154.7 147.8 158.4 163.8 164.8 160.5 170.3 159.3 164.0 154.6 154.7 155.3 149.0 159.0 165.3 166.2 162.1 171 .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 .6 152.9 163.6 170.0 171.4 168.0 175.7 164.0 170.8 159.9 159.7 161 .6 153.3 164.5 171.3 172.7 169.4 177.2 164.9 172.0 160.8 160.4 162.6 154.4 165.6 .9 .7 .6 .5 .7 .7 2.4 2 .5 2 .5 2 .7 2.9 2.6 1.4 2.9 2.3 2.1 2 .5 2 .7 2 .3 Service occupations .. 155.5 156.1 157.1 157.8 158.4 159.3 159.8 160.6 161 .4 1.9 Production and nonsupervisory occupations3 .... .. ............ 1 156.4 157.4 158.8 159.4 160.7 161 .7 163.1 163.4 164.5 2.4 156.3 155.4 160.0 158.0 153.8 150.6 158.0 160.1 157.7 156.3 158.8 156.6 157.4 156.5 161 .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 .6 162.3 161.2 166.0 163.6 159.8 157.1 163.8 166.1 163.5 162.1 164.5 162.8 162.4 161 .6 165.9 164.1 160.1 157. 0 164.0 166.1 163.9 162.4 164.7 162.9 163.6 162.8 167.3 165.3 161 .2 157.7 165.3 167.6 165.1 163.6 165.9 164.5 .7 .7 .8 .7 .3 .4 .8 .9 .7 .7 .7 1.0 2.3 2 .3 2.5 2 .4 2 .2 1.7 2.5 2.6 2.4 2 .4 2.5 2 .6 160.6 161 .7 163.0 165.3 153.2 155.1 154.8 150.5 160.4 161 .9 158.6 156.7 163.4 163.9 153.1 149.8 151 .0 161 .7 162.8 164.1 166.5 154.3 155.6 155.6 150.6 162.1 163.4 160.4 157.5 164.7 165.2 153.8 152.0 151.6 163.3 164.2 166.0 168.2 155.1 156.6 156.0 150.4 163.4 165.4 161 .0 159.2 164.8 165.7 156.3 153.1 152.2 163.9 165.0 166.6 169.0 155.4 157.4 156.5 150.8 164.1 165.9 161.8 159.5 165.3 166.3 156.5 153.6 152.8 165.0 166.0 167.8 170.2 156.2 158.0 157.6 151 .7 165.3 167.0 163.3 160.3 166.2 167.8 157.3 154.1 153.8 166.1 167.1 168.9 171 .2 157.8 158.8 159.1 153.4 166.4 167.5 165.1 161 .6 167.8 167.6 158.4 154.9 154.3 167.5 168.5 170.4 172.8 158.9 159.4 160.4 155.0 167.5 168.8 165.9 162.5 169.7 168.6 158.7 157.5 154.5 167.9 169.3 170.8 173.6 159.4 160.2 160.5 155.1 167.5 168.3 166.6 162.1 167.5 168.9 159.3 158.1 155.0 169.0 170.4 172.1 175.0 160.1 160.9 159.8 153.4 168.2 168.4 167.9 163.4 169.5 171 .5 160.3 159.3 155.8 .7 .6 .8 .8 2 .4 2.7 2 .6 2.8 2.5 1.8 1.4 1.1 1.8 Workers, by industry division : Goods-producing ...... Excluding sales occupations ... ......... ....... ... ... ...... . White-collar occupati ons ..... Excluding sales occupations .. Blue-collar occu pations ... Construction .. Manufacturing White-collar occupations .. Excl uding sales occupati ons Blue-collar occupations .. ... ... ..... ....... .. Durables ............................... . Nondurables .. Service-producing .. Excluding sales occupations White-collar occupations .......................... . Excluding sales occupations ..... .......... .... .. Blue-collar occu pations ......... .. .......... ..... .. . Service occupations .......... ... ..... ... .... . Transportation and public utilities ....... .. ...................... .. Transportation.. ........................... . Public utilities ......................................................... . Communications ...... ... ............. ... ....... . Electric, gas, and sanitary services ........................ .. Wholesale and retail trade. . . ........... .. .... ... . Wholesale trade ..... Excluding sales occupations. . . .. ... ... ............. .. Retail trade ............. .. ......... ...... ..... .. ......... .. General merchandise stores ... .. ............... .. Food stores .. .. ....... ............................. .. See footnotes at end of table. 110 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 .4 .4 -.4 -1 .1 .4 .1 .8 .8 1.2 1.5 .6 .8 .5 .8 2 .8 1.9 2.0 2 .2 1.9 3.4 1.3 31. Continued-Employme nt Cost Index, wages and salaries, by occupation and industry group [June 1989 = 100] 2004 2003 Series Mar. June Sept. Dec. Mar. June Percent change 2005 Sept. Dec. 3 months 12 months ended ended Mar. Mar. 2005 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 .. , ..................... 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 .9 177.7 182.9 2 11 .3 170.4 172.0 175. 0 171.9 173.8 176.8 173.6 179.2 184.6 210 .7 171 .7 173.4 175 .5 173.4 175.4 177.9 174 .6 0.8 .9 - .3 .8 .8 .3 .9 .9 .6 .6 2.3 3.0 1.9 4.0 3.2 2.6 3.3 3.5 3.5 3.0 Non manufacturing White-collar workers ... Excluding sales occupations Blue-collar occupations ..... . Service occupations ....... ....... ...... ... ..... .... 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. 1 172.3 157.1 159.2 166.6 170.5 173.1 157.5 160.1 167.7 171 .7 174 .4 158.2 160.8 .7 .7 .8 .4 .4 2.4 2.5 2.8 2.3 1.8 State and local government workers.... .. ... .......... ....... .. ... 162.6 163.2 165.9 166.8 168.0 168.7 171.5 172.6 174.1 .6 2.3 Workers, by occupational group: White-collar workers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . Professional specialty and technical .. Executive, administrative, and managerial Administrative support, including clerical .. Blue-collar workers .. ... . .. . . . . . . . . . . . . . . . . . . . . . . . . . . 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 .3 162.6 160.7 164.9 165.0 166.1 163.0 161.4 165.9 165.7 168.2 163.9 162.4 .6 .4 1.3 .6 .6 2.3 2.2 2.9 2.2 2.2 Workers , by industry division : Services .. .... .... ... ...... ..... 159.5 159.8 161 .6 162 .1 162.6 162 .7 164.8 165.5 166.2 .4 2.2 161.4 162.9 163.1 159.1 159.2 158.2 162.1 161.8 163.5 163.8 159.3 159.5 158.5 162.1 163.2 165.1 165.5 161.2 161.4 160.6 163.5 164.5 166.7 166.7 161 .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 .5 164.4 167.5 169.6 169.9 164.2 164.3 163.8 165.4 168.3 170.7 171 .0 164.9 165.0 164.5 166.3 169.4 171.9 172.0 165.5 165.6 164.8 167.9 .7 .7 .6 .4 .4 .2 1.0 2.6 2.7 2.7 2.2 2.2 22 2.2 157.2 158.0 159.4 160.0 161 .1 161.4 162 .6 163.5 165.0 .9 2.4 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 , admin istrative, and regulatory activities. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 3 This series has the same industry and occupation al coverage as the Hourly Earnings index, which was discontinued in January 1989. 4 Includes , for example , library, social, and health services . Monthly Labor Review June 2005 111 Current Labor Statistics: Compensati on & Industrial Relations 32. Employment Cost Index, benefits, private industry workers by occupation and industry group [June 1989 = 100] 2003 2004 2005 Series Mar. June Sept. Dec. Mar. June Sept. Dec. Mar. Percent change 3 months 12 months ended ended Mar. 2005 Private industry workers ..... .... ... .. ........ .... ........ ... ....... ... .. ..... 179.6 182.0 184 .3 185.8 192.2 195.3 196.9 198.7 203.3 2.3 5.8 Workers, by occupational group: Wh ite-col lar workers . . . . . . . . . . . . . . . . . . . . . . . . . . . ....... ...... ... ... . Blue-co llar worke rs ............................. , 183.6 172.7 185.5 176.1 187.7 178.4 189.2 179.9 194.4 188.3 197.4 191.8 199.1 193.3 201 .1 194.9 206.8 197 .8 2.8 1.5 6.4 5.0 Workers , by industry division: Goods-producing . . . . . . . . . . . . . . . . . . . . . . . . . ... .... .... .. ... .......... . Service-produci ng Manufacturing Nonmanufacturing ..... ..... ...... ...... 178.0 179.9 176.9 180.3 180.2 182.3 179.0 182 .8 182 .3 184.7 181.1 185.1 183.8 186.2 182.3 186.7 193.7 190.6 194.4 190 .9 196.2 194.1 196.9 194.3 198.1 195.5 199.2 195.7 20 1.2 196.5 200.4 197.6 207 .0 200.5 206.7 20 1.6 2.9 2.0 3.1 2.0 6.9 5.2 6.3 5.6 112 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 33 . Employment Cost Index, private industry workers by bargaining status, region , and area size [June 1989 = 100] 2003 2004 2005 Series Mar. June Sept. Dec. Mar. June Sept. Dec. Mar. Percent change 3 months 12 months ended ended Mar. 2005 COMPENSATI ON Workers , by barga in in g statu s1 Union .. ................... ... ......... .... .. .... . Goods-producing ................... ... .... Service-produc in g .. ········· ····· ··• .. ·.. Manufacturing ....... .. .. ... ... ...... .. Nonmanufacturing .. . ...... .. ........... 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 .6 175.3 176.0 174.4 178.4 173.0 176.2 176.7 175.4 178.9 174.1 177.5 178.2 176.6 180.6 175.2 0.7 .8 .7 1.0 .6 3.6 3.4 3.8 3.2 3.8 Nonunion Goods-producing Service-producing .... Manufacturing .... .... ............ .... Nonmanufacturing ................... 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 .1 172.7 170.9 173.2 172.0 172.6 174.2 172.4 174.6 173.8 174.0 174.9 173.5 175.1 174.3 174.7 177.1 176.5 177.0 177.5 176.6 1.3 1.7 1.1 1.8 1.1 3.4 4.0 3.1 4.0 3.2 163.8 160.6 169.0 167.3 165.2 161.6 170.4 169.5 166.9 163.2 171 .7 17 1.4 167.9 163.9 172 .5 172 .2 170.2 166.4 174.7 175.3 172.3 167.9 176.2 176.8 173.7 169.5 177.6 178.1 174.2 170.6 177.9 179.0 176.1 172.5 180.0 181 .4 1.1 1.1 1.2 1.3 3.5 3.7 3 .0 3.5 165.2 163.5 166.6 165.0 168.3 166.1 169.1 166.9 171 .5 170.2 173.1 172.1 174.6 173.3 175.3 174.3 177.4 176.4 1.2 1.2 3.4 3.6 Union .. .................. ......... ............. .. ... ... ... ... ... ....... Goods-producin g ................... .... ..... Service-producing ... ... ....... ...... ........................ . Manufacturing ..... ...... ... ........ ... .. ..... ... ...... .. .... ... . Nonmanufacturing . 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 .7 160.5 159.6 160.6 158.9 162.6 160.7 160.4 160.8 159.6 162.3 161.5 160.3 .1 - .2 .5 - .1 2.3 2.1 2.4 2.2 2.4 Nonunion ···· ···· ······ ······ ····· Goods-producing Service-producing Manufacturing. Nonmanufactu ring .. 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 .7 165.6 162.4 166.6 163.7 165.7 167.0 163.8 168.0 165.2 167.1 167.3 163.9 168.4 165.3 167.5 168.6 165.2 169.7 166.8 168.7 .8 .8 .8 .9 .7 2.4 2.4 2.5 2.6 2.4 157.3 155.3 164.1 161 .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 .6 169.2 169.1 165.0 162.3 169.2 169.5 166.0 163.6 170.6 170.3 .6 .8 .8 .5 2.5 2.8 2.2 2.1 159.6 156.8 160.7 158.0 162.2 158.9 162.7 159.5 163.8 160.8 164.9 162.1 163.3 162.1 166.6 163.8 167.7 165.1 .7 .8 2.4 2.7 ...................... Workers , by region 1 Northeast ....... ...... ... .. .... ... . . ... ...... ... ... ... .... South . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....... ... ... ... ... .......... Midwest (formerly North Central) .. ...... ..... ............... . West ························ ··· ······ ······· ········ ·············· ·· ······ · Workers, by area size 1 Metropolitan areas ··· ·· ··· ··• ··•··•·· •.. ···· .... ............ ... ... Other areas ................ ... ... .......... ....................... . WAGES AND SALARIES Wo rkers , by barga ining status 1 .... ........... .... ..... .... Workers , by reg ion ....... .. ............... 1 Northeast ......... .. .... ..... South ....... ...... .. ... .. .. ... .... ...... ... .. .. .. ..... .. ... .. Midwest (formerly North Central) West .................... ..... ..... .... ..... ...... ..... ............ ... .......... Wo rkers, by area size Metropolitan areas · ·· ·· ··· ·· · ·· · ··· · ··· Other areas .4 1 1 The indexes are calculated differently from those for the occupation and industry groups. For a detailed descri ption of the index calculation, see the Monthly Labor Review Technical Note, 'Estimation procedures for the Employment Cost Index,' May 1982. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 113 Current Labor Statistics: Compensation & Industrial Relations 34. Percent of full-time employees participating in employer-provide d benefit plans, and in selected features within plans, medium and large private establishments, selected years, 1980-97 Item 1980 Scope of survey (in 000's) .... ... ...... .. .... .. .... .. ... Number of employees (in 000's): With medical care .. ..... ........ .. .. ...... .. . ..... . With life insurance .. . . . . . . . . . . . . . . . .. . . ... . . . . . . . . . . . . . With defined benefit plan .. ........ ...... ....... . ... 1982 1984 1986 1988 1989 1991 1993 1995 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 9 25 76 25 9 26 73 26 11 - 10 26 71 26 99 10.0 99 9.8 3.3 97 9.2 8 30 67 28 80 3.3 92 10.2 9 29 68 26 83 3.0 91 - 99 10.1 29 72 26 85 3.2 96 9.4 - - 10 27 72 26 88 3.2 99 10.0 20 24 3.8 23 3.6 25 3.7 22 3.1 21 3.3 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 ... .... .. .. .... ...... .. ...... Average days per year ..... .. . .. .. ... . ... .... Paid personal leave . ········· ·· ·· ···· ···· ····· ······· ··· Average days per year .. ··· ··· ·· ··· ·· 75 - Paid sick leave 1 .. . ..... .. ..... , .. .. ... .. ... .. . ... .. .. Unpaid maternity leave ·············· ·· ·········· ···· ·· ···· Unpaid paternity leave ... .. .. .. .. ... ... ... ... .. ....... ..... Unpaid family leave . .. .. ....... ········· ·· · ·· ·· ·· .. .. . .. 9.4 - 80 3.3 89 9.1 81 3.7 89 9.3 22 3.3 20 3.5 100 99 99 100 98 97 96 21 3.1 97 96 95 62 67 67 - 70 - - 68 37 18 67 37 26 65 60 53 58 56 - 69 33 16 - - - - - - - - - 84 93 97 97 97 95 90 92 83 82 77 76 - - 58 62 - - 46 62 8 66 70 18 76 79 28 75 80 28 81 80 30 86 82 42 78 73 56 85 78 63 26 27 36 - 43 51 - - $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 46 $11 .93 58 $35.93 $107.42 67 $33.92 78 $118.33 69 $39.14 80 $130.07 96 96 96 96 92 94 94 91 87 87 69 72 74 78 71 7 71 76 5 77 6 42 44 41 37 74 6 33 42 43 - ···· ···· ··· ···· · Paid vacations . ................. .. ......... ... ........ ... 24 84 3.3 - - Insurance plans Partici pants in medical care plans .. ...... . ..... . Percent of participants with cove rage 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 . . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . . . .. . . . . . . ·•·· ·· Partici pants in short-term disability plans 1 •.. ..... .... 76 - - - 64 64 72 10 59 40 43 47 48 42 45 40 41 54 51 51 49 46 43 45 44 - - - - - - - - - - 53 55 84 84 82 76 63 63 59 56 52 50 55 58 97 - - 64 98 35 6 62 62 97 22 64 63 7 52 45 59 98 26 55 62 52 95 53 45 63 97 47 54 56 55 98 56 54 61 48 52 96 4 58 51 52 95 10 56 49 - - - 60 45 48 48 49 55 57 - - - 33 36 41 44 43 54 55 - 2 - 5 12 9 23 10 36 12 52 12 38 13 32 - - - 5 7 - 8 49 7 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 Security .. Participants in defined contribution plans .. ... . Participants in plans with tax-deferred savings arrangements ... ............ ...... . ........•.. 57 98 Other benefits Employees eligible for: Flexible benefits plans ... ... ....... .. ....... . .. .. .... .... .. - - Reimbursement accounts 2 ..... ..... .. ... ... . ... .. Premium conversion olans .. .. .. . .. .............. 1 The definitions for paid sick leave and short-term disability (previously sickness and accident insurance) were changed for the 1995 survey. Paid sick leave now includes only plans that specify either a maximum number of days per year or unlimited days. Short- - 5 fits at less than full pay. 2 Prior to 1995, reimbursement accounts included premium conversion plans, which specifically allow medical plan participants to pay required plan premiums with pretax dollars. Also, reimbursement accounts that were part of flexible benefit plans were terms disability now includes all insured, self-insured, and State-mandated plans available on a per-disability basis, as well as the unfunded per-disability plans previously reported as tabulated separately. sick leave. Sickness and accident insurance, reported in years prior to this survey, included only insured, self-insured, and State-mandated plans providing per-disability bene- NOTE: Dash indicates data not available. 114 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 35 . Percent of full-time em ployees participating in employer-provided benefit plans , and in selected fe a tures within plans, small private esta blishments and State and local governments , 1987 , 1990 , 1992, 1994 , and 1996 Small private establishm e nts Item 1990 Scope of survey (i n OOO 's) ... . . . .. ... . Number of employees (in OOO's) With medical ca re .. ... .. .. With li fe insurance .. .. ... ... .... .. . ······ •· • With de fined benefit plan ..... .... Time-off plans Participa nts with : Paid lunch time .. Averag e minutes per day Paid rest time Averag e minutes per day ... · • Paid funeral leave .. Av erage days pe r occurre nce Paid holidays . . . . . . . . . . . . . .. . ... .. .... ... . .. • · · ·•···• • ·· ·· ··· ·· · •·•• · ··· · · ··· ·· ··· · ·· ... ....... Av eraQe days per vear' Paid personal leave .. ...... , . . . . . . . . . Average days per year . Paid vaca tions . .. .. .. . .. • ·· .... Paid sick leave 2 . . Unpaid leave .. Unpaid paternity leave . Unpaid family leave . . . . . .. .. . . . .. . . .. .... . ··•· ·· · · •• · ... . · · ···• •• ·•• · ·•• · · • · • ···• .. ... Insu rance plans Participan ts in medical care plans .. . . . . . . . . . . . . . . . . . . . Perce nt of participants with coverage for· Home health ca re ····· • Extende d ca re fa cilit ies . .. . Phys ical exa m .. . ··· ···· Percen t of parti cipa nts with employee con tribution required for Self coverage . ······ ········ ····•··· ···•·· Av erage monthly co ntribution .. Family coverage . Av erage monthly contribution .. .... ··•· •· •·· Participants in life insurance plans .. ·· ······ ···· ·· .... Perce nt of participants with Accide ntal death and dismemberment insurance .. Survivor income benefits .. . . . . . . . . . .. ,, .......... Retiree protection available .. . . . . . . . . .. . Participants in long-term disa bility insurance plans ·· ••• · • " · •• ·•• · Participants in sickness and accident insurance plans . ..... Participants in short-term disability plans 2 Retirement plan s Participants in defined benefit pension plans Perce nt of participants with : Norm al retirem ent prior to age 65 Early retirement available .. Ad hoc pension increase in last 5 years Term inal earnings formula .. .. . Benefit coo rdinated with Social Security .. . .. Participa nts in defined co ntribu tion plans .. Participants in plans with tax-deferred savings arrangements .. 1992 1994 State and local governments 1996 1987 1990 1994 1992 32 ,466 34 ,360 35,910 39,816 10,321 12,972 12 ,466 12,907 22,402 20 ,778 6,49 3 24 ,396 21 ,990 7,559 23,536 2 1,95 5 5, 480 25 ,59 9 24 ,635 5, 88 3 9,599 8,773 9,599 12,064 11 ,415 11 ,675 11 ,219 11,095 10,845 11 ,192 11,194 11,708 8 37 48 27 47 29 84 9 37 49 26 50 3.0 82 - 51 3.0 80 11 36 56 29 63 3.7 74 10 34 53 29 65 3.7 75 - 50 3.1 82 17 34 58 29 56 3.7 81 62 3.7 73 95 11 2.8 88 92 12 2.6 88 7.5 13 2.6 88 7.6 14 3.0 86 10 9 38 2 .7 72 13.6 39 29 67 14 .2 38 29 67 11 .5 38 3.0 66 47 53 50 50 97 95 95 94 17 8 18 7 - - 57 30 51 33 59 44 - - - 47 48 - - - 93 69 71 66 64 93 93 90 87 79 83 26 80 84 28 - - 76 78 36 82 79 36 87 84 47 84 81 55 42 $25 .13 67 47 $36 .5 1 73 52 $40.97 76 52 $42 .63 75 35 $15.7 4 71 38 $25 .53 65 43 $28 .97 72 47 $30 .20 71 $109 .34 $150 .54 $ 159 .63 $181 .53 $71 .89 $ 11 7.59 $ 139 .23 $149 .70 64 64 61 62 85 88 89 87 78 1 19 76 1 25 79 2 20 77 1 13 67 1 55 67 1 45 74 1 46 64 2 46 19 23 20 22 31 27 28 30 - - 6 26 26 - 14 21 22 21 - - - 29 - - - - 20 22 15 15 93 90 87 91 54 95 7 58 49 50 95 4 54 46 - 47 92 53 44 92 90 33 100 18 89 88 16 100 8 92 89 10 100 10 92 87 13 99 49 31 33 34 38 9 9 9 9 17 24 23 28 28 45 45 24 - Oth er benefits Employees eligible for: Flexible benefits plans .. . . . . . . . . . . . . . . . . .. . .. . Reimbursement accounts 3 Premium co nversion plans 1 1 2 3 4 5 5 5 8 14 19 12 5 31 50 5 64 7 - - - - - - Meth ods used to ca lculate the average number of paid holidays were revised Sickness and accident insurance, reported in years prior to th is surve y, in 1994 to cou nt partial days more precisely. Average holidays for 1994 are included only insured , self-in sured, and State-mandated plans providi ng per- not comparable with those reported in 1990 and 1992 . disabil ity benefits at less th an 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 Pri or to 1996, reimbursem en t accounts incl uded premium conversio n plans , which specifically allow medical plan participants to pay required plan leave now incl udes only plans that specify either a maximum number of days premiums with pretax dollars . Als o, reimbursement accounts that were part of per year or unlimited days . Short-term disability now includes all insured, self- flexible benefit plan s were tab ulated sepa rately . insured , and State -mandated plans available on a per-disability basis , as well as the unfunded per-disability plans previously reported as sic k leave https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis NOTE: Dash indicates data not available . Monthly Labor Review June 2005 115 Current Labor Statistics: Comp ensation & Industrial Relations 36. Work stoppages involving 1,000 workers or more Annual totals Measure 2003 2004 2004 Apr. May June July 2005 Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr.P Number of stoppages: Beginning in period ... ........... ........ In effect during period ... .. ... . .. .. . . . . .. .. . 14 15 17 18 0 1 2 2 3 4 0 1 2 2 2 1 3 3 2 4 3 4 0 2 0 2 2 4 3 5 Workers involved : Beginning in period (in thousands) ..... In effect during period (in thousands) . 129.2 130.5 170.7 316.5 .0 2.2 103.0 103.0 27.6 28.6 .0 1.6 3.7 3.7 4.5 6.5 10.0 16.1 3.2 16.1 9.8 8.5 .0 2.5 .0 2.6 4.7 7.3 11 .0 14.0 4,091 .2 3,344.1 26.4 204.0 94 .0 3.2 52 .5 57.0 300.0 114.9 97.5 50.0 49.4 86 .0 48.5 .01 .01 (2) .01 (2) (2) (2) (2) .01 (2) (2) (2) (2) (2) (2) . Days idle: Number (in thousands) ... Percent of estimated workino 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 explanation of the measurement of idleness as a percentage of the total time worked is found in "Total economy measures of strike idleness," Monthly Labor Review 116 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 Monthly Labor Review, October 1968, pp . 54-56 . 2 Less than 0.005. NOTE: P = preliminary. 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 indicated] 2003 2004 2005 2004 Annual average Series Apr. May June July Aug . Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. CONSUMER PRICE INDEX FOR ALL URBA N CO NSUMERS All items All items (1967 = 100). .. · •· Food and beverages .. .. ... .. . Food .. •· ... .. ..... ... ... Food at home • ···· .... ... Cereals and bakery products .. Meats, poultry, fish, and eggs. Dairy and related products Fruits and veget:ibles .. .. .. ... 1 ·· •· ·· 184.0 188.9 188.0 189.1 189.7 189.4 189.5 189.9 190.9 19 1.0 190.3 190.7 191 .8 193.3 194.6 551 .1 565 .8 563.2 566.4 568.2 567.5 567.6 568.7 571 .9 572.2 570.1 571 .2 574.5 579.0 582 .9 180.5 186.6 185.0 184.5 186.8 186.3 186.8 187.3 186.8 187.2 186.7 188.4 187.9 188.6 188.2 188.9 188.5 189 .5 186.2 186.5 186.1 187.2 180.0 179.4 189.1 189.3 188.8 189.6 189.1 190.2 186.2 184.1 186.6 186.8 187.1 186.7 186.1 187.9 188.1 188 .5 188.9 188.0 188.1 189.8 202 .8 205.5 206.1 206.8 207.2 207.2 206.4 207.0 206.8 206.4 207 .6 208 .4 208.5 209.1 169 .3 206.0 181 .7 179.2 181 .1 182.3 183.7 183.7 183.4 182.9 182.4 183.1 183.4 183.9 184.3 184.7 167.9 225.9 180.2 232 .7 174.0 228.3 185.9 231.7 188.8 226.7 187.7 224 .5 184.9 224.0 181 .6 226.0 182.1 240.0 180.9 248.3 180.1 250.8 183.3 242.9 181 .8 234 .8 181.4 233 .7 182 .2 240.1 139.8 140.4 139.7 169.9 139.8 140.5 140.3 140.3 140.6 139.6 140.4 142 .2 142.5 143.6 144.8 162.6 164.9 165.0 165.4 165 .8 166.0 165.2 165.4 164.4 163.6 165.6 165.3 165.7 167.5 162 .8 166.2 164.4 163.5 162.6 163.0 164.2 167.4 170.4 178.9 178.3 180.3 169.3 179.7 162 .6 167.0 164.9 170.4 163.1 167.8 161 .3 169.7 181. 3 183.0 190.7 Nonalcoh ol ic beverages and beverage materials Other foods at home .. Sugar and sweets .. .. .. .. . 162.0 163.2 162.6 .. .. . .. .. 157.4 166.2 163.5 169 .4 171 .3 163.8 171 .9 ..... ···· • · 178 .8 167.8 179.7 180.4 180.1 180.5 180.3 180.9 179.4 170.2 180.1 110.3 110.4 110.5 110.8 110.9 109.4 111 .5 110.5 109.9 110.5 110.8 110.1 110.3 111 .9 110.8 182.1 187.5 186.2 186.7 187.0 187.8 188.4 188.9 189.4 189.6 189.9 190 .8 191.4 191 .7 192 .8 121 .3 187.2 125.3 192.1 124.7 191 .8 124.8 191.7 124.8 192 .4 125.1 192.2 125.4 192.5 125.9 193. 4 126.8 193.6 126.7 194.0 127.0 193.9 127.5 194.3 128.7 195.2 129.4 195.7 129.6 195.9 184.8 213.1 189.5 218.8 188.4 188.9 190.9 220.0 191 .0 192.7 194.1 194.4 220.2 190.8 2 19.9 191 .8 220.3 191 .0 220.6 190.7 218.7 190.3 219.2 191 .2 218.4 2 19.8 221 0 222 .5 224.4 224.4 205.5 211 0 209.7 210.2 210 .7 211.2 2 11 .9 2 12.4 212.8 2 13.2 2 13.9 21 4.5 215 .0 215.5 216.0 119.3 125.9 129.1 128.2 129.1 132.2 130.6 127.2 128.0 121 .9 118.7 122.6 128.9 138.3 136.2 219.9 224.9 223.9 224 .3 224 .7 225.1 225.7 226.1 226.5 226.8 227 .2 227 .8 228.4 228.7 229.0 114.8 154.5 138.2 116.2 161 .9 144.4 115.7 155.6 116.2 165 .5 148 .5 116.1 166.6 149.5 11 6.3 167.7 150.5 116.6 166.7 138.0 116.1 158 .1 140.4 149.3 116.3 162.8 144.9 11 7.7 165.6 147.8 118.7 165.7 148.0 118.5 166.9 149.0 118.7 166.4 148.1 119.0 166.7 148.4 118.2 169.6 151 .5 139.5 160 .5 149.6 150.4 150 .7 151.1 157.4 161 .6 177. 3 186.6 183.7 181 .2 188.5 195.5 145.0 150 .6 144.2 146.8 155.8 156.9 157.6 156.0 152 .7 153.0 154.3 152 .9 126 .1 152 .7 155 .9 126.1 126.3 ······• • . Fats and oils . . . . . . . . . . . . . Other foods 12 foods • Other miscellaneous Food away from home 1 12 Other food away from home · Alcoholic beverages .. Housing .. ... ... .. .. .... . Shelter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... .. . .... .. Rent of primary residence . Lodgi ng away from home .. ·· • ··· ••· · Owners ' equivalent rent of primary residence Tenants' and household insurance Fu els and utilities 3 12 • Fuels .. ... · · •· Fu el oil and other fuels .. ...... ...... . .. .. .... Gas (piped) and electricity. Household furnishi ngs and operations .. Apparel Men's and boys' apparel .. . . . . . . ... Women's and giris' appa rel •• · Infants' and todd lers' apparel Footwear ... . ·· • · 1 ..... .. ... .. ... Transportation ... .. Private transportati on 124 .8 125.0 125.8 125.5 115.9 11 6.5 121 .2 124.1 123.0 11 8.8 116.1 118.7 123.5 123 .7 118.0 11 7.5 120.3 116.9 11 7.7 115.2 113.8 118.3 11 8.9 116.3 116.3 119.6 117.1 120.4 116.6 126.1 11 3. 1 11 3.0 120.3 118.7 112.3 106.1 107.5 116.2 114.4 119.2 11 6.8 110.0 115.0 105.1 122. 1 118.5 120.5 118.1 116.2 114.5 11 5.0 119.5 120.6 120.3 118.6 11 7.5 11 8.1 119.0 121 .3 119.6 119.3 121.0 120.3 118.4 115.1 117.3 121.7 122.1 121 .8 120.3 119.4 121.1 122 .8 123.8 109.3 161 .8 165 .2 165.7 164.0 162.9 162.9 166.4 167.2 164 .8 164.0 166.1 168.8 173.2 161 .5 161 .9 160.0 159.1 159.4 162.9 163.6 161 .3 160.5 162.6 165 .2 169.6 96 .5 94.2 94.1 94.0 93 .6 93.5 93.4 93.9 94 .3 95.2 95.4 95 .8 95.9 95 .6 95 .6 137.9 137.1 137.6 137.4 137.2 135.9 134.9 134.9 135.9 137.9 138.8 139 .8 139.9 139.1 138 .8 142.9 135 .8 133.3 160.4 131 .3 155.9 131.8 170.5 130 .6 173 .3 132.1 165.2 133.8 162.0 136.5 161.2 136.8 173.1 136.7 171.9 171 .0 137.3 161.2 137.5 156.4 137.6 164 .3 137 .7 175.9 138 .1 193.9 163.4 11 0.9 175.0 193.9 11 0.9 204.7 110.8 205.0 ........ .. . . 2 12 • 2 Education and communication 2 Education Educational books and supplies .. Tuition , other school fees, and child ca re .. 12 Communication • 12 Information and information process ing • 12 Telephone services • Information and information processing 14 other tha n teleohone se rvices • Personal computers and peripheral 135 .1 159.7 155.3 169.8 172 .7 164.5 161 .2 160.5 172.2 160.4 155.6 107. 8 108.7 107.9 107.9 109.3 109.9 198.6 199.0 200.8 200.7 109.5 20 1.7 109.9 200.2 108.8 200.3 109.0 195.6 108.2 199.7 202 .9 203.3 11 0.6 204 .0 203.9 209 .3 209.1 211.5 210 .7 212 .3 214.4 209.7 205.3 206.5 208.6 205.4 204.4 205.9 210 .1 215 .0 297.1 308.3 309.0 310 .0 311 .0 311 .6 312 .3 313.3 314.1 319.3 269.1 269 .6 269.9 270.0 270.9 27 1.7 271.2 27 1.6 272 .8 320 .7 273 .2 321.5 268.5 314.9 270.8 316.8 262 .8 310.1 269.3 306 .0 261.2 394.8 321 .3 271.5 417 .9 319.2 270.6 413.6 319 .8 270 .9 414 .6 32 1.0 271 .6 416 .9 322 .3 272 .3 419.1 323.1 273.3 418 .8 323.7 273.3 420.3 324.8 273.7 422 .5 326.0 274 .2 329 .5 276 .2 4250 327 .3 274 .6 428.0 332 .5 278.6 434 .7 334 .3 279 .7 437 .3 335 .2 281 .0 437 .1 107.5 108.6 109.0 108.8 108 .9 108.7 108.5 108.6 108.7 108.7 108.5 108.9 109.0 109.0 109.2 103.6 104.2 104.7 104.6 104.4 104.4 104.1 104.0 104.2 104.0 103.9 104.2 104.3 104.6 104.8 109.8 111 .6 110.9 110.6 110.8 110.9 111.7 112.9 112.5 112.7 11 2.6 11 2.7 112.8 11 2.7 112.9 134.4 335.4 143.7 351 .0 140.7 349.5 140.9 349.6 141 .6 350.6 142.1 349.5 145.1 353.3 147.9 352 .8 148.3 353.8 148.4 354.4 148.5 355 .9 148.8 357.4 149.2 359.9 149.3 360.6 149.5 361.3 362 .1 414.3 404.9 405.6 418.3 427.4 428 .9 86.5 86.1 86.2 85.6 85.4 85.4 430.9 85.2 431 .4 86.9 429.7 85.4 430.6 87 .4 428.2 85.5 428.7 86.7 407.6 86.8 409 .4 89.7 87.8 84 .6 85.4 84.8 84.7 84 .5 84.0 84.1 83.4 83.5 83.3 83.2 83.3 83.1 83 .2 98.3 95.8 96 .5 95.9 95.8 95 .6 95.0 95.3 94.6 94.5 94.8 94.8 95.1 95.0 95.3 16.1 14.8 15.0 14.9 14.9 14.8 14.7 14.7 14.5 14.3 14.2 14.2 14 .0 14.0 13.9 431 .0 273 .5 85 4 17.6 15.3 15.9 15.7 15.5 15.3 15.1 15.0 14.6 14.2 13.9 14 .0 13.5 13.4 13.4 298.7 304.7 303.6 303.8 304 .1 305 .1 305.5 306.3 306.8 307.0 307 .8 309.3 310.8 311 .2 311 .5 469 .0 478.0 473.3 473.5 476.0 480.5 48 1.6 482 .9 482.3 481 .7 484 .8 493.9 496.1 496.6 497 .0 178.0 181 .7 181 .3 181.4 181 .4 181 .7 181 .9 182.3 182.8 83.0 183 .3 183.5 184.4 184.7 184.9 1 153 .5 153.9 154.5 154.6 153.8 153 .4 152.8 153.5 154.0 153.8 153.4 153.1 153.9 153 .0 153.4 1 193.2 197.6 196.1 196.6 196.9 197.5 198.9 199.1 199.4 200.0 20 1.2 201 .9 202 .9 203.3 203.3 June 2005 12 equipment ' Other goods and services ... Tobacco and smoking products ... 1 Personal care products Personal ca re services 125.2 120.1 157.9 Medical care co mm odi ties ... Medical care services ... . . . . . . . . . ... . . Professional services . .. ... Hospital and related services Personal care 125 .6 123.4 163.1 .. . . . . . . . . . Video and audio 125.4 124.3 159.4 Public transportation. Rec reation 125.6 120.4 153 .6 Motor vehicle parts and equ ipm ent. .. Motor vehicle maintenance and repai r .. Medical care ... 125.5 120.9 157.6 .... ...... . ... .... Gasoline (all types). 126.1 · •· · 1 . •• . 199.5 150.0 126.1 ·· •· New and used motor vehicles2 New vehicles ....... ....... ... .... ......... .. ... .. ... Used cars and trucks Motor fuel. . ........... ... 169.4 .. .... ..... ... ... .. ...... ..... See footn otes at end of table . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review 11 7 Current Labor Statistics: Price Data 37. Continued-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 indicated] Annual average Series 2003 Miscellaneous personal service s 2004 2004 Apr. May June July Aug. 2005 Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 283.5 293.9 292 .7 293 .1 .... 151 .2 154.7 154.3 156.0 155.8 154.5 154.2 154.9 157.1 157.2 155.8 155.4 156.5 158.2 160.3 Food and beverages .. 180.5 186.6 185.0 186.5 186.8 187.2 187.3 187.2 188.4 188.6 188.9 189.5 189.3 189.6 190.7 Commodities less food and beverages .. 134.5 136.7 136.9 138.6 138.2 136.1 135.6 136.7 139.4 139.4 137.2 136.4 138.1 140.4 142.9 Nondurables less food and beverages Apparel 149.7 157.2 157.2 160.9 160.5 156.7 156.1 157.8 162.6 162 .0 157.4 155.2 158.6 163.7 168 .9 120 .9 120.4 124.3 123.4 120.1 115.9 116.5 121 .2 124.1 123.0 118.8 116.1 118.7 123.5 123.7 171.5 183.9 117.5 114.8 181 .7 115.0 188.2 114.8 189.5 114.5 185.8 114.1 184.4 11 3.7 184.4 114.1 190.6 114.7 190.2 115.3 185.2 115.5 183.3 116.0 187.3 116.0 192.7 115.7 201 .0 115.6 •· Commodity and service group: Commoditi es .. Nondurabl es less fo od, beverages, and apparel. . ·· ·• Durables Services .. .. .... .......... . ... . ...... 294.4 295.2 295 .9 296 .3 296.9 297.7 298 .5 299 .8 300.8 301 .4 216.5 222 .8 221.5 22 1.9 223.3 224 .1 224.5 224.5 224.5 224.6 224.6 225.6 226.8 228.0 228.6 221 .9 216 .3 227 .9 220.6 227.4 227 .7 22 8.3 229.2 229.4 229.3 229.8 229.0 228.9 230.1 231 .7 233.7 233.7 ····· •·· 254 .4 261 .3 220.0 259.7 220 .0 259 .6 220 .5 260.2 221 .6 260.5 220.8 261.9 220.1 263.8 221.4 263.7 222 .8 264.2 221 .8 264.3 221 .7 265.1 222.4 265.8 223.3 266.1 224 .4 266.7 184.7 189.4 183.6 189.6 190.3 189.9 189.9 190.4 191.4 191 .5 190.6 190.9 192.3 194.0 195.3 . .... 174.6 179.3 178.2 179.6 180.2 179.6 179.5 180.1 181.4 181 .9 180.9 180.9 181 .9 183.2 185.1 3 Rent of shelter Transporatation services · •· Other services .. 293 .6 · •· ··· • Special indexes All items less food •· · All items less shelter .. .. All items less medical care .. Commodities less food ... Nondurabl es less food •· Nondurabl es less food and apparel. 178.1 182.7 181 .8 182 .9 183.5 183.2 183.2 183.6 184.6 184.7 183.9 184.2 185.3 186.8 188.1 136 .5 138.8 138.9 140.6 140.3 138.2 137.7 138.8 141.1 141 .4 139.3 138.6 140.2 142.5 144.9 158.2 184.3 159.9 164.2 159.5 185.1 160.8 165.6 170.6 190.0 163.9 189.7 157.5 184.4 183.5 187.2 192.1 199.7 151 .9 159.3 159.3 162 .8 162.4 158.8 172.1 183.8 181 .7 187.7 189.0 185.6 Nondurables .. 165.3 172.2 171 .4 174 .1 174.0 172.2 171 .9 172.8 175.8 175.6 173.3 172.5 174.2 177.0 180.3 Services les s rent of shelter3 Services less medical care se rvices Energy 226.4 233.5 231 .1 231.7 234 .2 235.0 235.6 235.9 235 .1 236.4 236.5 237.4 238.0 238.5 239.8 208 .7 136.5 2 14.5 151 .4 213.2 145.9 213 .6 154 .1 215.0 159.7 215.8 156.3 216 .2 155.3 2 16.1 154.3 216.0 157.7 216.1 158.6 216.0 153.7 217.0 151 .9 218.0 155.2 219.2 160.8 219.7 170.9 All item s less ene rgy · •· · All items less food and energy Commodities less foo d and energy Energy commodities .. Services less energy . 190.6 194.4 194.1 194.3 194.4 194.5 194.7 195.2 196.0 1196.0 195.8 196.4 197.3 198.3 198.6 193.2 196.6 196.5 196.5 196.6 196.6 196.8 197.4 198.2 198.1 197.8 198.4 199.5 200.7 200 .9 140.9 139.6 140.5 140.2 139.4 138.2 138.1 139.4 140.5 140.6 139.8 139.7 140.3 141 .1 141 .2 136.7 161 .2 156.3 170 .1 172.8 165.1 162.5 162.0 174.2 173.6 163.4 158.7 166.6 178.0 195.2 223 .8 230.2 229.4 229 .6 230 .2 231 .0 231.4 231 .6 232.1 231 .9 231 .9 232 .9 234 .3 235.7 236.0 CONSUMER PRICE INDEX FOR URBAN WAGE EARNERS AND CLERICAL WORKERS All items .. All items (1967 = 100) . ... .... Food and beverages .. Food .. . .. .. .. .. .. .. .. . ... ... ... . .. . . . . . . .. . . . .. ... Food at home · •• · Cereals and bakery products · · · •• · Meats, poultry, fish, and eggs .. Dairy and related produ cts Fruits and veg etables .. 1 179.8 184.5 183.5 184.7 185.3 184.9 185.0 185.4 186.5 186.8 186.0 186.3 187.3 188.6 190.2 535.6 549 .5 546.5 550 .2 55 1.9 550.8 551 .0 552 .4 555 .7 556.3 554 .2 554.9 557 .9 561 .9 566.4 179.9 186.2 184.5 186.0 186.4 186.8 186.9 186.8 187.9 188.1 188.4 189.0 188.8 189.1 190.1 179.4 185.7 183.9 185.6 185.9 186.3 186.4 186.2 187.4 187.6 187.9 188.5 188.2 188.5 189.6 188.0 207 .6 187.2 187.4 188.9 208.5 208.5 209.0 184.5 182 .1 237.5 178.5 185.4 183.3 185.8 186.1 186.3 186.1 185.5 187.1 187.3 187.6 202.8 206 .0 205.5 206 .0 206 .7 207.2 207 .0 206 .3 206.9 206.8 206 .3 169.2 181 .8 179.1 181 .1 182.4 183.7 183.7 183.4 183.0 182.4 183.2 183.4 183.9 184.3 167.6 224 .3 180.0 230.4 173.6 225.5 186.1 228 .9 189.0 224 .3 187.8 222 .3 184.9 222.2 181.4 223.9 181 .8 238 .0 180.8 246.4 179.9 248 .6 183.2 240.1 181 .6 232 .2 181 .3 231 .3 138.9 163.8 162.1 140.0 141 .6 141 .8 143.0 144.1 163.2 160.6 165.3 162.2 165.0 163.6 165.3 161 .8 167.0 163.9 167.3 178.6 170.4 180.8 169.1 167.2 180.2 181 .7 169.4 183.4 Nonalcoholic beverages and beverage materials .. Other foods at home Sugar and sweets ·• • · ·· • · · •· ... ...... . 139.1 139.7 139.1 139.3 139.3 139.8 139.6 139.7 140.0 162.2 161 .6 164.5 162.5 164.6 161 .9 165.1 162 .9 165.5 162.2 165.6 162.9 165.8 163.8 164.8 163.1 165.0 162.2 169.9 181.4 170.3 179.7 170.0 167.7 180.5 179.2 Fats and oil s .. 157.4 167.8 166.1 169.4 171 .4 172.0 Other foods 179.2 180.1 180.8 180.5 180.8 180.7 Other miscellaneous Food away from home 12 foods · 1 Other food away from .. 12 home ' Alcoholic beverages .. Housing Shelter. , Rent of primary residence Lodging away from home .. ... . ·· •· · 2 Owners' equivalent rent of primary residence Tenants' and household insurance Fu els and utilities 12 ' Fuels ... Fuel oi l and other fuels .. . .... Gas (piped) and electrici ty .. 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 vehicles 2 3 110.8 110.9 111 .0 111 .2 111.4 109.7 112.0 111 .0 110.3 111 .1 111 .3 110.7 110.9 112.5 111 .1 182.0 187.4 186.1 186.6 186.8 187.6 188.2 188.8 189.3 189.5 189.7 190.6 191 .2 191 .6 192.0 121 .5 187.1 125 .1 192.4 124.3 192.1 124.6 192 .0 124.7 192.7 124.9 192.2 125.2 192 .8 125.8 194.0 126.8 193.9 126.8 194.2 127.0 194.2 127 .3 194.4 128.4 195.2 129.1 196.0 129.2 196.2 180.4 185.0 183.6 184 .1 185.6 186.2 186.6 186.5 186.2 186.4 186.4 187.3 188.1 188.9 189.4 206.9 212 .2 211.5 211 .8 212 .2 213.0 213.4 213.4 213.8 213.4 213.5 214.4 215.7 216.8 216 .9 204.7 210.2 208.9 209.4 209.9 210.3 211 .0 211 .6 212.0 212 .4 213.0 213.7 214.2 214.6 215.2 119.8 126.4 129.8 128.2 128.8 133.0 131 .6 127.7 128.3 121 .8 118.6 122.2 129.1 137.1 135.2 199.7 204 .1 203.1 203 .6 203.9 204 .2 204.7 205.1 205.5 205.8 206.1 206.6 207.2 207.4 207.7 114.7 153.9 116.4 161 .2 116.0 155.1 116.4 157.4 116.5 165.0 116.3 166.1 116.5 167.2 116.8 166.2 116.5 161 .9 118.1 164.5 118.9 164.7 118.8 166.0 118.9 165.4 119.4 165.7 118.5 168.6 137.0 143.2 137.0 139.3 147.4 148.4 149.3 148.2 143.5 146.2 146.4 147.4 146.6 146.8 149.8 138.7 160.0 148.9 149.6 149.8 150.2 156.8 161 .1 177.2 186.5 183.4 180.9 187.7 195.3 199.2 152.0 121 .9 151 .8 155.0 121 .9 122.1 144.1 149.8 143.5 146 .1 155.1 156.2 156.8 155.3 149.1 151 .7 152.0 153.3 121 .9 12 1.1 121 .3 121 .1 121 .3 120.7 120.4 120.6 121 .7 121 .5 121 .3 121 .9 120.0 120.0 123 .8 122 .8 119.6 11 5.6 115.9 120.6 123.5 122.6 118.6 116.1 118.6 123.0 123.2 117.5 117.3 120.6 120.3 117.8 115.2 113.3 115.6 117.8 118.6 11 5.7 114.6 116.1 119.6 119.9 112.1 112.8 118.4 116.7 112.2 106.0 106.9 114.0 119.3 116.9 110.2 105.3 109.3 116.8 124.1 124.1 121 .3 123.4 120.9 118.8 117.0 117.6 122.3 123.3 123.1 121 .4 120.5 121 .0 121 .9 122.7 119.1 156.3 118.2 161 .5 119.6 159.9 119.0 163.6 117.0 164.0 114.4 162.2 116.3 161 .4 120.4 161 .6 120.6 165.3 120.6 165.8 11 9.4 163.4 118.8 1632.6 120.6 164.7 121 .7 167.6 122.7 172.2 153.5 158.8 157.1 160.9 161 .3 159.3 158.6 159.1 162.7 163.2 160.9 160.0 162.2 164.9 169.5 96.0 92 .8 92 .6 92 .5 92 .1 92 .1 92.2 92 .3 93.3 94 .0 94.3 94 .6 94.7 94 .5 94 .5 See footnotes at end of table . 118 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 37. Continued-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 indicated] Annual average Series New vehicles 2003 . .. . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . .. 1 Used cars and trucks Motor fuel .. ·· ··· Gasoline (all types) .... ......... ... .... ... ...... .......... . Motor vehicle parts and equipment.. . Medical care commodities ... Medical care services Sept. Oct. Nov. Dec. Jan. Feb. Mar. Apr. 139.0 138.1 138.7 138.5 138.2 137.0 136.0 136.0 136.9 138.9 139.8 140.7 140.7 140.0 139.7 143.7 134.1 132.1 132.6 131.4 133.0 134.6 137.3 137.6 137.5 138.1 138.3 138.4 138.5 138.9 194.5 136.1 160.9 156.5 171 .1 173.8 165.6 162.4 161.7 173.6 172.3 161.7 156.9 164.9 176.5 135.5 160.2 155.8 170.4 173.2 165.0 161 .7 161 .0 172.9 171 .6 160.9 156.1 164.1 175.7 193.7 107.3 108.2 107.5 107.5 107.8 108.2 108.4 108.7 108.9 109.4 109.3 110.1 110.4 110.5 110.4 200.4 200.8 201.5 202.1 202.7 202.7 203.8 204.9 205.3 206.0 206.1 206.9 207.2 208.8 210.0 212.1 208.0 203.1 204.2 207.1 204.2 203.4 204.9 209.0 213.3 .. .. ...... ........... ..... ....... .... . ..... 296.3 309.5 307.7 308.4 309.4 310.4 311 .0 311 .7 312.7 313.6 314.4 316.3 318.9 320.3 321 .1 257.4 263.2 262 .5 263.3 263.8 263.7 263.8 264.8 265.4 264.9 264.4 265.2 266.3 266.6 266.9 .. ... .... .. . ... . ···· · .. ... .. ..... 305.9 321 .5 319.4 320.0 321 .2 322.4 323.2 323.9 325.0 326.3 327.7 330.0 333.0 334.8 335.8 263.4 274.0 273.2 273.5 274.1 274.8 275.8 275.9 276.3 276.9 277.2 278.9 281 .2 282.3 283.6 ......... 391 .2 414.0 409.8 410.7 413.0 415.2 414.9 416.4 418.5 421 .0 424.2 427.4 430.9 433.6 433.4 105.5 106.3 106.7 106.6 106.7 106.3 106.1 106.2 106.2 106.3 106.1 106.5 106.5 106.5 106.8 102.9 103.4 103.9 103.9 103.7 103.7 103.4 103.3 103.5 103.3 103.2 103.4 103.5 103.9 104.0 110.8 . ······ ···"···· 2 2 Education Educational books and supplies .. Tuiti on, other school fees, and child care .. ('.,ommunication Aug. 209.4 12 ' Education and communication July 202.0 2 Vic1fl0 and audio June 207.1 . . . . . . . . .. . . . . . . . . Hospital and related services Recreati on May 197.3 .. . .. . .. .. . .. . . Professional services .. 2005 2004 Apr. 206.0 Motor vehicle maintenance and repair .... Public transportation .... Medical care ... 2004 12 · Information and information processing 12 • .. 12 Telephone services ' Information and information processing 14 other than teleohone services • Personal computers and peripheral 12 109.0 110.0 109.6 109.2 109.4 109.4 109.9 110.8 110.5 110.6 110.5 110.6 110.7 110.7 133.8 142.5 139.7 139.9 140.6 141.0 143.6 146.3 146.7 146.8 147.0 147.3 147.7 147.8 148.0 336.5 352.2 350.4 350.4 351 .5 350.4 354.7 354.8 355.6 356.1 357.6 359.0 361 .5 362.4 363.1 377.3 402.5 394.1 394.6 396.7 398.1 405.8 414.0 415.2 415.6 415.8 416.8 417.6 418.0 418.5 91 .2 88.3 89.0 88 ..4 88.4 88.1 87.6 87.8 87.1 87.2 87.0 87.0 87.0 86.8 87.0 89.9 86.8 87.5 87.0 86.9 86.7 86.2 86.3 85.6 85.7 85.5 85.5 85.5 85.3 85.5 98.5 96.0 96.7 96.1 96.1 95.8 95.2 95.5 94.8 95.1 95.0 94.9 95.3 95.1 95.4 16.7 15.3 15.5 15.4 15.4 15.3 15.3 15.2 15.0 14.9 14.8 14.8 14.6 14.5 14.5 equipment ' Other goods and services ........ ... ... .. ............ 17.3 15.0 15.6 15.4 15.2 15.0 14.9 14.8 14.3 13.9 13.7 13.7 13.3 13.2 13.2 307.0 312.6 311 .3 311.5 311 .8 313.2 313.5 314.4 314.7 314.9 315.9 318.0 319.4 319.6 319.9 Tobacco and smoking products ....... ...... 470.5 478.8 474.1 474.4 476.9 481 .6 482.6 483.9 483.0 482.5 485.7 494.9 496.9 497.4 497.8 177.0 180.4 180.1 180.2 180.0 180.3 180.5 180.9 181.4 181 .7 181 .9 182.1 182.9 183.0 183.2 154.2 154.4 155.1 155.1 154.3 153.9 153.1 154.0 154.3 154.3 153.8 153.3 154.2 153.3 153.6 Personal care 1 Personal care products 1 1 193.9 198.2 196.6 197.1 197.5 198.1 199.5 199.7 199.9 200.6 201 .8 202.4 203.3 203.6 203.6 . 283.3 294.0 292.9 293.1 293.5 294.7 295.4 296.2 296.6 297.5 298.4 299.2 299.8 300.8 301 .5 Commodities ... .. . ......... ...... ... .. ... .... ..... .. 151 .8 155.4 179.9 186.2 154.8 184.5 156.7 186.0 156.6 186.4 155.2 186.8 154.9 186.9 155.7 186.8 158.0 187.9 158.1 188.1 156.6 188.4 156.3 189.0 157.4 188.8 159.2 189.1 161 .5 190.1 Personal care services Miscellaneous personal services Commodity and service group: .. .. . .. . .. .. . Food and beverages .. Commodities less food and beverages ... 135.8 138.1 138.0 140.0 139.6 137.5 137.1 138.2 141 .0 141.0 138.8 138.0 139.8 142.2 145.0 Nondurables less food and beverages .. 152.1 160.6 160.5 164.7 164.4 160.4 159.5 161 .2 166.5 165.9 160.9 158.8 162.5 167.8 173.6 120.0 120.0 123.8 122.8 119.6 115.6 115.9 120.6 123.5 122.6 118.6 116.1 118.6 123.0 123.2 208.9 Apparel. Nondurables less food, beverages, and apparel . .. ...... ... ........ . .... . .. .. . .. . .. . .. •. . Durables ...... ..... ....... ..... .. ......... . ............. Services · ········•·· ............... ... ..... . ........... 3 Rent of shelter Transporatation services . .... ......... ... .... ..... .... Other services ..... ................. ... ..... ......... ...... 175.6 189.6 187.0 194.5 196.0 191 .8 190.2 190.1 196.9 196.5 190.8 188.8 193.3 199.4 117.4 114.0 113.9 113.9 113.5 113.2 113.1 113.7 114.3 114.8 115.1 115.5 115.5 115.3 115.3 212.6 218.6 217.1 217.6 219.0 219.7 220.2 220.3 220.0 220.4 220.5 221 .5 222 .3 223.2 223.8 199.2 204.3 203.7 216.2 220.9 220.2 203.9 220.3 204.4 220.7 205.1 221 .6 205.5 221.0 205.5 220.5 205.9 222.0 205.5 223.4 205.6 222.7 206.5 222.8 207 .7 223.4 208.8 224.0 208.9 224.8 248.5 254.1 253.0 252.7 253.3 253.5 254.4 2560 255.9 256.3 256.5 257.2 257.8 258.1 258.7 Special indexes: .AJI items less food . . . . .. . .. . . .. .AJI items less shelter .. .... ........ .............. . . . . .. . .. . .. . . . . . ·· ···•··· ··· . ..... .AJI items less medical care . .. ........ ... ... ............ Commodities less food . . . .. . .. . . . .. . . ... ......... .. ... Nondurables less food .. Nondurables less food and apparel .. Nondurables .. ...... .......... ... ......... 3 Services less rent of shelter Services less medical care services . .... ... . Energy .. ....... .AJI items less energy ... ....... ....... ... .......... .AJI items less food and energy ........ Commodities less food and energy .. ........... Energy commodities ..... Services less energy .. .......... ....... ... ... . ' Not seasonally adjusted. 2 Indexes on a December 1997 = 100 base. 3 Indexes on a December 1982 = 100 base. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 179.7 184.1 183.2 184.4 185.0 184.5 184.5 185.1 186.2 186.4 185.5 185.7 187.0 188.5 190.1 171.9 176.4 175.3 176.8 177.5 176.7 176.6 177.3 178.6 179.1 178.0 178.0 179.0 180.4 182.4 184.6 174.8 179.1 178.2 179.4 180.0 179.6 179.6 180.0 181 .1 181 .3 180.6 180.8 181 .7 183.1 137.7 140.0 139.9 141.8 141 .5 139.4 139.0 140.2 142.2 142.9 140.7 140.0 141 .7 144.1 146.8 154.2 162.6 162.4 166.4 166.2 162.3 161 .5 163.2 168.2 167.6 162.9 160.9 164.4 169.5 175.1 206.9 175.9 189.0 186.6 193.5 194.8 191 .0 189.6 189.7 195.6 195.4 190.3 188.5 192.7 198.3 166.4 173.9 173.0 175.9 175.9 174.0 173.6 174.5 177.7 177.5 175.1 174.3 176.1 179.0 182.5 201 .3 207.4 205.2 205.8 208.2 208.9 209.3 209.5 208.6 209.8 209.9 210.8 211 .2 211 .6 212.7 205.2 135.9 210.6 151 .3 209.2 146.0 209.7 154.5 211 .1 159.9 211 .8 156.2 212.2 155.1 212 .3 154.2 212.0 157.8 212.3 158.5 212.4 153.3 213.2 151.4 214.0 155.0 214.7 160.9 215.4 171.4 186.1 189.5 189.0 189.3 189.3 189.3 189.5 190.2 191 .0 191.1 191 .0 191 .5 192.2 192.9 193.3 187.9 190.6 190.4 190.4 190.3 190.3 190.5 191.4 192.1 192.2 192.0 192.4 193.4 194.2 194.5 141 .1 139.4 140.1 139.9 139.0 138.0 138.0 139.5 140.5 140.6 139.9 139.9 140.5 141 .3 141 .4 136.8 161 .5 156.7 170.7 173.3 165.5 162.8 162.3 174.5 173.7 163.4 158.7 166.6 178.1 195.5 220.2 226.2 225.3 225.5 226.0 226.7 227.1 227.4 227.9 228.0 228.1 229.0 230.1 231 .1 231.4 4 Indexes on a December 1988 = 100 base. NOTE: Index applied to a month as a whole, not to any specific date. Monthly Labor Review June 2005 119 Current Labor Statistics: Price Data 38. Consumer Price Index: U.S. city average and available local area data: all items = 100, unless otherwise indicated) [1982-84 Pricing schedule U.S. city average .. ...... .. ... .. ... ... ... . .. . . . . . . . . . . .. . .. Region and area size Nov. 191 .0 Urban Wage Earners 2005 Dec. 190.3 Jan. 190.7 Feb. 191 .8 2004 Mar. 193.3 Apr. Nov. 2005 Dec. 194.6 186.8 186.0 Jan. Feb. 186.3 187.3 Mar. 188.6 Apr. 190.2 2 Northeast urban .. ·• ·· • · • ·· • · Size A-More than 1,500,000 ... ...... . ..... Size B/C-50 ,000 to 1,500,000 M 1 All Urban Consumers 2004 ...... ..... . ... .. ..... . 3 4 Midwest urban Size A-More than 1,500,000 ......... ... ... ... ... . M 202 .6 201.9 202 .6 203.6 206.0 206 .9 200 .2 198.7 199.0 200.0 201 .8 202.9 M 204.6 204 .1 205.0 206.0 208 .6 209 .3 120.2 199.6 200.1 201.1 202 .8 203.8 M 120.1 119.2 119.4 120.1 121 .3 122.0 179.8 119.4 119.6 120.1 121.2 122.1 M 184.8 183.8 184.1 185.2 186.3 187.7 181 .2 178.8 179.1 180.2 181 .2 182.8 184.1 .. .......... M 186.9 185.7 185.9 187.1 188.3 189.6 116.9 180.1 180.4 181 .3 182.5 Size B/C-50,000 to 1,500,000 . • ··•· · Size D-Nonmetropolitan (less than 50,000) . ...... ... . M 117.7 117.3 11 7. 3 118.1 118.7 119.6 175.2 116.4 116.4 117.2 117.8 118.8 M 177.7 177.2 178.2 179.2 179.9 181.7 180.7 174.9 175.7 176.5 177.3 179.1 3 South urban .. ... .. .. . .. . .. M 183.7 183.3 183.6 184.7 185.9 187.3 182.5 180.3 180.5 181 .5 182.7 184.3 .. ..... . .. . M 185.0 184.9 185.2 186.6 187.9 189.9 182.5 182.4 182.6 184.0 185.3 186.7 Size B/C-50,000 to 1,500,000 Size D-Nonmetropolitan (less than 50 ,000) ····· ··· · ·· West urban .. . ..' ... ... .... ................. ........ ..' ... ... . M 117.4 117.1 117.1 117.7 118.4 119.3 116.0 115.6 115.7 116.3 117.0 117.9 M 182.5 181 .9 182.3 183.1 184.5 187.2 182.2 181 .5 181 .9 182.7 184.1 186.7 .. . ............................ Size A-M ore than 1,500 ,000 ....... ... ...... ... ... 3 Size A-More than 1,500,000 ..... . .. , ........ . ....... Size B/C-50,000 to 1,500,000 .. .. ... . 3 ···• ·• · · •··• · •· ·• ·· • ·· • ·· •• · •· Size classes: As · · · ·• ·· • ··· • ··· • ·· • ··•··•··•··•·· • · ·• ·• · · •· ·•·· • ·· • ·· • · Selected local areas M 195.1 194.2 194.5 195.7 197.1 198.6 190.2 189.4 189.5 190.5 192.0 193.7 M 197.6 196.5 196.7 198.3 199.8 201.3 191 .2 190.2 190.1 191 .6 193.2 194.9 M 119.3 119.0 119.5 119.6 120.4 121 .4 118.9 118.6 118.9 119.0 119.8 120.8 M M M 174.6 118.2 183.0 174 .0 117.7 182.4 174.3 117.9 183.0 175.5 118.5 183.7 177.0 119.2 184.8 178.1 120.1 186.9 173.0 117.3 181 .1 172.4 116.9 180.6 172.6 117.0 181 .0 173.7 117.5 181 .7 175.0 118.3 182.9 176.3 119.2 185.1 M 190.7 196.9 189.6 195.2 189.9 195.4 190.5 197.4 191 .3 199.2 193.2 201 .1 184.2 190.3 183.1 188.5 183.5 188.5 184.2 190.3 184.8 192.1 186.9 194.2 6 Chicago-Gary-Kenosha, IL-IN-WI ... . ·· · ······· ···""'' Los Angeles-Riverside-Orange County, CA .. M New York, NY-Northern NJ-Long Island , NY-NJ-CT-PA .. M 207.2 206.8 208.1 208.9 212.4 212.5 202.2 201.8 202.6 203.3 205.5 206 Boston-Brockton-Nashua, MA-NH-ME-CT .. 1 211 .7 - 211 .3 - 214.2 - 211 210 .3 1 185.2 - 183.3 186.3 - 173.9 181 .3 - 180.5 - 180.3 - 121 .3 - 122.7 - 120.4 - 120.7 - 213.1 Cleveland-Akron , OH ... . .... . ......... .. ...... .. . .... .. ........... - 122.3 - 185.3 - 188 181 .5 - 186.0 - 189.8 180.7 - 183.4 187.8 - 182.6 174.6 - 175 172.8 191.2 202.9 Dallas-Ft Worth, TX .... . ...... ................. . .. .. .... . .. Washington-Baltimore, DC-MD-VA-WV Atlanta, GA .. .... ... 7 .. ... .... .. ....... ... ... ... .... ... . 1 179.9 1 120.9 - 2 - 183.2 Detroit-Ann Arbor-Flint, Ml ... ...... . ......... . . .. ... . . . . 2 - 185.3 Houston-Galveston-Brazoria, TX .......... .. ..... ······ •··· ···· Miami-Ft. Lauderdal e, FL .. 2 - 170 2 - Philadelphia-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 al l areas: most other goods and services priced as indicated : 3 4 5 - 177.2 181 .6 167.7 - 171 .8 193.2 - 186.6 188.3 203.3 - 197.9 - - 200 .0 - - 202 .5 - 195.9 - 197.3 - 199.3 - 201 .3 - 190.3 - 192.4 - 196.2 188.6 - 190.6 - 197.8 - 200 .1 - 199.5 - 201.2 195.1 - 197.6 185.2 Report: Anchorage, AK; Cin cin natti , 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. 2-February, April , June, August, October, and December. 7 Indexes on a November 1996 = 100 base. Regions defined as the four Census regions . NOTE: Local area CPI indexes are byproducts of the national CPI program. Each local Indexes on a December 1996 = 100 base. index has a smaller sample size and is, therefore, subject to substantially more sampling The "North Central" region has been renamed the "Midwest" region by the Census Bureau . It is composed of the same geographic entities. 6 180.0 174.5 Labor Statistics strongly urges users to consider adopting the national average CPI for use Indexes on a December 1986 = 100 base. In addition , the following metropolitan areas are published semiannually and appear in tables 34 and 39 of the January and July issues of the CPI Detailed Monthly Labor Review 120 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis and other measurement error. As a result, local area indexes show greater volatility than the national index, although their long-term trends are similar. Therefore, the Bureau of June 2005 in their escalator clauses. Index applies to a month as a whole, not to any specific date. Dash indicates data not available. 39. Annual data: Consumer Price Index, U.S. city average, all items and major groups (1982-84 = 100) Series Consumer Price Index for All Urban Consumers : All items: Index. Percent ch ange Food and beverages: Index ......... .............. .... .... Percent chan ge Housing : Index ......... .. ......... ... . Percent ch ange .. Apparel : Index ···· ··· ·· ··· ··· ······ · ··· · ·· ····· Percent change Transportation : Index .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . Percent change Medical care: Index .. .... ................. Percent change .. .. ... .................... . Other goods and services: Index ... ......................................... Percent ch ang e ... Consumer Price Index fo r Urban Wag e Earn ers and Clerical Workers: All items : Index ................. .. .. ... . .. Percent ch ange ........ ................... ..... .. ....... ...... https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 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 2.3 188.9 2.7 144.9 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 2.1 186.6 3.3 144.8 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 2.5 189.5 2.5 133.4 - .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 .5 120.4 - .4 134.3 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 3.1 163.1 3.5 211 .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 4.0 310.1 4.4 198.5 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 1.9 304 .7 2.0 145.6 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 2.2 188.9 5.1 Monthly Labor Review June 2005 121 Current Labor Statistics: Price Data 40. Producer Price Indexes, by stage of processing [1982 = 100] 2003 2004 2005 2004 Annual average Grouping Apr. May June July Aug. Sept. Jan.P Feb.P Mar.P Apr.P 150.6 153.8 154.9 151 .5 154.7 154.2 152.2 155.8 155.6 153.5 157.5 156.2 154.4 158.7 156.5 Oct. Nov. Dec. 151 .7 155.4 154.7 Finished goods ..... . .... . .. .. ... ... .... .... ... .... Finished consumer goods .... ...... . ... .. . . .. Finished consumer foods ... .... ..... ..... .. .. . 143.3 145.3 145.9 148.5 151 .6 152.6 147.3 150.4 152.7 148.9 152.5 155.5 148.7 152.0 155.0 148.5 151.9 152.3 148.5 151 .8 152.2 148.7 152.1 152.7 152.0 155.7 155.1 Finshed consumer goods excluding foods .. Nondurable goods less food ... .. ..... Durable goods .. Capital equipment . . . . . . . . . . . . . ... . . . ... ..... 144.7 148.4 133.1 139.5 150.9 156.6 135.1 141.5 149.1 154.3 134.4 140.6 150.9 156.7 134.8 140.8 150.5 156.0 134.9 141 .1 151.4 158.0 133.6 140.7 151 .3 157.9 133.6 141 .2 151 .5 158.2 133.5 141 .2 155.6 162.1 137.8 143.4 155.3 161.8 137.4 143.4 153.0 158.5 137.2 143.6 154.5 160.5 138.0 144.4 155.5 162.2 137.3 144.0 157.7 165.5 137.0 144.3 159.3 167.9 137.0 144.5 Intermediate materials, supplies, and components ............ . .. ... . 133.7 142.5 140.2 142.0 142.8 143.5 144.8 145.3 146.5 147.7 146.9 148.0 148.9 150.4 151 .7 Materials and components for manufacturing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... Materials for food manufacturing ... Materials for nondurable manufacturing .. Materials for durable manufacturing .. Components for manufacturing .. 129.7 134.4 137.2 127.9 125.9 137.9 145.0 147.6 146.6 127.4 136.2 146.6 143.5 144.3 127.1 137.4 152.2 144.5 146.9 127.3 137.7 152.0 145.9 145.8 127.6 138.1 147.3 147.3 147.2 127.4 139.4 144.9 149.8 150.3 127.7 140.6 144.3 152.6 152.1 128.0 141 .5 144.2 154.4 153.0 128.2 142.0 143.9 155.5 153.6 128.3 142.8 145.2 156.8 155.2 128.5 143.9 145.7 157.8 157.8 129.1 144.5 146.0 158.1 159.3 129.6 145.2 146.6 160.7 158.7 129.5 145.3 146.6 160.4 158.9 129.9 Materials and components . . . . . . . . . . . . . . . .... for constructi on .. Processed fuels and lubricants ........ . ...... ... ..... Containers .. Suppl ies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. .... ... 153.6 112.6 153.7 141 .5 166.4 124.1 159.2 146.7 164.7 118.4 154.9 146.4 166.9 122.3 156.7 147.2 166.9 124.9 158.9 147.3 167.5 126.4 159.7 148.0 169.8 128.5 162.0 147.6 170.9 126.9 162.5 147.9 170.8 130.8 164.6 147.9 170.7 134.0 164.9 147.9 171 .3 128.9 165.2 148.5 173.1 129.0 166.5 149.7 174.7 130.7 166.8 150.0 175.2 135.8 166.8 150.6 175.3 141 .1 167.0 151 .2 Crude materials for further processing ........... .......... ... ... . .. ... .... .. . .. . Foodstuffs and feedstuffs ....... ... ... ... .. Crude nonfood mat erials .. .... ..... .. ....... 135.3 113.5 148.2 159.0 126.9 179.2 155.7 135.4 166.6 161 .8 141 .1 172.9 163.0 137.4 178.0 162.5 130.9 182.2 162.2 124.8 186.6 154.4 122.0 174.9 160.5 120.1 187.3 171 .5 119.5 207 .1 165.7 121 .5 195.3 163.7 123.8 189.9 162.2 121.3 189.3 169.4 127.6 197.0 174.1 125.0 207.3 Special groupings : Finished goods, excluding foods .. ... ... .. Finished en ergy goods . . . . . . . . . . . . . . . . . . . . . . .. . Finished goods less energy ... Finished consumer goods less en ergy .. . Finished goods less food and energ y .. . 142.4 102.0 149.0 153.1 150.5 147.2 113.0 152.4 157.2 152.7 145.7 109.5 151 .9 156.9 152.1 147.0 113.6 152.7 158.0 152.2 146.8 11 2.5 152.7 157.9 152.3 147.2 115.4 151 .7 156.5 151 .9 147.3 115.0 151 .9 156.6 152.2 147.5 115.1 152.1 156.9 152.3 150.9 121.1 154.5 159.3 154.7 150.7 120.1 154.4 159.2 154.7 149.2 114.5 154.6 159.4 154.9 150.5 11 6.4 155.2 159.8 155.9 151 .0 118.2 155.5 160.6 155.9 152.6 123.4 155.7 160.7 156.0 153.7 126.9 155.9 160.9 156.1 Finish ed consumer goods less food and energy... 157.9 160.3 159.8 159.9 160.0 159.4 159.6 159.7 162.2 162.3 162.5 163.6 163.9 163.8 164.0 Consumer nondurable goods less food and en ergy .... . 177.9 180.7 180.5 180.2 180.2 180.3 180.8 181 .2 181 .7 182.2 182.8 184.3 185.6 185.7 186.3 Intermediate materi als less foods and feeds .. Intermediate foods and feeds ....... .. .. . Intermediate energy goods .. Intermediate goods less energy .. . 134.2 125.9 111 .9 137.7 142.9 137.0 123.1 145.8 140.2 143.2 117.3 144.4 141 .9 147.7 121 .1 145.7 142.8 144.9 123.7 146.0 143.7 142.3 125.1 146.4 145.3 136.3 127.1 147.5 145.9 134.4 125.8 148.5 147.3 131 .2 129.9 149.0 148.3 130.7 132.7 149.4 147.8 131 .0 128.4 149.9 148.8 132.6 128.5 151.2 149.7 132.1 129.8 151 .9 151 .3 133.3 134.7 152.5 152 .6 134.2 139.4 152 .9 Intermediate materials less foods and energy.. . 138.5 146.5 144.6 145.7 146.2 146.8 148.3 149.5 150.1 150.6 151 .1 152.4 153.2 153.8 154.1 Crude energy materials .. . .. . ....... .... ... Crude materi al s less en ergy ... Crude nonfood materials less energy .. 147.2 123.4 152.5 174.7 143.9 192.8 158.8 148.7 187.6 172.1 150.1 177.9 180.0 147.0 176.3 177.9 147.5 195.4 181.9 144.6 200.8 166.6 141 .6 197.4 181 .8 141 .9 203.5 208.3 142.7 207.9 192.7 143.3 204.9 186.0 144.3 202.6 186.3 141 .7 199.4 196.5 146.8 201 .6 210.6 145.3 203.1 June 2005 Monthly Labor Review 122 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 41. Producer Price Indexes for the net output of major industry groups [December 2003 = 100, unless otherwise indicated] 2004 NAICS 2005 Industry July Aug. Sept. Oct. Nov. Dec. Jan.P Feb.P Mar.P Apr.P Total mining industries (December 1984=100) ......................... ........... .. 155.6 159.3 149.6 160.6 179.1 169.2 163.8 165.9 173.4 183.0 211 212 213 Oil and gas extraction(December 1985=100) ........ ... ..... ..... ... ........ . Mining, except oil and gas ............ ............... ..... ...... ... ... ... .. . Mining support activities .. .. .. ......................... ..... ......... .... . 196.6 110.2 103.7 202.7 110.4 105.3 184.0 112.3 106.4 203.0 112.8 109.2 234.8 114.0 111.4 214.7 116.4 114.9 204.4 118.4 114.2 205.3 120.2 123.5 217.4 121 .8 125.2 234.0 122.3 126.9 311 312 313 315 316 321 322 323 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 .. 143.2 146.5 100.6 101 .5 99.7 143.7 144.6 101 .1 101 .2 99.7 144.2 143.8 100.6 101 .4 100.2 146.5 143.5 101 .2 101 .6 100.3 146.1 143.3 101 .2 101 .7 100.4 145.0 144.2 101 .5 101 .5 100.5 146.2 144.9 104.1 102.2 100.4 147.2 145.2 104.7 102.5 100.3 148.9 146.0 104.7 103.0 100.3 149.7 146.6 104.4 103.2 100.2 143.7 106.8 103.2 101 .3 143.6 109.8 104.4 101 .3 143.6 110.7 105.0 101 .8 143.5 107.6 105.5 101 .8 143.8 105.1 105.7 102.0 143.9 105.9 105.8 102.0 144.2 106.9 106.2 102.3 144.3 108.8 106.4 102.8 144.6 109.5 106.8 102.7 144.5 108.8 107.1 102.5 324 325 326 331 332 333 334 335 336 337 339 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) .. Machine ry manufact uring .. Computer and electronic products manufacturina .. Electrical equipment, appliance, and components manufacturing .. Transportation equipment manufacturing ... Furniture and related product manufacturing(December 1984=100) .. Miscellaneous manufacturing .. 152.3 172.2 131 .2 144.7 142.5 102.1 98.9 103.6 99.7 152.0 101 .2 155.6 173.8 131 .7 148.3 143.4 102.3 98.9 103.8 99.8 152.7 101.4 158.9 175.5 133.1 150.8 144.2 102.5 98.7 104.2 99.9 152.8 101 .8 176.7 177.2 134.3 152.9 144.9 102.9 98.6 104.7 103.2 153.4 101 .3 170.4 179.3 135.3 154.2 145.4 103.2 98.4 104.6 102.7 154.6 101 .3 150.3 180.5 136.1 155.5 145.7 103.4 98.5 104.9 102.9 155.1 101 .6 153.6 183.1 137.1 158.3 146.7 104.3 98.4 106.1 103.5 155.6 102.8 163.6 184.0 138.7 159.2 147.7 104.8 98.3 106.6 102.6 156.0 102.5 182.5 185.2 139.0 158.1 147.9 105.1 98.1 107.0 102.5 155.9 102.7 189.3 186.5 139.4 157.9 148.9 105.2 97.9 107.5 102.6 156.8 102.7 454 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 .............. ... ........... ....... ...... ........ ..... ... .. . 103.3 102.6 98.6 101 .3 48.3 103.6 103.8 102.8 98.7 105.6 48.6 102.0 104.4 103.4 99.2 105.1 46.3 105.6 104.2 103.8 98.4 104.1 43.1 104.7 104.2 103.7 97.9 106.8 53.3 111 .5 104.2 104.6 93.6 107.2 59.8 117.4 104.9 105.8 98.5 103.3 47.1 11 9.1 104.3 106.8 96.9 105.1 46.4 121 .9 105.7 106.9 102.3 107.9 48.3 119.6 107.2 107.0 101 .1 106.2 49.5 121 .6 481 483 491 Transoortation and warehousina Air transportation (December 1992=100) ........ ..... ... ... ..... ... . Water transportation .. . ....................................... .. . Postal service (June 1989=100) ........... .. .... . .... ..... ... . ... . .. . 163.9 101 .5 155.0 163.4 102.1 155.0 159.8 103.2 155.0 160.9 103.8 155.0 162.2 103.7 155.0 161.4 103.5 155.0 165.4 103.9 155.0 166.5 104.1 155.0 171 .1 104.4 155.0 169.6 105.0 155.0 221 Utilities Utilities ..... ........... .... ..... ... ... . .... ..................................... . 107.1 107.4 105.2 104.3 108.8 108.9 108.6 107.0 107.9 110.2 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 ... .... ... ......... ... ........ ... .... . 114.3 100.0 119.7 141 .6 102.9 102.1 114.3 100.1 119.7 141 .6 103.0 102.1 114.4 100.1 119.8 141 .7 103.2 102.5 114.4 100.1 120.1 143.3 103.7 102 .5 114.4 100.1 120.2 143.5 103.9 102.5 114.5 100.1 120.3 143.8 103.9 102.5 114.7 100.1 120.5 144.7 104.4 103.4 115.3 100.5 120.6 145.3 104.5 103.4 115.1 104.4 120.6 145.3 104.9 103.7 115.2 104.3 120.9 145.5 105.1 103.7 101 .5 99.6 99.8 99.0 103.2 101.5 100.9 99.9 99.0 104.1 101.4 100.8 99.6 98.7 104.5 101 .8 104.3 99.4 98.7 104.3 102.1 103.2 99.2 98.6 105.8 101 .9 100.8 99.9 98.6 106.0 103.1 102.1 99.2 98.7 108.7 103.4 100.0 98.1 98.8 111 .8 103.2 100.8 97.8 98.6 109.8 103.6 102.4 98.4 98.7 110.1 103.5 101 .0 101 .4 110.0 131 .6 101 .3 104.0 101 .0 101 .0 110.8 131 .5 101.4 103.9 104.0 99.8 108.0 131 .8 101.4 104.6 103.1 101 .5 107.8 132.0 101 .6 103.0 103.1 101 .2 107.7 132.0 101 .7 104.2 105.9 102.3 108.1 132.0 101 .3 103.8 106.0 103.3 105.0 137.4 102.8 102.2 106.0 103.1 107.9 136.7 101 .9 103.4 106.0 101 .0 109.1 136.9 102.0 105.2 106.0 102.6 104.8 137.3 101 .9 127.0 100.0 114.6 95.1 101 .0 101 .4 126.6 127.0 100.3 114.6 94.7 101 .1 101.4 127.0 127.3 100.4 114.2 94.5 100.9 101 .4 127.2 127.3 100.3 115.2 95.8 101.4 101 .5 127.0 127.3 100.5 115.2 95.2 101.4 101 .5 125.1 127.7 100.5 114.4 96.1 101.4 101 .5 123.8 128.1 101 .6 115.2 96.5 101 .3 101 .5 126.8 128.7 101 .0 115.7 95.0 101 .7 101 .5 128.2 128.8 101 .0 115.2 96.2 101 .9 101 .5 127.9 129.2 101 .1 114.9 97.1 102.0 103.8 127.8 441 442 443 446 447 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. commoditv cont racts. and like activitv .. Lessors or nonresidental buildings (except miniwarehouse) ......... . Offices of real estate agents and brokers ... ... .. .. .. ... ... .................. . Real estate property managers .. . .............. .. . Automot ive 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) ..... .... ......... . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 123 Current Labor Statistics: Price Data 42. Annual data: Producer Price Indexes, by stage of processing [1982 = 100) 1994 Index 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Finished goods Total ··············· ···· ·" · ......... .. .. ... ... Foods. Energ y ... Other ......... ................ .......... . ............... . ............... . 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 88.8 150.2 143.3 145.9 102.0 150.5 148.5 152.6 113.0 152.7 11 8. 5 11 8.5 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 .9 138.5 142.5 145.0 123.1 146.5 101 .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 11 3.5 147.5 116.8 159.0 126.9 174.7 149.0 Intermediate materials, supplies, and components Total.... Foods Energy Other... ····· ··· ······ ··· ···· .. ... .. ..... ..... . ··· ··· ·· ······ ··· Crude materials for further process ing ......... ..... ...... ..... ...... ........ Total. Foods .. ....... ........... . Energy Oth er .. .. ... ..... .... . . . . ... . 124 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 43. U.S. export pric e indexes by Standard International Trade Classification (2000= 100] SITC 2004 Industry Rev. 3 Apr. May 0 Food and live animals ...................................... .. ...... Meat and meat preparations .. .. ....' ........ .... .• ........ ... 01 04 Cereals and cereal preparations ... Vegetables , fruit, and nuts, prepared fresh or dry... 05 126.1 127.6 147.7 109.5 126.7 127.7 146.0 113.3 123.9 127.3 141.2 111 .1 2 Crude materials, inedible, except fuels .......................... Oilseeds and oleaginous fruits . . . . . . .. . . . . . . . . . . . . . . . . .. . 22 24 Cork and wood ... ........ . ......... ..... Pu lp and waste paper ........................ ... .. .. ... ... 25 Texti le fibers and their waste .... ......... ... .. ...... 26 Metalliferous ores and metal scrap . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 132.8 197. 1 97.6 98.8 115.9 176.2 132.5 199.0 98 .2 100.4 114.9 170.6 3 Mineral fuels, lubricants, and related products ............. Petroleum, petroleum products, and related materials .. 33 123.2 119.8 5 Chemicals and related products, n.e.s . ..... .................... 54 Medici nal and pharmaceutical products ... Essential oils; polishing and cleaning preparati ons ....... 55 Plastics in primary forms .. 57 Plastics in nonprimary forms ···· ··"""'' ........... ....... ... 58 Chemical materials and products, n.e.s. 59 105.5 105.7 104.1 102.2 96.9 104.8 6 Manufactured goods classified chiefly by materials..... 62 64 66 68 Rubber manufactures. n.e.s .. Paoor. paoorboard. and articles of paper. pulp, and paperboard .. Nonmetallic mineral manufactures. n.e.s. .......... Nonferrous metals .. .. .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 7 Machinery and transport equipment.. .......................... ... 71 72 74 75 76 77 78 Power generating machinery and equipment. . Machinery specialized for particular industries ...... General industrial machines and parts, n.e.s., and machine parts ... ......... Computer equipment and off ice machines .. Telecommunications and sound recording and reproducing apparatus and equipment. .. Electrical machinery and equipment .. ............. .... .. ... Road vehicles .. . ' ....•...... 87 Professional, scientific, and controlling instruments and apparatus ................................ .. ... https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June July 2005 Aug. Sept. 119.8 123.0 128.0 110.0 116.4 126. 1 120.6 113.2 125.7 168.5 98.3 100.8 108.7 167.5 132.1 184.5 98.9 100.1 102.9 190.2 135.1 135.0 131 .8 129.7 105.6 105.7 104.4 102.9 96.7 104.8 105.8 105.8 104.3 103.2 96.5 104.9 Oct. Nov. Dec. Jan. Feb. Mar. Apr. 117.6 124.8 122.0 11 9.8 118.3 126.9 115.6 130.6 118.7 125.4 11 3.1 137.2 118.1 124.6 116.4 129.9 118.2 121 .3 119.2 127.4 118.3 125.2 116.2 128.1 120.1 128.3 121 .4 125.2 121 .0 132.4 117.0 130.5 11 8.0 117.4 98.8 99.5 101 .1 183.6 119.4 125.1 99.1 98.7 102.1 178.5 11 8.2 109.1 99.1 98.1 100.2 190.4 119.5 110.3 98.4 98.2 97.5 197.0 11 9.4 111 .1 98.8 98 .8 96.4 195.0 123. 1 115.2 98.7 100.0 98.4 205.8 122.0 109.7 98.9 100.7 98.8 206.0 127.4 128.9 99.2 103.0 104.4 206.4 129.3 124.6 98.7 101 .8 105.0 223.3 137.5 134.5 139.6 136.2 141 .2 138.0 156.0 156.4 151.1 151 .0 146.5 144.6 148.5 147.3 154.2 155 .7 170.8 177.1 183.2 192.1 107.0 107.9 104.1 104.8 97.2 104.6 108.6 108. 1 105.1 107.3 97.1 106.2 109.7 108.0 105.6 109.9 97.4 105.5 111 .6 106.7 106.6 113.2 98.1 105.2 112.9 106.9 107.5 11 7.2 98.7 105.3 114.0 107.2 109.1 118.9 99.9 105.8 116.1 108.3 109.8 126.6 101 .5 106.5 116.2 107.9 110.4 127.5 102.1 106.4 116.6 107.9 109.4 127.7 102.9 105.9 118.0 108.1 110.0 127.8 103.0 106.7 105.6 106.6 107.0 108.5 109.6 110.5 111 .3 111 .8 112.2 113.0 113.3 113.5 114.3 110.9 110.8 111 .2 111 .8 11 2.0 111.4 111 .6 11 2.4 11 2.9 11 3.8 114.2 114.4 115.2 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 96.5 102.7 100.4 99.0 104.0 101 .1 99.1 103.7 101 .3 100.6 104.2 101 .6 101 .5 104.1 101 .9 103.4 104.1 102.0 104.8 103.7 102 .2 106.4 104.1 102.5 108.5 98.4 98.4 98.2 98.2 98.2 98.2 98.4 98.4 98 .5 98.7 98.7 98.7 98.4 108.7 105.1 108.7 105.4 108.7 105.4 108.9 105.7 109.0 105.9 109.0 106.1 109.4 107.3 110.3 107.6 110.4 108.0 11 1.4 109.3 111 .4 109.2 111 .6 109.4 11 2.0 110.4 104.5 88.8 104.8 88.6 104.9 87.2 105.2 86.6 105.3 86.4 105.3 86.0 106.2 85.1 106.4 84.4 106 .6 83 .8 107.6 83.0 108.2 83.0 108.3 82 .0 108.8 79.4 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 88.1 102.4 90.5 87.9 102.8 90.5 87.7 102.8 90.4 87.9 103.0 90.5 87.8 103.0 90.5 87.6 103.0 90.4 87.7 103.0 89.8 87.5 103.1 102.2 102.1 102.0 101.7 101.9 101 .8 102.2 102.3 102.6 103.4 103.4 103.4 103.5 June 2005 Monthly Labor Review 125 Current Labor Statistics: Price Data 44. U.S. import price indexes by Standard International Trade Classification [2000= 100] 2005 2004 SffC Industry Rev. 3 Dec. Jan. Feb. Mar. Apr. 111 .0 111.9 110.9 112.6 117.3 117.3 131.8 133.0 134.5 134.8 135.9 137.5 85.6 114.5 84.7 116.3 85.0 112.2 86.0 107.7 87.0 107.8 88.5 122.0 88.9 121 .5 105.6 104.5 108.9 114.4 118.9 122.8 130.2 128.9 106.2 106.5 106.7 107.1 107.5 107.7 107.7 107.8 106.6 106.7 106.9 107. 1 107.6 107.9 108.1 108.2 108.3 Oct. Nov. 109.2 111.1 134.9 134.2 86.9 100.6 86.0 109.2 102.7 103.4 105.9 106.1 105.6 106.4 July Aug. Apr. May June 0 Food and live animals ......................... ..... . ............... 106.4 106.1 106.9 107.4 107.4 ........... .. .. .... 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 . .... . ... . .......................................... ... ... .... 121 .7 124.4 128.9 133.7 134.2 85.1 109.5 84.1 106.1 84.1 105.9 86.1 102.1 103.6 102.4 107.0 1 Beverages and tobacco .... ... ....... ........ ................... .. 105.3 105.4 105.3 Beverages .............. ......... ............... ................ ........ .... 105.5 105.7 2 Crude materials, inedible, except fuels .............. .... ........ 01 03 05 07 11 Sept. 122.9 127.3 125.8 125.7 134.0 135.1 125.1 121 .7 125.5 129.6 135.9 134.5 133.2 Cork and wood ... ....... .... .. .. ...... .. ......... .. ......... .... .. ... ... .. Pulp and waste paper... ............ ······ ... ....... ...... Metalliferous ores and metal scrap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . Crude animal and vegetable materials, n.e.s . . . . ......... . 127.8 100.8 148.2 99.3 139.0 103.4 143.5 102.1 136.1 106.5 140.4 98.0 132.1 108.0 145.3 101 .2 148.9 107.7 160.8 97.6 151 .1 105.5 162.6 98.7 126.3 99.8 166.2 96.3 117.1 98.0 167.0 96.5 124.7 100.3 167.3 98.3 127.0 103.6 170.8 110.1 132.1 107.2 170.8 137.5 137.2 108.7 179.8 102.8 132.6 109.7 188.3 96.3 3 Mineral fuels, lubricants, and related products ......... ... . Petroleum, petroleum products, and related materials .. 33 Gas, natural and manufactured ... 34 121 .1 120.3 123.3 131.6 131 .5 129.5 131 .5 130.0 140.0 133.9 133.0 134.8 144.2 144.8 136.3 146.8 149.5 121.9 161 .2 165.7 124.1 157.2 155.3 166.2 140.6 137.0 163.5 142.2 140.4 150.8 148.1 148.4 143.3 164.8 167.0 145.8 170.7 171 .2 164.0 5 Chemicals and related products, n.e.s . .......... ............... Inorganic chemicals . ........ .. ...... ..... .. .... ... ... .......... ... .... 52 Dying, tanning , and coloring materials ... ............... .•.. 53 Medicinal and pharmaceutical products ... 54 Essential oils; polishing and cleaning preparati ons ... 55 Plastics in primary forms. ·······.. 57 Plastics in nonprimary forms ..... ........... ... ..... .. ... ....... . 58 Chemical materials and products, n.e.s .. 59 103.5 115.9 100.6 107.7 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 94.7 105.1 123.8 98.4 107.3 93.4 108.4 103.2 94.1 106.7 124.1 98.4 106.6 93.4 109.6 103.8 94.4 108.4 125.5 98.5 106.4 93.6 109.9 104.4 95.3 108.9 126.8 98.7 107.4 93.7 113.2 105.1 95.8 109.6 126.7 98.7 108.9 94.4 116.1 105.7 96.1 110.2 127.6 97.9 110.5 94.9 123.0 106.7 96.2 111.8 128.5 98.6 110.3 95.3 124.2 106.5 97.7 112.0 129.3 98.6 110.2 95.5 126.6 106.5 97.8 113.8 130.9 99.8 111 .1 95.5 127.6 106.8 99.5 6 Manufactured goods classified chiefly by materials ..... 105.6 106.9 106.1 106.1 107.7 108.9 108.9 109.4 110.4 111 .4 111.9 113.0 113.7 ... .. Rubber manufactures, n.e.s. Paper, paperboard, and articles of paper, pulp, . ............................••...•. and paperboard .. Nonmetallic mineral manufactures, n.e.s .. ...... ......... Nonferrous metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ......... ... .. ....... ,,,. ....... .•' .. .... Manufactu res of metals, n.e.s ... 99.9 100.0 100.5 100.5 100.8 100.8 101.0 101 .3 101 .9 102.2 102.6 103.5 103.6 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 96.9 100.2 105.6 103.3 97.9 100.4 106.3 103.9 99.2 100.5 106.6 104.4 99.4 100.5 108.6 105.3 99.0 100.7 111 .0 106.7 100.0 100.9 112.1 108.1 99.9 100.8 114.1 108.5 100.3 100.9 116.2 108.8 101 .6 101.1 118.7 109.1 24 25 28 29 62 64 66 68 69 7 Machinery and transport equipment... .................. ........ .. 95.2 95.2 95.1 95.0 95.0 95.0 94.9 95.1 95.2 95.3 95.2 95.1 94.9 106.5 106.7 106.6 107.2 107.6 107.4 107.8 108.5 109.5 110.5 110.5 111 .2 111 .5 103.5 76.5 103.6 76.4 103.5 75.5 104.0 74.9 104.1 74.3 104.3 73.9 104.6 73.2 104.9 73.0 105.3 72.8 106.2 72.4 106.7 71.9 106.9 711 107.4 70.2 77 78 Machinery specialized for particular industries . . . . . . . . . .. . General industrial machines and parts, n.e.s., and machine parts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . Computer equipment and office machines .... ................ Telecommunications and sound recording and reproducing apparatus and equipment . ................. ••. . .. .. ..... ..... ... ... Electrical machinery and equipment... .................. ..... .. ..... Road vehicles .... 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 94.6 103.1 83.4 94.3 103.4 83.4 94.4 103.6 83.1 94.6 103.7 83.0 94.6 103.6 82.8 94.4 103.7 82.7 94.4 103.6 82.2 94.4 103.8 85 Footwear .. 100.6 100.6 100.4 100.4 100.1 100.5 100.5 100.5 100.5 100.3 100.3 100.3 100.2 88 Photographic apparatus, equipment, and supplies, and optical ooods n.e.s. ...... . ......... .... .. 99.4 99.3 99.0 98.2 98.2 98.2 98.2 98.3 98.6 99.1 99.1 99.1 99.3 72 74 75 76 Monthly Labor Review 126 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 45. U.S. export price indexes by end-use category (2000 = 100] 2004 Category All COMMODITIES ..... ....... .. ........ ............ .... ........ .... . Foods, feeds , and beverages .. ... .. . ... .....' . .. .. .. . Agricultural foods, feeds, and beverages .. Nonagricultural (fish , beverages) food products .. Industrial supplies and materials ..... ............. .. .. .. .. 2005 Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar. 103.7 104.1 103.4 103.9 103.4 103.8 104.4 104 .7 104.8 105.6 105.7 106.3 106.9 134.8 137.0 113.4 135.6 138.0 11 2.7 129.1 131.1 110.7 128.0 129.9 110.1 11 6.5 117.0 110.9 118.7 119.3 113.0 117.5 11 7.8 114.4 118.3 118.5 115.5 116.9 116.6 118.4 117.1 116.7 119.7 116.3 115.9 119.8 120.9 120.7 121 .8 120.9 120.8 121 .3 Apr. 109.1 110.2 109 .9 112.0 113.1 114.0 116.6 117.4 118.0 120.1 120.6 122.1 124.4 114.8 113.7 110.7 109.0 108.4 109.4 109.2 108.5 109.5 11 2.9 112.9 11 5.7 116.8 Fuels and lubricants · · ······ ··· · · ···· ····· ··· ·· ··· · ·· ·· ...... 109.6 Nonagricultural supplies and materials, excluding fuel and building materials .. ......... ...... 109.4 Selected building materials ... ....... . .. ...... 103.4 117.5 114.9 118.6 120.4 121 .5 132.2 128.3 125.4 128.3 133.0 144.0 153.9 109.9 103.9 110.0 103.4 11 2.4 102.8 113.5 103.3 114.4 104.0 116.4 103.9 117.9 104.0 118.9 104.4 121.0 104.6 120.9 104.8 121.1 105.3 122.7 105.2 98.1 101 .7 94.6 97.8 102.0 94.1 97.8 102.2 94.0 97 .8 102.2 94 .0 97.8 102.4 93.9 98 .0 103.3 93.9 98.1 103.5 93.8 98.2 103.6 93.9 98.4 103 .8 94 .0 98.5 103.5 94 .0 98.4 104.0 93 .8 98.1 104.0 93.4 Agricultu ral industrial supplies and materials .. Capital goods .. . . . . . . . . . . .. . . Electric and electrical generating equipment · ··· ····· Nonelectrical machinery .. 98.1 101.7 94.6 Automotive vehicles, parts, and engines .. . .. .. . . .. . . .. . 102.2 102.3 102.3 102.4 102.6 102.5 102.7 102.8 102.9 103.1 103.1 103.2 103.4 Consumer goods, excluding automotive . ... ... ... .. .... Nondurables, manufactured .. ... ... ... ... ... .......... Durables, manufactured .. .... ..... .. . .. . ....... ...... 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 .0 101.0 100.9 100.9 100.5 100.8 101.0 100.6 101 .0 101 .2 101 .0 101 .1 101 .7 101 .6 101.4 101 .6 101.4 101 .5 101 .6 101.4 101 .6 101 .9 101 .8 101 .8 Agricultural commodities .......... .. ... ................ Nonagricultural com modities .. .... ..... 133.0 101 .4 133.7 101 .7 127.4 101 .5 126.1 102.2 115.5 102.5 117.6 102.8 116.3 103.6 116.7 103.9 115.4 104.1 116.1 104.9 115.4 105.0 119.8 105.4 120.2 105.9 46. U.S. import price indexes by end-use category (2000 = 100] 2004 Category Apr. May All COMMODITIES ......... ... ........... .... ...... ....... ........ .. 100.4 101 .9 101.7 Foods , feeds , and beverages ··· ········ Agricultural foods, feeds , and beverages .. Nonagricultural (fish, beverages) food products . ... 107.2 114.2 91.7 106.8 114.0 90.6 106.9 114.3 90.3 Industrial supplies and materials .. ...... . ··•·· June July 2005 Aug . Sept. 102.1 103.6 107 .5 114.5 91.8 107.3 114.1 92 .3 Oct. Nov. Dec. Jan. Fe b. Mar. 104 .1 105.8 105.5 104.0 104.6 105.5 107.6 108.5 108.7 11 6.4 91.4 110.0 118.4 91 .1 11 0.3 119.1 90.7 11 1.5 120.7 91 .0 111 .1 119.6 92 .0 112.2 120.9 92 .8 115.8 125.7 93 .9 116.3 126.3 93 .8 Apr. 113.9 119.7 119.3 120.6 126.6 128.5 134.9 133.2 126.4 127.9 130.7 139.1 142.7 120.6 119.9 131.0 131 .2 130.9 129.7 133.2 132 .7 143.4 144.4 146.2 149.2 160.8 1658 157.0 155.9 141 .0 138.1 142.5 141 .2 147.8 148.2 163.9 166.4 170.5 171 .6 96.8 98.2 99.0 100.0 100.4 101 .1 101 .4 101 .1 101 .3 102.4 103.0 103.8 104.9 105.1 120.2 121 .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 98.5 107.7 124.0 129.8 98.5 108.0 125.6 133.1 98.8 108.7 115.3 134 .2 98 .9 109.3 111 .8 136.4 99.2 109.8 115.6 138.5 99 .7 111 .3 117.9 139.6 100.9 11 2.0 120.0 139.1 100.7 112.9 123.1 141 .5 100.5 114.0 120.8 144.7 100.7 92 .6 97.2 90.6 92 .6 97 .1 90.5 92. 2 97.0 90.1 92.2 97.5 90.0 92 .1 97.7 89.9 92 .0 97 .4 89.8 91 .8 97.4 89.5 91 .9 97.5 89.6 92.2 98 .0 89.9 92 .5 98.4 90.1 92 .4 98.7 90.0 92.2 98.7 89 .7 92 .1 98.9 89 .5 Automotive vehicles, parts, and engines .. .... ... ...... 102 .0 102.0 102.2 102. 3 102.5 102. 7 103.0 103 .1 103.2 103.2 103.2 103.2 103.4 Consumer goods, excluding automotive .... . Nondu rables , manufactured .. . . . . . . .. . .. . Durables, manufactured ... .... .. ..... .. ... . .. ..... ... . . Nonmanufactu red consumer goods .. .. . .... .. ... .. .. .. 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 98.5 100.9 96.0 97.9 98 .7 101 .1 96 .2 98 .0 99.0 101.4 96.5 98.2 99.6 102.2 96.8 100.1 100.1 102.8 96.7 105.0 99 .8 102 .8 96.7 99 .4 99.7 102.8 96.6 98.2 Fuels and lubricants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . Petroleum and petroleum products .. ...... Paper and paper base stocks .. ........... ....... ....... Materials associated with nondurable supplies and materials ............. .. Selected building materials .. Unfinished metals associated with durable goods .. Nonmetals associ ated with durable goods .. Capital goods . ....... .... .. .. .. .. .... .. .... . Electric and electrical generating equipment. . Nonelectrical machinery ........ ..... .. .. ....... .......... 47 . U.S. international price Indexes for selected categories of services (2000 = 100, unless indicated otherwise] 2003 Category Mar. June 2004 Sept. Dec. Mar. June 2005 Sept. Dec. Mar. Air freight (inbound) ... ···· ·········· ···"··· ····"···" '·····.. ··· Air freight (outbound) .. . ..... ... ... ···· ·· 108.8 97.2 109.4 95.4 112.5 95.5 112.9 94.9 116.2 96.1 116.6 99.0 118.7 100.7 125.2 104.7 126.3 103.7 Inbound air passenger fares (Dec. 2003 = 100) .. Outbound air passenger fares (Dec. 2003 = 100)) .. .. . Ocean liner frei ght (inbound) . .... .. .. - - - 94.0 11 6.1 116.2 100.0 100.0 117.7 105.1 99.3 119.1 106.1 114.2 121.1 110.1 114.2 120 .3 11 2.5 105.4 122 .7 114.5 105.0 121 .2 NOTE: Dash indicates data not available. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 2005 127 Current Labor Statistics: Productivity Data 48 . Indexes of productivity, hourly compensation , and unit costs, quarterly data seasonally adjusted (1992 = 100] I II Ill IV I II 2005 2004 2003 2002 Item Ill IV I II Ill IV I Bu siness 122.6 143.2 115.1 116.7 113.4 115.5 123.2 144.5 115.2 117.2 113.6 115.9 124.6 145.0 11 5.0 116.3 115.7 116.1 125.0 145.1 114.8 116.3 116.8 116.5 126.3 147.4 115.3 116.8 117.7 117.1 128.6 149.6 116.8 116.4 119.0 117.3 131 .1 151 .5 117.6 115.6 120.8 117.5 131 .8 152.9 118.5 116.0 120.7 117.8 133.1 154.0 11 8.2 115.7 122.9 118.4 134.1 156.0 118.5 116.4 124.4 119.4 134.7 158.2 119.6 11 7.4 123.5 119.7 136.0 160.1 120.0 117.8 124.8 120.4 136.7 161.8 120.6 118.4 126.0 121 .3 ............. .. .. ... ... ... ... Output per hour of all persons .. Compensation per hour ... ............ .. .. .. . ...... ... ... ..... Real co mpensation per hour ..... .. ....... . ........... .... ............... ........ . .. . ..... ...... . . Unit labor costs .. Unit nonlabor payments . . . . ..... ........ . .... ...... .. .. Implicit price deflator .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 122.4 142.5 114.6 116.4 115.1 116.0 122 .8 143.8 114.7 117.1 115.4 116.5 124.1 144.3 114.5 116.2 117.7 116.8 124.6 144.7 114.2 116.1 11 8.9 117.2 125.8 146.7 114.6 116.6 119.6 117.7 127.9 148.7 116.1 116.3 120.4 117.8 130.5 150.8 117.1 115.5 122.3 118.0 131 .5 152.3 118.0 115.9 121 .9 118.1 132.7 153.1 117.5 115.4 124.3 118.7 134.0 155.3 117.9 115.9 125.7 119.6 134.4 157.4 119.0 117.1 125.2 120.1 135.1 158.9 11 9.1 117.6 126.4 120.8 136.0 160.8 119.8 118.2 127.6 121 .7 Nonfinancial corporat ions Output per hour of all employees .. ... ...... ... ... ... ... ... ..... ........ . ... ... .. . .... , ...... Compensation per hou r .. Real compensation per hour .. ... .. ..... ... ...... ...... ... .... . . . . . . . . . . . . . . ...... .. .. . ·· ······· Tot al un it costs .. Unit labor costs . . . . . . . . . . . . . . ...... . .. . ·•· •· ·•· ······· •··• .... Unit nonlabor costs .. ····· •· · ... ····• .. ..... . . . . . . . . . . . . . . . . . . . . . Unit profits .............. .. .... ............ .................. .. . . . . . . . . .. Unit nonlabor payments ........... . ... ....... ..... ..... . . .. . Implicit pri ce deflator ... ...................... .. . . ...... .. ... .... 126.7 139.9 112.5 111 .3 110.4 113.6 88.8 107.0 109.3 128.2 141 .3 112.8 111 .0 110.3 112.7 94.5 107.9 109.5 129.0 142 .1 112.7 110.9 110.1 11 2.8 95 .8 108.3 109.5 129.6 142.8 112.8 110.9 110.2 11 2. 8 102.3 11 0.0 110.1 130.9 144.2 112.7 111 .6 110.7 114.0 100.0 110.3 11 0.5 132.7 146.4 114.3 110.9 110.3 11 2.6 112.2 112.5 11 1.0 135.8 148.4 115.3 110.5 109.8 112.6 120.3 114.7 111 .4 136.6 149.8 116.1 110.4 109.7 112.2 125.1 115.7 111 .7 136.9 150.8 115.7 110.4 110.2 111 .1 129.9 116.1 112.2 138.0 152.8 116.0 110.9 110.7 111.4 136.3 118.1 113.2 139.6 154.9 117.1 111.0 110.9 111.3 136.0 117.9 113.2 141 .4 156.5 117.3 110.6 110.7 110.2 147.5 120.2 113.9 - 144.4 143.8 115.6 99 .6 146.5 146.7 117.0 100.2 148.7 148.3 117.7 99.7 149.5 149.4 117.9 99 .9 151.6 155.5 121 .5 102.6 152.9 158.4 123.6 103.6 156.9 161.6 125.5 103.0 158 .1 163 .6 126.8 103.5 159.3 162.4 124.6 101 .9 162.2 165.1 125.3 101 .8 164.0 168.7 127.6 102 .9 166.5 171.7 128.4 103.1 168.1 173.7 129.4 103.3 ... . ....... ... Output per hour of all persons .. Compensation per hour ..... .. . ... ........ ... .. . . ... Real compensation per hour .. . ..... ..... .... ... Un it labor costs ... . . . . . . . . . . . . . . . . . . . . . . . . . .. .... . .... ········· Un it nonlabor payments . . . . . . . . . . . ... ........ .. .. ..... ...... Impl icit price deflator .. ························· · · ·· · ·· ··· ·· · · · Nonfarm bu siness . ' - - Manufacturing Output per hour of all pers ons .. . . ......... ... ... . . . . . . . Compensation per hour .. ...... . . . .. . ..... ····· ···.... , . . . Real compensati on per hour .. .. .. ....... .. ..... .... ..... Unit labor costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . .. ' NOTE: Dash indicates data not available. Monthly Labor Review 128 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 49. Annual indexes of multifactor productivity and related measures, selected years [2000 = 100, unless otherwise indicated] 1990 Item 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Private business Productivity: Output per hour of an persons ... ···············"······· Output per unit of capital services .. .. .. .... ···· ·· ······ Multifactor productivity .. .. .... ........ ..... Output ................... ............ ..... .. ........... . ... . Inputs: Labor input . . . . .. . . . . . . . .. ....... .. .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . Capital services .. . ... ...... .... Combined units of labor and capital in put · ·· ··· ·· ·· · · Capital per hour of all persons ... ........ ............ . .. .. 81.4 102.6 90.9 68.6 82 .7 99 .7 90.3 68.1 86 .2 101 .7 92. 7 70 .9 86.5 102.6 93.1 73.2 87.5 104.5 94.1 76.9 87.7 103.6 93.8 79.1 90.3 103.9 95.5 82 .8 91 .9 104.1 96 .3 87 .2 94.4 102.6 97.4 91 .5 97.2 101 .8 98.7 96.2 100.0 100.0 100.0 100.0 102.7 96.3 100.1 100.4 107.2 95 .5 102.0 102.3 80.1 66.9 75.5 79.3 79 .1 68.4 75 .4 83 .0 80 .0 69.7 76.5 84.8 82 .4 71.3 78.6 84.4 86.1 73.5 81.7 83.7 88.5 76.4 84 .3 84 .6 90.4 79.7 86.7 86.9 94 .0 83.8 90.5 88.3 96.2 89.2 93.9 92 .0 99.0 94 .5 97.5 95.4 100.0 100.0 100.0 100.0 98.6 104.2 100.4 106.6 97 .4 107.1 100.3 112 .2 81.7 104.2 91 .5 68 .6 83 .1 101 .1 91 .0 68. 1 86.5 102.2 93.2 70.8 86.9 103.8 93.6 73.2 87.9 105.4 94.5 76.7 88.4 104.7 94 .6 79.3 90 .8 104.7 96 .0 82 .9 92 .2 104.6 96.6 87.2 94 .7 103.0 97 .7 91 .5 97.3 102.1 98.8 96.3 100.0 100.0 100.0 100.0 102.6 96.3 100.0 100.5 107.2 95.4 102.0 102.4 79.8 65.8 75 .0 78.4 78.7 67.4 74 .8 82 .3 79.6 68 .8 75 .9 84.1 82.2 70.6 78.2 83.7 85.6 72 .8 81 .2 83.3 88 .0 75.7 83 .8 84.4 90 .0 79.2 86.3 86.7 93.7 83.3 90.2 88 .2 96.0 88 .8 93 .7 91 .9 99.0 94 .3 97 .5 95 .3 100.0 100.0 100.0 100.0 98.8 104.4 100.5 106.6 97.3 107.3 100.3 112.4 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 .9 100.0 100.0 100.0 100.0 103.8 101 .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 - 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 97.7 100.0 100.0 100.0 100.0 100.0 100.0 101 .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 93.6 11 7.9 98.9 105.5 97.7 101 .6 - - June 2005 129 Private nonfarm business Productivity: Output per hour of all persons .............. ..... Output per unit of capital servi ces ... ... ...... . · · ···••·· Multifactor productivity .... ......... .. .. .. . ... .. .... Output ·················· ··· ··· ······ ··· ·· ·· ··· ··· ... .. ...... .. ... .... Inputs: Labor input . . . . . . . . . . . . . . .... . .. .................... ... .. ....... Capital services .. . . .. . . .. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . Combined units of labor and capital input. . ...... Capital per hour of all persons .. Manufacturing [1996 = 100] 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 ...... - NOTE : Dash indicates data not available. https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review Current Labor Statistics: Productivity Data 50. Annual indexes of productivity, hourly compensation , unit costs, and prices, selected years (1992 = 100] 1960 Item 1970 1980 1990 1996 1997 1998 1999 2000 2001 2004 2003 2002 Business Output per hour of all persons . . . . . . . . . . . . ..... ..... .. .... Compensation per hour .... .. .... . . . . . .... .. . . . ... .. . .. . .... Real com pensation per hour ....... ··· •··· •··· · •· · . .......... ...... ...... . .. . .... . ... . Unit labor costs ... Unit nonlabor payments . ..... .. ····• ·· •·· . .... . .... ...... . ..... ........ .... . . . ... .. Implicit price deflator ....... . 48.9 13.9 60.9 28.4 24.9 27.1 66.2 23 .6 78 .8 35.6 31.4 34.1 79.2 54 .2 89.2 68.4 61.3 65.8 94.5 90.6 96.2 95.9 93.9 95.1 104.6 109.6 99.6 104.8 111 .8 107.4 106.5 113.1 100.6 106.1 113.8 109.0 109.4 119.9 105.1 109.6 109.8 109.7 112.6 125.6 107.9 111 .6 109.2 110.7 115.9 134.5 111 .8 116.1 107.2 112.7 118.8 140.1 113.3 118.0 109.9 114.9 123.9 144.5 115.0 116.6 114.9 116.0 129.5 150.5 117.0 116.2 119.6 11 7.4 134.6 157.2 11 9.2 11 6.8 123.9 11 9.5 Nonfarm business ..... .... ..... . ... , .. Output per hour of all persons .. Compensation per hour . .. .... . . .... . . .. . . .. . . . .. . . ... .... ... Real compensation per hou r .. .... ... .. ....... .. ... ... .... .. . .... .. .. .. ......... .. .. ........ Unit labor costs ...... Unit nonlabor payments . . . .. ... . .. ... . .. ..... . . . . . . . . . . . . .. ...... . ... .. ...... Implicit price deflator .. 51 .8 14.5 63 .3 27.9 24 .3 26.6 67 .9 23 .7 79 .1 34.9 31 .1 33 .5 80.6 54 .4 89.5 67.5 60.4 64.9 94.6 90.4 96.0 95.6 93.6 94.9 104.8 109.5 99 .5 104.5 112.0 107.3 106.5 112.9 100.4 106.0 114.5 109.1 109.3 119.6 104.9 109.4 110.8 109.9 112.3 125.1 107.5 111.4 110.7 111.1 115.5 134.0 111.4 116.0 108.7 113.3 118.3 139.3 112.7 117.7 111.5 115.4 123.5 143.8 114.5 116.5 116.8 116.6 128 .9 149.6 116.4 116.1 121 .1 117.9 134 .1 156.3 11 8.5 116.5 125.4 119.8 56.2 16.2 70.8 27.3 28.8 23 .3 50.2 30.5 29.4 69 .8 85.9 35.6 36.9 32 .2 44.4 35.4 36.4 80.8 57.2 94.1 69.2 70.8 64 .9 66 .9 65 .5 69 .0 95.4 91 .1 96.8 96.0 95.5 97.3 96.9 97.2 96.1 107.1 108.5 98.6 100.9 101 .3 100.0 150.0 113.3 105.3 109.9 111 .7 99.4 101.1 101 .7 99 .7 154.3 114.3 105.9 113.5 118.1 103.6 102.9 104.1 99.5 137.0 109.5 105.9 117.3 123.5 106.1 104 .0 105.3 100.4 129.1 108.0 106 .2 121 .5 132 .0 109.7 107.4 108.6 104.2 108.7 105.4 107.5 123.5 137.3 11 1.1 111 .6 111 .2 112.6 82 .2 104.5 108.9 128.4 141 .5 112.7 111 .0 110.3 113.0 95.4 108.3 109.6 133.7 147.2 114.6 110.8 110.1 112.9 114.6 113.3 111 .2 139 .0 153 .1 116.5 110.7 11 0.6 111 .0 137.5 11 8.1 113.1 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 90.5 96.1 97.3 100.8 99.5 113.9 109.3 99 .3 96 .0 110.7 105.2 118.0 112.2 99.8 95.1 110.4 104.6 123.6 118.7 104.2 96 .0 104.2 101.1 128.1 123.4 106.0 96.4 105.1 101.8 134.1 134.7 112.0 100.5 107 .1 104 .6 136.9 137.8 111 .5 100.7 105.9 103.9 147.3 147.0 117.0 99.8 154.8 159.7 124.3 103.2 163.0 167.0 126.5 102.4 - - - Nonfi nanc ial corporations ........ . . . . . ' . . Output per hour of all employees ... Compensati on per hour . .. .. .... . ...... ......... . ... . ..... Real compensation per hour. ....... ...... .. . .. . .. . . ...... Total unit costs .. · ··· ··· ·· ·· ··· ···· ·•·· ·· · ... . ... . . . . . .. Unit labor costs . . . . . .. .. .. . . . . . .. .. .. ........... ............ ...... Unit nonlabor costs ······· .. .. · • .. ........ ... .... .......... ... Un it profits ...... ..... .... ......... ........ .. ..... .... ................... Un it nonlabor payments ... ··········· •· ·· ··· ··· ·· •····· . Implicit price deflator .. . ...... .. . . . .. . ... .... .... . ... .. .. .. .. . . . 25 .: Manufacturing Output per hour of all persons . ····· ..... ······--······· ··· Compensati on per hour . . . . . . . . . . . . . . . ... . ...... .. .. ..... .. Real compensati on per hour ............. ······ ···· ··· ·· .. ... . .... . .. .. .. ...... Unit labor costs .. . Unit nonlabor payments .... .. ... . . . . . . . . . . . .. ... .. .... Implicit price deflator . .. ................ .... ... ... .. .. ..... .. Dash indicates data not available. Monthly Labor Review 130 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 51. Annual indexes of output per hour for selected NAICS industries, 1990-20U::l (1997=100] NAICS Industry 21 211 212 2121 2122 2123 Mining .. ........... ... .. . .......... ... .. ...... ... .... ... Oil and gas ext raction .. ... ... .... ........... .... . Mining , except oil and gas. ..... ....... ... ....... .. ...... Coal mining . . . .... . .. . ... .... .. ..... ... . . .. . .. . . .. .. . . . Metal ore mining .. . . ....... Nonmetallic mineral mining and quarrying .. . ..... .. 2211 2212 Power generation and suppl y . . . .. .. . . . . .. ... . . . . . . ... Natural gas distri bution .... ...... .. ...... ... .... 3111 3112 3113 3114 3115 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Mining 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 Animal food .... ........ ··· ··· ·· ···· ······· Grain and oilseed milling .. .......... .. .... ... Sugar and co nfectionery products . . . . . . . . . . . . . . . . . . . . Fru it and vegetable preserving and speci alty ... 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 96.0 127.2 11 7.3 109.9 11 7.0 96.2 - 3116 3117 3118 3119 3121 An imal sl aughtering and processing . ...... .... .. .. . Seafood product preparation and packaging .. Bake ries and tortilla manufacturing .. ....... Other food products ......... .... . .. .. ....... . . .. . .. . .. Beverages .... ........ ..... .... .. .... ... .. .. ...... ... .. ... 94.5 11 7.5 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 3122 3131 3132 3133 3141 Tobacco and tobacco products ... ..... ... .. ...... .... Fiber, yam , and th read mills . . . . . . .. . .. . . ..... . .... . Fabric mills .. ........ ... ... .. . Textile and fabric finishing mills .. ...... ..... ... Textile fu rnishings 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 kn itting mills .. ····· ···•· . . .... . . .... . . Cut and sew apparel . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . Accessories and oth er apparel ... ........... Leather an d 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 .. ............. ... Pl ywood 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 3231 3241 3251 Pul p, paper, and paperboard mills ... ........ . , ... Conve rted paper products . . . . . . . . . . . . . . . . . . . . ... . . . Pri nting 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 98.3 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 1049 113.2 118.4 11 7.5 101 .0 105.6 112.2 111 .0 3252 3253 3254 3255 3256 Resin , rubber, and artificial fi bers ...... .. .... ... ..... . Agricultu ral chemicals . . . . ........ ... .... . .. . .. . ... ..... Pharmaceuticals and medici nes .. ... .. ..... .. .... Paints , coatings, and adhesives . . . . . . . . . . . . . . . . . . . . . .. Soap, cl eaning compounds, and toiletries ... ...... 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 93.4 98.5 99.2 103.8 91 .1 97.4 102.1 102.7 3259 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 .3 102.7 109.2 109.3 101.4 103.5 108.6 120.0 111.2 103.9 103.6 109.7 113.3 104.2 97.6 105.2 - 3273 3274 3279 3311 3312 Cement and concrete products .. . ... .. .. .... ..... Lime and gypsum products .. . . . . . . . .. . . . . . . . . .. ..... Other nonmetallic mineral products ... ... ..... ... ..... Iron and steel mills and ferroall oy production .. Steel products from purchased steel .. 94.8 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 96.5 90.1 89.3 81.7 95.9 95.0 87.8 90.5 87.2 100.0 98.2 88 .8 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 .7 100.3 104.1 102.7 955 106.5 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 an d 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 89.8 94.6 91 .7 100.3 105.1 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 96.9 100.5 109.3 121 .8 110.2 - 3323 3324 3325 3326 3327 Architectural and structural metals .... ....... .. .. .... Boil ers, tan ks, and shipping containers ... ... .. .... .. Hardware ..... .............. ... ... ... .... . .... . .. . . Spri ng 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 93.4 94.8 89.6 95.3 86.9 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 - Utilities Manufacturing https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review June 111.3 2005 - - - - - - - - - - 131 Current Labor Statistics: Productivity Data 51. Continued-Annual indexes of output per hour for selected NAICS industries, 1990-2002 [1997=100] NAICS 3328 3329 3331 3332 3333 1990 Industry Coating, engraving, and heat treating metals .... Other fabricated metal products .. Agriculture, construction , and mining machinery Industrial machinery ... Commercial and service industry machinery .. 3334 3335 3336 3339 HVAC and commercial refrigeration equipment Metalworking machinery ............ ... .. ........... Turbine and power transmission equipment .... Other general purpose machinery .... 3341 Computer and peripheral equipment 3342 3343 3344 3345 3346 Communications equipment .. . ...... ..... ... 81 .6 86.7 82 .8 80.6 91 .4 88.8 85 .3 85.1 85.9 1991 78.1 85 .9 77 .2 81.1 89.6 1992 86.9 90 .6 79 .6 79.5 96.5 1993 91.9 92.1 84.1 84.9 101 .7 1994 96.5 95.0 91 .0 90.0 101.2 1995 102.8 97.1 95.6 97.9 103.0 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 1996 102.9 98 .9 95 .9 98.8 106.3 1997 100.0 100.0 100.0 100.0 100.0 1998 101 .7 102.3 104.2 94.4 107.5 1999 101 .5 100.2 95.0 105.2 111 .2 2000 2001 2002 105.9 100.8 101 .0 129.7 101.4 105.1 98.2 99.5 104.6 94.4 - 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 - - 14.3 15.8 20.6 27.9 35.9 51 .3 72 .6 100.0 138.6 190.3 225.4 237.0 - Audio and video equipment. . Semiconductors and electronic 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 4233 Wholesale trade Wholesale trade . . .... .. .. . ....... . .... ... . . . . ... .. ... . .... . ... ......... .. ... ······ ···· ···· .... Durable goods Motor vehicles and parts . . . .. . ..... . ··· ······· ····· Furniture and furnishings ... ... ..... .. . ... ... 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 93.9 100.0 100.0 100.0 100.0 100.0 118.2 102.4 105.9 103.5 104.2 141 .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 4245 4246 4247 4248 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 98.0 112.0 110.1 105.2 119.0 95.8 112.5 111.0 109.4 120.0 93.6 116.5 111 .6 111 .8 135.4 96.9 126.0 117.3 4249 425 42511 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 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 98.3 100.0 100.0 100.0 100.0 100.0 104.3 102.7 102.7 105.9 105.7 110.3 106.4 106.4 113.0 110.0 114.2 107.2 106.6 108.6 112.0 117.4 110.0 109.1 112.6 109.3 122.7 109.7 106.0 116.4 115.8 442 4421 4422 443 444 Furniture and home furnishings stores .. . ..... ........ ..... Furniture stores .. Home furnishings stores ........... . . . .. ..... . . ....... .... Electronics and 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 . . . .... . .. . .. ... . . .. . . Monthly Labor Review 132 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 - - - - 51. Continued--Annual indexes of output per hour for selected NAiCS industries, 1990-2002 [1997=100) 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 NAICS Industry 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 1030 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 .1 110.4 111 .8 446 447 448 Health and personal care stores . . . . . . . . . . . ... ....... Gasol ine 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 . ............ ..... .... .. ... Fl orists . . .. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ....... ..... .. 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 1000 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 11 7. 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 4539 454 4541 4542 4543 Office supplies, stationery and gift stores . .... ... . . . Used merchandise stores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other miscellaneous store retailers .. . .... . . . ... . Nonstore 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 98.7 140.3 121.4 92 .8 169.6 209.8 113.3 110.2 481 482111 48412 491 Transportation and warehousing Air transportation . .. .. . . . . . .. . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . Line-haul railroads .. ..... ······ · ····· General freight trucking , long-distance ····· ··· " ···· U.S. Postal service ..... .......... .., .. . .. .... .. 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 87.6 108.1 106.7 104.4 100.4 131 .4 130.6 192.0 93.5 52211 Finance and insurance Commercial banking ... . . .... ..... .. .... .. . . .... ..... . ... 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 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 Professional, scientific, and technical services .. . ... . . . . 7211 722 7221 7222 7223 7224 Accomodatlon 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 June 2005 . NOTE: Dash indicates data are not available . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Monthly Labor Review 133 Current Labor Statistics: International Comparison 52. Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data seasonally adjusted Annual average Country 2003 2004 2003 I II 2004 Ill I IV II 2005 Ill IV I United States ..... .. 6.0 5.5 5.8 6.1 6.1 5.9 5.6 5.6 5.5 5.4 5.3 Canada .. ...... .... .. 6.9 6.4 6.7 6.9 7.1 6.8 6.6 6.5 6.4 6.3 6.2 Australia . ....... .... 6.1 5.5 6.2 6.2 6.0 5.8 5.7 5.6 5.6 5.2 5.1 Japan .. ... . ····•·· .. . France ......... .... ... 5.3 4.8 5.4 5.5 5.2 5.1 4.9 4.7 4.8 4.6 4.6 9.6 9.8 9.3 9.5 9.7 9.8 9.7 9.8 9.8 9.8 9.9 Germany ............ 9.7 9.8 9.6 9.8 9.8 9.7 9.7 9.8 10.0 10.1 11.0 . Italy .... . . . . . . . . . . . . . . . 8.5 8.1 8.7 8.4 8.6 8.4 8.3 8.1 8.1 8.1 - Sweden ... ... ....... 5.8 6.6 5.3 5.5 5.8 6.3 6.7 6.8 6.6 6.4 6.3 United Kingdom ..... 5.0 4.8 5.1 5.0 5.0 4.9 4.8 4.8 4.7 4.7 - NOTE: Dash indicates data not available. Quarterly figures for Japan, France, Germany, Italy, and Sweden are calculated by applying annual adjustment factors to current published data, and for further qualifications and historical data, see Comparative Civilian Labor Force Statistics, Ten Countries, 1960-2004 (Bureau therefore http://www.bls.gov/fls/home.htm. should be viewed as less precise indicators of unemployment under U.S. concepts than the annual figures . See "Notes on the data" for information on breaks in series. Monthly Labor Review 134 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 of Labor Statistics, May 13, 2005) , on the Internet at Monthly and quarterly unemployment rates, updated monthly, are also on this site. 53. Annual data: employment status of the working-age population, approximating U.S. concepts, 10 countries [Numbers in thousands] Employment status and country 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 129,200 14,233 8,61 3 65,470 24,490 39,102 22,771 7,014 4,444 28,094 131 ,056 14,336 8,770 65 ,780 24,676 39,074 22,592 7,152 4,418 28 ,124 132,304 14,439 8,995 65 ,990 24,743 38,980 22,574 7,208 4,460 28 ,135 133, 943 14,604 9,115 66,450 24,985 39,142 22 ,674 7,301 4,459 28 ,243 136,297 14,863 9, 204 67 ,200 25,109 39,415 22,749 7,536 4,418 28,406 137,673 15,115 9,339 67,240 25,434 39,754 23,000 7,617 4,402 28, 478 139,368 15,389 9,414 67 ,090 25,764 39,375 23,172 7,848 4,430 28,782 142,583 15,632 9,590 66,990 26,078 39,301 23,357 8,149 4,489 28 ,957 143,734 15,892 9,752 66,860 26,354 39,456 23,520 8,338 4,530 29,090 144,863 16,367 9,907 66,240 26,686 39,499 23,728 8,285 4,544 29 ,340 146,510 16,729 10,092 66,010 26,870 39,591 24,021 147,401 16,956 10,244 65,760 66 .3 65 .5 63 .5 63 .3 55.4 57.8 48 .3 57 .9 64 .5 62. 6 66 .6 65 .1 63 .9 63 .1 55 .6 57.4 47 .6 58 .6 63.7 62.4 66.6 64 .8 64 .5 62 .9 55.4 57 .1 47 .3 58 .8 64 .1 62.4 66 .8 64.6 64.6 63 .0 67 .1 64 .9 64 .3 67 .1 65 .3 64 .3 62 .8 67 .1 65 .7 64 .0 62.4 67 .1 65 .8 64.4 62 .0 66 .8 65 .9 64.4 61 .6 66.6 66.7 64.4 60.8 66 .0 67 .3 64 .7 60.0 55 .9 57.7 47.6 611 62 .8 62 .5 56.3 56.9 47.9 62 .6 62 .8 62 .8 56.6 56.7 57.2 56.5 48 .5 64.7 64.0 62.9 56.4 49 .1 64 .9 64 .0 63 .0 - 48 .1 64 .5 63.8 62 .9 56 .9 56 .7 48 .2 65.6 63.7 62 .7 66 .2 67 .3 64 .6 60.3 57.4 120,259 12,694 7,699 63,820 21,714 35,989 20,543 6,572 4,028 25 ,165 123,060 12,960 7,942 63,860 21,750 35,756 20,171 6,664 3,992 25 ,691 124,900 13, 185 8,256 63,900 21 ,956 35,780 131,463 13,9 46 8,618 64,450 22,597 36,061 25, 696 20,366 7,321 4,034 26,691 133,488 14,314 8,762 63, 920 23,053 36,042 20,613 7,595 4,117 27,056 136,891 14,676 8,989 63,790 23,693 36,236 20,969 7,91 2 4,229 27,373 136,933 14,866 9,091 63,460 24,128 36,346 21,356 8,130 4,303 27,604 136,485 15,221 9,271 62,650 24,293 36,061 21,665 6,858 4,019 25 ,945 129,558 13,607 8,444 64,900 22, 169 35,508 20,165 7,163 3,973 26,418 61 .7 584 56.8 61 .7 49.2 62.5 58 .9 57 .8 61 .3 49 .0 52.6 42.5 54.6 57.6 57.0 62 .9 59 .2 59 .2 60.9 49 .2 52.4 42 .0 54 .9 58 .3 57 .0 63.2 59 .0 59.3 60.9 49 .1 52.0 42 .0 55.6 57.7 57 .3 63.8 59 .5 59.0 61 .0 49 .1 51 .6 41 .9 57 .8 56.9 58.2 64.1 60.3 59 .3 60.2 49 .7 52 .3 42.2 58 .7 57.6 58 .5 64 .3 61 .2 59 .6 59.4 504 52 .1 42 .6 60.6 584 59 .1 64.4 61 .9 60.3 59 .0 51.5 52 .2 43.2 62.7 60 .1 59 4 63.7 61 .9 60.1 584 52 .1 52 .2 43.8 63 .9 60.5 59 .5 62 .7 62.4 60.3 57 .5 52 .1 51 .6 44 .3 62 .9 60.7 59 .6 7,996 1,376 829 1,920 2,926 3,318 2,421 489 426 2,433 7,404 1,254 739 2,100 2,787 3,200 2,544 478 404 2,439 7,236 1,295 751 6,739 1,256 759 5,692 956 602 3,200 2,385 3,065 2,388 237 260 1,584 8,378 1,146 2,300 2,940 3,907 2,584 374 445 1,987 5,880 1,075 652 3,170 2,711 3,333 2,559 253 313 1,726 6,801 1,026 661 2,250 2,946 3,505 2,555 443 440 2,298 6,210 1,169 721 2,790 2,837 3,693 2,634 296 368 1,788 6.1 9.6 94 2.9 11 .9 8.5 10.7 5.6 8.7 8.2 3.2 11 .3 8.2 11.3 6.6 9.1 54 8.9 8.2 3.4 11 .8 9.0 11 .3 6.1 9.9 8.1 4.9 84 8.3 3.4 11 .7 9.9 114 4.5 7.7 7.7 4.1 4.2 7.0 4.0 6.1 6.3 4.8 9.1 7.8 10.2 2.9 5.8 5.5 Civilian labor force United States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. Canada . ·· · · ·· Au stralia . ... ... .. .. ..... -- . . . . . . .... .. Japan . . . • • ··· • ·· · • · • ·· France . . . . . . . . . . . . . . . . . .. . . . . . . . . . .. . . . . . .. . . .. .. .. Germany .. . · · ·· · · · ·· ·· ·· ·· ·· ·· " ... .. . ··· ·· ···· ·· ······ Italy . . . . . . . . . . . . . . ' .. . .. . . ·· · · ·· " ·" ' " ... .. ... .. Netherlands Sweden .. ... . ... .. .. ... ... · ··• · •· United Kingdom . .. Participation rate 8,353 4,567 29,562 39 ,698 24,065 8,457 4,576 29,748 1 United States Canada . . . . . . . . . . . . . . . . . .. . Au stralia . ·• •· · • · · • Japan •· • ··· .. . · ·· • • France. ·· · ·· . . . .. .. , . .. Germany . ···· • .. . ..... .. . . . . ... . . . . .. . . . .. . Italy . . .. . . . .. . Netherlands . . . . . . . . . . . .. . .. .. . · •• · · Sweden ................... . . .. . ... . . ··•· · • • ·· • · United Kingdom ...... ...... .... .... ..... . . ... . . . .. . .. 55 .7 57.1 47 .3 59.2 64 .0 62.4 63.2 55.6 57.3 47 .3 60.8 63 .3 62.5 49.1 65.5 63.7 63 .0 Employed United States Canada .. ·· • ··· • ·· •• ··· • · ·· ·• ·· · Australia . .. .. .. ..... .. ... .. . Japan · • France . . . . . . .. . . . . . . . . . . . Germany . . . . . . . .......... .. . . . . . . . . .. .. . . . .. . . .. ... Italy .. ······ · .. .. ... . ... .. . . Netherlands .. ....... . ... .. .. Sweden . . . . . . . . . . . . . . . . . . ... . · · · ·· ·· · · ·· United Kingdom .. 20,030 6,730 4,056 126,708 13,309 8,364 64,200 22,039 35,637 20,120 8,059 4,310 27,817 137,736 15,579 9,481 62,510 24,293 35,754 21,973 8,D35 4,303 28,079 139,252 15,864 9,677 62,630 62.3 63.0 60.7 57.1 51 .9 51 .0 44 .9 62 .3 63.4 61 .2 57 .1 35,796 22,105 8,061 4,276 28,334 Employment-population ratio 2 United States .. . .. .. .. ···· · Canada . .. . • · •• · ... ....... ... .. ... .... .... ... Australia . . . . . . . . . . . . . .. . . . . .. .. .... Japan .. . France. ·· · • · · .. .... .. . .. • ·· Germany .. ..... ... .. ... .. . ... ... .... ... Italy .... .... ..... Netherlands . . . . . . . . • · ·••··· • ... · • ·· Sweden ... .. . . ·· • . •• · United Kingdom .. .... . ... .............. . ... ... . .. . 53.2 43.6 54 .3 58 .5 56.0 62.4 60.3 59 .8 45 .1 62.4 59 .5 60 .0 Unemployed United States ... . Canada .. ... Australia . .. .. . .. . .... ... .. .. . . . . . . . . . .. . . ... . ... . . .. .. . .. .... . .. ... . . Japan .. . ·• · ··· • . ·•• · · . · • • ·· · · · • • ··· Fran ce ....... .... . .. .. ... . .. ... .. . ... Germany .... ····· ... ...... ... ... .. .. ....... . .. ... .... Italy . .. ... • ·· • • · · ·• ·· • · Netherlands ..... ······ · .. . . Sweden . . . . . . . . . . . . . . .. .. . . . .. . . . United Kingdom . .... . 8,940 1,538 914 1,660 2,776 3,113 2,227 442 416 2,930 3,400 2,226 3,109 2,164 208 227 1,486 636 3,590 2,393 3,438 2,062 227 234 1,524 4.7 6.5 6.8 5.1 8.4 7.9 9.2 5.8 7.0 6.4 5.4 9.0 8.7 8.7 2.5 5.0 5.1 2.7 5.1 5.2 8,774 1,150 611 3,500 2,577 3,838 2,048 318 264 1,484 8,149 1,092 567 3,130 2,630 3,899 1,960 396 300 1,414 6.0 6.9 6.1 5.3 9.6 9.7 8.5 3.8 5.8 5.0 5.5 6.4 5.5 4.8 9.8 9.8 8.1 4.7 6.6 4.8 Unemployment rate United States .. ... .... .. .. .. .. .. .. . ... .. .. Canada .. ...... .. • ·· Austral ia Japan . . · · •·· · .. . .... . .... . ... .. ... ... ... ... .. .... .. .... . . .. .. France . . . . . . . . . . . .. .. .... .... . .. .. Germany .. ···• Italy . ...... . ...... ··· ···· ... . .. . .... ... . ... .. ·· •· ···· Netherlands .. . .. .. ... . ... .. ... .. .. ........ .. ..... .. .. ... ... .. ..... .... ... Sweden . United Kingdom .. 1 6.9 10.8 10.6 2.5 11 .3 8.0 9.8 6.3 94 10.4 6.8 9.6 8.7 Labor force as a percent of the working -age population . 2 Employment as a pe rcent of the working-age population . NOTE: Dash indicates data not available . See 'Notes on the data' for 8.7 5.0 10.1 7.0 11.2 9.3 11.5 3.9 84 6.3 6.9 4.7 10.5 8.5 11.0 3.2 7.1 6.0 For fu rt her qualifications and historical data, see Comparative Civilian Labor Force Statistics, 'Ten Countries, 1960-2004 (Bureau of Labor Statistics, May 13, 2005) , on the Internet at htt p://www .bls.gov/fls/home .htm. for information on breaks in series . https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis Mo nt hly Labor Review June 2005 135 Current Labor Statistics: International Comparison 54. Annual indexes of manufacturing productivity and related measures, 15 economies (1992 = 100] Measure and economy Output per hour United States .. Canada .. ....... ... ..... ... Australia .... .. ... ..... ..... ..... Japan .... ...... ··· ···· . ...... .... Korea .. ... .. ..... ... .......... ..... .. . Taiwan ······· •·· ... ... .. . .. . . .. ... 1960 1970 1980 1990 1991 1993 1994 1995 - 0.0 54.9 96 .9 93.4 91.6 94.4 81.5 88.8 96 .8 98.4 97.9 95.3 96.4 99.0 91 .6 96.5 99 .1 100.3 102.1 105.8 106.1 101.7 108.5 102.8 102.5 100.2 107.3 110.8 104.9 103.3 118.2 106.7 108.4 112.6 113.8 112.4 105.8 111 .0 129.3 115.1 113.2 112.5 93 .9 99.0 96.6 97.0 98.3 96 .1 101 .0 101.8 101 .2 108.9 109.6 104.8 114.4 112.3 107.9 1996 1997 1998 1999 2000 2001 2002 2003 117.0 121 .3 113.5 115.2 121.0 160.4 129.3 125.5 118.0 121 .7 126.5 109.7 113.6 116.1 142.3 123.1 116.3 109.8 114.7 115.5 118.5 121 .2 178.8 135.9 126.9 117.4 127.9 143.5 129.3 145.2 127.0 102.5 129.5 105.4 102.0 141 .0 110.6 127.0 103.6 162.7 113.6 113.5 132.7 106.6 175.5 121 .0 132.4 135.9 214.3 160.8 132.6 127.2 148.0 127.8 114.0 171 .0 132.1 140.7 146.2 120.4 110.3 121.4 128.0 135.9 215.8 151 .0 130.8 126.6 142.5 127.0 160.0 130.5 136.2 139.9 235.2 170.9 141 .7 131 .3 114.7 108.3 119.3 132.8 122.1 119.9 126.7 198.9 143.4 125.5 123.1 133.0 121 .4 - - 13.9 37.7 70 .5 72.9 69.5 63.6 - - 47 .6 18.0 25.2 19.9 29.2 24.6 32 .9 46 .3 39.0 52.0 46.2 65.4 83 .2 61.6 77 .2 78.6 Netherlands ... ... ·· ··· · ··· ...... Norway ········ · ··· ·· ·· · ···· · ·· · · ·· Sweden . . . . . . . . . . . . . . . . . ......... United Kingdom .. . .. ........... . .. 18.8 37 .6 27.3 30.0 38.5 59.1 52 .2 43 .2 69.1 77.9 73.1 54.3 98.7 98 .1 94.6 89.2 99 .0 98.2 95.5 93.9 102.0 99 .6 107.3 103.8 113.1 99.6 117.8 108.0 117.3 100.7 124.5 106.2 Output United States . . . . . . . . . . . . . . . . .. . .. Canada . . . . . . . . . . . . . . . . .. . ....... . . Austral ia . . . . . . . . . . . . . ........ . .... Japan .. ..... ·····•·· - - 101 .6 106.0 104.1 111 .1 114.1 109.1 121.3 127.9 133.1 138.9 99.0 100.7 103.5 105.9 103.8 118.4 58.9 75.8 83 .6 89 .8 98.3 33.4 119.6 108.7 96.3 105.4 102.4 97.0 97 .0 94.9 116.8 108.5 101 .4 107.3 100.3 98.9 129.9 114.9 104.2 112.6 104.9 133.9 118.6 100.2 138.3 120.3 105.9 107.7 104.6 145.0 128.3 112.7 115.9 109.7 133.5 132.6 114.4 116.7 115.0 144.9 118.3 101 .9 162.6 139.6 153.6 97.1 86.7 90.0 101 .0 101 .7 99.1 99.1 99.4 127.7 115.1 106.5 147.6 159.2 60.8 29.9 44.0 78.2 94 .3 81.6 119.6 112.6 103.0 123.8 109.2 190.2 141 .5 114.4 117.9 118.7 151 .8 119.9 121.9 124.3 123.8 105.5 194.3 143.1 95.1 102.4 104.5 104.6 95.2 107.2 108.2 92 .5 105.4 108.9 110.3 95.7 108.8 111 .6 114.2 97 .7 110.7 114.9 113.7 95.8 110.3 117.6 113.6 136.4 108.6 146.5 110.7 158.3 111 .3 172.5 112.1 100.1 113.6 122.8 112.8 188.3 103.6 109.0 99.1 88.7 97 .2 97 .7 105.4 112.4 100.0 88.0 105.2 115.9 100.1 82 .7 74 .7 97 .6 103.5 90.2 99 .4 89 .9 80 .1 99.9 92.6 113.7 105.9 102.7 104.6 118.7 98.7 80.4 81.8 98.7 91 .2 95.8 89.2 78.9 99.8 92.6 109.6 106.0 98.7 102.9 123.1 96.7 80.3 88.1 100.5 91 .7 96.3 87.2 78.8 100.1 92.5 105.9 107.3 95.0 111 .5 109.3 124.6 117.4 111 .7 128.2 122.0 115.8 133.0 133.2 136.3 119.6 140.0 112.6 115.4 218.2 140.3 114.8 219.4 113.7 234.2 146.6 123.7 149.5 114.6 241 .7 150.0 120.6 130.2 122.8 127.2 136.5 128.3 137.4 127.8 132.0 140.5 142.0 132.5 138.2 148.9 136.8 137.6 143.8 144.3 Belgium .... . .... . . . ... .. . .. . Denmark . . . ..... ... ... ... .......... France .. ......... .. .. ... ..... .. .... . Germany .. ... .. .... .... ...... ·· ····· Italy .. .... ..... ... ..... .... . ····· Korea .... ... . . .... . .. . . . . . . . . . . . . Taiwan . . . . . . ... ..... ... .. .... . .... Belgium . . . . . . . . . . . . . ..... ... . Denmark .. ..... . .. ... . .......... France . . . . . . . . . . . . .... ·· ·· ····· Germany . . . . . .. . . . . . ·······•·· Italy ··· ·· ······· ... ·· • ......... . ... Netherlands .. . ... . . .............. Norway . . . . . . . . . . . . .. .. .. .. ... .... .. Sweden ................. .. .. ... ... .. . United Kingdom ····· ···· ···· ··· ··· Total hours United States ·················"·· Canada ............ ....... . ... .... . Australia ···· · · · · ·· · · · ········ ······· Japan .... ... .. .. ·· · · · · · ·········· · · Korea . . . . . . . . .. . . . . .. ............. Taiwan .. ...... . ... .... ........ Belgium . . . . . . . . . . ... . . . . . . . . . .. . . Denmark ..... ... ... ....... ... ...... France .... ...... .. ... ...... . . .... Germany ··· ······ ····· ...... Italy ....... ................ .... Netherlands . . . ... . . .. . . . . . . . . .. . Norway . . . . . . . . . . . . . . . . ........... Sweden ... ........ United Kingdom ..... .. . ... ..... .. Hourly compensation (national currency basis) United States . ... .. . ······ ...... Canada . . ..... .. ... .. ... . ... . . .... Australia . . . . . . . . . . . ... .. .. ....... ... Japan . . . . . . . . . . . . . .. . ... ...... ... 37 .8 - - 10.8 30.7 42 .0 27.9 41.5 39.4 7.0 12.7 57.6 72 .7 57.7 70.9 23.0 31 .9 57 .7 45 .9 67 .5 48.1 59.8 91 .0 80.7 90.2 92 .1 88.3 107.1 - 104.4 - 85.3 84 .4 76.9 104.9 90.7 87.2 - - 77 .8 104.3 107.5 114.6 129.2 95.5 - - 92 .4 170.7 166.7 140.3 142.3 93.5 169.8 153.6 168.3 224.6 174.7 157.1 147.8 136.3 104.0 155.5 153.9 154.7 208 .8 119.7 113.4 132.5 110.5 107.4 111 .2 134.7 124.0 160.5 14.9 23 .7 17.1 55 .6 47.5 10.0 - - - - 4.3 16.4 58 .6 Korea . . .. ... .... .. .... ··· ··· ··· ··· ·· - - - Taiwan . . .. . . . . . .. ······ · .. .... .... Belgium .. Denmark ... .. . .. . .. .. .......... . France ······ .. ... .. ... . ... . ........ - - 5.4 3.9 4.3 13.7 11.1 10.5 29.6 52 .5 45.1 41.2 8.1 1.8 6.2 4.7 20.7 5.3 19.4 11 .8 10.7 6.1 53 .6 30.4 60.5 39.0 37.3 32 .0 Germany ·· ···· · ··· ··· ··· •·"·""'' .... Italy .... ... ..... . . .. .. .. . . . Netherlands ... ... . . .. . .... ..... .. . Norway . . . . . . . . . . . .. . Sweden .. . . . . . . . . . . . . . ·········· United Kingdom .... 4.1 2.9 99.3 99.8 99.0 104.1 95 .7 92.4 96.5 97 .7 101 .7 101 .9 100.1 11 3.5 113.6 102.9 106.5 100.4 103.9 104.4 103.1 103.7 101 .4 104.3 103.3 105.6 100.1 102.9 100.3 103.4 116.4 118.1 99 .6 101.5 100.5 102.9 104.1 103.3 100.8 100.8 109.0 106.6 90 .8 88.3 86.3 95.6 95.0 94.0 90.6 68.6 96.5 86.2 85.2 90.1 93.5 90.9 89.4 93.5 97.3 97.9 96.4 91 .5 94.2 99.0 101.4 110.1 105.3 104.8 87.6 89.8 92 .3 87.8 82 .9 See notes at end of table. 136 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 102.0 95.0 96.1 100.7 100.7 99.8 102.3 2005 94.8 97 .5 95.5 93.8 101 .5 117.0 106.2 107.3 131 .9 107.8 101.4 100.1 97.8 94.7 103.6 103.0 103.9 91 .9 104.0 106.4 102.8 89.1 97.1 99.6 94.7 96.7 94.7 90.8 95 .4 95.8 102.1 94.9 97.7 98.8 101 .7 93.6 95.2 92.1 86.8 97.7 92.4 105.0 99.4 98 .4 100.4 99.8 92 .0 100.1 91 .7 84 .8 99.4 92 .3 106.6 105.9 101 .5 102.7 102.0 105.9 102.7 105.6 107.9 103.7 104.3 104.7 106.0 113.2 108.3 114.3 105.9 129.8 111 .1 158.3 120.2 104.8 102.4 103.1 109.2 108.1 110.4 106.4 105.7 104.5 106.1 106.0 106.5 111.8 106.8 109.0 101.5 97.4 104.5 104.4 99.8 107.3 109.2 106.8 108.8 11 7.6 111 .3 112.1 91 .0 98.1 91 .2 80.6 97.3 91 .2 107.6 105.3 103.1 109.4 107.0 122.8 109.1 184.3 128.2 111 .1 112.8 112.2 123.3 119.0 114.4 113.6 115.2 111.4 106.9 90.4 99.2 89.8 98.2 90.2 79.5 98.6 91 .9 112.0 103.9 200.3 132.4 115.2 116.6 111 .8 125.7 123.0 117.2 118.7 121 .0 115.7 122.0 110.8 124.1 99.9 149.5 108.4 117.0 119.6 112.7 127.6 122.2 122.0 125.7 125.6 123.0 144.3 118.5 127.3 116.6 130.6 124.2 126.0 133.0 130.3 129.9 115.0 132.5 109.8 170.3 125.1 120.4 121 .6 128.0 99.9 113.0 121 .9 112.3 183.1 113.4 96.2 120.9 93 .5 77.7 90.7 89.0 90.8 95.6 86.5 78.2 99.1 92.0 102.3 107.5 90 .7 155.1 131.0 112.1 135.4 111 .7 185.6 127.7 256.4 177.2 146.2 136.9 158.0 134.4 110.9 113.5 196.5 134.8 142.9 145.4 158.0 128.7 157.3 130.2 106.7 103.4 209.1 152.1 121 .6 120.8 129.1 99.6 111 .7 121 .0 111 .5 190.6 109.9 89.3 121 .1 94 .5 74.0 88.9 89.0 85 .8 92 .0 83.2 76.1 99.7 89.4 99 .8 102.7 86 .0 219.1 160.9 120.9 121.4 128.5 99.8 110.2 117.6 107.3 194.4 110.3 85 .0 119.1 92 .5 73.0 85.4 90.8 82 .7 88.7 81 .3 74.3 99.3 94 .5 98.9 81.9 145.4 126.8 154.7 157.8 131.4 122.8 266.1 123.8 290.9 146.7 145.8 136.5 143.2 135.2 145.5 135.7 147.3 157.9 148.8 152.2 - 150.0 139.1 148.9 140.0 164.6 154.3 160.3 54. Continued- Annual indexes of manufacturing productivity and related measures, 15 economies 1970 1980 1990 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 - - 26.4 31 .1 78.8 65.2 93.7 94.6 94.2 97.6 99 .6 100.6 96.4 99 .8 101 .0 105.4 103.0 94. 8 94.3 107.0 93.5 97. 5 108.1 94.0 91 .9 96 .2 108.2 92 .8 96.7 108.2 95.2 91 .9 94.9 110.9 92 .8 92 .5 109.4 109.8 104.1 83.6 108.5 97 .1 93.9 97.4 112.9 84.4 90.9 97.2 113.5 87.8 113.1 85.3 92.3 99.4 97 .5 97.5 94 .1 96 .8 98 .5 93 .6 99 .4 101 .4 92 .2 102.8 86 .2 1960 Measure and economy Unit labor costs (nation al currency basis) United States .. Canada .. Australia . ··· ····· · .............. .... .. - - - Japan . ···················· ...... ... .. Korea ... ..... . ......... .. . ... ... Taiwan ·········· ·· . . . . . . . . . 31 .1 43.6 92.1 Belgium ·········· · · · ··· · · .. . .. ..... Denmark ........... . . . France ........ ... .. . . . . . . . .. . . . . . . . Germany ......... . ... . . .. . ...... Italy . . ... .. .... ...... ..... .. Netherlands . . . . .. .. . . . . . . .. . . .. . . . . Norway .. ······ ··· ······· ·· ·········· Sweden .. ............... ........ .... United Kingdom .... ....... ... ..... Unit labor costs (U.S. dollar basis) United States . Canada ................. ... . .. Australia . . . . . . . . . . ............... Japan . . . . . . . . . .. . ..... ......... .. .. Korea ........ . . . . . . . . . . . . . . . . . . .. . Taiwan ............. . •· Belgium . . . . . . . . . . . . . ... . ....... . ... Denmark . . .... . ..... . .. ...... France .. ..... ..... . . . . .. . . . . .. . . . . Germany . ........ . .. . . .. . ... . ... Italy .... . ... ........ .. .. .. . ... . .. Netherlands .... , .. ....... ...... .... Norway ................ . ...... Sweden ... United Kingdom .. NOTE: Data for Germany for years https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis - - - - 23 .8 62.2 95.9 84.2 95 .9 30.1 15.3 21 .7 27.8 7.2 32 .9 41 .7 23 .9 26 .8 39.8 11.4 50.4 80 .3 54.2 67.0 69.4 38 .7 87.6 93.0 95 .0 96 .8 90.3 90 .7 91 .1 98 .1 97. 6 99 .3 93 .1 98.0 95 .7 102.3 102.2 102.0 104.5 104.5 102.4 97 .9 94.2 97 .8 102. 0 101 .9 96 .4 12.6 15.0 9.8 20 .0 20 .6 14.1 50.0 51 .0 59.0 94 .2 92 .9 93.0 99 .2 100.0 100.0 101 .9 90.8 100.7 104.8 84 .7 99.4 - - 32 .9 36 .0 78.8 67.4 93 .7 98 .0 100.1 97. 6 105.1 103.3 100.6 90.3 83 .9 93.0 89.7 89 .5 92 .7 94 .1 91 .8 100.3 91 .1 92. 3 98.5 82. 8 98.9 125.8 - - - 11 .0 15.4 51 .5 - - - - 14.9 27 .0 19.3 43.4 88.3 58.1 19.4 13.4 23.4 25 .7 10.4 17.1 14.3 22 .3 24.5 15.3 11 .0 17.4 16.9 23 .1 15.6 19.1 before 1991 are 83.9 59.6 55.7 77.5 62 .9 70.2 77.6 for the 92. 3 115.3 102.6 98 .1 95 .1 95.1 92 .0 93 .1 95 .3 87 .3 87.5 98 .7 93 .3 97. 3 81 .8 87 .9 90 .0 96. 9 93 .6 95 .0 89 .2 91 .3 96.3 67. 8 100.0 93 .9 85.6 former West Germany. Data 106.8 99 .0 94.2 89 .4 93 .4 98 .2 77. 9 93 .2 92 .3 64.0 97.5 122.4 104.5 96.4 96. 1 96.5 104.7 103.2 95 .6 108.4 85 .8 102. 5 94 .8 83 .0 107.8 131 .6 124.3 99.2 105.2 103.6 102.5 114.2 78.0 104.8 95.5 102. 8 97 .8 107. 5 109.8 95. 9 11 0.8 91 .8 98.8 91 .9 92.2 101 .9 88 .1 90.6 110.3 100.7 94.4 103.4 87.6 104.5 111.4 96.5 116.4 89.0 105.7 85.8 108.2 104.6 110.3 98.3 125.7 84 .0 107.6 112.3 99.1 128.4 80.1 108.1 112.6 99.5 131 .9 77 .9 104.3 135.6 84.4 113.5 114.3 113.7 115.4 93 .5 86.4 11 5. 1 109.5 91 .9 84.0 92. 8 78.8 91 .9 77 .2 92 .8 75.2 93.9 76.0 109.4 97.4 103.4 92.6 92 .2 97.3 101 .0 72 .7 86.5 98.4 75 .3 78 .3 80.2 89.3 75.3 78.1 67.8 76.7 64.2 79.4 88.0 68.5 69.4 91 .5 76.2 84 .3 102.2 56.4 104.7 1nd1cates 79.7 66 .2 73.3 93 .0 93.7 47 .6 49.5 94 .0 97.6 data not available 129.6 104.1 126. 3 95 .4 99 .1 107.0 101.2 93 .0 124.9 102.3 89 .5 82.4 90.2 83.3 122.0 103.2 68.4 77. 4 81 .6 91 .7 79.1 111 .6 94 .0 92 .9 87.7 80 .6 78.2 100 .0 87.0 87.2 106 .6 102.1 103.5 106.4 61 .5 70.0 77 .3 65.4 91 .6 93 .4 100.4 106.5 86.2 for 1991 onward are for unified Germany. Dash Monthly Labor Review 112.8 93.3 95.9 107.3 86.6 111 .2 116.2 68.4 77 .8 62 .6 79.5 66.2 74 .5 96.4 109.0 87.2 111 .1 121 .1 108.8 141 .3 80.2 119.2 84.7 113.5 82 .7 109.6 88.0 110.8 126.2 112.6 144.9 78.6 118.9 90.9 74.8 84.0 92 .3 85.8 88.9 71 .0 62 .1 92.6 74.7 60.5 72.6 83.5 66.5 83.9 72 .9 82 .1 110.0 48.1 101 .4 June 2005 - 100.6 80.4 100.1 90.9 101 .7 127.2 56.6 110.0 137 Current Labor Statistics: Injury and Illness 55. Occupational Injury and illness rates by Industry, 1 United States Industry and type of case 2 Incidence rates per 100 full-time workers3 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 39 86.5 89 39 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 ~7 100.9 11 .6 59 112.2 10.8 ~4 108.3 11 .6 ~4 126.9 11 .2 5.0 10.0 4.7 97 4.3 8.7 39 8.4 4.1 79 3.9 7.3 3.4 7.1 3.6 7.3 3.6 Mining Total cases ............................. . Lost workday cases .... ...... .. .. .... ... ... Lost workdays 8.5 4.8 137.2 8.3 5.0 1195 7.4 4 .5 129.6 7.3 4.1 204.7 6.8 39 6.3 39 6.2 39 5.4 3.2 59 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 14.3 6.8 143.3 14.2 6.7 1479 130 6.1 148.1 13.1 5.8 161 .9 12.2 5.5 11 .8 5.5 10.6 49 99 4.5 95 4.4 8.8 4.0 8.6 4.2 8.3 4.1 79 4.0 13.9 13.4 ~4 137.6 12.0 11 .5 5.1 10.9 5.1 98 4.4 90 4.0 8.5 3.7 8.4 39 8.0 3.7 7.8 3.9 6.9 3.5 132.0 12.2 ~4 142.7 General building contractors: Total cases . Lost workd ay cases .. Lost workdays as 137.3 ~s Heavy construction. except buildino: Total cases . ..................... .. ... . . . . ......... .. . Lost workday cases .. Lost workdays 13.8 ~3 144.6 10.2 5.0 99 4.8 90 4.3 8.7 4.3 8.2 4.1 7.8 3.8 7.6 3.7 7.8 4.0 160.1 12.1 ~4 165.8 11 .1 5.1 147.1 Special trades contractors: Total cases Lost workday cases. Lost workdays ..................................................... .. ... . 14.6 ~9 144.9 14.7 69 153.1 13.5 6.3 151 .3 13.8 ~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 13.1 13.2 12.7 12.5 ~8 ~8 ~6 ~4 12.1 5.3 12.2 5.5 11 .6 5.3 10.6 49 10.3 4.8 9.7 4.7 9.2 4.6 9.0 4.5 8.1 4.1 113.0 120.7 121 .5 124.6 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 18.4 ~4 177.5 18.1 a8 172.5 16.8 a3 172.0 16.3 ~6 165.8 15.9 7.6 15.7 7.7 149 7.0 14.2 6.8 13.5 6.5 13.2 6.8 130 6.7 12.1 6.1 10.6 5.5 16.1 7.2 16.9 7.8 15.9 ~2 14.8 ~6 128.4 14.6 6.5 15.0 7.0 139 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 Stone. clay, and olass products: 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 Primary metal industries: Total cases Lost workday cases Lost workdays 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 products: Total cases .. Lost workday cases Lost workdays .. 18.5 7.9 147.6 18.7 79 155.7 17.4 7.1 146.6 16.8 6.6 144.0 16.2 6.7 16.4 6.7 15.8 69 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 ~8 86.8 12.0 ~7 88 9 11.2 ~4 86.6 11 .1 4.2 87.7 11 .1 4.2 11 .6 4.4 11 .2 4.4 99 4.0 10.0 4.1 95 4.0 8.5 3.7 8.2 3.6 11 .0 6.0 Electronic and other electrical eauipment: Total cases .............................................. .. ... . . Lost workday cases Lost workdays 91 a9 77.5 91 a8 79.4 8.6 3.7 83.0 8.4 a6 81 .2 8.3 3.5 8.3 3.6 7.6 3.3 6.8 3.1 6.6 3.1 59 2.8 5.7 2.8 5.7 2.9 5.0 2.5 17.7 ~8 138.6 17.8 ~9 153.7 18.3 18.7 ~1 186.6 18.5 7.1 19.6 7.8 18.6 79 16.3 7.0 15.4 6.6 14.6 6.6 13.7 6.4 13.7 6.3 12.6 6.0 166.1 Instruments and related products: Total cases . . ... .. .. . . . . .................. .. . Lost workday cases ..... .. ... ..... ..... . Lost workdays .. ~6 25 55.4 ~9 2.7 57.8 2.7 64.4 59 27 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 91 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 13.8 ~s Manufacturing Total cases . .................... .. ... . . . . . . . . . . . . . . . . . ...... .... . Lost workday cases .. Lost workdays Durable goods: Total cases Lost workday cases . Lost workdays .. Lumber and wood products: Total cases . . . ....... .. ........... . Lost workday cases .. Lost workdays Furniture and fixtures: Total cases Lost workday cases Lost workdays .. Transportation eauipment: Total cases Lost workday cases ... Lost workdays See footnotes at end of table. 138 Monthly Labor Review https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis June 2005 12.8 ~o ~o ao 8 .8 4.3 55. Continued-Occupational injury and illness rates by industry, 1 United States Incidence rates per 100 workers Industry and type of case 2 1989 Nondurable goods : Total cases ... Lost workday cases .. Lost workdays ... 1 1990 1991 1992 1993 4 1994 4 1995 4 1996 4 3 1997 4 1998 4 1999 4 2000 4 2001 4 11 .6 ~5 107.8 11 .7 ~6 116.9 11 .5 5.5 119.7 11 .3 ~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 4.3 7.8 4.2 7.8 4.2 6.8 3.8 1&5 ~3 174.7 2Q0 ~9 202.6 1~5 9.9 207.2 1&8 ~5 211 .9 17.6 8.9 17.1 9.2 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 6.3 &7 a4 64.2 ~7 a2 62.3 &4 2.a 52 .0 &0 2.4 42 .9 5.8 2.3 5.3 2.4 5.6 2.6 6.7 2.8 5.9 2.7 6.4 3.4 5.5 2.2 6.2 3.1 6.7 4.2 10.3 ~2 a1.4 9.6 10. 1 ~4 aa.3 9.9 ~2 87 .1 9.7 4.1 8.7 4.0 8.2 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 85 .1 &6 as 80.5 as a9 92.1 ~2 ~2 99.9 9.5 ~o 104.6 9.0 3.8 8.9 3.9 8.2 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 12.7 ~a 132.9 12.1 5.5 124.8 11 .2 ~o 122.7 11 .0 5.o 125.9 9.9 4.6 9.6 4.5 8.5 4.2 7.9 3.8 7.3 3.7 7.1 3.7 7.0 3.7 6.5 3.4 6.0 3.2 Printina and oublishina : Total cases .......... _ ................. . Lost workday cases Lost workdays .... &9 a3 63.8 &9 a3 69.8 &7 a2 74.5 ~3 a2 74.8 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 Chemicals and allied oroducts : Total cases ..... ..................... ...... ... ... . Lost workday cases Lost workdays ~0 3.2 63.4 &5 3.1 61 .6 &4 3.1 62.4 &0 2.8 64.2 5.9 2.7 5.7 2.8 5.5 2.7 4.8 2.4 4.8 2.3 4.2 2.1 4.4 2.3 4.2 2.2 4.0 2.1 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 5.9 2.8 71 .2 5.2 2.5 4.7 2.3 4.8 2.4 4.6 2.5 4.3 2.2 3.9 1.8 4.1 1.8 3.7 1.9 2.9 1.4 Rubber and miscellaneous olastics oroducts : Total cases ............................. . Lost workday cases Lost workdays 16.2 ao 147.2 16.2 ~a 151.3 15.1 ~2 150.9 14.5 &a 153.3 13.9 6.5 14.0 6.7 12.9 6.5 12.3 6.3 11 .9 5.8 11 .2 5.8 10.1 5.5 10.7 5.8 8.7 4.8 Leather and leather oroducts : Total cases ............................. . Lost wo rkday cases Lost workdays . 13.6 &5 130.4 12. 1 5.9 152 .3 12.5 5.9 140.8 12.1 5.4 128.5 12.1 5.5 12.0 5.3 11.4 4.8 10.7 4.5 10.6 4.3 9.8 4.5 10.3 5.0 9.0 4.3 8.7 4.4 Transportation and public utilities Total cases ............................. . Lost workday cases Lost workdays .. 9.2 5.3 121 .5 ~6 ~5 134. 1 9.3 5.4 140.0 9.1 5.1 144.0 9.5 5.4 9.3 5.5 9.1 5.2 8.7 5.1 8.2 4.8 7.3 4.3 7.3 4.4 6.9 4.3 6.9 4.3 8.0 3.6 63.5 7.9 3.5 65.6 7.6 3.4 8.1 3.4 7.9 3.4 7.5 3.2 6.8 2.9 6.7 3.0 6.5 2.8 6.1 2.7 5.9 2.7 6.6 2.5 720 8.4 3.5 80.1 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 3.4 6.5 3.2 6.5 3.3 6.3 3.3 5.8 3.1 5.3 2.8 Retail trade : Total cases Lost workday cases .... Lost workdays .. 8.1 3.4 60.0 8.1 3.4 63.2 7.7 3.3 69.1 8.7 3.4 79 .2 8.2 3.3 7.9 3.3 7.5 3.0 6.9 2.8 6.8 2.9 6.5 2.7 6.1 2.5 5.9 2.5 5.7 2.4 2.0 2.4 1.1 24.1 2.9 1.2 32.9 2.9 1.2 2.7 1.1 2.6 1.0 2.4 2.2 .7 1.8 .9 .5 1.8 .8 1.9 9 .8 7 17.6 2.4 1.1 27.3 5.5 2.7 51.2 6.0 2.8 56.4 6.2 2.8 60.0 7.1 3.0 68.6 6.7 2.8 6.5 2.8 6.4 2.8 6.0 2.6 5.6 2.5 5.2 2.4 4.9 2.2 4.9 2.2 4.6 2.2 Food and kindred products: Total cases ............................ ...... . .. Lost workday cases Lost workdays ...... ...... .. .. .. Tobacco oroducts Total cases .................... , ....... . Lost workday cases .. Lost workdays ........... ... .. Textile mill oroducts Total cases Aooarel and other textile oroducts Total cases . Lost workday cases . Lost workdays Paoer and allied oroducts : Total cases .. Lost workday cases .... Lost workdays . Wholesale and retail trade Total cases ............................. . Lost wo rkday cases Lost workdays Finance, Insurance, and real estate Total cases ................... .. ....... . Lost workday cases .. . Lost workdays .... ~o Services Total cases . Lost workday cases Lost workdays .. 1 Data for 1989 and subsequent years are based on the Standard Industrial Class- ification Manual , 1987 Edition . For this reason, they are not strictly comparable with data N = number of injuries and illnesses or lost workdays ; EH = total hours worked by all employees during the calendar year; and for the years 1985-88, which were based on the Standard Industrial Classification 200,000 = base for 100 full-time equivalent workers (working 40 hou rs per week , 50 weeks Manual, 1972 Edition, 1977 Supplement. per year) . 2 Beginning with the 1992 survey, the annual survey measures only nonfatal injuries and 4 Beginnin g with the 1993 survey, lost workday estimates will not be generated . As of 1992, illnesses, while past surveys covered both latal 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 . 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 and https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis were calculated as IN/EH) X 200.000. where : Monthly Labor Review June 2005 139 Current Labor Statistics: Injury and Illness 56. Fatal occupational injuries by event or exposure , 1998-2003 Fatalities Event or exposure 1 2002 1998-2002 average 2 3 2003 Percent Number Number 6,896 5,534 5,559 100 2,549 1,417 696 136 249 148 2,385 1,373 636 155 202 146 2,367 1,350 648 135 269 123 42 24 12 2 5 2 27 33 17 (') 281 367 303 358 192 380 63 92 235 293 373 312 323 164 356 64 71 194 324 321 252 347 186 336 43 68 208 6 6 5 6 3 6 Shooting Stabbing .. Self-i nflicted injuries ..... ....... .. .... ....... ...... ......... .... ... .... .. .. ...... .. ... . 910 659 519 61 218 840 609 469 58 199 901 63 1 487 58 218 16 11 9 1 4 Contact with objects and equ ipment. .................................... .. .. ................. ... Struck by object Struck by falling object ..... ... .... ......... ... ..... .... ... . Struck by flying object .. ........ .. ... .... ..... ........ ... ...... ...... ..... . Caught in or com pressed by equ ipment or obj ects. Caught in running equipment or machinery .. Caught in or crushed in collapsing mate rials .. 963 547 336 55 272 141 126 872 505 302 38 231 110 116 911 530 322 58 237 121 126 16 10 6 Falls ....... ....... .... ...... ... .... ... ... .... ... ....... ..... ........ .......... ............. ... .. Fall to lower level. . Fall from ladder Fall from roof Fal l from scaffold, staging ...... ...... .... ....... ... ... ... ... . Fall on same level.. 738 651 113 152 91 65 719 638 126 143 88 64 691 601 113 127 85 69 12 11 2 2 2 Exposure to harmful substances or environments .. ... ..... ...... . Contact with elect ric current. Contact wit h ove rh ead power lines ....................... ... . Con tact with tem perature extremes .................. .. ............ .... . Exposu re to caustic, noxious, or allergenic substances ....... ..... ..... ... . Inhalation of su bstances. . Oxygen deficiency ...................................................... . Drowning, submersion .. 526 289 130 45 102 50 89 69 539 289 122 60 99 49 90 60 485 246 107 42 121 65 73 52 9 4 2 Fires and explosions ...... ... ..................................................... . 190 165 198 4 Total .... Transportation incidents ... ..... .... ..... .... .. .... .. ... .... ... ... .. ... .... .. ...... ... . Highway incident.. Collision between vehicles, mobile equi pment .................. . Moving in same direction .. Moving in opposite directi ons, oncoming ... Moving in intersection .. Vehicle struck stationary object or equi pment in roadway .. . Ve hicle struck stationary object, or equipment . ................. ....... .. on side of road .. __ . Noncollision incident.. Jackknifed or overturned-no coll ision .. Nonhighway (farm, industrial premises) incident .. . ...................... ...... ... ......... ... Overt urned .. Worker struck by a vehicle .. . Rail vehicle .. Water vehicle . Aircraft ............. ........ .......... .. .. .. Assaults and violent acts ..................... ... ...... .. ............................ .. ~m~m ....... .............. ......... ...... ........ ................... . 1 Based on th e 1992 BLS Occupa tional Injury and Illness Since then , an additional Classification Manual . Includes other events and exposures, identified , bringing such as bodi ly reaction, in ad dition to those shown separatel y. 2002 to 5,534 . 2 3 Exclu des fatalities from th e Sept. 11, 2001, terrorist attacts. The BLS news release of September 17, 2003, reported a total of 5, 524 fatal work inj uries for calendar year 2003. Monthly Labor Review 140 https://fraser.stlouisfed.org Federal Reserve Bank of St. Louis 4 the 4 2 2 2 were total job-related fatality count for Equal to or greater than 0.5 percent. NOTE: Totals for major categories may include sub- categories not shown separately. Percentages may not add to totals because of rounding . 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