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U.S. Department of Labor
Elaine L. Chao, Secretary
U.S. Bureau of Labor Statistics
Kathleen P. Utgoff, Commissioner
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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


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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

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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.


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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

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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


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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

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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


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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

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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

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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


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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
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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

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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.


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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


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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)

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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."
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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


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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

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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


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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

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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

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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


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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

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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

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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


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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
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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


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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
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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.


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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
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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


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Monthly Labor Review

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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

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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


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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
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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


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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

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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-


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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

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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


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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

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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.


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Monthly Labor Review

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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-

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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


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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,

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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 .. ........ ..... ..... .......... ..... .......... .


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.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.

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(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-


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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

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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 .


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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:

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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.


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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


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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


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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

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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.

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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


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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

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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

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"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


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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 ).

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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

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Producti vity, p. vii.

June

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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


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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

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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).

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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-


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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

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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.

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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


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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

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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

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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


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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

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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).


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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


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)'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-


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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

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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


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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
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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


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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

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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


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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

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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.

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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,


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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

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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
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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.


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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.

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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-

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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


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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

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2005

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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

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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


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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-

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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

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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.


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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


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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.


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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
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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

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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


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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.


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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


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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


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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

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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) ..... .... ......... .


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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

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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 ................................ .. ...


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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.


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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

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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

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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

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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

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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


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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

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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 .

June 2005

10 job-related fatalities

1
4

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BLSinfoKansasCity@bls.gov
BLSinfoNY@bls.gov
BLSinfoPhiladelphia@bls.gov
BLSinfoSF@bls.gov

Other Federal statistical agencies

http://www.fedstats.gov/


https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis


https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis