Full text of Monthly Labor Review : February 2005

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February 2005

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U.S. Department of Labor


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U.S. Bureau of Labor Statistics

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

REVIEW __________~_·e_rs~_
Volume 128, Number 2
February 2005

25 years of the National Longitudinal Survey-Youth Cohort
NLSY: 1979 cohort at 25

3

Charles Pierret

Antecedents and predecessors

8

James R. Walker

Educational data in the NLSY79

15

Kenneth I. Wolpin

The transition from school to work

21

Julie A. Yates

Job mobility and wage growth

33

Audrey Light

Self-employment and entrepreneurship

40

Robert W. Fair lie

Worker training

48

Harley J. Frazis and James R . Spletzer

Children of the NLSY79

59

Lawrence L. Wu and Jui-Chung Allen Li

The problem of respondent attrition

63

Randall J. Olsen

Departments
Labor month in review
Precis
Book reviews
Publications received
Current labor statistics

2

71
72
72
75

Editor-in-Chief: William Parks II • Executive Editor: Richard M. Devens • Managing Editor: Anna Huffman Hill • Editors: Bri a n I.
Baker, Kristy S. Christiansen, Richard Hamilton, Leslie Bro wn Joyner • Book Reviews: Richard Hamilton • Design and Layout: Catherine D.
Bowman , Edith W. Peters • Contributor: Mary Kokoski


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The February Review
This special issue commemorates the
25th year that the National Longitudinal
Survey-Youth Cohort (NLSY79) has
been in the field. Charles Pierret, the BLS
program manager for the survey, does a
far more thorough job of summarizing
the articles in this issue than would
normally have appeared in this space.
The articles themselves cover a wide
range of topics from some of the unique
issues of longitudinal methodology to
some of the more interesting questions
in labor economics. James Walker,
Kenneth Wolpin, Julie Yates, Audrey
Light, Robert Fairlie, James Spletzer and
Harley Frazis, Lawrence Wu and JuiChung Allen Li, and Randall Olsen made
contributions to this collection.

Union membership
In 2004, 12.5 percent of wage and salary
workers were union members, down
from 12.9 percent in 2003. The union
membership rate has steadily declined
from a high of 20.1 percent in 1983, the
first year for which comparable unionmembership data are available.
/\mcng private industry workers, the
2004 union membership rate was 7.9
percent, about half what it had been in
1983. Among major private industries,
transportation and utilities had the
highest union membership rate in 2004,
at 24.9 percent. Construction, information industries, and manufacturing also
had higher-than-average rates. Financial activities had the lowest unionization rate in 2004-2.0 percent.
About 36 percent of government
workers were union members in 2004.
Two groups-education, training, and
library occupations and protective
service occupations-had the highest

2 Monthly Labor Review

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February

unionization rates, at about 37 percent
each. Protective service occupations
include firefighters and police officers.
Find out more in "Union Members in
2004," news release USDL0S-112.

Ad agents profiled
Selling advertising space is the job of
advertising sales agents, who are often
called account executives or advertising
sales representatives. Most employers
pay an advertising sales agent using a
combination of salary, commissions, and
bonuses. Salary varies by geographic
location but is generally no more than
half of a sales agent's total compensation. Commissions are usually based
on a percentage of the agent's sales.
Bonuses are lump-sum financial awards
based on individual performance, the
performance of all sales agents in a
group, or the firm's overall performance.
Median annual earnings for all
advertising sales agents were $38,640 in
May 2003, including commissions and
bonuses. The lowest paid 10 percent
earned less than $19,920, and the
highest paid 10 percent earned more
than $87,360 per year. In addition to
earnings, advertising sales agents
usually get reimbursed for expenses
associated with making sales visits,
such as transportation costs and meals.
For more information about this
occupation, see "Sellers for the sellers:
Advertising sales agents" by Gregory
Niemesh, Occupational Outlook
Quarterly, Fall 2004.

State unemployment
trends
December 2004 unemployment rates
were lower than a year earlier in 43

2005

States, higher in 5 States and the District
of Columbia, and unchanged in 2 States.
Kentucky and Washington reported the
largest rate decreases from a year ago,
down 1.5 percentage points each,
followed by Hawaii, New Jersey, and
Oklahoma, all down 1.4 points. Nine
additional States registered rate decreases of at least 1.0 percentage point.
Mississippi reported the largest overthe-year unemployment rate increase,
0.8 percentage point. No other State had
a rate increase greater than 0.4 percentage point. See "Regional and State
Employment and Unemployment:December 2004," news release USDL 05-109.

Price trends in 2004
For the 12-month period ended in
December 2004, the Consumer Price
Index for All Urban Consumers (CPI-U)
rose 3.3 percent. This compares with an
increase of 1.9 percent in all of 2003.
From December 2003 to December 2004,
the Producer Price Index for Finished
Goods prices increased 4.1 percent, after
climbing 4.0 percent during 2003. Import
prices were up 6.9 percent over the year
ended in December 2004, compared with
a more modest 2.4-percent increase for
the year ended in December 2003. The
price index for overall exports rose 4.1
percent over the course of 2004.

Errata
Three rows of data were misaligned in
table 4 of the article, "Work-related
multiple-fatality incidents," in the
October issue. A revised version of
page 33 is available at:
http://www.bls.gov/opub/mlr/2004/
10/art2full. pdf.
□

The National Longitudinal Survey
of Youth: 1979 cohort at 25
The 1979 cohort of the National Longitudinal Survey of Youth
has been a font of information for researchers of all stripes;
the Monthly Labor Review brings together the results of research
on topics ranging from employment, to attrition in the survey,
to data on education, to the children of survey respondents

is issue of the Monthly Labor Review economics and demography journals contain at
elebrates the 25th anniversary of the least one article that uses NLSY79 data.
The main product of the NLS program, unlike
National Longitudinal Survey of Youth,
of most other BLS programs, is the actual
that
1979 Cohort (NLSY79). The National Longitudinal
generated by the surveys. For each
microdata
Surveys (NLS) program, of which the NLSY79 is
researchers can access a record that
respondent,
the flagship survey, is a bit of an anomaly among
her responses to every question in
or
his
details
the Bureau of Labor Statistics many data collecwith summary and supporting
along
survey,
the
tion efforts. None of the Bureau's key economic
this record is stripped of
course,
Of
information.
indicators relies on NLS data. Only a couple of
identify the individual
could
that
information
all
·the
the more than one hundred press releases
1
of data currently
rounds
20
With
respondent.
colBureau publishes each year involve data
an enormous
become
has
NLSY79
the
available,
lected by the NLS program. It is doubtful that
variables
75,000
than
more
comprising
set,
data
financial markets ever will react strongly to the
500
about
requiring
and
respondent
each
for
release of NLS data. And unlike the current
provides
NLSY79
The
storage.
its
for
megabytes
employment statistics, the inflation statistics, or
the unemployment rate, measures from the researchers with data from a nationally representative sample of 12,686 individuals who have
NLSY79 are not likely to be discussed in everyday
participated in up to 21 hour-long interviews over
conversation or even in the business news.
the last 25 years. 2 These individuals were 14 to
influential.
Yet, the NLSY79 has been extremely
Over the last 25 years, it has provided the data 22 years old when they were first surveyed in
for thousands of Ph.D. dissertations, working 1979; they are now in their forties. By observing
papers, journal articles, and books that have their lives over the 25-year period, researchers
shaped theory and knowledge in disciplines such can study the life course of a large sample of
as economics, sociology, education, psychology, American men and women born at the end of the
and health sciences. The survey's primary con- baby boom (1957 to 1964) as they navigate the
stituency includes hundreds of researchers years between adolescence or young adulthood
Pierret Is
Charles
Director of National
within universities, think tanks, and government and middle age.
Longitudinal Surveys,
Although the primary focus of the survey is
Office of Employment agencies both in the United States and abroad.
force behavior, the content of the survey is
labor
thoroughand
breadth,
and Unemployment Because of its quality,
Statistics, Bureau of
broader. The NLSY79 contains an
considerably
most
the
probably
ness, the NLSY79 has become
Labor Statistics.
questions ranging from childcare
of
set
expansive
social
the
in
set
data
analyzed longitudinal
E-mail:
For example, the survey
receipt.
welfare
to
costs
labor
leading
of
issue
every
Almost
sciences.
plerret.charles@bls.gov

Charles Pierret


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T:

Monthly Labor Review

February 2005

3

The

NLSY79

at 25

includes detailed questions on educational attainment,
investment in training, income and assets, health conditions,
workplace injuries, insurance coverage, alcohol and substance
abuse, sexual activity, and marital and fertility histories.
This wide array of subject matter is certainly one key to the
survey's broad utilization for academic research. Recognizing
that decisions made in one realm of life often affect and are
affected by events in other realms, the NLSY79 questionnaire
tries to get a comprehensive view of the lives of survey respondents. For example, health, childcare, and family constraints are important inputs to any labor market choices. By
collecting information in many domains, the NLSY79 gives
researchers the ability to simultaneously examine and control
for multiple correlates of complex phenomena.
The NLSY79 also benefits from the inclusion of information
collected outside of the main survey. For example, scores on
the Armed Services Vocational Aptitude Battery, a series of
10 tests measuring knowledge and skill in areas from
paragraph comprehension to electronics, are available for 94
percent of sample respondents. The Armed Forces Qualifying
Test score, a composite of the scores on four of these tests, is
one of the most popular variables in the survey, in that it can
be used to control for differences in cognitive development
prior to the start of the survey. Other ancillary data include a
survey of the secondary schools attended by NLSY79 respondents and detailed information from the respondents'
high school transcripts.
The breadth of the content in the survey is complemented
by the longitudinal design, a second key to the success of
the NLSY79. Not only can researchers correlate behavior in
multiple domains, but they can do so over long periods; for
example, they can investigate how family structure or educational experiences as a teenager affect employment decisions
in a person's twenties and thirties. To facilitate this type of
analysis, much of the information in the NLSY79 is gathered
in event history format, in which dates are collected for the
beginning and ending of important life events. Data on
respondents' labor market status, marital status, fertility, and
participation in government assistance programs such as
unemployment insurance and Aid to Families with Dependent
Children are all collected in this manner. The event history
format allows researchers to sequence key events so that a
first attempt at establishing causality can be made: only if A
precedes B can we postulate that A caused B.
But the real advantage of longitudinal data in terms of
studying causality is the existence of multiple observations
of the same person. A classic econometric problem is the
existence of unobserved personal characteristics that may be
correlated with both the dependent variable of interest and
an independent variable that is hypothesized to cause the
dependent variable. For example, consider the wage premium
associated with marriage among men. 3
4
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With cross-sectional data, one might compare the wages
of married nien with those of unmarried men to calculate the
wage premium. Even after controlling for all observable
characteristics, though, one might suspect that there are
some differences that are not easily measurable (for example,
interpersonal skills or ambitiousness), but that might make
one both more likely to be married and more likely to earn
higher-than-average wages. With longitudinal data, the
standard technique is to analyze changes either directly, by
regressing the change in wages on the change in marital
status, or indirectly, by using a fixed-effects framework. In
this way, longitudinal data give us the ability to control for
individual effects by using multiple observations of the same
individual.
A final key to the NLSY79's success is the high quality of
its data. The fundamental measure of quality in longitudinal
data is the retention rate-the percentage of initial respondents who respond in later rounds. Among social science
surveys, the NLSY79 is the undisputed leader in this regard.
In 1994, after 15 rounds of interviewing, more than 90 percent
of the survey's eligible initial respondents were still being
interviewed. 4 While attrition has picked up since that time,
more than 80 percent of living initial respondents were
interviewed in the 2002 survey.
Other dimensions of data quality are also high in the
NLSY79. Nonresponse to individual questions (either refusals
or "don't know" responses) is quite low; only a handful of
questions have nonresponse rates above 1 percent. Because
of the longitudinal design of the survey, missing data can
often be recaptured in subsequent interviews. As Randall J.
Olsen explains, this approach can lead to effective sample
sizes larger than the response rate would imply. 5
Given the size of the NLSY79 data set, using the data is
relatively easy. 6 Researchers can download the entire data
set, along with extraction software to pull off particular
variables on their own computer, or they can use the
extraction software at the Web site and download smaller
data sets. Typically, researchers then use statistical software
such as SAS, SPSS, or STATA to process the data and perform
their analyses. This accessibility can be combined with the
richness of the data to study many topics across myriad
disciplines. The remaining articles in this issue of the Review
highlight the contributions of the NLSY79 to research, with
special emphasis on issues in the area of labor economics.
James A. Walker points out that the introduction of
nationally representative longitudinal microdata sets,
combined with ever-increasing computing power, has created
a revolution in social science research over the last few
decades. 7 Previously, researchers had to rely on aggregate
statistics to grasp the workings of large complex systems.
Now they can study individual actors to build an understanding of social structure based upon those micro-

foundations. Before, analysts were forced to use small,
potentially unrepresentative samples to infer "normal"
behavior. Now, iarge surveys give them access to thousands
of individuals to study at very low cost. The combination of
massive amounts of data and growing computing power led
to the development of new statistical methods to exploit this
new source of information.
The first of these data sets, including the original NLS,
failed to fully exploit their longitudinal design. Instead, they
tended to ask questions as if the survey were cross sectional,
concentrating on the situation at the time of the interview.
The genius of the NLSY79 was that it attempted to capture
information about what was happening between interviews.
Now events of short duration-jobs, marriages, spells of
unemployment or public assistance, and the like-were
captured, and events across different domains could be
sequenced. Analysts now knew, for example, whether the job
change happened before or after the marriage, so issues of
causality could be addressed.
A second major improvement in the NLSY79 over previous
surveys using longitudinal data sets was the NLSY79 's strong
emphasis on reducing attrition and the effects of attrition on
the data. Even seemingly modest attrition can lead to sharp
declines in sample size. If participants who do not respond in
a particular round are never recontacted, and if 3 percent of
the remaining sample declines to participate in every round,
only slightly more than one-half of the initial sample will be
left after 20 rounds. The NLSY79's retention rate has required
interviewing an average of 99 percent of the number of the
previous round 's respondents every round. As Olsen's article
shows, this task involved increased efforts in terms of both
survey design and field operations. All respondents were
recontacted every round, regardless of how long it had been
since they were last interviewed. A surprising number were
reinterviewed after missing one or several rounds. In addition,
questions were written to recapture data that had not been
retrieved in interviews that had been missed. Respondents
were asked about events that had occurred since the last
interview, not those which were ongoing or had happened in
the past year.
Of course, even the highest-quality data are worthless if
they are unrelated to any valuable or interesting content.
The other six articles in this edition focus on particular
content areas. Kenneth I. Wolpin's article on education data
in the NLSY79 details a large number of correlates of educational attainment that can be found in the survey's database.8 Not only do more educated individuals earn more
money, on average, but they work more hours, are less likely
to be unemployed or receive welfare payments, have better
health and less obesity, drink less, wait longer to have children
and have fewer of them, and, among women in the survey,
bear children who weigh more at birth and score higher on
cognitive tests. These findings hint at the richness of the


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NLSY79 data, among which associations like these can be
explored in great depth.
In her article on early career job churning, Julie A. Yates
also touches on the richness of the education data in the
NLSY79. 9 She shows that the concept of educational attainment is not at all static: people leave school and return,
sometimes getting new educational credentials, sometimes
not. This ever-present activity adds significantly to the level
of education in the United States. Indeed, only 11 percent of
those born between 1961 and 1964 had a college degree when
they first left school, but more than 25 percent had a degree
by their 35th birthday.
The main focus of Yates 's article, though, is the length of
time required after leaving school to acquire a job that will
last 1, 3, or 5 years. She finds that, by age 35, about 60 percent
of those who left school with a high school degree or greater
have worked at a job for at least 5 years. For high school
dropouts, however, the figure is only 36 percent. Interestingly, the route high school graduates take to acquire a job of
this duration is different from that taken by college graduates:
the median high school graduate takes 10.1 years and 5
shorter jobs before finding the job that will last 5 years,
whereas a college graduate requires only 3.5 years and 2 jobs.
Audrey Light also uses the work history data of the NLSY79
to study employment dynamics-specifical ly job mobility
and wage growth. 10 She asks whether wage growth is greater
for those who stay in one job or those who change employers
frequently. The answer she arrives at depends at least partially on the reasons for mobility. Those who change jobs
voluntarily have wage growth of the same magnitude as those
who stay in jobs; those who change jobs involuntarily have
lower wage growth. Light admits that her analysis is "cursory." Given the complexities underlying her questionincluding the role of spells out of the labor force and their
interactions with education and gender-it is not surprising
that there is no simple answer to the question of how job
mobility affects wage growth. But by providing longitudinal
employment data along with comprehensive contextual data,
the NLSY79 will continue to play a key role in our growing
understanding of employment dynamics.
The NLSY79, states Robert W. Fairlie, has many features
that make it attractive for the study of self-employment: 11 a
large, nationally representative sample; detailed financial
information; dara on family and educational background; and
measures of cognitive and psychological suitability. But the
survey's strongest asset is its comprehensive work history,
which tracks transitions into and out of self-employment as
well as wage and salary employment. A great deal of selfemployment, at least among the young, is episodic, with
workers moving often between employment states. But as
workers age, their participation in self-employment grows and
becomes more stable. Only 5 percent of 22-year-old men are
self-employed; by age 42, 12 percent of men are self-employed.
Monthly Labor Review

February 2005

5

The

NLSY79

at 25

The NLSY79, because of its longitudinal design, is ideal for
investigating the dynamics of self-employment.
Since the 1950 's, the reigning paradigm of labor economics
has been human capital theory, in which workers invest in
productive skills in order to earn higher wages. The study of
human capital has divided skills acquisition into two components: education-in which general knowledge is acquired
largely prior to the start of one's working career; and training-in which more specific, job-related skills are acquired,
often during one's career. As Harley J. Frazis and James R.
Spletzer assert, empirical labor economics research on training has lagged behind research on education because research on training placed much greater demands on the
data. 12 Again, the NLSY79 has been a key data set in this
literature, not only because it captures details about training,
but because it contains a complete history of employment
and thousands cf other contextual variables.
'rhe final article in this volume introduces an additional
dimension of the NLSY79. In a unique collaboration between
Federal Government agencies with complementary interests,
the NLS program, with the financial support of the National
Institute of Child Health and Human Development, has been
surveying the children of all women respondents to the
NLSY79 every 2 years since 1986. Jn their article, Lawrence L.
Wu and Jui-Chung Allen Li describe this survey, called the
Children of the NLSY79. 13 Mothers are asked about pregnancy, prenatal care, and childcare, as well as the early
development of their children. Young children are given
cognitive and developmental assessments from the age of 4
to the age of 14. Children between ages 10 and 14 are
interviewed briefly about school and family life. Those older
than 15 years are given an interview that is similar to the
NLSY79 interview. What emerges is a unique data set with a
complete developmental history from birth and a comprehensive background of the mother, starting many years before
birth. Over time, the sample size has grown to rival that of the
NLSY79 itself. At its inception in 1986, the Children of the
NLSY79 survey comprised 4,971 children. In 2002, 7,467
children were assessed or interviewed, ranging in age from 2
to 30 years.
These articles only scratch the surface of what is available
in the NLSY79. Space limitations and the customary focus of
the Monthly Labor Review dictate a concentration on
employment, education, and training. But there is much more.
The NLS program tracks research using NLS data in the NLS
bibliography. 14 Among other topics that have been the
subject of at least 100 papers using NLSY79 data are income
and earnings, marriage, maternal employment, adolescent
fertility, alcohol and substance use, childcare, family
structure, and government assistance programs. Smaller
numbers of papers have used NLSY79 data to examine topics
as diverse as job satisfaction, depression, migration, breast-

6

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February 2005

feeding, parental leave, savings, and the involvement of
fathers in raising children. There truly are few topics in social
science that have not seen a contribution from NLSY79 data.
The success of the NLSY79 also led the Bureau of Labor
Statistics to introduce a new cohort to its own longitudinal
survey program. The NLSY97 is a survey of 8,984 youths who
were born between 1980 and 1984 and who were 12 to 17
years of age when first interviewed in 1997. While the basic
design of this survey is quite similar to that of the NLSY79,
several changes were introduced because of the NLS
program's experience with earlier surveys. The most
important change was that NLSY97 respondents were
younger when first interviewed than were those in the
NLSY79. It was felt that respondents should be observed
while still in school and still in the parental home, so that all
transitions to the world of work and to independent living
could be recorded. Also, starting with a younger sample
allowed the survey to collect more background on the
respondents' introduction to the labor market. Information
on informal jobs such as babysitting and yard work was
collected for those as young as 12 years. 15 In another attempt
to understand initial conditions, the NLSY97 interviewed a
parent for each respondent. These interviews focused on the
child's family, health, and schooling background, as well as
the resources available to the child in the parental home. The
eighth annual interview of the NLSY97 is currently in the field,
and the respondents are now in their twenties. Already, a
number of articles using these data have been published on
a wide array of topics. It appears that the NLSY97 is poised to
emulate the success of the NLSY79.
Many people have contributed to the success of the
NLSY79 program, which is managed and funded by the Bureau
of Labor Statistics. Since the survey's inception, the Center
for Human Resource Research at the Ohio State University
has directed survey operations under contract to the
Department of Labor. The Center is responsible for survey
development, data processing, and user support. In turn, the
actual interviewing has been performed under subcontract
by the National Opinion Research Center at the University of
Chicago. Designing, preparing, fielding, processing, and
disseminating a survey of the complexity of the NLSY79
requires many dedicated professionals. The success of the
survey is a tribute to their hard work and dedication.
The contributions of two groups are especially noteworthy.
Over the last 25 years, the field-interviewing staff at the
National Opinion Research Center has consistently exceeded
response rate targets by locating elusive respondents, convincing reluctant respondents to participate, and maintaining
high levels of data quality. Still, the most important group in
the success of the NLSY79 is the respondents. For 25 years,
the members of this group have endured thousands of
sometimes difficult and intrusive questions for little more than

the knowledge that they are involved in an important research
project. Their assistance has added immensely to academic
and policy research across a wide array of disciplines, helping

those involved to increase their knowledge,elaborate new
theory, and develop new ways of understanding critical
□
issues in the social sciences.

Notes
Under special arrangement, researchers can access certain
geographic information about respondents in order to link
environmenta l variables to the records of those respondents .
Personally identifying information, however, is never available to
researchers ; it is protected by law through the Confidential
Information Protection and Statistical Efficiency Act.
1

Much of the summary information that follows can be found on
Web site at http://www.bls.gov/nls/nlsy79.htm or in the
NLSY79 User 's Guide (Bureau of Labor Statistics, 2001) .
2

the

NLSY7 9

3
For a more thorough discussion of this issue, see Harry A. Krashinsky,
" Do Marital Status and Computer Usage Really Change the Wage
Structure?" Journal of Human Resources, summer 2004, pp. 774-91.

4
Two subsamples comprising roughly 3,000 respondents were
dropped from the sample in 1985 and 1991 to reduce costs . The
current sample size is 9,964 initial respondents, of which 346 were
deceased at the time of the 2002 survey.

5

Randall J. Olsen, "The problem of respondent attrition: survey
methodology is key," this issue, pp. 63- 70.
The data are available at no c0st on the Internet at http://
www.bls.gov/nls/nls_y79.htm .
6


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7
James A. Walker, "Antecedents and predecessors of the
paving the course," this issue, pp. 8-14.
8
Kenneth I. Wolpin , " Education data in the
research tool," this issue, pp. 15-20.

NLSY79:

NLSY79:

a premiere

9
Julie A. Yates, "The transition from school to work : education
and work experiences," this issue, pp. 21 - 32.
10

Audrey Light, "Job mobility and wage growth: evidence from the
this issue, pp . 33-39.

NLSY79,"
11

Robert W. Fairlie, "Self-employment, entrepreneurship, and the
this issue, pp. 40-47.

NLSY79,"

12
Harley J. Frazis and James R. Spletzer, " Worker training: what
we've learned from the NLSY79," this issue, pp. 48- 58.
13
Lawrence L. Wu and Jui-Chung Allen Li, "Children of the
a unique data resource," this issue, pp. 59---62.
14

NLSY79:

On the Internet at http://www.chrr.ohio-state.edu/nls -bib/.

See, for example, " Employment of Teenagers during the School
Year and Summer," news release 04-217, on the Internet at http://
www.bls.gov/news.release/pdf/nls yth.pdf.
15

Monthly Labor Review

February 2005

7

Antecedents and predecessors
of NLSV79: paving the course

A historical view of the NLSY79 development stages
highlights lessons learned during an era filled with new concepts
and innovations in sociology, economics, and computer science

James R. Walker

I

n 1965, at the prompting of the Assistant
Secretary of Labor, Daniel Patrick
Moynihan, individuals from the Department
of Labor (DOL) and Ohio State University
designed the National Longitudinal Surveys of
Labor Market Experience. At the time, the
participants did not realize that they were creating
one of the premier, large scale national longitudinal
surveys in the United States. Initially funded for 5
years by the Department of Labor, the "Parnes"
data, as the Original Cohorts were called,
continued for 37 years, with the last scheduled
fielding of the women samples in 2003. 1 The
success of the Original Cohorts led to the creation
of the National Longitudinal Survey of Youth
(NLSY79).

James R. Walker is
professor of
economics in the
Department of
Economics,
University of
WisconsinMadison. E-mail:
walker@ssc.wisc.edu.

This article explores antecedents and predecessors of the National Longitudinal Survey of
Youth, 1979. 2 Longitudinal data are now so
plentiful that it is difficult to imagine the world in
which they did not exist. Yet, in the mid- I 960s, the
large scale longitudinal household surveys that
came to dominate areas of sociology, demography,
and labor economics did not exist. Analyses that
are now commonplace were either not possible or
inference was restricted to small or specialized
samples.
Yet to suggest that there were no longitudinal
data sources prior to 1965 is wrong; several
longitudinal surveys predate the NLS. Two well-

8 Monthly Labor Review

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February 2005

known studies reflect the nature of longitudinal
data available before the start of the NLS. The
Glueck study of juvenile delinquents from the
Boston area followed 1,000 adolescents (500
juvenile delinquents and 500 non-delinquents) into
adulthood to examine criminal behavior and
contact with the justice system. 3 Sheldon and
Eleanor Glueck started interviewing at the end of
1938, completing the first wave of interviews in
1948. Two more waves of interviews followed as
the youth were interviewed at ages 25 and 32.
Interviews continued until 1965.
The other study available before the NLS, and
perhaps more visible to economists, is the National
Bureau of Economic Research (NBER)-Thorndike
sample, collected from Air Force volunteers during
WWII. In 1955, R. Thorndike and E. Hagen
randomly selected 17,000 of the 75,000 Air Force
volunteers who took the Aviation Cadet Qualifying
Test in the second half of 1943 (a test similar in
function to the Armed Services Vocational Aptitude
Battery (ASVAB) tests that NLSY79 respondents
took to set a norm in recruiting standards for the
Department of Defense). In 1969, with funding from
the NBER, Paul Taubman and his colleagues
reinterviewed about 5,000 of the original 17,000
members of the Thorndike sample, obtaining
information on current and retrospective earnings,
education, and occupation. These data have been
widely used to study the determinants of earnings,

ability bias, and the return to schooling (that is, benefits
associated with higher levels of schooling). 4
A number of other specialized longitudinal studies were
launched in the decade prior to the NLS. These efforts
surveyed teen mothers, drug users, gifted children, and
children from privileged and underprivileged backgrounds. 5
These studies shared features like the Gluecks' study and
the NEER-Thorndike study in that they were local in character
with limited or irregular longitudinal followups. However,
several studies are impressive and cover a long arc of their
respondents' lives. 6

Antecedents
Scientific frontiers. Two critical elements came together in
the 1960s supporting the development of large, household
surveys. First, the social science field had developed the
conceptual foundation supporting the use of longitudinal
data. Within the fields of psychology and sociology,
researchers and scientists fostered the life course
perspective, viewing human development as following a
sequence of stages. 7 And second, in the economics field,
human capital b~came the organizing conceptual framework.
In his 1960 Presidential Address to the American Economics
Association, T.W. Schultz presented his influential thoughts
on human capital. 8 The human capital theory quickly became
a central concept for understanding the determinants of
wages, the structure of earnings, and more generally, the
distribution of economic opportunities. Labor economists
sought to measure the return to schooling, labor market
experience, and tenure with an employer. Social scientists
sought to understand schooling decisions, both in terms of
quantity (the amount of schooling obtained) and in terms of
quality (types of post secondary schooling).
Intervention and experiments. The intellectual primacy of
measuring education and training fueled and was fueled by
the era of big social science and policy interventionism of the
mid-1960s. In 1964, the Johnson Administration am1ounced
the War on Poverty. Education and training programs were
among the most important anti-poverty programs proposed.
Thus, measuring and understanding the determinants and
consequences of poverty required the collection of new,
longitudinal household level data. The Department of Labor,
Office of Economic Opportunity initiated a survey of the same
name in 1967, followed by the Panel Study of Income
Dynamics (PSID), conducted by the Census Bureau in 1968.
The mid-1960s also witnessed the negative income tax
experiments in Gary, New Jersey; Seattle, Washington; and
Denver, Colorado. These experiments varied in size and focus,
but each was large with sizable treatment and control groups.
And, unlike the NLS and PSID surveys, which "only" collected


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information on the respondents, the experiments were more
ambitious, comprising both an important experimental design
component and an extensive data collection component.
Also, the experiments generated another source of
longitudinal data and provided additional demands for their
analysis and interpretation.
Yet, conducting the social experiments reflected certain
optimism (as it only makes sense to investigate the source of
the disease if remedies are available). Indeed, demand for
these new forms of data were perhaps driven by the belief in
the effectiveness of interventionist economic policies, and
particularly labor market policies to enhance human capital.
In 1962, Congress passed the Manpower Development and
Training Act, which generated an array of training programs
targeted to the low-skilled, unemployed, and underemployed
population. The Comprehensive Training Act of 1973
attempted to unify the existing Federal programs, and initiated
programs to additional groups (for example, welfare
recipients). The quasi-experimental designs of the 1960s and
1970s called for longitudinal data that could be used to
compare labor market outcomes for treatment groups and
control groups. These outcomes were matched with
observable personal characteristics for at least two points in
time (for the treatment group, before and after training). The
need for individual longitudinal data is transparent. Indeed, a
primary motivation for the NLSY79 cohort was "to permit a
replication of the analysis of the 1960s Young Men and Young
Women cohorts and to assist in the evaluation of the
expanded employment and training programs for youth
legislated in the 1977 amendments to the Comprehensive
Training Act of 1973.''9

The analysis gap. Analyses of longitudinal data started
appearing in the major journals about 10 years after data
collection. To prove this, Frank Stafford assessed empirical
practices within labor economics according to the content
and practices of labor papers published in the top economics
journals. 10
For example, Stafford reports that more than half of the
papers published in the six major journals in the first half of
the 1960s on labor market topics were theoretical, with no
empirical analyses. Of those reporting empirical analyses,
(national) time series data or aggregate (say, to the State or
metropolitan area) cross section data comprised the vast
majority of published work. Nearly one-fifth of the empirical
papers of the time period reported on tabulations and data
summaries published elsewhere (Stafford's term for
secondary analyses). Not surprisingly, given the (virtual)
absence of panel data, no papers during this period were
published using panel data. And, only one paper using panel
data appeared in the top economics journals in the second
half of the 1960s. The top journals witnessed a small but
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9

Antecedents of NLSY79

steady stream of papers using longitudinal data in the early
1970s; 11 a stream that turned into a river in the second half of
the 1970s and early 1980s.
Joshua Angrist and Alan Krueger update Stafford's
tabulations into the late l 990s. 12 By this time, microdata,
cross sectional, and longitudinal analyses have increased
their dominance- now fully 85 percent of empirical papers in
the top economics journals on labor market topics use microdata. The micro-files of the Current Population Survey
(especially the March Income Supplement) was the most
popular cross sectional source of data; and the Panel Survey
of Income Dynamics and the NLS cohorts dominate the
longitudinal-based studies. 13 However, the dominance of the
PSID and NLS as longitudinal data sources weakened as
economists increasingly (if not frequently) frame and collect
their own longitudina l data sources. The value of
longitudinal data used to address particular questions is
evident from the variety of longitudinal data collected.

The computer revolution. Implicit in Stafford's and Angrist
and Kreuger 's tabulations is that there is a 10-year lag between
the start of a panel and widespread use of the data. This lag
is surprisingly constant, though the reasons behind it vary
with each cohort. For the original cohorts of the NLS and the
PSID, longitudinal data were new and analytically and
physically cumbersome to use. Computing power in the midl 960s was a fraction of what it is today. Computing was done
in centralized locations, using mainframe computers
maintained by specialized staff. Commonly used equipment,
such as keypunch machines, card readers, magnetic tape
drives, and impact line printers can now be found only in
museums. The personal computer revolution was a solid 15
years in the future. Disk drives and other convenient large
scale storage devices did not exist. Tabulations easily
produced in a matter of seconds on a desktop computer today
required "spinning tapes" on the mainframe, assistance from
the tape machine operator, and literally hours of computer
time. Empirical researchers acquired nocturnal habits, as all
sigmficant computing was done at night. Notions of the
solitary scholar are almost always wrong, but certainly did
not apply to the early pioneers analyzing microdata.
It is also true that the profession had to develop the
statistical procedures and analytical skills for working with
the longitudinal data. With a few notable exceptions, most of
the initial statistical procedures for panel data were developed after 1965. The increased computational capacity of
the computer revolution was also necessary to support the
new statistical procedures. 14
Longitudina l versus household data. Besides acquiring
statistical techniques, researchers had to appreciate the
advantages and disadvantag es of longitudina l data.

10

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February 2005

Arguably, we continue to relearn these lessons. The chapters
by Stafford and Angrist and Krueger are informative on this
dimension and on the contemporar y research frontier.
Stafford's chapter enumerates the advantages and
disadvantage s of longitudinal data, and compares data
collected by household surveys versus those collected by
program or social experiments. In Angrist and Krueger, the
comparative advantage of panel data is presumed, and the
discussion focuses on empirical and modeling strategies for
recovering causal effects.

The original cohorts
From this intellectual and policy context, the original four
cohorts of the National Longitudina l Surveys of Labor
Market Experience were designed to represent the U.S. civilian
noninstitutional population at the time of the initial survey.
The surveys were funded by the Office of Manpower,
Automation , and Training (now, the Employmen t and
Training Administration) of the Department of Labor, and
conducted by the Center for Human Resource Research of
Ohio State University. Specifically, the original cohorts are:
Older Men Ages 45-59 in 1966; Mature Women ages 30-44
in 1966, and two cohorts of youth, Young Men ages 14-24 in
1966; and Young Women 14-24 in 1968. Initially, each cohort
was to be interviewed annually for 5 years for a total of six
interviews), with about 5,000 individuals per cohort. However,
cost considerations after the first wave of interviews changed
these plans. As a result, the older cohorts were interviewed
biennially, with the Mature Women interviewed in both 1971
and 1972 to place an interview year at the end of the 5-year
period. Because of high retention rates and widespread use
by the research community, the surveys secured another 5
years of funding in 1972, and again in 1977 when the decision
15
was made to start a new youth cohort, the NLSY79.
As previously noted, a fundamental purpose of the NLS
has been to provide relevant information on a variety of issues
to assist the research of economists, sociologists, and other
analysts. This mission motivated the selection of the original
cohorts. For example, the first cohort of Older Men (45-59 in
1966) was selected to study factors associated with declining
labor force participation, such as skill obsolescence, health
problems, and age discrimination. The Young Men's Cohort
(14-24 in 1966) and Young Women's Cohorts (14-24in 1968)
were selected because of the problems associated with the
preparation for, initial entry into, and adjustment to the labor
force. 16 Problems of the youth labor market generated concern
and added to contemporary debate on topics such as teen
unemployment, family effects on youth employment, the
effect of minimum wages, and barriers impeding the transition
from school to work. 17
Increased labor force participation by married women and

women with children is one of the great social changes of the
second half of the twentieth century. 18 The Mature Women's
Cohort (age 30-44, in 1967) was intended to enable
researchers to study women who were reentering the
workforce and balancing the roles of homemaker, mother, and
labor force participant. 19
Wea!!h of information. The initial survey instruments
focused on labor market activity. Instruments included the
Current Population Survey (CPS) questionnaire to summarize
current labor force status and a longer set of questions on
work experiences and attitudes to work. Attention was given
to collecting information on the respondent's current job (at
the interview date) and if not working, on the longest job
held since the prior interview. In addition, information on the
number of weeks worked in the last calendar year and the
reasons for not working are now collected. Information on
education is also concentrated on the status at the time of
the interview. Information on the current high school or
college is collected, if the respondent has dropped out of
school, why, or if the respondent returned to school, and
reasons for the return.
Even in the focused instruments of the early round:.;, the
surveys exhibited an eclectic mix of questions on employment
and (for the youth) education. Yet, the instrument also
obtained information on health, training, assets and income,
and family background. It did not take many years for the
content of the instrument to broaden significantly and attain
the breadth of coverage now associated with the NLS. As
James Sweet, noted, this breadth is natural because of the
diverse and competent set of scholars consulted about the
instrument's content, and more importantly, that virtually
every phase of life is and will be associated with some aspect
ofwork. 20
The original cohorts had a tremendous impact on policy
and on research. In October, 1977 the Social Science Research
Council held a 3-day conferenc(. to review the NLS. The
council's review was so extensive that it required five
volumes of papers to summarize and evaluate the research
contribution of the Original Cohorts. To give a flavor of the
topics, exhibit 1 lists the table of contents to the report on the
behaviors studied using the NLS Original Cohorts. Exhibit 2
lists some of the policy findings from individual assessments
done in the early 1980s.
The NLS Youth cohorts could be used, like the NBERThorndike sample, to study the return to schooling while
controlling for ability bias. One of the early uses of the NLS
was to estimate returns to schooling. Microdata are needed
to obtain information on the many background and contextual
variables that would confound the analysis. The primary
problem in estimating the return to schooling is controlling
for ability bias-are there person-spec ific unobserved


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variables (for example, motivation, parental support,
intelligence) that affects both the amount of education and
labor market earnings. Gary Chamberlain and Zvi Griliches
wrote an influential set of papers using the Young Men's
Cohort to articulate the issues involved and to provide initial
21
estimates of the return education controlling for ability bias.
convenience
Now, instead of using the NBER-Thorndike
sample, researchers could estimate the return to schooling
using a nationally representative sample. Research on the
return to schooling uncovered the weak-nesses of the pointin-time measuremen t of the educational attainment. The
NLSY79 schooling section was substantially extended and
22
provided researchers with a wealth of in-formation.
More issues from the panel. In the late 1970s, as economists
focused on life cycle events, they recognized the value of
panel data to distinguish between outcomes generated by
"state dependence;" that is, the true effects of the dynamic
path experienced or of "unobserved " heterogenei ty-fixed,
but unobserved personal characteristics that may contribute
to a set of outcomes. State dependence/unobserved heterogeneity debates arose in many literatures. A critical example
at the time was whether unemployment "scarred" workers:
did a long spell of unemployment damage the worker to make
them less employable in the future? Or were workers with the
longest spells of unemployment those with the lowest level

Table of contents from Social
Science Research Council
report, 1977
Labor supply
Female labor supply and fertility expectations; Child care
and welfare; Marital instability; Male labor supply
Labor demand
Dual and segmented labor markets; Racial discrimination
in the labor market; Sex discrimination in the labor market;
Unionization and labor market differentials; Labor demand
and structural factors - further considerations
Human capital and status attainment models
Human capital; Sociology of education status attainment
Unemployment
Job separation; Job search
Social psychological factors
Aging
Methodolo gical research using the NLS
SouRCE: William Bielby, Clifford Hawley, and David Bills "Research
Uses of the National Longitudinal Surveys," A Research Agenda for
the National Longitudinal Surveys of Labor Market Experience, part
V (Washington, oc, Social Science Research Council, Center for
Coordination of Research on Social Indicators, 1978).

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11

Antecedents of

Exhibit 2.

NLSY79

Topics of the NLS Cohorts
before 1979

Young Men and Young Women
School to work transitions; Effects of minimum wage;
Returns to schooling and ability bias; Impact of early
employment on later success; Consequences of early
childbearing among teenagers; Effects of unemployment
insurance benefits

Effect of discontinuous work experience on earnings and
labor supply; Balancing family and work demands; Market
availability of child care and women's employment; Effect
of income tax on labor supply of married women

Older Men
Retirement decisions of older men; Effects of unemployment insurance benefits; General issues relating to the
aging of the population; Relationship between health and
employment; Broad range of socio-economic considerations of the elderly; Analysis of age discrimination in
employment
SouRcE: Center for Human Resource Research, 'The National
Longitudinal Surveys and Public Policy" (Columbus, Ohio. The Ohio
State University, no date ); Duane Leigh, "The National Longitudinal
Surveys: A Selective Survey of Recent Evidence," paper presented at
the American Economic Association Meetings (Washington, DC, Dec.
28, 1981 ); and June O'Neill, "Review of the National Longitudinal
Surveys," unpublished paper (Washington, DC, The Urban Institute,
1982). Review prepared for the Office of Research and Evaluation,
Employment and Training Administration, Department of Labor.

of skills that made them susceptible to more and longer spells
in the future? As is well known, answers to these questions
determine the type of appropriate policy response. For
example, in the unemployment case, if scarring is present,
policies targeted at eliminating long employment spells may
be effective, but such policies would be ineffective if the
long spells are primarily generated by unobserved individual
characteristics. After nearly 30 years of professional experience in thinking about these issues (and with many
applications supported by the NLS79), some researchers
might view the early literature as na'ive and simplistic, yet,
there is no denying the authors' intellectual excitement and
vigor in these early papers. The authors were aware that they
were breaking new ground and were excited about the
analytical promises held by longitudinal data. 23
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The success of the Original Cohorts paved the way for the
NLSY79. The promise of longitudinal data and the policy
issues of the mid- to late 1970s called for another youth cohort.
Given a chance to field another cohort, the research
community had an opportunity to correct some of the
deficiencies of the original cohorts and to collect data on
new topics. The participants of the Social Science Research
Council review panel of 1977 were charged to:
•

Mature Women

12

Impact of the original cohorts

February 2005

•
•
•

Provide a comprehensive review of research based on
the NLS
Identify new directions
Suggest analytical strategies
Comment on survey content

Conference attendees took their job seriously and provided
thoughtful and broad perspectives of the NLS. Indeed,
participants developed several critical suggestions that
shaped the design of NLSY79. Most notably, the panel
advocated collection of more extensive labor market
experience data. A methodological paper by Burton Singer
illustrated the analytical advantages of collecting event
histories (that is, a full enumeration of the start and stop
dates of all jobs held since the last interview). 24 The state
dependence/unobserved heterogeneity analyses of the
original cohorts highlighted the need for precise timing
information to construct the correct temporal sequence of
education, marriage, employment and fertility decisions and
outcomes. The event histories collected in the NLSY79 is one
of its innovations. 25
As noted, the Social Science Research Council review
committee also recommended improved information on
schooling. Here, the interest was to gain improved
information on the type and nature of post secondary
schooling, and especially on vocational training. At the time
of the recommendation on schooling, the NLS program was
housed within the Employment and Training Administration
ofDOL.

Lessons learned. The Social Science Research Council
recommended that the definition of the sampling universe be
refined from the noninstitutionalized population. The Census
Bureau did the field work and gained expertise in following
the noninstitutionalized population. Yet, as the review panel
notes, this practice induced bias in the construction of the
Original Youth cohorts. For example, for the Young Men's
cohort, men in jail or in the military were defined out of scope
and excluded (at the height of the Vietnam War). The
definition also reflects the survey's point-in-time structure
and the cross sectional thinking behind it-persons

incarcerated and persons in the military at the time of the
interview would have little employment activity to report and
presumably could be excluded at little cost. An equally
pernicious fielding decision generated by the same cross
sectional mindset was to drop follow-up respondents who
missed two consecutive interviews. This could be view as
simply a mistake of adolescence-we did not know better
then. Indeed, it took the NLSY79 to teach us about the
possibility of retention and the significance of respondent
continuity.
Not all of the recommendations from the review committee
were accepted. As previously mentioned, one of the primary
motivations for the NLSY79 was to assist in the evaluation of
youth employment and training programs under the
Comprehensive Employment and Training Act of 1973 (CETA).
The Employment and Training Administration had front-line
responsibility for evaluating the programs. Yet, the Social
Science Research Council committee argued against
designing the survey for program evaluation:
Significant changes in study designs have been made for the
new youth cohorts [i.e., men and women of the NLSY79].
These include adding questions about participation in youthjob training programs, collecting supplemental data by
matching to program records on respondents who have

participated in such programs, and excluding from the panel
those over age 21 (rather than 24 as in the previous youth
panels or 25, which would be required to cover young people
not covered in existing [NLS] panels). The conferees were
nearly unanimous in perceiving these changes as motivated
by an intention to use the NLS as a vehicle for the evaluation
of these training programs. They present strong arguments
both for the impossibility of evaluating programs under the
proposed design, and for the danger of drawing evaluative
conclusions from the data produced by this design. 26

Conferees questioned whether respondents would be able
to provide sufficiently accurate program information to allow
researchers to identify their training program and specifically
their exact "treatment." Their concerns were well founded;
an encyclopedic review of the evaluation literature shows
that the NLS (and other large scale surveys) have been little
used for the evaluation of training programs. 27
This panel of experts earned their honorariums. Many of
their recommendations were incorporated into the design of
the NLSY79 Cohort. Indeed, the survey's continuous and
detailed recording of events related to the transition from
school to work, from adolescence into adulthood, and now
into middle age have made it the analytical workhorse within
several social science disciplines. The NLSY79 is viewed by
□
many to be the crown jewel of the NLS program.

Notes
This article was presented as a paper for the
ACKNOWLEDGMENT:
Twenty-Fifth Anniversary Celebration of the NLSY79, organized by
the National Longitudinal Studies Program at the Bureau of Labor
Statistics. The author thanks Frank Mott, Randy Olsen, Pat Rhoton,
and Ken Wolpin for comments, Amanda McClain, and Leslie Brown
Joyner for editorial assistance.
1
"Parnes" data are named after one of the designers of the NLS,
Herb Parnes, from Ohio State University.

Fnnk L. Mott, "Looking Backward: Post Hoc Reflections on
Longitudinal Surveys," in Erin Phelps, Frank F. Furstenberg, and Anne
Colby, eds., Looking at Lives: American Longitudinal Studies of the
Twentieth Century (New York, Russell Sage Foundation, 2002 ). This
offers another perspective on the history of the NLS program.
2

3
The Gluecks initiated a survey design that is difficult to match
today. They interviewed the youth, their families, employers, school
teachers, neighbors and justice officials. And they supplemented and
validated the interview data with administrative data obtained from
social welfare agencies. See Robert J. Sampson and John H. Laub,
Crime in the Making: Pathways and Turning Points Through Life
(Cambridge, MA, Harvard University Press, 1995), p. 90.

See Paul Taubman and Terence Wales, "Higher Education, Mental
Ability and Screening," Journal of Political Economy, 81 (Jan. - Feb.,
1973) pp. 28-55, for a description of the NEER-Thorndike sample.
4

5 Europe initiated a number of early longitudinal studies as well.
Erin Phelps and others, Looking at Lives: American Longitudinal
Studies of the Twentieth Century (New York, Russell Sage Foundation,


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2002), lists a few of the most noteworthy.
6
The Terman study of children with high ability followed a group
of 672 high-ability children from California for more than 65 years, with
an attrition rate of less than IO percent of the original respondents
(excluding those who died or became invalids)! See George E. Vaillant,
"The Study of Adult Development," in Erin Phelps and others, eds.,
Looking at Lives: American Longitudinal Studies of the Twentieth Century
(New York, Russell Sage Foundation, 2002), pp. I 16--132.

7
See Janet Zollinger Giele, "Longitudinal Studies and Life Course
Research: Innovation, Investigators, and Policy Ideas," in Erin Phelps
and others, eds., Looking at Lives: American Longitudinal Studies of the
Twentieth Century (New York, Russell Sage Foundation, 2002). Giele
discusses the synthesis in developmental psychology, sociology, and
history after World War II that led to a new conceptual framework for
understanding the forces and behavioral processes as people age.

8 See Theodore W. Schultz, "Investment in Human Capital,"
American Economic Review, March 1961, pp. 1-17.
9
NLSY79 User's Guide (Columbus,
Research) p. 4.

OH,

Center for Human Resource

°

1
Frank Stafford, "Forestalling the Demise of Empirical
Economics: The Role of Microdata in Labor Economics Research,"
in Orley Ashenfelter and Richard Layard, eds., Handbook of Labor
Economics, vol. I (New York, North Holland, 1986).

11
However, about 20 percent of these papers using a longitudinal
data source, used the data as a cross section.

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13

Antecedents of

NLSY79

12 Joshua D. Angrist and Alan B. Krueger, "Empirical
Strategies in
Labor Economics," in Orley C. Ashenfelter and David Card, eds.,
Handbook of Labor Economics, vol. 3A (New York, North Holland,
1986) .

13 With a small edge in papers to the original
cohorts of the NLS!
See for example, Stafford, "Forestalling the Demise of Empirical
Economics," 1986.

14 One of the first codifications of panel data
techniques appeared
in a special volume in the Journal of Econometrics in 1982. Also see
Gary Chamberlain, "Panel Data," in Z. Griliches and M. D. Intriligator,
eds., Handbook of Econometrics, vol. 2 (Amsterdam, North Holland,
1994). Chamberlain is considered a highly influential chapter on panel
data. Also see C. Hsiao, Analysis of Panel Data (Cambridge, MA,
Cambridge University Press, 1986), a readable gem from the
Econometric Society; and Jeffrey Wooidridge, Econometric Analysis
for Cross and Panel Data (Cambridge, MA. MIT Press, 2002).
Wooldridge's elegant text provides a unified treatment of what is now
an extensive literature.

15 Retention rates at the end of the first six interviews:
Older Men
83 percent, Mature Women 88 percent, Young Men 76 percent,
Young Women 86 percent. Please see the NLS Handbook or NLS User
Guide for each Cohort for additional information.
16
Social Science Research Council, "A Research Agenda for the
National Longitudinal Surveys of Labor Market Experience,'' Parts iIV. A Report on the Social Science Research Council's Conference on
the National Longitudinal Surveys. Prepared for the Employment
and Training Administration of the U.S. Department of Labor, October
1977 NBER volume.

17 See, for example, Richard B. Freeman
and David A. Wise, eds.,
The Youth Labor Market Problems: Its Nature, Causes, and
Consequences (Chicago, University of Chicago Press, 1982).

18
See Richard B. Freeman, "The Evolution of the American
Labor Market 1948-1980," in Martin Feldstein, ed., The American
Economy in Transition (Chicago, University of Chicago Press, 1980).
As Freeman notes, "In the early part of the post-wwn period, most
of the increase occurred among older women, many of whom were
returning to work as their children reached school age. Nearly 80
percent of the growth in the female work force between 1947 and
1965 resulted from increased numbers of women aged 35 and over,
whose labor force participation rate rcse sharply."

14

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19 Frank Mott, "Looking
Backward: Post Hoc Reflections on
Longitudinal Surveys," 2002, p. 67. Mott notes the strong policy
motivation supporting the cohorts, but also recognizes the Mature
Women's Cohort was "created in part because of internal pressures
applied within the Department of Labor by individuals in the
department's Women's Bureau."

20 James Sweet, remarks to a review
of the NLS program by the
Social Service Research Council in 1977. See William Bielby, Clifford
Hawley, and David Bills, "Research Uses of the National Longitudinal
Surveys," A Research Agenda for the National Longitudinal Surveys of
Labor Market Experience, Part V. (Washington, oc, Social Science
Reserach Council, Center for Coordination of Research on Social
Indicators, 1978).
21
See Gary Chamberlain and Zvi Griliches "Unobservables with a
Varianve-Components Structure: Ability, Schooling, and the
Economic Success of Brothers," International Economic Review,
1975, vol. 16, pp. 422-50.
22
For a summary of its influence, see Kenneth I. Wolpin,
"Educational data in the NLSY79: a premiere research tool," Monthly
Labor Review, February 2005, pp. 15-20.

23 See David Ellwood, "Teenage Unemployment:
Permanent Scars
or Temporary Blemishes," in Richard B. Freeman and David A. Wise,
eds., The Youth Labor Market Problem: Its Nature, Causes and
Consequences (Chicago, University of Chicago Press, 1981 ); and
James J. Heckman, "Heterogeneity and State Dependence," in Sherwin
Rosen, ed., Studies in Labor Markets (Chicago, University of Chicago
Press, 1981 ).
24
See Bielby and others, "Research Uses of the National
Longitudinal Surveys," 1978.
25
For a discussion of event histories and other methodological
contributions of the NLSY79, see Randy Olsen, "The problem of
respondent attrition: survey methodology is key," Monthly Labor
Review, February 2005, pp. 63-67.

26
Social Science Research Council, A Research Agenda for the National Longitudinal Surveys of Labor Market Experience, I 977, p. 31.

27 James J. Heckman, Robert LaLonde, and Jeffrey Smith, "Something on Evaluation Literature," in David Card and Orley Ashenfelter,
eds., Handbook of Labor Economics, vol. 3a (New York, North Holland, 1999), p. 1994.

Education data in the NLSY79:
a premiere research tool
Social science researchers widely use
the NLSY79 schooling data
because of its longitudinal nature and range of content

Kenneth I. Wolpin

Kenneth I. Wolpin is a
professor at the
University of
Pennsylvania
Department of
Economics.
E-mail:
wolpink@ssc.upenn.edu


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rhaps the most widely used data in social science research are those related to
measures of education; among such measures, years of schooling is the most ubiquitous. A search of the National Longitudinal Survey (NLS) Annotated Bibliography yields 1,803
articles, book chapters, dissertations, and so
forth , in which either the word "education" or
"schooling" appears in the title, abstract, or as
a keyword. 1 Of those, more than 1,000 were
based on the National Longitudinal Survey of
Youth , 1979 (NLSY79) data. 2
Researchers' use of education measures
found in the NLSY79 spans several social science disciplines, particularly economics and
sociology, and, to a lesser extent, psychology.
A large number of articles using NLSY79 education measures have appeared in major general
audience and specialty journals. (See table 1.) 3
In economics, there were ~ such journals, totaling 78 published articles, and in sociology,
6 journals with 47 articles. In psychology, one
journal specializing in child development published five articles, and one medical science
journal also published five. 4
The topics covered in these articles vary
widely, as is evident from looking at the titles
of the journals. These articles can be classified
into two broad categories: (i) articles that study
schooling decisions themselves (for example,
how much schooling to complete, whether to
drop out of high school, or choice of college
major), and (ii) articles that study the "effect"

P

of schooling on some other decision or outcome
(for example, on wages, fertility, or alcohol consumption). In both cases, the NLSY79 data is
chosen for its omnibus nature (that is, the data
include information other than schooling), and
because the data are longitudinal.
Although the NLSY79 was a pathbreaking survey in many ways, the collection of schooling
data has been relatively standard. The education section in 1979, the first year of the survey,
contained a total of just 25 questions (excluding interviewer check items), although respon dents were attending school levels ranging from
junior high through college. 5 In the first follow-up survey in 1980, respondents were asked
just 16 questions, primarily to update the
baseline schooling information obtained in the
1979 survey round. Essentially, the same questions were asked in the second follow-up in
1981, with the important addition of a monthly
attendance record obtained retrospectively back
to January 1980. This addition was in keeping
with the event history format of the NLSY79 with
respect to the collection of employment data
and was continued throughout all subsequent
rounds. The schooling section was unchanged
until 1984, when an extensive set of questions
was added to obtain information on colleges attended by respondents. Again, in keeping with
the overall format of the survey, the aim was to
obtain an event history of college attendance.
The rostering of colleges attended continued
prospectively through 1990, when the youngMonthly Labor Review

February 2005

15

NLSY79 Education Data

Number of articles pl.lblished since 1990 with
"education" or "schooling" as a keyword in the
title or in the abstract: journals with five or more
articles by field

Field / journal

Economics:
American Economic Review ... ... ...... ....... ....... ......
Economics of Education Review .. .................. .... .
Industrial and Labor Relations Review ..... ......... ..
Journal of Human Resources ...... .... ..... .. .... ... ..... .
Journal of Labor Economics ....... ........... .... ..... .... .
Journal of Political Economy ..... ..... ........ .......... ...
Monthly Labor Review .......... .... ..... ... ...................
Review of Economics and Statistics ....... ... ....... ...

Number
of
articles

10
7
11
18

7
5
10
13

Sociology, demography :
American Sociological Review ......... ................ .. .
Dernography .. ..... .. ...... .... ....... .... ........... .... ......... .. .
Family Planning Perspective .... ..... ... .... ....... ...... ..
Journal of Family Issues ......... ....... ... ....... ... ..... ... .
Journal of Marriage and the Family ... ........... ... ....
Social Forces ...... ...... .... ..... ....... .. ....... ....... ...... .... .

6
10
9

Psychology:
Child Development ..... ..... ... ... ........ ..... ......... ... ......

5

Others:
Pediatrics .. ........... .... .. ...... ..... ... .. .... .... ... .... .......... .

5

6
8
8

est respondents were age 25. The major elements of the
schooling data in the main survey include current school enrollment status, highest grade attended and completed, high
school curriculum, major field of study in college, degrees
obtained, names and locations of colleges attended, college
loans, and the schooling of household members.
Along with the main survey, there are several supplemental data collections pertinent to schooling. The School Survey, given in 1980, collected information from school administrators on school characteristics. The High School Transcript Survey collected and coded high school transcripts for
almost 9,000 of the respondents. Finally, the Children of the
NLSY79, begun in 1986, collected child development data for
all children born to the women of the NLSY79.
It is beyond the scope of this article to summarize the findings from the studies cited above; instead, some data are presented that fit with the purpose of this special issue, that of
celebrating the 25th anniversary of the NLSY79. In that vein,
the data presented illustrate the aforementioned two most important features of the survey, the longitudinal nature and the
range of content. These features are highlighted by documenting differences among completed schooling groups in a wide
array of behaviorally-rel ated characteristics. (See table 2.)
Cumulative differences are shown for many characteristics
over a substantial post-schooling age range. The table makes
clear the rationale for the intensive study of schooling by social scientists, and for the popularity of using the NLSY79 data.
16 Monthly Labor Review

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February 2005

In the table, schooling is based on completed years of
schooling as of age 25 and is divided into five categories:
high school dropouts (completed years of schooling reported
to be less than 12) who have not received a GED, high school
dropouts who have received a GED, high school graduates
(completed years of schooling exactly 12, no GED), those with
some college (completed years of schooling between 13 and
15), and college graduates (completed years of schooling 16
or more). The table, based on data through the 2000 survey,
considers differences in earnings, welfare takeup and payments, unemployment, hours worked, financial asset holdings, health status, obesity, alcohol consumption, fertility, and
birth weight and test scores of the first-born child.
About 20 percent of males and 17 percent of females in
this cohort did not complete high school with a regular diploma; of those, about a third received a GED. Notice that
counting a person with a GED as a high school graduate makes
a nontrivial difference to the calculation of the dropout rate. 6
A little less than 40 percent graduated high school with a
diploma and did not complete any further schooling. The rest,
about 40 percent of the cohort, went on to college, and about
half of those graduated.
The cumulative figures in the table span ages from 25
through 39. However, many of the variables are not available in every year. Thus, cumulative variables, such as total
earnings or weeks of unemployment, would be understated.
Averaging available data and multiplying by 15 would provide an unbiased estimate except that missing values tend to
occur more frequently at the older ages, both because of attrition and because the survey became biannual after 1994,
and many of the variables are age-trended. To obtain an estimate of the cumulative values, averages of three 5-year age
intervals (ages 25-29, 30-34, and 35-39) were calculated,
each average was multiplied by 5, and then the three totals
were added together. As long as the age trends are not severe
within each of the 5-year age intervals, this procedure should
provide a reasonable estimate.
The most striking feature of the table is how different high
school dropouts, particularly those without GED 's, and college graduates are relative to the rest. Following is a breakdown of each characteristic in turn.

Earnings. There are two sources of information on earnings in the NLSY79: a global question on the amount of wage
and salary income in the previous calendar year, and earnings obtained from the event history on reported jobs. The
figures in the table come from the former source and are in
1996 dollars. 7 Over the 15-year period between the ages of
25 and 39, a male high school dropout without a GED earned,
on average, $273 ,250, about $30,000 less than a high school
dropout with a GED, and $150,000 less than a high school
graduate with a regular diploma. On the other end of the edu-

11•1•n=--- Selected characteristics by completed schooling

Females

Males
Characteristic

High school dropout
No GED

GED

High
school
graduate

Some
college

High
Some
College High school dropout school
college
graduate No GED
GED
graduate

College
graduate

Percent ..... ..................

13.7

6.2

37.9

20.4

21 .8

11 .5

5.4

39.7

23.2

20.3

Cumulative earnings 1
.".ge 25-39 .... ...... .. ..

$273,250

$305 ,041

$429 ,813

$486,619

$775,206

$113,521

$187 ,214

$207,738

$284,859

$426,855

Cumulative welfare
payments 1
Age 25-39 ......... ..

$7 ,004

$7,185

$2,447

$1,216

$346

$28,032

$16,477

$8,198

$4 ,285

$271

Percent of years
receiving welfare
Age 25-39 .... .. .. ...

13.3

10.6

4.5

2.8

.5

31 .0

19.7

10.7

5.4

.7

Cumulative weeks
unemployed
Age 25-39 ... ... ... ..

71 .9

60.3

36.5

31 .0

11 .3

53.3

47.8

33.7

24.0

12 .2

Cumulative hours
worked : full-time
{2 ,080 hours) year
equivalents
Age 25-39 .. ... ......

13.1

12.7

15.6

15.3

16.6

7.3

9.6

10.4

11.2

12.2

Change in financial
assets between
1985 and 2000
10th percentile ....
50th percentile ....
90th percentile ....

-543
0
4,671

-814
0
7,865

-1 ,357
977
31,805

-2,360
1,928
55,119

-2,474
19,347
268,212

-87
0
3,661

-679
0
8,517

-1 ,368
467
20 ,642

-2,035
1,868
67,953

-950
12,927
178,281

36.4

32 .2

21 .1

18.4

9.0

51.6

36.1

36.0

31.7

21 .1

7.4

8.1

3.1

3.2

1.0

10.1

9.8

4.4

4.8

1.9

35.7

25.8

33.2

29.7

18.7

39.3

30.7

32 .2

27.5

17.8

16.3

12.2

17.8

16.2

8.2

26.1

18.7

17.1

14.7

6.2

Number of days per
month having had six
or more alcoholic
drinks
Age 18-24 ...........
Age 25-34 .... .. .... .

1.97
1.21

1.78
.86

1.70
.98

1.40
.93

1.39
.61

.80
.39

.59
.55

.46
.27

.40
.17

.54
.14

Number of children ever
born , greater than or
equal to age 35 .. .....

1.9

1.9

1.6

1.4

1.5

2.6

2.3

1.9

1.7

1.6

Percent at first birth,
less than or equal
to age 19 ....... ... ... ....

23.9

23.7

8.8

4.2

1.1

68.4

55.6

26.1

11.1

.7

Birth weight of first
born (ounces) .........

113.2

115.4

117.0

116.4

117.9

PPVT percentile score
of first born .... .... .... .

30

41

43

49

63

Percent reporting
health limitation ,
age 25-39
At least once .......
At least 50 percent
of the years ......
Percent obese
(BMI greater than
or equal to 30),
age25-39
At least once .. .... .
At least 50 percent
of the years ......

1

1996 dollars


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NOTE :

All figures use the 1979 sample weights. Dash indicates data not available.

Monthly Labor Review

February 2005

17

NLSY79 Education Data

cation spectrum, a male college graduate earned $775,206,
almost $300,000 more than a male who completed some college. Female differences by education are smaller in absolute
value, in part because they work less (see below). Nevertheless, a female high school graduate earned about $90,000
more than a dropout without a GED, and about $75,000 less
than what a female who completed some college earned. A
female college graduate earned $426,855, about $140,000
more than a female with some college.

(female) college graduates spent only 11 (12) weeks unemployed over the 15 years. With respect to the amount of time
spent working, measured in full-time year equivalents (2,080
hours), on average male dropouts (those with and without a
GED) worked about 13 of the 15 years, while male college
graduates worked more than full-time, 16.6 years or about
2,300 hours per year. In contrast, female dropouts without a
GED worked less than one-half of the years, while college
graduates worked four-fifths of the years.

Welfare payments and takeup. The NLSY79 collects data on
takeup and payments for several welfare programs. The figures in the table aggregate respondent information about the
Aid to Families with Dependent Children (AFDC), Food Stamp,
and Supplemental Security Income (ss1) programs. Because
of AFDC, aggregate welfare payments and takeup arc much
larger for females than for males. Over the 15 years, female
high school dropouts without a GED received welfare on average in about 30 percent of the years, totaling $28,032. In contrast, high school dropouts with a GED received welfare in
about 20 percent of the years, totaling $16,477. Comparable
figures for high school graduates were about 11 percent of
the years and $8,198, and for those with some college, 5 percent of the years and $4,285. Female college graduates, on
average, essentially received no welfare.
Perhaps somewhat surprisingly, as seen in the table, even
female high school dropouts without a GED had considerably
more market earnings over the period than they received in
welfare. In fact, only 35 percent of these women reported
receiving more in welfare payments than they earned over
the period. In contrast, 16 percent of high school dropouts
with a GED, 10 percent of high school graduates, and 4 percent of those with some college received more welfare over
the 15 years than they earned.
The takeup rates and average payments for males are, as
noted, considerably smaller than for females; for example,
male high school dropouts without a GED received some welfare in only 13 percent of the years, about the same as those
with a GED. Welfare payments over the entire 15-year period
were, on average, only about $7,000 for high school dropouts
regardless of GED status, and only 7 percent of dropouts without a GED received more in welfare benefits than they earned.

Financial asset accumulation. The NLSY79 began collecting
asset data in 1985. Table 2 presents statistics on the amount of
financial assets accumulated from the first report (when respondents were 20 to 28 years of age) to the 2000 report (when
respondents were 35 to 43). 9 Differences in financial savings
by schooling are large, although the relationship is quite
skewed. Median financial savings are zero for high school
dropouts of both sexes, slightly negative at the 10th percentile, and less than $10,000 at the 90th percentile. Median savings is less than $2,000 for those with some college, but savings increase to $55,000 for males and $68,000 for females at
the 90th percentile. College graduates are again different than
other schooling groups in terms of their financial savings. Median financial savings of males is almost $20,000, but more
than $250,000 at the 90th percentile; for females, the median
is about $13,000 and the 90th percentile, $178,000.

Labor force status (unemployment and hours worked). Male
high school dropouts without a GED were unemployed 72
weeks, and similar female dropouts were unemployed 53
weeks, cumulatively over the 15-year age period. 8 These figures are slightly higher than for dropouts with a GED. Those
who graduated from high school or completed some college
had significantly fewer cumulative weeks of unemployment.
However, as with earnings and welfare, college graduates
were distinctly different than the other schooling groups; male
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Health. In each round of the NLSY79, respondents were
asked separately about whether their health limited the amount
or the kind of work they could perform. Table 2 presents data
on the distribution of the number of times between the ages of
25 and 39 that the respondent answered affirmatively to either question. 10 The first row reports on the percent who gave
an affirmative response at least once, and the second row, on
the percent who gave an affirmative response in at least 50
percent of the years. High school dropouts without a GED are
the most prone to report a health limitation affecting work at
least once; more than a third of the men and more than half of
the women reported ever having a limitation. At the other
extreme, only 9 percent of male college graduates, and 21
percent of female college graduates, reported such an occurrence at least once. Chronic health conditions, as measured
by having reported a health limitation in at least half of the
years, are much less prevalent in all schooling groups, but
non-negligible for high school dropouts. Between 7 percent
and 8 percent of male high school dropouts, and about 10
percent of female high school dropouts, have a persistent
health condition, while this is true of only 1 percent of male
and 2 percent of female college graduates.
Obesity. The NLSY79 collected self-reported information on
a respondent's height and weight in 1981, 1982, and 1985,

and on the respondent's weight in all rounds since 1986, except 1987 and 1991. Body mass index (BMI) was calculated
in each year using the latest height information that was available.11 As with health limitations, table 2 reports the distribution of the number of times over the 15-year age period, 2539, that respondents have a BMI of 30 or more, indicating
obesity, again by schooling groups for each sex. Although
there is some prevalence of obesity among males with schooling below college completion, it is not large. Among males
with some college, 30 percent were obese in at least 1 year,
and 16 percent were obese in at least one-half of the years.
The same figures for high school dropouts are 36 percent
and 16 percent. Differences are larger for females, with 28
percent of those with some college, and 39 percent of high
schoo! dropouts without a GED, being obese in at least 1 year.
College graduates again are substantially different from all
of the other groups. For both sexes, less than 20 percent were
obese in any year, and less than 10 percent in at least half of
the years.
Alcohol consumption. In several years (1982, 1983, 1984,
and 1994), the NLSY79 has included a section of questions,
funded by the National Institute of Alcohol Abuse and Alcoholism (NIAAA), on alcohol use. A common question in those
rounds has been the number of days in the last month that the
respondent consumed six or more drinks. Table 2 shows the
average number of days over all of the observations in those
years for two age intervals, 18-24 and 25-34, by schooling
and sex. In all of the schooling groups, for both males and
females, alcohol consumption measured in this way fell substantially with age. Male high school dropouts without a GED
averaged just less than 2 days a month between the ages of
18 and 24, but 30 percent less between 25 and 34. Females
within that schooling group averaged only .8 days at the earlier age, but that fell in half at the later age. College graduates are less of an outlier in terms of alcohol consumption at
the earlier age, although their consumption falls proportionately more at the later age.
Fertility. Differences by schooling in the number of children ever born by age 35 are large. Female high school dropouts who have not earned a GED have had 2.6 children on
average by age 35, .3 more than their counterparts with a
GED, .7 more than those with a high school diploma, .9 more
than those with some college, and a full child more than those
with a college degree. Males at all schooling levels report
having had fewer children, and this is particularly pronounced
for high school dropouts. Even so, those male respondents
with some college report having fathered half a child less
than those who were high school dropouts.
Schooling differences in fertility are even more striking in
terms of the prevalence of teenage childbearing. More than


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two-thirds of female high school dropouts without a GED, and
more than one-half of those with a GED, gave birth to a child
as a teen. This figure drops to one-quarter for high school
graduates, to about 10 percent for those with some college,
and to less than 1 percent for college graduates.
Birth weight of children. Starting with the 1982 survey
round, with funding from the National Institute of Child
Health and Human Development (NICHD), the NLSY79 collected information about the pregnancy outcomes of the female respondents. 12 Table 2 compares mean birth weight of
first-born children across the schooling groups. 13 The differences observed among the respondent's own outcomes, such
as earnings and health, also emerge among the children of
the respondents. High school dropouts without a GED have
the lowest birth weight of children, almost 5 ounces less than
the first births of the college graduates.
Peabody Picture Vocabulary Test (PPVT) scores of children.
The Children of the NLSY79 survey was started in I 986 and
obtains information about the children born to the women of
the NLSY79, including cognitive test scores. Table 2 reports
the mean percentile scores (age adjusted) of first-born children by mother's schooling. As seen repeatedly in the table,
the lowest and highest schooling groups, high school dropouts without a GED and college graduates, appear to be outliers. Children of the former group have mean scores that fall
in the 30th percentile, while those of the latter fall in the 63rd
percentile. The other three schooling groups fall in between
and differ only slightly from each other.
analyzes the contributions of the NLSY79 to
the study of issues related to education. Space limitations,
combined with the vastness of the literature, precluded a critical assessment of the scientific contributions of the research
based on NLSY79 schooling data. Instead, this article demonstrates why the schooling data has played such an important
role in making the NLSY79 one of the premiere research inD
struments in the social sciences.

THIS OVERVIEW

Notes
1
Education alone yields 1,600 matches, schooling alone 588, with
385 matches using both. A search for the words "earnings" or .. wages"
yielded 1,437 matches, and for '•income," 1,027 matches. All of these figures are somewhat overstatements because of duplications that arise when
papers appear in multiple forms, for example, as a working paper and later
as a journal publication.

2
When referring to the NLSY79, I include the Children of the NLSY79.
Recall also that the NLS consists of five additional cohorts, four begun
between 1966 and I 968, and the latest begun in I 997.
3

I thank Terry Fahey at the Center for Human Resource Research

(CHRR) for performing the specialized search of the Bibliography 's data-

Monthly Labor Review

February 2005

19

NLSY79 Education Data

base on which these figures are based.
4

This is the only psychology journal publishing at least five such articles. There are about a dozen other psychology journals that have published fewer than five, though most have published only one article.
5
In the NLSY97, on the other hand, the schooling section contained
several hundred questions in the first round. Schooling data in the 1997
survey are obtained as a dated event history similar to the employment
data .
6

The Current Population Survey (CPS) counts both those with a
and those with a regular diploma as a high school graduate.

GED

7

The samples for the earnings and welfare statistics are both based on
respondent-years in which there is no missing information either on
earnings or on welfare benefits. In addition, the sample is restricted to
those who had at least one report in each 5-year age interval.
8

As with earnings and welfare, the sample is restricted to those who
have a valid report in at least I year of each 5-year age interval.
9

Financial assets consist of moneys in savings or checking accounts,
money market funds, credit unions, U.S. savings bonds, individual retirement accounts, 401 K or pre-tax annuities, certificates of deposits, per-

20 Monthly Labor Review

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February 2005

sonal loans to others or mortgages held, stocks, bonds, mutual funds, and
rights to an estate or investment trust.
10

The sample is based on respondent-years in which there is no missing information on the health limitation question. The average numbers of
years over the 25-39 age range in which these data are reported for each
schooling category are: 10.3, 10.0, 10.5, 10.4, and 10.3 for males, and
10.9, 10.6, 10.7, 10.8, and 10.7 for females.
11

More specifically, the 1985 height report was used whenever available (about 90 percent of the cases). If that was missing, the 1982 height
report was used, and if both were missing, the 1981 height report was
used. Differences in reported height in the 3 years are small; mean reported height in 1981 is .17 inches less than in 1985 for those that report
both. The BMI is defined as weight in kilograms divided by the square of
height in meters. Obesity is defined as having a BMI of 30 or more.
12
Information was also collected on maternal behaviors during each
pregnancy, such as prenatal care, smoking, drug use, and drinking.
13
Looking at first-born children avoids differences that might arise if
birth weight is related to birth order, given that less educated women have
more children as shown above. A similar comment applies to the Peabody
Picture Vocabulary Test scores addressed in the following section.

Data from the National Longitudinal Survey of Youth 1979
found that the average worker, approximately 5 years
after leaving school for the first time,
starts a job that will last 3 years;
however, there was considerable variation by education
Julie A Yates

Julie A Yates is a
research economist,
Employment
Research and
Program
Development Staff,
in the Office of
Employment and
Unemployment
Statistics, Bureau of
Labor Statistics.
E-mail :
Yates .J ulie@bls.gov


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Federal Reserve Bank of St. Louis

ouths experience different trajectories in
their transition from school to work.
Some youths jump from job to job and
do not develop a steady employment relationship until many years after leaving school, if at
all. Others settle into a longer-term employment
relationship soon after leaving school. Some
policymakers and educators express concern that
many new entrants to the job market tend to experience periods of churning, moving from one low
paying job to another, without settling into a
longer-term relationship.' This argument posits
that the time, sometimes many years, spent moving from one short-term job to another is nonproductive and steps should be taken to eliminate it.
Other analysts see this period of short employmeP.t spells in a more positive light. They
argue that early job mobility represents "job
shopping" where young workers learn about different work environments and their own skills and
interests. 2 As youths acquire different work experiences, they are able to move into jobs that
better match their skills and interests, often with
higher wages. In this light, the job-shopping
phase can be beneficial for both workers and their
employers.
Education is clearly linked to these employment processes. In high school, youths learn
mainly general skills. These include not only hard
skills such as literacy and numeracy, but soft skills
such as punctuality, dependability, and following
directions. Because of their youth, those seeking
jobs just after high school may know less about
the world of work and be less committed to a par-

Y

ticular occupation. Likewise, employers of these
youths have less information about their skills.
Both employer and employee may look at entrylevel jobs as a learning process by which each
can evaluate the long-term potential of their
"match." College graduates, on the other hand,
invest more in specific skills and may acquire a
greater knowledge of the job market within their
field. They can match their interests to skills and
reject potential career paths before entering the
labor market. Employers of new college graduates have potentially greater knowledge of the
particular skills of their new hires, and, because
of the higher wages they must pay, more incentive to find a good match. For these reasons,
matches between new college graduates and their
employers may be expected to last longer than
those between new high school graduates and
employers. Youths who have left school without
a high school degree are doubly disadvantaged;
they lack both general and job-specific skills, and
they face employers who have low expectations
and little incentive to invest in their matches.
Consequently, schooling choices may dictate the
speed and ease of the school-to-work transition.
This article documents the transition from
school to work for a nationally representative
sample of men and women from the time they first
left school for a year or more until age 35. The
tables in this article describe the duration of employment relationships and time since leaving
school until holding a job for a specific number of
years. This will help to answer a number of questions about the transition from school to work,

Monthly Labor Review

February

2005

21

School-to-Work Transition

such as: how does job changing evolve as individuals age;
how does job mobility behavior vary by education level and
other demographic characteristics; and finally, how long does
it take an average individual to settle into a longer-term job.
Other researchers have used the National Longitudinal
Survey of Youth 1979 (NLSY79) to study job changes, mostly
focusing on men. Some of the articles have been more descriptive like this one, although for earlier time periods. 3 Others engage in detailed empirical analysis, and generally try to
examine the causes and consequences of early job changes. 4
In this article, we use data through 2002, which allows us to
trace individuals' careers until age 35. Thus we can show
individuals' job transitions from the end of school into the
workforce and up until mid-career.

Data and methods
The data. To adequately measure the path an individual
takes from the completion of schooling to stable employment, one needs longitudinal data that track respondents
over their working lives. In this article, the education and
employment histories are examined using NLSY79 data.
These data describe a sample of men and women who were
ages 14 to 22 in 1979 and 37 to 44 when interviewed in 2002.
In 2002, the sample-which includes an overrepresentation
of blacks and Hispanics-had 7,724 respondents. This
article defines the transition from school to work to occur
at the point when the individual leaves school for 12 months
or more. However, work histories in the NLSY79 data generally begin in January 1, 1978, or the respondent's 16th
birthday, whichever is later. Thus, in order to view the
complete school-to-work transition without oversampling
those with higher education, only respondents born between January 1, 1961, and December 31, 1964-that is, the
last four birth cohorts of the NLSY79-are used. 5 After
dropping respondents with incomplete data, there were a
total of 3,845 respondents. In all computations, weights
are used to adjust for different sampling rates and
nonresponse rates so that the data are a nationally representative sample of all youths born between 1961 and 1964
and living in the United States in 1979.
A key feature of the NLSY79 data set is that it records
much of the information as event histories; thus, the dates
of transitions are documented and updated with each interview. In the event history of employment, or work history, the "start" and " stop" dates of each job the respondent has held are recorded, as well as dates of nonwork
(such as maternity leave or layoff) within each job. This
allows various job-related measures, such as the number
of jobs held, weeks worked, and job tenure, to be calculated. In addition, because the dates of these job-related

22

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February

2005

behaviors are recorded for each individual, these variables
can be calculated for a specific period-for example, the
number of jobs each worker held during the 2 years before
the start date of a certain job can be determined. Using
the NLSY79 work history data, it is possible to construct
and link monthly records of school attendance or nonattendance with employment.
Defining school leaving groups. In this analysis, individuals are grouped based on their education level when they
first left school. Individuals were assigned to a school-leaving group based on the highest degree, if any, they reported
earning as of the time they first left school for 12 months or
longer. 6 Thus, if an individual dropped out of school but
subsequently returned to finish high school within a 12month period, they would be classified as a high school
graduate and not a dropout. Similarly, individuals who completed high school and went to work full time, but within the
12-month period began taking night classes, would continue
to be viewed as 'in school,' despite their work schedule, until
they were no longer taking classes. Once determined that an
individual had left school for 12 months, even if the person
later returned to school and received a degree, the initial
school-leaving group to which they were assigned was not
changed. 7
Table 1 describes the sample used in this article by educational category as well as the average age respondents first
left school. 8 Twelve percent of the sample first left school at
approximately age 17 before attaining a high school degree
and are assigned to the category of dropouts. Approximately
55 percent of the sample first left school for at least 12 months
after attaining a high school degree; these respondents left
school approximately 1 year later (age 18) than dropouts.
Individuals with some college but who did not attain a
bachelor's degree before first leaving school make up 22 percent of our sample, while 11 percent are those who attained a
college degree before first leaving school. The educational
outcomes of men were more dispersed than those of women;
men were both more likely to drop out and more likely to
complete college. 9 Ethnic and racial differences in initial educational attainment are quite pronounced. Whites were more
likely than either blacks or Hispanics or Latinos to have either gone to college or earned a college degree and less likely
to drop out.
While the date first left school is a useful point to measure
the school-to-work transition, it does not always indicate the
final degree earned. Many individuals return to school, either by combining work and schooling or by leaving the labor force altogether. Table 2 shows that a large number of
individuals return to school at some point before they be-

Degree and age when first left school for 12 months or longer and degree when completed schooling,
by school-leaving group
Original school-leaving group and characteristic

Degree when first left
school

Average age when first
left school for
12 months or longer

High school dropouts .... ... .... ... ..... ... ..... ..... ........ .................... ........... ...
High school graduates .......... .. ...................... .... .. ... ...... .... ... ............... .
Some college .. ............. .. ...................... ................ ....................... .. ... ... .
College graduates ......... ............... ................. ..... ............................... ..

12.1
54.6
22.0
11 .3

17.0
17.9
21.4
23.8

7.1
42.8
23.7
26.5

19.6
12.6

19.3
17.1
18.0
21.6
24.0

7.4
44.4
21.1
26.1

Less than a high school diploma ......... ........................................... .
High school graduates, no college ..... ... .... .... ..................... ........... ..
Less than a bachelor's degree .......... ..... ... .. .... ........ ... ................ ... ..
Bachelor's degree or more .................................. ...................... ... ... .

10.6
55.0
24.5
9.9

19.2
16.9
17.9
21 .3
23.6

6.1
40.8
26.3
26.7

White (non-Hispanic) .......... .......................... .... ... ..... .... ... ..... ............. ..
Less than a high school diploma .... ....... ......... ..... .... .................... ... .
High school graduates, no college ... ...... ..... ........ ...... .................... ..
Less than a bachelor's degree ............................................... .... ... ..
Bachelor's degree or more ... ... ... .. ....... ... ... ..................... ........ ....... . ..

10.2
54.0
23.0
12.7

19.4
16.9
17.9
21.5
23.7

5.8
41.4
22.4
30.4

Black (non-Hispanic) ....................................... .... ........... .................... .
Less than a high school diploma ..... ................................... ........... ..
High school graduates, no college .......................... .......... ........... .. .
Less than a bachelor's degree ..... .... .. .. ..... ................... .... ... .... .... ... .
Bachelor's degree or more .......... .... ............. ....... .... .... ...... ...... ....... ..

17.0
58.7
19.1
5.1

18.9
17.4
18.1
21 .3
24.2

10.0
49.0
28.9
12.1

Hispanic or Latino ..... ....... .... .......... ................ .....................................
Less than a high school diploma .................................................... .
High school graduates, no college ..... ...... ... ...... ...... ...... ............ .. .. ..
Less than a bachelor's degree ... ... ..... ........ ..... ...... ..... .... ................ .
Bachelor's degree or more ... ............... .................... ..... ........ .... ....... .

22.7
54.2
16.9
6.1

18.7
16.9
18.0
21 .3
25.3

15.4
45.3
26.9
12.4

NorE: Educational attainment is defined as of the time they first left
school for 12 consecutive months. Our sample is 51 percent men; 49 percent

women; 77 percent non-Hispanic whites; 15 percent non-Hispanic blacks; and
7 percent Hispanics or Latinos.

come age 35; however, not everyone who returned to school
eventually received a degree. Fifty-three percent of high
school dropouts returned to school, as did 42 percent of high
school graduates. While 58 percent of initial dropouts never
received a high school diploma or GED (general equivalency
diploma) , 28 percent of dropouts did eventually earn a high
school diploma; an additional 10 percent received some college education; and 4 percent went on to receive a college
degree or more. Again, ethnic and racial differences are quite
striking in this regard. When retut ning to school, whites in all
education categories were more likely to complete their college degree. As already noted, whites generally had more
education when they first left school. Thus, the process of
returning to school to complete unfinished degrees did not
close the racial gap in educational attainment, but instead
seemed to widen it.
As seen in the last column of table 1, by age 35, whites are
significantly less likely to have less than a high school diploma or GED and twice as likely to have completed college.
Only 6 percent of whites had not received a high school de-

gree or GED by their 35th birthday compared with 10 percent
of blacks and 15 percent of Hispanics or Latinos. In contrast,
more than 30 percent of whites had received a college degree
compared with only 12 percent of blacks and Hispanics or
Latinos. In contrast, the gender gap in educational attainment is small; at 35, women have only slightly more education
than men.

Men .............. ..... .................. ... .. .. ...................................... .. .......... ..... .. .
Less than a high school diploma ..... ........ .. ............................ ........ ..
High school graduates, no college ................. .............. .... ...... ..... ....
Less than a bachelor's degree ...... ......... ... .................. ............ ....... .
Bact,elor's degree or more ............... .................. .. ........................... .

12.6

54.2

Women .. ... ...... ............. ... ............... ... ...................... ..... ..... ... ..... ........... .


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Degree when
completed schooling

The school-to-work transition
Duration of employment. The longitudinal nature and event
history data collection of the NLSY79 make the data set ideal
for studying job duration. A job-shopping model would suggest that the quality of job matches would increase with each
subsequent job. Thus the probability that one would find a
better match and switch jobs would decrease over time. This
tendency is reinforced by the development of job-specific
skills, which may not be valued by another employer. Theoretically, then, a successful transition to the labor market
should be characterized by increasing job tenure. Therefore,

Monthly Labor Review

February

2005

23

School-to-Work Transition

Distribution of completed schooling, by school-leaving group
Ever
returned to
school

High school dropouts .. ............. ..... ........ .......... .... ......
High school graduates .... ....... ........ .... ..... .... .. ........... .
Some college .. .. ... ........................ .................... ......... .
College graduates ............ ....... ... ........ ............. ....... ...

Highest degree ever reported receiving
High school
dropout

High school
graduates
(or GEO)

Some college

College
graduates

53.2
42.1
54.6
41.0

58.3

28.2
72.1

9 .7
22.9
45.5

3.7
5.0
54.5
100.0

50.1
37.2
54.8
34.5

58.8

27.7
75.5

9 .1
20.3
45.6

4.4
4.2
54.4
100.0

57.4
47.1
54.5
49.7

57.7

28.8
68.6

10.7
25.6
45.5

2 .8
5 .8
54.6
100.0

50.4
42 .1
53.4
41.2

56.8

27.5
71 .5

10.9
22.6
39 .5

4 .9
5 .9
60.5
100.0

61 .8
42.3
58.7
26.5

58.6

31 .5
74.3

8.4
23.6
71 .2

1.5
2 .1
28.8
100.0

49.9
39 .9
38.6

67.8

23.3
73.7

7.2
23.8

1.8
2 .5
100.0

Men
High school dropouts ... ..... ... ..... .. ......... ...... .... ....... ... .
High school graduates ... ....... ......... ......... ... ....... .... .. ..
Some college .... ...... .. .. ...... ....... ................. ...... .......... .
College graduates ...... ... .. ....... ....... ......... ..... ...... ...... ..

Women
High school dropouts .... ......... ........ ......... ...... ... .. .......
High school graduates ... ..... ....... .......... ....... ... ..... .. ... .
Some college .... .......... ..... .. .......... ......................... .... .
College graduates ... ... ............. .. ........ .. ....... ............ .. .

White (non-Hispanic)
High school dropouts .. ....... ... ...... .. ......... .................. .
High school graduates ... ........ .... ... ... .. .. ........... .... ..... .
Some college ...... ............. ... ............................... ....... .
College graduates ...... ........... .. ........ ...... ..... ..... ....... ...

Black (non-Hispanic)
High school dropouts .. ..... .............. .......... ....... ... ...... .
High school graduates .......... ..... .... ..... ... ........ .... ..... ..
Some college ......... .... ....... ... .... ... .. ......... .. .............. .. ..
College graduates .. ... .... .. ... ............. .. ............ ... ..... ... .

Hispanic or Latino
High school dropouts .......... ....... ....... ..................... .. .
High school graduates ... .... ... ...... .... .. .... .............. ..... .
College graduates ..... ... .......... .... .. .. ....... ....... ....... ... ...

NorE : Educational attainment is defined as of the time they first left school for 12 consecutive months.

tenure of the longest job ever held can be used as an indicator
of the successful transition from school to work and into a
stable employment relationship.
Table 3 shows the duration of the longest job held.
Youths with a high school degree or less can learn about
interests and gain job-specific skills while on the job, and
those with some post high school education can gain jobspecific skills in the classroom. If these schooling choices
are equally suited for preparing youths for stable employment, then the duration of their longest job by age 35
should be similar. A period of churning or job shopping
would be expected from those who do not yet know their
skills and preferences. However, high school dropouts and
graduates should eventually obtain job- or employer-specific skills; finct a compatible career path; and settle into
long-term jobs. From that point on they would, theoretically, have labor market behavior similar to youths who
sperit that time gaining those skills in a classroom. Again,
the data disprove this . Even though high school dropouts

24

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Feoruary

2005

left school the earliest, were in the labor market for the
longest time-and therefore had the most time to start
down a career path-they are the least likely to have had
stable employment relationships lasting more than 2 years.
At age 20, 14 percent of dropouts have never held a job
and 58 percent had yet to hold a job for more than 1 year.
At age 35, approximately 18 years after leaving school for
the first time, 2 percent of dropouts have never held a job;
9 percent of dropouts have never held a job for more than
a year; and an additional 15 percent have never held a job
for 2 years or longer. Only 36 percent of high school dropouts had held a job for 5 years or more by age 35.
In contrast, those with at least some college or a
bachelor's degree make the transition to stable employment-that is, a job lasting more than 2 years-the faste st.
By age 30, nearly 86 percent of those with some college
and 82 percent with a bachelor's degree or more have held
a job for more than 2 years. And by age 35, about 95 percent of individuals in these two education categories have

n•r•n:...,..

Duration of employment relationship with a single employer, for longest held job from first time left school to
age 35, by age and eduational attainment
Duration of longest employment relationship
Age and characteristic

No job

Less than
1 year

More than
1 year but less
than 2 years

More than
2 years but less
than5years

5 years or
more

Through age 20 ... ........... ... ....... ..... ....... ........ ....... ... ...
Less than a high school diploma ............. ....... .... ...
High school graduates, no college .... .... ... ....... ... .. .
Less than a bachelor's degree ... ..... .. .... .............. ..
Bachelor's degree or more ... ... ........ .......... .. .... .......

10.3
14.2
9.4
12.7
(2)

57.4
58.1
56.4
84.1
(2)

27.8
21.8
30.4
3.2
(2)

4.4
6.0
4.2
(1)
(2)

(1)
(1)
(1)
(1)
(2)

Through age 25 .... ..... .... .. .... .. ...... ......... ..... .......... .... .
Less than a high school diploma ... .. ......... ... ......... .
High school graduates, no college ...... ..... ........ .....
Less than a bachelor's degree ... ........ ....... .. ..... .... .
Bachelor's degree or more ....... ... ... .... ...... .. ............

2.2
4.2
1.7
1.5
2.2

19.6
28.2
15.5
18.3
37.6

28.8
24.5
24.4
36.2
45.0

41 .3
36.7
45.4
43.4
15.2

8.3
6.4
12.9
.6
(1)

Through age 30 .. ..... ..... ....... .... .. ...................... ... ...... .
Less than a high school diploma ... .... ... .. .. .. .... .......
High school graduates, no college ............ .... ...... ..
Less than a bachelor's degree ... .... ................. ..... .
Bach8lor's degree or more .. .... ... ... ....... ... ....... .. ..... .

1.0
2.2
1.0

6.2
15.9
5.7
3.5
3 .6

13.8
18.7
14.2
10.6
12.5

44.3
41.9
42.2
46.0
53.7

34.6
21.3
36.9
39.6
28.4

Through age 35 .... ...................... .. ...... ... ...... .. ...... ......
Less than a high school diploma ........ ......... ..........
High school graduates, no college ... ...... ...... ... ... ...
Less than a bachelor's degree .. ....... .... ..... ....... .... .
Bachelor's degree or more ... ........ ......................... .

.7
1.8

2 .9
9.1
2 .6
1.5
.4

6.9
14.5
7.4
3.1
4.1

33.1
39.0
33.7
28.8
31.8

56.4
35.7
55.7
66.4
62.5

.2
1.7

.6
.1
1.1

1
Estimates are not presented for these categories because most sample
members had not been out of school long enough to hold a job of this length .

done so. At age 35, 66 percent of those with some college
and 63 percent of those with a college degree had held a
job for 5 years or more .

Women in the labor market. During the last several decades , the "working mother" has become the norm rather
than the exception. 10 While women no longer automatically withdraw from the labor force upon marrying or after
having a child, it remains common for women with young
children to interrupt their careers for both childbearing and
childrearing. Women may also choose more intermittent or
seasonal work that correlates with school or other
childrearing activities. It is reasonable to expect that their
work trajectory will be different from men. However, much
of the difference between the school-to-work trajectory of
men and women-at least in the measures presented
here-appears to stem from the variability of work experience in female dropouts.
Table 4 breaks down the duration of longest held employment relationship by sex as well as age. At all ages,
female high school dropouts are significantly more likely
to have never held a job, and helJ jobs for less time. At age
20, 21 percent of female dropouts had never held a job,
while approximately 13 percent had held a job for between
1 and 2 years. Only 9 percent of male dropouts, in comparison, had never been employed, and 28 percent had held
their longest job between I and 2 years. While the differ-


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2 Estimates are not presented because most sample members in this
education category are not yet out of school.

ence in employment duration shrinks with age, it never
equalizes. At their 35th birthday, 4 percent of female dropouts had never held a job and only a quarter had held a job
more than 5 years. In comparison, nearly 44 percent of
male dropouts had held a job more than 5 years by age 35,
and almost all had held a job.
The work experiences of dropouts contrasts to the employment histories of those with more education. At age 25,
women with some college or a college degree are more likely
to have held a job since leaving school for the first time, and
were equally and often more likely to have held a job of a
specific duration as similarly educated men. However, differences emerged with age. These differences are probably due
to the fact that some women leave and re-enter the workforce
due to household responsibilities as they age. At age 35,
only 1 percent of male or female co11ege graduates had never
held a job, but 68 percent of the men had held a job for more
than 5 years, compared with 55 percent of the women. Only 3
percent of male co11ege graduates had never held a job for 2 or
more years, compared with 9 percent of women with a college
degree.

Racial differences. Table 5 examines the duration of employment relationships for the longest held job by race and
ethnicity. Compared with similarly educated whites or Hispanics, blacks consistently have less tenure. However,
blacks in particular benefit from increases in education. At

Monthly Labor Review

February

2005

25

School-to-Work Transition

■ r.1.,,,---.,-

Duration of employment relationship with a single employer, for longest held job from first time left school to
age 35-by sex, age, and eduational attainment
Duration of longest employment relationship

No job

Age and characteristic

Less than
1 year

More than
1 year but less
than 2 years

2 years but less
than 5 years

5 years or
more

More than

Men only through age 20 ..........................................
Less than a high school diploma ...........................
High school graduates, no college ........................
Less than a bachelor's degree ..............................
Bachelor's degree or more .....................................

9.8
8.7
10.1
(1)
(2)

58.0
57.6
57.7
(1)
(2)

28.7
28.3
29.1
(1)
(2)

3.5
5.4
3.1
(1)
(2)

(1)
(1)
(1)
(1)
(2)

Women only through age 20 .....................................
Less than a high school diploma ...........................
High school graduates, no college ........................
Less than a bachelor's degree ........................ ......
Bachelor's degree or more .....................................

10.9
21.2
8.7
13.3
(2)

56.9
58.6
55.1
85.4
(2)

27.0
13.3
31.0
1.3
(2)

5.3
6.8
5.2
(1)
(2)

(1)
(1)
(1)
(1)
(2)

Men only through age 25 ............................ .............
Less than a high school diploma .............. ... ..........
High school graduates, no college ........................
Less than a bachelor's degree ........... ...................
Bachelor's degree or more ....................... .......... ....

1.7
1.1
1.7
2.0
2.4

19.4
20.7
15.1
21.9
38.6

28.3
24.6
24.3
35.9
41.5

42.5
47.0
46.5
39.7
17.4

8.1
6.5
12.5
.4
(2)

Women only through age 25 ....................................
Less than a high school diploma ................. ..........
High school graduates, no college ........................
Less than a bachelor's degree ..............................
Bachelor's degree or more .....................................

2.3
8.3
1.8
1.1
2.0

19.9
38.1
16.0
15.2
36.3

29.4
24.4
24.6
36.4
49.1

40.0
23.0
44.3
46.5
12.6

8.4
6.2
13.3
.7
(2)

Men through age 30 ...................................... ............
Less than a high school diploma ...................... .....
High school graduates, no college ........................
Less than a bachelor's degree .......................... .. ..
Bachelor's degree or more .....................................

.8
.1
.9
.4
1.6

5.0
11.1
4.0
3.4
5.1

11.9
15.7
12.6
10.1
7.7

45.2
47.1
42.8
45.3
53.9

37.1
25.9
39.7
40.9
31.7

Women through age 30 .............................................
Less than a high school diploma ...........................
High school graduates, no college ........................
Less than a bachelor's degree ... ..... .... ..................
Bachelor's degree or more .....................................

1.3
4.9
1.0
.1
1.9

7.5
22.2
7.5
3.5
1.5

15.7
22.6
15.9
11.0
18.9

43.3
35.1
41.6
46.7
53.6

32.1
15.2
34.0
38.0
24.1

Men through age 35 .................................................
Less than a high school diploma ...........................
High school graduates, no college ........................
Less than a bachelor's degree ..............................
Bachelor's degree or more ............ ........ .................

.5
.1
.6
.1
1.4

2.3
7.3
1.9
1.7
.0

5.0
10.0
5.8
2.0
1.1

31.2
39.0
31.9
25.0
29.4

60.9
43.5
59.8
71.1
68.1

Women through age 35 .............................................
Less than a high school diploma ...........................
High school graduates, no college .................... ....
Less than a bachelor's degree ... ...........................
Bachelor's degree or more .....................................

.8
4.0
.6
.1
.7

3.5
11 .5
3.3
1.4
1.0

8.9
20.4
9.1
4.0
8.1

35.0
38.9
35.6
32.0
35.1

51.7
25.3
51.4
62.5
55.1

1
Estimates are not presented for these categories because most
sample members had not been out of school long enough to hold a job
of this length .

26 Monthly Labor Review

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Federal Reserve Bank of St. Louis

February

2005

2
Estimates are not presented because mcst sample members in this
education category are not yet out of school.

ll•l•U:.a.._

Duration of employment relationship with a single employer, for longest held job from first time left school to
age 35-by age, race, Hispanic or Latino ethnicity, and educational attainment
Duration of longest employment relationship

Age and characteristic

No job
Less than
1 year

More than
1 year but less
than 2 years

More than
2 years but less
than5years

S years or
more

Through age 20
White (non-Hispanic) ....... ............ ........... ........... ........
Less than a high school diploma ...........................
High school graduates, no college .... .... ...... ... .......
Less than a bachelor's degree .. ... ..... ... ... ... .... ... ....
Bachelor's degree or more ...... ... ..... .. .... .. .... ....... ....

6.7
10.5
6.0
(2)
(2)

56.9
58.3
55.5
(2)
(2)

31 .5
25.2
33.6
(2)
(2)

4.9
6.1
4.9
(2)
(2)

(1)
(')
(')
(2)
(2)

Black (non-Hispanic) ........ ..... ... .................................
Less than a high school diploma ..... ... .. ... .... ..........
High school graduates, no college ............... ... ... ...
Less than a bachelor's degree ..............................
Bachelor's degree or more ..... .. ....................... .......

25.1
25.7
24.6
(2)
(2)

58.4
56.7
58.6
(2)
(2)

14.3
13.2
15.1
(2)
(2)

2.2
4.4
1.7
(2)
(2)

(')
(')
(')
(2)
(2)

Hispanic or Latino ... .. ....... .................. .... .... ..... ..........
Less than a high school diploma ......................... ..
High school graduates, no college ........................
Less than a bachelor's degree ......... .... ........ ..... ... .
Bachelor's degree or more .....................................

12 .7
16.0
10.9
(2)
(3)

60.9
56.0
62.3
(2)
(3)

22.3
19.5
24.4
(2)
(3)

4.2
8.5
2.4
(2)
(3)

(')
(')
(')
(2)
(3)

White (non-Hispanic) ....................................... ..........
Less than a high school diploma ...........................
High school graduates , no college .. ... .. .................
Les~ than a bachelor's degree ... ... ... .. ...... .... .... .. ...
Bachelor's degree or more ......... .... ....... .... ... ... ... ....

1.3
2.3
1.0
2.3
2.4

17.6
23.3
13.4
16.5
37.9

29 .1
26.5
23.5
37.5
43.4

43.2
41.7
47.6
44.6
16.2

8.8
6.2
14.4
.4
(2)

Black (non-Hispanic) ........................... .. .. .... .... ..........
Less than a high school diploma .... ......... ..... ...... .. .
High school graduates, no college ..... .. ...... ..... .... ..
Less than a bachelor's degree ..... ....... ... .... ... .... ....
Bachelor's degree or more .. ... .................... ... ..... ....

5.7
11 .0
4.6
5.2
(3)

27.5
37.8
23.6
26.9
(3)

30.2
23.3
29.9
33.8
(3)

31.0
21.5
34.9
32.3
(3)

5.7
6.5
7.0
1.8
(3)

Hispanic or Latino ........... ................ .... ......................
Less than a high school diploma ........ .. .......... .. ... ..
High school graduates, no college .................. ... ...
Less than a bachelor's degree .......... ....... .............
Bachelor's degree or more .... .... ...... ........ ........ .......

2.2
3.2
1.6
2.2
(3)

22.3
32.5
19.1
18.2
(3)

24.7
18.0
33.0
28.9
(3)

43.6
38.7
45.5
50.8
(3)

7.4
7.7
9.8
.0
(3)

White (non-Hispanic) .................................... ...... .......
Less than a high school diploma ....... ..... .. ... ... .......
High school graduates, no college .. ... ... ......... .......
Less than a bachelor's degree ..............................
Bachelor's degree or more .....................................

.5
.8
.4
.9
1.7

4.5
11.5
4.3
2.6
3.5

13.0
17.6
13.4
10.0
12.9

45.2
47.8
42.4
46.4
52.3

36 .8
22.2
39.5
41.0
29.7

Black (non-Hispanic) .................................................
Less than a high school diploma .. ........ ... ......... .....
High school graduates, no college .... .. ........ .. ...... ..
Less than a bachelor's degree ......... ....... ... ......... ..
Bachelor's degree or more ..... ... .. ... ................. ... ....

3.1
6.5
28
1.4
·1.7

12.7
25.3
11.1
9.3
2.2

18.3
23.3
19.3
12.8
10.2

40.3
27.0
41.5
44.6
55.0

25.6
18.0
25.3
31.9
30.9

Hispanic or Latino .....................................................
Less than a high school diploma ... ... ................ .....
High school graduates, no college ..... .. .. ... ...... ......
Less than a bachelor's degree ....... ...... ... .. .......... ..
Bachelor's degree or more ...... ........ ....... ... .. .. ...... ...

1.8
2.5
1.3
1.2
(3)

9.1
18.7
7.6
.4
(3)

13.5
15.8
12.8
12.0
(3)

44.2
39.6
43.7
47.1
(3)

31.4
23.4
34.5
39.3
(3)

.4
.6
.5
.5
1.2

1.9
6.0
2.0
1.4
.1

5.7
13.9
5.8
2.6
3.8

32.6
29.7
31 .6
31.3
31 .8

59.4
29.7
60.0
64.2
62.4

Through age 25

Through age 30

Through age 35
White (non-Hispanic) .. ... .. ........ .... .. ....... .. .. ...... ......... ..
Less than a high school diploma ... .. ....... ...............
High school graduates, no college ...... ..... .. ... ..... ...
Less than a bachelor's degree .. .. ..... .... ..... ...... .. ....
Bachelor's degree or more .. .... ... ... ..... ...... ..... ..... ....
See notes at end of table.


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Federal Reserve Bank of St. Louis

Monthly Labor Review

February

2005

27

School-to-Work Transition

11•1•n=---

Continued Duration of employment relationship with a single employer, for longest held job from first time
left school to age 35-by age, race, Hispanic or Latino ethnicity, and educational attainment
Duration of longest employment relationship

No job

Age and characteristic

Less than
1 year

More than
1 year but less
than 2 years

More than
2 years but less
than5years

5 years or
more

Black (non-Hispanic) .................................................
Less than a high school diploma ...........................
High school graduates, no college ...................... ..
Less than a bachelor's degree ..............................
Bachelor's degree or more ... .... .... ..........................

1.7
4.6
1.5
.0
.0

7.1
18.0
5.5
4.2
.0

13.8
18.9
15.2
7.3
4.5

34.0
34.7
35.0
29.4
37.6

43.4
23.9
42.7
59.1
58.0

Hispanic or Latino .. ....... .. .... ......................................
Less than a high school diploma ...........................
High school graduates, no college .................. .. ....
Less than a bachelor's degree ..............................
Bachelor's degree or more ...... ............................. ..

1.5
2.5

4.4
9.6
4.1
.4
(3)

6.7
12.0
6.1
3.0
(3)

34.5
39.5
33.0
31 .8
(3)

52 .9
36.4
55.7
63.2
(3)

1.1

2.0
(3)

1
Estimates are not presented for these categories because most
sample members had not been out of school long enough to hold a job of
this length .

their 20th birthday, 27 percent of black dropouts and 24 _
percent of blacks with a high school degree had never held
a job. By their 25th birthday, 11 percent of black dropouts
had yet to hold a job, compared with 6 percent of blacks
with a high school degree. By their 35th birthday, 4 percent
of black dropouts had still never held a job, and less than a
quarter had ever held a job for 5 years or more. On the
other hand, less than 2 percent of black high school graduates had never held a job, and 43 percent had held a job for
5 years or longer. Moreover, at age 35 nearly all blacks with
some college or those with college degrees had held jobs,
and approximately 60 percent had held jobs for 5 years or
more.
A second way to use job duration to study the schoolto-work transition is to look at the tenure of all jobs ever
held, not just the longest job. Table 6 presents a measure
of job mobility or churning by averaging the tenure across
all jobs held since first leaving school. At their 25th birthday, 55 percent of the sample changed jobs, on average, at
least once a year, while approximately 7 percent had an
average tenure of 3 years or more. However, even at age
35, nearly a quarter of the sample had average tenure of
less than a year, meaning a nontrivial portion of the sample
continued to have a relatively large number of short-duration jobs as they approached middle age. What is striking
is that at age 35, half of all dropouts have an average tenure
across all jobs that is 1 year or less, while only 10 percent
have average tenure of 3 years or more. While table 3 shows

28 Monthly Labor Review

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Federal Reserve Bank of St. Louis

February

2005

2 Estimates are not presented because most sample members in this
education category are not yet out of school.
3
Estimates are not presented because cell size is less than 50.

that the majority of dropouts at some point have held a job
that lasted more than 2 years, table 6 indicates that for most
dropouts, jobs of long duration are accompanied by many
more jobs of short duration.
Again, the differences in the employment histories of
men and women became apparent with age. At age 25, the
average tenure across all jobs for men and women is
roughly the same: 55 percent of men and 56 percent of
women had average tenure less than 1 year, while 6 percent
of men and 7 percent of women had average tenure of more
than 3 years. By age 35, approximately 22 percent of men
had average tenure of less than 1 year, compared with 27
percent of women-and 28 percent of men had average
tenure of more than 3 years, compared with 22 percent of
women. More importantly, this difference holds true for all
education categories. Even female college graduates consistently have less average tenure than similarly educated
men. While 3 percent of college-educated men have average tenure of less than 1 year, 13 percent of college-educated women have the same. Moreover, 47 percent of these
men have average tenure of more than 3 years, compared
with only 30 percent of college-educated women.
Years or jobs until 'stable employment. ' As already demonstrated, individuals have a great deal of job mobility in
their first years out of school. Brief and intermittent periods of employment are common among many young workers, especially those with low levels of education. The

11•1•ir-.-.w

Percentage of individuals who have average tenure across all jobs less than a specific duration
Percent of people with average tenure lenght of:
To 25th birthday
Characteristic
1 year or
less

More than
1 year
but less

than

To 35th birthday

To 30th birthday

3 years or
more

1 year or
less

Morethan
1 year
3 years or
but less
more

than

1 year or
more

3 years

3 years

Morethan
1 year
3 years or
but less
more
than
3 years

Total .... ............ ..... ... .......... ..... ...... ....... ... .....
Less than a high school diploma ..... .......
High school graduates, no college ... ......
Less than a bachelor's degree .. ... ..........
Bachelor's degree or more ... .............. .....

55.3
70.1
52.0
52.2
64.5

38.0
25.9
39.2
42.6
35.0

6.7
4.0
8.8
5.2
.5

35.4
61 .5
38.1
21 .7
21.4

48.5
31 .7
46.9
57.3
56.6

16.1
6.8
15.1
21 .0
22.0

24.2
50.4
26.6
12.9
7.2

50.3
40.0
50.5
54.2
53.2

25.4
9.7
22.9
32.9
39.6

Men ... .. ... .. .... ... ..... .... .... ........... ........ ...... .. ....
Less than a high school diploma ..... .......
High school graduates, no college .........
Less than a bachelor's degree ........... ....
Bachelor's degree or more .. .. ...... ...... .....

55.1
65.2
52.1
52.2
64.5

38.6
30.4
40.3
41 .6
34.6

6.3
4.4
7.7
6.2
1.0

33.7
56.9
36.7
20.0
16.5

48.9
35.9
48.0
55.0
57.4

17.4
7.2
15.3
25.0
26.0

22.0
43.5
25.0
11.2
2.8

49.7
45.9
50.5
49.9
50.2

28.3
10.5
24.6
38.9
47.0

Women ........ ..... .. .. .... .... .. ... .... .. .... .... ....
Less than a high school diploma ............
High school graduates, no college ...... ...
Less than a bachelor's degree .. .. .. .... .. .. .
Bachelor's degree or more .... ....... ... ... .... .

55.6
77.1
51.9
52.1
64.5

37.3
19.5
38.2
43.4
35.5

7.1
3.4
9.9
4.5
.0

37.2
67.8
39.5
23.1
27.9

48.0
26.0
45.7
59.2
55.5

14.8
6.2
14.8
17.6
16.6

26.5
59.8
28.2
14.3
13.0

51 .0
31.8
50.5
57.9
57.1

22.4
8.5
21.3
27.9
29.9

White (non-Hispanic) ... .... ......... .... ..............
Less than a high school diploma ... .........
High school graduates, no college ... ..... .
Less than a bachelor's degree .... ..... ..... .
Bachelor's degree or more ......................

53.2
68.4
49.1
51.8
64.2

40.1
28.6
41 .6
43.7
35.3

6.7
3.0
9.3
4.5
.6

32.6
58.3
35.8
20.0
21.0

49.7
34.8
47.4
58.4
56.0

17.7
6.9
16.8
21.6
22.9

21.5
46.6
24.0
12.2
7.4

50.8
42.7
50.9
53.6
51.9

27.8
10.7
25.2
34.3
40.7

Black (non-Hispanic) .... ................ ........ ......
Less than a high school diploma .... ........
High school graduates, no college .... .....
Less than a bachelor's degree ....... ... ... ..
Bachelor's degree or more ...... .. ...... .... .. ..

65.4
74.2
64.5
59.7
(1)

28.0
19.3
29.0
32.5
(1)

6.5
6.5
6.5
7.8
(1)

47.4
68.6
49.2
30.7
22.6

41.9
23.7
42.2
52.6
57.2

10.7
7.7
8.7
16.7
20.2

36.3
59.3
38.5
17.6
9.1

47.3
32.0
47.3
57.7
57.7

16.4
8.7
14.3
24.7
33.2

Hispanic or Latino .. .. ... .. ..... ..... .... ....... ... .. ...
Less than a high school diploma .... .. .... ..
High school graduates, no college .........
Less than a bachelor's degree .... .. .. .......
Bachelor's degree or more ... .. ... ..............

57.4
70.1
56.4
39.9
(1)

34.6
24.4
35.3
48.2
(1)

8.0
5.5
8.3
11 .9
(1)

40.9
64.0
38.7
20.0
(1)

49.0
30.9
51.5
58.0
(1)

11 .2
5.1
9.7
22.0
(1)

27.8
51.9
26.5
9.1
(1)

52.5
41 .5
54.8
53.4
(1)

19.7
6.6
18.7
37.5
(1)

1

Estimates are not presented because cell size is less than 50.

question remains: how long does it take for young workers to find stable employment or a longer-term employment
relationship?
Table 7 presents the median number of years from first
leaving school until a worker holds a job for either 1, 3, or 5
years. Comparing the four school-leaving groups, it is apparent that the transition from school to work was quicker as
education increased. For example, the median high school
dropout took more than 3 years to start a job that would last
a full year, and nearly 11 years before they started a job that
would last 3 years. Because less than 50 percent of the high
school dropout sample had yet to hold a job for 5 years at age
35, we cannot determine the median number of years. In comparison, the median high school graduate took 6 years to
start a job that would last 3 years and IO years to start one
lasting 5 years. Those with a college degree settled into stable


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employment much more quickly; within a year and a half they
started a job that would last 3 years-and less than 4 years to
start a job that would last 5 years.
In other words, the median high school dropout started a
job that would last 3 years at age 29; the median high school
graduate, at age 24; and the median college graduate, age 26.
In addition, the median high school dropout had yet to hold a
job lasting 5 years by age 35, while a high school graduate
started one at age 28. A college graduate started a job lasting
5 years at age 27. It appears that high school graduates are
able to use the general skills gained in high school to obtain
additional on-the-job skills. While this article does not analyze
the wage potential of jobs and cannot determine if the career
paths are similar, the median high school graduate started jobs
of a significant duration-5 years-at approximately the same
age as the median college graduate.

Monthly Labor Review

February

2005

29

School-to-Work Transition

Median number of years between leaving school for the first time and starting a job that lasts a set amount of
time, to age 35, 1978-2002
Median number of years between leaving school for the first
time and starting a job that will last at least:
Characteristic
1 year

3 years

5 years or more

Total .....................................................................................................
Less than a high school diploma .. .................... ........................... ..
High school graduates, no college ..................................................
Less than a bachelor's degree .. ..................... ................. ............... .
Bachelor's degree or more ...............................................................

.9
3.3
1.3
.5
.2

4.6
10.8
5.8
2.7
1.3

9.2
(1)
10.1
5.0
3.5

Men ......................................................................................................
Less than a high school diploma .................................................... .
High school graduates, no college ................................................. .
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more ........................... .. ................. ... ............. .

.8
2.3
1.2
.4
.1

4.1
7.6
5.4
2.5
1.0

8.0
(1)
9.1
4.1
2.3

Women .................................................................................................
Less than a high school diploma ................................................... ..
High school graduates, no college ................................................. .
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more .............................................................. .

1.0
5.9
1.3
.5
.2

5.3
14.9
6.4
3.0
2.5

10.9
(1)
11 .7
5.9
5.4

White (non-Hispanic) ............... ....... .................... ................ ............... ..
Less than a high school diploma .................................................... .
High school graduates, no college ..................................................
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more .............................................................. .

.8
2.9
1.0
.4
.1

4.1
8.7
5.3
2.6
1.3

8.1
(1)
9.2
4.7
3.3

Black (non-Hispanic) ...........................................................................
Less than a high school diploma .....................................................
High school graduates, no college ................................................. .
LP.ss ttian a bachelor's degree ...... ................................ ................. .
Bachelor's degree or more ..... .. ....................................................... .

2.3
6.2
3.1
.9
.3

8.0
(1)
9.1
3.8
1.9

(1)
(1)
(1)
6.5
3.2

Hispanic or Latino ...............................................................................
Less than a high school diploma .................................................... .
High school graduates, no college .. .............. .. .......... ..................... .
Less than a bachelor's degree ........................................................
Bachelor's degree or more .............................................. ................ .

1.5
4.0
1.8
.2
(2)

5.7
10.5
6.2
.2
(2)

10.5
(1)
10.3
4.9
(2)

1
Estimates are not presented because less than 50 percent of the
sample had held a job of this length.

Female dropouts took longer to find stable employment
than male dropouts. It took the median female dropout approximately twice as long to hold jobs lasting 1 or 3 years
than male dropouts; 5.9 years versus 2.3 years before starting
a job that would last 1 year; and 14.9 years versus 7.6 years
before starting a job that would last 3 years. While women
with a college education started a job that lasted at least 1
year at the same time as similarly educated men, it took them
significantly longer to start a job that lasted 5 years (5.4 years
versus 2.3 years).
Compared with whites and Hispanics or Latinos, blacks
fared poorly in the labor market. At their 35th birthday, more
than 50 percent of both black high school dropouts and graduates had never held a job for 5 years or more, and more than 50
percent of black dropouts had never held a job lasting at least
3 years. It took white dropouts nearly 9 years to start a job
that would last 3 years, while Hispanic or Latino dropouts
took more than 10 years. In comparison, white college graduates started a job that would last 3 years approximately 1 year

30 Monthly Labor Review

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2

Estimates are not presented because cell size is less than 50 .

after leaving school; blacks with a college degree took approximately 1 extra year-but started a job lasting at least 3
years the same time as whites did.
Similar patterns existed for the median number of jobs held
from time first left school until workers settled into stable employment. (See table 8.) In all cases, measuring number of
jobs held from first leaving school until starting a job that
lasted 1, 3, or 5 years, college graduates made the transition to
stable employment with the least amount of churning or job
shopping. A high school dropout, on the other hand, seems
to do a fair amount of churning. The median high school
dropout held two jobs before starting a job that lasted 1 year
and held five jobs before starting a job that lasted 3 years. A
high school graduate, in comparison, held one job before holding a job for 1 year and five jobs before holding a job for 5
years. Finally, those with a college degree or more found a
long-lasting job with relatively little job shopping or churning.
College graduates held only two jobs before starting a job that
lasted 5 years or more.

11•1•n~:• Median number of jobs held between leaving school for the first time and starting a job that will last a set
amount of time, to age 35, 1978-2002
Median number of jobs held between leaving school for the first
time and starting a job that will last at least:
Characteristic
1 year

3 years

5 years or more

Total .....................................................................................................
Less than a high school diploma .............................. ...................... .
High school graduates, no college ................................................. .
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more ..................................... ......................... .

1
2
1
1
0

3
5
4
2
1

5
(1)
5

Men ..................................................................................... .. .............. .
Less than a high school diploma ................................................ .. ...
High school graduates, no college ..................................................
Less than a bachelor's degree ........................................................
Bachelor's degree or more .............................................................. .

1
2
1
1
0

3
5
4
2

4
(1)
5

Women ................................................................................................ .

1
3

4
6
4
3
2

5
(1)
5
4

3
6
4
2

4
(1)
5
3
2

Less than a high school diploma .................................................... .
High school graduates, no college ................................................. .
Less than a bachelor's degree .................................... ................... .
Bachelor's degree or more .............................................................. .

1

1
0

3
2

3
2

1

3

White (non-Hispanic) ...........................................................................
Less than a high school diploma .....................................................
High school graduates, no college ................................................. .
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more .............................................................. .

1
0

Black (non-Hispanic) .......................................................................... .
Less than a high school diploma .................................................... .
High school graduates, no college ................................................. .
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more ...............................................................

2
2
2
1
1

4
(1)
4
3
2

(1)
(1)
(1)

Hispanic ................................................................. .................... ......... .
Less than a high school diploma .....................................................
High school graduates, no college ................................................. .
Less than a bachelor's degree ....................................................... .
Bachelor's degree or more .............................................................. .

1
3
2
0
(2)

4
5
4
2
(2)

5
(1)
5

1
Estimates are not presented because less than 50 percent of the
sample had held a job of this length.

IT TAKES APPROXIMATELY 5 YEARS after leaving school for the first
time before the average worker starts a job that will last 3 years.
However, college graduates found stable, long-term employment
almost immediately, while high school dropouts continued to
have many short-term jobs 15 years after leaving school. By age
35, more than 62 percent of college graduates had held a job for
more than 5 years; at the median, this group had started their
career job less than 4 years after leaving school. In contrast,
most high school dropouts took many years to overcome their

1
3
1

2

1

3
2

3
(2)

Estimates are not presented because cell size is less than 50.

lack of skills: at age 35, only 36 percent of them had held a job for
more than 5 years, and more than 50 percent had an average
tenure of 1 year or less. The data also show significant differences by sex and race in the work experiences of individuals
between the end of schooling and age 35. While many of the
racial differences become insignificant with increases in education, the disparity between men and women often remain. Across
both genders and all races, increases in education smooth the
transition from school to work.
D

Notes
1
See, for example, Report by the Commission on the skills of the
American Workforce, 1990.

2
See, for example, Robert H. Topel and Michael P. Ward, "Job
Mobility and the Careers of Young Men," The Quarterly Journal of
Economics, May 1992, pp . 439-479; Boyan Jovanovic, ''Job Match-


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ing and the Theory of Turnover," Journal of Political Economy, May
1979, pp. 972-990; William R. Johnson, "The Theory of Job Shopping," The Quarterly Journal of Economics, May 1978, pp. 261-278.
3
Jacob Alex Klerman and Lynn A. Karoly, "Young Men and the
Transition to Stable Employment," Monthly Labor Review, August 1994,

Monthly Labor Review

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31

School-to-Work Transition

pp. 31-48; Jonathan R. Veum and Andrea B. Weiss, '·Education and the
Work Histories of Young Adults," Monthly Labor Review, Ap1il 1994,
pp. 11 - 20.
4

Rosella Gareecki and David B. Neumark, "Order from Chaos? The
Effects of Early Labor Market Experiences on Adult Labor Market
Outcomes," Industrial and Labor Relations Review, January 1998, pp.
299-322; and Audry Light, Kathleen McGarry, " Job Change Patterns
and the Wages of Young Men," The Review of Economics and Statistics,
May 1998, pp. 276-286.
5
If we do not restrict the sample to these birth cohorts, only individuais who had not exited school before 1978 would be included in the
1957 to 1960 sample . As a result , the sample including all cohorts
would have a much higher percentage of college graduates and a lower
percentage of high school dropouts.
6

An individual must be out of school for 12 consecutive months
before their education status is determined. However, once that 12month period has been reached, jobs and duration are measured retroac-

32

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2005

tively from the beginning of the 12 months, that is, from the very first
week they left school.
7
To account for individuals who initially overstate their educational
attainment, the highest degrees are compared to questions , asked in
1998 and afterwards, of highest degree ever earned and the date these
degrees were obtained. Approximately 100 individuals were reassigned
to lower categories based on their answers to this question.

8
Approximately 3 percent of the sample was still attending school
at their 35th birthday. However, much of this schooling appears to be
a use of leisure time as compared to the pursuit of a degree. These
individuals are characterized by intermittent school attendance, taking
only a few credits, and never completing any additional degree.
9

10

All comparisons are statistically significant.

According to the Current Population Survey (CPS) at the Bureau of
Labor Statistics, 64 percent of women with children under age 6 were in
the civilian labor force in 2003, as were 72 percent of women with
children under age 18.

Job mobility and wage growth:
evidence from the NLSV79
Data from the 1979 National Longitudinal Survey of Youth
provide an unusually complete history of employment experiences;
analyses of why workers separate from their employers,
frequencies of these separations, and job mobility's impact
on earnings reveal that today's labor markets
are far more dynamic than previously realized
Audrey Light

Audrey Light is an
associate professor
in the Department of
Economics and
Center for Human
Resource Research,
Ohio State University,
E-mail:
light.20@osu.edu


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ongitudinal data have contributed immeasurably to our understanding of individuals' labor market activities, especially when
it comes to analyzing job mobility and wage
growth. Without the ability to "see" workers
move from employer to employer, we would know
very little about why workers separate from their
employers, how often separations occur, and how
job mobility affects earnings. 1 Analyses of these
issues have revealed labor markets to be far more
dynamic than was previously realized.
One phenomenon that has received considerable scrutiny is the persistent, voluntary job mobility of young workers. In the mid 1970s,
economists began using search-theoretic models
to explain why information costs compel workers
to systematically "shop" for a better job. 2 The
idea is that workers cannot immediately locate
firms where their skills are valued the most highly,
so upon accepting a job offer they continue to
search for an even better 0utside opportunity.
Workers might also learn over time that their current job is not as productive as they initially predicted. New information regarding outside offers
or the current job is predicted to lead to a workerinitiated job separation. Empirical researchers
have used longitudinal data to determine which
theoretical models are supported by the data and
to identify the contribution of "job shopping" to
life-cycle wage growth.
A related issue of long-standing concern is
the effect of job immobility on wage growth.
Human capital models predict that wages rise
with job seniority when workers "lock in" and

L

invest in firm-specific skills. Because these skills
cannot be transferred to a new job if a separation
occurs, workers and firms agree to share the
costs and benefits of the investment-and the
worker's return on the shared investment takes
the form of within-job wage growth above and
beyond any gains due to the acquisition of general (transferable) skills. A variety of agency
models provide alternative explanations for upward sloping wage-tenure profiles. In these
models, employers defer wages as a means of
discouraging workers from quitting or shirking;
stated differently, they require workers to "post
a bond" as an incentive to sustain the employment relationship. 3 Longitudinal data have
proved to be essential for assessing the merits
of these theoretical models and identifying the
effect of tenure on wages.
Know ledge of the relative contributions of job
mobility and immobility to life-cycle wage growth
is fundamental to a number of important policy
issues. For example, the well-being of low-skill
labor market entrants is highly dependent on
whether they are consigned to a lifetime of lowwage jobs, or whether they can advance in the
wage distribution via life-cycle wage growth. As
a result, policymakers might ask what can be done
to enhance workers' wage growth. If job-specific
skill investments are an important source of wage
growth, then policies that promote on-the-job
training might be useful to the low-wage population. If "job shopping" provides the lion's share
of wage growth, then programs that provide jobsearch assistance might be warranted.

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33

Job Mobility and Wage Growth

Of course, not all job separations are worker-initiated quits,
so it is equally important to focus attention on issues related
to involuntary job displacements. Researchers have relied on
longitudinal data to determine which workers are particularly
vulnerable to layoffs; which industries are the most volatile;
and how wages are affected in both the short run and the long
run when workers are displaced from their jobs.

Advantages of NLSY79 data
Analysts have been studying job mobility and wage growth
for decades, but they gained an important new data source
when the 1979 National Longitudinal Survey of Youth
(NLSY79) was launched. The NLSY79 plays a central role in
this type of r~search because it provides an unusually complete history of each respondent's employment experiences,
inc1uJing a record of virtually every job held. In this section,
the key attributes of these data are highlighted; additional
details can be found in the NLSY79 User's Guide. 4
During each interview, NLSY79 respondents report information on every job currently in progress or held since the
last interview. When the first interview was conducted in
1979, respondents who were older than 18 retrospectively
identified each job held since age 18. (The 12,686 respondents
ranged in age from 14 to 22 at that time; 43 percent were older
than 18.) For the younger respondents, the job history begins between ages 15 and 17. As a result of this sampling and
data collection strategy, analysts can initialize respondents'
careers at a uniform point in the life cycle (the 18th birthday,
the first exit from school, and so forth) and obtain a remarkably complete record of jobs held from that point forward for a
large sample of individuals.
While the advantage of sampling young people is that complete histories (without left-censoring) are obtained, NLSY79based research has necessarily been limited to early-career
activities. The NLSY79 has taken a back seat to other longitudinal surveys-most notably, the Panel Study of Income Dynamics (PSID )-for the study of job mobility and wage growth
among prime-age workers. Now that the youngest respondents (those born in 1964) have entered their 40s, however,
the NLSY79 will be increasingly useful for the analysis of job
mobility in the mid-career.
The NLSY79 provides much more than a simple tally of
cumulative jobs held over the career. At each interview, respondents report the start date and stop date of any job that
began and/or "permanently" ended since the last interview.
Because the recall period is relatively short and respondents
report dates rather than time elapsed since the job began or
ended (which would invite them to "round" their responses),
analysts obtain high-quality data. Measurement error is inevitable in all survey data, but the NLSY79 is acknowledged to
identify job durations and job tenure more cleanly than other
surveys. 5

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In addition to start and stop dates, such job characteristics as industry, occupation, class of employer, rate of pay,
and weekly hours are identified for most jobs. These characteristics are usually known for as many as five unique jobs
held between each interview, although some characteristics
are identified only when the job lasts at least 9 weeks and the
respondent works at least 10 hours per week. When jobs last
long enough to span interviews, multiple reports of these
characteristics are recorded. For example, if a job begins 3
months before the 1980 interview and ends 3 months after the
1983 interview, the respondent reports his current wage, occupation, hours worked, and so forth during the 1980, 1981,
1982 and 1983 interviews; the stop date is then identified in
1984.
When respondents report that a job has ended, they are
asked to provide their reason for leaving and whether a new
job was lined up before they left. Analysts must contend
with missing data, ambiguous responses (especially when
reasons are recorded as "other"), and the possibility of
misclassification, but they can make considerable progress
in distinguishing between involuntary separations (layoffs,
firings) and voluntary "quits." In combination with job start
and stop dates, these data also allow analysts to classify job
exits as "job to job" or "job to nonemployment."
The survey also identifies temporary nonwork spells within
jobs-specifically, the start and stop date of each "within-job
gap" lasting at least 1 week, along with the reason for not
working. This information allows analysts to identify nonwork
spells due to strikes, temporary layoffs, health-related leaves
of absence, and so forth that do not lead to the permanent
termination of the employment relationship. Moreover, the
detailed information on work and nonwork spells collected at
each interview is used to create three weekly "work history"
arrays. One array identifies each respondent's labor market
status (working, out of the labor force, active military service,
and so forth) during each week from January 1, 1978, onward.
Another array identifies the usual hours worked on all jobs
held during each week, and the third array identifies the number of jobs held during each week. These variables allow
analysts to construct extraordinarily detailed measures of
cumulative labor market experience and job tenure, and to
identify transitions between employment, unemployment, and
nonemployment spells, as well as transitions into and out of
jobs.
As discussed in other articles in this issue, the NLSY79
also contains detailed data on schooling attainment and enrollment, job training, geographic location, household composition, family formation, and much more. These data provide a rich set of controls for models of job durations, job exit
probabilities, and wages, and they allow researchers to study
the interdependence of job mobility and other events such as
school completion, migration, and marriage.

Overview of NLSY79-based research
Given the range of substantive issues that compel analysts to
study job mobility and the advantages of using NLSY79 data
for this purpose, it should come as no surprise that the existing NLSY79-based literature is very large. Rather than attempt
a comprehensive survey of the literature, this article describes
a dozen studies that, as a group, illustrate the ways in which
NLSY79 data have been used to explore mobility- and wagerelated issues.
In the first set of studies, analysts identify the determinants of job mobility by estimating models of job durations or
separation probabilities. Studies of this nature include those
by Henry S. Farber, Derek Neal, Anne Beeson Royalty, and
Madeline Zavodny. 6 Farber focuses on the timing of job separations and the extent to which observationally equivalent
workers differ in their separation probabilities. He finds, among
other things, that the hazard rate rises with job tenure for about
3 months and declines thereafter-a pattern that is consistent
with the view that agents gather information before deciding
that a separation is optimal. Subsequent research has distinguished between different types of job separations. For example, Neal considers both "simple" job changes, where workers perform the same type of work on both jobs, and "complex" job changes that entail a change of career as well as a
change of employer. His analysis lends support to the idea
that workers first search for a suitable career and then concentrate on finding the best employer match within that career.
Royalty reconsiders the conventional wisdom that women are
more likely than men to leave their employers for nonemploymenc, but perhaps less likely to quit for a better job. By distinguishing between job-to-job and job-to-nonemployme~t tr~nsitions and estimating separation models for workers m distinct gender-schooling groups, she learns that this pattern
only applies to less educated workers; men and women with
more than 12 years of schooling prove to have similar separation patterns. Zavodny asks whether technology-intensive
industries (measured by computer usage, the fraction of workers in science and engineering, and so forth) have more or less
job stability than other industries. She finds that overall separation rates are lower in "high tech" industries than in "low
tech" industries, but that the difference is entirely due to lower
quit rates in the technology-intensive industries; among less
educated workers, involuntary separations may be more likely
in technology-intensive industries than in other sectors.
Wages are the outcome of interest in the next set of studies
discussed. To maintain the focus on mobility-related research,
studies that model wages as a function of past job mobility
and/or current tenure, among other factors, are considered. 7
Pamela J. Loprest, Kristen Keith, and Abagail McWilliams conduct gender comparisons of the contemporaneous wage
change associated with a change of employer. Loprest finds


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that men receive more wage growth than women over a 4-year
period, and that this premium is largely due to a higher return
to mobility. Keith and Mc Williams find that between-job wage
gains are greater for workers (both men and women) who engage in formal job search prior to their separation, but that men
are more likely than women to conduct such activities. Audrey
Light and Kathleen McGarry ask how "overall" mobility (defined as the number of job separations in the first 8 years of the
career) affects both the level and slope of men's wage paths.
They find that immobile workers have the highest and steepest wage paths, followed by moderately mobile men whose
mobility appears to conform to "job shopping," while highly
mobile workers fare the worse in terms of both wage levels and
wage growth.
Turning to studies that focus on the wage-tenure relationship, Bernt Bratsberg and Dek Terrell assess race differences
in the returns to tenure, using various instrumental variables
to contend with the fact that tenure is endogenous to the
wage-generating process. They find that estimated tenure
slopes are sensitive to the estimation method, but are roughly
similar for black and nonblack workers (all of whom are terminal high school graduates in their sample). However, blacks
receive significantly lower returns than nonblacks to general
labor market experience. The human capital interpretation of
these findings is that blacks invest less intensively than whites
in skills that are transferable across jobs, but receive similar
returns to investments in firm-specific skills. The role affirmspecific skill investments is given a closer look by Daniel Parent, who estimates wage models that include measures of both
job tenure (time with the current employer) and industry tenure (time with the current industry). He finds that tenure effects virtually disappear when industry tenure is included as a
control, which suggests that workers are investing in skills
that are specific to their industry rather than their current job.
Randall J. Olsen's study is distinguished by the fact that he
jointly estimates models of wages and job mobility. His unified, structural approach to assessing the relationship between
job mobility on wages suggests that cumulative work experience (general skill acquisition) and job mobility are more important sources of early-career wage growth than is tenure
(firm-specific skill acquisition).

Empirical patterns
In this section, some of the basic relationships between job
mobility and wage growth seen in the NLSY79 are highlightedspecifically, the distribution of cumulative jobs held by NLSY79
respondents in the first 8 years of their careers, and the unconditional relationships between job mobility and both cumulative and year-to-year wage growth.
The first step of the analysis is to define a career start datethat is, the date when individuals make a transition from school

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2005

35

Job Mobility and Wage Growth

to work. Many NLSY79 respondents are observed combining
school and work or cycling between the two activities, so a
judgment call is needed to determine when their work lives
begin. Therefore, careers are initialized at the start of the first
school exit that lasts at least 12 months. 8 A total of 5,321
respondents, not enrolled in school at the time of their 1979
interview, are eliminated from the sample. Reported school
enrollment data are used to determine career start dates for the
7,365 remaining respondents. This date falls between April
1979 and June 1990 for all respondents, and precedes May
1983 for 75 percent of the sample.
In order to track job mobility and wages over a reasonably
long period of time (but not so long that right-censoring affects a significant number of careers), respondents are required
to be observed for 8 years beyond the start of the careef. This
selection rule eliminates 51 individuals who drop out of the
survey before their 8-year window ends. To avoid having to
contend with missing data, respondents who miss one or more
interviews during the 8-year observation period are also eliminated. This leaves a final sample of 5,654 respondents.
Table 1 summarizes the number of jobs held by these 5,654
resrondents between the beginning and end of the 8-year
observation period. The cumulative job count includes jobs
that are in progress at the start of the career, as well as any job
whose start date precedes the end of the 8-year window. Table .
1 shows that men are slightly more mobile than women during
the first 8 years of their career: the mean job count is 4.8 for

-••leH-11'

men and only 4.3 for women, and a higher proportion of men
than women (25 percent versus 20 percent) hold seven or more
jobs. At the other extreme, 11-12 percent of women and men
hold no jobs or a single job during the period of observation.
In contrast to these relatively small gender differences, table 1
reveals that job mobility varies dramatically across schooling
levels. To assess the relationship between mobility and
schooling, the men are classified into a "high school"
subsample (those whose highest grade completed at the career start date is no greater than 12) and a "college" subsample.
The high school sample averages 5.2 jobs during the 8-year
window, which is almost one job more than the mean for the
college sample. Almost one-third of high school educated
men holds seven or more jobs, versus only 18 percent of the
college sample.
In table 2, the cumulative job count over the 8-year observation period is linked to cumulative wage growth. For this
exercise, attention is confined to 4,189 respondents for whom
a "valid" wage (an average, hourly wage between $1 and
$1,000) is reported to have been earned within 9 months of the
career start date and the career end date. Each average hourly
wage is divided by the gross domestic product (GDP) implicit
price deflator, and the 8-year difference in log-wages is used
as the measure of cumulative wage growth.
Table 2 reveals that, on average, overall wage growth declines with mobility for both men and women. Among women
who hold a single job in 8 years, the average change in log-

Distribution of number of jobs held during first 8 years of career
Women

Number
of
jobs

All schooling levels

Number

0
1
2
3

Percent
of
sample

Number

87
265
423
446
442
370
283
218
132
96
117

3.0
9.2
14.7
15.5
15.4
12.9
9.8
7.6
4 .6
3.3
4.1

58
241
353
374
408
325
313
240
173
94
196

2.1
8.7
12.7
13.5
14.7
11 .7
11.3
8.7
6.2
3.4
7.1

42
106
180
193
240
198
194
164
132
72
136

2.5
6.4
10.9
11.7
14.5
12.0
11.7
9.9
8.0
4.4
8.2

16
135
173
181
168
127
119
76
41
22
60

1.4
12.1
15.5
16.2
15.0
11.4
10.6
6.8
3.7
2.0
5.4

2 ,879
4.3
2.6
17

100.0

2,775
4.8
2.9
19

100.0

1,657
5.2
2.9
19

100.0

1,118
4.3
2.7
15

100.0

6 ······················· ·· ····
.. .. ........... ....... .... ...

'

8 ·····························
9 ........ .. ......... ..........
10 or more ..............

36

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Schooling greater
than grade 12

Number

4 ········· ········· ···· ·······
5 .............................

Mean .......................
Standard deviation ..
Maximum .. .. ............

Schooling less
than or equal to
grade 12

All schooling levels

Percent
of
sample

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

All ..... ..................... ..

Men

-

February

2005

-

Percent
of
sample

-

Number

Percent
of
sample

-

•

1

•1•n:__.- Wage growth during first 8 years of career by number of jobs held
Women

Number
of
jobs

Men

All schooling levels
Number
of
observetions
123
557
836
491
2,007

1 ·············"
2-3 .. .........
4--6 .... .. ... ..
7 or more ..
All ... ... ... ....

Schooling less than or equal
to grade 12

All schooling levels

Mean

Standard
deviations

.59
.57
.46
.40
.49

.56
.68
.63
.68
.66

Number
of
observetions

Mean

137
562
858
625
2,182

Standard
deviation

.66
.68
.51
.47
.55

.71
.69
.63
.77
.70

Number
of
observetions
42
257
489
435
1,223

Schooling greater than
grade 12

Mean

Standard
deviation

Number
of
observetion

.40
.58
.43
.45
.46

.44
.64
.60
.77
.67

95
305
369
190
959

Mean

Standard
deviation

.77
.77
.63
.51
.66

.77
.71
.65
.76
.71

observation period. Sample sizes are smaller than in table 1 because of
missing wages .

NOTE: Wage growth is defined as ln(W 8 )-ln(W 1), where W 1 and W 8 are
average hourly wages reported at the beginning and end of the 8-year

IJ·••ir=--- Percent of weeks employed during first 8 years of career by number of jobs held
Number
of
jobs

Women

Men

All schooling levels

Schooling less than or
equal to grade 12

Number
of
observetions

1 ·· ··· ··········
2-3 ....... ... .
4-6 .. .... .. ...
7 or more ..
All ... .. ........

123
557
836
491
2,007

Mean

90.0
83.6
77.4
74.8
79.2

All schooling levels
Standard
deviation
22 .0
23.5
23.1
19.0
22.7

Number
of
observetions
137
562
858
625
2,182

Mean

I

94.4
87.1
80.4
77.2
82.0

Standard
deviation
16.2
23.0
21 .6
18.3
21.4

Number
of
observetions
42
257
489
435
1,223

Mean

87.6
78.9
74.4
73.6
75.5

Schooling greater than
grade 12

Standard
deviation
22.2
28.7
23.5
18.7
23.4

Number
of
observetions
95
305
369
190
959

Mean

Standard
deviation

97.3
94.0
83.3
85.2
90.4

11.7
13.5
15.6
14.7
14.9

I

NOTE : The work history "status array" is used to identify the cumulative
number of weeks worked (excluding within-job employment gaps) during the

wage is 0.59. The average wage gain is virtually the same
among women who hold 1-2 jobs, but it is considerably smaller
(0.40--0.46) among the more mobile women. A similar pattern is
seen among the men, although their average wage growth is
markedly higher than the women's. However, when the sample
of men is broken down by schooling attainment, the negative
correlation between overall mobility and overall wage growth
holds only for the more highly schooled men. Among the high
school sample, the average change in log-wage is 0.58 for those
who hold 2-3 jobs, but only 0.40-0.46 for men in any other
mobility category, including those who hold a single job in 8
years.
What are the explanations for the patterns seen in table 2?
To the extent that "job shopping" dominates early-career mobility (that is, to the extent that workers move to jobs where
their skills are more highly valued), it should be associated
with wage growth. However, high mobility can also go hand in
hand with a high frequency of involuntary discharges and/or


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8-year observation period. Only respondents who report wages at the
beginning and end of this period are included in each sample.

a tenuous attachment to the workforce. Workers who are frequently fired or have frequent non work spells are not expected
to receive substantial amounts of wage growth.
To explore some of these issues, the work history "status
array" is used to identify the number of weeks in which respondents are known to be working during the 8-year window. In table 3, sample means for this employment measure
(expressed as a percentage of total weeks) are reported for the
same sample of workers used for table 2. Table 3 reveals that
the average percentage of weeks worked declines with job
mobility for each group of workers. In the "all schooling
levels" groups, the average worker (male or female) who holds
only one job in 8 years works at least 90 percent of the time,
while average work effort falls to around 75 percent for workers who hold seven or more jobs. Clearly, the negative relationship between overall mobility and overall wage growth
seen in table 2 reflects the fact that highly mobile workers
tend to be nonemployed for a substantial portion of their

Monthly Labor Review

February

2005

37

Job Mobility and Wage Growth

- ■ elelr-..-•-

Annual wage growth for job movers and job stayers
Women

Men

All schooling levels

Number
jobs

Job stayer ...
Job mover ...
Voluntary job
mover .......

Schooling less than or
equal to grade 12

All schooling levels

.:,:

Number
of
observetions

Mean

Standard
deviation

13,085
3,637

.048
.027

.44
.62

2,959

.044

.61

Number
of
observetions

Schooling greater than
grade 12

Mean

Standard
deviation

Number
of
observetions

Mean

Standard
deviation

14,265
3,755

.050
.046

.44
.69

7,173
2,277

.045
.042

.40
.60

7,092
1,478

.054
.052

.48
.81

3,210

.060

.70

1,869

.055

.61

1,341

.068

.80

NOTE: Wage growth is defined as the 1-year change in log average
hourly wages (or one-half the 2-year change) during the 8-year observation
period. Job movers change employers between wage reports; the subsample

early careers. Workers who change jobs less often are much
more likely to work continuously-and, perhaps, to engage
in productive "job shopping." Whether the above-average
wage growth of workers who undergo moderate mobility is
due to work continuity, job mobility, or a combination of the
two cannot be determined from tables 2 and 3.
Rather than look exclusively at cumulative wage growth,
the analysis concludes with an examination of year-to-year
changes in employers and log-wages. Beginning with each
worker's entire sequence ofreported wages during the 8-year
observation period, the change in log-wages for each successive pair of wages is computed and confined to those differences where the elapsed time between wages is approximately
I year (10-14 months) or 2 years (20-28 months); the 2-year
differences are divided in half. 9 Each year-to-year change in
log-wages is then classified according to whether the two
wages were earned on the same job or on different jobs.
Table 4 reports the mean log-wage changes for job stayers
and job movers. Among women, the average wage change is
quite a bit higher (0.048) for job stayers than for job movers
(0.027), while for each sample of men these two means are
almost identical. The bottom row of table 4 reports mean
changes in log-wages for a subsample of movers who make
voluntary job changes-defined as any change not reported
as a layoff, discharge, plant closing, or end of temporary work;
all quits and all separations for which the reason is "other" or

Number
of
observetions

Mean

Standard
deviation

of voluntary movers excludes those who report that they moved because
of a layoff, discharge , end of temporary job, or plant closing.

unknown are considered to be voluntary. By crudely narrowing the sample to job changes that might be voluntary, much
larger mean changes in log-wages are obtained. Women receive an average, annual boost in log-wages of 0.044-0.048
regardless of whether they maintain their current job or undergo a voluntary job transition. For men, the average wage
boost associated with a voluntary job change is about 0.01 log
points higher than the average wage change for job stayers,
although the difference in means is not always statistically
significant at conventional levels. Nonetheless, table 4 suggests that the average wage gain associated with a voluntary
job change is quite substantial for all groups of workers.
THIS BRIEF ANALYSIS HAS DEMONSTRATED that the typical worker
holds about five jobs in the first 8 years of the career, but that
workers vary considerably in their mobility rates. Highly mobile workers receive less cumulative wage growth, on average,
than their less mobile counterparts-a difference that is at
least partially attributable to the fact that employment continuity is negatively correlated with mobility. Finally, there is
cursory evidence that workers who change jobs voluntarily
receive significant contemporaneous wage boosts that, on
average, are at least as large as the wage gains received by job
stayers. Each of these patterns has been explored in greater
detail in other studies, and the NLSY79 will undoubtedly reveal
much more about these relationships in the future.
D

Notes
1
Throughout this article, job mobility refers to a change of employer, and not to an intra-firm change in position, rank, or work
assignment.

' Examples of such models include Kenneth Burdett, '·A Theory of
Employee Job Search and Quit Rates," American Economic Review, Vol.

38

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February

2005

68, no. 1, 1978, pp . 212-220; and Boyan Jovanovic, " Job Matching
and the Theory of Turnover," Journal of Political Economy, Vol. 87,
No. 5, part 1, 1979, pp. 972-990.
3
For a model of firm-specific human capital, see Gary Becker, ··Investment in Human Capital: A Theoretical Analysis," Journal of Politi-

cal Economy, Vol. 70, No. I, pp. 9-49. Agency models include Edward
Lazear, " Why is There Mandatory Retirement?" Journal of Political
Economy, Vol. 87, No. 6, 1979, pp. 1261-84; and Joanne Salop and
Steven Salop, "Self-Selection and Turnover in the Labor Market," Quarterly Journal of Economics, Vol. 90, No. 4, 1976, pp. 619-627.
4
The NLSY79 User's Guide is available at http://www.bls.gov/nls/

79guide/nls79usg.htm.
5
In contrast, the PSID makes it considerably more difficult to identify unique jobs and to measure tenure on each job. See James N. Brown
and Audrey Light, "Interpreting Panel Data on Job Tenure," Journal of
Labor Economics, Vol. IO, No. 3, 1992, pp. 219-257.
6
See Henry S. Farber, "The Analysis of Interfirm Worker Mobility,"
Journal of Labor Economics, Vol. 12, No. 4, 1994, pp. 554-593; Derek
Neal, "The Complexity of Job Mobility Among Young Men," Journal
of Labor Economics, Vol. 17, No. 2, 1999, pp. 237-261; Anne Beeson
Royalty, "Job-to-Job and Job-to-Nonemployment Turnover hy Gender
and Education Level," Journal of Labor Economics, Vol. 16, No. 2,
1998, pp. 392-443; and Madeline Zavodny, "Technology and Job Separation Among Young Adults," Economic Inquiry, Vol. 41, No. 2, 2003,
pp. 264-278.
7
Studies that control for past job separations include Kristen Keith
and Abagail McWilliams, "The Returns to Mobility and Job Search by
Gender," Industrial and Labor Relations Review, Vol. 52, No. 3, 1999,
pp. 460-477; Audrey Light and Kathleen McGarry, "Job Change Pat-


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terns and the Wages of Young Men," Review of Economics and Statistics, Vol. 80, .No. 2, 1998, pp. 276-286; and Pamela J. Loprest, "Gender
Differences in Wage Growth and Job Mobility," American Economic
Review, Vol. 82, No. 2, 1992, pp. 526-532. Studies that focus on the
wage-tenure relationship include Bernt Bratsberg and Dek Terrell , "Experience, Tenure, and Wage Growth of Young Black and White Men,"
The Journal of Human Resources, Vol. 33, No. 3, 1998, pp. 658-682;
Daniel Parent, "Industry-Specific Capital and the Wage Profile: Evidence from the National Longitudinal Survey of Youth and the Panel
Study of Income Dynamics," Journal of Labor Economics, Vol. 18, No.
2, 2000, pp. 306-323; and Randall J. Olsen, "Job Switching, Earnings
Growth and the Rate of Return to Tenure," Ohio State University working paper, November 2001.
8
For a discussion of the ambiguity in career start dates, see Audrey
Light, "Estimating Returns to Schooling: When Does the Career Begin?" Economics of Education Review, Vol. 17, No. I, pp. 31-45.
9
Because interviews were conducted annually from 1979 to 1994,
and the 8-year observation period ends well before 1996 for most respondents, the majority of successive wage reports are approximately l
year apart. One-half the 2-year differences is used to avoid discarding
wage data for those respondents whose 8-year period extends in the
mid- l 990s; these are invariably younger respondents who stay in school
a relatively long time. For this exercise, the dependent variable is again
the average hourly wages divided by the GDP implicit price deflator.

Monthly Labor Review

February

2005

39

Self-employment, entrepreneurship,
and the NLSY79
Researchers have used the rich data from the 1979 cohort
of the National Longitudinal Survey of Youth to investigate
the relationship between self-employment and various job
and earnings outcomes; future inquiry may afford valuable insights
into other interesting consequences of self-employment

Robert W. Fairlie

Robert W. Fairlie is
associate professor
and director of the
M.S. Program in
Applied Economics
and Finance,
Department of
Economics, University
of California, Santa
Cruz, California .
E-mail:
fairlie@ucsc.edu

elatively small, but growing, body of
terature uses microdata from the 1979
ational Longitudinal Survey of Youth
(NLSY79) to study self-employment _
and entrepreneur,hip among young adults. The topics
covered in these studies include, but are not
limited to, the determinants of entrepreneurship,
earnings growth among entrepreneurs, the returns to self-employment, the relationship
between criminal activities and self-employment,
and job satisfaction among the self-employed.
The NLSY79 is a nationally representative
sample of 12,686 men and women who were
between the ages of 14 and 22 when they were
first interviewed in 1979. 1 Survey respondents
were interviewed annually from 1979 to 1994 and
biannually starting in 1996. Most previous
studies using this survey exclude the sample of
1,280 youths designed to represent the population enlisted in the four branches of the military
as of September 30, 1978, but retain the supplemental sample of 5,295 civilian black, Hispanic,
and economically disadvantaged nonblack, nonHispanic youth. The NLSY79 contains a wealth
of information on the demographic, economic,
family background, educational, and psychological characteristics of respondents. Detailed
measures of the group's labor market and life
experiences from early adulthood to the midforties can also be created for survey respondents.
The NLSY79 is an excellent source of data for
conducting research on self-employment and

40
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February 2005

entrepreneurship. The wealth of information
available in the survey allows one to build rich
empirical models of the entrepreneurial process.
Measures of previous wage and salary, selfemployment, and unemployment experience can
be created, and the NLSY79 contains several
uncommon variables, such as those associated
with detailed asset categories, family background information, data on criminal activities,
Armed Forces Qualification Test (AFQT) scores,
and psychological characteristics. Furthermore,
a plethora of measures of the dynamics of selfemployment may be extracted from the longitudinal data in the survey. For example, measures
of transitions to and from self-employment,
number of years of self-employment, and whether
an individual ever tries self-employment can
easily be created. Finally, the returns to selfemployment, measured as earnings, job satisfaction, net worth, or other outcomes, can be estimated. Changes over time in labor market status
can be used to identify the effects of self-employment, potentially removing biases created by
unobserved heterogeneity across individuals.
Given these advantages, it is somewhat surprising that more researchers have not used the
NLSY79 to study self-employment. In the sections that follow, this article presents estimates
of self-employment from the NLSY79, reviews
findings from previous studies that used the
survey, and discusses some of the merits of the
data sets making up the survey.

Self-employment in the NLSY79
In most previous studies using the NLSY79, self-employed
workers are defined as those individuals who identify themselves
as self-employed in their own business, professional practice, or
farm in response to the class-of-worker question relating to the
current or most recent job. Unpaid family workers are not
counted as self-employed. Individuals who report being
enrolled in school and workers who report working fewer
than 300 hours in the previous calendar year are often
excluded. The hours restriction rules out very small scale
business activities.
Self-employment rates increase rapidly as the NLSY79
cohort ages. (See chart 1.) The self-employment rate is defined
as the fraction of workers that is self-employed. At age 22,
only 5.1 percent of men and 2.6 percent of women are selfemployed. By age 42, however, 12.1 percent of men and 9.8
percent of women are self-employed.
The following tabulation shows that self-employment rates
also differ substantially by race and its ethnicity:
NLSY79

data
Women

Men

Race or
ethnicity
Black ..................
Hispanic ............
White .................

SelfSelfemployment Sample employment
rate
rate
size
(percent)
(percent)
5.3
7.4
10.1

14,448
10,153
31,803

3.2
4.9
6.9

Sample
size
13,469
8,404
29,006

As in previous studies, blacks and Hispanics are much
less likely to be self-employed than are whites. 2 Only 5.3
percent of black men are self-employed, compared with 10.1
percent of white men. The Hispanic male rate of 7.4 percent is
also lower than the white rate, but higher than the black rate.
Among women, the black-white and Hispanic-white selfemployment rate ratios are similar to those for men. The main
difference is that women's self-employment rates are lower
than men's for all three racial and ethnic groups.

The determinants of self-employment. A few patterns are
beginning to emerge in the young and expanding literature
on self-employment. The empirical studies in this literature
generally find that being male, white, older, married, and an
immigrant and having a self-employed parent, more assets,
and more education increase self-employment. In contrast,
theoretical models of self-employment posit that attitudes
toward risk, entrepreneurial ability, and preferences for
autonomy are central to the individual's decision to become
self-employed or engage in wage and salary work. 3 Perhaps
not surprisingly, there is very little empirical evidence on the


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importance of these unobservable characteristics in the selfemployment decision. One article that uses the NLSY79 offers
indirect evidence by examining the relationship between drug
dealing and legitimate self-employment: 4 a review of ethnographic studies in the criminology literature indicates that
drug dealing may serve as a useful proxy for a low aversion
to risk, entrepreneurial ability, and a preference for autonomy.
The 1980 wave of the NLSY contains a special section on
participation in illegal activities, including questions on
selling marijuana and other "hard" drugs. The answers to
these questions, together with data from subsequent years
of the survey, are used to examine the relationship between
drug dealing as a youth and legitimate self-employment in
later years. Using various definitions of drug dealing and
specifications of the econometric model, the survey finds
drug dealers to be 11 percent to 21 percent more likely than
those who are not drug dealers to choose self-employment,
all else being equal. After ruling out a few alternative
explanations, this article interprets these results as providing
indirect evidence that aversion to risk, entrepreneurial ability,
and preferences for autonomy are important determinants of
self-employment.
In addition to offering detailed information on criminal
activities in the 1980 wave, the NLSY79 includes information
on whether respondents were interviewed in jail or prison in
each year. This information is useful because convictions
and incarcerations may have different effects on current and
future wage and salary and self-employment earnings. In
particular, ex-offenders who choose self-employment do not
face discrimination, either pure or statistical, by employers in
the labor market, but may face other forms of discrimination,
such as that by consumers or lending institutions. Using the
NLSY79, the aforementioned study by Robert Fairlie provides
evidence on the relationship between incarceration and selfemployment. 5 Estimates from probit regressions indicate that
having a previous incarceration increases the probability of
self-employment by 0.36 percentage point to 0.39 percentage
point, or 5.2 percent to 5.9 percent. Thus, self-employment
may provide an important alternative to wage and salary work
for at least some ex-convicts.
Another finding reported in Fairlie is that AFQT scores
have a small and insignificant effect in prob it regressions for
the probability of self-employment. 6
Interestingly, previous research using the NLSY79 finds
that AFQT scores have a large positive effect on earnings.
The general argument is that the scores represent a measure
of basic skills that help predict job performance. Although
youths who have low levels of these basic skills may have
limited opportunities in the wage and salary sector, that
barrier does not translate into higher probabilities of selfemployment.
Monthly Labor Review

February 2005

41

Self-Employment , Entrepreneurship

Self-employm ent rates by age, NLSY ( 1979-2002)
Percent

Percent

15

15

Men

12

12

9

9

6

6

3

3

0

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

0

Age

The longitudinal nature of the NLSY79 also allows one to
explore the effects of previous labor market experience on
current self-employment. Ellen Rissman analyzes one aspect
of the dynamic relationship between unemployment and selfemployment among men. 7 She finds that the probability of
being self-employed in the current year increases significantly if the person was unemployed in the previous year.
Stratifying her sample by race, she also finds a positive and
significant effect for whites, but not for nonwhites.

The dynamics ofself-employment. Previous research on selfem ploy men t generally takes a point-in-time focus;
longitudinal data in the NLSY79, however, allow for numerous
dynamic measures of the concept. For example, Rissman finds
that 3.4 percent of wage and salary workers in any given year
become self-employed the following year and, conversely,
36.9 percent of the self-employed during a given year make
the transition to wage and salary work the next year. 8 Also,
Marianne A. Ferber and Jane Waldfogel find that 24.8 percent
of men and 16.5 percent of women in their sample from the
NLSY79 report ever being self-employed. 9 By contrast, they
find current self-employment rates of 8.8 percent and 5.5
percent for men and women, respectively. Finally, Donald
Williams finds that, by 1987, just 3 .1 percent of NLSY79
42

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February 2005

respondents had 2 or more years of self-employmen t experience and only 1.2 percent of respondents had 3 or more
years of self-employment experience. 10
Although numerous possibilities exist for measuring selfemployment dynamics, most previous research has focused
on annual transitions to and from self-employment. Estimates
for transition matrices that include wage and salary employment, self-employment, and nonemployment are reported in
table 1. 11 One-year transition matrices are reported for the
1979-94 period, and 2-year transitions are reported for the
1994-2002 period. Estimates from 1979-94 indicate that 3.4
percent of young men who were wage and salary workers
became self-employed the following year. The entry rate for
the nonemployed is 2.2 percent. Estimates for 2-year transitions from 1994-2002 indicate a lower self-employment rate
from wage and salary work and a higher entry rate from
nonemployment. For men, the exit rates from self-employment
are 31.6 percent and 24.9 percent for the earlier and later
periods, respectively. Self-employment entry rates are generally lower, and exit rates higher, for young women. Overall,
the estimates indicate that substantial mobility exists between
sectors and into and out of employment among young workers.
The importance of assets has taken center stage in the
literature on the determinants of self-employment. Several

Labor market transition matrices, National Longitudinal Survey of Youth, 1979-2002
[In percent]

Gender, category, and year

t

Nonemployment, 12
t+1/t+2

Wage and salary
employment, 2
t+1/t+2

Selfemployment,2
t+1/t+2

72.1
1.0
.7

25.7
95.5
30.8

2.3
3.4
68.4

7.0
85.1
7.9

3,675
33,917
2,661

65.6
2.8
2.0

29.1%
94.0
22.8

5.3
3.1
75.1

6.9
83.6
9.5

1,284
11,249
1,112

75.4
3.7
4.6

21.4
94.1
29.0

3.3
2.2
66.5

19.1
76.1
4.8

9,584
31,452
1,641

68.7
6.1
10.2

28.1
91.5
28.6

3.2
2.4
61.2

18.8%
75.0%
6.2%

2,844
10,585
727

Share of total, year

t

N

Men

1979-94, year t:
Non employment ...............................
Wage and salary employment .........
Self-employment .............................

1994-2002, year t: ............... ..............
Nonemployment ...............................
Wage and salary employment .........
Self-employment ........... ..................

Women

1979-94, year t:
Non employment ....... .... .. ..................
Wage and salary employment .........
Self-employment .............................

1994-2002, year t:
Non employment ...............................
Wage and salary employment .........
Self-employment ... .... ............. ... ......

Those unemployed and not in the labor force.
Measured in year t + 1 for 1979-94 and year t + 2 for 1994-2002.
NorE: The sample consists of youths aged 22 to 45 years who are not
1

2

recent studies explore this issue by modeling the decision of
wage and salary workers or other nonbusiness owners to
switch into self-employment over a fixed period. 12 The focus
on transitions to self-employment attempts to avoid the
endogeneity problem of including assets in a static model of
self-employment. The problem is that a positive relationship
found in a cross-sectional analysis may simply reflect the
possibility that business owners accumulate more wealth
instead of wealth increasing the likelihood of owning a
business. Although individuals may save in anticipation of
becoming self-employed, a measure of assets in year t - 1
should be more exogenous to the entrepreneurial decision
than a contemporaneous measure of assets.
Fairlie follows this approach, using net worth data from
the NLSY79. 13 Specifically, he estimates probit regressions
for the probability of entry into self-employment from wage
and salary work that include a measure of net worth. The
NLSY did not collect information on assets prior to 1985 and
in 1991. For other years, a measure of net worth can be created
from the detailed NLSY questions on assets. 14 The coefficients
on net worth and its square are statistically significant and
indicate a concave relationship. Evaluated at the mean level
of net worth (which equals $36,900), the coefficients imply
that increasing net worth by $10,000 raises the probability of
a transition into self-employment by 0.00044. This percentage
represents only 1.5 percent of the sample's entry rate into
self-employment. Thus, the estimates of the coefficients pro-


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enrolled in school. All estimates are calculated with the use of annual
sample weights provided by the National Longitudinal Survey of Youth.

vide some evidence that young men face liquidity constraints, but these constraints do not appear to be overly
restrictive.
Creating detailed measures of previous work experience to
include in her regressions, Hiromi Taniguchi examines the
determinants of transitions from nonemployment to selfemployment and to wage and salary employment among
women in the NLSY79. 15 Her results indicate that both
cumulative work experience and the number of jobs ever held
increase the rate of entry into self-employment and wage/
salary employment. She also finds that previous selfemployment increases the rate of entry into self-employment
and has a negative effect on entry into wage and salary
employment.

Self-employment earnings in the NLSY79
Although self-employment income can be identified in the
NLSY79, earnings among self-employed business owners are
typically measured as total annual earnings, which are calculated by summing the responses to questions on military
income, wage and salary income, and business or farm income
(after expenses) in the previous calendar year. The income
from all three sources is added because more than half of the
self-employed with positive earnings in the NLSY79 report
16
wage and salary income, but do not report business income.
Monthly Labor Review

February 2005

43

Self-Employm ent, Entrepreneurship

This is partly due to incorporated business owners reporting
their income as wage and salary income, as roughly 50 percent
of unincorpora ted business owners with positive total
earnings report zero business income. As suggested by
Zagorsky, it may also be due to the ordering of questions on
the questionnaire. 17 Respondents were asked, ( 1) "How much
money did you get from the military?"; (2) "Excluding military
pay, how much money did you get from wages, salary,
commissions, or tips?"; and (3) "Excluding anything you
already mentioned, did you receive any business income?"
Thus, some of the self-employed may have reported their
income in the second question and did not correct their mistake. Another possibility is that the self-employed report only
their labor income from the business under wage and salary
income.
The following tabulation shows the mean, median, and
standard deviation of total annual earnings for self-employed
and wage and salary workers:
Men

Women

SelfWage and
employed
salary

Mean ..................... $52,300
Median .................. 38,000
Standard deviation . 48,159
Sample size ...........

3,725

Selfemployed

Wage and
salary

$38,258
33,021
25,769

$28,217
20,029
30,040

$27,131
23,407
17,794

43,852

1,570

35,367

Only full-time workers, defined here as those working at
least 1,400 hours in the previous calendar year, are included,
in order to control for differences in hours worked. Earnings
observations in all years are inflated to 2002 dollars. The
responses for each of the three sources of income (the selfempioyed, wage and salary workers, and the military) are top
coded at $75,000 from 1979 to 1984, $100,000 from 1985 to
1994, and the top 2 percent for more recent years. Instead of
these top codes, however, the 1994 top code, in 2002 dollars,
which equals $121,390, is used for all years in what follows. 18
As is customary, all top-coded values are set to $150,000.
For men, the self-employed earn substantially more, on
average, than do wage and salary workers. Men's average
earnings are $14,042 higher among the self-employed, and
median earnings are $4,979 higher. For women, average earnings among the self-employed are slightly higher than average earnings among wage and salary workers, but median
earnings are lower. For both men and women, the standard
deviation of self-employment income is substantially greater
than that of wage and salary income.

Returns to capital. One issue that arises in comparing selfemployment earnings with wage and salary earnings from
survey data is the treatment of returns to capital. In the NLSY,
the question regarding self-employment income asks, "How
44

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February 2005

much did you receive after expenses?" from your farm or
business in the past calendar year. Although some uncertainty is involved in answering this question, respondents
are likely to interpret the question to include both the returns
to labor and the returns to capital. As noted earlier, however,
most of the self-employed report their earnings as wage and
salary income and not business income. In the case of the
respondent who does report self-employm ent income as
business income, it would be preferable to remove the returns
to capital before making comparisons with the earnings of
wage and salary workers.
Unlike most other data sets, the NLSY79 contains detailedenough information on assets to enable researchers to
conduct a careful analysis of the issue of how returns to
capital are treated. The NLSY79 contains data on the market
value of the individual's farm, business, and other real estate
and the total amount of debt owed on those assets. This
information can be used to calculate the opportunity cost of
capital and remove it from business income. With Standard &
Poor's 500 as the alternative investment, adjusted self-employment earnings are 5.2 percent lower than unadjusted selfemployment earnings for white men and 4.0 percent lower for
white women. 19 Simple adjustments for the opportunity cost
of capital also have a small effect on self-employment earnings for blacks and Latinos. Overall, estimates from the
NLSY79 suggest that unadjusted self-employm ent earnings
from survey data may provide reasonably accurate measures
of the returns to labor.

Earnings regressions. As mentioned earlier, the NLSY79
contains detailed information on individual characteristics
such as age, race, education, AFQT scores, and various measures of previous work experience. Earnings regressions that
include these observable controls can be used to estimate
the between self-employment earnings and wage and salary
earnings. Unobserved differences, however, such as entrepreneurial ability and aversion to risk, may also exist between
self-employed business owners and wage and salary workers. To address this issue, fixed-effects earnings regressions
can be estimated with the longitudinal data in the NLSY79.
The individual-level fixed effects control for all observable
and unobservable characteristics that do not change over
time. Because, over time, individuals make transitions between self-employment, on the one hand, and wage and
salary work, on the other, comparisons of self-employment
earnings with wage and salary earnings for the same individual in different years contribute to identifying the associated
coefficients.
Estimating fixed-effects earnings regressions for young
men from disadvantaged families yields some evidence that
self-employed business owners earn more than do wage and
salary workers. 20 Estimates for young women, however,

provide some evidence oflower earnings among self-employed
business owners than among wage and salary workers. The
results from these earnings comparisons are somewhat
sensitive to the use of different measures of income and
diffcrent econometric models.
In a related study, Justin van der Sluis, Mirjam van Praag
and Arjen van Witteloostuijn (2004) estimate the returns to
education for entrepreneurs and for wage and salary
workers. 21 Using instrumental-variable regressions, they find
that the returns to education are 14 percent for the selfemployed, much higher than the 10-percent estimated return
for wage and salary workers. The detailed data available in
the NLSY79 allow these researchers to control for ability and
to use family background characteristics, including the
mother's and father's education, the presence of library cards
in the household at age 14, and magazines present in the
household at age 14, as instrumental variables for education.

Earnings profiles. The longitudinal nature of the NLSY79
enables one to compare earnings profiles for self-employed
workers and wage and salary workers. Charts 2 and 3 display
earnings-age profiles for full-time self-employed and wage
and salary workers. For men (chart 2), average self-employment earnings are always higher and appear to grow at a rate
similar to that of wage and salary earnings. For women,
average self-employment earnings start out lower than wage
ancl ~::J.lary earnings, but then grow at a faster rate.
To investigate the question of whether the self-employed
experience faster earnings growth than do wage and salary
workers, the NLSY79 allows fixed-effects regressions that
include interactions between self-employment, on the one
hand, and experience, potential experience, or tenure, on the
other, to be estimated. Estimating fixed-effects regressions
for hourly earnings for a sample of white, non-Hispanic men,
Daiji Kawaguchi finds flatter earnings-experience/tenure
profiles for self-employed workers than for wage and salary
workers. 22 At 10 years of experience and job tenure, selfemployed business owners earn 18 percent less than wage
and salary workers. An earlier work by Fairlie compares men's
and women's earnings profiles for whites, blacks, and
Hispanics. 23 For white men, the point estimates from these
earnings regressions indicate that the self-employed initially
experience slower earnings growth than do wage and salary
workers. After several years, the trend reverses, and selfemployed persons experience faster earnings growth and
higher earnings. For Hispanic men, the relative self-employment earnings coefficients suggest that the self-employed
start at much lower earnings levels than do wage and salary
workers, but experience faster growth rates. For white women,
relative self-employment earnings start out positive and
then become negative. Relative self-employment earnings
coefficients are not statistically significant for black men,


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black women, or Hispanic women, possibly due to small
sample sizes.

Self-employment and other outcomes. The detailed information available in the NLSY79 also allows for the analysis of
the relationship between self-employment and other outcomes, such as future wage and salary income, job satisfaction, and net worth. One possibility is to examine the
relationship between early-career self-employment experience and future labor market outcomes. The NLSY79 is an
excellent instrument for this type of analysis because it
follows individuals from ages 14 to 22 in 1979 to 37 to 45 in 2002.
A previously mentioned work by Fairlie examines the
earnings patterns of less educated individuals who are selfemployed early in their careers and makes comparisons with
young, less educated wage and salary workers. 24 Selfemployment status is determined between ages 22 and 26,
and earnings are measured starting at age 27. Estimates from
fixed-effects regressions indicate that the self-employed
experience faster earnings growth, on average, than do wage
and salary workers after a few initial years of slower growth.
In a similar vein, Williams examines the relationship between
self-employment at ages 16 to 20 and outcomes at ages 25
and 27. 25 He finds that self-employment as a youth is
associated with a substantially higher probability of being
self-employed in early adulthood (age 27), but also is
associated with lower earnings at that age.
Another interesting question that can be answered with
the NLSY79 is whether self-employment experience is
rewarded in the wage and salary sector. Do self-employment
spells limit opportunities for acquiring valuable labor market
experience, especially firm- and sector-specific human capital,
or do they provide workers with skills that are rewarded in
the wage and salary sector? Again using data from the
NLSY79, as well as data from the National Longitudinal Survey
(NLS) of Young Women, Williams examines the effects of selfemployment experience on future wage and salary earnings
of men and women. 26 His estimates indicate a negative return
for women and little or no return for men.
Most of the focus in the self-employment literature is on
earnings, but other outcomes also are of interest. In particular,
lower hourly earnings among the self-employed with high
levels of tenure may be explained by nonpecuniary factors of
the job, such as being one's own boss. Kawaguchi uses the
NLSY79 to investigate whether self-employment is associated
with higher levels of job satisfaction. 27 He finds that 65
percent of the self-employed report liking their job "very
much," whereas only 45 percent of wage and salary workers
report that level of job satisfaction. Estimates from regression
models which control for individual heterogeneity confirm
that the self-employed have higher levels of job satisfaction
than wage and salary workers have.

Monthly Labor Review

February 2005

45

Self-Emplo yment, Entrepreneurship

Average annual earnings by age for men, NLSY (1979-2002)
Earnings

Earnings

$70,000 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - . : 7 0 , o o o

60,000

60,000

50,000

.-·
-- -·
..-

••

••• -

-----·

•• ••

..
50,000

••

40,000

••• -

••
•• •

30,000

--

••

-· .-----\··

40,000

••

.••

30,000

Wage/salary

20,000 .___.__ _ _.....___ ___.__ _ _.......__ ___.__ _ __.___ ___.__ _ _.....__ ___.__
_ _....__ ___.__. 20,000
22-23
24-25
26-27
28-29
30-31
32-33
34-35
36-37
38-39
40-41
42-43

Age

Average annual earnings by age for women, NLSY ( 1979-2002)
Earnings

Earnings

$45,000 , - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , $ 4 5 , 0 0 0

40,000

Self-employed

40,000

35,000

30,000

Wage~/sa lary

25,000

20,000

15,000

----------··

""

, ""

••

••

.- -·

24-25

•• ••

••
25,000

20,000

26-27

28-29

30-31

32-33

Age

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30,000

••

L - J . . - - - ' - -_ _....____ __.__ _ _ _ _ _ _ ___._

22-23

-----·

35,000

February 2005

_ _.......__ _.......__ _____.__ ____.__ _ _..__,

34-35

36-37

38-39

40-41

42-43

15,000

has undoubtedly improved
our understanding of the determinants of entrepreneurship,
the dynamic process of self-employment, and self-employent
earnings patterns. Although the relationship between selfemployment and a few outcomes, such as future wage and
salary earnings and job satisfaction, has been explored with
the exceptionally rich data avaiiable in the NLSY79, more

RESEARCH USING THE NLSY79

research may provide valuable insights into the consequences of self-employment. For example, the detailed data
available in the survey allow one to explore the causal relationship between self-employment and several outcomes
of interest, such as net worth, business net worth, health
insurance and other fringe benefits, and public assistance
programs.
□

Notes
1 See Center for Human Resource Research, NLSY79 Users' Guide
(Columbus, OH , The Ohio State University, 1999) , for a detailed
description of the NLSY79.

2 Estimates from the NLSY79 are comparable to those from 1990
census microdata using a similar age group. See Robert W. Fairlie,
"Does Business Ownership Provide a Source of Upward Mobtlity for
Blacks and Hispanics?" in Douglas Holtz-Eakin, ed., Entrepreneurship
and Public Policy (Cambridge, MA, MIT Press, 2004), pp. 634--59 . The
census shows slightly lower rates, but the relative differences between
the races are similar.
3 See, for example, Richard Kihlstrom and Jean-Jacques Laffont,
" A General Equilibrium Entrepreneurial Theory of Firm Formation
Based on Risk Aversion," Journal of Political Economy, vol. 87, no.
4, 1979, pp. 719-48; Robert E. Lucas,"On the Size Distribution of
Firm s, " Bell Journal of Economics, vol. 9, no. 2, 1978, pp. 508-23;
Boyan Jovanovic, " Selection and the Evolution of Industry,"
Econometrica , vol. 50, no. 3, 1982, pp . 649-70; and David Evans
and Boyan Jovanovic, "An Estimated Model of Entrepreneurial Choice
under Liquidity Constraints ," Journal of Political Economy, vol. 97,
no. 4, 1989, pp. 808-27.
4
Robert W. Fairlie , "Drug Dealing and Legitimate SelfEmployment,"Journal of Labor Economics, vol. 20, no. 3, 2002, pp.
538 - 67.

5

Ibid.

6

Ibid.

Ellen R. Rissman, "Self-Employment as an Alternative to
Unemployment," working paper no . 2003-34 (Chicago, Federal
Reserve Bank of Chicago, 2003) .
7

8

Ibid.

9 Marianne A. Ferber and Jane Waldfogel , " The long-term
consequences of nontraditional employment," Monthly Labor Review,
May 1998, pp. 3-12.
10 Donald R. Williams, "Youth Self-Employment: Its Nature and
Consequences," Small Business Economics, vol. 23, no. 4, 2004, pp.
323-36.

All estimates are calculated with annual sample weights provided
by the NLSY. "Nonemployment" denotes those not in the labor force.
11

See, for example , Evans and Jovanovic , "Estimated Model of
Entrepreneurial Choice"; David Evans and Linda Leighton, " Some
Empirical Aspects of Entrepreneurship," American Economic Review,
vol. 79 , no . 3, 1989, pp. 519-35; Douglas Holtz-Eakin, David
Joulfaian, and Harvey Rosen, "Entrepreneurial Decisions and Liquidity
Constraints," Rand Journal of Economics, vol. 23, no . 2, 1994 pp.
334-47; Thomas A. Dunn and Douglas J. Holtz-Eakin, " Financial

Capital, Human Capital, and the Transition to Self-Employment:
Evidence from Intergenerational Links," Journal of Labor Economics,
vol. 18, no. 2, 2000, pp. 82-305; Robert W. Fairlie,"The Absence of
the African-American Owned Business: An Analysis of the Dynamics
of Self-Employment," Journal of Labor Economics , vol. 17, no . I,
1999, pp. 80-108; and Erik Hurst and Annamaria Lusardi, "Liquidity
Constraints, Household Wealth, and Entrepreneurship," Journal of
Political Economy, vol. 112, no. 2, 2004, pp. 319- 47 .
13

Fairlie, "Drug Dealing."

The variable having to do with assets is not available in the publicuse data, but can be obtained from Jay L. Zagorsky at the Center for
Human Resource Research. See Jay L. Zagorsky, "Young Baby Boomers'
Wealth," working paper (Columbus, OH , Center for Human Resource
Research, 1998), for more details on the construction of this variable.
14

15 Hiromi Taniguchi, "Determinants of Women 's Entry into SelfEmployment, "Social Science Quarterly, vol. 83, no . 3, 2002, pp.
875 - 93.
16 Fairlie, "Does Business Ownership Provide a Source of Upward
Mobility for Blacks and Hispanics?"
17

18 Especially problematic is the fact that 36 individuals have topcoded wage and salary income of more than $4 million each in 1996.
19

Fairlie, "Earnings Growth."

Robert W. Fairlie, "Entrepreneurship and Earnings among Young
Adults from Disadvantaged Families," Small Business Economics ,
forthcoming.
20

21 Justin Van der Sluis, Mirjam van Praag, and Arjen van
Witteloostuijn, " Comparing the Returns to Education for
Entrepreneurs and Employees ," working paper (Amsterdam,
University of Amsterdam, 2004).
22 Daiji Kawaguchi, " Positive, Non -Earnings Aspects of SelfEmployment: Evidence from Job Satisfaction Scores," working paper
(Tsukuba City, University of Tsukuba, Institute of Policy and Planning
Sciences, 2004).

23 Fairlie, "Does Business Ownership Provide a Source of Upward
Mobility for Blacks and Hispanics?"
24

Fairlie, "Earnings Growth ."

25

Williams, "Youth Self-Employment."

12


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Telephone conversation, August 1999 .

Donald R. Williams, "Consequences of Self-Employment for
Women and Men in the United States," Labour Economics, vol. 7,
no. 5, 2000 , pp. 665-87.
26

27

Kawaguchi, "Aspects of Self-Employment."

Monthly Labor Review

February 2005

47

Worker training: what we've
learned from the NLSY79
The 1979 cohort of the National Longitudinal Survey
of Youth has been a wellspring of knowledge
about worker training and a valuable
means of empirically testing human-capital theory

Harley J. Frazis
and
James R. Spletzer

Harley J. Frazis and
James R. Spletzer are
research economists on
the Employment
Research and Program
Development Staff,
Office of Employment
and Unemployment
Statistics, Bureau of
Labor Statistics. E-mail:
frazis .harley@bls.gov or
spletzer.j im@bls.gov

48

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H

w individuals obtain their skills and every additional year of schooling they have
how they are paid for the use of those completed. 2 Kenneth I. Wolpin's article on edukills are concepts that are fundamental cation in this special edition of the Monthly
to the field of labor economics. Productive skills Labor Review shows that, over the 15-year
are often referred to as "human capital." The period between ages 25 and 39, a male college
basic idea of human-capital theory is that graduate earns 80 percent more than a male high
workers invest in their own skills in order to school graduate without any college, and a male
earn higher wages, much as persons invest in high school graduate earns 57 percent more than
financial or physical assets to earn income. a high school dropout.
Although this idea goes back at least to Adam
However, empirical research on training-the
Smith, modern human-capital research was other key component of human capital-has
originated in the late 1950s by economists lagged research on the economics of education.
Theodore Schultz, Jacob Mincer, and Gary The human-capital model yields straightforward
Becker. Their ideas, focusing on investments predictions about the relationship of on-the-job
in and returns to education and training, have training to wages, wage growth, and job mobility;
provided the theoretical and empirical basis for still, as will become clear, testing these predictions
decades of ensuing research. 1
requires good longitudinal microdata.
Much of the empirical research on the topic
The need for high-quality longitudinal microof human capital has analyzed the relationship data with detailed information about wages,
between education and wages. This focus on mobility, and on-the-job training has led reeducation is due to the abundance of high- searchers to the National Longitudinal Surveys
quality data sources with information on both for empirical analyses of training. This article
education and wages. For example, analysts both provides a brief summary of the humanusing cross-sectional data from the Current capital model as it relates to on-the-job training
Population Survey (CPS) have found that indi- and summarizes the empirical training literature,
viduals in the United States receive earnings with a special focus on the contributions that
that are approximately 10 percent higher for analyses of the data from the 1979 cohort of the
February 2005

National Longitudinal Survey of Youth (NLSY79) have made
to that literature.

The human-capital model
Models of competitive labor markets imply that wages paid
to wrn kers reflect their productivity. For example, if education
makes workers more productive, then higher wages are paid
to more highly educated persons. Similarly, if on-the-job
training makes workers more productive, then trained workers
should receive higher wages than workers with no training.
But education and on-the-job training differ in one key aspect:
most workers finish their schooling before entering the labor
market, whereas most on-the-job training occurs during a
worker's tenure with an employer. While education and onthe-job training are both productivity-enhanci ng investments, they potentially differ with regard to whether the
worker or the employer pays the costs.
Any investment in human capital involves current costs
and future benefits. The costs associated with on-the-job
training involve both direct costs, such as the salaries of the
persons doing the training and any costs of materials, and
indirect costs, such as the cost of taking trainees away from
their current productive tasks. The benefits of on-the-job
training accrue to both the firm providing the training and the
worker receiving the training: because the worker is more
productive after the training, the firm benefits from higher
productivity and greater output, and the worker benefits from
his m her higher productivity in the form of higher wages.
One key theoretical issue regarding on-the-job training
concerns the division of these costs and returns between the
firm and the worker.
Gary Becker made progress on this cost-sharing issue by
defining two types of training: general training and specific
training. Completely general on-the-job training is training
that provides the worker with skills that are productive at
firms other than his or her current employer. Examples of
completely general training are learning to use a wordprocessing or data-processing computer program that is
available for purchase by any firm, pilots learning to fly a
type of jet airplane that is in the fleet of several airlines, and
doctors learning a new surgical technique that could be
conducted at any hospital. By contrast, completely specific
on-the-job training is training that enhances the productivity
of the worker at only the firm providing the training. Examples
of completely specific training are astronaut training (presumably specific to the National Aeronautics and Space Administration), learning to drive a tank (presumably specific to the
U.S. Military), and learning to operate a machine that was
developed and is used by only one manufacturing firm to
produce its product. In the real world, almost all training
involves a combination of both general and specific skills.


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In a competitive labor market, workers are paid for the
skills they possess. Becker reasoned that, because general
training provides skills which are useful at all firms, the firm
offering the general training will need to pay the trained worker
a wage that reflects these skills; otherwise the worker will
leave the firm to receive a higher wage at a different firm.
Anticipating this possibility, a profit-maximizing firm will not
pay any of the costs of general training because it cannot
extract any of the returns from the training. In that case, the
worker will pay all the costs of the general training-not just
the direct costs, but also the indirect costs that reflect the
worker's lost productivity to the firm. Human-capital theory
thus predicts that, relative to workers who do not receive
training, workers who receive general training will be paid
lower wages while receiving the training and higher wages
after the training is complete. This hypothesis is depicted in
exhibit 1, which compares the wage profile of a worker who
receives no training with that of a worker who receives general
training.
Sharing the costs and returns of specific training is more
complicated. On the one hand, assume, for the moment, that,
as with general training, a worker pays all the costs and
receives all the benefits of specific training. In such a case, a
worker who might be fired or laid off after receiving the
training would receive no future returns from his or her
investment in specific training; thus, the worker would have
less incentive to pay for the training, because the decision to
lay the worker off is made by the firm. On the other hand,
assume that a firm pays all the costs and receives all the
benefits of specific training. In this case, the firm would
receive no future returns from the investment if the trained
worker quit for another job; thus, the firm would have less
incentive to pay for the training, because the decision to quit
is made by the worker. The solution to this dilemma is for the
worker and the firm to share the costs and returns of specific
training, with the exact division of the returns depending
upon the wage elasticity of the worker's propensity to quit
and the firm's propensity to lay the worker off. 3 This sharing
is portrayed in exhibit 1: the wage profile of a worker who
receives specific training shows the worker paying some (but
not all) of the costs and receiving some (but not all) of the
returns.
The sharing of the costs and returns to training has
implications for worker mobility. Workers who have received
specific training have higher productivity at their current
employer than at other employers, and their wage at the
current employer is higher than the wage they could obtain
from other employers. This asymmetry results in workers with
specific training having lower probabilities of quitting than
workers with no specific training. Similarly, because the
productivity of workers with specific training exceeds their
wage, the employer is less likely to lay off workers with

Monthly Labor Review

February

2005

49

Worker Training

Wage profiles

Wages

General training
Specific training

1 - - - - - - - - - - - - - - - L - - - - - - - - - - - - - - - - - - No training

Time

specific training relative to workers with no specific training.
By contrast, because workers with general training have the
same productivity at the current employer as at other employers, and because the wage they receive from their current
employer equals the wage they would expect from other
employers, the provision of general training does not lower
expected probabilities of quitting. Similarly, in the simple
model presented here, firms could replace a worker with
completely general training without loss of any productivity,
so the provision of general training does not lower expected
probabilities of layoffs.
In sum, this simple, yet elegant, human-capital model has
several testable predictions. First, training lowers the starting
wage: during training, a worker accepts a lower wage relative
to a worker not receiving training, all other things being equal.
Second, training raises future wages at the employer providing the training: a worker who has received on-the-job training
should receive higher wages relative to a worker with no
training-again , all other things being equal. Third, by
definition from the preceding two predictions, training raises
wage growth at the employer providing the training. Fourth,
the foregoing three prediction~ vary in magnitude as a
function of whether the training is specific or general. Finally,
specific training lowers worker mobility, whereas general
50

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February 2005

training has no effect on worker mobility, all other things
being equal.
These testable predictions provide the framework for
empirical analysis. It is obvious that several demands are
being placed on the data. First and foremost, there needs to
be information on training and individuals' wages. Furthermore, longitudinal microdata are necessary for analyzing
wage growth and mobility. Finally, the information needs to
be quite detailed in order to distinguish general training from
specific training. The NLSY79 data satisfy all these criteria
and make up one of the few data sets that provide detailed
longitudinal information on all the necessary analytical
variables. It is not surprising, then, that much of what we
know about on-the-job training has come from analyses of
the NLSY79 data.
Before we turn to the empirical findings, the importance of
the phrase "all other things being equal" needs to be mentioned. A common finding from all data sets with training
information is that individuals who receive training are not a
random sample from the population of all workers. For example,
those who are college educated and those with higher ability are
more likely to receive training. 4 This nonrandom selection affects
how we interpret the empirical analysis that follows. (This issue
is more fully explained later in the article.)

Questions about training, NLSY79, 1988-2000 1
Mean 2

Question
Since [date of the last interview] did you attend any training program
or any on-the-job training designed to help people find a job, improve job
skills, or learn a new job?
Continue to 20 .......................................................... .......................................................... ................ ..
Yes :
Skip to next section of questionnaire ............................................................ ............................................ .
No:

.158
.842

Which category on this card best describes where you received
this training? [Code one only]
Business school ................................................................................................................................................................. ..
Apprenticeship program ....... ............. ............. ............ ..................... ....... ........... ...... ........ ....... ...... ............ ... ..... ........ .. ..... .
A vocational or technical institute ................................................................................................................................. .
A correspondence course ........................ .... .. .................. ..................... ............ ................................................................ ..
Formal company training run by employer or military training ............................................................................... .
Seminars or training program at work run by someone other than employer ....................................................... ..
Seminars or training programs outside of work ................................................................................................................. .
Vocational rehabilitation center ........................ ........................................................................ .......... .......... ................ ..
Other (Specify: __________ ) ....................................................................................................................................... .

.030
.021
.097
.029
.375
.158
. 184
.0 13
.078

19.

20.

21. Who paid for this training program? [Code all that apply]
Self or family ........................................................................................................... ............................. ... ......................... ..
Employer ....................................... ................................................................................................................................... ..
Job Training Partnership Act ............................................................................................................................... ... ...... .. .
........................................................................................ .. ................ ............ ............ ........ ... ..... .
Trade Adjustment Act
Job Corps Program ........................... ...................................................................................................................... ... ........ .
Work Incentive Program ........................................................................... .. .................................................................... .
Veteran's Administration ............................................................. ................ ................................................................... ..
Vocation Rehabilitation ........................ ........................... ... .......... ................ ... ................................................................ ..
Other (Specify _ _ _ _ ) ..................................................................... ........................................................................ ..

.139
.739
.021
.001
.001
.004
.002
.011

25. Altogether, for how many weeks did you attend this training? ........................................................................ ..

5.7

31. How many hours per week (do/did) you usually spend in this training? .......................................................... ..

20.1

1
The training questions in 1988, 1996, 1998, and 2000 had a 2year reference period. The training questions in 1989-1994 had
a I -year reference period.
2
All entries are unweighted tabulations from the 1988-2000
NLSY79 microdata. The sample size for question 19 is N = 91,144.

Empirical findings from the early literature
Early on in the development of human-capital theory,
economists recognized that on-the-job training was an
important source of investment in human capital. Because
on-the-job training data were not available, the earliest
attempts to measure such training were indirect. As has been
noted, models of competitive labor markets imply that workers
will be paid in accordance with their productivity. The
tendency of wages to increase with labor market experience
was interpreted as evidence of training-induced increases in
productivity. With additional assumptions, the wage-experience relationship could be used to infer an investment path
and returns to the training investment.
Mincer's 1962 article attempted to estimate the amount of
training by comparing the earnings path of individuals with


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All means for questions 20,
sample of respondents who
for question 25 is computed
training program had been
view.

.092

21, 25, and 31 are computed from the
said "yes " to question 19. The mean
from the sample of respondents whose
completed by the date of the inter-

different amounts of education and assuming that returns to
training were the same as returns to schooling. 5 Mincer's
197 4 book was probably more influential; in it, he showed
that if time spent in training increased the logarithm of wages
linearly, and if the percentage of working time spent in training
declined with experience in a linear manner, then wages would
be well described by a quadratic function of experience. 6 The
quadratic earnings function was found to be a fair approximation of earnings and won wide acceptance. 7
This evidence was clearly imperfect. Moreover, in the late
1970s, economists developed other theories to explain the
tendency of wages to rise with experience, ranging from
improvements in job matches through a worker's career to
firms tilting their wage profiles to discourage shirking or
encourage more stable workers to apply. 8 Evidently, more
direct measures of on-the-job training were needed.
Monthly Labor Review

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51

Worker Training

One such measure was included in the Panel Study of
Income Dynamics (Psm), a longitudinal survey administered
annually since the 1960s. In 1976 and 1978, the PSID asked the
question, "On a job like yours, how long would it take the
avefage new person to become fully trained and qualified?"
Articles by Greg J. Duncan and Saul Hoffman in 1979 and
James N. Brown in 1989 used this question to identify periods
of on-the-job training and adjust earnings equations
accordingly. 9 The longitudinal structure of the PSID allowed
Brown to directly examine the effect of training on wage
increases, rather than inferring wage increases from crossperson comparisons. Both articles found a substantial effect
of training on wages, providing evidence for the humancapital interpretation of wage increases. However, the PSID
question clearly affords only a limited measure of on-the-job
training. As Duncan and Hoffman noted, the intensity of
training during the training period may vary between persons
with identical answers to the question, and the type of
training-form al, informal, or learning by doing-is
completely unspecified.
Aside from the NLSY79, other attempts to measure on-thejob training in surveys of individuals include earlier cohorts
of the National Longitudinal Surveys (NLS) and supplements
to the CPS i.n 1983 and 1991. Lee A. Lillard and Hong W. Tan
used the 1983 CPS and the early cohorts of the NLS to examine
training and its effect on labor market outcomes. 10 The CPS
supplement asks what training was needed for the
respondent to obtain his or her current or previous job and
inquires about training to improve skills on the current job.
Because the CPS is not a longitudinal survey and because the
period during which the training took place is unclear, only
the association of training with differences in wages between
persons (which is substantial) can be examined; wage
changes due to training for a given individual cannot be
tracked. The training questions in the earlier cohorts of the
NLS are broadly similar to, but less extensive than, those in
the NLSY79 (and the employment data in the earlier cohorts
are not as good). Moreover, they cover only the "longest"
training event between surveys, so they do not provide a
comprehensive record of formal training. Exploiting the
longitudinal nature of the NLS to examine the effect of training
on wages several years later, Lillard and Tan found evidence
that training does depreciate.

Training data in the

NLSY79

This section describes several of the key training questions
in the NLSY79 survey instrument. Readers interested in more
docnmentation about the NLSY79 training questions may
consult the NLS Users' Guide. 11 As mentioned there, the
training questions in the survey changed in the mid- l 980s.
The initial, 1979-86 rounds of the NLSY79 were funded by the
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Employment and Training Administration (ETA) of the U.S.
Department of Labor. The ETA was concerned with the efficacy
of various federally funded employment and training
programs in helping youths to acquire skills and secure
employment. Data collection during the 1979-86 interviews
was limited to only those training programs in which the
respondent had been enrolled for 1 month or more; analysis
of the microdata from 1988 to 2000 indicates that 66 percent
of completed training spells are less than 4 weeks in duration.
In 1987, when funding of the NLS shifted to the Department of
Labor's Bureau of Labor Statistics, the collection of extensive
information on government training ceased, and the "Other
Training" section of the questionnaire was restructured. The
limitation of 1 month's duration was dropped in the 1988 and
later questionnaires. The 1987 survey was an abbreviated
telephone interview, and only one question was asked about
whether any training or assistance had been received from
any government-sponsored program.
The key training questions from the 1988-2000 NLSY79
surveys are listed in table 1, along with unweighted means.
In each of the surveys between 1988 and 2000, the incidence
of training is elicited with the question, "Since [date of the
last interview] did you attend any training program or any
on-the-job training designed to help people find a job, improve
job skills, or learn a new job?" The statistics in table 1 tell us
that 15 .8 percent of persons have received training since the
date of their last interview. 12
One advantage of a longitudinal survey such as the
NLSY79 is that one can examine the incidence of training over
several years. Table 2 reports the cumulative incidence of
training spells for the sample of individuals who responded
to every interview between 1989 and 1994. Surveys for these
6 years were chosen because each has an annual reference
period for the training question. Over the course of those 6
years, when individuals in the NLSY79 ranged from 24 to 37
years of age, 53.2 percent of respondents never received any
training. Of those persons who did get training at least once
during the 6-year period, roughly half (24.1/46.8) received
Cumulative incidence of training, sample of
8,095 individuals who responded to each
interview, 1989-94
Number of training spells
0 ........................................ .

1 ....................................... ..
2 ........................................ .
3 ....... ........... ........... .... ....... .
4 ....................................... ..
5 ........................................ .
6 ··································· .... .

Count

4,307
1,947
994

Percent

208
99

53.2
24.1
12.3
6.4
2.6
1.2

24

.3

516

NorE: Entries are unweighted tabulations from the 1989-94
microdata.

NLSY79

only one spell of training, and roughly one-quarter (12.3/46.8)
received two spells of training.
Individuals who answer "yes" to the question on the
incidence of training are then asked where they received their
training. The most frequent type of training is formal company
training (37.5 percent of all training spells); noncompany
seminars or training programs also are a frequent type of
training (34.2 percent of all training spells, broken down into 18 .4
percent consisting of seminars or training programs outside of
work, and 15.8 percent seminars or training programs at work
run by someone other than the employer). Vocational or technical
institutes are the fourth most-frequent type of training (9.7
percent of all training spells). As mentioned later in the article,
researchers have found that this question about the type of
training provides important information about the generality
of training.
The next question in the training sequence asks who paid
for the training program. The most frequent response is "the
emrl0yer," who pays for 73.9 percent of all training spells.
Researchers have made several interesting points regarding
this question. First, the question supplies some of the data
that are necessary to analyze the interesting theoretical
question of who pays for general and specific training. (The
empirical literature on the subject is summarized in a later
section of the article.) Second, many researchers restrict their
analyses to employer-paid training spells; the human-capital
model is an on-the-job training model, and deleting
nonemployer paid spells of training aligns the theory with
the empirical work.
The duration of training is just as important as the
incidence. Table 1 indicates that the mean completed training
spell lasts 5.7 weeks and 20.1 hours per week. Table 3 gives
statistics on the distribution of the duration of completed
training spells. The distribution of weeks of training is heavily
skewed to the right, with half of all completed training spells
less than or equal to 1 week in duration, but 5 percent greater
than 24 weeks. Total hours of training follow a similar pattern:
half of all completed training spells are less than or equal to
35 total hours, but 5 percent are greater than 520 total hours.

Empirical findings
Wages and wage growth. Lisa Lynch's 1992 article in the
American Economic Review is the most prominent early
study using NLSY79 data to examine the effect of training on
wages. 13 Lynch uses data from 1980 through 1983 to estimate
the effect of training on 1983 wages for youths who had
completed their education by 1980 with less than a college
degree. (Note that 1980 is too early to have a substantial
sample of college graduates from the 1979 survey.) She
classifies training as on the job, off the job, and apprenticeship, and she reports descriptive statistics showing that


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-••1• =--11

Distribution of training durations, NLSY79,
1988-2000

Percentile
Mean.. .. .... .. .....
25th....................
50th ....................
75th ...... ... ......... ..
95th ... ...... ..... .. ....
99th ....................

Number of weeks

Hours per week

5.7
1
1
6
24
52

20.1
6
16
40
40
65

Total hours
118.3
12
35
80
520
1,440

NorE: Entries are unweighted tabulations from the 1988-2000 NLSY79
microdata.

4.2 percent of individuals received on-the-job training for an
average of 31.2 weeks, 14. 7 percent of individuals received
off-the-job training for an average of 40.9 weeks, and 1.8
percent of individuals received apprenticeship training for
an average of 63.5 weeks. She takes advantage of the
longitudinal nature of the NLSY79 to construct measures of
cumulative weeks of training in each category.
Lynch estimates both an equation for 1983 wages and an
equation for wage growth from 1980 to 1983. The wage growth
equation is used to eliminate possible selection bias in the
wage-level equation: workers who receive training may have
some unobservable characteristic, such as high ability, that
is positively correlated with both wages and training. In that
case, because more able workers would get trained, comparing wage levels of different workers would bias wage
differentials between trained and untrained workers. But
examining wage changes for a given worker will correct this
source of bias if ability is fixed over time for a given individual.
Lynch's wage-level equation implies that off-the-job training
and apprenticeship training from previous employers, and
on-the-job training and apprenticeship training with the
current employer, significantly raise wages. In addition, the
wage growth equation implies that off-the-job training and
apprenticeship training raise wages, but that on-the-job
training has no effect on wages.
In a later study paralleling Lynch's methods, Jonathan
Veum uses data from the 1986-90 surveys to measure the
impact of training on wages. 14 Unlike Lynch, Veum is able to
include in his analysis all training spells, whether they were
less than or greater than 1 month in duration. His 1990 wagelevel equation yields no statistically significant effect of any
form of training when training is measured continuously, but
does show some significant effects of company training and
off-the-job seminars when training is measured in terms of its
incidence. His results for wage growth between 1986 and
1990 are similar.
Daniel Parent uses a specification similar to that of Lynch
and Veum, with data from the 1979-91 surveys. 15 Although
the wage growth equations in the earlier papers eliminate
bias due to unchanging personal characteristics, Parent notes
that jobs with higher wages may also have more training

Monthly Labor Review

February

2005

53

Worker Training

irrespective of the individual. Parent gets around this problem
by using information on the deviation of the stock of training
from within-job means. He finds fairly substantial effects of
both off-the-job and on-the-job training. His correction for
job bias substantially reduces the effect of apprenticeships,
although the effect of previous jobs' apprenticeship training
remains statistically significant.
Paul Lengermann used the NLSY79 to examine the question
investigated by Lillard and Tan: How does the effect of
training on wages evolve over time? 16 Unlike those researchers, Lengermann examines wage growth in contrast to
wage levels, so as to avoid bias due to the differing abilities
of workers. He examines both spells of training that lasted 4
weeks or longer (available throughout the sample) and spells
of training ofless than 4 weeks (for which detailed information
is available only after 1986). His data cover 1979-93, and his
results indicate that ( 1) long spells of company training have
substantial effects on wages and (2) those effects do not
depreciate-indeed, they are estimated to increase from 4.4
percent in the first year to 8.2 percent after 9 years. The effect
of long spells of school-based training is not statistically
significant, although it also does not appear to depreciate.
Short spells of training, perhaps not surprisingly, have less
consistent effects.
So far, this article has concentrated on whether training
increases wages by a statistically significant amount, rather
than discussing the economic significance of the increase.
This approach reflects the emphasis in the papers presented.
Another question is, "Considering training as an investment,
how does the rate of return compare with that from other
investments, such as schooling?" Harley Frazis and Mark
Loewenstein investigate this question, using data from the
1979-2000 surveys of the NLSY79. 17 Their analysis is
restricted to training at least partially paid for by the employer.
(See question 21 in table 1; for years prior to 1988, Frazis and
Loewenstein impute whether the employer pays for training.)
Like Parent, Frazis and Loewenstein control for jobs by
restricting their investigation to within-job wage changes.
Frazis and Loewenstein show that estimates of the effect
of training are highly dependent upon the assumed functional form of the relationship between wages and training.
They find that the best-fitting functional form is one in which
the logarithm of wages increases proportionately with the
cube root of training. Using linear hours instead of the cube
root results in a drastic underestimation of the effect of
training at typical values, which may explain the insignificant
effects found in some of the aforementioned papers. Frazis
and Loewenstein find that, in their base specification, the
median positive value of employer-financed training of 60
hours increases wages by about 5 percent, which, when
annualized, implies a rate of return of 159 percent.
Frazis and Loewenstein consider several explanations for
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this very high estimated rate of return. Correcting for
promotions (for which the NLSY79 collected data in 1988-90
and 1996-2000), direct costs of training, and heterogeneity in
wage growth (as well as in wage levels) reduces estimated
rates of return to 30 to 40 percent. While this estimate is still
several times estimated rates of return to schooling in the
literature, Frazis and Loewenstein note that returns to training
appear to vary across jobs: managers and professionals have
higher rates of return than do blue-collar workers, for example.
In the presence of such variation, estimated rates of return
can be regarded as the return of training to the trained.
However, they are likely to be greater than the return that
could be realized by employees who did not receive training.
Frazis and Loewenstein 's research highlights the
strengths of the NLSY79 data set in studying training. The
large sample size, the long length of the panel, and detailed
survey data about other labor market information, such as
promotions, allow for relatively precise estimation of the
effects of training while controlling for confounding influences.
Mobility. The most prominent early article analyzing the
empirical relationship between worker mobility and training
was written by Lynch. 18 Using data from the 1979-83 surveys
of the NLSY79, Lynch estimated the probability of leaving the
first job as a function of tenure for individuals who have
permanently left school. Her estimates show that young
persons who received formal on-the-job training from their
employer are less likely to leave their job, whereas those who
participated in training obtained from for-profit proprietary
institutions outside the firm are more likely to leave their job
(although this latter effect is not statistically different from
zero). These results are consistent with the human-capital
model if one makes the straightforward assumption that onthe-job training provides firm-specific skills and off-the-job
training provides general labor market skills.
Further analysis of how training affects worker mobility is
provided by Loewenstein and James Spletzer, who use
NLSY79 data from the 1988-91 survey years. 19 After
controlling for individual and job characteristics, they find
that individuals who have receiv.e d company training have a
job separation rate that is 8 percent lower than individuals
without such training, and individuals who received school
training (business school, apprenticeship, vocational or
technical institute, or correspondence courses) have no
differences in job separation probabilities relative to persons
who did not receive school training. The mean job separation
rate in Loewenstein and Spletzer's sample is 53 percent. Assuming that company training is more specific than general
and that school training is more general than specific, these
empirical results are consistent with the basic predictions
about worker mobility from the human-capital model.

Loewenstein and Spletzer use their empirical findings to
build on the predictions from the human-capital model.
Because the returns to specific training are lost when a job
match terminates, the model predicts that specific training
should be selectively provided to workers who are less likely
to leave the job. (Evidence supporting this theoretical
prediction is mentioned shortly). If there is uncertainty about
workers' future mobility, and if information about the quality
of the employer-worker match is revealed over time, it may be
optimal to delay training as a means of avoiding making a
costly investment in a worker who may soon leave the firm.
Such a decision to delay training may be optimal even though
it entails forgoing the returns to training during the early part
of the employment relationship. Loewenstein and Spletzer
find that the NLSY79 data show a substantial amount of
delayed training; for example, their estimates show that a
similar proportion of workers get their first spell of training in
their second year of tenure as in their first year of tenure.
As part of his research mentioned earlier, Parent found
that both on-the-job and off-the-job training at the current
employer reduced a worker's mobility, while training at
previous employers appeared to increase mobility, although
by a lesser magnitude. For workers with more than one spell
of employment, it is possible to correct for bias caused by
differences across workers in propensities to leave jobs.
When Parent makes this correction, the effect of training at
the current employer is strengthened.
It is interesting to flip the training-mobility relationship
around and ask whether individuals with higher expected
future job separation rates receive lower amounts of company
training. This is the question asked by Anne Beeson Royalty,
who uses NLSY79 data from survey years 1980-86 in her
analysis. 20 She finds that a higher incidence of companyprovided training is given to individuals with lower estimated
probabilities of leaving the employer. Assuming that company training imparts firm-specific skills, Royalty's analysis
shows that profit-maximizing employers target the provision
of specific training toward those individuals who are less
likely to leave.

General and specific training
Human-capital theory distinguishes between general human
capital, which is useful at many employers, and specific
human capital, which is useful only at the current firm.
Researchers have associated types of training that raise
wages at both current and future employers with general
human capital, and types of training that raise wages only at
the current employer with specific human capital. Furthermore, types of training that reduce mobility have been associated with specific training. Much of the empirical work in
the training literature has taken advantage of the wealth of


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information in the NLSY79 data to explore the measurement
and theoretical implications of general and specific training.
The findings reviewed in this article up to now present a
fairly consistent fit between the theoretical human-capital
model and the empirical training results. From her wage-level
estimation, Lynch finds that wages are raised by on-the-job
training from the current employer, but not from previous
employers, whereupon she concludes that on-the-job training
is primarily specific. She also finds that off-the-job training
taken before the worker's tenure on the current job does raise
wages, consistent with such training being primarily general.
Lynch's analysis of mobility leads her to a similar conclusion:
that on-the-job training is more specific, whereas off-the-job
training is more general; Loewenstein and Spletzer's analysis
of worker mobility finds similar results. The one study mentioned that does not slot nicely with the theoretical model is
Parent's, which finds little difference between. off-the-job and
on-the-job training, with similar returns for training provided
by current and previous employers (consistent with general
training) and with both reducing mobility (consistent with
specific training).
A detailed analysis of the costs and returns to training
within and across jobs was conducted by Loewenstein and
Spletzer. 21 The motivation for this study was to analyze
questions in the NLSY79 that ask about who pays for the
training (question 21 in table 1). Recall from the discussion of
the human-capital model that there are two costs to employerprovided training: the direct costs, plus an indirect cost of
lower wages during training. It is assumed that asking workers
who pays for the training refers to direct costs only; it is a stretch
to believe that noneconomists would think of indirect costs
(lower wages due to reduced productivity) when answering this
survey question. Loewenstein and Spletzer find that employers
pay for 96 percent of formal company training spells. This
percentage is not surprising, because formal company training
almost surely has a large component of specificity and the
human-capital model predicts that employers will share the costs
of specific training with the worker. Loewenstein and Spletzer
also show that employers pay for 42 percent of training spells in
the aggregate category of "business school, apprenticeship,
vocational or technical institute, and correspondence course."
This aggregate category, referred to as school training in the
discussion that follows, should have a large component of
generality, and according to the human-capital model,
employers should pay the direct costs of general training
only if they can pass on the costs to workers by paying them
lower wages during the training.
Loewenstein and Spletzer find no significant evidence that
workers receiving employer-provided training-either
general or specific-accept lower wages during the training
period. While this finding may seem to contradict the human
capital model, it is consistently found in the empirical

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2005

55

Worker Training

literature-for example, by Lynch and Parent, as well as by
researchers using training data from employer surveys. 22 The
most likely explanation for this anomaly is that it is due to
differences between trained and untrained workers that are
difficult to control for empirically. If workers who receive training
have higher ability than workers who do not receive training,
and if this higher ability is observable to the employer, but
unobservable to the data analyst, then workers who receive
training will have higher wages relative to workers who do not
receive training. Even if training lowers the starting wage, as is
predicted by the human-capital model, higher wages attributable
to differences in ability may make a lower starting wage difficult
to observe in the data when one compares wages of untrained
workers with wages of workers receiving training.
The human-capital model predicts that if employers are
paying both the direct and indirect costs of training, as the
NLSY79 data suggest, employers should also be realizing some
of tl1c 1 eturns. The empirical strategy that Loewenstein and
Spletzer use to test this prediction is to compare the return to
training when a worker remains at the employer providing the
training with the return to training when the worker moves to
a new employer. Loewenstein and Spletzer find that the return
to training received from a previous employer is higher
relative to the return to training received from the current
employer when the training is arguably more general.
Lengermann reports a similar result for long spells of company
training. In combination with the absence of a starting-wage
effect, Loewenstein and Spletzer's analysis shows that
employers pay for SOIJle of the costs of general training and
receive some of the returns. This finding is at odds with the
standard human-capital model, but can be reconciled with
several theoretical modifications to that model. For example,
minimum wages, liquidity constraints, or contract
enforcement considerations can result in the employer
sharing the costs and returns of general training. 23
This evidence that employers share the costs and returns
of general training has led researchers to seek additional
questions that measure the generality of training. In 1993, for
the first and only time, the NLSY79 included the question "How
ma~y of the skills that you learned in this training program do
you think could be useful in doing the same kind of work for
an employer DIFFERENT than [current employer]?" There are
five possible responses to this question: (1) all or almost all
of the skills, (2) more than half of the skills, (3) about half of
the skills, (4) less than half of the skills, and (5) none or almost
none of the skills. In follow-up research, Loewenstein and
Spletzer analyze the 1993 NLSY79 data and find that 63 percent
of workers receiving employer-provided formal training respond
that "all or almost all" of the skills they learned at one employer
are useful in doing the same kind of work for a different
employer. 24 This finding suggests that the skills individuals are
learning in their employer-provided

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training have a large general component.
Loewenstein and Spletzer estimate wage and mobility
equations with the 1993 general and specific training data as
the key explanatory variable. Their wage regressions show,
first, that there is no systematic relationship between the
degree of generality of the training and its wage return in the
job that provided the training and, second, that the return to
training received from previous jobs exceeds the return to
training received at the current job, for all degrees of
generality. If these results are compared with those from a
similar specification using data on the type of training
(question 20 in table 1) instead of on the degree of generality,
then the first result holds, whereas the second result holds
for school training, but not for company training. This finding
leads Loewenstein and Spletzer to discuss the pros and cons
of the two training measures. They hypothesize that the type
of training data conveys different information than does the
self-assessed generality of training data. For training to be
truly general, not only must the skills be useful at other
employers, but also, other employers must observe and value
the generality of those skills. The generality-of-training
question in the 1993 NLSY79 asks the individual's opinion
about whether the skills learned in the training are useful
elsewhere, but this is not equivalent to asking alternative
employers about the transferability of skills. By contrast, the
question on type of training not only proxies for the
generality of the skills imparted by training, but also conveys
information about how likely other prospective employers
are to observe these skills. For example, school training might
easily be certified for other employers to see its value, but it
may be difficult for prospective employers to observe the
usefulness of skills learned in company training. Such
reasoning leads Loewenstein and Spletzer to speculate that
information on the type of training may be preferable to a
directly asked question as a measure of generality. However,
as they suggest, the evidence for such speculation is limited,
and the research community would benefit from asking the
question on degree of generality in more than 1 year.
HUMAN-CAPITAL THEORY GAINED ITS PRESENT PROMINENCE in labor
economics more than four decades ago. The simple humancapital model has empirically testable predictions regarding
the relationships among wages, mobility, and training. Testing
these predictions requires microdata with detailed
longitudinal information on training, individual wages, and
job mobility-data that were not available at the time humancapital theory was originally developed. As we celebrate the
25th anniversary of the NLSY79 cohort, we are not surprised
that much of the empirical knowledge about worker training
has come from analyses of the data in that survey.
But any good literature review raises as many questions
as it answers, and this article has tried to highlight some of

the issues that could benefit from further analysis and
enhancements to the questionnaire. Three specific topics
warrant mention. First, there is new theoretical and empirical
research into the topic of why employers appear to pay for
general training, and the NLSY79 data are likely to play an
important role in the continuing development of this literature.
Second, training is not always a well-defined concept, and
any survey's measures of the incidence and duration of

training undoubtedly contain measurement error; analyzing
the amount, consequences of, and statistical remedies for
measurement error is a topic that is well worth exploring. 25
Finally, any distinctions there are among formal training,
informal training, and learning by doing have been ignored in
this article; the NLSY79 has asked questions regarding
informal training, but economists have not yet studied the
responses to those questions in depth. 26
D

Notes
1
See Gary Becker, "Investment in Human Capital: A Theoretical
Analysis," Journal of Political Economy, October 1962, pp. 9-49,
and Human Capital: A Theoretical and Empirical Analysis with
Special Reference to Education (Cambridge, MA, National Bureau of
Economic Research, 1964); Theodore W. Schultz, "Investment in
Human Capital," American EconomiL Review, March 1961, pp. 117; and Jacob Mincer, "Investment in Human Capital and Personal
Income Distribution," Journal of Political Economy, August 1958,
pp. 281-302.

2

See David A. Jaeger, "Estimating the Returns to Education Using
the Newest Current Population Survey Education Questions,"
Economics Letters, March 2003, pp. 385-94.
3
The formal mathematical model was worked out by Masanori
Hashimoto in "Firm-Specific Human Capital as a Shared Investment,"
American Economic Review, June 1981, pp. 475-82.

4
This greater likelihood has been shown by many authors using
many data sets. For example, see the analysis of the National
Longitudinal Survey of the High School Class of 1972 (NLSHS72) data
by Joseph G. Altonji and James R. Spletzer, "Worker Characteristics,
Job Characteristics, and the Receipt of On-the-Job Training,"
Industrial and Labor Relations Review, 1991, pp. 58-79; the analysis
of Current Population Survey (cPs) data by Norman Bowers and Paul
Swaim, "Recent Trends in Job Training," Contemporary Economic
Poli1.,y, 1994, pp. 79-88; the analysis of Panel Study of Income
Dynamics (Psm) data by Greg J. Duncan and Saul Hoffman, "On-theJob Training and Earnings Differences by Race and Sex," Review of
Economics and Statistics, 1979, pp. 594-603; and the analysis of the
NLSY79 data by the authors cited in the later sections of this article.

5

Jacob Mincer, "On-the-job training: Costs, returns and some
implications," Journal of Political Economy, October 1962, pt. 2,
pp. 50-79.
6
Jacob Mincer, Schooling, Experience, and Earnings (New York,
Columbia University Press, 1974).

7
Later research found that earnings profiles were better described
by a quartic function of experience; see Kevin M. Murphy and Finis
Welch, "Empirical Age-Earnings Profiles," Journal. of Labor
Economics, April 1990, pp. 202-29.

Statistics, November 1979, pp. 594-603; and James N. Brown, "Why
Do Wages Increase with Tenure? On-the-Job Training and Life-Cycle
Wage Growth Observed within Firms," American Economic Review,
December 1989, pp. 971-91.
10
Lee A. Lillard and Hong W. Tan, "Private Sector Training: Who
Gets It and What Are Its Effects?" Research in Labor Economics,
vol. 13, 1992, pp. 1-62.

11
Found at http://www.nlsinfo.org/nlsy79/docsn9htmln9texU
training.htm.

12
As mentioned in footnote I of table I, the training questions in
1988, 1996, 1998, and 2000 have a 2-year reference period, whereas
the training questions in 1989-94 have a I -year reference period.
13
Lisa M. Lynch, "Private Sector Training and the Earnings of Young
Workers," American Economic Review, March 1992, pp. 299-312.

14
Jonathan R. Veum, ·'Sources of Training and their Impact on
Wages," Industrial and Labor Relations Review, July 1995, pp. 812-26.

15
Daniel Parent, "Wages and Mobility: The Impact of EmployerProvided Training," Journal of Labor Economics, April 1999, pp.
298-317.

16
Paul A. Lengermann, "How Long Do the Benefits of Training
Last? Evidence of Long Term Effects across Current and Previous
Employers," Research in Labor Economics, vol. I 8, 1999, pp. 439-6 I.

17
Harley Frazis and Mark A. Loewenstein, "Reexamining the
Returns to Training: Functional Form, Magnitude, and Interpretation," BLS working paper no. 367, Journal of Human Resources,
forthcoming, spring 2005.

18
Lisa M. Lynch, '·The Role of Off-the-Job vs. On-the-Job
Training for the Mobility of Women Workers," American Economic
Review Papers and Proceedings, May 1991, pp. 151-56.

19
Mark A. Loewenstein and James R. Spletzer, "Delayed Formal
On-the-Job Training," Industrial and Labor Relations Review, October
1997, pp. 82-99.

8
See Boyan Jovanovic, "Job Matching and the Tl-ieory of
Turnover," Journal of Political Economy, October 1979, pp. 97290; Edward Lazear, "Agency, Earnings Profiles, Productivity, and
Hours Restrictions," American Economic Review, September 1981,
pp. 606-20; and Joanne Salop and Steven Salop, "Self-Selection and
Turnover in the Labor Market," Quarterly Journal of Economics,
November 1976, pp. 619-27.

20
Anne Beeson Royalty, "The Effects of Job Turnover on the
Training of Men and Women," Industrial and Labor Relations Review,
April 1996, pp. 506-21.

9
Greg J. Duncan and Saul Hoffman, "On-The-Job Training and
Earnings Differences by Race and Sex," Review of Economics and

22 See, for example, John M. Barron, Dan A. Black, and Mark A.
Loewenstein, "Job Matching and On-the-Job Training," Journal of Labor


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21
Mark A. Loewenstein and James R. Spletzer, "Dividing the Costs
and Returns to General Training," Journal of Labor Economics,
January I 998, pp. 142-71.

Monthly Labor Review

February

2005

57

Worker Training

Economics, January I 989, pp. 1-19; and John M. Barron, Mark C.
Berger, and Dan A. Black, "Do Workers Pay for On-the-Job Training?"
The Journal of Human Resources, spring 1999, pp. 235-52.
23
For recent studies on the growing literature of why employers
may share the costs of, and returns to, general training, see John
Bishop, "What We Know about Employer-Provided Training: A
Review of the Literature," Research in Labor Economics, vol. 16,
1997, pp. 19-87; Daron Acemoglu and Jorn-Steffen Pischke, "Why
Do Firms Train? Theory and Evidence," Quarterly Journal of
Economics, February 1998, pp. 79-119, and "The Structure of Wages
and Investment in General Training," Journal of Political Economy,
June 1999, pp. 539-72; Alison Booth and Mark Bryan, "Who Pays
for General Training? New Evidence for British Men and Women,"
discussion paper no. 486 (Bonn, Institute for the Study of Labor (IZA),
2002); and Alison Booth and Gylfi Zoega, "Is Wage Compression a
Necessary Condition for Firm-Financed General Training?" Oxford
Economic Papers, January 2004, pp. 88-97.

58 Monthly Labor Review

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February 2005

24
Mark A. Loewenstein and James R. Spletzer, "General and
Specific Training: Evidence and Implications," Journal of Human
Resources, fall 1999, pp. 710-33.
25
For an analysis of the extent of measurement error in training in
an employer data set, see John M. Barron, Mark C. Berger, and Dan A.
Black, "How Well Do We Measure Training?" Journal of Labor
Economics, July 1997, pt. I, pp. 507-28. For a start on the extremely
complicated topic of statistical consequences of, and remedies for,
measurement error, see Harley Frazis and Mark Loewenstein,
"Estimating Linear Regressions with Mismeasured, Possibly
Endogenous, Binary Explanatory Variables," Journal of Econometrics,
November 2003, pp. 151-78, and the working-paper version of
"Reexamining the Returns to Training."

26
A first pass at analyzing these responses is Mark A. Loewenstein and
James R. Spletzer, "Formal and Informal Training: Evidence from the
NLSY79," Research in Labor Economics, vol. 18, I 999, pp. 403-38.

Children of the NLSY79:
a unique data resource
The survey provides a wealth of information on the education,
socioeconomic background, and cognitive, social, and emotional
development of children aged 14 and younger; and on the workforce
participation, education, marital, and fertility behaviors of young
adults aged 15 or older; the data have been heavily used by
researchers across a wide range of disciplines
Lawrence L. Wu
and
Jui-Chung Allen Li

remarkable design aspect of the Naional Longitudinal Survey of Youth
979 (NLSY79) is the availability of longitudinal data on all children born to women in
the original NLSY79 sample. The resulting data
from the Children of the NLSY79 provide a resource that is unique in many respects. Perhaps not surprisingly, these data have been
used by researchers across a wide range of disciplines, including child development, demography, economics, epidemiology, family studies, social policy, and sociology. Much of the
usefulness of these data stem from two key factors: they can be linked to the rich longitudinal data for the NLSY79 mothers, and the child
and young adult surveys are themselves longitudinal, covering a wide range of ages from
early childhood and adolescence through the
young adult years.

Sample design

Lawrence L. Wu is
professor and chair,
and Jui-Chung Allen Li
is a Ph.D. candidate, in
the Department of
Sociology at New York
University.
E-mail:
lawrence.wu@nyu.edu


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As noted in other articles in this issue of the
Monthly Labor Review, the main respondents
in the NLSY79 are a n<1.ti0nally representative
sample of individuals aged 14-22 in 1979, with
surveys conducted annually through 1994 and
biennially since 1996. The child sample-consisting of offspring aged 14 or younger-was
begun in 1986, while the young adult sample-

consisting of offspring aged 15 or older-was
begun in 1994, with both the child and young
adult samples fielded biennially since initial
data collection.' The survey instruments differ
substantially in the child and young adult surveys, as reviewed below. Because of the longitudinal design of the child and young adult
samples, offspring are interviewed initially in
the child sample, and then in the young adult
sample as they reach adolescence. Thus by design, sample sizes in the two samples will vary
from wave to wave, but as of the 2002 wave,
the child sample contained 11,340 children, and
the young adult sample contained 4,648 young
adults.
These data do not provide a nationally representative sample of children or young adults,
although they are appropriately regarded as
representative of the population of offspring
born to U.S. women who were aged 14-22 in
1979. Sample coverage of this latter population is excellent. However, the child and young
adult samples do not cover children of NLSY79
women who were concealed from the survey
(for example, children born to a NLSY79
woman but given up for adoption), children
who died before the initial 1986 children survey, or children in NLSY79 households who were
lost to the survey because they were not interviewed. The numbers of children in the latter
Monthly Labor Review

February 2005

59

Children of the NLSY79

category far outweigh the numbers in the other two categories, but, by current survey standards, sample attrition is
modest. In the 2002 wave, for example, roughly 84 percent
of NLSY79 women who had one or more births were successfully interviewed, compared with only 7 4 percent of women
who had no children.

many other studies (usually small-scale studies) have collected BPI data, a unique aspect of the data from the Children
of the NLSY79 survey is its longitudinal nature, with BPI measures repeated every 2 years since 1986. Thus, for some children, BPI is measured at as many as six longitudinal data
points.

Child assessment battery

Cognitive development. The major instruments evaluating
the cognitive development for the NLSY79 children are three
subscales of the Peabody Individual Achievement Tests
(PIAT)-Mathematics, Reading Recognition, and Reading
Comprehension-for children between ages 5 to 14 and the
Peabody Picture Vocabulary Test-Revised (PPVT-R) for children. The Memory for Digit Span subscale of the widely
known Wechsler Intelligence Scale for Children - Revised
(wisc-R) is also included. These assessments of cognitive
ability are administered in all waves since 1986. Thus, as in
the case of BPI, repeated measures of cognitive ability are
available for individual children, which have proven of great
value to the research community. Less widely used measures of cognitive ability in these data include "body parts
recognition" (for children between ages 1 to 3 years; in the
1986 and 1988 waves), "memory for locations" (for children
between 8 months and 4 years of age; in the 1986 and 1988
waves), and the short-term memory of auditory stimuli
subscale from the McCarthy Scales of Children's Abilities
(for children between ages 3 and 7 years; in the 1986 to 1994
waves). Taken together, these multiple-repeated measures
provide a comprehensive developmental portrait of early
cognitive ability.

A major interview component of the Children of the NLSY79
is the child assessment battery, which gauges the child's
socio-emotional and cognitive development from birth to age
14 and provides measures of the home environment that are
thought to be important for child development. The validity
and reliability for these child assessments are high, with these
measures available for roughly 90 percent of surveyed children. (An exception is the Home Observation of the Environment-Short Form (HOME-SF) (see below) for infants under
age 3.) While these child assessment instruments were designed to be administered in a face-to-face interview, they
are nevertheless close in quality to similar diagnostic instruments used in the clinical settings of child assessments. The
battery covers multiple domains of a child's developmental
trajP.ctories, as well as the home environment for the child.

Home observation of the environment. The quality of a
child's home environment is measured by the HOME-SF. 2 This
instrument assesses the cognitive stimulation and emotional
support for children under the age of 15. Examples of questions include, "How many children 's books does your child
have?" and "If your child got so angry that he/she hit you,
what would you do?" with the specific questions administered varying according to age suitability. These data have
been widely analyzed, with the instrument adopted by other
surveys (for example, the child assessment module in the New
Immigrant Survey3).
Socio-emotional development. Data on child temperament
are obtained from mothers for children under age 3 and from
interviewers for children between ages 3 and 6. This temperament scale is intended to measure the child's activity
level, affective attributes, attachment styles, compliance, sociability, and more generally, how a child usually acts. It was
adapted from Rothbart's Infant Behavior Questionnaire and
Kagan's compliance scale. 4 The Behavior Problem Index
(BPI), completed by mothers with children from age 4 to age
14, has been widely used to gauge problem behaviors in children.5 Researchers have typically distinguished between two
major BPI subscales: "externalizing behaviors" measuring
behaviors such as aggression, over-activity, and conflictual
relations with peers; and "internalizing behaviors" measuring frustration and negative affect toward oneself. Although
60 Monthly Labor Review

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February 2005

Education, health, neighborhoods
Educational experiences are also assessed longitudinally, including the child's participation in the Head Start program,
grade retention, number of the child's friends that the parent
knows (a classic measure of "social capita1"6), attendance in
advanced classes, school activities, and basic school information. Researchers may request permission to view data on
school characteristics, which were gathered in a 1995/ l 996
school survey, and on neighborhood characteristics, which
are available from geocode information. These items, taken
collectively, provide an unusually comprehensive portrait of
the child 's early formal schooling experiences in ways that
exceed the educational data available for the original NLSY79
respondents. 7
Various health-related questions include detailed perinatal conditions (for example, mother's smoking and drug use
during pregnancy, her access to prenatal care, and child's
birth weight), hospitalization history, general health conditions, specific illnesses, height, and weight. Many of these
variables are available longitudinally.

Young adult data
Yet another extremely important design decision in the offspring data is that the young adult survey instrument-administered when offspring are aged 15 or older-in many
ways mirrors the survey instrument given to their NLSY79
mothers. The young adult ages are similar to NLSY79 mothers
when they were first surveyed, and the period marks when
adolescents and young adults begin transitioning from school
to work. Thus, behavioral domains cover the continuing educational experiences of offspring, but also their employment
histories, income, and program participation. Health-related
behavioral items include detailed batteries on substance use
(cigarette, alcohol, marijuana, and other drugs/substance), but
also items related to both general and specific mental and
physical health conditions. Demographic data include information on exposure to sex education, pregnancy, and detailed
fertility and marital histories. Since 1994, a series of questions on computer use has been available, reflecting the
survey's continuing sensitivity to various sources of social
change. 8

Advantages of the Children of the NLSY79
Several design elements of the Children of the NLSY79 make
these data unique. For example, offspring data are available
in the Panel Study oflncome Dynamics (Psm), which is also
a nationally representative longitudinal survey of respondents
in the United States. However, the PSID child supplement
consists of only two waves of data to-date; hence, the PSID
parent/child data consists of a long panel of data for parents
but only a short panel for children. By contrast, the National
Survey of Families and Households (NSFH), another national
probability survey, contains longitudinal data on both parents and offspring, but limits data on offspring to a single
focal child within a specific age range. In addition, the NSFH
contains only three waves of data, with interviews occurring
5 or more years apart. By contrast, the NLSY child and young
adult surveys are administered biennially, contain all children
born to a given mother, and provide a long panel of closelyspaced longitudinal data for both mothers and offspring.
As noted earlier, the major limitation of the NLSY79 data
has to do with how the sample is generated. The sample cannot be regarded as a nationally representative sample of any
cross-sectional population of children in the United States,
but is representative of the population of children whose
mothers were born between 1958 and 1965. Because children are added as they are born, the initial data in the child
sample overrepresented children born to the youngest mothers, who tended to be from disadvantaged socioeconomic
backgrounds. Children in the initial rounds of data were observed to do worse than other children of similar ages on a


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variety of indicators for cognitive, physical, and socio-emotional developments. These disadvantages are likely due to
their disadvantaged social and economic circumstances.
However, with time, more children have been added to the
child sample as the NLSY79 women have completed their
childbearing; hence, what was problematic in the early history of the child sample has become much less so as the joint
mother-child age distribution has "normalized." Similar issues hold for the young adult data. The research community
has been sensitive to these issues, and a variety of modeling
strategies can be employed to deal with them. 9

Retrospect and prospect
Although the majority of published refereed research papers
have been in journals specializing in the areas of the family
and child development, studies using these data have also
appeared in fields such as demography, economics, epidemiology, and sociology. (See table 1.) The use of these data by
researchers in child psychology, pediatrics, and psychiatry is
particularly noteworthy because, traditionally, these fields
have not relied on analyses of national probability samples,
but have rather focused beavily on smaller experimental and
clinical samples.
The topics examined with these data are very diverse.
Published studies to-date have examined the consequences
on children's well-being and cognitive and socio-emotional
development of factors such as maternal employment, povArticles using data from the Children of the
published in academic journals

NLSY79

Number
Journal name

Journal of Marriage and the Family ............... .. .
Child Development ........................................... .
Journal of Human Resources ........................... .
Demography ...................... ............................... .
Pediatrics ...........................................................
Social Forces .................................................... .
Developmental Psychology ............................. ..
Journal of Family Issues ............ .... ........... ... ..... .
Perspectives on Sexual & Reproductive Health
(previously, Family Planning Perspectives) .... .
American Sociological Review ......................... .
American Journal of Public Health ................... .
Intelligence ........................... ...................... ...... .
Journal of Health and Social Behavior ............ ..
Archives of Pediatric & Adolescent Medicine .. .
Journal of Economic Literature ........... ... .. ........ .
Journal of Family and Economic Issues .......... .
Journal of Health Economics ........................... .

Rank

of
articles
31
13
12

10
9
9

8
8
9

7

10
11
11

11
14
14
14
14

NmE: Journals that have only published less than three papers are not
included, but they include such flagship journals as American Journal of

Sociology, Journal of Political Economy, American Economic Review,
American Educational Research Journal, and Population and Development
Review.

Monthly Labor Review

February 2005

61

Children of the NLSY79

erty and program participation (for example, the Temporary
Assistance for Needy Families (TANF) program and the Special Supplemental Nutrition Program for Women, Infants,
and Children (w1c)), parenting practices, and parental marital history. Similarly, pediatric researchers have used these
data to estimate the association of child obesity and television viewing and how parents deal with children during
mealtimes. 10
Use of the young adult sample has increased over time as
offspring have aged into this sample. A bibliography maintained by the NLS lists 45 journal articles, book chapters, theses and dissertations, conference papers, and working papers
that have used the young adult data. 11 As noted above, early
waves of the young adult data heavily overrepresented offspring born to very young mothers, but selection of offspring
on mother's age has become far less problematic as offspring
born to older mothers have aged into the young adult sample.
As a consequence, research uses of the young adult data
should continue to gain momentum as more waves of data
become available and as parent/offspring characteristics become less skewed.

Researchers can employ advanced statistical techniques
exploiting unique design elements in these data. For example,
researchers have often noted that siblings are more likely to
resemble one another on a variety of behavioral outcomes
than are two otherwise similar but unrelated individuals chosen randomly from the same population. Such resemblance,
it is argued, may reflect characteristics of siblings-how they
were raised, characteristics of their parents and home environment, and genetic influences-that are not captured by the
rich array of variables available in data such as the NLSY79.
To deal with this issue, researchers have increasingly used a
variety of statistical modeling techniques (for example, fixed
effect models) that rely on the availability of sibling data to
purge out the influence of hypothesized unobserved factors.
An innovative elaboration of this idea involves comparisons
of siblings, cousins, and unrelated individuals, a research design that is possible with the offspring data because both siblings and cousins can be identified. A similar idea relies on
the identification of half-siblings, both among the original
NLSY79 respondents and in the offspring data, which has been
0
used to examine certain models of genetic similarity.

Notes
1
Members of the Center for Human Resource Research (CHRR) and, in
particular, Frank Mott played a crucial role in developing and designing the
child and young adult surveys. Because plans to collect data on offspring
were not part of the original NLSY79 data design, external funding sources
were required to collect these data. As a consequence, the availability of
offspring data were dependent on the foresight of Frank Mott, who in the
early 1980s spearhe:ided efforts by staff at CHRR and NLS to secure external
support for collecting data on the Children of the NLSY79. It is also important
to recognize that at the time of initial data collection, many of the substantive issues, as well as statistical and methodological techniques appropriate
for these data, were in their infancy. In these and others ways, those who
envisioned these data in the early 1980s were well ahead of their time.

2 HOME-SF is a modification of the HOME inventory, see Bettye M.
Caldwell and Robert H. Bradley, Home Observation for Measurement of
the Environment (Little Rock, AR, University of Arkansas, Center for Child
Development and Education, 1984).
3

For further information about the New Immigrant Survey, see http://
nis. princeton.edu.
4
At the time of the first child survey in 1986, there was no existing
temperament scale appropriate for use in a survey setting. As a result, the
temperament scale developed and fielded by NLS investigators lacked a
national norm. Subsequent work has established the psychometric and
measurement properties of this scale. See Frank L. Mott, Paula C. Baker,
David E. Ball, Canada K. Keck, and Steven M. Lenhart, The NLSY Children
1992: Description and Evaluation (Columbus, OH, Ohio State University, Center for Human Resource Research, 1995).

5
The BPI was developed by Nicholas Zill and James L. Peterson, Behavior Problems Index (Washington, DC, Child Trends Inc., 1986) by
adapting primarily the Achenbach Behavior Problems Checklist. See Tho-

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February 2005

mas M. Achenbach and C. S. Edelbrock, Manual for the Child Behavior
Checklist and Revised Child Behavior Profile (Burlington, VT, University of Vermont, Department of Psychology, 1983).
6
See James S. Coleman, "Social Capital in the Creation of Human
Capital," American Journal of Sociology 94, (The University of Chicago
Press, 1988), pp.S95-S 120.
7
See Kenneth I. Wolpin, "Education data in the NLSY79: a premiere
research tool," Monthly Labor Review, February 2005, 15-20.

8
Data on substance use, health conditions, sex education, and computer use are also available in the child sample. The fact that similar items
are present in both the child and young adult surveys permits investigators
to compare and combine data from the two samples to assess, for example,
issues of reliability and stability/change in these variables.
9
See Mark R. Rosenzweig and Kenneth I. Wolpin, "Are There Increasing Returns to the Intergenerational Production of Human Capital? Maternal Schooling and Child Intellectual Achievement," Journal of Human
Resources 29 (The University of Wisconsin Press, 1994), pp. 670-93.

10
See Laura K. Certain and Robert S. Kahn, "Prevalence, Correlates,
and Trajectories of Television Viewing Among Infants and Toddlers," Pediatrics l 09 (American Academy of Pediatrics, 2002), pp.634-42; Myles
S. Faith, Stanley Heshka, Kathleen L. Keller, Bettylou Sherry, Patty E.
Matz, Angelo Pietrobelli, and David B. Allison, "Maternal-Child Feeding
Patterns and Child Body Weight: Findings from a Population-Based
Sample," Archives of Pediatric and Adolescent Medicine 157 (American
Medical Association, 2003), pp.926-32.

11
A searchable online bibliography of all sources that have used the
data is on the Internet at http://www.nlsbibliography.org.

NLS

The problem of respondent attrition:
survey methodology is key
Longitudinal surveys will suffer from attrition
and nothing will change that; however,
years of lessons learned in the field show that
straightforward survey methodology
can minimize the impact of losing respondents

Randall J. Olsen

e central problem oflongitudinal surveys
s attrition. The National Longitudinal
Survey of Youth in 1979 (NLSY79), which
this issue of the Monthly Labor Review features,
is the gold standard for sample retention against
which longitudinal surveys are usually
measured. However, we cannot understand how
the NLSY79 has done so well without considering
what was done differently in the other cohorts of
the NLS and what we have learned by formal
evaluations of attrition ave1sion measures that
evolved over a quarter crntury of field work. The
lessons here are hard-won and, to some,
unconventional.

11

Background

Randall J, Olsen is
professor of
economics at the
Ohio State University,
E-mail:
olsen@postoffice.chrr.
ohio-state.edu.


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The NLS began in 1965 at the urging of an Assistant Secretary of Labor, Daniel Patrick Moynihan.
He believed that although the Current Population Survey provided crucial snapshots of the
Nation's labor force and labor market, the Nation
needed a data source that was more dynamic and
capable of tracking the long-run evolution of careers. Tile task of starting the study went to
Howard Rosen at the Department of Labor, who
enlisted Herb Parnes from Ohio State University,
to assemble a team, design the surveys, and analyze the data. This team comprised representatives from the Census Bureau, Ohio State University, and the Department of Labor.

The original plan was to follow the cohorts
for 5 years to study some of the pressing questions of the time-the shrinking labor force participation rate of older men, the problem of youth
unemployment and the transition from school to
work, and the growing labor force participation
of women whose children were entering school,
leading to steady growth in the number of working mothers. Childcare was an important issue
along with the problem of how the family would
pay for a college education for the children of the
baby boom.
Over time, the project has expanded. (Table I
shows the various cohorts of the NLS, their start
and stop dates, sizes, and age ranges covered.)
Because the project began with a 5-year horizon,
neither the Census Bureau, Ohio State, nor the
Department of Labor had a plan for sample retention over the long run; after all, longitudinal surveys were still quite rare. The studies shortly
proved their worth and the project became openended in terms of duration. However, the original
limitations on intended duration led to some problems with attrition that conflicted with a revised
plan to follow the respondents over the balance
of their lives. In particular, the "following-rule" 1
that the Census Bureau used specified that when
a respondent missed two consecutive interviews,
the Census Bureau would drop that respondent
from the study.
The following-rule and the original 5-year hoMonthly Labor Review

February

2005

63

Respondent Attrition

Illa'

Survey groups, sample sizes, interview yeors, and survey status, National Longitudinal Survey, 1966-2004
Age cohort
at first
interview

Survey group

Original
sample

Initial year/
latest year

4/1 /06-3/31 /21
4/1 /22-3/31 /37
4/1/41-3/31/52
1943-53

5,020
5,083
5,225
5,159

1966/1990
1967/2003
1966/1981
1968/2003

13
21
12

12,686

1979/2004
1986/2004
1994/2004

21

1997/2004

OlderMen ....................
Mature Women .. .. ..... .. ..
Young Men ............ ... .. ..
You1 ·1g Women ..............

45-59

NLSY79 ............. . .. ..........

14-21
birth-14
15 and older

1957-64
(4)

(5)

(4)

(5)

12-16

1980-84

8,984

NLSY79
NLSY79

Children ............
Young Adults ....

3(}-44

14-24
14-24

6

NLSY97 . .. . ... . ............ . .... .

2

1
Interviews in 1990 also were conrlucted with 2,206 widows or other
family members of deceased respondents .

2
After dropping the military (in 1985) and economically disadvantaged
non-black/non-Hispanic oversamples (in 1991 ), the sample contains 9,964
respondents eligible for interview.
3
The latest sample size available is from the 2002 survey. The 2004
survey is currently being fielded.
4

NLSY79 Children and young adults included by relation to NLSY79

rizon struck with the greatest force on the Young Men's cohort. In 1981, about two-thirds of the cohort responded to
the survey. Some analysts believed that the rate of attrition
reflected veterans' refusing to participate in a Government
survey. Although the rate of attrition among black veterans
was a few percentage points higher than that for
nonveterans, for whites, the differential for veterans as a
whole was essentially zero. Within the Young Men's cohort,
blacks had the highest attrition rates. For whites, attrition in
the Young Men's cohort was a bit higher than that for two
women's cohorts, but male respondents have always had
higher attrition than females.
The two-and-out-following-rule that the Census Bureau
employed had serious ramifications, given the attrition pattern for the young men, and high attrition among blacks. By
1981, the Census Bureau had stopped tracking 11 percent of
the young men because they had, at some point, missed two
consecutive interviews. Blacks make up 28 percent of the
young men's sample, but 57 percent of the cases dropped
because of the following-rule were black. Our current ruleof-thumb is that in the next round, one can obtain an interview on about 25 percent of the respondents who have
missed two interviews in a row. When interviewing began
for the NLSY79, performance specifications did not allow respondents to be dropped simply based on consecutive
missed interviews.
The original design for the surveys alternated in-person
interviews with telephone and mail-out surveys, with the inperson version conducted every 5 years. 2 As a result, the
content of the interview was more comprehensive every 5
64 Monthly Labor Review

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Number
of
surveys

Birth year
cohort

February 2005

Number
at last
interview

1

Status

2,092
2,236
3,398
2,857

Ended
Ended
Ended
Ended

6

7,724
3,229
3
4,238

Continuing
Continuing
Continuing

7

7,756

Continuing

22
10

3

3

females, not birth year. Children are still being born.
5

The size of the NLSY79 child sample depends on the number of children
born to female NLSY79 respondents , attrition over time , and the gradual
aging of the children into the young adult sample. The size of the young
adult sample depends on the number of children who reach age 15 in each
survey year.
6
In 1998 only, the young adults eligible for interview were limited to
those ages 15 to 20.

years, with smaller updates in between. The NLS approach to
the Mature Women's cohort is emblematic of the general approach of the survey. The 1967 interview of the women ages
30-44 focused on the longest held jobs between schooling
and marriage, between marriage and the birth of her first child,
and after the birth of the first child. The survey sought the
most important (that is, longest held) job holdings, probed
for significant periods not working, and ascertained why the
woman did not work. The respondent answered CPS questions about the previous week~ these questions accounted
for a significant part of the interview.
This approach to collecting labor force behavior data left
unanswered questions about work history, especially for
women with frequent job transitions and women who missed
the in-person interview. There were modules that collected
retrospective data about fertility and marriage, but in the
1960s, marriages ending in divorce were less frequent, compared with current divorce rates. The NLS did not attempt to
collect an event history on marriage, but nonetheless, the
survey probably collected most of the transitions in marital
status and cohabitation for the Mature Women and Older
Men's cohorts.
The original cohort data collection effort frequently captured data on respondents' behavior by asking retrospective
questions, sometimes at wide intervals, to capture particular
data domains. For example, rather than collecting pregnancy
roster data on the Young Women 's cohort as those events
occurred, the NLS would ask about many years' experience all
at once. As Frank Mott documents, this strategy for data
collection opens the way for more measurement error. 3

'.:\.'ith the strategy used then, missing one interview can
leave an important part of the data record distressingly incomplete.
It is in this context that we start this article by focusing on
the historical record of the completion rates for the various
cohorts of the NLS and how the strategy for both data collection and the rules for continuing to follow nonrespondents
generate startling impacts on the completeness of the data
coming out of a longitudinal study. This article continues by
describing some of the fielding techniques the NLS program
has employed to offset the secular trend toward lower completion rates.

The historical record
The remainder of this article describes the two original
women's cohorts: the NLSY79 and the NLSY97. The two original men's cohorts were cancelled in the early l 980s. 4 In 1981,
the Census Bureau completed interviews of 65 percent of the
original respondents for the Young Men and 52.5 percent of
the Mature Men. However, corrected for mortality, the numbers are higher, with 66.8 percent completed of the respondents "-till alive for the Young Men and 74.8 percent of the
Mature Men. After 15 years, the completion rate for the Mature Women was 69.7 percent (73.5 percent of those still alive),
and for the Young Women it was 68.8 percent (69.4 percent of
those still alive). As mentioned earlier, the lower completion
rate for the Young Men reflects a following-rule that dropped
blacks at an unusually high rate.
The Census Bureau experience with the original cohorts
was more favorable than its recent experience with respondents from the Survey of Program Dynamics (SPD). That survey continues the 1992-93 Survey of Income and Program
Participation (SIPP) panel. By the end of the SIPP phase for
the SPD respondents (the SIPP phase contained nine waves
each 4 months apart), the completion rate was about 73.4
percent. The 1997 wave of the SPD completed 58.7 percent of
the SIPP respondents, and by 2002, the completion rate stood
at 53 percent of the 1992-93 SIPP respondents. The 10-year
retention rate for the SIPP/SPD panel is below the 15-year
retention rate for the original cohorts, whether or not one
corrects the latter retention rates for mortality. A more striking contrast is the experience with the NLSY79, for which the
National Opinion Research Center (NORC) does the data collection. The 15-year retention rate from 1979 to 1994 was 89.2
percent-almost 20 percentage points higher than the rate
for the original cohorts. The disparity of experience with
retention in longitudinal studies grows when we compare the
NORC experience with the NLSY97 to their experience with the
NLSY79. After seven rounds, NLSY97 retains 86.4 percent of
the respondents, which is below the NORC experience in the
NLSY79 after more than 19 years Even if we examine the


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records of the Census Bureau and NORC separately, we see
marked variation in outcomes. The reason for this disparity is
complex.

Bounded interview event histories
The most important feature that distinguishes the NLSY79
(and NLSY97) from the initial design of the original cohorts of
the NLS is the use of event histories in the NLSY79 and
NLSY97. The article by James Walker in this issue addresses
this feature in more detail, but when the case for event histories in the NLSY79 was made by Burton Singer, he had in mind
their analytic usefulness and not their effect on attrition. (See
accompanying articles in this issue.) As it turns out, event
histories in longitudinal surveys force us to rethink our views
on attrition.
As implemented in the NLSY79, event histories carried forward respondents' answers to questions from the previous
interviews in some domain. For example, if the respondent
was interviewed on July 21, 1987, and he was married, we
would ask, "According to our records, when we last interviewed you on July 21, 1987, you were married. Is that correct?" If the respondent agrees, he would be asked whether
his marital status has changed since that date, and if so,
when that happened, what the change was, and the characteristics of that transition, such as the demographics of the
new spouse, and so forth. 5 If the respondent disagrees with
the data carried forward in our records, the interviewer obtains the corrected data and then carries the event history
collection forward from the point of correction. 6 This approach allows one to deal with the "seam problem," namely
how to deal with recollections of the same event whose timing differs across survey waves. Indeed, when the NLSY79
switched to computer-assisted interviewing and, at the same
time, to collecting a true event history for education, the incidence of seam problems declined dramatically.
Perhaps more importantly, the use of bounded interview
event histories makes the data collection protocol less dependent on the interview date. If a respondent misses an
interview, at the next round he is asked to pick up the collection of the event history on, say, marital status at the point he
left off on the most recent completed interview. We recover
event history data from a respondent whenever he or she
returns to the survey. This approach generates a substantially more complete data history for respondents than suggested by a simple examination of completion rates.
Chart I illustrates the effect of returning to respondents.
The lower line is the round-by-round completion rate for the
NLSY79 from the 1st through 20th rounds. The higher line
shows the fraction of the data for the year preceding each of
the 20 rounds that we recovered either from an interview in
that or a subsequent round. Because we fill in missing data

Monthly Labor Review

February

2005

65

Respondent Attrition

Comparison of data recovery rates from ordinary round-by-round interviews and event
history with bounded interviewing, NLSY79 youths, ages 14-21 in 1979
Recovery rate

100

Recovery rate

~ -~ ~ ~ ~ ~ - - - - - - - - - - - - - - - - - - - - 7

100

90

90

80

80

70

70
Completion rate from event history
...... Ordinary completion rate

60

60

0

5

10

15

20

25

30

35

40

Years from first interview

NOTE: Year "0" is 1979.
Author's calculations.

SOURCE:

for the event histories whenever we interview the respondent, analysts have well in excess of 90 percent of the data
for 20 years after the initial interview. Bounded interviewing
requires a sophisticated instrument, adds to interview length,
and works best with salient events. With long retrospectives,
accuracy may suffer, but missing data are the worst data. In
the NLSY79 and NLSY97, we employ event histories for employment, marriage and cohabitation, fertility, training, education, and program recipiency. These histories are the core
of the NLS.
Using bounded interviewing event histories generates a
more complete data record, especially when combined with a
fielrl ~trategy that emphasizes returning to past
nonrespondents. Charts 2 and 3 illustrate how the decision
on which respondents to contact in a longitudinal survey is
crucial to generating a complete data record.
In the Young Women's and Mature Women's cohorts, the
Census Bureau originally adopted the strategy that it would
not return to any respondent who refused two straight interviews. In the mid-1980's, the Census Bureau changed that
strategy. Starting in 1985, the Census Bureau returned to
past nonrespondents to the Young Women's survey, and in
I 986, the Census Bureau no longer dropped respondents
missing two straight interviews, but did not return to past
nonrespondents.
Chart 2 shows that the rate of attrition slowed after the
Census Bureau no longer dropped nonrespondents after they
missed two straight interviews, effective in 1985. The event
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history completion rate naturally shows less data loss once
the Census Bureau changed their following-rules. Chart 3
illustrates the impact of the Census Bureau's decision in 1986
to return to some (but not all) of the Young Women who had
been dropped from the survey roster after two straight noninterviews. The completion rate jumped up when the Census
Bureau started the new following-rules, and in subsequent
rounds, the completion rate dropped more slowly. The impact
on the event history completion rate of former nonrespondents,
(especially in contrast to chart 2 for the Mature Women), provides stark evidence that an aggressive following-rule plus
bounded interviewing event histories are valuable data-collection strategies for the NLS. Next, we tum to encouraging the
respondents to give us more interviews.

Encouraging respondent cooperation
The NLS has a life-cycle perspective; it tries to follow respondents from their first interview to the end of their life. Sometimes funding constraints limit how long it can follow a cohort, but the project tries to keep its focus on how lives of
individuals evolve in their entirety. This scientific agenda
requires patience and a focus on long-term cooperation. The
other long-term survey best known among social scientists
is the Panel Study of Income Dynamics (PSID). That study
focuses on the household rather than on the individual, so if
we lose the cooperation of one reference person for the
household, we seek another informant for that household. In

Comparison of data recovery rates from single-round inteviewing and event history
with bounded interviewing from women ages 14-24 in 1967
Recovery rate

Recovery rate

100 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - , 100
~

Bounded interview

~ Single-round interview

90

90

80

80

70

70

60

60

5

10

15

20

25

30

40

35

Years from first interview
NOTE: Year 11 011 is 1979.
SOURCE: Author's calculations.

Comparison of data recovery rates between single-round inteviewing and event history
bounded interviewing, women ages 30-44 in 1967
~Gcovery rate

Recovery rate

100 - - - - - - - - - - - - - - - - - - - - - - - - - - - -- ~ 100
..,.._..

Bounded interview

...... Single-round interview

90

90

80

80

70

70

60

60

NOTE: Year 11 0 11 is 1979.
SOURCE: Author's calculations.


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Years from first interview

Monthly Labor Review

February

2005

67

Respondent Attrition

the case of the NLS, we only use proxy respondents for ill
individuals or in other unusual cases. This puts a premium
on retaining the good will of respondents. We seek the goodwill of the respondents in three major ways: informing the
respondent about survey objectives and findings, using incentive payments, and employing the mode of interview that
the respondent prefers.
Fundamentally, to gain respondent cooperation, field staff
must stress that the study is important, if not for the respondent, then for society at large. The questions have to be valid
and must relate to the objectives of the study that form the
basis for its societal usefulness. For the doubting respondent, the study must provide evidence that it is substantiated by findings, recommendations, or a body of research
consistent with the message that it is important. Interviewers play a pivotal role in the "selling of the survey." Thus, a
less appreciated corollary of convincing respondents that
the study is important is that the interviewers must be convinced the study is important. What good is a salesperson
who does not believe in the product?
ThP NLS program mails out informative brochures to respondents and provides the interviewers with packets of
materials that provide support for the central thesis that the
study has societal importance. It also provides items to rebut common objections to cooperating with the survey. However, the most important tool we have is the enthusiasm and
commitment of the interviewers. We communicate this in two
forms-first by engaging and "seliing" the interviewers when
they attend training, and second through their interaction
with the field managers who have day-to-day responsibility
for overseeing the field.work. Tepid and boring training sessions for interviewers are not only ineffective venues for
learning, but they communicate a subliminal message to interviewers that the larger survey is equally boring and, hence,
unimportant.
Although we attempt to communicate the scientific importance and social utility of the survey, some respondents remain unconvinced. Respondents are not monolithic. About
half the respondents are extremely cooperative and easy to
work with. As of 2002, almost exactly 60 percent of the respondents in continuing sample types 7 had completed every
interview. There is also a core group of very disaffected
respondents for whom we have little hope they will complete
the survey, although from time to time a few rejoin. For these
hard-core refusers there is not much that will make a difference in their cooperativeness, at least nothing we have been
able to identify and use. However, for a sizeable minority of
the sample, respondent incentives, either money or in-kind,
can play a useful role in securing cooperation with the survey. Small gifts that are tailored explicitly to the interests and
situation of the respondent can be very effective-they say
in a tangible way that we care about the respondent and pay
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February 2005

attention. Although incentive payments likely influenced
some of the respondents who have remained with the survey
throughout, incentives appear to work on a minority of respondents-a fact that needs to be integrated with any plan
to use incentives in longitudinal surveys.
In 2000, we conducted an experiment, with randomization
of subject and treatment, investigating the effects of a $40
versus $80 incentive. With the two incentive amounts, we
computed how much additional money we spent to obtain an
additional interview. On the one hand, when additional incentives were offered to respondents not interviewed in 1998
(the previous round-the NLSY79 is now administered every
2 years), we made $167 in additional incentive payments, on
average, for every additional interview when increasing the
payment from $40 to $80. On the other hand, for respondents
who had cooperated in 1998 we paid an additional $264 for
each additional interview. This experiment was conducted
when we had encountered strong resistance and sharply
higher field costs when using an across-the-board $20 incentive. Had the experiment been mounted at the beginning of
the field period, the higher payments would have been even
more cost-ineffective.
Across-the-board incentive payments increase response
rates at a substantial monetary cost. However, one unexpected result of the experiment in 2000 was a reduction in
field costs that coincided with the higher incentive payments-field costs fell in a manner we had not seen in previous rounds. The savings in field costs very nearly offset the
higher respondent fee costs. Survey organizations should
consider the effects of incentive fees, not only on respondent cooperation, but also on the overall cost-effectivene ss
of a survey campaign.
The most frequent objection to differential fees cites equity issues. This objection, together with the surprising finding in the 2000 round that higher fees reduced field costs, led
us to experiment in 2002 with a strategy aimed at using higher
incentives with cooperative respondents. For this experiment, called the "Early Bird," we mailed a flyer to respondents offering a higher fee if they called in to make an appointment for an interview. Because interviewers expend
substantial time locating, contacting, and making an appointment with respondents, the "Early Bird" offer saves money
despite the higher respondent fee involved. Once the conventional field period begins, the offered incentive payment
reverts to the normal level. 8 Respondents requesting a higher
fee are encouraged to participate in the Early Bird programinterviewers redirect requests for a higher payment toward
enrolling the respondent in a mutually advantageous compact exchanging more cooperation for a higher fee.
Although incentive payments encourage some respondents to participate, these payments cannot, within a reasonable range, convert all or even nearly all nonrespondents.

Fees work for some and fail for others. In addition, for some
groups of respondents, such fees can be very expensive in
terms of how many cases the extra money spent produces. In
short, respondent reactions to incentive fees are remarkably
heterogeneous. 9
Heterogeneity also rules when it comes to the interview
mode that respondents prefer. The survey choices available
to respondents are via telephone, in-person interview, or, to a
lesser degree, Webpage. However, the NLS surveys are too
complex to encourage most respondents to take a mail survey or a Web survey-most interviewers require about 2 days
of training on the survey before they are ready to administer
it. Some respondents, having done the survey 20 times before, are more "experienced" with the survey than new interviewers, but there are sections of the interview where interviewer training plays an important role. For this reason, the
NLS program does not routinely offer respondents the choice
to do the interview over the Web, although we could offer
this option. 10 When it comes to telephone versus in-person
interviews , some re spondents insist on a telephone interview and others insist on in-person interviews. The initial
approach to each respondent is usually over the telephone,
except in unusual circumstances. At that point, respondents
for the NLSY79 can choose how they want to do the interview. With the NLSY97 , the survey contains a substantial
self-administered section containing sensitive questions, and
interviewers emphasize in-person interviews. The latter constraint generates comments from the field staff indicating the
emphasis on in-person interviewing tends to increase costs;
however, if the choice is between a telephone interview and
no interview, the field staff always goes for the interview.
The essential point is that respondents vary in their preferences, and insisting on either telephone or in-person interviews carries substantial risk of alienating respondents.
In this same vein, organizations conducting longitudinal

-..,.1.

surveys often question whether having the same interviewer
do a case year after year encourages response. 11 Knowing
whether continuity in the assigned interviewer encourages
response would allow a field organization to assign cases
more strategically and to make a more intelligent trade-off
between reducing travel costs and reducing assignment turnover. Our ability to infer the relationship between attrition
and interviewer turnover is reduced by the fact that field
operations rarely employ random assignment of interviewer
and respondent to a dyad. If one interviewer has trouble with
a case, we assign a new interviewer and hope for better results. In short, random influences that make a respondent
less likely to do an interview can also lead to a change in the
assigned interviewer. Indeed, a case may have several interviewers assigned to it over the course of the field period.
Because of these problems, we look at respondent attrition
using variables that summarize his or her past tendency to do
the survey, his or her attitude at the most recently completed
interview, and respondent age. To capture the effect of interviewer continuity, we use two additional variables, whether
the interviewer doing the respondent's most recent interview
is still on staff and, if so, how many times that interviewer had
interviewed the respondent. These two variables only measure the ability of the field staff to exploit interviewer continuity in the current round, not whether interviewer continuity held for the current round.
The results, in table 2, using 202,245 observations from
rounds 2 through 20 of the NLSY79, suggest that interviewer
continuity is not a major factor. There is a net advantage to
interviewer continuity after the respondent has been interviewed twice by the same interviewer, and after that, having
the same interviewer decreases attrition by about 0.7 percent
for each additional round. If an interviewer had interviewed
the respondent for the previous round, having that interviewer on staff again generates no positive effect (for the

Probability of respondent cooperating with current
Variable

NLSY79

Coefficient

round
Effect 1 (dP/dX)

Standard error

t-ratio

Sample size, N=202,245
Intercept .......... ................... ..... ... ... ... ............ ... .... .. ... ..... .
Respondent did round before? ..... ........... .. .. .... .............. ..
Previous interviewer on staff? .... .... ... ........ .. ... ... .. ... ...... ..
Previous interview by phone? ... ............ .. ... ... .. ... .. ..... .... ..
Number of interviews done by previous
field interviewer, previous interviewer on staff ..... .. ...
Percent of previous interviews done ..... ..................... ... .
Respondent hostile at previous interview? ....... ... ... ... ... .
Respondent impatient, restless? ... .. .... ... .......... ... .. .. ..... ..
Respondent cooperative, not interested .... .............. ..... .
AgeR .......... .. ....... ....... ... ... .... .. .... .... ....... ... .... ............ ...... .
Age R squared ... .................. .. ........ ... .... ................. ......... .

1

-2.4002
2.8604
- .0914
- .5067

0.212
- .007
- .037

0.2219
.0296
.0289
.0256

10.82
96.64
3.16
19.79

.0881
3.8157
-1.5691
- .9364
- .5107
.0142
- .0011

.007
.282
- .116
-.069
- .038
.000
.000

.0089
.0721
.0917
.0421
.0249
.0141
.0002

9.90
52.92
17.11
22 .24
2.51
1.01
5.50

The effect on the probability of an interview on a unit of change in the explanatory variable.


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Monthly Labor Review

February

2005

69

Respondent Attrition

if the same interviewer is available, she will most
likely have any of her previous cases that are in the same
geographic area).
Also in table 2, the estimated model is a logit. The third
column gives the effect on the probability of an interview of
a unit change in the explanatory variable. The standard error
is for the coefficient. Results show that it is, by far, more
important to keep the respondent interested in the project
and happy with how they are treated than to keep the same
interviewer. Of course, interviewer continuity may make it
easi~r for the field staff to remember how best to deal with a
particular respondent.
NLSY79,

from attrition and nothing
will change that. However, certain survey methodologies can
minimize the impact of attrition. First, if consistent with its
objectives, the survey should utiiize event histories to recover data not collected when a respondent misses a round.

LONGITUDINAL SURVEYS WILL SUFFER

Second, the following-rules must emphasize persistence. If a
respondent refuses a round, return in the next round. When
respondents miss a round, in about half the cases they will
grant an interview for the next round. If they miss two straight
interviews, the probability of success drops to about 25 percent, but certainly not to zero. Third, targeted incentive payments should be used because they are cost effective ways
of holding attrition in check. Fourth, allow respondents to
choose the interview mode. Just as differences in respondents
make incentives effective only for some respondents, differences among respondents make it important to acquiesce to
their preferences over interview mode (phone versus personal
or even Web). Finally, longitudinal surveys must be "sold."
They must be sold to the interviewers who face the job of
convincing the respondent that the survey is important, and
they must be sold to the respondent who, in the majority of
cases, will offer their cooperation so long as the study engages their attention and they are confident that they are
□
providing their time for a worthy endeavor.

Notes
1
This is the rule determining to which respondents interviewers
would return in the event the respondent did not complete one or
more interviews .

2
Census interviewed the young men and young women ir. person
for several consecutive years and then reverted to the interview
pattern for the two older cohorts-personal, skip a year: telephone,
skip a year, telephone, personal. In the late 1980s the women's
interviews were done in person, although many cases were done over
the phone when circumstances dictated.

6
The discrepancy is handled at Ohio State. This style of data
collection generates fewer "seam problems" than histories that are
not collected using bounded interviewing. Our current practice is to
accept the date given initially and place the "seam" on the day after
the date of interview.

7
The low-income, non-black, non-Hispanic oversample was nearly
eliminated in 1993 and most of the military oversamples were dropped
earlier.
8

See Frank Mott, "Looking Backward: Post Hoc Reflections on
Longitudinal Surveys," in Erin Phelps, Frank Furstenberg, and Anne
Colby, eds., Looking at Lives: American Longitudinal Studies of the
Twemic;r, Century (New York, Russell Sage, 2002).

Currently the normal incentive is $40.

3

4
The two original men's cohorts-NLSY79 and NLSY97 were cancelled
in the early 1980s.

5 With the rise of nonmarital unions, event histories on marriage
and cohabitation have become more complex. The approach of asking
the retrospective question with explicit reference to the respondent's
previous answers is referred to as "bounded interviewing."

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February 2005

9
In the past I 5 years, we have also experimented with monetary
incentives to interviewers. Unfortunately, those experiments failed.
At best, they simply shifted the timing of when cases were completed.
10
Our CAP! system is based entirely on Web browsers. Some interviews
done over the phone utilize the Web to present the interview to the
interviewer, so offering a Web interview to respondents is a minor step.

11
The other side of repeated contact is that the respondent may be
more likely to give normative responses to an interviewer once they
have established rapport.

Measuring health
As Thomas Hale wrote almost 4 years
ago in this Review, measuring health or
disability status in household surveys
is a difficult and often frustrating task.
Michael Baker, Mark Stabile, and
Catherine Deri show that the difficulties
and frustrations continue. A broad
range of analysts, they say in a recent
article in The Journal of Human
Resources, recognize that subjective
self reports of physical capacity can be
biased as respondents may report a
spurious incapacity to justify nonparticipation in the labor market. Baker,
Stabile, and Deri go on to ask if self
reports of "objective" health measuresthe existence of specific diseases or
ailments-share any of the same
weaknesses.
Unfortunately, the evidence seems to
suggest that such objective measures
are also, as we say in the statistics trade,
difficult to interpret. The authors find
that there is considerable erro1, both
false positives and false negatives when
comparing responses to the Canadian
National Population Health Survey that
could be linked to the administrative
records of the Ontario Heath Insurance
Plan. They also find that these errors
are statistically related to labor market
status. Thus, they conclude that the self
reports of objective health indicators
"share many of the weaknesses of other
measures of health commonly used in
the literature."

Affording gas
Recent fluctuations in gas prices could
make one wonder if there could soon be


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evidence of a presumed complementary
goods relationship between gasoline
and low-gas-mileage passenger vehicles.
William T. Gavin doubts it. In the
November 2004 Federal Reserve Bank
of St. Louis publication National
Economic Trends, Gavin notes that
while it was true that the amount of gas
an hour of work could buy fell from a
little more than 14 gallons in February
1999 to ju~t under 8 gallons in May 2004,
it was imp01tant to realize that February
1999 Vias the record high point for that
calculation. In his view, the subsequent
rises in gas prices relative to wages
served to bring gasoline affordability
back to something only a little below its
long-term average. Thus, he concludes,
"Unless this modestly higher price
persists and continues to rise in tandem
with or faster than wages, we should not
expect it to dent consumer demand for
suvs." [Editor's note: As of the January
payroll survey reference week, an hour
of work was worth 8. 9 gallons of
gasoline.]

Raising productivity
As has been the case in the past,
producti ~-ity growth slowed at the
beginninp: uf the 2001 recession and
sped up again once the recession was
over, according to a recent report in the
Current Issues in Economics and
Finance series published by the Federal
Reserve Bank of New York. But, note
authors Dale W. Jorgenson, Mun S. Ho,
and Kevin J. Stiroh, "the drop-off in
productivity in 2001 was not as large as
it had been in earlier recessions and the
productivity recovery was much
stronger." As they are quick to point

out, this created some problems for
business cycle analysts who had to deal
with the differing trends in output and
employment growth as they sought to
identify the trough of the recession.
Using the standard techniques of
growth accounting, Jorgenson, Ho, and
Stiroh attribute much of the recent
vigorous growth in productivity to
accelerated capital deepening attributable to information technology and
to a rebound in the rate of total factor
productivity growth to about the rate
that was recorded in the 1960s and very
early 1970s. The rebound in total factor
productivity itself reflected a disproportionate contribution from
information technology: Despite
accounting for only 5 percent of
aggregate
output,
information
technology producers accounted for
about 35 percent of the increase in total
factor productivity.
The authors project a continuation
of these trends through 2014. Their
base-case scenario implies an annual
average growth in productivity of about
2.6 percent. This can be compared to
the 2.2-percent per year rate of
productivity growth the same team of
authors projected in a report released in
2002. The authors attribute their
revision to a projected continuation of
recent productivity trends, particularly
in the high-tech sectors, offset only
slightly by a projection of slightly
higher "drag" on productivity growth
from demographic trends. Jorgenson,
Ho, and Stiroh conclude modestly that
"there is little evidence to suggest that
the technology-led productivity
resurgence is over or that the U.S.
economy will revert to the slower pace
of productivity growth observed in the
1970s and 1980s."
□

Monthly Labor Review

February

2005

71

Essays on economics
The Lost Art of Economics: Essays on
Economics and the Economics
Profession. By David Colander.
Northampton, MA, Edward Flgar,
2003, 224 pp., $30/paperback.
Few students of economics have managed to avoid the question of whether
their chosen field qualifies as a true science. In this thought-provoking collection of essays, David Colander, a professor of economics at Middlebury College in Vermont, addresses this question
with well-written, and often entertaining
prose. A self-described "economic gadfly" and "slightly out of sync" economist, he steps back from the theories,
practices, and educational establishment
of the field to critique how this "science"
is taught and done. He argues that, in
the real world where economic policies
are implemented, successful economic
analysis must account for institutions
as they exist. It must consider the complex aspects of the economy and economic behavior that cannot be easily
quantified, or derived from a simplified,
codified theoretical model. In short, he
argues for the practice of economics as
an art, where theoretical models provide
background and elements of judgment,
intuitive thought, and even some ad hoc
empirics enter into the process before
policy prescriptions are drawn.
The author organizes his essays into
six parts. In the first part he presents
Milton Friedman's Theory of Positive
Economics as the foundation for the
current methodological approach of the
economics field. Under this approach
theoretical models are constructed,
based on (necessarily) simplified assumptions, and the (presumably observable, or testable) economic implications
are then derived from these models. He
argues that, ironically, Friedman himself
was mainly more of a practitioner of economics as an art, and that this h3s been
lost to the literature on history of eco-

72

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nomic thought. In the second part, he
lays out the methodology of the "art of
economics," and, using monetary policy
as the example, shows how academic
economists are too far into the realm of
abstract theory to provide many useful
policy ideas for applied problems. He
argues that the theoretical models
should be separated from applied policy
questions because, in the complex real
world, one cannot really test the hypotheses of the abstract model. Instead, the
model should serve as a general guide,
because "the question in applied policy
economics concerns whether the theory
fits the application, not whether the
theory is true."
Taking this basic premise, he describes in the third part of his essay collection how it can be used to better educate and interest students in economics. Particularly at the introductory (or
principles courses) level, he decries the
attempt to combine the basic theories of
economic behavior with modeling techniques as denying justice to both; it
makes for economic stories that are probab1y uninteresting to students. In his
fourth section are two more personal
essays, one of which details his own
story as an economics student and induction into the profession. The second essay provides suggestions for
making a living within the academic
world of economics as one with his
iconoclastic views. While these essays
may be of mterest mainly to those who
are Ph.D students and academic economists, they do reveal the faults of a rigid
"publish-or-perish" institutional structure that may discourage innovative
thinking (a problem not necessarily
unique to economics as a scholarly field).
He argues that this structure has encouraged economists to pursue problems and
policy issues that more easily yield to
quantification rather than those that are
more important to address and thuseconometrics has ascended from the role
of toolkit to the final arbiter of which issues an economist will choose to study.

February 2005

The essays in the fifth and sixth parts
of the book provide a critique of the educational institution of graduate education in economics, and a speculative scenario of what the profession, and thus
the graduate curriculum, may look like in
the year 2050. The author proposes a
second track of economics education
that will appeal to the generalist
inductivist student-one in which formal abstract theorem-and-proof is
deemphasized, and more weight is given
to interpreting and understanding the
basic theory of economics and doing
technical work at a more practical level.
Interestingly, this appears to already be
happening in other fields; some universities are now offering a professional
science master's degree, combining natural and physical sciences with finance
and business courses for students who
will need both in a practical career (Wall
Street Journal, Aug. 3, 2004). The author predicts that, by 2050, economists
will have abandoned positivism and will
be addressing more complex and specific problems with more emphasis on
computational simulation-making use
of new computation horsepower to mine
data for patterns and to create data by
simulation, but also combining this with
more general, intuitive insight and
knowledge of economic institutions in
the real world.
-Mary Kokoski
Division of Price and Index Number
Research,
Bureau of Labor Statistics

Publications received
Economic and social statistics
Aldy, Joseph E. and W. Kip Viscusi, Age
Variations in Workers' Value of Statistical Life. Cambridge, MA, National Bureau of Economic Research, Inc., 2003,
51 pp. (Working Paper 10199) $10 per
copy, plus $10 for postage and handling
outside the United States.

Clark, Robert L., Richard V. Burkhauser,
Marilyn Moon, Joseph F. Quinn, and
Timothy M. Smeeding, The Economics
of an Aging Society. Malden, MA,
Blackwell Publishing, 2004, 362 pp.,
$34.95/paperback.
Collins, William J. and Robert A. Margo,
The Labor Market Effects of the 1960s
Riots. Cambridge, MA, National Bureau
of Economic Research, Inc., 2004, 36 pp.
(Working Paper 10243) $10 per copy,
plus $10 for postage and handling outside the United States.
Dietzenbacher, Erik and Michael L. Lahr,
eds., Wassily Leontief and Input-Output
Economics. New York, Cambridge University Press, 2004, 396 pp., $80/hardcover.
Jacobs, Eva E., ed., Handbook of U.S. Labor
Statistics: Employment, Earnings, Prices,
Productivity, and Other Labor Data, Seventh Edition, 2004. Lanham, MD, Beman
Pre<:,;;, 2004, 471 pp., $147/hardcover.
Leopold, Ronald S., A Year in the Life of a
Million American Workers. New York,
MetLife Group Disability, 2003, 214 pp.,
softcover.
Wright, Daniel B., First Steps in Statistics.
Thousand Oaks, CA, Sage Publications,
2002, 147 pp., $23/softcover.

Economic growth
and development
Acs, Zoltan J., Innovation and the Growth
of Cities. Northampton, MA, Edward
Elgar Publishing, 2002, 264 pp., $90/hardback; $35/paperback.
Cahue, Pierre and Andre Zylberberg, Labor
Economics. Cambridge, MA, The MIT
Press, 2004, 844 pp., $90/cloth.
Cameron, Samuel, The Economics of Sin:
Rational Choice or No Choice At All?
Northampton, MA, Edward Elgar Publishing, 2002, 240 pp., $90/hardback.
Colar,Jcr, David, The Lost Art of Economics: Essays on Economics and the Economics Profession. Northampton, MA,
Edward Elgar Publishing, 2001, 203 pp.,
$30/paperback.
Hendry, David F. and Neil R. Ericson, eds.,
Understanding Economic Forecasts.
Cambridge, MA, The MIT Press, 2001, 225
pp. , $17.95/paperback.


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Lazear, Edward P. and Paul Oyer, Internal
and External Labor Markets: A Personnel Economics Approach. Cambridge,
MA, National Bureau of Economic Research, Inc., 2003, 50 pp. (Working Paper
10192) $10 per copy, plus $10 for postage and handling outside the United States.
Niederle, Muriel and Alvin E. Roth, Market
Culture: How Norms Governing Exploding Offers Can Affect Market Performance. Cambridge, MA, National Bureau
of Economic Research, Inc. , 2004, 57 pp.
(Working Paper I 0256) $10 per copy,
plus $10 for postage and handling outside thi:: United States.

Education
Hanushek, Eric A., Some Simple Analytics of
School Quality. Cambridge, MA, National
Bureau of Economic Research, Inc., 2004,
27 pp. (Working Paper 10229) $10 per
copy, plus $10 for postage and handling
outside the United States.

Industrial relations
Aitchison, Will, The FMI.A: Understanding
the Family and Medical Leave Act. Portland, OR, Labor Relations Information
System Publications, 2003, 320 pp.,
$39. 95/paperback.
Hogler, Raymond, Employment Relations in
the United States: Law, Policy, and Practice. Thousand Oaks, CA, Sage Publications, Inc., 2004, 301 pp., $42.95 /
softcover.

International economics
Balducchi, David E., Randall W. Eberts, and
Chri-;tor:,'.1~r J. O'Leary, eds., Labor Exchange Policy in the United States.
Kalamazoo, MI, W.E. Upjohn Institute for
Employment Research, 2004, 295 pp.,
$45/cloth; $20/paperback.
Budd, John W., Labor Relations: Striking a
Balance. New York, McGraw-Hill/Irwin,
2005, 553 pp., hardcover.
Troy, Leo, The Twilight of the Old Unionism. New York, M.E. Sharpe, Inc., 2004,
200 pp., $64.95/cloth; $24.95/paperback.

New Century. Cambridge, MA, The
Press, 2003, 565 pp., $39.95/cloth.

MIT

Kugler, Adriana D., The Effect of Job Security Regulations on Labor Market Flexibility: Evidence from the Colombian
Labor Market Reform. Cambridge, MA,
National Bureau of Economic Research,
Inc., 2004, 65 pp. (Working Paper 10215)
$10 per copy, plus $10 for postage and
handling outside the United States.

Labor and economic history
Galenson, David W., A Portrait of the Artist
as a Young or Old Innovator: Measuring the Careers of Modern Novelists.
Cambridge, MA, National Bureau of Economic Research, Inc., 2004, 78 pp.
(Working Paper 10213) $10 per copy,
plus $10 for postage and handling outside the United States.
Hardwick, M. Jeffrey, Mall Maker: Victor
Gruen, Architect ofan American Dream.
Philadelphia, University of Pennsylvania Press, 2004, 288 pp., $29.95/cloth.
McCann Jr., Charles Robert, ed., The Elgar
Dictionary of Economic Quotations.
Northampton, MA, Edward Elgar Publishing, 2003, 315 pp., $150/hardback.
Nicholson, Philip Yale, Labor's Story in the
United States. Philadelphia, Temple University Press, 2004, 376 pp., $74.50/
cloth; $27.95/cloth.
Quartey, Kojo A., A Critical Analysis of the
Contributions of Notable Black Economists. Burlington, VT, Ashgate Publishing Company, 2003, 125 pp., $79.95/
hardback.

Labor force
2003 Compendium of Regulatory Impact
Assessments. London, Department of
Trade and Industry, 2004, 291 pp.

Labor organizations
Roth, Silke, Building Movement Bridges: The
Coalition of Labor Union Women.
Westport, CT, Praeger Publishers, 2003,
207 pp., $64.95/hardback.

Management and organization
theory

Industry and government
organization
Cnossen, Sijbren and Hans-Werner Sinn,
Public Finance and Public Policy in the

Bevan, Stephen, Sally Dench, Heather
Harper, and Sue Hayday, Employment
Relations Research Series No. 25. Lon-

Monthly Labor Review

February 2005

73

Book Reviews

don, Department of Trade and Industry,
2004, 183 pp., spiral-bound.
Carey, Dennis C. and Dayton Ogden, The
Human Side ofM&A: How CEOs Leverage the Most Important Asset in Deal
Making. New York, Oxford University
Press, 2004, 193 pp., $27/cloth.
Jackson, Kevin T., Building Reputational
Capital: Strategies for Integrity and Fair
Play That Improve the Bottom Line. New
York, Oxford University Press, 2004, 300
pp., $30/hardcover.
Jacoby, Sanford M., Employing Bureaucracy: Managers , Unions, and the Transformation of Work in the 20th Century,
Revised Edition. Mahwah, NJ, Lawrence
I::r!Laum Associates Publishers, 2004, 315
pp., $79.95/cloth; $34.50/paperback.
Oyer, Paul and Scott Schaefer, Why Do Some
Firms Give Stock Options to All Employees?: An Empirical Examination of Alternative Theories. Cambridge, MA, National Bureau of Economic Research, Inc.,
2004, 41 pp. (Working Paper 10222) $10
per copy, plus $10 for postage and handling outside the United States.
Schneider, Benjamin and Susan S. White,
Service Quality: Research Perspectives.
Thousand Oaks, CA, Sage Publications,
2004, 200 pp., $34.95/paperback.
Weiss, Donald H., Fair, Square & Legal,
Fourth Edition. New York, AMACON
(American Management Association),
2004, 384 pp., $35/hardcover.
West, Michael, Motivate Teams, Maximize
Success: Effective Strategies for Realizing Your Goals. San Francisco, Chronicle
Books, 2004, 160 pp., $16.95/paperback.

ing Paper 10212) $10 per copy, plus $10
for postage and handling outside the
United States.
Kremp, Elizabeth and Jacques Mairesse,
Knowledge Management, Innovation and
Productivity: A Firm Level Exploration
Based on French Manufacturing CIS3
Data. Cambridge, MA, National Bureau
of Economic Research, Inc., 2004, 45 pp.
(Working Paper 1023 7) $10 per copy,
plus $10 for postage and handling outside the United States.
Lerner, Josh, The New New Financial Thing:
The Sources of Innovation Before and
After State Street. Cambridge, MA, National Bureau of Economic Research, Inc.,
2004, 54 pp. (Working Paper 10223) $10
per copy, plus $10 for postage and handling outside the United States.

Social institutions and
social change
Lee, Sandra S., ed., Traumatic Stress and Its
Aftermath. Binghamton, NY, The
Haworth Press, 2004, 99 pp., $34.95/
hardcover; $19.95/softcover.
Presser, Harriet B., Working in a 2417
Economy: Challenges for American
Families. New York, Russell Sage Foundation, 2003, 286 pp., $39.95/cloth.

Wages and compensation
Appelbaum, Eileen, Annette Bernhardt, and
Richard J. Murnane, eds., Low-Wage
America: How Employers Are Reshaping Opportunity in the Workplace. New
York, Russell Sage Foundation, 2003, 536
pp., $45/cloth.

Bai, Chong-En and Chi-Wa Yuen, Technology
and the New Economy. Cambridge, MA,
The MIT Press, 2003, 312 pp., $32.95/cloth.

Biesebroeck, Johannes Van, Wages Equal
Productivity, Fact or Fiction? Cambridge,
MA, National Bureau of Economic Research, Inc., 2003, 52 pp. (Working Paper 10174) $10 per copy, plus $10 for
postage and handling outside the United
States.

Hall, Bronwyn H., Innovation and Diffusion.
Cambridge, MA, National Bureau of Economic Research, Inc., 2004, 33 pr. rwork-

Connelly, Rachel, Deborah S. DeGraff, and
Rachel A. Willis, Kids at Work: The Value
of Employer-Sponsored On-Site Child

Productivity and technological
chu,1ge

74

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February 2005

Care Centers. Kalamazoo, MI, W.E.
Upjohn Institute for Employment Research, 2004, 184 pp., $40/cloth; $17/
paperback.
Oyer, Paul and Scott Schaefer, Compensating Employees Below the Executive
Ranks: A Comparison of Options, Restricted Stock, and Cash. Cambridge, MA,
National Bureau of Economic Research,
Inc., 2004, 28 pp. (Working Paper
10221) $10 per copy, plus $10 for postage and handling outside the United
States.

Welfare programs
and social insurance
Benitez-Silva, Hugo, Moshe Buchinsky, and
John Rust, How Large Are the Classification Errors in the Social Security Disability Award Process ? Cambridge, MA,
National Bureau of Economic Research,
Inc., 2004, 52 pp. (Working Paper 10219)
$10 per copy, plus $10 for postage and
handling outside the United States.
Chetty, Raj, Consumption Commitments,
Unemployment Durations, and Local
Risk Aversion. Cambridge, MA, National
Bureau of Economic Research, Inc., 2004,
64 pp. (Working Paper 10211) $10 per
copy, plus $10 for postage and handling
outside the United States.
Wolff, Edward N., Retirement Insecurity: The
Income Shortfalls Awaiting the Soon-toRetire. Washington, DC, Economic Policy
Institute, 2002, 95 pp., softcover.

Worker training
and development
Giloth, Robert P., ed., Workforce Development Politics: Civic Capacity and Performance. Philadelphia, Temple University Press, 2004, 296 pp., $72.50/cloth;
$24.95/paperback.
Phelps, Edmund S., ed., Designing Inclusion:
Tools to Raise Low-end Pay and Employment in Private Enterprise. New York,
Cambridge University Press, 2004, 165
pp., $55/hardback.
0

Notes on labor statistics ..............................
Comparative indicators

76

1. Labor market indicators .................................................... 89
2. Annual and quarterly percent changes in
compensation, prices, and productivity ....................... 90
3. Alternative measures of wages and
compensation changes ............................................... .... 90

Labor force data
4. Employment status of the population,
seasonally adjusted ....................................................... 91
5. Selected employment indicators,
seasonally adjusted ....................................................... 92
6. Sekcted unemployment indicators,
seasonally adjusted ....................................................... 93
7. Duration of unemployment,
seasonally adjusted ....................................................... 93
8. Unemployed persons by reason for unemployment,
seasonally adjusted ....................................................... 94
9. Unemployment rates by sex and age,
seasonally adjusted ....................................................... 94
10. Unemployment rates by States,
seasonally adjusted ....................................................... 95
11. Employment of workers by States,
seasonally adjusted ....................................................... 95
12. Employment of workers by industry,
seasonally adjusted ....................................................... 96
13. Average weekly hours by industry,
seasonally adjusted ....................................................... 99
14. Average hourly earnings by industry,
seasonally adjusted ................................................... .. .. 100
15. Average hourly earnings by industry ................................ 101
16. Average weekly eamil)gs by industry .................... ........... 102
17. Diffusion indexes of employment change,
seasonally adjusted ....................................................... 103
18. Job openings levels and rates, by industry and regions,
seasonally adjusted......................................................... 104
19. Hires levels and rates by industry and region,
seasonally adjusted .......................................................... l 04
20. Separations levels and rates by industry and region,
seasonally adjusted .......................................................... l 05
21. Quits levels and rates by industry and region,
seasonally adjusted .......................................................... I 05
22. Quarterly Census of Employment and Wages,
10 largest counties ......................................................... 106
23. Quarterly Census of Employment and Wages, by State.. 108
24. Annual data: Quarterly Census of Employment
and Wages, by ownership ............................................. 109
25. Annual data: Quarterly Census of Employment and Wages,
establishment size and employment, by supersector ... 110
26. Annual data: Quarterly Census of Employment and
Wages, by metropolitan area ......................................... 1 11
27. Annual data: Employment status of the population ........ 116
28. Annual data: Employment levels by industry .................. 116
29. Annual data: Average hours and earnings level,
by industry ..................................................................... 117


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Labor compensation and collective
bargaining data
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 ...........

30.
31.
32.
33.

120
121
122
123
124
125
126

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, all items
and major groups...........................................................
40. Producer Price Indexes by stage of processing ............ .....
41. Producer Price Indexes for the net output of major
industry groups .............................................................
42. Annual data: Producer Price Indexes
by stage of processing...................................................
43. U.S. export price indexes by Standard International
Trade Classification ..... .... .. .. ...... .. .. .. .. ... .... ..... .... ......... ..
44. U.S. import price indexes by Standard International
Trade Classification ..... .... .... .... ... ..... ... .. ........ .... .... ... .. ...
45. U.S. export price indexes by end-use category .................
46. U.S. import price indexes by end-use category ................
47. U.S. international price indexes for selected
categories of services.....................................................

127
130
131
132
133
134
135
136
137
137
137

Productivity data
48. Indexes of productivity, hourly compensation,
and unit costs, data seasonally adjusted .......................
49. Annual indexes of multifactor productivity ......................
50. Annual indexes of productivity, hourly compensation,
unit costs, and prices ....................................................
5 1. Annual indexes of output per hour for select
industries ... .. .. .. .. .. .. .... .... .... .... .... .. .. .... .. .. .... .... .... .......... ..

138
139
140
141

International comparisons data
52. Unemployment rates in nine countries,
data seasonally adjusted ................................................. 144
53. Annual data: Employment status of the civilian
working-age population, 10 countries............................ 145
54. Annual indexes of productivity and related measures,
15 economies.................................................................. 146

Injury and Illness data
55. Annual data: Occupational injury and illness
incidence rates ................................................................. 148
56. Fatal occupational injuries by event or exposure............. 150

Monthly Labor Review

February 2005

75

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.

Genera l 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 condition s, industry productio n
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
an<l p::\st experiences. When new seasonal
factors are computed each year, revisions
may affect seasonally adjusted data for several preceding years.
Seasonally adjusted data appear in tables
1-14, 17-21, 48, and 52. Seasonally adjusted labor force data in tables 1 and 4-9
were revised in the February 2005 issue of
the Review. Seasonally adjusted est:tblishment survey data shown in tables 1, 12-14,
and 17 were revised in the March 2004 Review. A brief explanation of the seasonal
adjustment methodology appears in "Notes
on the data."
Revisions in the productiv ity 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
76

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F&bruary

index number of 150, where 1982 = 100,
the hourly rate expressed in 1982 dollars is
$2 ($3/150 x 100 = $2). The $2 (or any other
resulting values) are described as "real,"
"constant," or "1982" dollars.

Sources of information
Data that supplement the tables in this section are published by the Bureau in a variety of sources. Definitions of each series and
notes on the data are contained in later sections of these Notes describing each set of
data. For detailed descriptions of each data
series, see BLS Handbook of Methods, Bulletin 2490. Users also may wish to consult
Major Programs of the Bureau of Labor Statistics, Report 919. News releases provide
the latest statistical information published
by the Bureau; the major recurring releases
are published according to the schedule appearing on the back cover of this issue.
More information about labor force, employment, and unemployment data and the
household and establishment surveys underlying the data are available in the Bureau's
monthly publicatio n, Employm ent 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.govnpd
For additional information on interna2005

tional comparisons data, see International
Comparis ons of Unemploy ment , 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;
productivi ty; internatio nal compariso ns;
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 availabilit y 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 Employm ent
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
compensa tion and wage measures because
it provides a comprehe nsive measure of
employer costs for hiring labor, not just
outlays for wages, and it is not affected
by employme nt shifts among occupatio ns
and industries.
Data on changes in compens ation,
prices, and productivity are presented in

table 2. Measures of rates of change of compensation and wages from the Employment
Cost Index program are provided for all civilian nonfarm workers (excluding Federal
and household workers) and for all private
nonfarm workers. Measures of changes in
consumer prices for all urban consumers;
producer prices by stage of processing; overall prices by stage of processing; and overall export and import price indexes are
given. Measures of productivity (output per
hour of all persons) are provided for major
sectors.
Alternative measures of wage and
compensation rates of change, which reflect the overall trend in labor costs, are summarized in table 3. Differences in concepts
and scope, related to the specific purposes
of the series, contribute to the variation in
changes among the individual measures.

Notes on the data
Definitions of each series and notes on the
data are contained in later sections of these
notes describing each set of data.

Employment and
Unemployment Data
(Tables 1; 4--29)

Household survey data

not work during the survey week, but were
available for work except for temporary illness and had looked for jobs within the preceding 4 weeks. Persons who did not look
for work because they were on layoff are also
counted among the unemployed. The unemployment rate represents the number unemployed as a percent of the civilian labor force.
The civilian labor force consists of all
employed or unemployed persons in the civilian noninstitutional population. Persons
not in the labor force are those not classified as employed or unemployed. This group
includes discouraged workers, defined as
persons who want and are available for a
job and who have looked for work sometime in the past 12 months (or since the end
of their last job if they held one within the
past 12 months), but are not currently looking, because they believe there are no jobs
available or there are none for which they
would qualify. The civilian noninstitutional population comprises all persons 16
years of age and older who are not inmates
of penal or mental institutions, sanitariums,
or homes for the aged, infirm, or needy. The
civilian labor force participation rate is
the proportion of the civilian noninstitutional population that is in the labor
force. The employment-population ratio is
employment as a percent of the civilian
noninstitutional population.

Notes on the data

Description of the series
Employment data in this section are obtained from the Current Population Survey,
a program of personal interviews conducted
monthly by the Bureau of the Census for the
Bureau of Labor Statistics. The sample consists of about 60,000 households selected to
represent the U.S. population I 6 years of
age and older. Households are interviewed
on a rotating basis, so that three-fourths of
the sample is the same for any 2 consecutive months.

Definitions
Employed persons include (1) all those
who worked for pay any time during the
week which includes the 12th day of the
month or who worked unpaid for 15 hours
or more in a family-operated enterprise and
(2) those who were temporarily absent from
their regular jobs because of illness, vacation, industrial dispute, or similar reasons.
A person working at more than one job is
counted only in the job at which he or she
worked the greatest number of hours.
Unemployed persons are those who did


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From time to time, and especially after a decennial census, adjustments are made in the
Current Population Survey figures to correct for estimating errors during the
intercensal years. These adjustments affect
the comparability of historical data. A description of these adjustments and their effect on the various data series appears in the
Explanatory Notes of Employment and
Earnings. For a discussion of changes introduced in January 2003, see "Revisions
to the Current Population Survey Effective
in January 2003" in the February 2003 issue of Employment and Earnings (available
on the BLS Web site at: http://www.bls.gov/
cps/rvcps03.pdf).
Effective in January 2003, BLS began using the X-12 ARIMA seasonal adjustment program to seasonally adjust national labor force
data. This program replaced the x-11 ARIMA
program which had been used since January
1980. See "Revision of Seasonally Adjusted
Labor Force Series in 2003," in the February 2003 issue of Employment and
Earnings (available on the BLS Web site
at http:www.bls.gov/cps/cpsrs.pdf) for a
discussion of the introduction of the use of

ARIMA for seasonal adjustment of the
labor force data and the effects that it had
on the data.
At the beginning of each calendar year,
historical seasonally adjusted data usually
are revised, and projected seasonal adjustment factors are calculated for use during
the January-June period. The historical seasonally adjusted data usually are revised for
only the most recent 5 years. In July, new
seasonal adjustment factors, which incorporate the experience through June, are produced for the July-December period, but no
revisions are made in the historical data.
FOR ADDITIONAL INFORMATION on national household survey data, contact the
Division of Labor Force Statistics: (202)
691-6378.
X-12

Establishment survey data
Description of the series
Employment, hours, and earnings data in
this section are compiled from payroll
records reported monthly on a voluntary basis to the Bureau of Labor Statistics and its
cooperating State agencies by about
160,000 businesses and government agencies, which represent approximately
400,000 individual worksites and represent
all industries except agriculture. The active
CES sample covers approximately one-third
of all nonfarm payroll workers. Industries
are classified in accordance with the 2002
North American Industry Classification System. In most industries, the sampling probabilities are based on the size of the establishment; most large establishments are
therefore in the sample. (An establishment
is not necessarily a firm; it may be a branch
plant, for example, or warehouse.) Self-employed persons and others not on a regular
civilian payroll are outside the scope of the
survey because they are excluded from estab1ishment records. This largely accounts for
the difference in employment figures between
the household and establishment surveys.

Definitions
An establishment is an economic unit
which produces goods or services (such as
a factory or store) at a single location and is
engaged in one type of economic activity.
Employed persons are all persons who
received pay (including holiday and sick
pay) for any part of the payroll period including the 12th day of the month. Persons
holding more than one job (about 5 percent
of all persons in the labor force) are counted

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2005

77

Current Labor Statistics

in each establishment which reports them.
Production workers in the goods-prod uc ing industries cover employees, up
through the level of working supervisors,
who engage directly in the manufacture or
construction of the establishment's product.
In private service-providing industries, data
are collected for nonsupervisory workers,
which include most employees except those
in executive, managerial, and supervisory
positions. Those workers mentioned in
tables 11-16 include production workers in
manufacturing and natural resources and
mining; construction workers in construction; and nonsupervisory workers in all private service-providing industries. Production and nonsupervisory workers account
for about four-fifths of the total employment
on private nonagricultural payrolls.
Earnings are the payments production
or nonsupervisory workers receive during
the survey period, including premium pay
for overtime or late-shift work but excluding irregular bonuses and other special
payments. Real earnings are earnings adjusted to reflect the effects of changes in
consumer prices. The deflator for this series is derived from the Consumer Price Index for Urban Wage Earners and Clerical
Workers (CPI-W).
Hours represent the average weekly
hours of production or nonsupervisory
workers for which pay was received, and are
different from standard or scheduled hours.
Overtime hours represent the portion of average weekly hours which was in excess of
regular hours and for which overtime premiums were paid.
The Diffusion Index represents the percent of industries in which employment was
rising over the indicated period, plus onehalf of the industries with unchanged employment; 50 percent indicates an equal balance between industries with increasing and
decreasing employment. In line with Bureau
practice, data for the 1-, 3-, and 6-month
spans are seasonally adjusted, while those
for the 12-month span are unadjusted. Table
17 provides an index on private nonfarm
employment based on 278 industries, and a
manufacturing index based on 84 industries.
These indexes are useful for measuring the
dispersion of economic gains or losses and
are also economic indicators.

Establishment survey data are annually adjusted to comprehensive counts of employment (called "benchmarks"). The March
2003 benchmark was introduced in February 2004 with the release of data for January 2004, published in the March 2004 is-

sue of the Review. With the release in June
2003, CES completed a conversion from the
Standard Industrial Classification (SIC) 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 and Earnings
and "Recent changes in the national Current
Employment Statistics survey," Monthly Labor Review, June 2003, pp. 3-13.
Revisions in State data (table 11) occurred with the publication of January 2003
data. For information on the revisions for
the State data, see the March and May 2003
issues of Employment and Earnings, and
"Recent changes in the State and Metropolitan Area CES survey," Monthly Labor Review, June 2003, pp. 14-19.
Beginning in June 1996, the BLS uses the
X-12-ARIMA methodology to seasonally adjust establishment survey data. This procedure, developed by the Bureau of the Census, controls for the effect of varying survey intervals (also known as the 4- versus
5-week effect), thereby providing improved
measurement of over-the-month changes
and underlying economic trends. Revisions
of data, usually for the most recent 5-year
period, are made once a year coincident with
the benchmark revisions.
In the establishment survey, estimates for
the most recent 2 months are based on incomplete returns and are published as preliminary in the tables (12-17 in the Review).
When all returns have been received, the estimates are revised and published as "final"
(prior to any benchmark revisions) in the

78

2005

Notes on the data

Monthly Labor Review


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February

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 l) 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 l 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 10)
or (202) 691-6559 (table 11).

Quarterly Census of
Employment and Wages
Description of the series
Employment, wage, and establishment data
in this section are derived from the quarterly tax reports submitted to State employment security agencies by private and
State and local government employers sub-

ject to State unemployment insurance (u1)
laws and from Federal, agencies subject
to the Unemployment Compensation for
Federal Employees (uCFE) program. Each
quarter, State agencies edit and process the
data and send the information to the Bureau of Labor Statistics.
The Quarterly Census of Employment
and Wages (QCEW) data, also referred as ES202 data, are the most complete enumeration
of employment and wage information by industry at the national, State, metropolitan
area, and county levels. They have broad economic significance in evaluating labor market trends and major industry developments.

Definitions
In general, the Quarterly Census of Employment and Wages monthly employment data
represent the number of covered workers
who worked during, or received pay for, the
pay period that included the 12th day of the
month. Covered private industry employment includes most corporate officials, executives, supervisory personnel, professionals, clerical workers, wage earners, piece
workers, and part-time workers. It excludes
proprietors, the unincorporated self-employed, unpaid family members, and certain
farm and domestic workers. Certain types
of nonprofit employers, such as religious organizations, are given a choice of coverage
or exclusion in a number of States. Workers
in these organizations are, therefore, reported to a limited degree.
Persons on paid sick leave, paid holiday,
paid vacation, and the like, are included. Persons on the payroll of more than one firm
during the period are counted by each u,subject employer if they meet the employment definition noted earlier. The employment count excludes workers who earned no
wages during the entire applicable pay period because of work stoppages, temporary
layoffs, illness, or unpaid vacations.
Federal employment data are based on
reports of monthly employment and quarterly wages submitted each quarter to State
agencies for all Federal installations with
employees covered by the 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 physical location and
engaged in one, or predominantly one, type
of economic activity for which a single industrial classification may be applied. Occasionally, a single physical location encompasses two or more distinct and significant
activities. Each activity should be reported
as a separate establishment if separate
records are kept and the various activities are classified under different NAICS
industries.
Most employers have only one establishment; thus, the establishment is the predominant reporting unit or statistical entity for
reporting employment and wages data. Most
employers, including State and local governments who operate more than one establishment in a State, file a Multiple Worksite Report each quarter, in addition to their quarterly 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
u, report. Some very small multi-establishment employers do not file a Multiple
Worksite Report. When the total employment in an employer's secondary establishments (all establishments other than the largest) is 10 or fewer, the employer generally
will file a consolidated report for all establishments. Also, some employers either cannot or will not report at the establishment
level and thus aggregate establishments into
one consolidated unit, or possibly several
units, though not at the establishment level.
For the Federal Government, the reporting unit is the installation: a single location at which a department, agency, or other
government body has civilian employees.
Federal agencies follow slightly different criteria than do private employers when breaking down their reports by installation. They
are permitted to combine as a single statewide unit: 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 installations may be
combined and reported with the major installation. Last, if a Federal agency has fewer
than five employees in a State, the agency
headquarters office (regional office, district
office) serving each State may consolidate
the employment and wages data for that State
with the data reported to the State in which
the headquarters is located. As a result of
these reporting rules, the number of reporting units is always larger than the number
of employers (or government agencies) but
smaller than the number of actual establishments (or installations).

Data reported for the first quarter are
tabulated into size categories ranging from
worksites of very small size to those with
1,000 employees or more. The size category
is determined by the establishment's March
employment level. It is important to note that
each establishment of a multi-establishment
firm is tabulated separately 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, specify
that wages be reported for, or based on the
period during which services are performed
rather than the period during which compensation is paid. Under most State laws or
regulations, wages include bonuses, stock
options, the cash value of meals and lodging, tips and other gratuities, and, in some
States, employer contributions to certain deferred compensation plans such as 401 (k)
plans.
Covered employer contributions for oldage, survivors, and disability insurance
(OASDI), health insurance, unemployment insurance, workers' compensation, and private
pension and welfare funds are not reported
as wages. Employee contributions for the
same purposes, however, as well as money
withheld for income taxes, union dues, and
so forth, are reported even though they are
deducted from the worker's gross pay.
Wages of covered Federal workers represent the gross amount of all payrolls for
all pay periods ending within the quarter.
This includes cash allowances , the cash
equivalent of any type of remuneration, severance pay, withholding taxes, and retirement deductions. Federal employee remuneration generally covers the same types of
services as for workers in private industry.
Average annual wage per employee for
any given industry are computed by dividing total annual wages by annual average employment. A further division by 52 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-time
workers as well as the number of individuals in high-paying and low-paying occupations. When average pay levels between
States and industries are compared, these
factors should be taken into consideration.
For example, industries characterized by
high proportions of part-time workers will

Monthly Labor Review

February

2005

79

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.

Beginning with the release of data for 200 l,
publications presenting data from the Covered Employment and Wages program have
switched to the 2002 version of the North
American Industry Classification System
(NAICS) as the basis for the assignment and
tabulation of economic data by industry.
NAICS is the product of a cooperative effort
on the part of the statistical agencies of the
United States, Canada, and Mexico. Due to
difference in NAICS and Standard Industrial
Classification (SIC) structures, industry data
for 2001 is not comparable to the SIC-based
data for earlier years.
Effective January 200 l, 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) pro~ram 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.

Changes resulting from improved employer
reporting also are introduced in the first
quarter. For these reasons, some data, especially at more detailed geographic levels, may not be strictly comparable with
earlier years.
County definitions are assigned according to Federal Information Processing Standards Publications as issued by the National
Institute of Standards and Technology. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those areas designated by the Census Bureau where counties
have not been created. County data also are
presented for the New England States for
comparative purposes, even though townships are the more common designation used
in New England (and New Jersey).
The Office of Management and Budget
(0MB) defines metropolitan areas for use in
Federal statistical activities and updates
these definitions as needed. Data in this table
use metropolitan area criteria established by
0MB in definitions issued June 30, 1999
(0MB Bulletin No. 99-04). These definitions
reflect information obtained from the 1990
Decennial Census and the 1998 U.S. Census Bureau population estimate. A complete
list of metropolitan area definitions is available from the National Technical Information Service (NTIS), Document Sales, 5205
Port Royal Road, Springfield, Va. 22161,
telephone 1-800-553-6847.
0MB defines metropolitan areas in terms
of entire counties, except in the six New
England States where they are defined in
terms of cities and towns. New England data
in this table, however, are based on a county
concept defined by 0MB as New England
County Metropolitan Areas (NECMA) because county-level data are the most detailed
available from the Quarterly Census of Employment and Wages. The NECMA is a countybased alternative to the city- and town-based
metropolitan areas in New England. The
NECMA for a Metropolitan Statistical Area
(MSA) include: (1) the county containing the
first-named city in that MSA title (this county
may include the first-named cities of other
MSA, and (2) each additional county having
at least half its population in the MSA in
which first-named cities are in the county
identified in step 1. The NECMA is officially
defined areas that are meant to be used by
statistical programs that cannot use the regular metropolitan area definitions in New
England.
FOR ADDITIONAL INFORMATION on the
covered employment and wage data, contact
the Division of Administrative Statistics and
Labor Turnover at (202) 691-6567.

80

2005

Notes on the data

Monthly Labor Review


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February

Job Openings and Labor
Turnover Survey
Description of the series
Data for the Job Openings and Labor Turnover Survey (JOLTS) are collected and compiled from a sample of 16,000 business establishments. Each month, data are collected
for total employment, job openings, hires,
quits, layoffs and discharges, and other separations. The JOLTS program covers all private
nonfarm establishments such as factories,
offices, and stores, as well as Federal, State,
and local government entities in the 50 States
and the District of Columbia. The JOLTS
sample design is a random sample drawn from
a universe of more than eight million establishments compiled as part of the operations
of the Quarterly Census of Employment and
Wages, or QCEW, program. This program includes all employers subject to State unemployment insurance (UI) laws and Federal
agencies subject to Unemployment Compensation for Federal Employees (UCFE).
The sampling frame is stratified by ownership, region, industry sector, and size class.
Large firms fall into the sample with virtual
certainty. JOLTS total employment estimates are
controlled to the employment estimates of the
Current Employment Statistics (CES) survey.
A ratio of CES to JOLTS employment is used to
adjust the levels for all other JOLTS data elements. Rates then are computed from the adjusted levels.
The monthly JOLTS data series begin with
December 2000. Not seasonally adjusted data
on job openings, hires, total separations, quits,
layoffs and discharges, and other separations
levels and rates are available for the total nonfarm sector, 16 private industry divisions and
2 government divisions based on the North
American Industry Classification System
(NAICS), and four geographic regions. Seasonally adjusted data on job openings, hires, total
separations, and quits levels and rates are available for the total nonfarm sector, selected industry sectors, and four geographic regions.

Definitions
Establishments submit job openings information for the last business day of the reference month. A job opening requires that (1)
a specific position exists and there is work
available for that position; and (2) work
could start within 30 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 l 00.
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 I00.
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-tenn employees, and terminations of
seasonal employees. Other separations include
retirements, transfers to other locations, deaths,
and separations due to disability. Separations
do not include transfers within the same location or employees on strike.
The separations rate is computed by dividing the number of separations by employment, and multiplying that quotient by 100.
The quits, layoffs and discharges, and other
separations rates are computed similarly,


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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 currentiy 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: (1) 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!) 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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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 le"els 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.

benefits (such as Social Security, workers'
compensation, and unemployment insurance).
Excluded from wages and salaries and
employee benefits are such items as payment-in-kind, free room and board, and tips.

Notes on the data
The Employment Cost Index for changes in
wages and salaries in the private nonfarm
economy was published beginning in 1975.
Changes in total compensation cost-wages
and salaries and benefits combined-were
published beginning in 1980. The series of
changes in wages and salaries and for total
compensation in the State and local government sector and in the civilian nonfarm
economy (excluding Federal employees)
were published beginning in 1981. Historical indexes (June 1981 = I 00) 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

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

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.

82

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Definitions

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February

Definitions
Employer-provided benefits are benefits
that are financed either wholly or partly by
the employer. They may be sponsored by a
union or other third party, as long as there is
some employer financing. However, some
benefits that are fully paid for by the employee also are included. For example, longterm care insurance and postretirement life
insurance paid entirely by the employee are
included because the guarantee of insurability and availability at group premium rates
are considered a benefit.
Participants are workers who are covered by a benefit, whether or not they use
that benefit. If the benefit plan is financed
wholly by employers and requires employees to complete a minimum length of service for eligibility, the workers are considered participants whether or not they have
met the requirement. If workers are required to contribute towards the cost of a
plan, they are considered participants only
if they elect the plan and agree to make the
required contributions.
Defined benefit pension plans use predetermined formulas to calculate a retirement benefit (if any), and obligate the employer to provide those benefits. Benefits
are generally based on salary, years of service, or both.
Defined contribution plans generally
specify the level of employer and employee
contributions to a plan, but not the formula
for determining eventual benefits. Instead,
individual accounts are set up for participants, and benefits are based on amounts
credited to these accounts.
Tax-deferred savings plans are a type
of defined contribution plan that allow participants to contribute a portion of their salary to an employer-sponsored plan and defer income taxes until withdrawal.
Flexible benefit plans allow employees
to choose among several benefits, such as
life insurance, medical care, and vacation
days, and among several levels of coverage
within a given benefit.

Notes on the data
Surveys of employees in medium and large
establishments conducted over the 197986 period included establishments that employed at least 50, 100, or 250 workers,
depending on the industry (most service
industries were excluded). The survey conducted in 1987 covered only State and local governments with 50 or more employ-

ees. The surveys conducted in 1988 and
1989 included medium and large establishments with 100 workers or more in private
industries. All surveys conducted over the
1979-89 period excluded establishments
in Alaska and Hawaii, as well as part-time
employees.
Beginning in 1990, surveys of State and
local governments and small private establishments were conducted in even-numbered years, and surveys of medium and
large establishments were conducted in oddnumbered years. The small establishment
survey includes all private nonfarm establishments with fewer than 100 workers,
while the State and local government survey includes all governments, regardless of
the number of workers. All three surveys include full- and part-time workers, and
workers in all 50 States and the District of
Columbia.
FOK 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 = 100 for many Consumer Price Indexes
(unless otherwise noted), and 1990 = 100 for
International Price Indexes.

Consumer Price Indexes

Work stoppages

Description of the series

Description of the series

The Consumer Price Index (CPI) is a measure of the average change in the prices paid
by urban consumers for a fixed market basket of goods and services. The CPI is calculated monthly for two population groups,
one consisting only of urban households
whose primary source of income is derived
from the employment of wage earners and
clerical workers, and the other consisting of
all urban households. The wage earner index (CPI -W) is a continuation of the historic
index that was introduced well over a halfcentury ago for use in wage negotiations.
As new uses 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 1998 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 PP! organizes
products by class of buyer and degree of fabrication (that is, finished goods, intermediate goods, and crude materials). The traditional commodity structure of PP! 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 I 992, price changes for
the various commodities have been averaged
together with implicit quantity weights representing their importance in the total net
selling value of all commodities as of 1987.
The detailed data are aggregated to obtain
indexes for stage-of-processing groupings,
commodity groupings, durability-of-product groupings, and a number of special composite groups. All Producer Price Index data
are subject to revision 4 months after original publication.
FOR ADDITIONAL INFORMATION, contact
the Division of Industrial Prices and Price
Indexes: (202) 691-7705.

International Price Indexes
Description of the series

pleted during the first week of the month.
Survey respondents are asked to indicate
all discounts, allowances, and rebates applicable to t!1e reported prices, so that the
price used in the calculation of the indexes
is the actual price for which the product
was bought or sold.
In addition to general indexes of prices for
U.S. exports and imports, indexes are also
published for detailed product categories of
exports and imports. These categories are defined according to the five-digit level of detail
for the Bureau of Economic Analysis End-use
Classification, the three-digit level for the Standard International Trade Classification (SITC),
and the four-digit level of detail for the Harmonized System. Aggregate import indexes by
country or region of origin are also available.
BLS publishes indexes for selected categories of internationally traded services,
calculated on an international basis and on
a balance-of-payments basis.

Notes on the data
The export and import price indexes are
weighted indexes of the Laspeyres type. The
trade weights currently used to compute
both indexes relate to 2000.
Because a price index depends on the
same items being priced from period to period, it is necessary to recognize when a
product's specifications or terms of transaction have been modified. For this reason, the
Bureau's questionnaire requests detailed descriptions of the physical and functional
characteristics of the products being priced,
as well as information on the number of units
bought or sold, discounts, credit terms, packaging, class of buyer or seller, and so forth.
When there are changes in either the specifications or terms of transaction of a product,
the dollar value of each change is deleted from
the total price change to obtain the "pure"
change. Once this value is determined, a linking procedure is employed which allows for
the continued repricing of the item.
FOR ADDITIONAL INFORMATION, contact
the Division of International Prices: (202)
691-7155.

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
forfign 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-

The productivity measures relate real out-

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February

Productivity Data
(Tables 2; 48-51)

Business and major sectors
Description of the series

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. Multifactor 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 are based
on changing weights which are averages of the
shares in the current and preceding year (the
Tornquist index-number formula).

Notes on the data
Business sector output is an annuallyweighted index constructed by excluding
from real gross domestic product (GDP) the
following outputs: general government, nonprofit institutions, paid employees of private
households, and the rental value of owneroccupied dwellings. Nonfarrn 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.
_A !though 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 t"he 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/mlr/2000/06/
artlfull.pdf).

Definitions
For the principal U.S. definitions of the labor force, employment, and unemployment,
see the Notes section on Employment and

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2005

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Current Labor Statistics

Unemployment Data: Household survey
data.

The foreign country data are adjusted as
closely as possible to U.S. concepts, with the
exception of lower age limits and the treatment
of layoffs. These adjustments include, but are
not limited to: including older persons in the
labor force by imposing no upper age limit,
adding unemployed students to the
unemployed, excluding the military and family
workers working fewer than 15 hours from the
employed, and excluding persons engaged in
passive job search from the unemployed.
Data for the United States relate to the
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.
conrepts 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 I 6 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 inA11stralia
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

passive jobseekers as unemployed. An
adjustment is made to exclude them in Canada,
but not in the European countries where the
phenomenon is less prevalent. Persons waiting
to start a new job are counted among the
unemployed for all other countries, whether
or not they were actively seeking work.
The figures for one or more recent years
for France, Germany, and the Netherlands are
calculated using adjustment factors based on
labor force surveys for earlier years and are
considered preliminary. The recent year
measures for these countries are therefore
subject to revision whenever more current
labor force surveys become available.
There are breaks in series for the United
States (1994, 1997, 1998, 1999, 2000, 2003),
Australia (2001), and Germany (1999).
For the United States, beginning in 1994,
data are not strictly comparable for prior years
because of the introduction of a major
redesign of the labor force survey questionnaire and collection methodology. The
redesign effect has been estimated to increase
the overall unemployment rate by 0.1
percentage point. Other breaks noted relate
to changes in population controls that had
virtually no effect on unemployment rates.
For a description of all the changes in the
U.S. labor force survey over time and their
impact, see Historical Comparability in the
·'Household Data" section of the BLS publication Employment and Earnings (available
on the BLS Web site at http://www.bls.gov/
cps/eetech_methods.pdf).
For Australia, the 2001 break reflects the
introduction in April 2001 of a redesigned
labor force survey that allowed for a closer
application of International Labor Office
guidelines for the definitions of labor force
statistics. The Australian Bureau of Statistics
revised their data so there is no break in the
employment series. However, the reclassification of persons who had not actively
looked for work because they were waiting to
begin a new job from "not in the labor force"
to "unemployed" could only be incorporated
for April 2001 forward. This reclassification
diverges from the U.S. definition where
persons waiting to start a new job but not
actively seeking work are not counted in the
labor force. The impact of the reclassification
was an increase in the unemployment rate by
0.1 percentage point in 2001.
For Germany, the 1999 break reflects the
incorporation of an improved method of data
calculation and a change in coverage to
persons living in private households only.
For further qualifications and historical
data, see Comparative Civilian Labor Force
Statistics, Ten Countries, on the BLS Web site
at http://www.bis.gov/fls/flslforc. pdf

86

2005

Notes on the data

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February

FOR ADDITIONAL INFORMATION on this
series, contact the Division of Foreign
Labor Statistics: (202) 691-5654 or
flshelp@bls.gov

Manufacturing productivity
and labor costs
Description of the series
Table 54 presents comparative indexes of
manufacturing labor productivity (output per
hour), output, total hours, compensation per
hour, and unit labor costs for the United States,
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 measures--output, total labor hours, and total compensation. The hours and compensation measures
refer to all employed persons (wage and salary earners plus self-employed persons and
unpaid family workers) 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 1998-2003,"
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 Australia, 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
available, the measures are developed by BLS
using employment figures published with the
national accounts, or other comprehensive employment series, and estimates of annual hours
worked.
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, comrensation 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 average
for wage and salary employees.

mining as well.
The measures for recent years may be
based on current indicators of manufacturing output (such as industrial production indexes), employment, average hours, and
hourly compensation until national accounts
and other statistics used for the long-term
measures become available.
Official published data for Australia are
in fiscal years that begin on July 1. 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 fiscal year data.
FOR ADDITIONAL INFORMATION on this series, contact the Division of Foreign Labor
Statistics: (202) 691-5654.

Occupational Injury
and Illness Data
(Tables 55-56)

Survey of Occupational
Injuries and Illnesses
Description of the series
The Survey of Occupational Injuries and Illnesses collects data from employers about
their workers' job-related nonfatal injuries and
illnesses. The information that employers provide is based on records that they maintain under the Occupational Safety and Health Act of
1970. Self-employed individuals, 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 maintain records of nonfatal work-related injuries and illnesses that
involve one or more of the following: loss
of consciousness, restriction of work or motion, transfer to another job, or medical

treatment other than first aid.
Occupational injury is any injury such
as a cut, fracture, sprain, or amputation that
results from a work-related event or a single,
instantaneous exposure in the work environment.
Occupational illness is an abnormal condition or disorder, other than one resulting
from an occupational injury, caused by exposure to factors associated with employment. It includes acute and chronic illnesses
or disease which may be 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 capacity, or both, because of an occupational injury or illness. BLS
measures of the number and incidence rate
of lost workdays were discontinued beginning with the 1993 survey. The number of
days away from work or days of restricted
work activity does not include the day of injury or onset of illness or any days on which
the employee would not have worked, such
as a Federal holiday, even though able to
work.
Incidence rates are computed as the
number of injuries and/or illnesses or lost
work days per 100 full-time workers.

Notes on the data
The definitions of occupational injuries and
illnesses are from Recordkeeping Guidelines for Occupational Injuries and Illnesses (U.S. Department of Labor, Bureau
of Labor Statistics, September 1986).
Estimates are made for industries and employment size classes for total recordable
cases, lost workday cases, days away from
work cases, and nonfatal cases without lost
workdays. These data also are shown separately for injuries. Illness data are available for
seven categories: occupational skin diseases
or disorders, dust diseases of the lungs, respiratory conditions due to toxic agents, poisoning (systemic effects of toxic agents), disorders due to physical agents (other than toxic
materials), disorders associated with repeated
trauma, and all other occupational illnesses.
The survey continues to measure the number of new work-related illness cases which
are recognized, diagnosed, and reported during the year. Some conditions, for example,
long-term latent illnesses caused by exposure
to carcinogens, often are difficult to relate to
the workplace and are not adequately recog-

Monthly Labor Review

February

2005

87

Current Labor Statistics

nized and reported. These long-term latent illnessc:; are believed to be understated in the
survey's illness measure. In contrast, the overwhelming majority of the reported new illnesses are those which are easier to directly
relate to workplace activity (for example, contact dermatitis and carpal tunnel syndrome).
Most of the estimates are in the form of
incidence rates, defined as the number of injuries and illnesses per l 00 equivale11t fulltime workers. For this purpose, 200,000 employee hours represent 100 employee years
(2,000 hours per employee). Full detail on
the available measures is presented in the annual bulletin, Occupational Injuries and Illnesses: Counts, Rates, and Characteristics.
Comparable data for more than 40 States
and territories are available from the BLS Office of Safety, Health and Working Conditions. Many of these States publish data on
State and local government employees in addition to private industry data.
· Mining and railroad data are furnished to
BLS by the Mine Safety and Health Administration and the Federal Railroad Administration. Data from these organizations are included in both the national and State data published annually.
With the 1992 survey, BLS began publishing details on serious, nonfatal incidents resulting in days away from work. Included are
some major characteristics of the injured and
ill workers, such as occupation, age, gender,
race, and length of service, as well as the circumstances of their injuries and illness..::s (nature of the disabling condition, part of body
affected, event and exposure, and the source
directly producing the condition). In general,

The Census of Fatal Occupational Injuries
compiles a complete roster of fatal job-related injuries, including detailed data about
the fatally injured workers and the fatal
events. The program collects and cross
checks fatality information from multiple
sources, including death certificates, State
and Federal workers' compensation reports,
Occupational Safety and Health Administration and Mine Safety and Health Administration records, medical examiner and autopsy reports, media accounts, State motor
vehicle fatality records, and follow-up questionnaires to employers.
In addition to private wage and salary
workers, the self-employed, family members, and Federal, State, and local government workers are covered by the program. To be included in the fatality census, the decedent must have been employed (that is working for pay, compensation, or profit) at the time of the event,
engaged in a legal work activity, or
present at the site of the incident as a requirement of his or her job.

88

2005

Monthly Labor Review


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Federal Reserve Bank of St. Louis

February

these data are available nationwide for detailed
industries and for individual States at more
aggregated industry levels.
FOR ADDmONAL INFORMATION on occupational injuries and illnesses, contact the Office of Occupational Safety, Health and
Working Conditions at (202) 691-6180, or
access the Internet at: http://www.bls.gov/iif/

Census of Fatal
Occupational Injuries

Definition
A fatal work injury is any intentional or unintentional wound or damage to the body resulting in death from acute exposure to energy, such as heat or electricity, or kinetic
energy from a crash, or from the absence of
such essentials as heat or oxygen caused by
a specific event or incident or series of events
within a single workday or shift. Fatalities
that occur during a person's commute to
or from work are excluded from the census, as well as work-related illnesses, which
can be difficult to identify due to long latency periods.

Notes on the data
Twenty-eight data elements are collected,
coded, and tabulated in the fatality program,
including information about the fatally injured worker, the fatal incident, and the machinery or equipment involved. Summary
worker demographic data and event characteristics are included in a national news release that is available about 8 months after
the end of the reference year. The Census of
Fatal Occupational Injuries was initiated in
1992 as a joint Federal-State effort. Most
States issue summary information at the
time of the national news release.
FOR ADDITIONAL INFORMATION on the
Census of Fatal Occupational Injuries contact the BLS Office of Safety, Health, and
Working Conditions at (202) 691-6175, or
the Internet at: http://www.bls.gov/iif/

1. Labor market indicators
2003

Selected Indicators

2002

2004

2003

IV

II

2004
Ill

IV

II

Ill

IV

Employment data
Employment status of the civilian noninstitutional
population (household survey) :

1

I

Labor force participation rate ........ ............. ..
Employment-population ratio ..... .
Unemployment rate .....
Men ..
16 to 24 years ..... .
25 years and older ...
Women .....
16 to 24 years
25 years and older
EmpIuy1ne11t, nonfarm (payroll data), in thousands:

66.2
62.3

66.0
62.3

66.4

66.3

66.4

62.4

62.3

66.2
62.1

66.1
62.2

66.0
62.2

66.0
62.3

66.0
62.4

66.0

62.5

6.0

5.5

5.9

5.8

6.1

6.1

5.9

5.6

5.6

5.5

5.4

6.3
13.4

5.6

6.1

6.1

6.5

6.4

6.1

5.7

5.7

12.6

12.5

12.8

13.9

13.7

13.0

12.6

12.9

5.6
12.5

5.6
12.6

5.0

4.4

4.9

5.0

5.2

5.1

4 .9

4.5

4.5

4.4

4 .3

5.7

5.4

5.7
11.8

5.8
11.5

5.6
10.9

5.6
11.1

5.4
10.9

5.2

11.0

5.5
11.2

5.3

11.4

5.6
11.4

10.9

10.9

4.6

4.4

4.5

4.5

4.6

4.7

4.6

4.5

4.4

4.3

4.2

62.4

1

Total nonfarm ........................................... .

129,931

131,481

108,664

130,047

129,878

130,002

130,367

131 ,125

131,731

132,294

108,356

109,863

108,654

108,428

108,309

108,260

108,453

108,986

109,737

110,095

110,593

Goods-producing

21,817

21,885

22,252

22,025

21,848

21,718

21,676

21,725

21,868

21,932

21,995

Manufacturing.

14,525

14,329

14,979

14,775

14,570

14,410

14,340

14,285

14,338

14,353

14,337

Service-providing

108,114

109,597

107,995

108,022

108,030

108,102

108,326

108,816

109,457

109,799

110,300

Total private.

129,820

Average hours:
Total private
Manufacturing .
Overtime ................ .. ....... .

Employment Cost lndex

33.7

33.7

33.8

33.8

33.7

33.6

33.7

33.8

33.7

33.7

33.8

40.4
4.2

40.8
4.6

40.4
4 .2

40.4

40.2

4.2

4.1

40.2
4.1

40 .6
4.4

41 .0
4 .5

40.8
4.5

40.8
4.6

40 .6
4 .5

2

Percent change in the ECI, compensation:
All workers (excluding farm, household and Federal workers)
Private industry workers .....
Goods-producing3 ....
3

Service-providing ..
State and local government workers
Workers by bargaining status (private industry):
Union
Nonunion
1
2

3.8
4.0

.6

1.4

.5

1.4

.9

1.0

.4

1.5

.9

1.0
.8

.5

1.7

.8
.8

1.1

.4

4.0

.9

1.8

.9

.7

.5

2.3

.9

.9

.6

4.0

.2

1.5

.8

1.1

.5

1.1

1.0

.8

.3

3.3

.9

.7

.4

1.7

.5

.7

.4

1.7

.6

4.6

.9

1.6

1.2

1.0

.7

2.8

1.5

.8

.5

3.9 !

.4

1.6

.8

1.0

.4

1.3

.8

.9

.4

.5

Quarterly data seasonally adjusted.

NOTE:

Annual changes are December-to-December changes. Quarterly changes are calculated

controls. Nonfarm data reflect the conversion to the 2002 version of the North American

Beginning in January 2003, household survey data reflect revised population

using the last month of each quarter.

Industry Classification System (NAICS), replacing the Standard Industrial Classification (SIC)

3

system. NAICS-based data by industry are not comparable with sic-based data.

Goods-producing industries include mining, construction, and manufacturing. Service-

providing industries include all other private sector industries.


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Federal Reserve Bank of St. Louis

Dash indiates data not available

Monthly Labor Review

February 2005

89

Current Labor Statistics:

Comparative Indicators

2. Annual and quarterly percent changes in compensation, prices, and productivity
Selected measures

2002

2004

2003

2003

2004
Ill

II

IV

II

IV

Ill

IV

12

Compensation data '

Employment Cost Index-compensation (wages,
salaries. benefits):
Civilian nonfarm ................................................................. .
Private nonfarm ............................................................. .
Employment Cost Index-wages and salaries:
Civilian nonfarm ........................ ....... ..... ........ ..... .... ..
Private nonfarm ......................................... ................. ..
Price data

3.8
4.0

0.6
.4

1.4
1.7

0.8
.8

1.1
1.0

0.5
.4

1.4
1.5

0.9
.9

1.0
.8

0.5
.5

2.9
3.0

.4
.3

1.0
1.1

.6
.7

.9
.8

.3
.4

.6
.7

.6
.7

.9
.9

.3
.2

1

Consumer Price Index (All Urban Consumers): All Items ......

2.3

3.3

-.1

1.8

-.3

- .2

-.2

1.2

1.2

.2

.2

3.2
4.2
.4
4.6
25.2

4.1
4.6
2.4
9.1
18.0

-.1
-.3
.6
.1
6.5

3.7
2.4
.6
6.5
28.0

-.8
1.8
-.6
-2.1
-10.6

.3
.3
-.1
-.1
3.4

.0
.0
.0
.0
14.4

1.2
1.5
.6
2.5
6.0

1.2
1.4
.5
3.0
7.6

.0
-1.7
.4
1.9
-5.1

1.1
.9
1.6
.9
8.3

4.5
4.4
5.4

4.0
4.1

1.2
1.6
3.4

3.9
3.7
3.2

7.6
6.7
9.1

8.5
9.0
9.4

2.4
3.1
5.0

3.9
3.7

1.5
3.9
2.7

2.3
1.9

2.5
.8

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:
Business sector ....................................................................
Nonfarm business sector ..................................................... .
Nonfinancial cornorations

4

..

2

.1

3

' Annual changes are December-to-December changes. Quarterly changes are
calculated using the last month of each quarter. Compensation and price data are not
seasonally adjusted, and the price data are not compounded.

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.
4

Excludes Federal and private household workers.

Output per hour of all employees.
NOTE: Dash indicates data not available.

3. Alternative measures of wage and compensation changes
Quarterly change

2003

Components

Four quarters ending-

2004
II

IV

2003
Ill

IV

2004
II

IV

Ill

IV

1

Average hourly compensation:
All persons, business sector ......... .. ............. ............ .................... .
All persons, nonfarm business sector. ......................................... .

4.0
4.4

2.8
2.0

5.2
5.9

3.8
3.5

4.2
3.1

5.3
5.4

4.6
4.5

4.4
4.6

4.0
3.9

4.0
3.6

.5
.7
.4
.5

1.4
1.5
2.8
1.3
.7

.9
.9
1.5
.8
.4

1.0
.8
.8
.9
1.7

.5
.5
.5
.4
.6

3.8
4.0
4.6
3.9
3.3

3.8
3.9
5.7
3.6
3.3

3.9 1
4.0
6.0
3.5
3.4

3.8
3.7
5.8
3.4
3.4

3.7
3.8
5.6
3.4
3.5

.3
.4
.6
.2
.4

.6
.7
.6
.7
.4

.6
.7
1.0
.6
.2

.9
.9
.8
.8
1.0

.3
.2
.4
.2
.5

2.9
3.0
2.4
3.1
2.1

2.5
2.6
2.5
2.6
2.1

2.5
2.6
2.9
2.5
1.9

2.4
2.6
3.0
2.5
2.0

2.4
2.4
2.8
2.4
2.1

Employment Cost Index-compensation:
2

Civilian nonfarm .. .............. . .. . ............... ...... .... ..•....
Private nonfarm ........................................................................ .
Union ..................................................................................... .
Nonunion ... .. .... .......................................................................
State and local governments .................................................... .

.4

Employment Cost Index-wages and salaries:
2

Civilian nonfarm
........................................ .
Private nonfarm .......................................... .............. ........... .... .
Union ..................................................................................... .
Nonunion ................................................................................
State and local governments .................................................... .

90

1

Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate.

2

Excludes Federal and household workers.

Monthly Labor Review


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Federal Reserve Bank of St. Louis

February 2005

4. Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adJusted
[Numbers in thousands]
Employment status

2004

Annual average

2003

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

221,168
146,510
66.2
137,736

223,357
147,401
66.0
139,252

222,509
146,808
66.0
138,409

222,161
146,785
66.1
138,481

222,357
146,529
65.9
138,334

222,550
146,737
65.9
138,408

222,757
146,788
65.9
138,645

222,967
147,018
65.9
138,846

223,196

223,422

223,677

223,941

224,192

224,422

224,640

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
140,1 56

TOTAL
Civilian noninstitutional
1

population ..
Civilian labor force ....... .... .
Participation rate ... ......
Employed .. .... ...... .... ... ..
Employment -pop2

ulation ratio .. .
Unemployed ...................
Unemployment rate ...
Not in the labor force .....

62.3

62 .3

62.2

62 .3

62.2

62 .1

62.2

62.2

62.3

62 .5

62.4

62.3

62.4

62 .5

62.4

8,774
6.0
74,658

8,149
5.5
75,956

8,399
5.7
75,701

8,303
5.7
75,377

8,195
5.6
75,828

8,330
5.7
75,812

8,143
5.6
75,969

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

98,272

99,476

98,927

98,866

98,966

99,065

99,170

99,279

99,396

99,512

99,642

99,776

99,904

100,017

99,476

74 ,623
75.9
70,415

75,364
75.8
71,572

75,103
75.9
71,135

75,139
76.0
71,283

74,854
75.6
71,014

75,035
75.7
71,158

74,908
75.5
71 ,158

75,095
75.6
71,226

75,631
75.8
71,575

75,567
75.9
71 ,830

75,615
75.9
71,847

75,462
75.6
71,701

75,632
75.7
71 ,895

75,866
75.9
71,134

75,754
75.7
72 ,020

71.7

71.9
3,791
5.0
24,113

71.9

71.8
3,840
5.1
24,112

71 .8
3,877
5.2
24 ,029

71 .8
3,751
5.0
24,261

72.0

71 .9

72.0

3,869
5.2
24 ,184

3,786
5.0
24,035

72.2
3,737
4.9
23,945

72.1

3,968
5.3
23,824

72.1
3,856
5.1
23,726

71 .7

4,209
5.6
23,649

3,768
5.0
24,026

3,761
5.0
24,314

3,736
4.9
24,272

72.1
3,733
4.9
24,151

3,733
4.9
24,372

106,800

107,658

107,404

107,131

107,216

107,299

107,389

107,483

107,586

107,687

107,801

107,920

108,032

108,129

107,658

64 ,716
60 .6
61,402

64,923
60 .3
61 ,773

64,743
60.3
61 ,523

64,475
60.2
61 ,237

64,636
60.3
61,456

64,723
60 .3
61,424

64 ,776
60.3
61,591

64,803
60.3
61 ,723

64,989
60 .4
61,731

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

Men, 20 years and over
Civilian noninstitutional
1

population .. .... .. .. .......... ...
Civilian labor force ... ... .......
Participation rate ....
Employed ......................
Employment-pop2

ulation ratio ...
Unemployed · · ···· ··· ··
Unemployment rate ....
Not in the labor force .... ...

71.9

Women, 20 years and over
Civilian noninstitutional
1

population ..
Civilian labor force .... .... .... .
Parti cipation rate .........
Employed ......................
Employment-pop2

ulation ratio ...
Unemployed ......... ..........
Unemployment rate ....
Not in the labor force ..

57.5

57.4

57.3

57 .2

57.3

57.2

57.4

57.4

57.4

57.5

57.4

57.4

57.4

57.5

57.5

3,314
5.1
42,083

3,150
4.9
42,735

3,302
5.1
42,661

3,238
5.6
42,657

3,179
4.9
42,580

3,299
5.1
42,576

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
42,961

16,096

16,222

16,178

16,164

16,175

16,186

16,198

16,205

16,214

16,222

16,234

16,246

16,257

16,293

16,222

7,170
44.5
5,919

7,1 14
43.9
5,907

6,961
43.0
5,836

7,171
44.4
5,962

7,039
43.5
5,864

6,979
43.1
5,825

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

36.9
1,209
16.9
8,993

36.3
1,175
16.7
9,136

36.0

36.4

36.4

36.1

36.4

36.6

36.2

36.3

36 .9

1,154
16.5
9,207

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

36.4
1,262
17.6
9,104

Both sexes, 16 to 19 years
Civilian noninstitutional
1

population . .
Civilian labor force ...
Participation rate ...
Employed ...... ........ ........
Employment-pop2

ulation ratio . ..
Unemployed ....... .........
Unemployment rate ....
Not in the labor force ..

White

36.8

36.4

36.1

1,251
17.5
8,926

1,208
17.0
9,108

1,125
16.2
9,235

181,292

182,643

182,185

181,879

182,00,

182,121

182,252

182,384

182,531

182,676

182,846

183,022

183,188

183,340

183,483

120,546
66.5
114,235

121,686
66.3
115,239

120,703
66.3
114,626

120,743
66.2
114,771

120,590
66.3
114,615

120,598
66.2
114,500

120,713
66.2
114,779

120,997
66.3
115,006

121,212
66.4
115,199

121,383
66.4
115,610

121,278
66.3
115,526

120,995
66.1
115,318

121 ,273
66.2
115,618

121,606
66.3
115,966

121,509
66.2
115,910

63.0
6,311
5.2
60,746

63.1
5,847
4.8
61 ,558

62.9
6,077
5.0
61,482

63.1
5,972
4.9
61 ,136

63.0
5,975
5.0
61,411

62.9
6,098
5.1
61,522

63.0
5,934
4.9
61,539

63.1
5,991
5.0
61 ,387

63.1
6,013
5.0
61,319

63.3
5,773
4.8
61,293

63.2
5,752
4.7
61,568

63.0
5,677
4.7
62 ,027

63.1
5,655
4.7
61,915

63.3
5,640
4.6
61,735

63.2
5,600
4.6
61 ,973

25,686

26,065

25,894

25,827

25,900

25,932

25,967

26,002

26,040

26,078

26,120

26,163

26,204

26,239

26,273

16,526
64.3
14,739

16,638
63.8
14,909

16,362
63.2
14,697

16,603
64.2
14,875

16,427
63.4
14,825

16,603
64.0
14,917

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

57.5
1,728
10.4
9,264

57.3
1,598
9.7
9,473

57 .5
1,685
10.2
9,330

57.4

57.1
1,642
10.0
9,523

56.9
1,696
10.3
9,520

57.3

57.3

1,838
11.0
9,303

1,749
10.5
9,399

57.3
1,730
10.4
9,452

57 .3
1,808
10.7
9,384

56.8

1,612
9.8
9,462

1,814
10.8
9,512

56.7
1,806
10.8
9,559

3

Civilian noninst itutional
1

population ..
Civilian labor force .... .... .....
Participation rate .....
Employed ................ .....
Employment-pop2

ulation ratio . ..
Unemployed. ..........
Unemployment rate ..
Not in the labor force ..

Black or African American

3

Civilian noninstitutional
1

population ..
Civilian labor force ..............
Participation rate .... .....
Employed ........ ......... .. ..
Employment-pop2

ulation ratio
Unemployed .... ... .... .. ..
Unemployment rate ...
Not in the labor force ....

57.4

57.2

56.8

1,787
10.8
9,161

1,729
10.4
9,4?.8

1,665
10.2
9,559

See footnotes at end of table .


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

Monthly Labor Review

February 2005

91

Current Labor Statistics:

Labor Force Data

4. Continued-Employme nt status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Annual average

Employment status

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

27,551
18,813
68.3
17,372

28,109
19,272
68.6
17,930

28,116
19,051
68.7
17,794

27,619
18,849
68.2
17,476

27,705
18,702
67.f17,315

27,791
19,036
68.5
17,633

27,879
19,081
68 .4
17,724

27,968
19,297
69.0
17,959

28,059
19,302
68.8
18,013

28,150
19,432
69.0
18,102

28,243
19,463
68.9
18,128

28,338
19,444
68.6
18,079

28,431
19,524
68.7
18,213

28,520
19,552
68.6
18,238

28,608
19,544
68.3
18,252

63.1
1,441
7.7
8,738

63.8
1,342
7.0
8,837

63.3
1,257
6.6
9,065

63 .3
1,373
7.3
8,770

62 .5
1,387
7.4
9,003

63.5
1,403
7.4
8,755

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

64.2
1,335
6.9
8,780

63.8
1,366
7.0
8,894

64.1
1,311
6.7
8,907

63.9
1,313
6.7
8,968

63.8
1,292
6.6
9,064

Hispanic or Latino
ethnicity
Civilian noninstitutional
1

oooulation . . . . . ' . . . . . . .
Civilian labor force ...........
Participation rate .... ....
Employed ... ........... .........
Employment-pop2
ulation ratio ••••.•••••••.
Unemployed ...................
Unemployment rate .. .
Not in the labor force .........
1

The population figures are not seasonally adjusted.

2

Civilian employment as a percent of the civilian noninstitutional population.
3
Beginning in 2003, persons who selected this race group only; persons who selected
more man 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.

NOTE: Estimates for the above race groups (white and black or African American) do not sum
to totals because data are not presented for all races . In addition, persons whose ethnicity is
identified as Hispanic or Latino may be of any race and, therefore, are classified by ethnicity as
well as by race. Beginning in January 2003, data reflect revised population controls used in the
household survey.

5. Selected employment indicators, monthly data seasonally adjusted
[In thousands]

Selected categories

Annual average

2003

2004

137,736
73,332
64,404

139,252
74,524
64,728

Married men, spouse
present. ........... ..... .

44,653

Married women, spouse
present .....................

34,695

Characteristic
Employed , 16 years and over ..

Men ............. .................... .....
Women ...

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

2003
Dec.

2004
Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

138,409
74,122
64 ,286

168,481
74,284
64,197

138,334
73,937
64 ,397

138,408
74,062
64,345

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

45,084

45,383

45,443

45 ,044

45,000

44,759

44 ,763

44,958

44 ,948

45,099

45 ,093

45 ,127

45,462

45,315

34 ,600

34,897

34,546

34,481

34 ,283

34,375

34,536

34,487

34,607

34,494

34,704

34 ,808

34,961

34,878

Persons at work part time'
All industries:
Part time for economic
reasons ............
Slack work or business
conditions ....................
Could only find part-time
work .......... ... .... ..
Part time for noneconomic
noneconomic reasons ... ..
Nonagricultural industries:
Part time for economic
reasons ... .. .. ... .. .. .....
Slack work or business
conditions ....... .............
Could only find part-time
work ... ......................
Part time for noneconomic
reasons ..... ....... ....... . ....
1

4,701

4,567

4,782

4,703

4,445

4,708

4,557

4,634

4,504

4,488

4,509

4,476

4,762

4,533

4,474

3,118

2,841

3 ,197

2,972

2,841

2,984

2,813

2,845

2,801

2,642

2,816

2,805

3,052

2,761

2,735

1,279

1,409

1,305

1,400

1,363

1,430

1,431

1,449

1,400

1,472

1,403

1,312

1,385

1,420

1,440

19,014

19,380

18,656

18,986

19,020

19,091

19,130

19,570

19,564

19,737

19,657

19,410

19,704

19,499

19,502

4,596

4,469

4,704

4,604

4,335

4,595

4,451

4,567

4,423

4,390

4,408

4,400

4,656

4,404

4,382

3,052

2,773

3, 149

2,894

2,768

2,899

2,747

2,801

2,753

2,580

2,722

2,750

2,971

2,685

2,682

1,272

1,405

1,350

1,415

1,425

1,458

1,382

1,484

1,388

1,320

1,363

1,396

1,397

18,416

18,711

18,775

18,791

18,844

19,145

19,123

19,327

19,204

19,061

19,288

19,141

19,176

1,264
18,658

1,399 1
19,026

Excludes persons "with a job but not at work" during the survey period for such reasons as vacation, illness, or industrial disputes.

NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.

92

Monthly Labor Review


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

February 2005

6. Selected unemployment indicators, monthly data seasonally adjusted
[Unemployment rates]
Annual average
Selected categories

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

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

5.7
16.2
5.3
5.1

5.7
16.9
5.1
5.0

5.6
16.7
5.1
4.9

5.7
16.5
5.2
5.1

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

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

5.0
14.7
17.0
13.3
4.7
4.3

4.9
14.1
14.0
14.2
4.5
4 .4

5.0
15.3
15.6
15.1
4.6
4.2

5.1
14.8
16.3
13.3
4.7
4.4

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

Black or African American, total 1 • • • • • • • • •
Both sexes, 16 to 19 years ... .... ... .. ...
Men, 16 to 19 years .. .. ... ...... .. ...... ..
Women, 16 to 19 years .. ..... ...........
Men, 20 years and older. .... ..............
Women, 20 years and older ..............

10.8
33.0
36.0
30.3
10.3
9.2

10.4
31.7
35.6
28.2
9.9
8.9

10.2
27 .6
28.2
27.1
9.3
9.5

10.4
33.1
42 .2
25.9
9.5
9.0

9.7
25.2
29.1
22.4
9.3
8.8

10.2
30 .1
37.0
23.5
9.2
9.3

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

10.8
32.7
38.1
27.0
10.5
9.0

10.8
30 .8
37.7
24.0
10.7
9.1

Hispanic or Latino ethnicity ....... .... .... .
Married men, spouse present.. ........... ..
Married women, spouse present.. ... .... .
Full-time workers ... .. .. .......... ...... ... .. ......
Part-time workers .. .. ....... ....... ................

7.7
3.8
3.7
6.1
5.5

7.0
3.1
3.5
5.6
5.3

6.6
3.4
3.8
5.8
5.3

7.3
3.3
3.7
5.7
5.4

7.4
3.4
3.6
5.7
5.2

7.4
3.2
3.7
5.8
5.4

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
3.1
3.5
5.5
5.2

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

Educational attalnment2
Less than a high school diploma .... ...........

8.8

8.5

8.1

8.8

8.6

8.8

8.7

8.7

8.7

8.3

8.2

8.9

8.2

8.0

8.3

High school graduates, no college 3 •• ••••• • ••
Some college or associate degree .... .. .....

5.5
4.8

5.0
4.2

5.5
4.4

4.9
4.5

5.0
4.3

5.3
4.7

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

3.1

2.7

3.0

2.9

2.9

2.9

2.9

2.7

2.7

2.7

2.6

2.5

2.5

2.5

White, total

1

••• • ••• • •• • • ••• • •••• • • • • • •• •• •••••••

Bachelor's degree and higher

4

• • • • ••••••••••• •

1

Beginning in 2003, persons who selected this race group only; persons who
selected more than one race group are not included. Prior to 2003, persons who
reported more than one race were included in the group they identified as the
main race .
2

Includes high school diploma or equivalent.
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

Annual average

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

2,449
2,418
3,252
1,382
1,870

2,623
2,417
3,321
1,330
1,991

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

20.3
10.2

19.9
10.2

19.7
9.4

19.8
9.9

19.8
10.8

18.5
8.9

19.2
9.5

Less than 5 weeks .. ........... ... ..........
5 to 14 weeks ........... .. ..... ........... .....
15 weeks and over .... .. ....................
15 to 26 weeks .... ... ... ... ... .. ...... .. ...
27 weeks and over .... ... ......... .......

2,785
2,612
3,378
1,442
1,936

2,696
2,382
3,072
1,293
1,779

2,595
2,453
3,389
1,496
1,893

2,623
2,402
3,339
1,447
1,89?

Mean duration, in weeks ............ .....
Median duration, in weeks ..............

19.2
10.1

19.6
9.8

19.8
10.4

19.G
10.6

I

Oct.

Nov.

Dec.

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

19.6
9.5

19.7
9.5

19.8
9.8

19.3
9.5

Sept.

NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.


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Monthly Labor Review

February 2005

93

Current Labor Statistics: Labor Force Data

8. Unemployed persons by reason for unemployment, monthly data seasonally adjusted
[Numbers in thousands]
Reason for
unemployment
1

Job losers ... . ....... . .... .... ....
On temporary layoff. ........... .........
Not on temporary layoff ........... .....
Job leavers ......................................
Reentrants ......................... ... .. .........
New entrants ............... ... .......... .... ..

Annual average

2003

2004

4,838
1,121
3,717
818
2,477 ;
641

4,191
998
3,199
858
2,408
686

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

4,569
1,054
3,144
759
2,387
696

4,380
1,030
3,350
807
2,514
677

4,284
1,060
3,224
835
2,421
671

4,475
1,035
3,440
845
2,419
629

4,322
993
3,329
835
2,310
650

4,190
920
3 ,270
855
2,437
723

4,117
1,009
3,108
909
2,426
642

4,228
1,068
3,160
896
2,333
686

3,978
971
3,007
885
2,440
699

4,014
919
3,094
830
2,417
697

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

Percent of unemployed
1

Job losers . ... ... .... .... ..... . .. ... .. . ..
On temporary layoff .....................
Not on temporary layoff... .............
Job leavers ......................................
Reentrants ....................... ... ............
New entrants ............................... ...

55.1
12.8
42.4
9.3
28.2
7.3

51 .5
12.2
39.3
10.5
29.5
8.4

54.3
12.5
41 .8
9.0
28.4
8.3

52.3
12.3
40.0
9.6
30.9
8.1

52.2
12.9
39.3
10.2
28.4
8.2

53.5
12.4
41 .1
10.1
28.4
7.5

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
13.1
38.8
11 .0
28.6
8.4

49.7
12.1
37.6
11 .1
30.5
8.7

50.4
11.6
38.9
10.4
30.4
8 .8

50.5
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

3.3
.6
1.7
.4

2.8
.6
1.6
.5

3.1
.6
1.6
.5

3.0
.6
1.7
.5

2.9
.6
1.7
.5

3.0
.6
1.6
.4

2.9
.6
1.6
.4

2.8
.6
1.7
.5

2.8
.6
1.6
.4

2.9
.6
1.6
.5

2.7
.6
1.7
.5

2.7
.6
1.6
.5

2.8
.6
1.6
.5

2.7
.6
1.6
.5

2.8
.6
1.6
.5

Percent of civilian
labor force
1

Job losers ..........• •.. .. •..••...• .. •..•• . .
Job leavers ...................... .......... ......
Reentrants .......................................
New entrants ..................................
1

.1ncludes persons who completed temporary jobs.

NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.

9. Unemployment rates by sex and age, monthly data seasonally adjusted
[Civilian workers]
Sex and age

Annual a,;erage

2003

2004

2003
Dec.

2004
Jan.

Feb.

Mar.

Apr.

June

July

Aug.

Sept.

Total, 16 years and older .............. ...
16 to 24 years ................ ~············
16 to 19 years ...........................
16 to 17 years ........................
18 to 19 years ........................
20 to 24 years ...........................
25 years and older .. ..... .......... ......
25 to 54 years ... .................... .
55 years and older .................

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

5.7
11 .7
16.2
18.5
14.5
9.6
4.6
4.8
3.9

5.7
12.1
16.9
18.5
15.9
9.8
4.5
4.7
3.l

5.6
11.8
16.7
18.1
15.6
9.5
4.5
4.6
3.8

5.7
11.8
16.5
19.7
14.4
9.6
4.6
4.8
3.8

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

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

5.8
12.5
17.2
18.3
16.4
10.4
4.6
4.9
3.9

5.7
12.7
17.5
19.9
16.1
10.6
4.5
4.6
3.7

5.7
12.3
17.3
20.1
15.7
10.1
4.5
4.7
3.7

5.8
12.6
18.3
22.4
15.8
10.1
4.6
4.8
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

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
4.3

5.5
12.2
17.8
21.2
15.9
9.7
4.4
4.5
3.8

Women , 16 years and older ...... .....
16 to 24 years ............................
16 to 19 years ......... .... ... .... .....
16 to 17 years ....... ...... .... ..
18 tO 19 years ...................
20 to 24 years .................... .....
25 years and older .....................
25 to 54 years ......................

5.7
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

5.6
10.8
15.1
18.6
12.4
8.7
4.6
4.9

5.6
11.3
16.2
17.1
15.6
8.9
4.6
4.8

5.5
11 .3
16.0
16.2
15.5
8.9
4.5
4.6

5.6
10.8
14.7
17.3
12.8
8.9
4.6
4.9

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

55 years and older 1 ••••••••••••

3.7

3.6

3.7

4.1

3.9

3.5

3.3

3.3

3.8

3.8

3.9

3.5

1

Data are not seasonally adjusted .

NOTE: Beginning in January 2003, data reflect re:-,1ised population controls used in the household survey.

94

May

Monthly Labor Review


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Federal Reserve Bank of St. Louis

February 2005

4.4

4.4

3.9

Nov.

Dec.

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

5.6
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
11.3
15.1
19.0
12.5
9.4
4.2

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

3.6

3.6

Oct.

4.4 1
3.3


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Federal Reserve Bank of St. Louis

10. Unemployment rates by State, seasonally adjusted
State

Nov.

Oct.

Nov.

2003

2004P

2004P

State

Nov.

Oct.

Nov.

2003

2004P

2004P

Alabama ............................. ................. . .
Alaska ...................................................... .
Ari zona .. ............................ .... ..... ...... ... . .
Arkansas ... ... .... .... ... .. .............. ................. .
California ................................... ...... ..... .

5.9
8.1
5.0
6.5
6.6

5.5
7.2
4.8
5.7
5.8

5.2
7.2
4.5
5.6
5.7

Missouri .. .... ... .... ........ ............. .
Montana ......... ........................... ... ............ .
Nebraska ... ... ....... ..... .. .. .... ... ............ ..... .
Nevada ............. .... .. .................................. .
New Hampshire ........... ......... ... ............. . .

5.4
4.8
4.0
4.9
4.2

5.6
4.8
3.5
3.6
3.4

5.8
4.2
3.5
3.7
3.1

Colorado ............................ .. ................... ...
Connecticut... .. ........................... ........... .
Delaware ...................................................
District of Colum bia .. ......... ...................... .
Florida ............................ .......................... .

5.9
5.4
4.4
7.0
4.9

5.0
4.6
4.0
8.5
4.6

5.0
4.7
4.0
8.8
4.3

New Jersey ................................ .. ....... .. ....
New Mexico ............. ... ....... .. .. ... ... ..... .... .
New York ........................ ......................... .
North Carolina ....... .
North Dakota .............. .. .......... .. ... .

5.5
6.5
6.3
6.3
3.8

4.7
5. 2
5.2
4.8
3.7

4.4
5. 1
4.9
5.0
3.2

Georgia ............................. .......... ......... .
Hawaii .. .................................................. ...
Idaho ............................. ... .......... .. .. ..... .
Ill inois ................. .... .......... ............. .
Indiana ................................ .... ..... ........ .

4.3
4.4
5.0
6.7
5.1

4.2
3.3
5.2
6.1
5.4

4.3
3.3
5.1
6.0
5.3

Ohi o ......... ... .... ... ................ .. .. ..... ......... .
Oklahoma ... .... ......... .. .... ........................... .
Oregon ............ ... .... ....... .. .. ... ............. ... .
Pennsylvania .. .. ............... .. ... ... ....... .. ....... .
Rhode Island ... .. ... .................. .... ..... ... ... .

6.1
5.7
7.7
5.2
5. 1

6.4
4.5
7.2
5.5
4.5

6.5
4.5
7. 1
5.4
4.4

Iowa .................. ......................... ......... .
Kansas ....................... ..................... ... ... ... .
Kentucky .............................. ...... ........... .
Louisiana ..... ..... .. ..................................... .
Mai ne ........... .

4.6
5.3
6.0
6.2
5.1

4.8
4.8
4.8
5.6
4.6

4.7
4.6
4.5
5.7
4.5

South Carolina ......... .. .. ...................... .... .
South Dakota ........... .. .. ..... .. ........ ............. .
Tennessee ................. ............... ..... ..... .. .
Texas ........................ ... ..... ... ... .......... ... .. .. .
Utah .. ... .. .. .... ......... .. .......... .. ... ...... .. ... . .

6.9
3.8
6.1
6.6
5.3

6.4
3.3
5.1
5.6
4.7

6.6
3.2
5.0
5.7
4.6

......... .. ...... .
Maryland .......
Massachu setts ......................................... .
Michigan ............................ ...... ..... ....... . .
Minnesota ............. .......... .................. ....... .
Mississippi ................................ ... ......... .

4.4
5.7
7.6
5.1
5.7

3.9
4.7
6.6
4.3
6.3

3.9
4.6
7.0
·4.2
6.5

Vermont... ... ... .. ... ............... .... .... ..... ..... .
Virginia .............. .................. ..................... .
Washington .. .. ....... ... .... .. ................. .. ... . .
West Virginia .. .. .. .. ............. .. ..................... .
Wisconsin .. ....... ..... ....... .. ...... ............. ... .
Wyoming .............. ... .... ..... ......... ............... .

4.6
3.9
7.4
5.6
5. 4
4.3

3.1
3.3
5.6
5.1
5.0
3.8

3. 1
3.3
5.7
4.8
4.8
3.5

1

P

= prelim inary

11 . Employment of workers on nonfarm payrolls by State, seasonally adjusted
State

Nov.

Oct.

Nov.
State

2003

Alabama .................. ... ................ .
2, 163,787
Alaska ................ ... ...... .. .... ... ..........
335,373
Arizona .... .... ................. ........... ... .
2,694,191
Arkansas .................................... ... .
1,262,145
California ............... ... ................... . 17,480,211

Nov.

Oct.

Nov.

2003

2004P

2004P

2,162,788
347,143
2,794,442
1,331 ,533
17,750 ,890

2,160,452
347,393
2,786,387
1,329,075
17,755,732

Missouri. ........ .. ......... ....... ...... . .
Montana .... ............. .......... ...... ....... .
Nebraska .. ... .. .... ....................... ... .
Nevada ........................... ........... .. . .
New Hampshire ....... ..................... .

3,034,372
477,929
980,613
1,148,419
725 ,220

3,039,012
487,422
992,059
1,184,969
730,638

3,043,033
488,865
991 ,844
1,191 ,704
730 ,215

Colorado ..................................... ...
Connecticut... ......................... ..... .
Delaware ..................................... .
District of Columbia .. ......... ... ......... .
Florida .............. .... .. ..... .................. .

2,487,239
1,797,444
418,986
301 ,284
8,202,592

2,542 ,868
1,788,408
428,931
306,411
8,457,181

2,533,458
1,792,573
425,341
306,723
8,465,515

New Jersey ...... ........................ ..... .
New Mexico ................ ... .............. .
New York ....................... ......... ...... .
North Carolina .. ..................... ....... .
North Dakota ................ .......... .

4,379 ,881
902 ,950
9,296 ,357
4,255,896
347,043

4,414,138
912 ,006
9,312,761
4,175,865
354.440

4,411 .603
910 ,953
9,323,858
4,185,077
352 ,740

Georgia ...................... .... ....... ..... .
Hawaii ..... ..... ..... ... .................... ... ...
Idaho ........................... ...... ... .. .... .
Illinois ... ................................ ......... .
Indiana ........................................ .

4,447,384
626 ,165
694,359
6,346,042
3,1 88,742

4,430,274
632,110
710 ,594
6,443,178
3,169,584

4,424,782
635,341
710 ,111
6,424,158
3,167,246

Ohio ........... .... .... .. .. ....... ... .... .
Oklahoma .. .. ................ ....... .. ........ .
Oregon ................... ...... .......... .... .
Pennsylvania ......................... ...... ..
Rhode Island .. ........... ... ................ .

5,920 ,696
1,692,221
1,847,218
6,138,893
573,446

5,888,552
1,711 ,742
1,849,709
6,299.287
565,452

5,889 ,638
1,715,779
1,866,878
6,316 ,600
564,354

Iowa ..... .............. ... ...... ..... .......... .
Kansas ..... .. .. ........ ........ .... ... ... ... ... ..
Kentucky ......... ........ ..... .... ... ........ .
Louisiana .. .. ... ........ ......... .. .. .. .. ... ... .
Maine ........................... .... ...... .. . .

1,602 ,979
1,438,851
1,963,328
2,048,121
697,929

1,635,603
1,478,259
1,980 ,574
2,062 ,831
701,233

1,631,591
1,473,344
1,979,622
2,066,007
703,012

South Carolina .. .. .... ... .................. .
South Dakota .......... ... ..... ... .......... ..
Tennessee ............. .. ..... ......... .... . .
Texas ... ...... .. .... ... .... ............ .......... .
Utah ......... ........ .. .. ......... ... .. .. ...... .

2,014,543
426,867
2,909,970
10,963,282
1,192,257

2,080 ,923
425 ,076
2,945,699
11 ,008,283
1,218,682

2,084,162
425,338
2,942,082
11 ,020,478
1,221,511

Maryland .... ............................. .... .
Massachusetts ............. ................ ..
Michigan ......... .. .. ... ... ...... ........... .
Minnesota ......................... ..... ....... .
Mississippi ........ .................... ....... .

2,905,697
3,390 ,712
5,068,224
2,927,291
1,312 ,944

2,956,279
3,396,232
5,085,966
2,962 ,303
1,326,002

2,960,894
3,387,399
5,076,044
2,961 ,972
1,328,183

Vermont.. .............. ... .......... ...... ... .
Virginia ...... ... ...... .. .. .. .... .. .... ........... .
Washington ........ .... ........ ..... .
West Virginia ........, .... ....... ..... ....... .
Wisconsin ........................ .. ...... .... .
Wyoming .... .. .. ...... ... ..... .... ......... .... .

350,859
3,784,740
3,149,726
779,611
3,084,292
281,551

351 ,563
3,861 ,265
3,203,962
803,905
3,121 ,422
280 ,256

352 ,864
3,848,497
3,242 ,826
798,955
3,128,068
279,525

P

= preliminary.

NOTE: some data in this table may differ from data published elsewhere because of the continual updating of the data base.

Monthly Labor Review

February 2005

95

Current Labor Statistics:

Labor Force Data

12. Employment of workers on nonfarm payrolls by Industry, monthly data seasonally adjusted
[In thousands]
Annual average
Industry

2003

2004

2003
Dec.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

129,931

131,481

130,035

130,194

130,277

130,630

130,954

131 ,162

131 ,258

131,343

131,541

131,660

131,972

132,109

132,266

108.356
21,817

109.863
21,885

108.491
21,668

108.667
21,696

108.738
21 ,684

109.077
21 ,778

109.382
21,822

109.618
21 ,894

109.730
21 ,891

109.771
21 ,906

109.912
21 ,939

110.008
21 ,958

110.297
22,016

110.422
22,017

110.550
22,030

571
68.5
502.3
122.9

591
67.9
523.2
123.0

570
65.9
504.3
124.6

570
65.1
505.1
126.9

572
64.2
508.1
128.9

581
65.9
514.9
130.0

585
66.7
518.5
131.0

589
65.6
523.2
132.3

587
64.5
522.7
132.0

592
64.5
527.5
132.2

591
64.6
526.6
132.7

593
64.9
527.7
132.9

592
64.2
527.5
132.7

595
63.6
531 .0
133.5

598
65.1
532.8
133.2

Mini no. exceot oil and aas ... ..
Coal minina .... ... .... ........ .. ..
Support activities for mining ....

202.7

207.0

202.0

200 .0

200.6

202.8

205.2

207.8

207.9

211.2

209.2

209.4

209 .0

210.7

210.6

70.4
176.8

71 .7
193.2

69 .8
177.7

69.6
178.2

70.2
178.6

70.6
182.1

71 .8
182.3

72.9
183.1

73.5
182.8

75.0
184.1

74.6
184.7

74.8
185.4

74.4
185.8

75.0
186.8

75.2
189.0

Construction ..............................

6,722

6,965

6,774

6,812

6,791

6,853

6,872

6,909

6,911

6,916

6,930

6,958

7,018

7 ,025

7,032

Construction of buildinas .. ...
Heavv and civil enaineerina ....
Soecialitv trade contractors .. ...
Manufacturing ............................

1.575.9
910.7
4.235.5
14,525

1.632.7
902.2
4.429.7
14,329

1.585.1
920.7
4.268.4
14,324

1.593.3
928.0
4.290.2
14,314

1.590.9
924 .0
4.276.5
14,321

1.607.6
926.8
4.318.9
14,344

1.609.8
924.7
4 .337.3
14,365

1,622.9
924.3
4.362.2
14,396

1.625.9
920.9
4.364.6
14,393

1.629.7
920.2
4.365.6
14,398

1.635.5
921 .9
4.378.9
14,412

1.648.8
922 .5
4.386.8
14,407

1.661 .6
928.4
4.427.5
14,406

1.665.1
930.9
4.428.8
14,397

1.669.3
931 .6
4.430.6
14,400

Production workers ..............
Durable goods ..........................

10.200
8,970

10.083
8,922

10.044
8,868

10.035
8,869

10.038
8,882

10.058
8,889

10.085
8,924

10.123
8,946

10.128
8,955

10.141
8,955

10.162
8,986

10.150
8,979

10.150
8,985

10.141
8,979

10.143
8,979

Production workers ......... ... ..
Wood oroducts ................... .. ....
Nonmetallic mineral oroducts
Primarv metals .......... .. .............
Fabricated metal oroducts .......
Machinerv .. .. ........ ......... . .... ..
Comouter and electronic

6.157
536.1
492.6
476.7
1.478.4
1.153.5

6.137
548.4
504.8
465.8
1.141.5
1.141.5

6.079
536.6
487.5
464.6
1.471.2
1,140.4

6.081
536.3
492.7
432.2
1.471 .8
1.138.7

6.088
538.4
490 .5
462.2
1.476.6
1.141 .2

6.101
539.7
493.2
462.0
1.478.5
1.145.1

6.126
540.0
497.8
462.5
1.486.7
1.152.0

6.152
543.0
501.4
464.0
1.494.5
1.153.3

6.164
543.8
501.7
465.4
1.497.6
1.156.7

6.167
544.1
502.6
467.0
1.501.3
1.160.4

6.195
545.9
501.6
465.4
1.504.7
1.163.3

6.184
544.8
503.2
464.1
1.505.8
1.161.7

6.188
549.7
503.0
464 .5
1.508.5
1,161.4

6.180
548.5
502.9
464.6
1.507.5
1.162.0

6.182
550.3
502.7
464.3
1.508.0
1.162.0

oroducts • • ••• • ••• •• • • •• •• • •• • •• • •
Comouter and oerioheral
equipment.. .... .... ... ... ..........
Communications equipment..
Semiconductors and
electronic components .........
Electronic instruments .... .... ..
Electrical equipment and
appliances ..... ....... ......... ........
Transportation equipment.. ....
Furniture and related
products ............. .... .. ..... .
Miscellaneous manufacturing

1,360.9

1,326.1

1,332.2

1,333.2

1,333.9

1,338.0

1,339.7

1,345.8

1,346.2

1,351 .9

1,353.0

1,350.7

1,348.6

1,344.6

1,342.8

225.7
157.0

212.0
150.5

217.8
153.0

219.4
154.8

219 .0
154.8

218.6
155.0

218.1
155.1

218.8
155.9

217.7
157.1

217 .2
158.2

217.9
158.5

217.1
158.1

215.6
158.0

214.4
158.6

215.4
158.7

461 .8
429.3

452.8
431 .8

451 .3
425.3

450.2
423.7

451.4
423.3

452.1
426.8

453.4
427.5

455.8
430.1

458.0
429.8

460.7
432.4

460.2
433.0

459.4
433.1

457.2
435.4

455.2
434.1

452.2
434.5

459 .9
1,775.4

446.8
1,763.3

451.2
1,762.7

449.8
1,760.6

448.6
1,766.5

446.8
1,769.1

446.5
1,768.8

447.3
1,764.4

448.6
1,765.1

449.2
1,745.9

449.6
1,774.4

449.1
1,771.7

447.3
1,774.3

447.8
1,772. 3

445.5
1,774.1

573.5
662.8

572.7
655.4

569.3
651 .9

571 .3
652.0

571.2
653.0

573.4
653.0

576.5
653.0

577.6
654.4

575.0
654.6

576 .7
655.5

574.6
653.6

573.8
653.7

573.7
654 .0

574.1
654.6

574.3
655.1

Nondurable goods...................
Production workers ..............

5,555
4,043

5,407
3,946

5,456
3,965

5,445
3,954

5,439
3,950

5,445
3,957

5,441
3,959

5,450
3,971

5,438
3,964

5,443
3,974

5,426
3,967

5,428
3,966

5,421
3,962

5,418
3,961

5,421
3,961

Food manufacturing ......... ... ... ..
GGverages 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 ...... .... ............... ... ....

1,518.7

1,497.5

1,506.3

1,500.7

1,502.4

1,504.5

1,502.7

1,507.0

1,502.8

1,508.0

1,499.6

1,502.5

1,504.5

1,506.5

1,512.9

200.6
260.3
179.8
312.7
45.2
519.0

194.3
238.6
177.8
285.0
42.9
499.1

198.3
241 .0
174.3
297.7
44.3
510 .3

197.7
239 .2
176.9
296 .1
44.6
509.8

195.9
237.3
176.6
297.1
44.8
508.0

197.2
237.1
179.7
294 .3
44.8
508.8

197.8
235.8
180.1
292.7
44.6
507.0

197.5
236.1
181.4
290.8
45.1
508.1

197.6
235.0
179.7
286.8
44.7
506.7

198.4
235.6
179.3
284.8
45.3
509.0

197.2
234.4
179.4
284.2
44.8
509.8

198.5
233.8
179.6
282.7
45.4
508.6

197.0
233.0
180.1
277.4
45.3
508.0

199.8
231.2
180.0
273.5
45.8
505.7

197.4
230.4
180.2
272.5
45.2
506.1

680.0
114.6
7.9

665.1
112.7
887.1

670 .1
112.4
895.9

667.6
114.3
893.7

665.0
112.9
894 .7

664.4
113.1
894.9

663.6
112.6
896.4

665.9
113.1
895.0

667.0
113.8
895.2

663.8
113.6
894.2

662.2
114.1
891 .9

660.3
114.3
892.7

660 .6
114.2
891.3

660. 3
114.2
890.3

661 .7
113.2
890.4

TOTAL NONFARM ......... ......
TOTAL PRIVATE................ ......
GOODS-PRODUCING ................
Natural resources and
mining ...................................
Logging .... .... .... .... ......... ..... ....
Mining .................... ....................
Oil and gas extraction ............
1

1

Plastics and rubber products ..

815.9

806.7

805.8

804.8

803.9

806 .3

807.5

810.2

808.6

811.2

808.8

809.5

809.2

811 .1

811 .0

SERVICE-PROVIDING ..................

108,114

109,597

108,367

108,498

108,593

108,852

109,132

109,268

109,367

109,437

109,602

109,702

109,956

110,092

110,236

PRIVATE SERVICEPROVIDING .........................

86,538

87,978

86,823

86,971

87,054

87,299

87,560

87,724

87,839

87,865

87,973

88,050

88,281

88,405

88,520

25,275
5,605.0
2,949 .2
2,002 .1

25,511
5,655.3
2,949.2
2,007.2

25,211
5,598.4
2,945.8
1,991.8

25,312
5,611.4
2,954 .9
1,993.7

25,331
5,612 .2
2,953.8
1,994.5

25,415
5,623.5
2,963.4
1,995.3

25,448
5,632.5
2,967.5
1,996.3

25,477
5,636.7
2,969.7
1,997.2

25,497
5,639.5
2,975.6
1,994.3

25,499
5,649.6
2,986.0
1,994.3

25,516
5,652.8
2,989.6
1,992.5

25,522
5,662.8
2,992.3
1,996.6

25,562
5,670.4
2,995.6
2,000.2

25,580
5,678.4
2,996.2
2,002.5

25,580
5,695.4
3,005.8
2,005.3

698.9

660 .8

662 .8

663.9

664.8

668.7

669.8

669.6

671.5

670.7

673.9

674 .6

679.7

684.3

Trade, transportation,
and utilities ..............................
Wholesale trade .......................
Durable goods ...... . . .. . .. . .. ...
Nondurable goods ....... ··· ··· ·
Electronic markets and
agents and brokers .............

654 .3

Retail trade .. .......... ................... 14.911 .5
Motor vehicles and parts
dealers' .. .. ... .... .... .. .. ... ... ..
Automobile dealers .......... .....
Furniture and home
furnishings stores ..................
Electronics and appliance
stores ................... ..................

15.034.4 14.876 .0 14.944 .8 14.963.0 15.013.0 15.037.1 15.047.6 15.054.9 15.038.1

Monthly Labor Review


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

15.048.8 15.030.5 15.055.6

15.064.5 15.044.9

1,883.5
1,255.1

1,900.9
1,254.2

1,893.7
1,259.5

1,895.4
1,261 .3

1,900.9
1,262.9

1,906.9
1,263.9

1,910.9
1,264.7

1,911.4
1,263.6

1,908.5
1,262.3

1,908.1
1,259.2

1,904.9
1,256.8

1,904.8
1,253.7

1,903.4
1,251 .6

1,907.3
1,254.7

1,909.1
1,254.7

542.9

560.4

547.2

546.4

544.5

544.8

544.5

545.7

546.3

546.4

548.7

548.7

880 .0

550.8

551 .8

511 .9

514.4

511 .9

509 .3

508.2

511.7

514.1

512.6

511 .5

510.7

511 .6

512.6

517.8

519.8

515.5

See notes at end of table .

96

2004
Jan.

February 2005

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 stores1.
Department stores ..............
Miscellaneous store retailers ..
Non store retailers ... ............ ..

Transportation and
warehousing ..........................
Air transportation .. ... ..... ......
Rail transportation ...... ..........
Water transportation ...... .. .....
Truck transportation ... .. .... ....
Transit and ground passenger
transportation ........ .......... .
Pipeline transportation ... ... .. .
Scenic and sightseeing
transportation ........ .. .... .... ..
Support activities for
transportation ....... .... .... . ....
Couriers and messengers ...
Warehousing and storage
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 ...... .. .... .. ..... ...

Annual average

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

1,191 .1
2,840.9

1,226.1
2,826.4

1,209.5
2,813.9

1,221.4
2,826.3

1,231.4
2,831.3

1,243.5
2,838.9

1,247.3
2,839.9

1,248.7
2,845.3

1,245.8
2,839.7

1,246.9
2,834.5

1,251 .7
2,832.9

1,256.5
2,832.2

1,258.7
2,832.3

1,262.4
2,827.9

1,263.9
2,822.6

943.1
879.9

941 .6
87,.3

952.6
871 .1

954.1
875 .. 1

954.9
871.8

958.2
873.0

957.9
872.4

957.1
871.6

957.2
870.3

956.7
869.9

956.4
870.3

956.4
871 .8

956.3
869.6

957.5
867.1

956.8
864.7

1,296.7

1,361.8

1,301.0

1,304.3

1,311.3

1,321.8

1,328.0

1,335.5

1,346.5

1,349.0

1,355.2

1,349.9

1,353.0

1,354.8

1,353.8

645.0
2,815.2
1,618.8
934.1
427.5

639.0
2,843.2
1,612.4
918.5
424.7

633.2
2,793.4
1,601.3
924.4
424.1

635.9
2,822.7
1,603.4
929.6
424.3

636.8
2,822.5
1,602.7
924.6
424.8

636.5
2,824.4
1,604.9
926.9
427.4

635.8
636.1
2,831.0
2,830.5
16.7
1,610.9
927.9
925.7
427.4
429.8 1

635.7
2837.4
1,614.9
928.4
427.6

635.5
2825.3
1,609.9
926.2
428.9

638.4
2823.8
1,607.9
927.1
427.8

635.0
2810.9
1,599.4
924.7
427.0

636.5
2822.8
1,609.3
927.7
427.5

636.3
2828.2
1,615.3
924.0
428.4

631 .7
2822.9
1,614.5
923.1
429.0

4,176.7
527.3
215.4
52.5
1,328.0

4,250.7
515.1
224.1
57.0
1,350.9

4,157.0
512.9
215.5
50.0
1,338.7

4,175.9
510.2
215.4
50.6
1,343.6

4,175.8
511.6
215.7
48.8
1,344.1

4,197.0
512.9
216.0
49.2
1,346.4

4,196.5
513.3
216.3
50.6
1,352.2

4,209.9
514.7
216.4
51 .1
1,353.9

4220.9
513.8
217.3
51.7
1,353.9

4228.3
512.4
217.8
51 .7
1,361 .9

4232.5
511.8
217.4
50.3
1,363.7

4246.0
510.0
217.9
50.1
1,363.8

4254.4
511 .5
217.8
50.7
1,367.4

4256.0
510.0
217.1
50.2
1,366.7

4260.5
509.6
216.9
50.2
1,374.7

380.3
40.0

385.6
38.8

385.0
38.8

382.3
38.3

380.1
38.2

380.5
38.1

372.3
38.1

381.5
38.3

374.6
38.4

374.2
38.5

374.5
38.5

380.2
38.6

362.7
38.4

383.6
38.3

384.8
38.2

28.0

26.8

29.4

28.7

29.7

31.4

31.1

30.6

32.6

32.6

32 .7

32.7

31 .6

31.8

31 .3

516.3
566.6
522.3
580.8
3,198

535.8
560.8
555.7
570.1
3,138

511 .6
559.0
516.1

515.5
567.7
524.4

579.3
3,175

514.1
566.9
525.8
580.2
3,163

580.0
3,169

518.5
572.1
531.9
581.2
3,169

519.1
570.9
532.6
582.1
3,173

519.5
572.8
531.1
582.3
3,177

520.8
578.2
534.0
581.7
3,182

523.7
579.2
536.3
582.6
3,173

525.1
580.4
538.1
582.0
3,166

525.9
581 .1
541.4
582.4
3,159

528.3
580.0
546.0
581.5
3,163

531.4
581.3
545.6
580.8
3,164

535.5
576.5
542.8
579.6
3,161

926.4

909.9

917.4

914.0

915.1

915.3

916.3

916.2

916.6

914.7

914.3

913.8

913.2

914.0

913.0

376.1
327.0

389.3
326.5

385.2
329.5

379.7
329.7

382.7
331.8

381 .2
333.0

385.7
333.3

390.8
335.4

394.9
335.5

391 .0
336.4

388.7
336 .6

389.4
337.3

395.0
338.4

388.7
338.9

386.4
339.5

30.0
1,082.6

31.3
1,042.3

30.4
1,061.2

30.8
1,061.3

31.9
1,058.2

31 .9
1,055.0

32.5
1,051.9

32.5
1,047.3

33.6
1,044.8

33.6
1,042.3

34.2
1,037.5

34.5
1,030.0

35.7
1,026.4

36.4
1,032.3

36.9
1,032.5

407.5
48.1

388.2
50.9

402.6
48.2

400.1
47.8

401.1
48.0

403.7
48.6

404.0
49.6

405.1
49.6

406.5
50.0

404.9
49.8

404.3
50.0

404.7
49.6

404.9
49.0

404.6
48.7

404.1
48.6

7,974
5,920.5

8,052
5,965.7

7,981
5,916.5

7,981
5,917.1

7,989
5,924.7

8,003
5,933.0

8,015
5,947.7

8,029
5,946.0

8,049
5,960.4

8,044
5,951 .9

8,053
5,962.4

8,Q78
8,976.2

8,092
5,990.7

8,107
6,002.9

8,121
6,017.1

22.7

21.6

22.5

22.4

22.4

22.3

22.3

21.8

21.9

21.8

21.8

21.7

21.5

21.3

21.1

2,785.6

2,832 .3

2,783.3

2,785.3

2,787.2

2,793.8

2,802.1

2,800.8

2,809.9

2,804.1

2,807.3

2,818.3

2,824.6

2,838.0

2,847.2

1,752.1
1,281.1

1,761.3
1.285.4

1,757.1
1.278.9

1,758.7
1,280.4

1,762.6
1,283.5

1,762.8
1,284.1

1,765.0
1.285.0

1,765.2
1.284.2

1,768.8
1,285.9

1,766.9
1,284.0

1,768.3
1,283.0

1,772.7
1.287.5

1,776.3
1.290.1

1,781 .5
1.295.0

1,784.5
1,297.4

Credit intermediation and
related activities 1 ••••••
Deoositorv credit
intermediation 1 •• •• ... .. . . . . . ..
Commercial bankino ··· ···· "·
Securities, commodity
contracts, investments .........
Insurance carriers and
related activities ........ .. ...
Funds, trusts, and other
financial vehicles ...... .. .
Real estate and rental
and leasing ........ .
Real estate ... ... ............ .. . ..
Rental and leasing services ..
Lessors of nonfinancial
intangible assets ....

Professional and business
services ...............................
Professional and technical
services' .. ...... .. ...... .. ... ... . ...
Legal services .. .. .......... ......
Accounting and bookkeeping
services ... .... ..... ...... ......
Architectural and engineering
services .. .. ...... ... ........ ...

764.4

766.8

771.9

773.8

778.2

780.8

781 .0

782.8

787.2

787.8

791 .6

793.6

800.6

800.2

802 .7

2,266.1

2,260.3

2,258.1

2,255.8

2.2'i7.4

2,257.1

2,259.5

2,262.7

2,263.8

2,260.2

2,263.9

2,265.1

2,266.7

2,266.6

2,269.1

81.7

84.7

80.7

79.8

79.5

79.0

78.8

77.9

77.6

78.0

77 .8

77.5

77.3

76.8

77.0

2,053.6
1,384.4
640.8

2,086.1
1,416.9
643.8

2,064.0
1,395.7
638.3

2,063.6
1,397.7
636.0

2,064.5
1,400.2
634.2

2,069.5
1,405.8
634.1

2,071 .6
1,409.2
633.2

2,083.1
1,418.7
635.4

2,088.1
1,418.8
640.5

2,092.0
1,422.1
641.4

2,090.6
1,424.1
638.0

2,101 .8
1,431 .6
641.9

2,101 .6
1,433.4
639.9

2,103.8
1,437.7
637.6

2,104.3
1,437.3
638.5

28.4

25.4

30.0

29.9

30.1

29.6

29.2

29.0

28.8

28.5

28 .5

28.3

28.3

28.5

28.5

15,999

16,413

16,159

16,172

16,196

16,237

16,363

16,432

16,457

16,490

16,518

16,548

16,643

16,664

16,705

6,623.5
1,136.8

6,760.7
1,161 .7

6,669.3
1,140.5

6,657.9
1,138.7

6,658.1
1,139.2

6,679.8
1,138.4

6,701.4
1,141.9

6,708.1
1,143.3

6,732.6
1,146.3

6,739.9
i,148.2

6,762.0
1,146.2

6,783.3
1,148.4

6,817.4
1,148.5

6,835.7
1,147.1

6,864.2
1,148.7

815.6

814.0

826.6

815.2

813.3

812.8

818.5

806.3

811.6

811.9

815.3

815.7

826.3

830.3

835.3

1,228.0

1,261.0

1,235.2

1,230.9

1,240.0

1,246.4

1,254.1

1,258.3

1,261.9

1,264.4

1,269.3

1,275.1

1,284.3

1,291.3

1,298.2

See notes at end of table.


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

Monthly Labor Review

February 2005

97

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

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept

Oct.

1,108.3

1,147.7

1,105.7

1,104.6

1,099.8

1,103.5

1,103.5

1,110.1

1,117.7

1,120.5

1,129.7

1,136.5

1,142.9

1,153.3

1,163.0

747.3

779.1

764.0

765.4

767.9

774.0

780.9

785.9

791.4

792 .2

794.3

793.9

796.7

795.4

797.9

1,675.5

1,718.0

1,670.2

1,675.1

1,675.6

1,676.6

1,679.7

1,683.3

1,684.5

1,685.9

1,682.5

1,679.1

1,678.2

1,679.3

1,678.6

7,982.3

8,040.1

8,040.0

8,064.3

8,073.0

8,085.4

8,147.2

8,148.7

8,162.5

Nov.P

Dec.P

Computer systems design
and related services .. .......
Management and technical
consulting services ........ ...
Management of companies
and enterprises ...... .... ..... ...
Administrative and waste
services ..............................

7,698.3

7,934.0

7,819.2

7,838.5

7,862.4

7,880.1

Administrative and suooort
services' ...... ............... . .

73,764.0

7,608.9

7,496.3

7,517.5

7,539.6

7,556.8

7,657.0

7,715.6

7,713.0

7,738.1

7,746.6

7,759.5

7,821 .5

7,822.1

7,837.0

3,336.2

3,470.2

3,461.3

3,473.8

3,493.8

3,492.3

3,553.7

3,591 .5

3,573.4

3,606.8

3,607.8

3,633.6

3,692 .9

3,696.5

3,719.9

2.243.2
747.4

2.393.6
754 .3

2.355.3
745.1

2.344 .3
739.0

2.370.4
739.8

2.380.3
746.0

2.423.8
748.6

2.451.7
751.2

2.449.4
754.0

2.460.2
749.9

2.474 .7
751.5

2.501 .4
744.3

2.554.2
747.8

2.551.7
748.8

2.561.1
749.2

1.631.7

1.694.5

1.635.9

1.637.1

1.639.5

1.646.2

1.674.5

1.686.0

1.694.1

1.691 .5

1.691.6

1.691.7

1.688.3

1.686.2

1.677.3

321 .9

325.2

322.9

321

322.8

323.3

325.3

324 .5

327

326.2

326.4

325.9

325.7

326.6

325.5

16,577
2,688.5

16,955
2,766.4

16,731
2,728.0

16,746
2,729.3

16,764
2,727.4

16,813
2,736.0

16,854
2,740.8

16,871
2,731.1

16,897
2,727.4

16,901
2,731.2

16,965
2,746.4

16,980
2,749.6

17,049
2,773.0

17,086
2,775.9

17,133
2,787.5

13,888.0

14,188.3

14,003.2

14,017.1

14,036.8

14,077.1

14,113.1

14,140.1

14,169.8

14,169.3

14,218.3

14,230.0

14275.6

14309.8

14345.3

4,776.0
2,003.8
423.1
727.1

4,946.9
2,054 .0
446.1
773.4

4,831 .0
2,030.0
425.0
739.9

4,840.3
2,032.3
427 .8
740.2

4,855.3
2,034.4
431.1
741.5

4,868.0
2,043.5
430.3
743.8

4,883.6
2,046.1
432.2
748.4

4,896.8
2,049.6
435.1
751.7

4,909.6
2,053.9
436.0
754.2

4,920.8
2,057.5
437.6
756.8

4,935.1
2,062.1
438.0
760.1

4,938.4
2,068.1
436.9
761 .5

4,964.6
2,078.6
437.7
766.2

4,978.1
2,083.6
438.3
773.5

4,995.2
2,088.9
438.8
778.2

4,252.5

4,2'l3.6

4,283.9

4,287.8

4,284.1

4,298.0

4,305.1

4,315.4

4,318.3

4,322.0

4,330 .5

4,332.1

4,337.5

4,346.7

4,351 .6

2,784.3
1.582.8
2,075.2
760.5
12,128

2,814.9
1.575.5
2,132.8
767.2
12,479

2,793.0
1.581 .7
2,095.3

2,792.1
1.580.3
2,096.9

2,791.1
1.578.7
2,106.3

2,798.4
1.582.1
2,112.7

2,802.8
1.584.0
2,121 .6

2,806.3
1.585.3
2,121.6

2,809.0
1.586.5
2,132 .9

2,812 .0
1.586.7
2,114.5

2,814 .0
1.586.3
2,138.7

2,820.3
1.587.1
2,139.2

2,820.5
1.587.1
2,153.0

2,826.3
1.591 .4
2,158.7

2,826.9
1.590.6
2,171.6

770
12,192

766.3
12,218

772.2
12,229

773.7
12,271

777.6
12,303

777.1
12,331

786
12,339

752.1
12,344

792.7
12,341

783.3
12,353

789.9
12,362

792.4
12,387

798.2
12,399

1,833.1

1,795.2

1,801.4

1,796.7

1,798.7

1,791.1

1,793.1

1,792.0

1,791.9

1,785.6

1,793.8

1,787.6

1,783.4

1,772.3

364 .7

368.8

369.4

366.5

364 .6

361.4

358.8

359.3

357.1

356.0

360.3

361.0

359.8

357.3

117.2

113.1

113.4

113.7

114.2

114.6

115.6

116.1

116.6

116.7

116.2

115.7

115.6

114.8

1,315.1

1,318.7

1,316.6

1,318.2

1,312.9

1,317.3

1,310.9

1,308.0

1,300.2

10,511.8 105,837.9
1,758.5
1,758.'5

10,546.7

10,551.7

10,555.6

10,559.3

10,574.0

10,603.9

10,626.4

1,764.7

1,764.4

1,767.9

1,771.4

1,769.2

1,786.7

1,790.6

Employment services

1

••

Temoorarv helo services ...
Business suooort services ....
Services to buildinos
and dwellinas ... ........
Waste management and
remediation services .... .....

Edur~t:onal and health
services .. ... .............
Educational services .. .......
Health care and social
assistance .... .. ... .... .... ..
Ambulatorv health care
services' .... .... ............. ....
Offices of physicians ...........
Oµtpatient care centers ....
Home health care services ...
Hospitals ......................... ...
Nursina and residential
r.;:trA f;:tr.ilitiA~ 1

Nursina care facilities ... ..
Social assistance 1 • •••••••• •• •••• ••
Child day care services .. .. .. .
Leisure and hospitality ...........
Arts, entertainment,
and recreation . .. . . . . . . . . .. . .
1,801 .0
Performing arts and
spectator sports ..... ...... .....
370.2
Museums. historical sites,
zoos, and parks ... .... ..... ....
114.1
Amusements, gambling , and
recreation ..... .. .. .. .............. 1,316.6
Accommodations and
food services .... ... ........ ........ 10,324.4
Accommodations .... .. ........... . 1,765.2
Food services and drinking
places ........ . ...................
8,559.2
Other services ....................... .
5,393
Repair and maintenance ....... ~ 1,236.2
Personal and laundry services
1,258.2
Membership associations and
organizations ... ..... .... .........
2,898.0
Government................................
21 ,575
Federal ........................ ..............
2,756
Federal, except U.S. Postal
Service ............. ........... ........ ... 1,947.0
U.S. Postal Service .. .. .... .......
809.1
State ............. ............................
5,017
Education ............................... 2,266.4
Other State government... .....
2,750.7
Local ........ .. .......... ....... ... . ........
13,802
Education .. .. ............ ·······
7,699.1
Other local government ... .....
6,104.0

1,351 .1

1,313.3

1,318.6

1,316.5

1,319.9

10,645.6

10,396.3

10,416.5

10,432.3

10,742.0

1,795.6

1,763.0

1,752.1

1,754.4

1,753.4

8,849.9
5,431
1,227.5
1,273.9

8,633.3
5,374
1,228.5
1,250.2

8,664.4
5,379
1,233.5
1,251 .2

8,677.9
5,376
1,230.5
1,247.6

8,718.6
5,391
1,239.4
1,255.9

8,753.3
5,404
1,238.2
1,260.5

8,779.4
5,407
1,237.7
1,265.5

8,782.0
5,418
1,235.1
1,268.4

8,787.7
5,414
1,236.3
1,262.1

8,787.7
5,414
1,235.2
1,259.9

8,787.9
5,410
1,235.2
1,255.7

8,804.8
5,410
1,226.6
1,252.9

8,817.2
5,417
1,236.4
1,255.6

8,835.8
5,421
1,237.1
1,259.1

2,929.3

2,895.7

2,894.5

2,898.3

2,895.2

2,904.8

2,903.7

2,914 .9

2,915.9

2,919.1

2,918.8

2,920.3

2,924.5

2,924.8

21,619
2,728

21,544
2,720

21,527
2,715

21,539
2,716

21,553
2,710

21,572
2,727

21 ,544
2,712

21,528
2,716

21,572
2,710

21 ,629
2,712

21,652
2,713

21,675
2,706

21,687
2,713

21,716
2,706

1,943.3
784 .1
4,986
2,249.1
2,736.6
13,906
7,763.0
6,142.9

1,928.9
791.4
5,027
2,285.7
2,740.9
13,797
7,687.1
6,109.7

1,921.5
793.1
5,007
2,268.0
2,738.9
13,805
7,692 .2
6,112.7

1,923.8
791 .7
5,018
2,279.6
2,738.4
13,805
7,694.3
6,110.8

1,921 .1
789.1
5,023
2,283.2
2,739.7
13,820
7,704.7
6,114 .8

1,939.5
787.3
5,019
2,278.3
2,740.6
13,826
7,710.9
6,115.4

1,925.7
786.5
5,004
2,261.4
2,742.8
13,828
7,710.2
6,117.9

1,930.5
785.4
5,004
2,257.8
2,746.1
13,808
7,695.1
6,113.3

1,922.5
787.2
5,019
2,271 .1
2,747.8
13,843
7,715.7
6,116.8

1,926.3
785.3
5,035
2,285.2
2,749.4
13,882
7,758.4
6,123.2

1,927.6
784 .9
5,047
2,299.7
2,747.5
13,892
7,760.4
6,131.6

1,923.6
781 .9
5,058
2,307.0
2,751 .1
13,911
7,774.9
6,136.3

1,930.4
782.3
5,066
2,311.4
2,754.5
13,908
7,779.9
6,128.1

1,931 .5
774.6
5,076
2,317.3
2,758.7
13,934
7,793.5
6,140.8

' Includes other industries not shown separately.
p : preliminary.

98

Monthly Labor Review


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

Classification System (NAICS), replacing the Standard Industrial Classification (SIC) system.
NAICS-based data by industry are not comparable with sic-based data. See "Notes on the

NOTE: Data reflect the conversion to the 2002 version of the North American industry

February 2005

data" for a description of the most recent benchmark revision .

13. Average weekly hours of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry, monthly
data seasonally adjusted
Industry

Annual average

2003

2004

2003
Dec.

2004
Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

TOTAL PRIVATE .................... .. .. .....

33.7

33.7

33.6

33.8

33.8

33.8

33.7

33.8

33.6

33.8

33.7

33.8

33.8

33.7

33.8

GOODS-PRODUCING ........ .. ................

39.8

40.0

39.9

40.2

40.3

40.2

40.0

40.3

40.0

40.1

40.1

40.1

40.0

39.9

39.9

Nat1Jral resources and mining ............

43.6

44.5

43.6

44.5

44.1

44.2

44.3

44.2

43.9

44.1

44.4

44.6

44.8

44.9

44.9

Construction ....................................

38.4

38.3

38.1

38.5

38.5

38.6

38.2

38.3

38.1

38.4

38.1

38.4

38.3

38.4

38.4

Manufacturing ....................................
Overtime hours .................................

40.4
4.2

40.8
4.6

40 .6
4.5

41.0
4.5

41.0
4.6

40 .9
4.6

40.7
4.5

41.1
4.6

40.8
4.6

40.9
4.6

40.9
4.6

40 .8
4.6

40.6
4.5

40 .5
4.5

40.5
4.5

Durable goods ... ...... ... ....... ... ............
Overtime hours .................................
Wood products ...................................
Nonmetallic mineral products .............
Primary metals ... .......... ............ .. ...... .. .
Fabricated metal products ..................
Machinery .. .......... ......... ..... ... .. ... ..
Computer and electronic products .... .
Electrical equipment and appliances ..
Transportation equipment. ..................
Furniture and related products .. ..... ...
Miscellaneous manufacturing .............

40.8
4.3
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
42.4
43.1
41.1
41.9
40.4
40.7
42.5
39.5
38.5

41 .2
4.7
41.0
42.3
42 .7
40.8
41.1
40.4
40.7
42 .7
39.7
38.5

41.5
4.7
40.9
42.5
43.1
41.2
41.8
40.8
41.1
42 .8
39.7
39.0

41.5
4.8
41.1
42.5
43.0
41 .2
41.8
41 .2
40.7
42.9
39.4
38.7

41.4
4.8
41.0
42.9
43.2
41 .1
41.7
40.7
40.8
42.8
39.6
38.7

41.2
4.7
41.0
42.3
43.1
41.0
41.6
40.5
40.8
42.4
39.5
38.3

41.6
4.8
41.4
42.0
43.4
41 .3
42.3
40.8
41.6
42.8
40.0
38.9

41.2
4.7
40.5
41.8
43.5
41 .0
42.0
40.5
40.8
42.3
39.7
38.4

41.3
4.7
40.7
42.1
43.3
41.2
42.0
40.9
40.8
42.4
39.4
38.5

41.3
4.7
40.9
42.3
43.3
41.2
42.1
40.5
41.0
42.5
39.5
38.5

41.2
4.7
40.3
42.4
43.1
41 .2
42.3
40.3
40.5
42.4
39.3
38.3

41.1
4.7
40.2
42 .4
43.1
41 .0
42.2
40 .2
40.4
42.4
39.1
38.3

41.0
4.6
40.0
42.4
43 .1
40.8
42.2
39.9
40 .1
42.2
39.4
38.2

41.1
4.6
40.0
42.5
43.1
40.9
42 .1
40.2
39.6
42.4
39.7
38.5

Nondurable goods ................... .............
Overtime hours ................... ... ...........
Food manufacturing ............................
Beverage and tobacco products ....... ..
Textile mills ....................... .. .. ... ....
Textile product mills ...... .................
Apparel ................. ..............................
Leather and allied products ................
Paper and paper products ....... ........
Printing and related support
activities ... .. ............................... ... .. ...
Petroleum and coal products ...... ......
Chemicals ....................................
Plastics and rubber products ... .........

39.8
4.1
39.3
39.1
39.1
39.6
35.6
39.3
42.1

40.0
4.4
39.3
39.3
40.2
38.9
36.0
38.4
42.1

39.9
4.2
39.1
39.1
39.7
39.8
35.8
40 .3
41.8

40.2
4.3
39.5
39.6
40.0
39.4
35.7
39.8
41.9

40.3
4.3
39.4
40.3
40.0
39.9
36.2
39.5
42.0

40.1
4.3
39.3
39.4
40.2
38.8
36.3
39.4
41.8

40.0
4.3
39.1
39.6
39.5
38.3
35.9
39.1
41.9

40.3
4.4
39.6
39.2
40.3
38.8
36.1
38.4
42.6

40.1
4.4
39.4
38.7
40.3
38.9
35.9
38.0
42.0

40.1
4.4
39.3
39.2
40.5
38.5
36.1
37.2
42.4

40.2
4.4
39.3
39.5
40 .5
38.7
36.1
37.8
42.5

40.1
4.4
39.4
39.1
40 .1
39.0
36.2
38.1
42.1

39.8
4.3
38.9
38.5
40.1
39.0
36.0
38.3
42.2

39 .7
4.3
38.9
38.5
40 .0
38.8
35.9
38.1
42.0

39.6
4.3
38.7
38.4
40.1
38.4
36.3
36.8
42 .0

38.2
44.5
42.4
40.4

38.4
45.0
42.7
40.4

38.2
44.2
42.5
40.4

38.6
43.8
42.9
40.8

38.6
44.1
43.2
40.9

38.4
43.7
43.0
40.9

38.4
43.9
43.0
40.7

38.6
45.0
42.9
40.9

38.5
45.0
42 .6
40.8

38.6
45.0
42.8
40.5

38.5
46.3
42.8
40.5

38.3
45.8
42.8
40.2

38.2
44.9
42.6
40 .0

38.3
45.4
42 .3
39.6

38.2
45.0
42.2
39.7

32.4

32.3

32.2

32.4

32.4

32.4

32.3

32.4

32.3

32.4

32.4

32.5

32.5

32.4

32.5

33.5
37.8

33.5
37 .8
30 .8
36.7
40 .8
36.2
35.3

33.6
37.9
31.0
36.9
40.8
36.2
35.7

33.7
38.0
30 .9
37.2
41 .0
36.3
35.5

33.6
38.0
30.8
36.9
41 .2
36.3
35.5

33.5
38.0
30.7
36.9
41.2
36.3
35.6

33.5
37.8
30.7
37.3
41.3
36.4
35.8

33.3
37.6
30 .5
36.9
41.1
36.5
35.5

33.4
37.8
30.6
37.1
41.0
36.4
35.6

33.5
37.6
30.7
37.2
40.9
36.4
35.5

33.6
37.8
30 .8
37.4
41.4
36.4
35.5

33.6
37.7

30 .9
36.9
41.1
36.2
35.5

33.5
37.8
30.7
37.2
41 .0
36.3
35.6

30.8
37.4
40.7
36.4
35.7

33.5
37.6
30.7
37.3
40 .5
36.4
35.6

33.5
37.6
30.8
37.2
40.6
36.4
35.8

34.1
32.3
25.6
31.4

34.2
32.4
25.7
31.0

33 .8
32.4
25.6
31 .0

34.1
32.4
25.7
31.1

34.2
32.4
25.8
31.1

34.1
32.4
25.7
31.2

34.1
32.4
25.7
31.1

34.2
32.5
25.7
31.2

33.9
32.5
25.7
31.0

34.2
32.6
25.6
31.1

34.2
32.5
25.6
31.1

34.5
32.6
25.6
31 .1

34.3
32.6
25.7
31.0

34.2
32.5
25.7
31.0

34.2
32.6
25.8
31 .0

PRIVATE SERVICEP~OVIDING ..................................
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 hospltallty ......................
Other services ......................................
1

Data relate to production workers in natural resources and mining and manu-

facturing , construction workers in construction, and nonsupervisory workers in the
service-providing industries.

NOTE:

Data reflect the conversion to the 2002 version of the North American

Industry Classification System (NAICS), replacing the Standard industrial Classification
(SIC) system . NAICS-based data by industry are not comparable with SIC-based data.
See "Notes on the data" for a description of the most recent benchmark revision.

p = preliminary.


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

Monthly Labor Review

February 2005

99

Current Labor Statistics: Labor Force Data

14. Average hourly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry,
monthly data seasonally adjusted
Annual average

Industry

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

TOTAL PRIVATE
Current dollars .. . . .. .. .. . . . . . ... . . ..
Constant (1982) dollars .. . . . . . . . . . . .

$15.35

$15.67

$15.45

$15.49

$15.52

$15.55

$15.59

$15.63

$15.66

$15.71

$15.76

$15.78

$15.82

$15.84

8.27

8.23

8.30

8.27

8.27

8.24

8.25

8.21

8.20

8.23

8.26

8.25

8.22

8.22

$15.86
8.22

GOODS-PRODUCING ........................... ..

16.80

17.19

16.97

17.00

17.06

17.08

17.13

17.13

17.16

17.19

17.24

17.30

17.33

17.35

17.38

Natural resources and mining .............
Construction .........................................
Manufacturing .......................................
Excluding overtime ..........................
Durable goods .. . .. ...... ....... ... ........ ..
Nondurable goods ... .... .. . .... ...... ... ...

17.58
18.95
15.74
14.96
16.46
14.63

18.06
19.23
16.14
15.29
16.82
15.05

17.91
19.04
15.93
15.09
16.64
14.81

17.95
19.11
15.94
15.11
16.63
14.85

18.01
19.18
15.99
15.14
16.68
14.89

18.10
19.17
16.01
15.16
16.69
14.93

18.08
19.20
16.08
15.24
16.75
15.00

18.10
19.20
16.08
15.23
16.75
15.02

18.24
19.19
16.13
15.27
16.78
15.08

18.15
19.22
16.16
15.30
16.81
15.12

18.12
19.25
16.23
15.37
16.90
15.15

18.11
19.27
16.29
15.42
16.98
15.19

18.19
19.33
16.29
15.43
16.99
15.16

18.32
19.34
16.30
15.44
17.00
15.18

18.34
19.34
16.36
15.50
17.07
15.20

PRIVATE SERVICEPROVIDING ..... .............. .. ... .............. ..

14.96

15.26

15.05

15.08

15.10

15.13

15.17

15.23

15.26

15.31

15.36

15.38

15.41

15.43

15.46

14.34
17.36
11 .90
16.25
24 .76
21.01
17.13

14.59
17.66
12.08
16.53
25.62
21.42
17.53

14.41
17.46
11 .95
16.33
25.13
20.99
17.30

14.45
17.53
11 .95
16.46
25.32
21 .15
17.35

14.49
17.54
11 .98
16.52
25.35
21 .24
17.32

14.50
17.54
11 .99
16.53
25.38
21 .25
17.41

14.57
17.60
12.01
16.71
25.67
21 .29
17.46

14.61
17.63
12.06
16.75
25.46
21.42
17.49

14.65
17.67
12.10
16.82
25.44
21 .30
17.50

14.70
17.71
12.12
16.89
25.57
21.45
17.55

14.73
17.70
12.16
16.99
25.54
21 .53
17.58

14.74
17.74
12.17
16.91
25.73
21 .61
17.61

14.77
17.80
12.17
16.97
25.95
21 .60
17.68

14.79
17.81
12.22
16.97
25.85
21 .58
17.65

14.82
17.84
12.23
17.04
25.95
21 .78
17.70

17.20

17.46

17.25

17.24

17.25

17.27

17.29

17.36

17.42

17.44

17.56

17.52

17.59

17.62

17.65

15.64
8.76
13.84

16.15
8.91
13.98

15.81
8.84
13.80

15.87
8.85
13.84

15.90
8.86
13.84

15.96
8.87
13.87

15.99
8.86
13.84

16.06
8.86
13.85

16.12
8.85
13.88

16.18
8.87
13.90

16.19
8.91
13.92

16.23
8.95
13.95

16.24
8.99
13.99

16.27
9.02
14.02

16.26
9.03
14.03

Trade,transportatlon, and
utilities ..................... ...................
Wholesale trade ... ..... ............... .. .........
Retail trade ......................... ................
Transportation and warehousing ... ... .
Utilities .. .... ... ... .. .. ... .. ... .. . .. ..... .... ...
Information ................ ............................
Financial activities................................
Professional and business
services ...............................................
Education and health
services ...............................................
Leisure and hospitality ........................
Other services.................. ................ .....
1

Data relate to production workers in natural resources and mining and manufacturing , construction workers in constru ction, and nonsupervisory workers in the
service-providing industries.
p

100

= preliminary.

Monthly Labor Review


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

February 2005

NOTE: Data reflect the conversion to the 2002 version of the North American industry
Classification System (NAICS), replacing the Standard Industrial Classification (SIC) system. NAICS
based data by industry are not comparable with SIC-based data. See "Notes on th e data" for a
description of the most recent benchmark revision .

15. Average hourly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry
Industry
TOTAL PRIVATE ............... ...... .... ..
Seasonally adjusted .... .... ....... ...

2003

Annual average

2003

2004

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

$15.56
15.49

$15.60
15.52

$15.55
15.55

$15.59
15.59

$15.63
15.63

$15.57
15.66

$15.59
15.71

$15.67
15.76

$15.80
15.78

$15.83
15.82

$15.86
15.84

$15 .89
15.84
17.43

$15.35

$15.68

15.47

-

$15.48
15.45

GOODS-PRODUCING .............................

16.8

17.19

17.03

16.94

16.95

17.00

17.09

17.10

17.14

17. 18

17.28

17. 41

17.39

17.38

Natural resources and mining ... ...... ..

17.58

18.13

17.97

18.00

18.05

18.17

18.14

18.06

18.18

18.07

18.01

18.04

18.14

18.32

18.41

Construction ... ... .................................

18.95

19.24

19.19

19.01

19.07

19.07

19.15

19.15

19.12

19.25

19.33

19.42

19.47

19.37

19.35

Manufacturing ..... ......... ............ .. ... ..

15.74

16.15

16.05

15.98

15.99

16.01

16.07

16.05

16.09

16.04

16.17

16.36

16.27

16.33

16.47

Durable goods ... ..... ...... ...... ............
Wood products .... ..... .................... ....
Nonmetallic mineral products .... .. ...
Primary metals .................................
Fabricated metal products ...............
~,ldGhinery .... .. ... ....... ..... .... .........
Computer and electronic products ...
Electrical equipment and appliances
Transportation equipment ................
Furniture and related products .. .......
Miscellaneous manufacturing .. ........

16.46
12.71
15.77
18.13
15.01
16.30
16.68
14.35
21 .25
12.98
13.30

16.84
13.01
16.27
18.57
15.30
16.66
17.26
14.85
21 .54
13.16
13.84

16.78
12.93
15.98
18.39
15.23
16.62
16.85
14.68
21 .74
13.08
13.60

16.66
12.90
16.03
18.39
15.20
16.53
16.81
14.50
21 .38
12.95
13.68

16.68
12.91
16.00
18.36
15.18
16.50
16.92
14.58
21 .37
12.92
13.75

16.69
12.93
16.02
18.33
15.25
16.49
16.93
14.68
21.34
12.96
13.78

16.72
13.00
16.19
18.52
15.21
16.53
17.01
14.80
21 .36
13.09
13.70

16.71
13.03
16.18
18.48
15.20
16.53
17.11
14.83
21.29
13.04
13.76

16.75
12.98
16.24
18.51
15.23
16.56
17.21
14.88
21.36
13.10
13.81

16.61
13.03
16.38
18.66
15.26
16.68
17.29
14.88
20.77
13.11
13.89

16.85
13.01
16.29
18.58
15.27
16.72
17.37
14.98
21.54
13.27
13.87

17.08
13.13
16.52
18.89
15.42
16.85
17.47
15.03
21.96
13.39
13.96

16.99
13.02
16.38
18.74
15.37
16.83
17.51
15.00
21.84
13.26
13.91

17.05
13.09
16.50
18.69
15.44
16.80
17.60
15.02
21 .95
13.29
13.97

17.22
13.13
16.48
18.73
15.54
16.89
17.86
15.08
22.27
13.16
13.96

Nondurable goods .... ....... .. .. ... .. .. ....
Food manufacturing ............ ... ....... .. .
Beverages and tobacco products ....

14.63
12.80
17.96

15.06
12.97
19.14

14.88
12.95
18.58

14.89
12.91
18.88

14.88
12.87
18.76

14.90
12.89
19.13

15.01
12.96
19.60

14.98
12.94
19.55

15.03
13.00
19.39

15.14
13.05
19.29

15.09
12.99
19.10

15.24
13.08
19.20

15.12
12.93
19.20

15.17
12.97
18.84

15.24
13.01
18.72

Textile mills ............................... .. .....
Textile product mills .........................
Apparel ..... ................ .......................
Leather and allied products .... .......
Paper and paper products .. ...........
Printing and related support activitiei
Petroleum and coal products ....... ..
Chemicals .... ... .... .... .... ..... .. .. ..... .
Plastics and rubber products ....... ....

1.2.00
11 .24
9.56
11 .67
17.32
15.37
23.64
18.52
14.18

12.14
11 .43
9.76
11.65
17.85
15.71
24.41
19.22
14.59

12.21
11.44
9.80
11 .90
17.60
15.56
24.06
18.79
14.47

12.11
11.45
9.74
11 .94
17.63
15.53
24.13
18.83
14.43

12.13
11 .40
9.58
11 .76
17.55
15.57
24.32
18.85
14.45

12.09
11.37
9.60
11 .64
17.59
15.61
24.82
18.87
14.45

12.23
11 .33
9.71
11 .65
17.84
15.54
24.48
19.02
14.58

12.08
11 .30
9.55
11.49
17.88
15.51
24.41
19.05
14.55

12.15
11.29
9.60
11 .59
17.86
15.54
24.24
19.20
14.59

12.07
11 .48
9.74
11 .68
17.91
15.71
24.35
19.36
14.69

12.08
11.46
9.73
11 .68
17.84
15.86
24.07
19.29
14.66

12.26
11.51
9.93
11 .56
18.16
15.94
24.47
19.49
14.75

12.12
11 .44
9.97
11 .57
17.87
15.94
24.35
19.47
14.55

12.12
11 .44
10.02
11 .53
18.10
15.90
24.78
19.47
14.60

12.19
11 .68
10.02
11.74
17.99
15.86
24.56
19.76
14.73

PRIVATE SERVICEPROVIDING .. .. ................. ... ........ . .. .

14.96

15.28

15.07

15.19

15.24

15.16

15.20

15.24

15.14

15.17

15.24

15.37

15.41

15.46

15.48

Trade, transportation, and
utilities .. ... ..........................................
Wholesale trade .. ...... .. .. ..... ..... ......
Retail trade ...... .. ..... .. .... ...... .... .....
Transportation and warehousing .... ..
Utilities ..... ... ... .. ..... .... ... ....... .. ...... .

14.34
17.36
11 .90
16.25
24.76

14.65
17.66
12.10
16.81
25.60

14.31
17.46
11.87
16.33
25.26

14.50
17.56
11.98
16.46
25.38

14.58
17.60
12.04
16.58
25.29

14.53
17.47
12.03
16.51
25.36

14.64
17.60
12.08
16.73
25.69

14.64
17.67
12.08
16.72
25.53

14.61
17.58
12.09
16.80
25.33

14.62
17.66
12.07
16.86
25.43

14.66
17.69
12.09
16.98
25.33

14.78
17.72
12.23
16.91
25.87

14.78
17.77
12.18
16.98
26.00

14.77
17.81
12.18
17.00
25.99

14.72
17.83
12.14
17.08
26.05

21.01

21.44

21.10

21.21

21 .28

21 .17

21 .24

21.41

21 .18

21 .30

21 .44

21.73

21 .69

21 .71

21 .86

17. 13

17.52

17.26

17.35

17.47

17.37

17.45

17.62

17.38

17.44

17.58

17.60

17.67

17.62

17.65

17.20

17.43

17.29

17.38

17.47

17.28

17.26

17.45

17.28

17.31

17.46

17.43

17.50

17.59

17.70

16.29

Financial activities .............................
Professional and business
services ...... .. ... ....... ......... .... ... ... ...
Education and health
services ......... .. ..... .. .. ... ................

15.64

16.11

15.86

15.94

15.95

15.94

15.99

16.00

16.06

16.18

16.16

16.25

16.25

16.28

Leisure and hospitality ............... ... ..

8.76

8.91

8.94

8.89

8.92

8.89

8.84

8.85

8.78

8.78

8.80

8.94

9.01

9.06

9.17

Other services..... ... . ......... .................

13.84

13.91

13.88

13.89

13.90

13.85

13.87

13.90

13.82

13.78

13.84

13.97

13.97

14.04

14.11

1

Data relate to production workers in natural resources and mining and

NOTE: Data reflect the conversion to the 2002 version of the North American Industry

manufacturing, construction workers in construction, and nonsupervisory workers in

Classification System (NAICS), replacing the Standard Industrial Classification (SIC)

the service-providing industries.


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

system. NAICS-based data by industry are not comparable with SIC-based data. See
"Notes on the data" for a description of the most recent benchmark revision.

Monthly Labor Review

February 2005

101

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 ........ ............ $517.36
Seasonally adjusted ·········
GOODS-PRODUCING ............ ... .
Natural resources
and mining ...... ..........

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

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

$528.97

$518.15
523.56

$527.28
524.58

$520.93
525.59

$522 .27
525.38

$531.42
528.29

$524.71
526.18

$528.50
531.00

$535.91
531.11

$530.88
533.36

$535.05
534.72

$534.48
533.81

$537.08
536.07

681 .70

678.47

690.84

689.03

687.20

698.11

691 .18

699.08

696.94

702.43

822.93

-

-

$520.13
519.12

669.23

688.85

682.90

674.21

674.61

766.83

804.90

786.98

797.66

794.53

802.31

806.85

712.88

711.31

732.29

721 .96

738.03

754.60

755.80

797.37
730.19

821 .74
755.44

831 .73

736.78

798.25
741 .11

809.01

727.11

781 .70
714.34

784.80

Construction ..................... .....

739.93

739.17

Manufacturing ... ................. . ...

636.07

658.94

662.87

650.39

652.39

653.21

652 .44

659.66

659.69

646.41

661 .35

664.22

662.19

666.26

678.56

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

671 .53

694.90

703.08

688.06

688.88

690.97

687 .19

695.14

695.13

674.37

695.91

698.57

699.99

702.46

721.52

Wood products .. ..........
.......
Nonmetallic mineral products ....
Primary metals ......... ............
Fabricated metal products ........
Machinery .. ..................... .....
Computer and electronic
products ........... ................ .. ...
Electrical equipment and
appliances ... . . ' . . . .. . . .... .. ......... .
Transportation equipment . .....
Furniture and related
products .. ... ........ ... ........ ..
Miscellaneous
manufacturing ........ ...... ..........

513.92
665.11
767.63
610.33
664.79

528.73
690.13
801 .78
628 .65
699.19

531.42
669.56
799.97
635.09
696.38

517.29
663.64
796.29
626.24
689.30

521 .56
664.00
787.64
623.90
691.35

524.96
680.85
790.02
625.25
690.93

530.40
684.84
800.06
620.27
987 .65

544.65
684.41
803.88
627.76
700.87

533.48
690.20
808.89
627.48
698.83

531 .62
694.51
791.18
621.08
692.22

538.61
700.47
798.94
627.60
697.22

521 .26
710.36
808.49
627.59
699 .28

526.01
701 .06
803.95
633.24
706.86

526.22
702.90
809.28
634.58
710.64

527.83
698.75
822.25
648.02
724.58

674.68

699.17

695.91

680.81

695.41

690.74

683.80

694.67

698.73

696.79

700.01

700.55

705.65

709.28

732.26

582.68
890.32

604.21
916.24

616.56
950.04

594.50
915.06

591 .95
916.77

596.01
917.62

599.40
905.66

613.96
915.47

611.57
912.07

599.66
841 .19

611.18
911.14

601 .20
928.91

612.00
928.20

609.81
930.68

609.23
968.75

505.23

519.66

528.43

510.23

505.17

510.62

517 .06

517.69

521 .38

515.22

529.47

519.53

515.81

524.96

548.51

510.69

533.28

533.12

532 .15

533.50

534.66

524.71

535.26

530.30

527.82

534.00

529.08

534.14

536.45

547.23

Nondurable goods .... ........ . .... ...

582.65

602.85

602.64

594.11

595.20

596.00

595.90

602 .20

604.21

602.57

606.62

611 .12

604.80

608.32

612.65

Food manufacturing ... .. .. .... ....
Beverages and tobacco
'""'.1L1CIS ... . ...... .... .. .... . .. .. .. .
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 ... .. .. ......... .. .. ........

502 .61

508.97

514.12

504.78

499.36

498.84

497 .66

511 .13

512.20

512.87

514.40

521 .89

508.15

513.61

511 .29

702.75
469.47
445.08
340.22
458.26
719.21

749 .38
487.43
444.04
352 .04
445.95
752 .38

722.76
490.84
464.46
352 .80
485.52
751.52

728.77
485.61
447.70
343.82
471 .63
738.70

737.27
486.41
450.30
345.84
464.52
731 .84

744.16
490.85
441 .16
350.40
464.44
731 .74

780.08
484.31
435.07
347.76
460.18
745 .71

774.18
486.82
436.18
346.67
441 .22
756.32

760.09
490.86
444.83
348.48
442 .74
748.33

760.03
481 .59
435.09
348.69
422.82
750.43

762.09
489.24
443.50
353.20
441 .50
754.63

764.16
489.17
445.44
352.52
430.03
771 .80

735.36
482.38
448.45
357.92
445.45
755.90

727.22
486.01
446.16
361 .72
440.45
767.44

718.85
496.13
455.52
369.74
428.51
775.37

587.42

603.66

602.17

593.25

597.89

600.99

593.63

594.03

593.63

600.12

610.61

612 .10

613.69

616.92

616.95

1,052.97
784.56

1,096.71
820.73

1,061 .05
806.09

1,068.96
804.04

1,074.94
816.21

1,079.67
811.41

1,062.43
814.06

1,091 .13
815.34

1,095 .65
819.84

1,120.10
816.99

1,097.59
823.68

1,120.73
832 .22

1,098.19
827.48

1,132.45
829.42

1,110.11
837.82

572.23

589.73

596.16

585.86

588.12

589.56

594.86

595.10

599.65

583.19

589.33

590 .00

583.46

582 .54

595.09

484.00

494.46

485.25

484.56

496.82

486.64

487.92

496.82

489.02

493.03

501.40

496.45

499.28

499.36

501 .55

490.69

480.82

477.05

488.43

482.40

486.05

493 ..37

489.44

494.46

498.44

496.61

495.13

491.84

494.59

666.52
371 .80

659.99
367.97

656.74

658.62
365.71

665.28
367.23

674.99

361 .80

670.56
368.42

372.06

661 .01
372.37

665.78
376.58

672.22
378.42

666.27
377.91

668.15
373.93

669.66
370.27

668.63
376.34

624.01
1,048.44

602.58
1,028.08

597.50
1,032.97

613.46
1,039.42

604.27
1,039.76

610.65
1,053.29

627.00
1,054.39

621.60
1,046.13

627.19
1,032.46

641 .84
1,030.93

630.74
1,073.61

635.05
1,066.00

637.50
1,060.39

638.79
1,052.42

Durable goods ....

.

PRIVATE SERVICEPROVIDING ...............................

Trade, transportation,
and utilities............ .. .... ... .. .... 481 .10
Wholesale trade .... .. ... . ... .. . .....
657.12
Retail trade .. ......... . ............ ... 367.28
Transportation and
warehousing .. .. ....... .... ........ 597 .79
Utilities ..... .. ........... .. ... . ...
1,016.94

1

2003

2004

Information ................... ..........

761.13

778.77

761.71

763.56

776 .72

760.00

764 .64

777.18

775.19

773.19

788.99

788.80

789.52

792.42

793.52

Financial activities ... ...... ... ... ...

608.87

623 .02

607.55

612.10

630.67

611.42

615.99

637.84

613.51

617.38

634.64

619.52

627.29

625.51

630.11

Professional and
business services ........ . ... ..... .

586.68

595.32

582 .67

583.97

602.72

587.52

588 .57

603.77

587.52

590.27

604.12

592.62

598.50

599.82

603.57

Education and
health services ... ...................

505.76

523.26

512 .28

514.86

519.97

513.27

516.48

521 .60

520.34

527.47

530.05

528 .13

528.13

529.10

529.43

Leisure and hospitality..... .......

224.35

228.74

225.29

221 .36

230.14

225.80

224.81

229.22

227.40

230.91

234.08

226.18

230.66

230.12

233.84

Other services ...... ...... ... ....... ..

434.49

432.02

430.28

429.20

433.68

428.73

428.58

435.07

428.42

429.94

434.58

431 .67

433.07

433.84

437.41

Datc1 •elate to production workers in natural resources and mining and manufacturing,

Industry Classification System (NAICS). replacing the Standard Industrial Classifification (SIC)
system. NAICS-based data by industry are not comparable with sic-based data. See "Notes on

construction workers in construction, and nonsupervisory workers in th e serviceproviding industries.

the data" for a description of the most recent benchmark revision .

NOTE :

Dash indicates data not available. p - preliminary.

Data reflect the conversion to the 2002 version of the North American

102 Monthly Labor Review

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

February 2005

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

17. Diffusion indexes of employment change, seasonally adjusted
[In percent]
Timespan and year

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug. Sept.

Oct.

Nov.

Dec.

Private nonfarm payrolls, 278 industries
Over 1-month span:
2000 .. ...... ..................................... .
2001 ......... .. ....... ......... ····· ············ ·
2002 ............................................. .
2003 ......................... ......... ........... .
2004 ........... .. ... ........... .. .. .............. .
Over 3-month span:
2000 .. .......... ................ .. ............... .
2001 ............................................. .
2002 ........ ... .......... .. .......................
2003 ............................................. .
2004 ................................... .. ........ .
Over 6-month span:
2000 ....................................... .. .... .
2001 ....... ... .... .. ........................ ......
2002 ............................................. .
2003 ... ..................... ......................
2004 ............ .. ............................... .
Over 12-month span:
2000 .. ... .. .. ..................... ............... .
2001 ..............................................
2002 ................ ..... .. ... ....................
2003 ............................................. .
2004 .... .... .. ................................... .

61 .9
52.2
40.1
41.2
52.3

62.9
47.8
35.1
35.1
56.1

63.3
50.4
41.0
38.1
68.7

59.5
34.4
41 .5
41.4
67.6

46.9
41.4
41 .7
42.8
63.8

61.7
39.2
47.8

63.1
37.1
44 .1

52.5
38.8
44.1

40.1
60.6

40.5
55.2

39.7
56.3

69.2
52.7
34.0

66.2
50.4
37.4

67.8
50.4
35.1

68.3
43.5
36.2

60.1
38.8
36.7

58.1
34.9

61.5
37.9
40.8

36.5
54.0

32.6
55.2

36.3
62.8

35.1
70.0

40.5
74.5

39.4
42.6
68.7

56.3
36.2
39.9
37.4
64.6

35.4
57.2

67.3
51 .8
29.5

69 .1
50.0
30.0

75.2
51 .8
31.1

72.5
47.3
31.1

67.4
43.5
31 .7

67.8
41 .5
37.1

66.7
38.1
37.2

60.8
35.4
39.0

33.6
48.9

31.1
54.1

31.7
59.6

31.7
64.7

33.5
67.8

37.8
71.2

36.2
68.3

36.5
71.6

70 .9
59.5
33.6
34.5

69.2
59.5
31.7
31..5

69.8
48.6
30.2
36.2
54.9

70.0
43.3
32.0

70.3
43.9
31 .3

43.2 1

71.0
49.3
30.4
33.5
50.7

71 .0
45.0
29.1

37.8 1

73.2
53.4
30.2
32.9
47.3

34.4
60.3

34.7
64.0

33.1
63.8

51.5
38.3
42.8
49.3
56.8

53.4
32.4
39.0
46.0
58.3

56.8
36.7
38.7
51.1
57.4

53.8
34.9
34.5
49.1
57.6

56.5
34.7
38.7
40.1
60.6

53.2
35.3
37.1
45.5
57.7

52 .9
30.8
34.4
50 .5
58.6

56 .8
32.0
34.7
51 .1
58.1

59.0
32.2
34.7
40.5
67.1

55.0
33.1
36.5
39.4
65.1

59.7
31.5
35.3
42 .6
61.0

54.0
31.1
33.3
41.7
56.3

70.3
39.9
30.0
37.6
65.3

65.6
37.8
29.5
37.4
66.5

63.8
37.1
32.9
33.1
68.7

62.1
34.9
34.7
35.4
66.5

Manufacturing payrolls, 84 industries
Over 1-month span:
2000 ........... .. .... .......... .. .. ...... ... ..... .
2001 ....... ... .. ....... .. .. ......... ............. .
2002 ............... ............... ......... ...... .
2003 ............................................. .
2004 ....................................... .......

48.2
22.6

58.3
22.0

50.0
21.4

50.0
16.1

41.1
15.5

57.1
23.2

60.7
13.7

28.6
14.3

25.0
19.0

35.1
17.9

39.9
14.9

41.1
10.1

21.4
26.2
42 .9

18.5
15.5
55.4

23.8
22.6
60.1

35.1
13.7
66.1

29.8
26 .2
64.9

32.7
25.0
54.2

40.5
28.0
57.1

28.0
26.2
48.2

31.0
27.4
44.0

11.9
28 .6
47.6

15.5
51.2
47.6

17.9
45.8
51.2

Over 3-month span:
2000 ............................................. .
2001 ............................................ ..
2002 ..............................................
2003 ....... .. ................... ................. .
2004 .. ....... ......................... .. ... ...... .

53.6
35.7
9.5
13.7
48.8

53.6
21 .4
10.1
13.1
51.8

56.0
16.1
11 .3
16.7
59.5

54.8
14.3
17.9
10.1
66.1

44.0
13.1
17.3
13.1
71.4

44.0
13.7

51.2
11.9

47.6
8.9

19.0
14.9
65.5

28.0
16.1
65.5

22.0
16.1
51.8

32 .7
8.3
23.8
16.1
53.0

25.0
13.1
15.5
24.4
43.5

23.2
8.9
6.5
27.4
44 .6

38.7
10.1
4.8
41 .7
42 .3

Over 6-month span:
2000 ..... .. .. .....................................
2001 ...................... ....................... .
2002 ............................... .............. .
2003 .......... .............. .................... .
2004 ............... ..... .... .. ................... .

44.0
22.0
6.5
11.3
28.6

52.4
23.8
8.9
9.5
36.9

55.4
22.0
7.7
6.0
46.4

57.7
20 .8
8.3
7.1
56.5

47.6
14.3
7.7
8.9
61 .3

51.8
13.7
14.3

56 .0
14.3
14.9

45.2
10.1
10.7

13.1
64.9

8.9
66.7

13.1
66.1

39.3
10.7
12.5
13.1
58.3

34.5
5.4
10.1
16.7
54.8

32.1
7.1
8.9
19.0
45.2

27.4
4.8
8.9
19.6
46.4

Over 12-month span:
2000 .......... ............ ............. ...... .... .
2001 ........ ......................................
2002 .................. ........................ ... .
2003 ... ...... .....................................
2004 ..................... .. ...... ...... ........ .. .

41.7
29.8
7.1
10.7
9.5

39.3
32.1
6.0
6.0
19.0

47.0
20.8
6.0
6.5
16.7

50.0
19.0
6.5
5.4
26.2

46.4
13.1
7.1
8.3
29.8

52.4
12.5
3.6

51.8
10.7
4.8

49.4
11 .9
6.0

9.5
40.5

9.5
50.0

9.5
50.6

46.4
11.9
4.8
10.7
52.4

40 .5
10.1
7.1
11 .9
55.4

35.1
8.3
4.8
9.5
52.4

33.3
6.0
8.3
11.3
48.2

NOTE: Figures are the percent of industries with employment
increasing plus one-half of the industries with unchanged
employment, where 50 percent indicates an equal balance
between industries with increasing and decreasing
employment.

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

Monthly Labor Review

months

are

preliminary.

February 2005

103

Current Labor Statistics:

Labor Force Data

18. Job openings levels and rates by industry and region, seasonally adjusted
1

Levels (in thousands)
Industry and region
June
Total

2

Rates

2004
July

Aug.

2004

Sept.

Oct.

Dec.P

Nov.P

June

July

Aug.

Sept.

Oct.

Nov.P

Dec.P

3,022

3,237

3,195

3,294

3,420

3,204

3,204

2.3

2.4

2.4

2.4

2.5

2.4

2.4

Total private 2 •••• . ••• ..• •••• .. ••.••...• .••.••.•• •••

2,640

2,894

2,859

2,934

3,042

2,867

2,867

2.3

2.6

2.5

2.6

2.7

2.5

2.5

Construction ......................... .. .. ... ..

94

88

121

113

114

108

108

1.3

1.3

1.7

1.6

1.6

1.5

1.5

Manufacturing .... ............ ........... .....

247

240

234

251

263

236

236

1.7

1.6

1.6

1.7

1.8

1.6

1.6

••....•.•..... •• ...•.•...•.... . .•. ••. .. • .....•.....•

Industry

Trade, transportation, and utilities ... .. ..

503

567

551

591

630

551

551

1.9

2.2

2.1

2.3

2.4

2.1

2.1

Professional and business services ....

494

594

564
543

614

595
540

595
540

2.9

3.4

3.5

3.3

3.6

3.4

3.4

Education and health services ..... ......

496

583
537

3.1

3.1

3.1

3.1

435

425

550
405

3.1

421

536
410

2.9

Leisure and hospitality .. .... ............ ...

385

385

3.3

3.4

3.2

3.3

3.2

3.0

3.1
3.0

Government. ..................................... .

380

343

337

350

403

335

335

1 .. 7

1.6

1.5

1.6

1.8

1.5

1.5

546
1,164

545

540

562

606

2.1

2.2

2.3

2.0

2.0

1,259

1,245

1,385

523
1,214

2.1

1,280

523
1,214

2.1

South ..................... ... .. ........... .. ....

2.4

2.7

2.6

2.6

2.9

2.5

2.5

Midwest. .............. ...... ..... .. ..... .... ...

631

635

613

699

711

713

713

2.0

2.0

1.9

2.2

2.2

2.2

2.2

West. ..... .......................... ...... ..... .

677

738

771

790

756

750

750

2.3

2.5

2.6

2.7

2.6

2.5

2.5

Reglon 3

Northeast. ......... .. .................. ........

West Virginia; Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota,
Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona,
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Washington, Wyoming.
NOTE: The job openings level is the number of job openings on the last business day of
the month; the job openings rate is the number of job openings on the last business day of
the month as a percent of total employment plus job openings.

' Detail will not necessarily add to totals because of the independent seasonal
adj11~t..,,<:1nt of the various series.
2
Includes natural resources and mining, information , financial activities, and other
services, not shown separately.

Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey,
New York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas,
Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland,
Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia,

= preliminary.

P

19. Hires levels and rates by industry and region, seasonally adjusted
1

Levels (in thousands)
Industry and region

Rates

2004
June

Total 2 •.•• .••••.•••.•• •• .•••.•.•.•.•.•..•.•.•.•...•.•.•.••

July

Aug.

Sept.

2004
Oct.

Nov.

Dec.P

June

4,433

4,229

4,375

4,253

4,469

4,780

4,488

Total private 2 ........... ... ....... . ............... .

4,110

3,930

4,058

3,906

4,149

4,467

4,198

3.7

Construction ... .......... ..... .... ...... ......

436
370

401 I
356
984

388
379

388
376
1,081

385
327
1,022

6.3
2.6

3.4

July
3.2

Aug

Sept.

Oct.

Nov

Dec.P

3.3

3.3

3.4

3.6

3.6

3.7

3.8
5.1
2.3
3.8

3.8

5.8
2.5

3.6
5.5
2.6
3.4

4.0

5.3
2.4

5.5
2.6
4.2

5.5
2.3
4.0

3.4

Industry

Trade, transportation, and utilities ..... ..

945

368
352
957

864

361
333
976

3.7

3.8

3.9

Professional and business services .. ..

692

621

690

689

783

801

718

4.2

3.8

4.2

4.2

4.7

4.8

4.3

Education and health services ....... ....

418

401
782

411
769

439

2.8

2.6

803

6.2

6.1

2.4
6.3

2.4

802

2.6
6.1

2.5

760

470
760

447

Leisure and hospitality ....... ......... .....

428
749

6.2

6.5

2.6
6.5

Government. ........ .... ........... ............. ..

328

310

322

337

321

325

287

1.5

1.4

1.5

1.6

1.5

1.5

1.3

745
1,635

755
1,694

793
1,799

796
1,704

2.8

2.9

3.7

3.5

3.0
3.5

2.9
3.5

3.0
3.6

3.1
3.9

3.1
3.6

Manufacturing ........... .. ............... ....

Reglon 3

Northeast. ....... ..... .......... .... .. ...... ...

703

720

763

South ...........................................

1,709

1,640

1,643

Midwest .......... .......... .. ........... ..... ..

1,009

935

945

942

1,054

1,114

998

3.2

3.0

3.0

3.0

3.4

3.6

3.2

West. .. ............ ..... .. .. ....... ..... ...... ...

1,023

685

1,018

942

928

1,022

951

3.6

3.0

3.5

3.3

3.2

3.5

3.3

Detail will not necessarily add to totals because of the independent seasonal
adjustment of the various series.
2

Includes natural resources and mining, information, financial activities, and other
services, not shown separately.

Illinois, Indiana, Iowa,
Kansas, Michigan, Minnesota, Missouri,
Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona,
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Washington, Wyoming .
Midwest:

3

Northeast: Connecticut, l,1aine, Massachuse.ts, New Hampshire, New Jersey, New
York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas, Delaware,
District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee , Texas, Virginia, West Virginia;

104

Monthly Labor Review


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

February 2005

NOTE: The hires level is the number of hires during the entire month; the hires rate
is the number of hires during the entire month as a percent of total employment.
P

= preliminary.

20. Total separations levels and rates by industry and region, seasonally adjusted
Rates

Levels' ,in thousands)

June
Totai2. ...... ..

2004

2004

Industry and r~gion
July

Sept.

ALtJ.

Oct

Nov.

Dec.P

June

4,069

4,074

4,134

4,158

4,129

4,131

4,278

Total private 2 • • • ••• ••• • ••• • • • •••• • • • • • • • • • • • • • • •••.

3,789

3,793

3,894

3,856

3,877

3,832

3,994

3.5

Construction ... ... ...... .... .... ... .. .... ... ...

382

364

391

350

423

360

343

5.5

Manufacturing ... ........ ...... ..... ...... ....

343

367

379

381

338

334

372

2.4

························ ··················

Aug.

July

3.1

Sept.

3.1

3.2

3.5

3.5

3.5

5.3

5.6

5.0

2.5

2.6

2.6

3.1

Dec.P

Nov.

Oct.
3.1

3.1

3.2

3.5

3.5

3.6

6.0

5.1

4.9

2.3

2.3

2.6

Industry

Trade, transpo,1ation, and utilities ..... ..

927

972

951

909

922

934

1,073

3.6

3.8

3.7

3.6

3.6

3.7

4.2

h ofessional and business services ....

607

613

575

649

3.7

3.7

3.5

3.6

3.5

4.2

3.9

362

363

380

580
373

694

Education and health services .......... .

590
384

422

380

2.1

2.1

2.2

2.3

2.2

2.5

2.2

Leisure and hospitality .......... ........... .

734

694

760

756

747

692

750

5.9

5.6

6.2

6.1

6.0

5.6

6.1

Government. ............. ..... ............. .. ... ..

270

273

246

306

260

307

277

1.3

1.3

1.1

1.4

1.2

1.4

1.3

738
1,572

782

2.8

2.7

2.8

2.9

2.6

2.9

3.1

1,599

3.3

3.3

3.2

3.4

3.4

3.4

3.0

3.0

3.1

3.0

3.5

3.2

2.8

3.3

Reglon

3

Northeast. .... ... ... .. ....... ..... .. .. .........

704

674

717

730

670

South .... .... .............. .... ... .......... ....

1,533

1,545

1,527

1,506

1,568

853

935

831

931

948

986

949

2.7

3.0

3.3
2.7

979

945

1,087

978

914

817

938

3.4

3.3

3.8

Midwest. ......... .. .. .... ...... .. .... ........
I

West. ........ .. ... ... ... ... .... ..... .. .... ...... j
1

Detail will not necessarily add to totals because of the independent seasonal adjustment

Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska,

North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona, California,

of the various series.
Includes natural resources and mining, information, financial activities, and other

Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon , Utah, Washington,
Wyoming.

services, not shown separately.
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 entire

District of Columbia, Florida, Georgia, Kentucky,

month; the total separations rate is the number of total separations during the entire

Louisiana,

Mary'.: nd,

Mississippi,

North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia l ' !est Virginia;

month as a percent of total employment.
P = preliminary.

21. Quits levels and rates by industry and region, seasonally adjusted
1

Rates

Levels (in thousands)
Industry and region

2004

2004
June

Totai2 ........ ..... ...... ... ............................. .

July

Aug.

Sept.

Oct.

Nov.

Dec.P

June

July

Aug.

1.7

1.8

1.8

1.8

1.9

2.1

2.0

2.1

2.3

2.2

2.2

2.3

1.2

1.2

1.3

1.3

2.2

2.0

2.2

2.2

2.2

1.9

2.0

1.8

2.1

1.4
3.9

1.4
3.9

1.4
3.9

1.5
3.7

2.1
1.4
4.0

.5

.6

.5

.5

.6

1.7

1.7

2,283

2.0

2.0

1.9

162

2.3

1.5

2.1

182

191

1.2

1.2

1.1

515

551

553

2.1

2.2

325
240
439

296
242
476

357
258
453

356
234
498

2.0
1.3
3.9

2.0
1.6
3.6

130

122

119

127

.6

.6

1.7

Dec.P

Nov.

Oct

Sept.

2,284

2,265

2,252

2,248

2,283

2,325

2,408

Total private 2 • • • ••• • ••••• • ••••• •••• ••••• •• ••••••• • •

2, 162

2,141

2,140

2,118

2,147

2,206

Construction .... .................... ..... .....

156

101

147

138

161

155

Manufacturing ...... .......... ...... ..........

171

174

165

183

172

Trade, transportation, and utilities ...... .

536

559

552

536

Professional and business services ...
Leisure and hospitality ...... ..... .. ........

322
225
480

322
271
442

308
239
476

Government. .. .... ......... .... .. ......... ... .....

123

126

116

338
901

339

325

316

355

350

1.3

1.3

1.3

1.2

1.4

1.4

1.4

903

910

971

995

2.0

1.9

1.9

2.0

2.0

2.1

2.1

Industry

Education and healtn services .......... . '

Region 3

1

Northeast. ...................... ...............

334

South .... ... ...... ..............................

910

Midwest. ....... .... ............ .. ..............

485

505

8971
447

472

510

508

512

1.6

1.6

1.4

1.6

1.8

1.6

1.6

West. ..... .... ...... ............................

573

519

566

546

539

468

519

2.0

1.8

2.0

1.9

1.7

1.6

1.8

Detail will not necessarily add to totals because of the independent st-asonal adjustment
Includes natural resources and mining , information, financial activities, and other

Illinois, Indiana,

Iowa,

Kansas, Michigan, Minnesota,

Missouri,

California, Colorado, Hawaii , Idaho, Montana, Nevada, New Mexico, Oregon , Utah,
Washington, Wyoming.

services, not shown separately.
3

Midwest:

Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona,

of the various series.

Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New

York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas, Delaware,
District of Columbia, Florida, Georgia,

Kentucky,

Louisiana,

Maryland,

Mississippi,

North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;


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

NOTE: The quits level is the number of quits during the entire month; the quits rate
is the number of quits during the entire month as a percent of total employment.
P

= preliminary.

Monthly Labor Review

February 2005

105

Current Labor Statistics:

Labor Force Data

22. Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003.

County by NAICS supersector

Establishments,
fourth quarter

2003
(thousands)

December

2003

Percent change,
December

(thousands)

2002-032

Fourth
quarter

Percent change,
fourth quarter

2003

2002-032

United States 3 •..• ..•. ..... ... ... . .•.. ...... ...•..•.••...••.•....•....•.....•.......•. .... .
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 ........... .......... ................................ .. .

8,314.1
8,048.7
123.7
804.9
376.8
1,853.6
145.2
767.0
1,329.4
732.2
669.9
1,080.6
265.3

129,341.5
108,215.1
1,557.8
6,689.5
14,307.8
25,957.3
3,165.9
7,874.7
16,113.2
15,974.0
12,042.8
4,274.1
21,126.3

0.0
.0
.1
1.2
-4.2
-.3
-4.0
1.2
.6
2.1
1.7
-.1
-.2

$767
769
703
837
943
665
1,139
1,138
945
731
335
494
757

3.6
3.9
4.9
2.3
6.7
3.4
3.9
5.9
3.8
3.8
3.4
3.1
2.4

Los Angeles, CA ... ............... .......... .. ................... .
Private industry ............................. .................................. ....... .
Natural resources and mining ..............................................
Construction ......................... .. ..... ........................... ........... ...
Manufacturing ..................................................................... .
Trade, transportation, and utilities ....................................... .
Information .......... .................................................................
Financial activities ..... ................. ............................. .......... ...
Professional and business services .................................... .
Education and health services ............................................ .
Leisure and hospitality .........................................................
Other services ........................................ .. ........... .... .......... ...
Government ....... ......................................................... .

356.0
352.2
.6
12.9
17.8
53.9
9.2
23.0
40.1
26.6
25.6
142.1
3.8

4,075.3
3,486.3
11 .0
133.9
485.2
794.6
194.9
237.9
575.0
456.5
375.9
220.7
589.0

-.5
-.2
.7
-1.1
-7.1
-1.2
-2.0
.9
1.6
1.9
5.6
3.5
-2.3

903
898
955
883
900
735
1,627
1,258
1,043
820
766
422
930

4.2
4.2
16.9
1.7
6.5
2.7
5.2
7.0
3.7
3.9
6.5
5.0
3.3

Cook, IL .................................... ....................................................
Private industry .................... ............................................ ..... .. .
Natural resources and mining ............................................. .
Construction .............................................. .. ....... ................ .
Manufacturing ............. ............................................ ............ .
Trade, transportation, and utilities ....................................... .
Information ...... ...... .......... ............... ................. ... ...... ... ........ .
Financial activities ......... ................................ ..... ........... ...... .
Professional and business services .... ............ .................... .
Education and health services ...................................... ...... .
Leisure and hospitality .........................................................
Other services .............. .............. .... .......... ............ .
Government ... ..................................... ................................. ... .

126.7
125.5
.1
10.5
7.9
26.7
2.5
13.8
26 .1
12.3
10.5
12.6
1.2

2,539.8
2,221 .9
1.3
96.7
265.7
499.4
66.1
219.4
405.5
350.8
217.7
95.1
317.9

-1.2
-.9
-3.6
.0
-5.1
-.8
-4.1
-.8
-1.3
1.0
2.8
-2.0
-3. 1

922
929
1,037
1,169
975
753
1,164
1,471
1,206
791
375
655
871

3.0
3.2
3.2
-.8
6.3
.4
.1
8.1
4.1
3.7
-.3
3.0
.9

New York, NY .................................... ...... .................................. .. .
Private industry .............................. ..... ................................... .
Natural resources and mining ...... .................................... .
Construction ..... ................................. ....... ........................... .
Manufacturing ................................... ...... ............................ .
Trade, transportation, and utilities ..................... ........... ....... .
Information ... ... ............... ................... ............ .... ... ..... .......... .
Financial activities ............................................................... .
Professional and ,l)usiness 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
7!:i9

2.1
2.1
-.9
2.6
2.3
1.0
.4
4.9
3.2
1.8
-.9
.4
3.1

Maricopa, AZ. ..............................................
Private industry ...... .... .... .................... .......................... ........... .
Natural resources and mining ................................... .
Construction ............................. ..... ......................... ... ..... ..... .
Manufacturing ................................. ... .. ..... ... .. ..................... .
Trade, transportation, and utilities ....................................... .
Information ...........................................................................
Financial activities ................................................................
Professional and business services ......... ... .... .................. .. .
Education and health services .. ..... ..... ................................ .
Leisure and hospitality .. ............ ....... ............................. .
Other services ........ ......
............................................ .
Government ........................ ........... ........................................ .

80.9
80.5
.5
8.4
3.3
18.6
1.6
9.5
18.1
7.6
5.6
5.7
.5

1,621.2
1,401 .8
9.8
131 .7
128.0
336.4
36.6
133.3
261 .5
160.5
155.8
44.7
219.4

(4)

2.2
-2.6
5.9
-2.5
1.5
-4.1
1.5
4.2
5.6
.8
-2.6
1.6

757
755
545
779
1,050
712
872
933
776
842
364
500
766

4.0
3.9
4.4
2.1
8.2
3.2
.5
3.7
3.5
5.0
2.8
2.2
3.7

See footnotes at end of table.

106

Average weekly wage 1

Employment

Monthly Labor Review


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

February 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

Fourth
quarter

2002-032

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 .. .............. .. .. .................. ..
Information ..... ............... ................... .. ... ........ .. .....................
Financial activities .. ... .. .. ..... ...... .......... .......... .... ............ .... ... .
Professional and business services .. .. .. .............................. .
Education and health services ........................................ .. .. .
Leisure and hospitality .................. .......... ........ .. .......... .. .. .... .
Other services ............. .. ..................... ....... .. ..... .. ................ .
Government .......................... .......... .... .. ................................ .. .

88.8
87.4
.3
6.4
6.1
17.3
1.5
9.7
17.4
9.1
6.6
12.9
1.4

1,436.6
1,305.5
6.1
85.5
179.9
278.8
33.8
127.8
261.0
126.6
159.9
46.0
131.1

1.3
2.1
8.3
4.4
-3.0
.6
-4.4
9.9
1.0
6.1
2.5
6.3
-5.7

874
875
579
969
1,036
802
1,152
1,354
942
849
358
518
859

5.3
5.2
.2
5.9
11.4
2.7
5.3
6.2
2.8
3.7
3.8
3.0
6.0

San Diego, CA ....................... ....... .. .................... ....................... ..
Private industry ..................... ... ............... .. ..... .. ... ......... .. ... .. .. .. .
Natural resources and mining ............................................ ..
Construction ...................... .................. .. ............... .. ..... ... .. .. ..
Manufacturing ........ ................................................. ........... ..
Trade, transportation, and utilities ...................................... ..
Information ..... ........ ............. ....... .............. .. ......................... .
Financial activities ................... ........... ....... .. .........................
Professional and business services .... .. .............................. .
Education and health services ............................................ .
Leisure and hospitality .............. .................... ........ .. .. ........ .. .
Other services ... ......... ......................................................... .
Government ...................... ............................. ........ .. ............... .

85.3
83.9
.9
6.4
3.6
14.2
1.4
8.8
14.9
7.6
6.5
19.5
1.3

1,278.2
1,060.2
11.0
81.1
105.4
220.4
36.7
81 .6
208.1
122.6
141 .5
51.6
218.0

1.3
1.5
-5.4
4.7
-4.2
2.2
-4.5
4.8
1.5
1.6
3.5
1.8
.1

815
809
491
869
1,129
655
1,582
1,058
989
778
346
449
843

2.6
2.5
1.0
.7
11.5
.9
-2.0
.4
2.8
5.7
2.4
2.7
2.9

King, WA ..... .......... ....... .. .... .. ................... .. ............. .... ....... ... ...... ..
Private industry .................................. ................ .. .... .. .. ............
Natural resources and mining ............................................ ..
Construction ............... .. .. ... ... .. .. ... ... .. ..... .............. ..... .. ..........
Manufacturing ....... .............. ................................................ .
Trade, transportation, and utilities ........................ .. ........ .. .. ..
Information .. ...... .. .. .. ...................... ................................... .
Financial activities .......................... .. ............ .. .. .. .......... .. ..
Professional and business services .... .. ............................ .. .
Education and health services ............................................ .
Leisure and hospitality .......... .. .......................................... .. .
Other services .... ... ............................................................. ..
Government ............................................................... .. ......... .. .

81.6
81.0
.4
6.2
2.7
14.8
1.5
6.1
11.7
5.9
5.4
26.4
.6

1,100.6
945.5
2.8
53.4
101.9
225.5
69.2
77.5
158.3
108.3
100.5
48.1
155.1

.2
.1
-11.3
-.4
-8.2
1.1
.8
2.4
.7
1.5
2.9
1.2
1.0

935
944
1,109
921
1,176
804
1,829
1,114
1,160
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 ...................... .............. .. ......... .... ........... ..
Professional and business services .. .. .. ...... .. .. ................... ..
Education and health services .............................................
Leisure and hospitality ........................................................ .
Other services ............... .................. .. .................................. .
Government ...................... ........... .. .................... ... ........ ... .. ..... .

80.2
79.9
.5
4.9
2.8
23.2
1.7
8.2
15.9
7.8
5.3
7.5
.3

980.8
827.5
9.9
40.7
49.4
247.2
28.5
65.5
132.0
123.4
92.8
34.5
153.3

-.5
-.7
-1.8
.3
-9.8
-1 .7
-3.2
.7
-.2
1.4
2.1
-1.8
.5

765
742
421
788
695
689
990
1,062
948
748
432
450
886

3.5
3.6
4.0
2.7
5.8
4.2
1.7
-1 .1
5.2
2.3
9.9
3.0
2.8

1

Average weekly wages were calculated using unrounded data.

2

Percent changes were computed from quarterly employment and pay data
adjusted for noneconomic county reclassifi cations. See Notes on Current Labor
Statistics.
., 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

February 2005

107

Current Labor Statistics:

Labor Force Data

23. Quarterly Census of Employment and Wages: by State, fourth quarter 2003.
Establishments,
fourth quarter
2003
(thousands)

State

December
2003
(thousands)

Percent change,
December
2002-03

Fourth
quarter
2003

Percent change,
fourth quarter
2002-03

United States2 ...................................

8,314.1

129,341.5

0.0

$767

3.6

Alabama ............................................
Alaska ...............................................
Arizona ..............................................
Arkansas ...........................................
California .......................................... .
Colorado .......................................... .
Connecticut .................................... ..
Delaware .............................. .......... .
District of Columbia .......................... .
Florida .............................................. .

111.8
20.0
126.9
75.2
1,190.8
160.0
109.1
27.1
30.0
504.1

1,838.1
282.7
2,352.1
1,133.6
14,922.3
2,134.6
1,648.9
408.4
654.8
7,424.5

-.1
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

Georgia ............................................ .
Hawaii ...............................................
Idaho·················································
Illinois ............................................... .
Indiana ............................................. .
Iowa ................................................. .
Kansas ................. ............................ .
Kentucky .......................................... .
Louisiana .......................................... .
Maine ............................................... .

245.6
37.4
48.5
325.7
152.1
90.6
82.2
105.7
114.0
47.4

3,845.6
583.0
577.5
5,738.7
2,852.2
1,418.5
1,298.3
1,740.6
1,870.9
595.8

.2
1.3
.6
-1.2
-.3
.0
-.9
.3
.5
.7

734
678
579
827
675
626
631
645
628
631

2.8
3.7
1.8
3.2
3.5
4.7
2.8
3.5
2.4
4.6

Maryland .......................................... .
Massachusetts ................................. .
Michigan ........................................... .
Minnesota ........................................ .
Mississippi ........................................ .
Missouri ............................................ .
Montana ............................................
Nebraska ...........................................
Nevada ..............................................
New Hampshire ............................... .

150.4
206.6
25U
159.0
65.6
165.4
42.0
55.3
60.3
47.0

2,466.4
3,154.6
4,365.8
2,591.9
1,108.1
2,633.6
396.6
884.4
1,111.2
614.9

.7
-1.9
-1.1
-.5
.4
-.7
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 Mexico ..................................... .
New York ..........................................
North Carolina .................................. .
North Dakota .................................... .
Ohio ................................................. .
Oklahoma ..........................................
Oregon ............................................. .
Pennsylvania .................................... .
Rhode Island .................................... .

268.1
50.4
550.3
227.8
24.0
294.2
91 .6
118.8
326.9
34.7

3,912.8
757.1
8,379.2
3,759.6
317.6
5,322.4
1,423.4
1,579.8
5,524.5
480.5

.1
1.4
-.4
-.1
.9
-.7
-1.3
.2
-.2
1.2

945
612
959
679
563
713
597
694
750
738

3.4
4.1
5.2
4.5
4.3
3.8
4.2
3.3
4.7
5.1

South Carolina ................................. .
South Dakota ...................... . ......... .
Tennessee ........................... .
Texas ............................................... .
Utah ........... ...... ................................ .
Vermont .. ................................... ...... .
Virginia ................... .......................... .
Washington ...................................... .
West Virginia .................................... .
Wisconsin ..........................................

108.4
28.1
128.4
505.3
73.9
24.1
202.6
222.7
47.2
157.6

1,781.0
365.4
2,648.0
9,300.1
1,066.2
300.7
3,477.5
2,654.7
685.2
2,715.4

.3
.3
.4
-.3
1.2
.3
1.2
1.0
.1
.0

623
559
689
754
630
661
786
759
587
683

3.1
4.1
4.2
3.1
2.3
5.1
5.2
1.3
2.1
4.1

Wyoming .......................................... .

22.0

241.6

1.7

616

4.1

Puerto Rico .......................................
Virgin Islands ................................... .

50.2
3.2

1,074.1
42.5

3.5
-.2

450
629

4.7
2.4

1

Average weekly wages were calculated using unrou 1cied data.

2 Totals for the United States do not include data fu: Puerto Rico

or the Virgin Islands.

108

Average weekly wage 1

Employment

Monthly Labor Review


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

Feb, ~Jry 2005

NOTE: Includes workers covered by Unemployment Insurance (UI)
and Unemployment Compensation for Federal Employees (UCFE)
programs. Data are preliminary.

24. Annual data: Quarterly Census of Employment and Wages, by ownership
Year

Average
establishments

Average
annual
employment

Total annual wages
(In thousands)

Average annual wage
per employee

Average
weekly
wage

Total covered (UI and UCFE)
1993 ·································· ··· ·············
1994 .......................... ... ................ .... .
1995 .............. ... ...... .. ....................... ..
1996 ···· ·· ·· ·············· ·· ····················· ·· ···
1997 ·········································· ········
1998 ···························· ······················
1999 .. ................... .................. ... .. .. .
2000 ·· ···· ············· ···· ··· ··· ·· ···················
2001 ............... ....... .. ....... .................. .
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
36,219
36,764

$507
518
536
557
584
614
641
679
697
707

$26,055
26,633
27,567
28,658
30,058
31,676
33,094
35,077
35,943
36,428

$501
512
530
551
578
609
636
675
691
701

$25,934
26,496
27,441
28,582
30,064
31,762
33,244
35,337
36,157
36 ,539

$499
510
528
550
578
611
639
680
695
703

$28,643
29,518
30,497
31,397
32 ,521
33,605
34,681
36,296
37,814
39,212

$551
568
586
604
625
646
667
698
727
754

$26,095
26,717
27,552
28,320
29,134
30,251
31,234
32,387
33,521
34,605

$502
514
530
545
560
582
601
623
645
665

$36,940
38,038
38,523
40,414
42,732
43,688
44,287
46,228
48,940
52,050

$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
1899
2000
2001
2002

··········· ······················ ··· ····· ·· ·······
··· ·································· ····· ·· ··· ···
······ ··· ····· ·· ····················· ·········· ···
·· ······················ ··· ··· ·· ··· ···············
.................... ... .... .. .............. ...... .

6,454,381
6,596,158
6,803,454
6,946,858
7,121,182
7,381,518
7,560,567
7,622,274
7,724,965
7,839 ,903

91 ,202,971
94,146,344
96 ,894,844
99,268,446
102,175,161
105,082,368
107,619,457
110,015,333
109,304,802
107,577,281

$2,365,301,493
2,494,458,555
2,658,927,216
2,837,334,217
3,071,807,287
3,337,621,699
3,577,738,557
3,887,626,769
3,952,152,1 55
3,930,767,025

State government covered

1993 ························ ·· ·· ···· ··· ···············
1994 ........ .................. ... ..... .. ..............
1995 ···· ··· ·· ·· ···· ···· ·······························
1996 ·································· ················
1997 .. .... ... .......... ....... .. .... ........... ... .
1998 ················· ·· ·· ················ ·· ·· ··· ····· ·
1999 ············ ··· ··· ··········· ·····················
2000 ··················································
2001 ····· ·· ···········································
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
140,083
141,491
143,989
146,767

11,059,500
11,278,080
11,442,238
11,621 ,074
11,844,330
12,077,513
12,339,584
12,620,081
13,126,143
13,412,941

$288,594,697
301,315,857
315,252 ,346
329,105,269
345,069 ,166
365,359,945
385,419,781
408,721,690
440,000,795
464,153,701

Federal Government covered (UCFE)
1993 ... .... ........................ .... .. ............ .
1994 ··· ·· ··········································· ··
1995 .... ........ .... ........... ... ........... .. ...... .
1996 ............. ................ ................... ..
1997 ············ ·· ···· ····· ··· ······ ·· ···· ····· ···· ·· ·
1998 ······ ············· ············· ················· ·
1999 ················ ··· ··· ············ ···· ···· ········
2000 ·· ·········· ···· ············ ······················
2001 ··· ·· ·· ···· ··· ·········· ············ ·· ······ ······
2002 ···· ·· ··· ········ ···· ··· ··· ················· ··· ···

47 ,714
48,377
50 ,083
51,524
52,110
47,252
49,661
50,256
50,993
50,755

3,071,140
3,023,098
2,948,046
2,881,887
2,810,489
2,782,888
2,786,567
2,871,489
2,752,619
2,758,627

$113,448,871
114,992,550
113,567,881
116,469,523
120,097,833
121,578,334
123,409,672
132,741,760
134,713,843
143,587,523

NOTE: Detail may not add to totals due to rounding . Data reflect the movement of Indian Tribal Counci l establishments from private industry to
the public sector. See Notes on Current Labor Statistics.


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

Monthly Labor Review

February 2005

l 09

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

Total

5 to9
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 industrles 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

1,851,662
24,683,356

992 ,1 80
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

lnforn,at!on
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

1,761
1,220,921

1,692
3,814,460

Leisure and hospitality
Establishments, first quarter ..................
Employment, March ...............................

657,359
11,731,379

260,149
411,192

110,499
744,144

118,140
1,653,470

122,168
3,683,448

34,166
2,285,550

9,718
1,372,780

1,609
545,304

599
404,831

311
630,660

Other services
Establishments, first quarter ..................
Employment, March .. .. ................... ........

1,057,236
4,243,633

851,231
1,037,360

116,940
761,518

56,238
740,752

24,235
703,957

5,451
371,774

2,561
376,832

454
150,421

109
71,453

17
29,566

Trade, transportation, and utilities
Establishments, first quarter ..................
Employment, March ·······························

110

Fewer than
5 workers 1

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

February 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
Metropolitan area1

Percent
change,
2001-02

2001

2002

Metropolitan areasJ .................. .. ................... .. ... ................. .

$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
34,037
29,913
35,994
32,475
27,300
34,789
27,360
28,274
39,112

1.5
3.4
3.6
1.8
2.6
3.8
3.6
1.8
3.1
2.9

Ann Arbor, Ml ............................................... ......................... .
Anniston, AL .......................................................................... .
Appleton-Oshkosh-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
28,771
29,942
41,123
32 ,201
26,405
31,743
39,540

4.4

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

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
34,519
27,116
28,013
35,111
31 ,624
45,766
44,310
35,655

28,515
31,832
35,940
27,993
28,855
36,133
31,955
45,685
44,037
36,253

Bremerton, WA ..................................................................... ..
Brownsville-Harlingen-San Benito, TX .................................. .
Bryan-College Station, TX .................................................... ..
Buffalo-Niagara Falls, NY ...................................................... .
Burlington, VT ........... .. .......... ...... ...................... .... .. ... .... ........ .
Canton-Massillon, OH .......................................................... ..
Casper, WY .................. .... ......................... ... ... ...................... .
Cedar Rapids, IA ................................................................... .
Champaign-Urbana, IL .......................................................... .
Charleston-North Charleston, SC .......................................... .

31,525
22,142
25,755
32,054
34,363
29,020
28,264
34,649
30,488
28,887

33,775
22,892
26,051
32,777
35,169
29,689
28,886
34,730
31 ,995
29,993

Charleston, WV ........... ......... ..................... .... ................. ..... .. .
Charlotte-Gastonia-Rock Hill, NC-SC .................................... .
Charlottesville, VA ................................................................. .
Chattanooga, TN-GA ............................................................. .
Cheyenne, WY .................. .. ................... .......... ..................... .
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
35,514

32,136
38,413
33,328
30,631
28,827
43,239
27,190
37,168
26,940
36,102

Colorado Springs, CO .......................................................... ..
Columbia, MO ........................................................................ .
Columbia, SC ........................................................................ .
Columbus, GA-AL ................................................................. ..
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

4.0
1.1

.9
3.4

1.4
3.0
2.5
3.6
-3.2
3.6
3.3
2.1
3.2
3.4
2.4
2.0
4.8

1.1
2.4

.6
2.1
4.1
3.2
3.0
2.9
1.0

-.2
-.6
1.7
7.1
3.4

1.1
2.3
2.3
2.3

2.2
.2
4.9
3.8

1.9
3.1
2.8

2.2
4.5
1.3
2.6
3.1
5.4
1.7

.8
2.3
2.7
2.8
3.2
2.7
3.5
4.7

.7
2.6

See footnotes at end of table .

Monthly Labor Review

February 2005

111

Current Labor Statistics:

Labor Force Data

26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area 1
2001

2002

Percent
change,
2001-02

Davenport-Moline-Rock Island, IA-IL ........ .. ..................... .. .. ...
Dayton-Springfield, OH .......................... .. .. ............................ .
Daytona Beach, FL .................................. .. ..................... ....... .
Decatur, AL ........... ... .. .... ......................................... ............... .
Decatur, IL .... .. ................................................. .......................
Denver, CO ................................................................... ......... .
Des Moines, IA ......................... ................ .. .. ... ...................... .
Detroit, Ml ...............................................................................
Dothan, AL .................................................................... ........ ..
Dover, DE ...............................................................................

$31,275
33,619
25,953
30,891
33,354
42,351
34,303
42,704
28,026
27,754

$32,118
34,327
26,898
30,370
33,215
42,133
35,641
43,224
29,270
29,818

2.7
2.1
3.6
-1 .7
-.4

Dubuque, IA ............................... .. ..... .. ... ............................ .... .
Duluth-Superior, MN-WI ........................................................ .
Dutchess County, NY ............................................................ .
Eau Claire, WI ....................................................................... .
El Paso, TX .............................................................................
Elkhart-Goshen, IN ................................................................ .
Elmira, NY ..............................................................................
Enid, 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 ...................................................... .
Fayetteville, NC ......................................................................
Fayetteville-Springdale-Rogers, AR .. ............... .................... ..
Flagstaff, AZ-UT ............ ............ ............................................ .
Flint, Ml ...................................................................................
Florence, AL ....................................................... ................... .
Florence, SC ...........................................................................
Fort Collins-Loveland, CO .................................................... ..
Fort Lauderdale, FL ............................................................... .

31,042
27,899
26,981
29,940
25,890
35,995
25,639
28,800
33,248
33,966

31,977
29,053
28,298
31,090
26,846
36,507
26,591
29,563
34,215
34,475

3.0
4.1
4.9
3.8
3.7
1.4
3.7
2.6
2.9
1.5

Fort Myers-Cape Coral, FL .................................................... .
Fort Pierce-Port St. Lucie, FL ................................................ .
Fort Smith, AR-OK ................................................................ ..
Fort Walton Beach, FL .......................................................... ..
Fort Wayne, IN ..................................................................... ..
Fort Worth-Arlington, TX ........................................................ .
Fresno, CA ................................................. ........................... .
Gadsden, AL ...........................................................................
Gainesville, FL ....... ................................ .... .. ....... .... ............... .
Galveston-Texas City, TX ...................................................... .

29,432
27,742
26,755
26,151
31,400
36,379
27,647
25,760
26,917
31,067

30,324
29,152
27,075
27,242
32,053
37,195
28,814
26,214
27,648
31,920

3.0
5.1
1.2
4.2
2.1
2.2
4.2
1.8
2.7
2.7

Gary, IN ..................................................................................
Glens Falls, NY .. ...................................... ...............................
Goldsboro, f\iG ....................................................................... .
Grand Forks, ND-MN ..............................................................
Grand Junction, CO ................................................................
Grand Rapids-Muskegon-Holland, Ml .................................. ..
Great Falls, MT ................................................................ ...... .
Greeley, CO .. .. ... ................................... ........................... ..... . .
Green Bay, 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

Greenville, NC .. ............................. ................... ..................... .
Greenville-Spartanburg-Anderson, SC .... ............................. ..
Hagerstown, MD ................................................................... ..
Hamilton-Middletown, OH ...................................................... .
Harrisburg-Lebanon-Carlisle, PA ........................................... .
Hartford, 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

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

See footnotes at end of table.

112

Monthly Labor Review


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

February 2005

-.5
3.9
1.2
4.4
7.4

3.4
3.5

3.3
2.0
2.7
2.5
3.5
2.0

3.3
1.2
3.6
2.5

4.4
1.4

-.2
3.1
5.0
1.7
2.9
2.5

2.4
3.5

4.2
4.1


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 area1
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
2.2
4.0
3.1
2.6

Kenosha, WI ................................ .... ... .... .................... .... .. .. ... .
Killeen-Temple, TX ................................................................ .
Knoxville, TN ... ......... .... ................ ................................... ... ... .
Kokomo, IN ............................................................................ .
La Crosse, WI-MN ................................................................. .
Lafayette, LA .. ................................. .......................... .............
Lafayette, IN .......................................................................... .
Lake Charles, LA ...... .......................................................... .. . .
Lakeland-Winter Haven, FL .................................... ............... .
Lancaster, PA ... ......... .... ... ...................... .. .. .................... ... ... ..

31,562
26,193
30,422
39,599
27,774
29,693
31,484
29,782
28,890
31,493

32,473
27,299
31,338
40,778
28,719
30,104
31,700
30,346
29,505
32,197

2.9
4.2
3.0
3.0
3.4

Lansing-East Lansing, Ml .. .... .............. .................................. .
Laredo, TX .. ...................... .... ...... ...................................... ..... .
Las Cruces, NM ..................................................................... .
Las Vegas, NV-AZ ................................................................. .
Lawrence, KS ........................................................................ .
Lawton, OK ............................. .......... ................... ............. .. .. ..
Lewiston-Auburn, ME ... .. .......................................... ... .......... .
Lexington, KY ........................................................................ .
Lima, OH .................................................. ............................. .
Lincoln, NE ..................................... ........... .... ... ..................... .

34,724
24,128
24,310
32,239
25,923
24,812
27,092
31,593
29,644
29,352

35,785
24,739
25,256
33,280
26,621
25,392
28,435
32,776
30,379
30,614

3.1
2.5
3.9
3.2
2.7
2.3
5.0
3.7
2.5
4.3

Little Rock-North Little Rock, AR .................... ... ................... ..
Longview-Marshall, TX ...... .. ..... ........................... .. .... ............ .
Los Angeles-Long Beach, CA ............................................... .
Louisville, KY-IN .................................................................... .
Lubbock, TX .......................................................................... .
Lynchburg, VA .................................. .... ................................ . .
Macon, GA .... .. .. ................................ .. ................................... .
Madison, WI ............... .... ... ..... .. ..... .......................... ...... ... .... .. .
Mansfield, OH ... .................. ... .... .. ..... .... .............................. ... .
McAllen-Edinburg-Mission, TX ... ... .. .......... .... ........................ .

30,858
28,029
40,891
33,058
26,577
28,859
30,595
34,097
28,808
22,313

31,634
28,172
41,709
33,901
27,625
29,444
31,884
35,410
30,104
23,179

2.5

Medford-Ashland, OR ............................................................ .
Melbourne-Titusville-Palm Bay, FL ............ ............................ .
Memphis, TN-AR-MS ................................ ......... .................. .
Merced, CA .......................... ................. ............. .................. .
Miami, FL .. ................................ .... .. ..... ............. ..................... .
Middlesex-Somerset-Hunterdon, NJ ..................................... .
Milwaukee-Waukesha, WI ............... ... .. ................... .... ........ ...
Minneapolis-St. Paul, MN-WI ...... ........ .................................. .
Missoula, MT ....................... ......................... .... .. .......... ... ...... .
Mobile, AL ..................... ....................... .. ................. ............... .

27,224
32,798
34,603
25,479
34,524
49,950
35,617
40,868
26,181
28,129

28,098
33,913
35,922
26,771
35,694
50,457
36,523
41,722
27,249
28,742

3.2
3.4
3.8
5.1
3.4
1.0
2.5
2.1
4.1
2.2

Modesto, CA ..... ... ..... .............. ....... .................................. ...... .
Monmouth-Ocean, NJ ................................... ........................ .
Monroe, LA ............................................................................ .
Montgomery, AL ......... ......... .. .. .............. ................. ... ............ .
Muncie, IN ........................................ ..................................... .
Myrtle Beach, SC ................................................................... .
Naples, FL ............................................................................. .
Nashville, TN ................................... ..................................... ..
Nassau-Suffolk, NY ............................. .................... ...... ... ... .. .
New Haven-Bridgeport-Stamford-Waterbury-Danbury, CT ... .

29,591
37,056
26,578
29,150
28,374
24,029
30,839
33,989
39,662
52,198

30,769
37,710
27,614
30,525
29,017
24,672
31,507
35,036
40,396
51,170

4.0
1.8
3.9
4.7
2.3
2.7
2.2
3.1
1.9
-2.0

New London-Norwich, CT ................................... .................. .
New Orleans, LA ........................................................... .. ...... .
New York, NY .................. .. .......... .. .................. .... .. ............... ..
Newark, NJ ............................................ ...... .. .. .... .................. .
Newburgh , NY-PA ... ........................ ............... ............ ........... .
Norfolk-Virginia Beach-Newport News, VA-NC ..................... .
Oakland, CA .... ....................... ... ........................................ .... .
Ocala, FL .... ................... ... ........................................... ... ....... .
Odess.:i-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

4.2
-2.4
2.2
3.7
3.2
2.1
2.4
.1
3.2

1.4
.7
1.9
2.1
2.2

.5
2.0
2.6
3.9
2.0
4.2
3.9
4.5
3.9

.4

See footnotes at end of table.

Monthly Labor Review

February 2005

113

Current Labor Statistics:

Labor Force Data

26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropoli\an area 1

2001

2002

Percent
change,

2001-02

Olympia, WA .......................................................................... .
Omaha, NE-IA ........................................................................
Orange County, CA .......................... ......... ........... ...... .. ....... .. .
Orlando, FL .... .......................... .............................................. .
Owensboro, KY ............... .. ................. .. ................................. .
Panama City, FL .. ....... ........................................................... .
Parkersburg-Marietta, WV-OH ................. ..... .. ... ................... .
Pensacola, FL ........................................... ... .... ....... ..... .......... .
Peoria-Pekin, IL ... ............. ..................................... .. .............. .
Philadelphia, PA-NJ ............................................................... .

$32,772
31,856
40,252
31,276
27,306
26,433
27,920
28,059
33,293
40,231

$33,765
33,107
41,219
32,461
28,196
27,448
29,529
28,189
34,261
41 ,121

Phoenix-Mesa, AZ .................. ............... ... ... ... ....................... .
Pine Bluff, AR ............................... ................ ..... ... ....... .......... .
Pittsburgh, PA .......................... ........ ...................................... .
Pittsfield, MA .. .. ...................................................................... .
Pocatello, ID .... ............... .. ........... .. ... ........ ....... .......................
Portland, ME ......................... ............ ........... ... ....................... .
Portland-Vancouver, OR-WA .... ... ........ .............. ................ ... .
Providence-Warwick-Pawtucket, RI ...................................... .
Provo-Orem, UT ........................... ... ...................................... .
Pueblo, CO ............................................... ............ .. ... .. ...........

35,514
27,561
35,024
31,561
24,621
32,327
37,285
33,403
28,266
27,097

36,045
28,698
35,625
32,707
25,219
33,309
37,650
34,610
28,416
27,763

Punta Gorda, FL .................... .......................... ...... .............. .. .
Racine, WI ........................................ .. .... .................... .. ......... .
Raleigh-Durham-Chapel Hill, NC ........................................... .
Rapid City, SD .. ...................................... ......... ..... .......... ..... .. .
Reading, PA ....................................................... ... ..... ........... .
Redding, CA ............. .................... ........... ... ....... ... ...... ... ....... ..
Reno, NV ... .. ..... .... ................. .. ................ ... ... ....... ... .. .. ... ... .... .
Richland-Kennewick-Pasco, WA .................. .. ....... .. .. ............ .
Richmond-Petersburg, VA ..................................................... .
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

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

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

Santa Cruz-Watsonville, CA ....................... ........................... .
Santa Fe, NM .................. ........ ......... ........................... .......... .
Santa Rosa, CA ........................................................... .......... .
Sarasota-Bradenton. FL ... ... ...... .. ... ......................... ............. ..
Savannah, GA ....................................................................... .
Scranton-Wilkes-Barre-Hazleto n , 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

4.2
-1 .0

Shreveport-Bossier City, LA ........ ... ... ............... ..................... .
Sioux City, IA-NE ............... ............. ... .. ... .. ............................. .
Sioux Falls, SD .............. ........................................................ .
South Bend, IN ................ ............................. .......... ..... ..... .. .. ..
Spokane, WA ... ................ ... ... ... .......... ..... ....... ...... ..... ... ........ ..
Springfield, IL .......................... ........ .. ..................................... .
Springfield, MO .. .................................................................... .
Springfield, MA ..................... .. ............................................... .
State College, PA .................... ........... ..................................
Steubenville-Weirton, OH-WV .................. ................... .. .... .... .

27,856
26,755
28,962
30,769
29,310
36,061
27,338
32,801
29,939
28,483

28,769
27,543
29,975
31 ,821
30,037
37,336
27,987
33,972
30,910
29,129

3.3
2.9
3.5
3.4
2.5
3.5
2.4
3.6
3.2
2.3

See footnotes at end of table.

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3.0
3.9
2.4
3.8
3.3
3.8
5.8

.5
2.9
2.2
1.5
4.1
1.7
3.6
2.4
3.0
1.0
3.6

.5
2.5
2.8
3.1

.9
3.6
3.4
3.0
1.5
5.4
2.4
3.5
4.8
3.4
1.5
2.3

.4
3.5

.0
4.5
2.8
2.2
3.1
2.3
2.0

.7
2.2
2.3
-5.1
-4.4
3.1
2.2
2.0
5.2
1.0
3.5
2.1
2.4
1.8

4.4


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
2002

2001

Percent
change,
2001-02

Stockton-Lodi, CA .......... .................................................. ... ... .
Sumter, SC ........... ......................... .............. .. ........................ .
Syracuse, NY ................................... .. .... ..... ........ .................. ..
Tacoma, WA ............... .............. .............................. ... ............ .
Tallahassee, FL ........................................................ ...... .. .... ..
Tampa-St. Petersburg-Clearwater, FL ................ .................. .
Terre Haute, IN ................ ..... ............................................. .... .
Texarkana, TX-Texarkana, AR .............................................. .
Toledo, OH ............ ................................................................ .
Topeka, KS .............................................................................

$30,818
24,450
32,254
31,261
29 ,708
31,678
27,334
26,492
32 ,299
30,513

$31,958
24,982
33,752
32,507
30,895
32,458
28,415
27,717
33,513
31,707

3.7
2.2
4.6
4.0
4.0
2.5
4.0
4.6
3.8
3.9

Trenton, NJ ..................................................................... ...... ..
Tucson, AZ ........................................................ ........ .......... .. .
Tulsa, OK .................................. .. ........................... .................
Tuscaloosa, AL .......................................................................
Tyler, TX .............................................................................. .. .
Utica-Roms, NY .......................................... .. ........................ ..
Vallejo-Fai1t1 c 1d-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

Visalia-Tulare-Porterville, CA ........................ ....................... ..
Waco, TX ................................................................................
Washington, DC-MD-VA-WV ................................................ ..
Waterloo-Cedar Falls, IA ........ .............................................. ..
Wausau, WI ............................................................................
West Palm Beach-Boca Raton, FL ....................................... ..
Wheeling, WV-OH ............... ... ................ .... ...................... .... ..
Wichita, KS ................................................ .. .......................... .
Wichita Falls, TX .............. ..................................................... ..
Williamsport, PA ................................................................... ..

24,732
28,245
47,589
29,119
29,402
35,957
26 ,282
32,983
25,557
27,801

25,650
28,885
48,430
29,916
30,292
36,550
26,693
33,429
26,387
27,988

3.7
2.3

Wilmington-Newark, DE-MD ................................................. .
Wilmington , NC ..................................................................... .
Yakima, WA ......................... ............... ................................... .
Yolo, CA ............................................ ... ... ... .. ....................... ..
York, PA ............................................................................... ..
Youngstown-Warren, OH ............... ...................................... ..
Yuba City, CA ............................................... ......................... .
Yuma, AZ ................................................................................

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

Aguadilla, PR .................. .................. ........... .. ........................ .
Arecibo, PR ........................................................................... .
Caguas, PR .............. ................. ............................................ .
Mayaguez, PR .................... ..................... ... .......................... ..
Ponce, PR ............................................................................ ..
San Juan-Bayamon , PR .. .. .. .. ................................................ .

18,061
16,600
18,655

19,283
18,063
19,706
17,500
18,187
21 ,930

17,101
17,397
20,948

3.2

1.1
2.6
1.6
2.6
1.9

1.1

.3
3.2

1.8
2.7
3.0
1.6
1.6

1.4
3.2

.7
-.4
3.0

.7
2.1
3.5
4.3
4.5
6.8

8.8
5.6
2.3
4.5
4.7

1 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

February 2005

115

Current Labor Statistics:

Labor Force Data

27. Annual data: Employment status of the population
[Numbers in thousands]

1994 1

1995

1996

199i

1998 1

1999 1

2000 1

2001

2002

2003

2004

196,814

198,584

200,591

203,133

205,220

207,753

212,577

215,092

217,570

221 ,168

223,357

131,056

132,304

133,943

136,297

137,673

139,368

142,583

146,510

147,401

66.6

66.8

67.1

67.1

67.1

67.1

143.734
66.8

144,863

66.6

66.6

66.2

66.0

Employed ...................................... .

123,060

124,900

126,708

129,558

131,463

133,488

136,891

136,933

136,485

137,736

139,252

Employment-population ratio .........
Unemployed .......... ... ... ...................

62.5

62.9

63.2

63.8

64.1

64.3

64.4

63.7

62.7

62 .3

62.3

7,996

7,404

7,236

6,739

6,210

5,880

5,692

6,801

8,378

8,774

8,149

Unemployment rate ....... ................
Not in the labor force .............................

6.1

5.6

5.4

4.9

4.5

4.2

4.0

4.7

5.8

6.0

5.5

65,758

66,280

66,647

66,836

67,547

68,385

69,994

71,359

72,707

74,658

75,956

Employment status
- - - -·
Civilian noninstitutional population ...........
Civilian labor force .... ... ..... .... .... .... ... .. ...
Labor force participation rate .. ............

' Not strictly comparable with prior years.

28. Annual data: Employment levels by industry
[In thousands]
Industry

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Total private employment... ..........................

95,016

97,866

100,169

103,113

106,021

108,686

110,996

110,707

108,828

108,356

109,863

Total nonfarm employment.. ........ .. ..........
Goods-producing .....................................
Natural resources and mining ................
Construction ...................................... ...
Manufacturing ....... .. .. ... .. ... .......... ... ..... .

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
6,1 49
17,560

128,993
24,465
598
6,545
17,322

131 ,785
24,649
599
6,787
17,263

131,826
23,873
606
6,826
16,441

130,341
22,557
583
6,716
15,259

129,931
21,817
571
6,722
14,525

131,481
21 ,885
591
6,965
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
4,69C

79,227
24,700
5,663.9
14,388.9
4,026.5
620.9
3,084
7,178
14,335
14,087
11 ,018
4,825

81 ,667
25,186
5,795.2
14,609.3
4,168.0
613.4
3,218
7,462
15,147
14,446
11 ,232
4,976

84,221
25,771
5,892.5
14,970.1
4,300.3
608.5
3,419
7,648
15,957
14,798
11 ,543
5,087

86,346
26,225
5,933.2
15,279.8
4,410.3
601.3
3,631
7,687
16,666
15,109
11,862
5,168

86,834
25,983
5,772 .7
15,238.6
4,372 .0
599.4
3,629
7,807
16,476
15,645
12,036
5,258

86,271
25,497
5,652.3
15,025.1
4,223.6
596.2
3,395
7,847
15,976
16,199
11 ,986
5,372

86,538
25,275
5,605 .6
14,911 .5
4,176.7
580.8
3,198
7,974
15,997
16,577
12,125
5,393

87,978
25,511
5,655.3
15,034.4
4,250.7
570.1
3,138
8,052
16,413
16,955
12,479
5,431

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.. .. ... ... ...... ........................
19,275
19,432
19,539
19,664
19,909
20,307
20,790
21,118
21 ,513
21 ,575
NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System (NAICS), replacing the Standard lndustrrial Classification (SIC)
system. NAICS-based data by industry are not comparable with SIC-based data. See "Notes on the data" for a description of the most recent benchmark revision .

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21,619

29. Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm
payrolls, by industry
Industry

1995

1994

1996

1997

1999

1998

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
412.74

34.5
12.49
431.25

34.5
13.00
448.04

34.3
13.47
462.49

34.3
14.00
480.41

34.0
14.53
493.20

33.9
14.95
506.07

33.7
15.35
517.36

33.7
15.67
528.56

Goods-producing:
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
14.71
599.99

40.7
15.27
621 .86

39.9
15.78
630 .04

39.9
16.33
651.61

39.8
16.80
669.23

40.0
17.19
687.99

45.3 "
14.41 1
653.14

45.3
14.78
670.32

46.0
15.10
695.07

46.2
15.57
720.11

44.9
16.20
727.28

44.2
16.33
721 .74

44.4
16.55
734.92

44.6
17.00
757.92

43.2
17.19
741 .97

43.6
17.58
766.83

44.5
18.06
802.95

38.8
14.38
558.53

38.8
14.73
571 .57

38.9
15.11
588.48

38.9
15.67
609.48

38.8
16.23
629.75

39.0
16.80
655.11

39.2
17.48
685.78

38.7
18.00
695.89

38.4
18.52
711.82

38.4
18.95
727.11

38.3
19.23
735.70

41.7
12.04
502.12

41 .3
12.34
509.26

41 .3
12.75
526.55

41 .7
13.14
548.22

41 .4
13.45
557.12

41 .4
13.85
573.17

41 .3
14.32
590 .65

40.3
14.76
595.19

40 .5
15.29
618.75

40.4
15.74
636.07

40.8
16.14
658.49

32 .7
10.87
354.97

32.6
11.19
364.14

32.6
11 .57
376.72

32 .8
12.05
394.77

32.8
12.59
412.78

32.7
13.07
427.30

32 .7
13.60
445.00

32 .5
14.16
460.32

32 .5
14.56
472 .88

32.4
14.96
484.00

32.3
15.26
493.70

34.3
10.80
370.38

34.1
11 .10
378.79

34.1
11.46
390 .64

34.3
11 .90
407.57

34.2
12.39
423.30

33.9
12.82
434.31

33.8
13.31
449.88

33.5
13.70
459.53

33.6
14.02
471 .27

33.6
14.34
481 .10

33.5
14.59
488.61

38.8
12.93
501 .17

38.6
13.34
515.14

38.6
13.80
533.29

38.8
14.41
559.39

38.6
15.07
582.21

38.6
15.62
602 .77

38.8
16.28
631 .40

38.4
16.77
643.45

38.0
16.98
644.38

37 .8
17.36
657.12

37.8
17.66
666.92

30.9
8.61
501 .17

30 .8
8.85
515.14

30.7
9.21
533.29

30.9
9.59
559.39

30.9
10.05
582.21

30 .8
10.45
602.77

30 .7
10.86
631 .40

30 .7
11 .29
643.45

30.9
11.67
644.38

30 .9
11 .90
657.12

30.7
12.08
666.92

39.5
12.84
507.27

38.9
13.18
513.37

39.1
13.45
525.60

39.4
13.78
542.55

38.7
14.12
546.86

37.6
14.55
547.97

37.4
15.05
562.31

36.7
15.33
562.70

36.8
15.76
579.75

36.8
16.25
597.79

37.2
16.53
614.86

42 .3
18.66 1
789.G8

42.3
19.19
611.52

42.0
19.78
830 .74

42.0
20.59
865.26

42.0
21 .48
902.94

42 .0
22 .03
924.59

42.0
22.75
955.66

41.4
23.58
977.18

40.9
23.96
979.09

41.1
24.76
1,016.94

41 .0
25.62
1,049.10

36.0
15.32
551.28

36.0
15.68
564.98

36.4
16.30
592.68

36.3
17.14
622.40

36.6
17.67
646.52

36.7
18.40
675.32

36.8
19.07
700.89

36.9
19.80
731 .11

36.5
20.20
738.17

36.2
21.01
761.13

36.3
21.42
777.13

35.5
11 .82
419.20

35.5
12.28
436.12

35.5
12.71
451.49

35.7
13.22
472.37

36.0
13.93
500.95

35.8
14.47
517.57

35.9
14.98
537.37

35.8
15.59
558.02

35.6
16.17
575.51

35.5
17.13
608.87

35.6
17.53
623.14

34.1
12.15
414.16

34.0
12.53
426.44

34.1
13.00
442.81

34.3
13.57
465.51

34.3
14.27
490.00

34.4
14.85
510 .99

34.5
15.52
535.07

34.2
16.33
557.84

34.2
16.81
574.66

34.1
17.20
586.68

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
505.76

32.4
16.15
523.95

Leisure and hospitality:
Average weekly hours ..... ...................................
Average hourly earnings (in dollars) .... .. ... .. ... .. ..
Average weekly earnings (in dollars) ......... ... .. .. .

26.0
6.46
168.00

25.9
6.62
171.43

25.9
6.82
176.48

26.0
7.13
185.81

26.2
7.48
195.82

26.1
7.76
202.87

26 .1
8.11
211.79

25.8
8.35
215.19

25.8
8.58
221.26

25.6
8.76
224.25

25.7
8.91
228.79

Other services:
Average weekly hours ........................ ................
Average hourly earnings (in dollars) ................. .
Average weekly earnings (in dollars) ... ... ... ........

32.7
10.18
332.44

32.6
10.51
342 .36

32.5
10.85
352.62

32 .7
11 .29
368.63

32.6
11.79
384.25

32 .5
12.26
398.77

32 .5
12.73
413.41

32 .3
13.27
428.64

32.0
13.72
439.76

31.4
13.84
434.49

31 .0
13.98
433.04

Natural 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, transportation, and utllltles:
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) .... .............
Transportation and warehousing :
Average weekly hours ........... .............................
Average hourly earnings (in dollars) ..... .. ... ... .. ....
Average weekly earnings (in dollars) ........ .. .......
Utllltles:
Average weekly hours .... ....... ............ ... ... ... .. .. ....
Average hourly earnings (in dollars) ... ... ... .........
Average weekly earnings (in dollars) .. .. .... ....... ...
Information:
Average weekly hours ........................................
Average hourly earnings (in dollars) .. .. ..............
Average weekly earnings (in dollars) ..... .. ..........
Financial activities:
Average weekly hours ... ..... ... ..... .. ... ........... ........
Average hourly earnings (in dollars) ............... ...
Average weekly earnings (in dollars) .................
Professional and business services:
Average weekly hours ... ... .... .. ......................... .. .
Average hourly earnings (in dollars) ... ............ ...
Average weekly earnings (in dollars) ...... ....... ... .
Education and health services:
Average weekly hours .... ... ... ...................... ........
Average hourly earnings (in dollars) ..................
Average weekly earnings (in dollars) .................

I

NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System (NAICS), replacing the Standard Industrial Classification
(SIC) system. NAICS-based data by industry are not comparable with SIC-based data.


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

Monthly Labor Review

February 2005

117

Current Labor Statistics:

Compensation & Industrial Relations

30. Employment Cost Index, compensation, 1 by occupation and industry group
[June 1989

= 100]
2002
Series

Dec.

2003
Mar.

June

2004

Sept.

Dec.

Mar.

June

Percent change

Sept.

Dec.

3 months
ended

12 months
ended

Dec. 2004
Clvlllan workers

2

162.2

164.5

165.8

167.6

168.4

170.7

172.2

173.9

174.7

0.5

3 .7

164.3
162.4
166.7
166.1
157.5
162.2

166.7
164.1
171 .1
168.3
159.8
164.1

167.9
165.0
172.0
170.0
161 .4
165.0

169.9
167.0
174.0
171.7
162.9
166.8

170.7
168.0
174.9
172.5
163.7
167.9

172.7
170.2
175.8
175.3
166.9
169.7

174.0
171 .2
177.1
177.2
168.8
170.9

175.8
173.6
178.2
178.7
170.1
172.7

176.6
174.7
179.4
180.0
170.9
173.6

.5
.6
.7
.7
.5
.5

3.5
4.0
2.6
4 .3
4.4
3.4

Public administration .... .
Nonmanufacturing ...... ....................................................... .

169.2
160.5
162.8
163.9
164.5
167.6
162.8
161 .7
162.4

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

4 .7
5.0
3.3
3.5
4.1
4.0
3.2
4 .3
3.4

Private industry workers .... ... ........ ......................... .... . .
Excluding sales occupations ... ..................................... ..

162.3
162.4

165.0
165.1

166.4
166.6

168.1
168.1

168.8
169.0

171 .4
171.6

173.0
173.2

174.4
174.6

175.2
175.6

3.8
3.9

Workers. by occupational group:
White-collar workers ........................ ......... .. .... ................. .
Excluding sales occupations ................... .. .................. .
Professional specialty and technical occupations ......... .
Executive, adminitrative. and managerial occupations ..
Sales occupations .................. .................................... ..
Administrative support occupations, including clerical. ..
Blue-collar workers ............................................ .. ........... .
Precision production, craft, and repair occupations ...... .
Machine operators, assemblers. and inspectors .......... ..
Transportation and material moving occupations ......... ..
Handlers, equipment cleaners. helpers. and laborers .. ..

165.2
165.9
164.4
167.2
161 .9
166.7
157.3
157.8
156.7
151 .8
162.9

168.1
169.1
166.5
172.1
163.5
169.0
159.7
160.0
159.9
153.2
164.9

169.4
170.4
167.7
173.1
165.1
170.9
161.4
162.0
161 .1
155.1
166.8

171 .2
172.1
169.4
175.0
167.2
172.3
162.8
163.1
162.6
156.7
168.6

172.0
173.0
170.5
175.9
167.1
173.2
163.6
164.2
163.2
156.9
169.5

174.2
175.3
173.4
176.8
169.2
176.1
166.9
167.1
168.7
158.5
171 .7

175.7
176.7
174.7
178.1
171.2
178.1
168.8
169.1
170.5
160.6
173.2

177.3
178.3
176.8
179.2
173.1
179.4
170.1
170.2
172.2
161.8
174.3

178.1
179.5
178.1
180.2
171.4
180.7
170.8
171 .2
172.5
162.3
175.3

Service occupations ..... ..... ........................................... ..

159.8

161 .7

162.6

163.8

164.3

166.9

168.2

168.9

160.5

162.6

164.1

165.7

166.6

169.3

171.0

172.4

Workers, by industry division:
Goods-producing .. ..... ......... ..... ...................................... ...
Excluding sales occupations ....... .. ........ ... ................ .
White-collar occupations ....: .......................... .. .... .... .. .. .
Excluding sales occupations ........ .. ......................... ..
Blue-collar occupations .. ................................. .... .. ... .. . .
Construction .......................................... ........................ .
Manufacturing ...................... .. ............................... ....... ..
White-collar occupations ............................................. .
Excluding sales occupations .. ....................... .......... ..
Blue-collar occupations ........................................... ..
Durables .................... ... ..................... .. ....... ... ... ... .......... .
Nondurables .. .......... .............. ..................................... .. .

160.1
159.2
164.3
162.3
157.3
157.9
160.5
163.3
160.7
158.3
160.6
160.3

163.0
162.4
167.8
166.3
159.9
159.1
164.0
167.1
165.1
161 .6
164.4
163.1

164.5
163.8
169.2
167.5
161 .5
161 .1
165.4
168.7
166.4
162.8
165.5
164.9

165.7
165.0
170.1
168.5
162.9
162.3
166.5
169.5
167.4
164.1
166.6
166.0

166.5
165.9
170.5
169.2
163.9
163.3
167.1
169.6
167.8
165.1
167.3
166.6

170.3
169.8
173.5
172.2
168.1
164.6
171.7
173.2
171.3
170.4
172.4
170.4

171.8
171 .2
174.7
173.3
169.8
165.9
173.2
174.6
172.6
172.0
174.0
171.7

173.3
172.5
176.4
174.5
171 .3
167.0
174.9
176.4
174.1
173.7
175.8
173.1

Service-producing ......................... ...... .... .. ....................... .
Excluding sales occupations .................................... .
White-collar occupations ................... .. ..... ................... .
Excluding sales occupations ................................... ..
Blue-collar occupations ............ .... .. ...............................
Service occupations ... ....... .......................................... .
Transportation and public utilities .... ... .. ........................ .
Transportation ........................... ............................ ... .. ..
Public utilities ...................................... ..... .... .......... .. .... .
Communications ........................................ ... ... ..........
Electric, gas, and sanitary services .......................... .
Wholesale and retail trade ........................... .. .............. ..
Excluding sales occupations .................................... .
Wholesale trade .. ............... ................... .................... . ..
Excluding sales occupations .................................... .
Retail trade .. ........ ... ... ..................... .. ................ .......... .
General merchandise stores ............................... ..... ..
Food stores .......................................... ......... ..... ...... ..

163.1
164.0
165.1
167.0
156.9
159.3
161 .7
156.1
169.2
170.1
168.1
159.7
160.4
166.7
167.2
155.8
155.1
156.3

165.6
166.6
167.9
169.9
158.7
161.1
163.2
157.8
170.5
171 .3
169.5
161.3
161 .8
169.5
168.4
156.6
156.4
157.5

167.0
168.0
169.2
171 .3
160.8
162.0
165.4
158.9
174.2
175.5
172.6
162.5
162.7
171 .3
169.9
157.4
159.2
158.6

168.8
169.7
171.2
173.1
162.2
163.2
166.5
159.4
176.4
178.4
173.8
164.3
165.0
172.0
171 .2
159.9
161 .2
159.3

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

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

Workers, by occupational group:
White-collar workers .. .... ....................... ........... .... .. ... .. ...... . .
Professional specialty and technical. .............................. .
[;;,;:cutive, adminitrative, and managerial. ................ .... .
Administrative support, including clerical. ..................... .
Blue-collar workers .................... ... .............. ..................... ..
Service occupations .................................... ...................... .
Workers, by industry division:
Goods-producing ............................. .................................. .
Manufacturing ......... .. ............................................. ..........
Service-producing .. ............................................ ............... .
Services ............................................. .. ............ ............... .
Health services ...... ............... .. ..................... .................. .
Hospitals ...................... .... .. ......................................... .
Educational services ............. ... .................. .. .. .......... .. ... .
3

Production and nonsupervisory occupations

4

........ .

.5
.7
.7
.6
-1.0
.7
.4
.6
.2
.3
.6

3.5
3.8
4 .5
2.4
2.6
4 .4
4.4
4 .3
5.7
3.4
3.4

169.7

.5

2.9

173.0

.3

3.8

174.3
173.7
177.8
176.4
172.0
167.3
175.4
176.7
174.7
174.3
176.3
173.6

.6
.7
.8
1.1

4 .7
4 .7
4 .3
4.3
4.9
2 .4
5.0
4.2
4 .1
5.6
5.4
4 .2

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

.3
.5
.3
.6
.4
.5

.4

.2
.3
.2
.3
.3
.3
.3

- .1

.0
-.1

-.2
.0
.0
.5
- .7
.6
.3
.4
.3

3.3
3.5
3.4
3.6
3.4
2.8
3.9
4.1
3.6
2.5
5.0
2.5
2.7
2.7
2 .9
2.3
2.4
2 .3

'------'---------'-----.J----'-----'-----'---..__----'---------'--------'--------

See footnotes at end of table.

118
Monthly Labor Review

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

February

2005

30. Continued-Employment Cost Index, compensation, 1 by occupation and industry group
[June 1989 = 100]

2002
Series

Dec.

2003
Mar.

June

2004

Sept.

Dec.

Mar.

June

Percent change

Sept.

Dec.

3 months

12 months

ended

ended

Dec. 2004
Finance, insurance, and real estate ...............................

168.5

176.7

178.3

180.2

180.9

182.5

183.6

184.8

186.0

0.6

2.8

Excluding sales occupations ......... ............................
Banking, savings and loan, and other credit agencies.
Insurance .............. .......................... .................. .. ..........
Services ................... ............................................... ....... .
Business services .. ... .. ....... ..... .. ........ ... ........................
Health services ... .................. ... ............................. ........
Hospitals ...... ......... ........................................... .. ........
Educational services ................................ .. .. .............. ..
Colleges and universities ................... ........................

173.1
185.3
167.9
165.4
167.5
164.4
168.1
175.2
173.7

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

.6
.9
.8
.6
.3
.6
.8
.8
.5

2.7
1.6
4.2
3.8
3.8
4.2
4.2
4.0
3.8

Nonmanufacturing ................................................... .. .....

162.5

164.9

166.4

168.1

169.0

170.9

172.5

173.9

174.7

.5

3.4

White-collar workers ....... ..............................................
Excluding sales occupations ....................................
Blue-collar occupations ........................... .. ... ................
Service occupations .................................. .................

165.3
167.1
155.9
159.2

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

.5
.7
.5
.5

3.4
3.7
3.5
2.9

State and local government workers ...................................

161 .5

162.6

163.2

165.9

166.8

168.0

168.7

171 .5

172.6

.6

3.5

160.7
159.4
163.8
162.4
159.8

161.7
160.2
165.3
163.8
161.3

162.2
160.8
165.7
164.4
161 .7

164.9
163.4
168.0
167.9
163.6

165.7
164.1
169.1
168.5
165.2

166.8
165.1
170.1
170.4
166.7

167.5
165.6
171.0
171.8
167.5

170.0
168.4
172.1
174.3
169.9

171 .2
169.4
174.3
175.5
171 .0

.7
.6
1.3
.7
.6

3.3
3.2
3.1
4.2
3.5

160.9
162.8
165.5
166.2
160.3
160.7
158.8
165.8
161.7

161 .8
164.0

162.3
164.2
166.7
167.3
161.7
162.0
160.0
167.5
164.3

164.9
166.8
169.5
170.3
164.3
164.7
163.0
169.2
167.3

165.7
168.2
171.0
171 .4
165.0
165.3
163.7
170.0
168.1

166.5
169.4
172.2
172.4
165.7
166.0
164.4
170.7
170.1

166.8
170.1
172.9
173.2
165.9
166.3
164.6
171.0
171.4

169.7
173.0
175.7
176.3
168.8
169.2
168.0
172.4
174.1

170.8
173.8
176.8
177.4
169.9
170.3
169.2
173.2
175.4

.6
.5
.6
.6
.7
.7
.7
.5
.7

3.1
3.3
3.4
3.5
3.0
3.0
3.4
1.9
4.3

Workers, by occupational group:
White-collar workers ................................ ... ........................
Professional specialty and technical. ........ ........ ... ... .........
Executive, administrative, and managerial. ..... .... .... .......
Administrative support, including clerical. ........ ..............
Blue-collar workers ............................................................
Workers, by industry division:
Services ............................................................ .. ..............
5

Services excluding schools ....
····· ······ ···"··········
Health services ......................... ................................... .
Hospitals .................... ................................................
Educational services ................................... .................
Schools ................. ........... .. ... .. .............................. .....
Elementary and secondary ............................ .........
Colleges and universities ........................................
Public administration

3

..

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

166.4
167.0
161 .1
161.4
159.4
167.0
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

3

Consists of legislative, judicial, administrative, and regulatory activities.

4

This series has the same industry and occupational coverage as the Hourly
Earnings index, which was discontinued in January 1989.
5

Includes, for example, library, social, and health services.

Monthly Labor Review

February

2005

119

Current Labor Statistics:

Compensation & Industrial Relations

31. Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989

= 100]
2002

2003

2004

Percent change

Series
Dec.

Mar.

June

Sept.

Dec.

Mar.

June

Sept.

Dec.

3 months
ended

12 months
ended

Dec.2004
Civilian workers

1

157.8

159.3

160.3

161.8

162.3

163.3

164.3

165.7

166.2

0.3

2.4

Workers, by occupational group:
White-collar workers ........................ .................................. .
Professional specialty and technical. .............................. .
Executive, adminitrative, and managerial. ..... ............ .... .
Administrative support, including clerical. ... .... .............. .
Blue-collar workers .... ............... ........ ............................ .....
Service occupations ........................................... ...... ... ...... .

160.1
158.6
163.8
160.6
152.6
156.9

161.9
159.3
167.9
161 .8
153.8
158.0

162.9
160.1
169.0
163.1
154.8
158.7

164.5
161.8
170.5
164.3
155.8
159.8

165.1
162.5
171 .2
164.9
156.3
160.6

166.1
163.8
171.4
166.3
157.3
161 .2

167.1
164.4
172.4
167.5
158.4
161 .9

168.7
166.5
173.4
168.8
159.7
162.8

169.1
167.0
174.4
169.7
160.0
163.6

.2
.3
.6
.5
.2
.5

2.4
2.8
1.9
2.9
2.4
1.9

vvun<ers, by industry division :
Goods-producing .... ........................................ ................... .
Manufacturing .... .. ............................................ ... ... ......... .
Service-producing .......................................... ................... .
Services ..... .......... ................ ........................................... .
Health services .............................................................. .
Hospitals ..................................................................... .
Educational services ....................... ... ....... .................... .

155.1
156.5
158.8
161.1
160.9
162.2
160.1

156.3
158.0
160.5
161.9
162.0
163.5
160.4

157.5
159.0
161 .4
162.8
163.2
164.4
160.7

158.3
159.7
163.0
164.7
164.7
166.3
162.7

160.6
160.1
163.6
165.4
165.9
167.7
163.2

159.9
161.3
164.6
166.5
167.7
169.0
163.6

161 .0
162.4
165.5
167.4
168.6
169.9
163.8

162.3
163.8
167.0
167.3
170.8
171.8
166.0

162.4
164.0
167.5
170.1
171.7
173.2
166.8

2.3
2.4
2.4
2.8
3.5
3.3
2.2

Public administration ..
Nonmanufacturing .......... ............... ........................... .. ...... .

155.8
158.0

157.2
159.6

158.0
160.5

159.4
162.1

160.0
162.7

161 .1
163.7

161 .4
164.6

162.6
166.0

163.5
166.5

2.2
2.3

Private Industry workers ... ....................... .... .. .... ....... .. .
Excluding sales occupations ......................................... .

157.5
157.9

159.3
159.4

160.4
160.5

161 .7
161 .7

162.3
162.4

163.4
163.5

164.5
164.5

165.9
165.8

166.2
166.5

2.4
2.5

Workers, by occupational group:
White-collar workers............ .. ..... ............................ .... ......
Excluding sales occupations. ...... ............................ .. ...
Professional specialty and technical occupations..........
Executive, adminitrative, and managerial occupations..
Sales occupations.. .. ... ..... .... ........ .. ............... .......... .. ...
Administrative support occupations, including clerical. ..
Blue-collar workers. ....... .. ................................. ....... ..... ...
Precision production, craft, and repair occupations. ......
Machine operators, assemblers, and inspectors............
Transportation and material moving occupations...........
Handlers, equipment cleaners. helpers, and laborers....

160.4
160.8
158.5
164.5
156.8
161 .3
152.4
152.3
153.2
146.9
157 .2

162.6
163.6
159.5
169.1
158.1
162.6
153.6
153.4
154.7
147.8
158.4

163.8
164.8
160.5
170.3
159.3
164.0
154.6
154.7
155.3
149.0
159.0

165.3
166.2
162.1
171 .8
161 .6
165.1
155.6
155.5
156.8
149.8
159.9

165.9
167.0
163.0
172.5
161 .1
165.7
156.1
156.2
156.9
149.8
160.6

167.1
168.1
164.7
172.7
162.6
167.2
157.2
157.1
158.6
150.4
161.8

168.2
169.2
165.5
173.9
163.9
168.6
158.3
158.3
159.8
151.8
162.7

169.7
170.6
167.6
174.9
165.9
169.7
159.5
159.3
161 .6
152.9
163.6

170.0
171 .4
168.0
175.7
164.0
170.8
159.9
159.7
161.6
153.3
164.5

.2
.5
.2
.5
-1 .1
.6
.3
.3
.0
.3
.6

Service occupations.... ....... ............................ ............. ...

154.4

155.5

156.1

157.1

157.8

158.4

159.3

159.8

160.6

.5

1.8

155.2

156.4

157.4

158.8

159.4

160.7

161 .7

163.1

163.4

.2

2.5

155.0
154.0
158.6
156.3
152.6
150.2
156.5
158.6
155.9
154.7
157.3
155.2

156.3
155.4
160.0
158.0
153.8
150.6
158.0
160.1
157.7
156.3
158.8
156.6

157.4
156.5
161 .4
159.2
154.8
152.4
159.0
161 .6
158.9
156.9
159.7
157.8

158.3
157.4
161.9
159.9
155.9
153.6
159.7
162.0
159.5
157.9
160.6
158.3

158.7
158.0
162.1
160.4
156.4
154.0
160.1
162.1
160.0
158.5
160.9
158.7

159.9
159.2
163.2
161 .5
157.7
155.1
161 .3
163.3
161.2
159.8
161.9
160.4

160.9
160.2
164.5
162.7
158.6
155.9
162.4
164.7
162.5
160.6
162.9
161.6

162.3
161 .2
166.0
163.6
159.8
157.1
163.8
166.1
163.5
162. 1
164.5
162.8

162.4
161 .6
165.9
164.1
160.1
157.0
164.0
166.1
163.9
162.4
164.7
162.9

.1
.2

2.3
2.3
2.3
2.3
2.4
1.9
2.4
2.5
2.4
2.5
2.4
2.6

2

Production and nonsupervisory occupations

3

Workers, by industry division :
Goods-producing................................ ........... ...................
Excluding sales occupations...... ...............................
White-collar occupations. ............. .. ... ....... .. ...... .. ..........
Excluding saies occupations............... .. ....................
Blue-collar occupations................................................
Construction ............................. .. ....................................
Manufacturing ............ .. ........... ... ....... .............................
White-collar occupations....... .. .......... ........................ ....
Excluding sales occupations.......... ............ ...............
Blue-collar occupations.. .......... .................................... .
Durables.. ........................................................... .. ..........
Nondurables... ........................... .....................................

-.1

.3
.2
-.1
.1

.0

.2
.2
.1
.1

2.5
2.6
3.1
1.9
1.8
3.1
2.4
2.2
3.0
2.3
2.4

Service-producing. ................................................... .. ........
158.6
160.6
163.9
161 .7
163.3
166.1
165.0
167.5
167.9
.2
2.4
Excluding sales occupations... .. ............ .. ... ...............
159.6
161 .7
165.0
162.8
164.2
167.1
166.0
168.5
169.3
.5
2.6
White-collar occupations........................ ...... ......... ........
160.7
163.0
166.6
164.1
166.0
168.9
170.8
167.8
170.4
.2
2.5
Excluding sales occupations................... ............. .....
162.8
166.5
168.2
165.3
169.0
171.2
170.2
172.8
173.6
.5
2.7
Blue-collar occupations........................... ..... ............ ....
152.0
153.2
155.4
154.3
155.1
157.8
159.4
156.2
158.9
.3
2.6
Service occupations.....................................................
154.1
155.1
157.4
155.6
156.6
158.8
158.0
159.4
160.2
.5
1.8
Transportation and public utilities ..................... .............
154.1
154.8
156.5
155.6
156.0
159.1
157.6
160.4
160.5
.1
2.6
Transportation ... .. .. .... .. .......................................... .......
150.1
150.5
150.8
150.6
150.4
153.4
155.1
151 .7
155.0
.1
2.9
Public utilities.. ........................ ........... ... ........................
159.3
160.4
164.1
162.1
163.4
166.4
165.3
167.5
167.5
.0
2.1
Communications. .......................................................
160.7
161 .9
165.9
163.4
165.4
167.5
167.0
168.8
168.3
- .3
1.4
Electric, gas. and sanitary services. ..........................
157.4
158.6
161.8
160.4
161.0
165.1
166.6
163.3
165.9
.4
3.0
Wholesale and retail trade. ................ ............................
155.5
156.7
159.5
157.5
159.2
161 .6
162.1
160.3
162.5
-.2
1.6
Wholesale trade... .. .. ................................................ ....
161 .0
164.7
164.8
163.4
165.3
166.2
167.5
167.8
169.7
-1 .3
1.3
Excluding sales occupations........... ..... .......... ... .........
163.7
163.9
166.3
167.8
165.2
165.7
167.6
168.9
168.6
1.6
.2
Retail trade.............. ........ ...... ... ...................................
152.7
153.1
153.8
156.3
156.5
158.4
157.3
159.3
158.7
1.8
.4
General merchandise stores................. .......... .. ...... ...
149.2
149.8
153.6
154.9
154.1
152.0
153.1
158.1
157.5
.4
2.9
Food stores ............................................... .................
150.3
151.6
152.2
151 .0
152.8
154.3
153.8
155.0
154.5
1.4
.3
See footnotes at end of table.
'-----'-----~---'-----'-------'----~---,.___ ___.___ _~ - - - - - - - ' - - - - - -

120 Monthly Labor Review

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

February

2005

31. Continued-Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989

= 100]
2002

2003

Percent change

2004

Series
Dec.

Mar.

June

Sept.

Dec.

Mar.

June

Sept.

Dec.

3 months
ended

12 months
ended

Dec. 2004
Finance, insurance, and real estate ............... .......... ... .. .
Excluding sales occupations .....................................
Banking, savings and loan , and other credit agencies.
Insurance .................................................................... ..
Services .......... ............. ...................................................
Business services .......... .......... ....... ... .. ............... .. .. ... ..
Health services .............................................................
Hospitals .................... .......... .. ................................. ...
Educational services ........................ ............................
Colleges and universities ...........................................

162.6
167.3
183.9
159.1
161.7
164.8
160.7
162.1
166.5
164.3

171.1
176.7
206.4
161 .6
162.8
165.6
161.9
163.6
167.1
164.4

172.4
178.5
208.7
163.0
164.0
166.4
163.2
164.6
167.5
165.1

174.1
179.2
209 .1
163.9
165.9
169.1
164.6
166.5
170.3
167.6

174.5
210.2
164.5
164.5
166.7
169.8
135.8
167.9
171.0
168.4

175.2
179.2
206.7
165.1
168.1
171.0
167.8
169.4
171 .9
169.5

175.3
180.5
207.6
167.2
169.3
172.7
168.8
170.5
172.6
170.0

176.5
181.8
209.5
168.9
171 .1
174.3
170.9
172.4
175.5
172.9

177.7
182.9
211.3
170.4
172.0
175.0
171 .9
173.8
176.8
173.6

0.7
.6
.9
.9
.5
.4
.6
.8
.7
.4

1.8
1.7
.5
3.6
3.2
3.1
3.7
3.5
3.4
3.1

Nonmanufacturing ............ ..............................................
White-collar workers .....................................................
Excluding sales occupations .. ......... .........................
Blue-collar occupations .. ................................. .............
Service occupations ...................................................

157.5
160.5
162.5
150.2
154.0

159.4
162.8
164.9
151.1
155.0

160.5
163.9
166.1
152.4
155.5

162.1
165.7
167.7
153.4
156.5

162.6
166.3
168.5
153.8
157.3

163.7
167.5
169.7
154.7
157.9

164.8
168.6
170.7
156.1
158.7

166.2
170.1
172.3
157.1
159.2

166.6
170.5
173.1
157.5
160.1

.2
.2
.5
.3
.6

2.5
2.5
2.7
2.4
1.8

State and local government workers .......... ..... ................

161.5

162.6

163.2

165.9

166.8

168.0

168.7

171 .5

172.6

.5

2.1

White-collar workers .............................................. .............
Professional specialty and technical. ......... ... ...................
~xecutive, administrative, and managerial. ... ...... ...........
Administrative support, including clerical. ......................
Blue-collar workers .. ................ ..................... ......... .. .... ......

158.4
158.4
160.1
156.0
155.1

158.9
158.8
160.9
156.9
156.2

159.2
159.1
161.0
157.2
156.5

161.0
161.0
162.5
159.1
157.6

161 .5
161.4
163.3
159.5
158.3

162.1
162.1
163.5
160.4
158.9

162.4
162.3
163.8
160.8
159.2

164.1
164.4
164.3
162.6
160.7

164.9
165.0
166.1
163.0
161.4

.5
.4
1.1
.2
.4

2.1
2.2
1.7
2.2
2.0

Workers, by industry division:
Services ............................................................................

159.2

159.5

159.8

161.6

162.1

162.6

162.7

164.8

165.5

.4

2.1

Services excluding schools 4. ···························· ··············
Health services ............................................ .. ... .. ..........
Hospitals ........... ......... ......................... .. .................... .
Educational services ............................... .................... .
Schools ............................................................. ........ .
Elementary and secondary ....... ............ .. ....... .........
Colleges and universities ..... .. ....................... ... .......

160.3
162.2
162.5
158.9
159.0
158.1
161.6

161.4
162.9
163.1
159.1
159.2
158.2
162.1

161.8
163.5
163.8
159.3
159.5
158.5
162.1

163.2
165.1
165.5
161.2
161.4
160.6
163.5

164.5
166.7
166.7
161.6
161.8
160.9
164.0

165.1
167.4
167.4
162.0
162.1
161.3
164.3

165.6
167.8
167.9
162.1
162.3
161.5
164.4

167.5
169.6
169.9
164.2
164.3
163.8
165.4

168.3
170.7
171.0
164.9
165.0
164.5
166.3

.5
.6
.6
.4
.4
.4
.5

2.3
2.4
2.6
2.0
2.0
2.2
1.4

155.8

157.2

158.0

159.4

160.0

161.1

161.4

162.6

163.5

Workers, by occupational group:

Public administration

2

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

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

Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
2

Consists of legislative, judicial, administrative, and regulatory activities.


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

.6

3

This series has the same industry and occupation:~I coverage
Earnings index, which was discontinued in January 1989.
4

as,

2.2
the Hourly

Includes, for example, library, social, and health services.

Monthly Labor Review

February 2005

121

Current Labor Statistics:

Compensation & Industrial Relations

32. Employment Cost Index, benefits, private industry workers by occupation and industry group
[June 1989

= 100]
2002

2003

2004

Percent change

Series
Dec.

Mar.

June

Sept.

Dec.

Mar.

June

Sept.

Dec.

3 months

12 months

ended

ended

Dec .2004
Private industry workers ......................................................

174.6

179.6

182.0

184.3

185.8

192.2

195.3

196.9

198.7

0.9

6.9

Workers, by occupational group:
White-collar workers ....................................... ..................
Blue-collar workers . ................. ...................... .. ..................

178.5
167.8

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

1.0
.8

6.3
8.3

Workers, by industry division:
Goods-producing ............ ..... .. ................................. .......... ..
Service-producing .. ..... .......................................................
Manufacturing .....................................................................
Nonmanufacturing ..............................................................

171.0
175.9
168.9
176.3

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

201.2
196.5
200.4
197.6

1.6
.5
.6
1.0

9.5
5.5
9.9
5.8

122

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February

2005

33. Employment Cost Index, private nonfarm workers by bargaining status, region, and area size
[June 1989

= 100]
2002

2004

2003

Percent change

Series
Dec.

Mar.

June

Sept.

Dec.

Mar.

June

Sept.

Dec.

3 months

12 months

ended

ended

Dec.2004
COMPENSATION

Workers, by bargaining status

1

Union ............................ .. .................... .... ............. .
Goods-producing .................... ............................................ .
Service-producing .............................................................. .
Manufacturing ............... .................. ...... .............................. .
Nonmanufacturing ..................... .................. .......... .. ........... .

159.5
157.8
161.1
157.9
159.9

162.1
161 .4
162.6
162.3
161 .4

164.1
163.4
164.6
163.8
163.7

165.7
164.7
166.5
165.0
165.5

166.8
165.9
167.5
166.3
166.5

171.4
172.3
170.2
175.0
168.8

173.9
174.6
172.9
177.0
171.6

175.3
176.0
174.4
178.4
173.0

176.2
176.7
175.4
178.9
174.1

0.5
.4
.6
.3
.6

5.6
6.5
4.7
7.6
4.6

Nonunion .......... .. ............................ ... ...... ................................
Goods-producing ...... ........................................................ .. .
Service-producing .................................................. .......... ...
Manufacturing ..................................................................... .
Nonmanufacturing .... .. ........................................................ .

162.8
160.8
163.3
161 .3
162.9

165.4
163.6
165.9
164.5
165.4

166.8
164.9
167.2
165.8
166.7

168.4
166.1
169.0
166.9
168.5

169.1
166.7
169.8
167.3
139.3

171 .3
169.7
171.6
170.6
171.1

172.7
170.9
173.2
172.0
172.6

174.2
172.4
174.6
173.8
174.0

174.9
173.5
175.1
174.3
174.7

.4
.6
.3
.3
.4

3.4
4.1
3.1
4.2
3.2

161 .3
159.0
164.6
165.0

163.8
160.6
169.0
167.3

165.2
161.6
170.4
169.5

166.9
163.2
171 .7
171.4

167.9
163.9
172.5
172.2

170.2
166.4
174.7
175.3

172.3
167.9
176.2
176.8

173.7
169.5
177.6
178.1

174.2
170.6
177.9
179.0

.3
.6
.2
.5

3.8
4.1
3.1
3.9

162.5
169.8

165.2
163.5

166.6
165.0

168.3
·166.1

169.1
166.9

171 .5
170.2

173.1
172.1

174.6
173.3

175.3
174.3

.4
.6

3.7
4.4

Union ............................ .. .... .. .......... ... ......... ........ .................... .
Goods-producing .. ....................... .. ....... ..................... ... ...... .
Service-producing .............................................................. .
Manufacturing ................................................. ............ ........ .
Non manufacturing .................................. .......................... ..

152.5
151 .2
154.1
153.1
152.1

153.3
152.4
154.6
154.6
152.5

154.3
153.9
155.1
155.9
153.5

155.3
154.8
156.3
156.7
154.6

156.2
155.4
157.3
157.1
155.6

157.2
156.3
158.5
158.1
156.6

158.7
157.5
160.3
159.2
158.4

160.0
158.7
161.7
160.5
159.6

160.6
158.9
162.6
160.7
160.4

.4
.1
.6
.1
.5

2.8
2.3
3.4
2.3
3.1

Nonunion ............................ ............. ............ ........................... .
Goods-producing .. .. ...... .. .... ........................ ........................ .
Service-producing .. .. ... .. ................. ... ... .. ........................ .... .
Manufacturing ..... .............. ... ................................................
Nonmanufacturing .................. ... ...... ............. ... ........... ........

158.5
156.6
159.0
157.8
158.3

160.4
157.8
161 .2
159.3
160.4

161.5
158.9
162.3
160.2
161.5

163.0
159.7
164.0
160.9
163.1

163.4
160.1
164.5
161 .3
163.7

164.6
161.4
165.6
162.6
164.7

165.6
162.4
166.6
163.7
165.7

167.0
163.8
168.0
165.2
167.1

167.3
163.9
168.4
165.3
167.5

.2
.1
.2
.1
.2

2.4
2. 4
2.4
2.5
2.3

155.7
154.6
160.2
160.1

157.3
155.3
164.1
161.3

158.4
156.1
165.0
163.1

160.0
157.4
166.1
164.7

160.9
157.9
166.5
165.2

162.0
159.1
166.9
166.8

163.6
160.1
167.7
167.9

164.9
161.6
169.2
169.1

165.0
162.3
169.2
169.5

.1
.4
.0
.2

2.5
2.8
1.6
2.6

157.9
154.8

159.6
156.8

160.7
158.0

162.2
158.9

162.7
159.5

163.8
160.8

164.9
162.1

163.3
162.1

166.6
163.8

.2
.3

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

Workers, by bargaining status 1

Workers, by region 1
Northeast. ........................................ .... ......... .... ... .... .............. .
South ............................................................... ... ................... .
Midwest (formerly North Central) ........................ .................. ..
West. ....... . ..................... ....................................................... .
Workers, by area size

1

Metropolitan areas ................................... ............................ .. .
Other areas ... .... .............. .. .......... ...... ... .. ......................... .. ..... .

: The indexes are calculated differently from those for the occupation and industry groups. For a detailed description of the index calculation , see the Monthly Labor Review
Technical Note, "Estimation procedures for the Employment Cost Index," May 1982.


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Monthly Labor Review

February

2005

123

Current Labor Statistics:

Compensation & Industrial Relations

34. Percent of full-time employees participating In emp!.:>yer-provlded benefit plans, and In selected features within plans,
medium and large private establishments, selected ye_o..-'...;'•_1_9_8_0-_9..-7_ _ _..-------.-----,------,-----,--------,,--1980

Item
Scope of survey (in OOO's) ........... ....... .
Number of employees (in OOO'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
27
72
26

10
26
71
26
84
3.3
97
9.2

8

9

30
67
28
80
3.3
92
10.2

29
68
26
83
3.0
91
9.4

80
3.3
89
9.1

81
3.7
89
9.3

22
3.1

21
3.3

21
3.1

22
3.3

20
3.5

Time-off plans
Participants with:
Paid lunch time ................ .
AvGray'- ,riinutes 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 ....... ... ...... .. .

10

9

9

75

25
76
25

26
73
26

99
10.0

9.8

99
10.0

24
3.8

23
3.6

25
3.7

11
29
72
26
85
3.2
96
9.4
24
3.3

100

99

99

100

98

97

96

97

96

95

62

67

67

70

69
33
16

68
37
18

67
37
26

65
60
53

58

56

84

93

88
3.2

99
10.1
20

Paid vacations ... .... .. ............ .
1

Paid sick leave •••••••••••••••••••••••••••••••••••
Unpaid maternity leave ............. . ...................... .
Unpaid paternity leave ........ ... .................. .... .... .
Unpaid family leave ....................................... .

99

Insurance plans
Participants in medical care plans .... ......... ... .. ..... .
Percent of participants with coverage for:
Home health care
Extended care facilities
Physical exam ............... .
Percent of participants with employee
contribution required for:
Self coverage ................................ .
Average monthly contribution ......................... .
Family coverage .................. .. .......... ...... . .
Average monthly contribution
Participants in life insurance plans .................... .
Percent of participants with:
Accidental death and dismemberment
insurance .... .... ......... ......... .. ......... .
Survivor income benefits ........... .
Retiree protection available ................ .
Participants in long-term disability
insuranr:e plans ....... .
Participant:, in sickness and accident
insurance plans ................................. .

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

36
$11.93
58
$35.93

43
$12.80
63
$41.40

44
$19.29
64
$60.07

47
$25.31
66
$72.10

51
$26.60
69
$96.97

61
$31.55
76
$107.42

67
$33.92
78
$118.33

69
$39.14
80
$130.07

26
46

51

96

96

96

96

92

94

94

91

87

87

69

72

74

78
8
49

71
7
42

71

76

77

6

5

74
6
33

64

64

72
10
59

44

41

7
37

40

43

47

48

42

45

40

41

42

43

54

51

51

49

46

43

45

44
53

55

Participants in short-term disability plans '

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

84

84

82

76

63

63

59

56

52

50

55
98

58
97

98
7
56
54

52
95
6
61
48

52
96

62

62
97
22
64
63

55

52
45

64
98
35
57
62

59
98
26

53
45

63
97
47
54
56

58
51

52
95
10
56
49

60

45

48

48

49

55

57

33

36

41

44

43

54

55

Participants in defined contribution plans .... .... .. ... .. .
Participants in plans with tax-deferred savings
arrangements ...... ... ............ .......... ... .

55

4

Other benefits
Employees eligible for:
Flexible benefits plans .. .... ................................. .
2

Reimbursement accounts .••••..•...... ..... • . ..•...•••••
Premium conversion olans .............. .... ............. .
'

The definitions for paid sick leave and short-term disability (previously sickness and

2

5

9

10

12

12

13

5

12

23

36

52

38
5

32
7

fits at less than full pay.

accident insurance) were changed for the 1995 survey. Paid sick leave now includes only

2

plans that specify either a maximum number of days per year or unlimit~•c:t

specifically allow medical plan participants to pay required plan premiums with pretax

L!.;.yS.

Short-

Prior to 1995, reimbursement accounts included premium conversion plans, which

terms disability now includes all insured, self-insured, and State-mandated plans available

dollars.

on a per-disability basis, as well as the unfunded per-disability plans previously reported as

tabulated separately.

Also, reimbursement accounts that were part of flexible benefit plans were

sick leave. Sickness and accident insurance, reported in years prior to this survey, included
only insured, self-insured, and State-mandated plans providing per-disability bene-

124

Monthly Labor Review


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Federal Reserve Bank of St. Louis

February

2005

NOTE: Dash indicates data not available.


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Federal Reserve Bank of St. Louis

35. Percent of full-time employees participating In employer-provided benefit plans, and In selected features
within plans, small private establishments and State and local governments, 1987, 1990, 1992, 1994, and 1996
State and local governments

Small private establishments

Item

1990

1994

1992

1987

1996

1992

1990

1994

Scope of survey (in OOO's) .... ..... ... .... .

32,466

34,360

35,910

39,816

10,321

12,972

12,466

12,907

Number of employees (in OOO's):
With medical care ....... ..... ..... ......... .. .. .... ........ .
With life insurance ..... ....... ..... ... .... ... ... ... ......... .
With defined benefit plan .. .... ... ... .... .

22,402
20,778
6,493

24,396
21,990
7,559

23,536
21,955
5,480

25,599
24,635
5,883

9,599
8,773
9,599

12,064
11 ,415
11 ,675

11,219
11 ,095
10,845

11 ,192
11 ,194
11 ,708

11
36
56
29
63
3.7
74

10
34
53
29
65
3.7
75

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

37
48
27
47
2.9
84

37
49
26
50
3.0
82

50
3.1
82

51
3 .0
80

17
34
58
29
56
3.7
81

Averaqe days per year' ... .. .. ...... .. .... ... .. .... .... .. .
Paid personal leave ...... ...... .. ..... ..... .... ... .. .. ... .. .
Average days per year ... .... .. ... ..... ...... ..... .. .... .
Paid vacations ....... ... .. .......... ..... ...... ... .. .. .. .. .

9.5
11
2 .8
88

9.2
12
2.6

7.5
13
2.6

88

88

7 .6
14
3.0
86

10.9
38
2.7
72

13.6
39
2.9
67

14.2
38
2 .9
67

11 .5
38
3.0
66

47

53

50

50

97

95

95

94

17

18
7

57
30

51

59
44

Paid sick leave

2

. .. ..• • • • . •.•• • •• • • •• • • •• • •

Unpaid leave ..
Unpaid paternity leave .... ...... ..... ... . .
Unpaid family leave ... ... ... .... .... .

8

8

47

48

66

64

33

62

3.7
73

93

Insurance plans
93

93

90

87

76
78
36

82
79
36

87
84
47

84
81
55

52
$42 .63
75

35
$15.74
71

38
$25.53
65

43
$28.97
72

47
$30 .20
71

$159.63

$181 .53

$71 .89

$117.59

$139.23

$149.70

61

62

85

88

89

87

79

67
1
55

67
1
45

74
1
46

64

20

77
1
13

23

20

22

31

27

28

30

26

26

14

21

22

21

93

90

87

91

89

92
87
13
99
49

69

71

79
83
26

80
84
28

42
$25.13
67

47
$36.51
73

52
$40.97
76

Average monthly contribution .... ... ..... .... .. .... ... .

$109.34

$150.54

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

64

64

78
1
19

76
1
25

19

Participants in medical care plans ... .. .
Percent of participants with coverage for:
Home health care ..... .. ..... ... ..... .. ...... ... ............ . .
Extended care facilities .... .. .... ..... . .
Physical exam ...
Percent of participants with employee
contribution required for:
Self coverage ......... ..... .. .... .....
Average monthly contribution .
Family coverage ... .. .. ......... ........ ..... . .

Participants in short-term disability plans

2

2
46

29

2

Retirement plans

22

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

20
54
95
7
58
49

54
46

Participants in defined contribution plans ..
Participants in plans with tax-deferred savings
arrangements .... .. ... ................ .... ............. ...

31

33

34

17

24

23

15

50
95

15
47
92

92
90

53

44

100
18

16
100
8

92
89
10
100
10

38

9

9

9

9

28

28

45

45

24

33

4

88

Other benefits
Employees eligible for :
Flexible benefits plans ..... ....... .
3

Reimbursement accounts • . ••.• • ••• • • . ••
Premium conversion plans ..... .. .... ...... .... .. .... ... .
1

1

2

3

4

5

5

5

8

14

19

12

31

50

64

7
Sickness and accident insurance, reported in years prior to this survey,

Methods used to calculate the average number of paid holidays were revised

in 1994 to count partial days more precisely. Average holidays for 1994 are

included only insured, self-insured, and State-mandated plans providing per-

not comparable with those reported in 1990 and 1992.

disability benefits at less than full pay.

2

The definitions for paid sick leave and short-term disability (previously

sickness and accident insurance) were changed for the 1996 survey. Paid sick

3

Prior to 1996, reimbursement accounts included premium conversion plans,

which specifically allow medical plan participants to pay required plan

leave now includes only plans that specify either a maximum number of days

premiums with pretax dollars. Also, reimbursement accounts that were part of

per year or unlimited :iays. Short-term disability now includes all insured, self-

flexible benefit plans were tabulated separately.

insured, and State-mandated plans available on a per-disability basis, as well
as the unfunded per-disability plans previously reported as sick leave.

NOTE: Dash indicates data not available.

Monthly Labor Review

February

2005

125

Current Labor Statistics:

Compensation & Industrial Relations

36. Work stoppages involving 1,000 workers or more
Measure

Annual totals

2003

2003

Dec.

2004

2004P
Jan.

Feb.

Mar.

Apr.

May

June

Aug.

July

Sept.

Oct.

Nov.

Dec.

Number of stoppages:
Beginning in period ...................... ·····
In effect during period .. .... ... ... ..... ·····

14
15

15
16

0
2

0
1

1
2

1
1

0
1

2
2

3
4

0
1

2
2

2

-

1

3

-

3

2
2

Workers involved:
Beginning in period (in thousands) ...
In effect during period (in thousands).

129.2
130.5

168.2
316.5

.0
70.5

.0
61.3

6.5
66.5

2.2
2.2

.0
2.2

103.0
103.0

27.6
28.6

.0
1.6

3.7
3.7

4.5
6.5

10.0
16.1

2.0
16.1

8.5
8.5

4,091.2

3,290.9

1,473.4

1,203.9

1,146.5

44.0

26.4

204.0

94.0

3.2

52.5

57.0

300.0

107.7

51.5

.01

.01

.05

.05

.05

(2)

(2)

.01

(2)

(2)

.00

(2)

.01

(2)

(2)

Days idle:
Number (in thousands) .............. ...
Percent of estimated workina time
1

1

.•

Agricultural and government employees are included in the total employed and total

working time; private household, forestry, and fishery employees are excluded. An

Monthly Labor Review. October 1968, pp.54-56.
2

Less than 0.005.

explanation of the measurement of idleness as a percentage of the total time worked
is found in "Total economy measures of strike idleness,"

126

Monthly Labor Review


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

February

NOTE: Dash indicates data not available. P = preliminary.

2005

37. Consumer Price Indexes for All Urban Consumers and ft>f Urban Wage Earners and Clerical Workers: U.S. city average,
by expenditure category and commodity or service group
[1982-84 = 100, unless otherwise indicated]
Annual average
Serles

2003

2004

2004

2003
Dec.

Jan

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

CONSUMER PRICE INDEX
FOR ALL URBAN CONSUMERS
All items. ....... .. .................. .. .... ................ ..............
All items (1967 = 100) .. ··············································

184.0
551.1

188.9
565.8

184.3
552.1

185.2
554.9

Food and beverages ...... ............ .......... ......................

180.5
180.0

186.6
186.2

184.7

184.3
183.8
184.0

557.9

187.4
561.5

188.0
563.2

189.1
566.4

189.7
568.2

189.4
567.5

189.5
567.6

189.9
568.7

190.9
571.9

184.5
184.1

184.9
184.4

185.0
184.5

186.5
186.1

186.8
186.3

187.2
186.8

187.3
186.8

187.2
186.7

188.4
187.9

184.1
205.5

186.8
206.8

187.1
207.2

186.7
207.2

186.2

191 .0
572.2
188.6
188.2
188.1

190.3
570.1
188.9
188.5

179.4

186.2

180.0
184.1

202.8
169.3

206.0
181.7

202.9
181.1

203.9
179.9

184.0
204.4
179.7

184.3
204.8
179.5

179.2

186.6
206.1
181 .1

182.3

183.7

167.9
225.9

180.2
232.7

173.0
232.4

172.4
232.4

172.1
229.7

171 .9
230.1

174.0
228.3

185.9
231.7

188.8
226.7

187.7
224.5

139.8

140.4
164.9
163.2

139.3
163.0
161.0

140.7
162.8
163.0

141.4
163.7

140.8
165.1

139.7
165.0

169.9
165.4

139.8

162.6
162.0
157.4

157.7
179.6

160.7
178.0

163.9
162.3
178.9

163.3
166.2
180.4

162.6
166.2
180.4

163.5
169.4

178.8

167.8
179.7

165.8
162.8
171.3

180.1

180.5

110.3

110.4

109.8

109.1

109.5

111.7

110.5

110.8

110.9

182.1

187.5

184.3

184.9

185.5

185.8

186.2

186.7

Other food away from home 1.2 ... ··· •·· · ·····••··
Alcoholic beverages ..................... ............. ....... ..
Housing ............. ..... ···· ················ ....... ....... •.... ... ....

121.3
187.2

125.3
192.1

122.9
188.7

123.9
189.4

124.0
189.9

124.1
190.8

124.7
191.8

124.8
191 .7

184.8

189.5

185.1

186.3

188.4

188.9

190.3

190.9

191.2

191.0

191.0

190.8

190.7

213.1

218.8

213.1

215.2

187.0
216.0

187.9

Shelter ···· ······························· ............... ...............
Rent of primary residence .... ...... ... ...................
Lodging away from home ....... ........ ... . ... . .. .. ..

217.8

218.4

218.7

219.2

220.0

220.3

220.2

220.6

219.9

219.8

205.5

211.0

205.5

208.3

208.8

209.2

209.7

210.2

210.7

211.2

211.9

212.4

212.8

213.2

213.9

119.3

125.9

119.3

117.2

120.0

128.1

129.1

128.2

129.1

132.2

130.6

127.2

128.0

121.9

118.7

219.9

224.9

219.9

222.6

222.9

223.3

223.9

224.3

224.7

225 .1

225.7

226 .1

226.5

226.8

227.2

Tenants' and household insurance •
Fuels and utilities .. ............. ... ....•................•...
Fuels ··················--···-- ....... .... .... ....... .. ...............
Fuel oil and other fuels .................... .......... .....
Gas (piped) and electricity ... .... .... .... ......... ....

114.8
154.5
138.2
139.5

116.2
161.9
144.4

114.8
15~ 5

114.8
156.3
139.2
149.9

115.0
156.9
139.5
155.1

115.. 1
155.2
137.6

115.7
155.6
138.0

116.1
158.1
140.4

116.2
165.5

116.1
166.6

116.3
167.7

117.7
165.6

149.6
144.2

150.4
146.8

148.5
150.7
155.8

157.6

161.6
156.0

118.7
165.7
148.0
183.7

145.5

144.9
177.3
150.0

147.8
186.6

145.5

152.5
143.5

150.5
157.4

116.6
166.7
149.3

116.3
162.8

149.5
151.1

Household furnishings and operations ..... ........ .
Apparel ...... ············ ··· ·· ····················· ................... ....
Men's and boys' apparel. .. .. ........ .. .. . .. ..... ... ..... ....
Women's and girls' apparel. .... ..... .... ..... ... ........

124.8

125.0

126.1

······· ............... ................................................
Food at home ..... ········ ····· ·· ················ .. ... .... . ...
Cereals and bakery products ....... ....... ········
Meats, poultry, fish, and eggs ........ ···· ··· •· ·· · . ....

Food

1

Dairy and related products
Fruits and vegetables .. .. .. ....... .............................
Nonalcoholic beverages and beverage
materials ................................
Other foods at home ........................... ......... .......
Sugar and sweets .............. ···············•····
Fats and oils ..... . ........................ ........ ...........
Other foods ............................... ... ........ . ..........
Other miscellaneous foods
Food away from home

1

12
·

..

Owners' equivalent rent of primary residence

3

...

12

186.1

187.9
207.0
182.9

206.8

188.5
206.4

183.7

206.4
183.4

182.4

183.1

184.9
224.0

181.6
226.0

182.1
240.0

180.9
248.3

180.1
250.8

140.5

140.3

166.0
163.8
171.9

166.2
164.4

140.3
165.2
163.5

140.6
165.4
162.6

139.6
164.4

140.4
163.6

170.4
179.4

170.2
180.1

163.1
167.8

161 .3
167.4

180.3

169.7
180.9

178.9

178.3

109.4

111.5

110.5

109.9

110.5

110.8

187.0

187.8

188.4

188.9

189.4

189.6

189.9

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

145.0

160.5
150.6

138.7
139.1
145.0

126.1

125.5

124.7

125.3

125.7

125.7

125.6

125.4

125.6

156.9
125.2

120.9
118.0
113.1

120.4
117.5
113.0

119.0
118.0
110.9

115.8
115.5
105.7

118.6
117.1
110.3

123.5
119.8
117.6

124.3
120.3
118.7

123.4
120.3
116.9

120.1
117.7
112.3

115.9
115.2
106.1

116.5
113.8
107.5

121 .2
116.2
114.4

124.1

152.7
125.8

153.0

118.3
119.2

123.0
118.9
116.8

118.8
116.3
110.0

125.5

Infants' and toddlers' apparel ............ ..... .. ..
....... ...............
Footwear ..
Transportation ........... . . .... . .. .. . .. ....... . ....... .. ... .........
r'rivate transportation. ..... ...... ................... ........

1

122.1

118.5

119.2

117.7

119.3

121.9

120.5

118.1

116.2

114.5

115.0

119.5

120.6

120.3

118.6

119.3

118.5

120.1

118.4

115.1

117.3

160.5
156.6

165.2
161 .5

165.7
161.9

164.0
160.0

162.9
159.1

121.8
167.2
163.6

120.3

158.8
154.9

121 .7
162.9
159.4

122.1

154.7
150.8

121.0
161 .8
157.9

120.3

163.1
159.4

115.9
157.0
153.2

117.0

.

119.6
157.6
153.6

New and used motor vehicles2 ....... .... .........
New vehicles ....... ............. . . . . .... . .. . ................. .

96.5

94.2

94.4

94.3

94.4

94.2

94.1

94.0

93.6

93.5

93.4

93.9

94.3

95.2

95.4

137.9

137.1

138.0

138.0

138.3

137.9

137.6

137.4

137.2

135.9

134.9

134.9

135.9

137.9

138.8

142.9
135.8
135.1
107.8

133.3
160.4
159.7
108.7

131.0
127.8
127.2

131.0
143.1
142.5
108.0
198.2

131.2
150.5
149.8
107.8
198.5

131 .3
155.9
155.3
107.9
198.6

131.8
170.5
169.8
107.9
199.0

130.6
173.3
172.7
108.2
199.7

132.1
165.2
164 .5
108.8

133.8
162.0

136.5
161 .2

136.7
171.9

161.2
109.0

109.5
201.7

137.3
161 .2
160.4
109.9
203.3

206.3

208.1

209.9

211.5

210.7

212.3

200.8
209.7

171 .0
109.9
202.9

205.6

200 .3
214.4

160.5
109.3
200.7
205.3

136.8
173.1
172.2

107.8
198.0

130.8
136.7
136.1
108.0
198.2

206.5

208.6

205.4

302.1

303.6
265.5

306.0
266.7

307.5
267.3

308.3
268.5

309.0
269.1

310.0
269.6

311.0
269.9

311.6
270.0

312.3
270.9

313.3
271.7

314.1

314 .9

313.8

318.4

270.8
327.3

.

1

Used cars and trucks . . . .. . . . . . . . . . . ......... ......... ..
Motor fuel ··························--·· ·-- ........ ................
Gasoline (all types) .............. .. .. . . . .. . . . .. ....... .. .. .
Motor vehicle parts and equipment ..... .. .. .. ... .. ...
Motor vehicle maintenance and repair ........ ... ...

.

195.6
209.3

200.2
209.1

Medical care . . . .. .. . .. . . . . .. . . . .. . . . . . .. . . ... . . ....... .... ...... .. ...
Medical care commodities ..... ..... ..... ........ .. .........
Medical care services .......... ............ ... . ......... .....

297.1
262.8

310.1
269.3

306.0

Professional services ················· --· ·· .... .........
Hospital and related services ... .... .... ···············

261.2
394.8

321.3
271.5
417.9

2

107.5

1
VirlP.n ;inrl >i11rlin ·2

103.6

Public transportation ......... .......... ...... .. ....

... ........

.

166.4
162.9

164.8
161.3

323.1

323.7

324.8

269.7
413.8

270.9
414.6

321.0
271.6
416.9

322 .3

262.5
409.7

319.2
270.6
413.6

319.8

261.2
407.0

316.6
268.0
412.5

27 1.2
326.0

272.3
419 .1

273.3
418.8

273.3
420.3

273.7
422.5

274.2
425.0

274.6
428.0

108.6

107.7

107.9

108.4

108.8

109.0

108.8

108.9

108.7

108.5

108.6

108.7

108.7

108.5

104.2

103.3

103.6

104.1

104.3

104.7

104.6

104.4

104.4

104.1

104.0

104.2

104.0

103.9

109.8

111.6

110.9

111.1

111.2

111 .1

110.9

110.6

110.8

110.9

111.7

112.9

112.5

112.7

112.6

Education
Educational books and supplies ...... ..... ........ . ..

134.4
335.4

143.7
351.0

139.4
342.8

140.1
345.4

140.4
348.6

140.6
348.9

140.7
349.5

140.9
349.6

141.6
350.6

142.1
349.5

145.1
353.3

147.9
352.8

148.3
353.8

148.4
354.4

148.5
355.9

..........

362.1
89.7

414.3
86.7

401.7
88.2

403.6
88.1

404 .2
88.1

404.7

404.9

405.6
86.9

409.4
86.5

418.3
86.1

427.4
86.2

428.2
85.5

428.9

87.4

407.6
86.8

428.7

87.7

85.6

85.4

87.8

84.6

86.2

86.1

86.1

85.7

85.4

84.8

84.7

84 .5

84.0

84.1

83.4

83.5

83.3

98.3

95.8

97 .2

97.0

97.1

96.7

96.5

95.9

95.8

95.6

95.0

95.3

94.6

94.5

94.8

16.1

14.8

15.3

15.3

15.2

15.2

15.0

14.9

14.9

14.8

14.7

14.7

14.5

14.3

14.2

RP.r.rP.>itinn

Education and commun ication

2

.. ·

...

2

Tuition, other school fees, and child care
12

Cnmm,mir.;itinn ·
12
Information and information processinQ ·
12

Telephone services • ..
Information and information processing
14

nthP.r th;in tP.IP.nhnnP. sP.rvir.P.s •
Personal computers and peripheral
12

equipment ·
Other goods and services .......... . . .. ... .. . . . .. ....... .. ..
Tobacco and smoking products .. .. ... ..... ..... .....
Personal care

17.6

15.3

16.2

16.2

16.0

15.8

15.9

15.7

15.5

15.3

15.1

15.0

14.6

14.2

13.9

298.7
469.0

304.7
478.0

300.2
470.4

301.4
473.0

302.3
472.6

303.1
473.6

303.6
473.3

303.8
473.5

304.1
476.0

305.1
480 .5

305.5
481.6

306.3
482 .9

306.8
482.3

307.0
481 .7

307.8
484.8
183.3

178.0

181.7

179.0

179.7

180.4

180.9

181.3

181.4

181.4

181.7

181.9

182.3

182.8

83 .0

1

153.5

153.9

153.4

153.8

154.5

154.5

154.5

154.6

153.8

153.4

152.8

153.5

154.0

153.8

153.4

1

193.2

197.6

194.3

194.6

195.2

195.8

196.1

196.6

196.9

197.5

198.9

199.1

199.4

200.0

201.2

February

2005

1

Personal care products
Personal care services

265.0
311.9

See fnotnotes at end of table .


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

Monthly Labor Review

127

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
overage, by expenditure category and commodity or service group
[1982-84 = 100 unless otherwise indicated]
Annual average
Series

2003

Miscellaneous personal services ...

.......... ·····

Commodity and service group:
Commodities ..... ... . . . . . . . . . . . . . . . . . . . . . . . . .. ... ........ ......
Food and beverages .........................................
Commodities less food and beverages .............
Nondurables less food and beverages ..........
Apparel ...... .......... ···················· ········· .. .......
Nondurables less food, beverages,
and apparel. ................ ................ .. ··········
Durables ............. ........... . . . . .. . .. . . . .. . ...... ... .
Services ...............

··········· ..............

2004

2003
Dec.

2004
Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

283.5

293.9

287.1

288.8

290.4

291 .6

292.7

293.1

293.6

294.4

295.2

295.9

296.3

296.9

297.7

151 .2
180.5
134.5
149.7

154.7
186.6
136.7
157.2

151 .1
184.3

154.3
185.0

156.0
186.5

155.8
186.8

154.9
187.2

157.1
188.4

157.2
188.6

136.9
157.2
124.3

138.6
160.9
123.4

138.2
160.5
120.1

135.6
156.1
116.5

136.7
157.8

139.4

120.4

136.0
155.3
123.5

154.5
187.2
136.1
156.7
115.9

154.2
187.3

132.6
148.4
115.8

152.3
184.5
134.2
151.4
118.6

153.7
184.9

120.9

150.4
184.1
131 .7
146.7
119.0

121.2

162.6
124.1

139.4
162.0
123.0

155.8
188.9
137.2
157.4
118.8

171.5

183.9

117.5

114.8

167.7
115.0

172.3
115.1

175.6
115.3

179.1
115.1

181.7
115.0

188.2
114.8

189.5
114.5

185.8
114.1

184.4
113.7

184.4
114.1

190.6
114.7

190.2
115.3

183.9
114.8

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

216.5

222.8

217.9

219.1

219.9

221 .0

221.5

221 .9

223.3

224.1

224.5

224.5

224.5

224.6

224.6

Rent of shelter ·········• ·• •· · · · •· • ·•·•··•··•··
Transporatation services. ........ ............ .... .....
Other services .. ....... .... .. ........... .. . . .. ... . .. . . . . .. .

221.9
216.3

227.9
220.6
261.3

224.1
218.7
258.4

224.9
219.3
259.2

226.8
219.7
259.5

227.4

254.4

222.9
217.7
257.4

220.0
259.7

227.7
220.0
259.6

228.3
220.5
260.2

229.2
221.6
260.5

229.4
220.8
261 .9

229.3
220.1
263.8

229.8
221.4
263.7

229.0
222 .8
264.2

228.9
221 .8
264.3

184.7
174.6

189.4
179.3

184.4 1

185.5
175.6

186.6
176.7

188.0
177.6

188.6
178.2

189.6
179.6

190.3
180.2

189.9
179.6

189.9
179.5

190.4
180.1

191.4
181.4

178.1
136.5
151.9
172.1
165.3

182.7

174.7 1
178.2

181 .8
138.9

182.9
140.6

183.5
140.3

183.2
138.2

183.2

183.6
138.8

184.6
141.1

191 .5
181 .9
184.7
141.4

190.6
180.9
183.9

133.8
149.2
168.8
165.4

180.1
136.3

181.3

138.8
159.3
183.8
172.2

179.1
134.7
150.8
173.0
166.4

153.7
176.1
168.1

159.3
181 .7
171.4

162.8
187.7
174.1

162.4
189.0
174.0

158.8
185.6
172.2

159.9
184.4
172.8

164.2
190.0
175.8

163.9
189.7
175.6

3

Special indexes:
All items less food . .. ... .... .......... .. ... . .. ... .. .......
All items less shelter ......... .. ..........., .. . .. .... .. . .
All items less medical care .... ........... ....... ......

.

Commodities less food ......... .......... ....... ..... .
Nondurables less food . .................... ... ... .
Nondurables less food and apparel .................
Non durables ....................................... ..... .......
Services less rent of shelter3 .......
.........
Services less medical care services .... ..........
Energy ............ ................................ ................
All items less energy ........................................
All items less food and energy .... ···················
Commodities less food and energy ..............
t::nE<rgy commodities .. .
Services less energy. ............................ .....

138.0
157.5
179.4
170.3

137.7
158.2
184.3
171.9

226.4

233.5

228.4

229.7

230.6

230.7

231 .1

231 .7

234.2

235.0

235.6

235.9

235.1

236.4

208.7
136.5
190.6

214.5
151.4
194.4

212.7
143.1
193.7
196.1
140.3
151 .3
228.9

215.0
159.7
194.4

215.8
156.3
194.5

216.0
157.7
196.0

196.5
140.5

196.5
140.2

196.6
139.4

156.3
229.4

170.1
229.6

172.8
230.2

196.6
138.2
165.1

216.2
155.3
194.7
196.8
138.1
162.5

216.1
154.3
195.2

129.0
225.5

211.7
140.6
192.7
194.9
139.3
144.6
227.5

213.6
154.1
194.3

196.6
139.6
161.2
230.2

211.0
137.4
191.9
194.0
138.5
138.2
226.6

213.2
145.9
194.1

193.2
140.9
136.7
223.8

209.9
131 .8
191 .5
193.6
139.0

197.4
139.4
162.0

198.2
140.5
174.2

216.1
158.6
1196.0
198.1
140.6
173.6

231 .0

231.4

231.6

232.1

231 .9

179.8
535.6

184.5
549.5

179.9
536.0

180.9
538.7

181.9

182.9

183.5

184.7

185.3

184.9

185.0

185.4

186.5

186.8

541 .7

544.8

546.5

550.2

551 .9

550.8

551.0

552.4

555.7

556.3

179.9
179.4
178.5
202.8

186.2
185.7
185.4
206.0

183.8
183.3
183.2
203.8

184.0

184.4

184.5

186.0

183.5
183.2
204.4

183.8
183.5
204.9

186.4
185.9
186.1
206.7

186.8
186.3

186.9
186.4

186.8
186.2

181.8

179.9

179.7

179.6

183.9
183.3
205.5
179.1

185.6
185.8
206.0

169.2

183.6
183.1
183.3
202.4
181.0

181 .1

Dairy and related products .................. .....
Fruits and vegetables ...... ... .. ... ... ........ ........

167.6
224.3

180.0
230.4

172.7
229.7

172.2
229.7

171 .7
227.5

171 .3
227.8

173.6
225.5

Nonalcoholic beverages and beverage
materials .............. ·········· ···· ·· ···· ······ ···· ····· ...
Other foods at home ................. ........... .. ... ... .
Sugar and sweets ... . . ... .. . .. . .. . . .......... ..........
Fats and oils ....... .. .... ... . ..... ...... ..... .. .. ..
Other foods .. ... .. .... ................. .......... ........

139.1
162.2
161 .6
157.4
179.2

139.7
164.5
162.5
167.8
180.1

138.6
162.5
160.5
157.7
180.0

140.0
162.3
162.4
160.7
178.4

140.8
163.3
163.2
162.2
179.4

140.1
164.7
162.6

139.3
159.5
185.1
173.3
236.5
216.0
153.7
195.8
197.8
139.8
163.4
231.9

CONSUMER PRICE INDEX FOR URBAN
WAGE EARNERS AND CLERICAL WORKERS
All items ....... .... ... ........
........ .... ......... ..........
All items (1967 c 100) ......... .. .. .... ... . .. .. . . ....... .

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

Food and beverages ....

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

Food ...................... . . . . . . . . . . . . ... .. . ... ..
Food at home ..... .. ..... ... . . . . . . . . . . . . . . . . . ......... . . . . i
Cereals and bakery products .. ... .. ......... .. ......
Meats, poultry, fish, and eggs . ... .. . ..... . ...
1

Other miscellaneous
Food away from home

1

12
foods ·

·········•····

..
12

Other food away from home • .. ...........
Alcoholic beverages ···· ····· · ... .......... ........... ....
Housing .. ..... ........... ................................ ... ..... ..
Shelter ... . . . . . .. . . . . .. . .. . .. . ... . ... . . . .. . .. .. .... ....... .........
Rent of primary residence ........ ...... ... .. .... ..

166.0
180.8

187.9

188.1
187.6
187.3

186.0
554.2
188.4
187.9
187.6
206.3

186.3

186.1

185.5

187.4
187.1

182.4

207.2
183.7

207.0
183.7

206.3
183.4

206.9
183.0

206.8
182.4

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

139.1
164.6
161 .9
166.1
180.8

139.3
165.1
162.9
169.4
180.5

139.3
165.5
162.2
171.4
180.8

139.8
165.6
162.9
172.0
180.7

139.6
165.8
163.8
169.9
181.4

139.7
164.8
163.1
170.3
179.7

140.0
165.0
162.2
170.0
180.5

138.9
163.8
162.1
167.7
179.2

140.0
163.2
160.6
167.3
178.6

183.2

110.8

110.9

110.3

109.6

110.1

112.2

111 .0

111.2

111.4

109.7

112.0

111 .0

110.3

111 .1

111 .3

182.0

187.4

184.2

184.8

185.3

185.6

186.1

186.6

186.8

187.6

188.2

188.8

189.3

189.5

189.7

121.5
187.1

125.1
192.4

123.1
188.9

123.6
189.5

123.8
190.0

123.8
191 .2

124.3
192.1

124.6
192.0

124.7
192.7

124.9
192.2

125.2
192.8

125.8
194.0

126.8
193.9

126.8
194.2

127.0
194.2

180.4

185.0

181 .0

182.1

182.6

183.2

183.6

186.2

186.6

186.5

186.2

186.4

186.4

212.2

208.2

209.2

209.8

211.0

211.5

184.1
211.8

185.6

206.9

212.2

213.0

213.4

213.4

213.8

213.4

213.5

204.7

213.0

210.2

207.0

207.4

208.0

208.4

208.9

209.4

209.9

210.3

211.0

211 .6

212.0

212.4

119.8

126.4

113.4

118.5

121 .1

128.8

129.8

128.2

128.8

133.0

131 .6

127.7

128.3

121 .8

118.6

3

199.7

204.1

201.7

202.1

202.3

202.7

203.1

203.6

203.9

204.2

204.7

205.1

205.5

205.8

206.1

Tenants' and household
Fuels and utilities ................. ....................
Fuels ......... ..•....•. . ..... .. . . .• . . ····························
F1Jel oil and other fuels ...... .. . . . . ... .. . . .. . ......
Gas (piped) and electricity ...... . . .. . . . . . . . . .. .. . .. .
Household furnishings and operations ..... ..
Apparel ..................... ··········································
Men"s and boys' apparel. . .. .. .... ............ .. ......
Women's and girls' apparel. .... ........ ........... ....

114.7
153.9
137.0
138.7
144.1
121 .9
120.0
117.5
112.1

116.4
161.2
143.2
160.0
149.8
121.1
120.0
117.3
112.8

114.4
153.0
135.4
136.2
142.5
120.4
118.7
117.8
110.5

114.9
155.6
138.0
149.6
144.7

115.1
156.2
138.3
154.5
144.7

115.2
154.7
136.6
152.0
142.9

116.0
155.1
137.0
148.9
143.5

116.4
157.4
139.3
149.6
146.1

116.5
165.0
147.4
149.8
155.1

116.3
166.1
148.4
150.2
156.2

116.8
166.2
148.2
161.1
155.3

121.4
118.3
117.4
109.8

121.4
122.9
120.0
117.4

121.3
123.8
120.6
118.4

121 .1
122.8
120.3
116.7

121.3
119.6
117.8
112.2

120.7
115.6
115.2
106.0

116.5
161 .9
143.5
177.2
149.1
121.7
123.5
117.8
119.3

118.1
164.5
146.2
186.5

121 .0
115.7
115.6
105.5

116.5
167.2
149.3
156.8
156.8
120.4
115.9
113.3
106.9

151 .7
121.5
122.6
118.6
116.9

118.9
164.7
146.4
183.4
152.0
121 .3
118.6
115.7
110.2

124.1
119.1
156.3

121.4
117.8
152.5
149.7

120.1
115.6
154.9

125.2
118.6
158.5
155.7

123.4
119.6
159.9
157.1

118.8

119.0
163.6
160.9

117.0
164.0
161 .3

117.0
114.4
162.2

152.2

122.2
116.4
156.8
154.0

120.9

153.5

121.3
118.2
161 .5
158.8

96.0

92.8

92 .8

92 .7

92.8

92 .6

92.6

92.5

92 .1

Lodqinq away from home2 ..
Owners' equivalent rent of primary residence
12
insurance · ...

1

Infants' and toddlers' apparel ....... .. ... . ........ .. .
Footwear . .... ... ........... .................. ...... .........
Transportation ... ....... ···················· ......................
Private transportation ....... ...... ···-·-----· ......
New and used motor vehicles2 ....

..........

.. ...L

See footnotes at end of table .

128

Monthly Labor Review


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

February 2005

120.6
120.6
115.6
114.0
122.3

121.4

120.4
161 .6
159.1

123.3
120.6
165.3
162.7

123.1

159.3

117.6
116.3
161.4
158.6

120.6
165.8
163.2

119.4
163.4
160.9

92.1

92.2

92.3

93.3

94.0

94.3

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]
2004
Annual average 2003
Serles
New vehicles .. .

2003

... .. . . .. . . . . . . . . . . . . ..... ... ... ...
1

.

Used cars and trucks . . . . . . . . .. . . . . . . . ... . ... . . .... .
Motor fuel .. ... ...... ... ... . . .. . .. ... . . .. ... ........ .. .....
Gasoline (all types) .. ............................ .. ....
Motor vehicle parts and equipment.. ...... ...... ..

.

Motor vehicle maintenance and repair .. .. .......
Public transportation . . . . . . . . . . . . . . . . . . . . . . . . . . .. .. ... . . .

.

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

139.0

138.1

139.2

139.2

139.5

139.0

138.7

138.5

138.2

137.0

136.0

136.0

136.9

138.9

139.8

143.7

134.1

131.7

131 .6

131.7

132.0

132.1

132.6

131.4

133.0

134.6

137.3

137.6

137.5

138.1

136.1

160.9

128.1

137.1

143.6

150.9

156.5

171.1

173.8

165.6

162.4

161 .7

173.6

172.3

161 .7

135.5

160.2

127.6

136.6

143.0

150.3

155.8

170.4

173.2

165.0

161 .7

161 .0

172.9

171 .6

160.9

107.3
197.3

108.2
202 .0

107.3

107.6
199.9

107.6

107.4

107.5

107.5

107.8

108.2

199.8

200.1

200.3

200.4

200.8

201 .5

202.1

108.4
202.7

108.7
202.7

108.9
203.8

109.4
204 .9

205.3

109.3

206.0

207.1

203.6

204.6

206.2

208.0

209.4

208.8

210.0

212.1

208.0

203.1

204 .2

207.1

204.2

Medical care ............ .. ............ ... .. ......... .•. ... . . .. ••..
Medical care commodities .... .. .... .. . . . . . . . . .. . . .....

296.3

309.5

302.8

305.4

308.4

309.4

310.4

311.0

311 .7

312.7

313.6

314.4

263.2

259.8

260.9

306.9
261 .5

307.7

257.4

301.4
259.4

262.5

263.3

263.8

263.7

263 .8

264.8

265.4

264.9

264 .4

Medical care services ··· ·· ···· ··· ·· · · ······ ·· ··· ···· ········
Professional services .. .. ... .. .. ....... ........ .... .. .

305.9

321 .5

311 .9

313 .8

316.8

318.6

319.4

320.0

321 .2

322.4

323.2

323 .9

32 6.3

327 .7

263.4

274.0

266 .5

267 .8

270.6

272.3

273.2

273.5

274.1

274.8

275.8

275 .9

325.0
276 .3

276.9

277.2

391.2

414.0

403.4

405.9

408.7

409.9

409.8

410.7

413.0

415 .2

414.9

416 .4

418 .5

42 1.0

424 .2

105.5

106.3

105.5

105.6

106.2

106.5

106.7

106.6

106.7

106.3

106.1

106.2

106.2

106.3

106.1

102.9

103.4

102.5

102.7

103.2

103.5

103.9

103.9

103.7

103.7

103.4

103.3

103.5

103.3

103.2

109.0

110.0

109.7

109.8

110.0

109.8

109.6

109.2

109.4

109.4

109.9

110.8

110.5

110.6

110.5

Education ..
············· ···· ••··
F'r11!:ational books and supplies .. ........

133.8
336.5

142.5
352 .2

138.0
343.8

139.1
346.1

139.4
349.5

139.6
349.9

139.7
350.4

139.9
350.4

140.6
351.5

141 .0
350.4

143.6
354.7

146.3
354.8

146.7
355.6

146.8
356.1

147.0
357.6

Tuition, other school fees, and child care .....

377 .3
91 .2

402 .5

390.7

392.8

394.1
89.0

394.6
88 .. 4

405 .8

414 .0

415.8

88.4

88.1

87.6

87 .8

415 .2
87 .1

415.6

89.6

393.8
89.3

398.1

89.7

393.3
89.6

396.7

88.3

87.2

87.0

89.9

86.8

88.3

88 .2

88.2

87.9

87 .5

87.0

86.9

86.7

86 .2

86 .3

85.6

85.7

85.5

98.5

96.0

97.4

97.2

97.3

96.9

96.7

96.1

96.1

95 .8

95.2

95 .5

94 .8

95.1

95.0

16.7

15.3

15.8

15.8

15.8

15.7

15.5

15.4

15.4

15.3

15.3

15.2

15.0

14.9

14.8

.

Hospital and related services
RP.r.rP.Hlinn

······ ·• .. .. ..... .. .. ...

2

Vir1P.n Hnrl HI Jrlin

12
·

Education and communication

2

2

C:nmm, inir.Hlinn

12
·

Information and information processinq

12
· ..

12

Telephone services · ..
Information and information processing
14

nthP.r lhHn IP.IP.nhnnP. ~P.rvir.P.!'; •
Personal computers and peripheral
12

17.3

15.0

15.9

15.8

15.7

15.5

15.6

15.4

15.2

15.0

14.9

14.8

14.3

13. 9

13.7

307.0

312 .6

308.1

309.3

310.0

310.8

311.3

311.5

311 .8

313.2

313.5

314.4

314.7

3 14.9

315.9

470.5

478.8

471.5

473.8

473.2

474.2

474.1

474.4

476.9

481 .6

482.6

483 .9

483 .0

482.5

485.7

177.0

180.4

177.8

177.4

179.1

179.7

180.1

180.2

180.0

180.3

180.5

180.9

181 .4

181 .7

181 .9

154.2

154.4

154. 2

154.3

155.0

155.0

155.1

155.1

154.3

153.9

153.1

154.0

154.3

154.3

153.8

193.9

198.2

194.9

195.1

195.7

196.3

196.6

197.1

197.5

198.1

199.5

199.7

199.9

200.6

201 .8

283.3

294.0

286.6

288.4

290.2

291 .6

292.9

293.1

293.5

294.7

295.4

296 .2

296 .6

297.5

298.4

Commodities ...... .... .. ...... ....... .. ... .. ... .... . ··· ·······
.. . .. ........ ..... .... .....
Food and beverages ..

151 .8

155.4

179.9

186.2

150.7
183.6

151.5
183.8

152.7
184.0

154.1
184.4

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

Commodities less food and beverages ..... .......
Nondurabl es less food and beverages .... ... ....

135.8

138.1

132.5

133.5

135.2

137.0

138.0

140.0

139.6

137.5

137.1

138.2

141 .0

141 .0

138.8

152.1

160.6

149.0

151 .0

154.3

158.4

160.5

164.7

164.4

160.4

159.5

161.2

166.5

165.9

160.9

120.0

120.0

118.7

115.7

118.3

122.9

123.8

122.8

119.6

115.6

115.9

120.6

123 .5

122. 6

118.6

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

175.6

189.6

180.2

184.1

187.0

194.5

196.0

191 .8

190.2

190.1

196.9

196.5

190.8

117.4

114.0

171.6
114.0

176.5

.

114.0

1142.0

114.0

113.9

113.9

113.5

113.2

113.1

113.7

114.3

114.8

115.1

212.6

218.6

214.2

215.3

216.0

216.7

217.1

217.6

219.0

219.7

220.2

220.3

220.0

220.4

220.5

199.2
216.2

204.3
220.9

200.6
218.0

201.4
219.1

202.0
219.7

203.2
220.0

203.7
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

248.5

254.1

250.9

251.8

252.6

252.9

253.0

252.7

253.3

253.5

254.4

256.0

255.9

256.3

256.5

equipment · ....
Other goods and services ....... . . .. .. .. . .. .. . ... .....
Tobacco and smoking products ...... .. ...... ...

.

1

Personal care ······ ···· ·· ··· ······· ···· ······· ·· .....
1
Personal care products ....
········ · ......
1

Personal care services ...... . ........ . .. . .• ··•··
Miscellaneous personal services ... .... ... ...
Commodity and service group:

Apparel . . ...... ..... ..... .... .. ........ ....... ..
Nondurables less food, beverages,
and apparel ..

Durables. .... ...... ... ... ... ...... .........
Services .......... ... .. .. ..... ....

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

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

Rent of shelter3 .. .................... ... ......... .. . ··•··
Transporatation services ... ... ... .. .... ..... ..... .. .
Other services. .... .... .... . ..... .. . .. .. ... ....... ... ....
Special indexes:
All items less food . ........ ... .. .. ... ..... ....... ... .. .....
All items less shelter ..... ...... .. .... .... .... ..... ..
All items less medical care ....... ......... ....... .. ..
Co;r,,nodities less food .... .. .. ... ......... . ···•· ·· · .....
... . ...
Nondurables less food . .. ..... ...
Nondurables less food and apparel ... .. ....... .. .
Non durables

.

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

Services less rent of shelter3. ·· •··· ········· · ·•·· ·•··
Services less medical care services ....... ... ...
........ .. ... ... ...... .. .... ..
Energy. ............ .. ....

.

All items less energy . . . . . . . . . . . . . . . . . . . . . . ........ ... ... .
All items less food and energy. . . . . . . . . . . . . . . . . . .
Commodities less food and energy
Energy commodities ....... ...... ... .. ..

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

Services less energy ..

179.7

184.1

179.2

180.2

181.4

182.6

183.2

184.4

185.0

184.5

184.5

185.1

186.2

186.4

185.5

171 .9

176.4

171 .6

172.5

173.7

174.7

175.3

176.8

177.5

176.7

176.6

177.3

178.6

179.1

178.0

174.8
137.7
154.2
175.9
166.4

179.1
140.0
162.6
189.0
173.9

174.7
134.5
151.4
172.1

176.6
137.1
156.4
180.2

179.4
141 .8
166.4
193.5

180.0
141.5
166.2
194.8

179.6
139.4
162.3
191.0

179.6
139.0
161 .5

173.0

175.9

175.9

174.0

173.6

180.0
140.2
163.2
189.7
174.5

181 .1
142 .2
168.2
195.6
177.7

181.3
142.9
167.6
195.4

169.5

177.6
138.9
160.4
184.0
171 .8

178.2
139.9
162.4
186.6

166.6

175.6
135.5
153.3
176.9
167.8

177.5

180.6
140.7
162.9
190.3
175. 1

201 .3

207.4

202 .9

204.1

204.9

204.9

205.2

205.8

208.2

208.9

209.3

209.5

208.f\

209.8

209.9

205.2
135.9

210.6
151.3

206.6
131 .1

207.6
136.9

208.2
140.2

208.8
143.0

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

186.1

189.5

189.0

189.3

189.5

190.2

191 .0

191 .1

191 .0

190.1

190.4

189.3
190.4

189.3

188.3

187.9
189.1

188.7

190.6

186.9
188.0

187.2

187.9

190.3

190.3

190.5

191.4

192 .1

192.2

192.0

141 .1

139.4

141.1

138.2

139.0

140.0

140.1

139.9

139.0

138.0

138.0

139.5

140.5

140.6

139.9

136.8

161 .5

136.8

138.3

144.7

151 .5

156.7

170.7

173.3

165.5

162.8

162.3

174.5

173.7

163.4

220.2

226.2

222.1

223.1

223.9

224.9

225.3

225.5

226.0

226.7

227 .1

227.4

227.9

228.0

228.1

189.6

4

1

Not seasonally adjusted.

2

Indexes on a December 1997

3

Indexes on a December 1982 = 100 base.


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

= 100 base.

Indexes on a December 1988 = 100 base .
Dash indicates data not available.
NOTE: Index applied to a month as a whole, not to any specific date.

Monthly Labor Review

February

2005

129

Current Labor Statistics:

Price Data

38. Consumer Price Index: U.S. city average and available local area data: all items
[1982-84 = 100, unless otherwise indicated]
Pricing

All Urban Consumers

sched-

2004

ule1
U.S. city average ........ .. .......... .. ...... .... ...... .... ........

July

M

189.4

....

M

201.0

Size A-More than 1,500,000 ...... ....................... .. ...........

M

203.0

Region and area size 2
Northeast urban ... .. ........... .... ........................ .............
3

Aug.
189.5

Sept.

Urban Wage Earners

2004

Oct

Nov.

Dec.

189.9

190.9

191 .0

201.0

201.2

202.5

202.6

201.9

203.1

203.2

204.5

204.6

204.1

190.3

July
184.9

Aug.

Sept.

Oct.

Nov.

Dec.

185.0

185.4

186.5

186.8

197.3

197.2

197.7

199.0

200.2

198.7

198.0

198.1

198.4

199.7

120.2

199.6

186.0

M

119.2

118.9

119.2

120.1

120.1

119.2

119.1

118.7

119.2

120.1

179.8

119.4

Midwest urban ..... ... .... .. ... ... ..........
·· ····· ···· ··· ············· ·
Size A-More than 1,500,000 ................ .. .......... .. .. ... .......

M

183.2

183.3

183.6

184.5

184.8

183.8

178

178.2

178.6

179.5

181 .2

178.8

M

185.4

185.6

189.5

186.8

186.9

185.7

179.5

179.8

180.2

181.1

116.9

180.1

Size B/C-50,000 to 1,500,000 3 . ..... ...... .. .... ........ .. .....
Size D-Nonmetropolitan (less than 50,000) .................

M

116.3

116.5

116.8

117.4

117.7

117.3

115.5

115.7

115.9

116.6

175.2

116.4

M

177.1

176.3

176.4

177.1

177.7

177.2

173.7

173.4

173.7

174.4

180.7

174.9

South urban ... ...... ........ ..................... ......................... .. .. ....

M

182.6

182.6

182.8

183.7

183.7

183.3

179.3

179.4

179.7

180.6

182.5

180.3
182.4

Si z6 8/C-50,000 to 1,500,000 ..... ... .. ..... .. ... .... . ... ......
4

Size A-More than 1,500,000 ..........................................

M

183.7

183.7

184.0

185.0

185.0

184.9

181.2

181 .2

181.4

182.5

182.5

Size B/C-50,000 to 1,500,000 3 .......................... .... . . .... ..
Size D-Nonmetropolitan (less than 50,000) .... ...... .......

M

116.9

116.9

116.9

117.4

117.4

117.1

115.2

115.3

115.4

115.9

116.0

115.6

M

180.1

180.0

181.2

182.8

182.5

181.9

179.4

179.5

180.7

182.3

182.2

181.5

West urban ... .... .................................................................

M

192.9

193.0

193.8

195.0

195.1

194.2

188.0

188.0

188.8

190.0

190.2

189.4

Size A-More than 1,500,000 .. ... ..... ..... ... ... ................ .. ...

M

195.4

195.5

196.4

197.5

197.6

196.5

188.9

188.9

189.9

191.0

191.2

190.2

M

117.9

118.1

118.4

119.2

119.3

119.0

117.4

117.6

117.8

118.7

118.9

118.6

M
M
M

173.1
117.3
181.3

173.2
117.3
181 .0

173.6
117.4
181.8

174.6
118.1
182.9

174.6
118.2
183.0

174.0
117.7
182.4

171 .3
116.2
179.0

171.4
116.2
178.8

171.8
116.5
179.7

172.8
117.2
180.8

173.0
117.3
181 .1

172.4
116.9
180.6

Chicago-Gary-Kenosha , IL-IN-WI. ... ... ... .... ... ........... ....
Los Angeles-Riverside-Oran ge County, CA ....... ..... ...... ..

M
M

189.2
193.4

190.2
193.1

190.0
194.5

190.8
196.3

190.7
196.9

189.6
195.2

182.4
186.8

183.2
186.5

183.1
187.8

184.0
189.8

184.2
190.3

183.1
188.5

New York, NY-Northern NJ-Long Island, NY-NJ-CT-PA ..
Boston-Brock1on-Nashua, MA-NH-ME-CT .................. .

M

205.5

205.7

205.9

207.3

207.2

206 .8

200.1

200.3

200 .6

201 .9

202 .2

201 .8

1

208.9

209.8

-

211 .7

-

207.9

181.7

183.8

-

185.2

172.8

173.9

-

-

179.4

-

119.7

-

120.4

-

180.5

120.9

-

-

211

1

-

208.8

Cleveland-Akron, OH ................................ ............... ..
Dallas-Ft Worth, TX .. .... ..... ..... .. ... .. .... ..... ......... .... ......

-

120.4

-

3

Size B/C- 50,000 to 1,500,000 .. ... .... ... .... .. ... .... .. ....
Size classes:
As .......... .......... .... ..... ............... ..........
············· . ... ...... ....
3
B/C .......... . . . .. ..... ................. .. ........ ..... . .. .... .......... ...... .. .....
0 ........ ......... ... ....... .............. ........................................

Selected local areas 6

Washinqton-Baltimore, DC-MD-VA-WV 7 ..
. ......... ....
Atlanta, GA .......... .... .... ..... .. ... .. .... .. .. ... ... ........ ....... ... .
Detroit-Ann Arbor-Flint, Ml. ..... .. .. .. ........... .................. .
Houston-Galveston-Brazoria, TX ... .... ........ ..... .... ........ .
Miami-Ft. Lauderdale, FL .... ....... .... ...... ..... ... .... .. .........
Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD .....
~an Francisco-Oakland-San Jose, CA ... ....... .... .... ........
Seattle-Tacoma-Breme rton, WA. .. ....................... ...... ..

1

179.1

-

179.7

1

120.2

-

120.8

2

-

184.1

-

183.9

-

183.2

-

182.5

181.7

181.5

187.6

-

185.3

-

-

186.8

-

-

-

181.5

-

183.0

-

180.7

169.5

-

167.7

185.1

-

186.6

2

-

169.1

2

-

185.1

2

-

199.1

2

-

198.7

2

-

194.6

Foods, fuels, and several other items priced every month in all areas; most other
goods and services priced as indicated:
M-Every month.
1-January, March, May, July, September, and November.
2-February, April, June, August, October, and December.
Regions defined as the four Census regions.
Indexes on a December 1996 = 100 base.
4
The "North Central" region has been renamed the "Midwest" region by the
Census Bureau. It is composed of the same geographic entities.
3

5

= 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
Indexes on a December 1986

130

Monthly Labor Review


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

February 2005

180.0

2

1

6

179.9

174.8

-

-

171.8
187.0

170

-

167.4

188.6

-

182.9

200 .2

-

197.8

-

198.0

-

197.9

-

199.5

-

195.0

-

199.8

200.3

196.4

195.1

-

189.6

-

191.6

-

190.3

196.5

195.9

Report: Anchorage, AK; Cincinnatti, OH-KY-IN ; Kansas City, MO-KS; Milwaukee-Racine,
WI; Minneapolis-St. Paul, MN-WI; Pittsburgh, PA; Port-land-Salem , OR-WA; St Louis,
MO-IL; San Diego, CA; Tampa-St. Petersburg-Clearwater, FL.
7
Indexes on a November 1996 = 100 base.
NOTE: Local area CPI indexes are byproducts of the national CPI program. Each local
index has a smaller sample size and is, therefore, subject to substantially more sampling
and other measurement error. As a result, local area indexes show greater volatility than
the national index, although their long-term trends are similar. Therefore, the Bureau of
Labor Statistics strongly urges users to consider adopting the national average CPI for use
in their escalator clauses. Index applies to a month as a whole, not to any specific date.
Dash indicates data not available.

39. Annual data: Consumer Price Index, U.S. city average, all items and major groups
(1 982-84 = 100]
Series
Consumer Price Index for All Urban Consumers:
All items:
Index ..................................................................... ..
Percent change .................................................. .
Food and beverages:
Index ...................................................................... .
Percent change .................................................. .
Housing:
Index .................................................................. ..
Percent change .................................................. .
Apparel:
Index ...................................................................... .
Percent change .................................................. .
Transportation:
Index ................ ........ ............................................. .
Percent change .................................................. .
Medical care:
Index ..................................................................... ..
Percent change .................................................. .
Other goods and services:
Index ..................................................................... ..
Percent change .................................... .
Consumer Price Index for Urban Wage Earners
and Clerical Workers:
All items:
Index .................................................................... .
Percent change .................................................. .


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

1994

1995

1996

1997

1998

1999

2000

2001

2002

2004

2003

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.~

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

February

2005

131

Current Labor Statistics:

Price Data

40. Producer Price Indexes, by stag~ of processing
[1982

= 100]
Grouping

Annual average

2003

2004

2003

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.P

Oct.P

Nov.P

Dec.P

Finished goods...................... ......... .....
Finished consumer goods ........................
Finished consumer foods ............. ......... .
Finshed consumer goods
excluding foods .... ................................
Nondurable goods less food .................
Durable goods ................... .. ............... ..
Capital equipment. .................................

143.3
145.3
145.9

148.5
151.6
152.6

144.5
146.7
150.3

145.4
147.8
148.1

145.3
147.8
148.4

146.3
149.0
150.7

147.3
150.4
152.7

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
152.0
152.2

151 .9
155.5
154.7

151 .7
155.3
154.5

150.4
153.5
154.5

144.7
148.4
133.1
139.5

150.9
156.6
135.1
141 .5

145.0
148.2
134.3
140.2

147.4
151.7
134.3
140.5

147.3
151.6
134.2
140.2

148.0
152.4
134.7
140.5

149.1
154.3
134.4
140.6

150.9
156.7
134.8
140.8

150.5
156.0
134.9
141 .1

151.4
158.0
133.6
140.7

151.3
157.9
133.6
141.2

151 .5
158.1
133.8
141 .3

155.5
162.0
137.7
143.5

155.2
161.8
137.5
143.4

152.8
158.2
137.3
143.6

Intermediate materials,
supplies, and components............... ....

133.7

142.5

134.5

136.2

137.3

138.3

140.2

142.0

142.8

Materials and components
for manufacturing .....................................
Materials for food manufacturing .............
Materials for nondurable manufacturing ..
Materials for durable manufacturing ........
Components for manufacturing .. ..... ... .... ..

143.5

144.8

145.3

146.2

147.2

146.7

129.7
134.4
137.2
127.9
125.9

137.9
145.0
147.6
146.6
127.4

130.9
140.7
137.9
131 .2
125.8

131 .9
138.4
140.2
132.9
125.9

133.2
139.3
141.0
137.3
126.2

134.3
141 .7
141.4
140.7
126.5

136.2
146.6
143.5
144.3
127.1

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

140.8
144.2
152.1
153.3
128.0

141 .2
144.2
153.5
152.8
128.2

141.8
144.0
154.9
153.3
128.4

142.8
145.1
156.8
154.8
128.6

Materials and components
fnr (Y':1Struction .........................................
Processed fuels and lubricants ..................
Containers ..................................................
Supplies ............................... .... ..................

153.6
112.6
153.7
141 .5

166.4
124.1
159.2
146.7

155.6
111 .7
153.5
142.8

156.2
116.8
153.9
143.2

159.0
116.8
153.7
143.8

161.9
116.5
154.1
144.8

164.7
118.4
154.9
146.4

166.9
122.3
156.7
147.2

166.9
124.9
158.9
147.3

167.5
126.4
159.7
148.0

169.8
128.5
162.0
147.6

171 .1
127. 1
162.5
147.7

170.7
130.4
164.1
147.8

170.6
133.8
164.3
147.9

171 .2
127.7
165.2
148.6

Crude materials for further
processing ............................ .... ........ ...
Foodstuffs and feedstuffs .......................... .
Crude nonfood materials ............................

135.3
113.5
148.2

159.0
126.9
179.2

141 .1
124.7
149.5

147.8
117.1
167.3

150.1
122.2
167.3

152.9
131 .7
164.8

155.7
135.4
166.6

161.8
141.1
172.9

163.0
137.4
178.0

162.5
130.9
182.2

162.2
124.8
186.6

153.8
121 .7
174.1

159.7
119.9
186.1

171.9
119.3
208.1

166.5
121.6
196.6

Special groupings:
Finished goods, excluding foods ................
Finished energy goods ...............................
Finished goods less energy ....... ................
Finished consumer goods less energy .......
Finished goods less food and energy ....... ..

14?.4
102.0
149.0
153.1
150.5

147.2
113.0
152.4
157.2
152.7

142.8
101 .0
150.9
155.5
151.4

144.5
106.0
150.6
154.9
151 .8

144.3
105.7
150.5
155.0
151 .7

144.9
107.0
151.3
156.1
152.0

145.7
109.5
151 .9
156.9
152.1

147.0
113.6
152.7
158.0
152.2

Finished consumer goods less food
and energy ....... .. ............ .................. .... ....

146.8
112.5
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
114.9
152.1
156.8
152.5

150.9
120.9
154.4
159.1
154.7

150.7
120.3
154.3
159.1
154.6

149.1
114.5
154.4
159.1
154.7

157.9

160.3

159.0

159.4

159.4

159.7

159.8

159.9

Consumer nondurable goods less food
and energy .. ...........................................

160.0

159.4

159.6

160.0

162.2

162.2

162.2

177.9

180.7

178.9

179.7

179.8

179.8

180.5

180.2

180.2

180.3

180.8

181.3

181.6

182.0

182.2

Intermediate materials less foods
and feeds .................................................
Intermediate foods and feeds .....................
Intermediate energy goods .........................
Intermediate goods less en ergy .................

134.2
125.9
111 .9
137.7

142.9
137.0
123.1
145.8

134.7
134.1
110.9
139.0

136.5
132.2
115.e
133.8

137.6
133.7
115.8
141 .1

138.4
137.0
115.6
142.4

140.2
143.2
117.3
144.4

141.9
147.7
121.1
145.7

Intermediate materials less foods
and energy ......................................... ......

142.8
144.9
123.7
146.0

143.7
142.3
125.1
146.4

145.3
136.3
127.1
147.5

146.0
133.8
126.0
148.5

147.0
131 .2
129.5
148.7

148.1
130.6
132.6
149.2

147.5
131.5
127.2
149.9

138.5

146.5

139.5

140.4

141.7

142.9

144.6

145.7

146.2

146.8

148.3

149.5

149.9

150.4

15U

Crude energy materials ... ...... .. ...................
Crude materials less energy .......................
Crude nonfood materials less energy ........

147.2
123.4
152.5

174.7
143.9
192.8

141 .8
136.2
170.1

163.5
133.2
179.3

158.9
139.8
189.9

153.0
148.0
195.2

158.8
148.7
187.6

172.1
150.1
177.9

180.0
147.0
176.3

177.9
147.5
195.4

181 .9
144.6
200.8

166.3
140.9
195.4

179.5
142.0
204.6

210.1
142.3
207.0

194.7
143.2
204.3

132

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February

2005

41. Producer Price Indexes for the net output of major Industry groups
[December 2003 = 100, unless otherwise indicated]

2004
NAICS

Industry

211
212
213

311
312
313
315
316
321
322
323
324
325
326
331
332
333
334
335
336
337
339

Mar.

Apr.

May

June

July

Aug.

Sept.P

Oct.P

Nov.P

Dec.P

155.6

159.3

148.8

158.9

180.5

170.0

136.6

140.9

149.5

155.5

165.4
105.9
100.8

171 .7
108.5
101.0

188.1
107.3
101.3

198.0
108.1
102.2

196.6
110.2
103.7

202.7
110.4
105.3

182.8
111 .6
107.5

199.9
112.3
110.1

237.5
112.7
112.7

216.7
116.1
113.1

Total manufacturing Industries (December 1984:100)........................
Food manufacturing (December 1984=100) ...................................
Beverage and tobacco manufacturing ...................................................
Textile mills .................................................................... ............ .... ........
Apparel manufacturing .............................................. ...... ......... ..
Leather and allied product manufacturing (December 1984=100) ...... ...
Wood products manufacturing ............................................. ........
Paper manufacturing ............................ .. ........... ............................. ........
Printing and related support activities ............ ................ ........................

140.3
142.4
100.7
100.2
99.8

141 .8
146.1
101.5
100.7
99.9

143.3
149.1
100.2
101 .1
100.0

142.9
148.6
101 .2
101.3
99.8

143.2
146.5
100.6
101 .5
99.7

143.7
144.6
101 .1
101 .2
99.7

144.1
143.3
101 .0
101 .2
99.9

146.5
142.9
101 .6
101.7
100.1

146.0
142.9
101 .6
102.0
100.1

144.7
144.0
101.7
101.6
100.1

143.8
105.9
99.5
100.4

143.5
108.1
100.1
100.8

143.4
110.2
101 .1
100.8

143.5
108.3
102.3
101 .0

143.7
106.8
103.2
101 .3

143.6
109.8
104.4
101 .3

143.5
110.8
104.9
102.0

143.7
107.4
105.7
101 .9

143.9
105.0
105.7
102.1

144.1
106.0
106.0
102.0

Petroleum and coal products manufacturing (December 1984= 100) ....
Chemical manufacturing (December 1984= 100) ....................... .......
Plastics and rubber products manufacturing (December 1984=100) ....
Primary metal manufacturing (December 1984=100) .......... ... .... .......
Fabricated metal product manufacturing (December 1984= 100) .........
Machinery manufacturing ...................... .............................. ........
Computer and electronic products manufacturina ............................
Electrical equipment. appliance, and components manufacturing ........
Transportation equipment manufacturing ............... ..... ... ...........
Furniture and related product manufacturing(December 1984=100) ... .

134.3
168.8
129.6
132.3
137.5
100.9
99.3
101.8
100.4
149.0
100.8

141 .9
169.7
130.0
138.4
139.4
101.3
99.5
102.7
100.2
149.7
101 .0

152.0
170.3
130.4
142.2
140.8
101.6
99.3
103.3
100.4
151.4
100.9

144.1
171.6
130.8
142.3
141.9
101.8
99.1
103.5
100.6
151 .7
101.2

152.3
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
175.6
132.5
150.9
144.2
102.5
98.9
104.1
99.9
152.7
101 .6

176.7
177.1
134.3
152.0
144.7
103.1
98.9
104.4
103.2
153.5
101 .6

170.6
178.3
134.7
154.1
145.2
103.2
98.6
104.4
102.7
154.6
101 .6

148.5
180.2
135.9
154.9
145.5
103.5
98.4
105.0
102.8
155.0
102.1

Total mining Industries (December 1984:100)......................................
Oil and gas extraction(December 1985=100) .......................................
Mining, except oil and gas ............................................. .............
Mining support activities .......................... ... ... .............................

Miscellaneous manufacturing.

············· ··· ························· ········· ··

441
442
443
446
447
454

Retail trade
Motor vehicle and parts dealers. ... . ... .. ...... .. .... . ...... . .... .... .....
Furniture and home furnishings stores ..... ... ..... ... ..... ............. ...
Electronics and appliance stores .. ............. .............
Health and personal care stores ........................................
Gasoline stations (June 2001=100) ....................... ...... ... .. ... ... ...... .
Non store retailers ........................ .......................... ..................

103.2
101 .8
99.9
96.9
55.4
113.2

103.8
102.0
101.2
97.4
56.6
108.6

103.7
101.4
101.2
97.5
53.2
107.0

103.7
102.8
98.8
98.7
59.3
108.7

103.3
102.6
98.6
101 .3
48.3
103.6

103.8
102.8
98.7
105.6
48.6
102.0

103.5
103.6
101 .6
107.3
45.8
107.5

104.2
104.0
100.6
106.8
42.0
103.1

104.0
105.1
97.9
104.6
52 .0
111 .7

104.3
104.1
93.8
107.7
62.5
117.5

481
483
491

Transoortatlon and warehouslna
Air transportation (December 1992=100) ............................
Water transportation ... .............................. .......... .. .......... .. ....... .. .
Postal service (June 1989=100) ........ ... .................. ... ... ..

162.0
99.4
155.0

162.3
100.1
155.0

162.2
100.3
155.0

162.8
100.3
155.0

163.9
101.5
155.0

163.4
102.1
155.0

160.6
103.0
155.0

161.6
103.6
155.0

160.4
103.4
155.0

163.0
103.5
155.0

221

Utilities
Utilities .... ..... ..... .... . ···· ··· ··· ····· ················· ····· ··· .... ... ............... .

101.2

101 .8

103.1

106.9

107.1

107.4

105.1

104.0

108.5

108.5

6211
6215
6216
622
6231
62321

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
99.8
119.6
140.3
101.6
99.9

114.4
99.8
119.7
140.7
101.9
99.9

114.4
100.0
119.7
140.8
102.0
100.5

114.3
100.0
119.7
140.9
102.0
100.5

114.3
100.0
119.7
141.6
102.9
102.1

114.3
100.1
119.7
141.6
103.0
102.1

114.5
100.0
119.7
142.4
103.1
100.6

114.4
100.1
119.9
142.9
103.5
100.9

114.4
100.1
120.0
143.3
103.6
102.0

114.4
100.1
120.1
143.6
103.3
101 .9

101.3
100.3
100.2
98.4
101.7

101.4
101.6
100.1
98.5
102.3

101.3
103.1
99.9
98.9
102.4

101.4
102.7
99.9
99.0
102.7

101.5
99.6
99.8
99.0
103.2

101.5
100.9
99.9
99.0
104.1

101 .0
101.9
99.5
98.8
103.2

101 .5
103.6
99.2
98.9
104.0

102.0
105.5
99.0
98.5
105.3

101.7
104.9
98.9
98.5
106.0

99.6
100.7
101 .1
107.4
131 .7
100.8

101.0
100.8
101.3
106.0
131.8
101 .1

102.6
100.8
101.9
104.5
131.8
101.2

102.1
101.0
98.5
105.6
131.8
101.1

103.5
101.0
101.4
110.0
131.6
101 .3

104.0
101 .1
101 .0
110.8
131.5
101.4

104.7
101.0
100.7
108.2
132.3
101 .8

104.1
99.5
98.5
108.0
132.5
102.0

104.2
99.6
100.1
107.9
132.1
102.3

103.1
100.1
101 .5
108.5
132.0
102.1

126.5
99.8
113.2
98.7
100.4
100.8
124.9

126.6
99.9
113.1
98.7
100.5
101.3
124.8

126.5
99.9
113.4
98.7
100.6
101.5
124.4

126.6
99.9
113.8
97.4
101.0
101.5
125.6

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

127.2
100.4
114.6
94.8
100.9
101.3
125.4

127.4
100.4
115.3
96.9
101 .5
101.4
125.4

127.3
100.7
115.2
96.4
101.3
101.4
124.7

127.4
100.6
114.1
96.1
101 .1
101.5
122.6

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 contracts. and like activitv .. ............. ........ ....... .
Lessors or nonresidental buildings (except miniwarehouse) ...............
Offices of real estate agents and brokers ....... .... ..... ... ..... ... ............
Real estate support activities ............................................. ......... .
Automotive equipment rental and leasing (June 2001=100) ... .. .. ........
Legal services (December 1996=100) .. ..... .... .. .. ..... ............... ........
Offices of certified public accountants .................................. .. .......
Architectural, engineering, and related services
(December 1996=100) .. .... ............................ ..........................
Advertising agencies ............................................ .... .... ...... ...... ..
Employment services (December 1996=100) ................. ....... ........
Travel agencies ..... ......... ... .. ..... ............................. ... ... ... ........ .
Janitorial services ........ ... .. ... .......... ......... .................. .... ......... ..
Waste collection .... ........... .. .... .. ........... .... ........................ ... .......
Accommodation (December 1996= 100) ... .... ...................... ..... .......

NOTE: Data reflect the conversion to the 2002 version of the North American Industry Classification System
(NAICS), replacing the Standard Industrial Classification (SIC) system .


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Monthly Labor Review

February

2005

133

Current Labor Statistics:

Price Data

42. Annual data: Producer Price Indexes, by stage of processing
[1982

= 100]
Index

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Finished goods
Total. .... ........................ ........ ............ ... ..... ................. ... ..
Foods ... ........................ .. ... ......... ........ .. ... .... ........ .
Energy ............ ..... .... ...... ... . ....... ..... . ... ... .. .... .... .. .
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
146.0
102.0
150.5

148.5
152.6
113.0
152.7

Intermediate materials, supplies, and
components
Total. ........... ... .. ... .. .. ...... ............................... ............ .... .
Foods .... .... .. ....... .... . ...... .... .... .... ...... ... .. ... .. ..... .. . .
Energy .. ............ .............. ..... .. . .... ... ...... ..... ... .... .. ... .
Other .... ......... ..... ..... ...... ... ..... ... ... ......... .. .... ... .. ..... .

118.5
118.5
83.0
127.1

124.9
119.5
84.1
135.2

125.7
125.3
89.8
134.0

125.6
123.2
89.0
134.2

123.0
123.2
80.8
133.5

123.2
120.8
84.3
133.1

129.2
119.2
101.7
136.6

129.7
124.3
104.1
136.4

127.8
123.3
95.9
135.8

133.7
134.4
111.9
138.5

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
113.5
147.5
116.8

159.0
126.9
174.7
149.0

Crude materials for further processing
Total. .. ........ .. .... ........... ... .. ................... ............ .. .... ... ... ...
Foods ... .. ...... .. ........... ...... ... ... ..... ..... .... .... .. ........ .. .
Energy ..... ......... ..... .. ..... .. .... .. ... .... .... ..... ..... .. ... ... .
Other ......... .... .. .... ........ ........ . .... .... ..... ......... .. ........ .

134

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February

2005

43. U.S. export price Indexes by Standard International Trade Classification
[2000 = 100]

2004

2003

SITC

Industry

June

Aug.

July

Sept.

Nov.

Oct.

Dec.

Dec.

Jan.

Feb.

Mar.

Apr.

May

0 Food and live animals ..............................................
Meat and meat preparations .............. ................. ............
01
Cereals and cereal preparations ...... ...... .... ........... ........ ..
04
Vegetables, fruit, and nuts, prepared fresh or dry .... ...... .
05

116.5
123.0
130.8
103.2

117.0
122.8
131.6
103.1

119.9
125.0
135.2
108.4

122.7
127.1
139.6
110.1

126.1
127.6
147.7
109.5

126.7
127.7
146.0
113.3

123.9
127.3
141.2
111 .1

119.8
123.0
128.0
110.0

116.4
126.1
120.6
113.2

117.6
124.8
122.0
119.8

118.2
126.9
115.5
130.6

118.5
125.4
113.0
136.7

119.8
124.4
116.3
138.5

2 Crude materials, inedible, except fuels ...........................
Oilseeds and oleaginous fruits ... .. ...... ...... ..... ........... ..... ..
22
Cork and wood ................... ... ... .... .. ... ............ .... .......... ..
24
Pulp and waste paper ........ ..... ..... ..... .. .. .... ......... ..............
25
Textile fibers and their waste .. ... ... .... ........... ........ ...........
26
Metalliferous ores and metal scrap ......... ....... .. ...........
28

116.9
152.5
93/
91 .7
121.2
136.6

120.2
157.2
94.5
91 .7
123.7
148.9

122.3
160.9
95.6
92 .5
122.2
156.8

129.0
181.6
96.5
94.2
121.9
171.4

132.8
197.1
97.6
98.8
115.9
176.2

132.5
199.0
98.2
100.4
114.9
170.6

125.7
168.5
98.3
100.8
108.7
167.5

132.1
184.5
98.9
100.1
102.9
190.2

118.0
117.4
98.8
99.5
101 .1
183.6

119.4
125.1
99 .1
98.7
102.1
178.5

118.2
109.1
99.1
98.1
100.2
190.4

120.1
110.3
98.4
98.2
97.5
200.4

119.0
111 .1
98.8
98.9
96.4
192.4

3 Mineral fuels, lubricants, and related products .... ..........
Coal, coke, and briquettes ... ... ....... ..... ....... ........ ..............
32
Petroleum, petroleum products, and related materials ...
33

110.7
112.9
106.2

120.5

119.3

123.0

123.2

135.1

131.8

137.5

139.6

141.2

156.1

151.4

141 .3

-

-

-

-

-

-

-

-

-

-

-

-

116.8

114.7

120.1

119.8

135.0

129.7

134.5

136.2

138.0

156.4

151.0

135.1

5 Chemicals and related products, n.e.s. ..........................
Medicinal and pharmaceutical products .... ...... ........ ... .....
::>4
Essential oils; polishing and cleaning preparations ........ .
55
Plastics in primary forms ... .. ...... .............. ..... ................. .
57
Plastics in nonprimary forms .... ... .... ...... ...... .............. .. ....
58
Chemical materials and products, n.e.s.. .... ..... ............ ..
59

101.4
105.8
100.1
96.5
97.2
102.6

102.9
105.4
104.3
98.3
96.8
105.0

104.0
105.3
104.2
100.9
97.2
105.2

104.9
105.5
104.3
102.1
97.4
104.8

105.5
105.7
104.1
102.2
96.9
104.8

105.6
105.7
104.4
102.9
96.7
104.8

105.8
105.8
104.3
103.2
96.5
104.9

107.0
107.9
104.1
104.8
97.2
104.6

108.6
108.1
105.1
107.3
97 .1
106.2

109.7
108.0
105.6
109.9
97.4
105.5

111.5
107.1
106.0
113.2
98.2
105.2

112.6
107.3
105.7
116.6
98.8
105.5

113.7
107.7
106.4
119.2
99.2
106.0

6 Manufactured goods classified chiefly by materials .....

100.8

101 .7

103.0

104.1

105.6

106.6

107.0

108.5

109.6

110.5

111.4

111.9

112.3

Rubber manufactures. n.e.s . ...... ........... ............ ...... .......
Paoer. oaoerboard. and articles of oaoer. oulo.
and oaoerboard ... ..... ... ..... ... ... .. ... .... ...... .... .. .........
Nonmetallic miner-,,I manufactures. " · ' .S ...... .......... .......
Nonferrous metals .................. ... ... ...... ......... ....... ........ .....

109.9

110.4

110.9

110.4

110.9

110.8

111 .2

111 .8

112.0

111.4

111.6

112.4

112.9

101 .9
100.2
96.5

102.7
100.4
99.0

104.0
101 .1
99.1

103.5
101 .3
100.6

103.8
101.7
101 .5

Rev. 3

62
64
66
68

7 Machinery and transport equipment. ..............................
Power generating machinery and equipment... .. ...... .... ..
Machinery specialized for particular industries ...... ... .... . .
General industrial machines and parts, n.e .s.,
and machine parts ................ .. .................. ...... ...............
Computer equipment and office machines ......... .......... .. .
75
Telecommunications
and sound recording and
76
reproducing apparatus and equipment... .... ........... ...... .
Electrical machinery and equipment... .......... . ................
77
Road vehicles ................. ....... ........... ...... .........................
78

71
72
74

87 Professional, scientific, and controlling
instruments and apparatus ......................... ............


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

97.6
99.8
84.5

97.9
99.7
85.9

97.8
99.6
90 .9

97.9
99,7
94.1

98.7
99.7
98.1

99.0
99.5
97.6

99.2
99.9
95.4

101 .2
99.9
95.4

97.8

97.9

98.1

98.2

98.4

98.4

98.2

98.2

98.2

98.2

98.4

98.4

98.5

108.7
103.4

109.3
103.9

109.4
104.0

109.4
104.2

108.7
105.1

108.7
105.4

108.7
105.4

108.9
105.7

109.0
105.9

109.0
106.1

109.5
107.2

110.2
107.5

110.3
108.0

102.8
88.6

103.3
87 .7

103.5
88.2

104.0
88.4

104.5
88.8

104.8
88.6

104.9
87.2

105.2
86.6

105.3
86.4

105.3
86.0

106.4
85.4

106.6
84.7

106.8
84.5

92 .0
88.1
101 .5

92.6
88.0
101 .7

92.5
88.3
101.9

92.4
88.6
101.9

92.2
88.5
102.3

92.0
88.6
102.3

91.8
88.2
102.4

91.5
88.3
102.5

90.7
88.2
102.5

90.7
88.1
102.4

90.4
87.9
102.8

90.4
87.7
102.8

90.4
87.6
103.0

102.3

11)2.2

102.3

102.3

102.2

102.1

102.0

101.7

101.9

101.8

102.2

102.3

102.6

Monthly Labor Review

February

2005

135

Current Labor Statistics:

Price Data

44. U.S. Import price Indexes by Standard International Trade Classification

= 100]

(2000
SITC

2004

Dec.

Jan.

Feb.

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

0 Food and live animals ..............................................
01
Meat and meat preparations ..... ....... .... .................... .......
03
Fish and crustaceans, mollusks, and other

101.0
120.4

102.2
117.7

104.7
118.0

105.4
120.4

106.4
121.7

106.1
124.4

106.9
128.9

107.4
133.7

107.4
134.2

109.2
134.9

111 .0
134.1

110.9
131.8

111 .7
132.9

aquatic invertebrates .. ... .. ......... ..... .... ... .......... ....... ..
Vegetables, fruit, and nuts, prepared fresh or dry ...........
Coffee, tea, cocoa, spices, and manufactures
thereof ... ... .... ...... ......... ..... .. .... .. .. .. .. .... ... .... ..... .....

79.2
109.4

78.2
112.3

80.0
115.7

83.3
111.3

85.1
109.5

84.1
106.1

84.1
105.9

86.1
102.1

86.9
100.6

86.0
109.2

85.5
114.5

84.4
116.1

84.7
111.5

96.0

100.1

101.9

101.7

103.6

102.4

107.0

102.7

103.4

105.6

104.5

108.9

114.4

1 Beverages and tobacco ...........................................
11
Beverages .. ........ ... .............. ........ ........ ....... ........ ..........

104.4
104.3

104.7
104.9

105.0
105.2

105.3
105.5

105.3
105.5

105.4
105.7

105.3
105.6

105.9
106.4

106.1
106.6

106.2
106.7

106.5
106.9

106.6
107.0

107.1
107.5

2 Crude materials, inedible, except fuels ...........................
24
Cork and wood ...... ................. ......... ........ ......... ...............
25
Pulp and waste paper ............................ .............. ....... ....
28
Metalliferous ores and metal scrap .. .. .............. ..............
29
Crude animal and vegetable materials, n.e.s . ...... ........ ..

107.9
108.0
92.8
115.3
99.6

109.5
108.9
93.3
124.2
98.9

114.1
115.7
91 .9
134.6
99.5

120.0
123.3
95.4
148.0
99.7

122.9
127.8
100.8
148.2
99.3

127.3
139.0
103.4
143.5
102.1

125.8
136.1
106.5
140.4
98.0

125.7
132.1
108.0
145.3
101 .2

134.0
148.9
107.7
160.8
97.6

135.1
151 .1
105.5
162.6
98.7

125.1
126.2
99.8
166.2
96.3

121.7
117.2
98.0
167.0
96.5

125.4
124.9
99.5
167.1
98.3

3 Mineral fuels, lubricants, and related products ............ ..
33
Petroleum, petroleum products, and related materials .. ..
34
Gas, natural and manufactured ................ ... ....................

108.2
106.9
113.9

117.3
114.0
138.0

117.7
114.5
137.1

120.8
120.0
122.9

121 .1
120.3
123.3

131 .6
131 .5
129.5

131.5
130.0
140.0

133.9
133.0
134.8

144.2
144.8
136.3

146.8
149.5
121.9

161.5
165.9
124.7

158.4
156.4
168.0

141 .9
137.6
170.2

5 Chemicals and related products, n.e.s. .........................
52
Inorganic chemicals ............................. ...........................
53
Dying, tanning, and coloring materials ........... .................
54
Medicinal and pharmaceutical products ... ... ....................
55
Essential oils; polishing and cleaning preparations ..... ....
57
Plastics in primary forms ...................... ....................... ....
58
Plastics in nonprimary forms ............ .... .. ........... ........... ...
59
Chemical materials and products, n.e.s .................. .......

101 .1
114.0
99.6
103.4
91.6
105.5
101 .8
93.3

103.0
119.3
99.9
107.2
92.7
104.4
102.1
94.3

103.4
120.6
99.7
107.7
93.3
105.2
102.4
94.9

103.8
120.5
99.5
108.1
93.7
106.9
102.9
95.8

103.5
115.9
100.6
107.7
93.5
105.5
102.9
95.4

103.5
117.5
100.8
107.3
93.4
105.8
102.9
95.1

103.8
119.8
100.3
107.1
93.5
104.6
102.3
95.2

104.6
122.2
98.3
107.3
93.5
107.8
103.0
94.7

105.1
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

108.2
125.6
98.5
106.1
93.3
110.4
104.4
94.8

108.4
126.5
98.7
106.7
93.2
113.7
105.4
95.3

108.9
126.8
98.7
108.0
93.3
116.7
105.9
95.6

6 Manufactured goods classified chiefly by materials.....
62
Rubber manufactures, n.e.s ... ................. ......... ....... .......
64
Paper, paperboard, and articles of paper, pulp,

97.8
98.8

98.9
99.0

101.4
99.2

103.6
99.7

105.6
99.9

106.9
100.0

106.1
100.5

106.1
100.5

107.7
100.8

108.9
100.8

108.9
101 .0

109.3
101 .3

110.4
101.7

and paperboard .... ... ....... .. ....... .. ..... .. .... ... ..............
Nonmetallic mineral manufactures, n.e.s ...... .................
Nonferrous metals ..... ..................... ...................... ......... ..
Manufactures of metals, n.e.s . .. ........ ... ...... ....... .............

93.7
98.1
87.7
99.5

94.1
98.5
92.3
99.7

94.5
98.9
97.0
100.3

95.0
99.0
102.6
101 .1

94.8
99.3
105.8
102.3

95.5
99.4
106.1
102.4

95.5
99.4
101.6
102.4

96.4
99.3
102.3
102.7

96.9
100.2
105.6
103.3

97.9
100.4
106.3
103.9

99.2
100.6
106.6
104.1

99.4
100.7
108.8
104.7

99.2
100.7
111.3
105.7

7 Machinery and transport equipment. ..............................
72
Machinery specialized for particular industries .... ............

95.3
103.6

95.4
104.9

95.5
106.4

95.5
106.7

95.2
106.5

95.2
106.7

95.1
106.6

95.0
107.2

95.0
107.6

95.0
107.4

95.0
107.7

95.1
108.1

95.2
109.1

05
07

66
68
69

7,l

I

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

101.2
78.2

101.8
78.0

102.5
78.0

103.3
77.7

103.5
76.5

103.6
76.4

103.5
75.5

104.0
74.9

104.1
74.3

104.3
73.9

104.6
73.0

104.8
72.8

105.2
72.6

86.7
95.3
101 .6

86.4
95.4
101 .9

85.4
95.7
102.0

85.1
95.6
102.0

84.9
94.9
102.2

84.9
94.8
102.3

85

84.7
94.7
102.4

84.3
94.6
102.6

84.0
94.7
102.8

83.8
94.6
103.1

Footwear ...... ........... .... .......... .......................... ........... ...

83.5
94.4
103.6

83.5
94.6
103.8

100.1

83.2
94.8
103.9

100.5

100.5

100.6

100.6

100.6

88

100.4

100.4

100.1

Photographic apparatus, equipment, and supplies,
and optical aoods n.e.s .... .......... .................................

100.5

100.5

100.6

100.7

99.9

99 .9

100.3

100.0

99.4

99.3

99.0

98.2

98.2

98.2

98.2

98.5

99.3

75
76
77
78

136

2003

Industry

Rev. 3

Monthly Labor Review


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

February 2005

45. U.S. export price indexes by end-use category
[2000"' 100)

2004

2003
Category
ALL COMMODITIES .................... ..............................

Feb.

Jan.

Dec.
100.8

Mar.

Apr.

May

June

July

Aug.

Sept.

Oct.

Nov.

Dec.

103.0

103.7

104.1

103.4

103.9

103.4

103.8

104.4

104.7

104.9

129.1
131 .1
110.7

128.0
129.9
110.1

116.5
117.0
110.9

118.7
119.3
113.0

117.6
117.8
114.4

118.1
118.3
115.3

118.6
118.5
118.9

102.2

101.5

Foods, feeds, and beverages .................... ... ... .....
Agricultural foods, feeds, and beverages ................
Nonagricultural (fish, beverages) food products .....

122.4
123.8
108.5

123.1
124.6
109.5

125.6
127.2
110.7

130.5
132.4
112.1

134.8
137.0
113.4

135.6
138.0
112.7

Industrial supplies and materials ...... .... ..................

102.5

105.1

106.4

108.1

109.1

110.2

109.9

112.0

113.1

114.0

116.6

117.5

117.5

113.7

110.7

109.0

108.4

109.4

108.7

107.2

107.7

132.5

128.6

120.5

Agricultural industrial supplies and materials ..... .. ...

117.5

114.8

117.2

116.6

118.6

Fuels and lubricants ............ ..... .. ........... ................
Nonagricultural supplies and materials,
excluding fuel and building materials ............... .. .
Selected building materials ... ................................. .

99.0

106.1

106.5

108.9

109.6

117.5

114.9

118.6

120.4

121.5

102.5
99.5

104.7
98.7

106.4
100.9

108.1
102.3

109.4
103.4

109.9
103.9

110.0
103.4

112.4
102.8

113.5
103.3

114.4
104.0

116.4
103.9

118.0
104.0

119.1
104.1

Capital goods ..... .... ..... ............ ....................... ....
Electric and electrical generating equipment.. ........
Nonelectrical machinery ..... ............ ... .. .. ....... .........

97.5
101.7
94 .1

97.5
102.0
93.9

97.8
101 .9
94.3

98.0
102.0
94.5

98.1
101.7
94.6

98.1
101.7
94.6

97.8
102.0
94.1

97.8
102.2
94.0

97.8
102.2
94 .0

97.8
102.4
93.9

98.0
103.3
93.9

98.1
103.4
93.9

98.2
103.4
93.9

Automotive vehi cles, parts, and engines ...... .. .. .......

101 .8

101 .9

102.0

101.9

102.2

102.3

102.3

102.4

102.6

102.5

102.7

102.8

102.9
101.4
101 .3
101.1
116.6
104.0

Consumer goods, excluding automotive ... ...... .........
Nondurables, manufactured ............... ................... .
Durables, manufactured ... ............ .. ....................

99.9
99 .2
100.3

100.2
99.9
100.1

100.1
99.9
100.0

100.2
99.9
100.1

100.4
100.1
100.5

100.5
100.1
100.6

100.4
100.0
100.7

100.9
100.8
100.8

101 .1
101.0
101.0

101 .0
101 .0
100.9

101.0
100.7
100.8

101.1
100.9
101.0

Agricultural commodities .......... ..... ... .. .... ..............
Nonagricultural commodities ............................... ..

122.7
99 .1

123.5
99.8

125.3
100.4

129.7
100.9

133.0
101.4

133.7
101.7

127.4
101.5

126.1
102.2

115.5
102.5

117.6
102.8

116.2
103.6

116.4
103.9

46. U.S. import price indexes by end-use category
[2000"' 100)

2004

2003
Category

Oct.

Nov.

Dec.

ALL COMMODITIES ..................................... .............

97.5

99.0

99.4

100.2

100.4

101.9

101 .7

102.1

103.6

104.1

105.8

105.6

104.2

Foods, feeds, and beverages.. .............. . . . ...... ...
Agricultural foods, feeds , and beverages ................
Nonagricultural (fish, beverages) food products .. ...

103.2
110.9
86.0

103.7
112.0
85.1

105.3
113.4
87.2

105.9
113.0
90.1

107.2
114.2
91.7

106.8
114.0
90 .6

106.9
114.3
90.3

107.5
114.5
91.8

107.3
114.1
92.3

108.7
116.4
91.4

110.0
118.4
91.0

110.2
119.0
90.5

111 .3
120.4
90 .7

Industrial supplies and materials ... .. ....... .... ... ..... ... .

103.6

108.5

110.0

112.7

113.9

119.7

119.3

120.6

126.6

128.5

135.0

133.6

127.2

120.2
120.1

120.6
119.9

131.0
131.2

130.9
129.7

133.2
132.7

143.4
144.4

146.2
149.2

161.1
166.1

158.0
156.7

142.4
138.7

Dec.

Feb.

Jan.

Mar.

Apr.

May

July

June

Sept.

Aug.

Fuels and lubricants ...................................... ...... ..
Petroleum and petroleum products ................ ...

107.2
106.0

116.5
113.7

117.0
114.3

Paper and paper base stocks ... ............................. .
Materials associated with nondurable
supplies and materials ........ .. .. ...................... .......
Selected building materials .................................. .. .
Unfinished metals associated with durable goods ..
Nonmetals associated with durable goods .... .. .......

93.9

94.1

94.2

95.6

96 .8

98.2

99.0

100.0

100.4

101 .1

101.4

101.1

101 .4

104.4
108.0
99 .2
98.2

104.7
106.8
104.5
98.5

104.8
113.7
109.5
99.2

105.4
118.4
114.9
99.3

105.1
120.2
121.7
99.3

105.4
123.6
126.2
99 .1

106.0
120.5
124.4
98.7

106.5
117.6
126.1
98.5

107.7
124.0
129.8
98.5

108.0
125.6
133.1
98.8

108.6
115.3
134.2
98.8

109.4
110.9
136.9
99.2

109.9
115.2
138.9
99.7

Capital goods ... .. ...... .................. .......................
Electric and electrical generating equipment.. .. ......
Nonelectrical machinery .. ......... ......... .. ..................

92.9
96.8
91.1

93.1
,17.4
91 .2

93.1
97.9
91 .2

93.1
97.8
91.2

92.6
97.2
90.6

92 .6
97.1
90.5

92 .2
97.0
90.1

92.2
97.5
90.0

92.1
97.7
89.9

92.0
97.4
89.8

91.7
97.4
89.4

91.8
97.5
89.5

92.1
98.1
89.8

Automotive vehicles, parts, and engines .... ......... ....

101 .4

101.6

101.7

101 .8

102.0

102.0

102.2

102.3

102.5

102.7

103.1

103.3

103.5

Consumer goods, excluding automotive ..................
Nondurables, manufactured ......... ................ ..........
Durables, manufactured ....... .... .... ....... .......... ..... .
Non manufactured consumer goods .. ..... ..............

98.1
100.1
96.2
96.2

98.6
101.1
96.3
95.9

98.7
101.2
96.3
96.2

98.7
101.3
96.3
96.4

98.6
101 .1
96 .3
96.4

98.5
101.0
96.0
97.3

98.5
100.9
96 .1
96.8

98.5
101.0
95.9
97.4

98.4
100.9
95.9
97 .9

98.4
100.8
95.9
97.9

98.4
100.7
96.0
97.9

98.5
100.8
96.1
98.0

98.8
101 .1
96.4
98.1


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Federal Reserve Bank of St. Louis

I

47. U.S. international price Indexes for selected categories of services
[2000 "' 100, unless indicated otherwise)
Category

2004

2003

2002

Dec.

Sept.

June

Mar.

Dec.

Mar.

June

Sept.

Dec.

Air freight (inbound) ....................... ..... .. .. ...................
Air freight (outbound) ..... .. ................................. . ..

105.9
95.4

108.8
97.2

109.4
95.4

112.5
95.5

112.9
94.9

116.2
96.1

116.6
99.0

118.7
100.7

125.2
105.2

Inbound air passenger fares (Dec. 2003 = 100) .. ....... .
Outbound air passenger fares (Dec. 2003 = 100)) ..... ..
Ocean liner freight (inbound) .... .......... .... .... ..............

-

-

-

-

93.3

94.0

116.1

116.2

100.0
100.0
117.7

105.1
99.3
119.1

106.1
114.2
121.1

110.1
114.2
120.3

112.5
108.4
122.7

-

NOTE: Dash indicates data not available .

Monthly Labor Review

February

2005

137

Current Labor Statistics:

Productivity Data

48. Indexes of productivity, hourly compensation, and unit costs, quarterly data seasonally adjusted
[1992

= 100]
2001

Item

Business
Output per hour of all persons .......... ............................
Compensation per hour ..... ...... .... ..... ........ ... ....... ...
Real compensation per hour ......... .. ...... ..... .. ... ....... .
Unit labor costs .........................................................
Unit nonlabor payments ..... ...... .. ... .................... ... ....
Implicit price deflator .............................................

2002

2003

!V

I

II

Ill

IV

120.9
141 .5
114.2
117.0
113.1
115.6

122.7
143.2
115.2
116.7
113.4
115.5

123.2
144.4
115.2
117.2
113.6
115.9

124.7
145.0
115.0
116.3
115.7
116.1

125.0
145.5
114.8
116.3
116.8
116.5

120.4
140.7
113.5
116.8
114.7
116.0

122.4
142.6
114.7
116.4
115.1
116.0

122.8
143.8
114.7
117.1
115.4
116.5

124.1
144.3
114.4
116.2
117.7
116.8

124.5
139.3
112.5
113.0
111 .9
115.7
75.5
105.0
109.6

126.8
139.9
112.6
111 .3
110.4
113.6
88.8
107.0
109.3

128.1
141.3
112.7
111.0
110.3
112.7
94.5
107.9
109.5

140.8
139.1
112.3
98.8

144.5
142.8
115.7
99.6

146.4
146.7
117.0
100.2

I

2004

II

Ill

IV

I

II

Ill

IV

126.21
147.4
115.3
116.8
117.7
117.1

128.6
149.6
116.8
116.4
119.0
117.3

131 .2
151 .7
117.7
115.6
120.8
117.5

132.0
153.2
118.7
116.0
120.7
117.8

133.3
154.2
118.4
115.7
122.9
118.4

134.2
156.2
118.6
116.4
124.4
119.4

135.0
157.7
119.1
116.8
124.6
119.7

135.8
159.3
119.3
117.3
125.3
120.3

124.6
144.7
114.3
116.1
118.9
117.2

125.8
146.6
114.7
116.6
119.6
117.7

127.8
148.7
116.1
116.3
120.4
117.8

130.6
150.9
117.1
115.5
122.3
118.0

131.7
152.5
118.2
115.9
121.9
118.1

132.8
153.3
117.7
115.4
124.3
118.7

134.1
155.5
118.0
115.7
125.7
119.6

134.7
156.8
118.5
116.4
126.3
120.1

135.0
158.0
118.4
117.1
126.8
120.7

129.0
142.1
112.7
110.9
110.1
112.8
95.8
108.3
109.5

129.6
142.9
112.8
110.9
110.2
112.8
102.3
110.0
110.1

130.2
144.1
112.7
111 .6
110.7
114.0
100.0
110.3
110.5

132.7
146.3
114.2
110.9
110.3
112.6
112.2
112.5
111.0

135.3
148.5
115.3
110.5
109.8
112.6
120.3
114.7
111.4

136.8
150.0
116.2
110.4
109.7
112.2
125.1
115.7
111.7

137.0
150.9
115.9
110.4
110.2
111 .1
129.9
116.1
112.2

138.1
152.9
116.1
110.9
110.7
111.4
136.3
118.1
113.2

139.4
154.4
116.6
111 .0
110.7
111 .7
136.5
118.3
113.2

-

148.7
148.3
117.6
99.7

149.7
149.6
118.1
99.9

151 .6
155.4
121.6
102.6

152.7
158.2
123.5
103.6

156.7
161.3
125.2
103.0

158.1
163.6
126.7
103.5

158.8
161.9
124.3
101.9

161.7
164.6
124.9
101.8

163.4
166.4
125.7
101.8

Nonfarm business
Output per hour of all persons .......................................
Compensation per hour ... .... .......... ..... ...... ........ .. ...
Real compensation per hour .... ....... ....... .................
Unit labor costs .. ............... ...... .. ... .. ... ...... ..... .............
Unit non labor payments .. ......... ...... ..... ... .. ....... ... . ....
Implicit price deflator ... .... ...... ... ....... ...... ... .... .. .......
Nonfinancial corporations
Output per hour of all employees .. ...... ... ... .. .. .. ....... .... .. ..
Compensation per hour .........................................
Real compensation per hour ...................................
Total unit costs .........................................................
Unit labor costs .................. ..................... .... .... .............
Unit nonlabor costs ................. ... .... ... ..... .. ................ ...
Unit profits .... ......................... ... .. .. ..................................
Unit nonlabor payments ................... ................... .....
Implicit price deflator ............ ································

-

-

Manufacturing
Output per hour of all persons ........ .................. .............
Compensation per hour .........................................
Real compensation per hour ...................................
Unit labor costs ................................ ... ... ...... .. ..... ... ...

138

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Federal Reserve Bank of St. Louis

February

2005

165.6
168.8
126.5
101 .9

49. Annual indexes of multifactor productivity and related measures, selected years
[1996

= 100]
Item

1980

1990

1991

1992

1993

1994

1995

1997

1999

1998

2000

2001

Private business
Productivity:
Output per hour of all persons .................. ....... .... .. ...
Output per unit of capital services .. .. .. .. .... .... ...... ....
Multifactor productivity ........ ........ ... .... ...... ......... ...
Output. ................................... ... ..... ..... .. .. ......... ... ....
Inputs:
Labor input. ... ... .. ... ... ... .. ... ..... ... .. ........ .... ....... ...... .........
Capital services .... ............................................. ...
Combined units of labor and capital input.. ..............
Capital per hour of all persons ........................ ............

75.8
103.3
88.8
59.4

90.2
99.7
95.5
83.6

91.3
96 .5
94.5
82.6

94.8
98.0
96.7
85.7

95.4
98.7
97.1
88.5

96.6
100.4
98.2
92.8

97.3
99.8
98.4
95.8

102.2
100.3
101.2
105.2

105.0
99.3
102.5
110.5

107.7
98.2
103.4
115.7

111 .0
96.6
105.0
120.4

112.4
92 .8
103.9
120.2

71.9
57.6
67.0
73.4

89.4
83.8
87.5
90.4

88.3
85.7
87.4
94.6

89.3
87.5
88.7
96.8

91.8
89.7
91 .1
96.6

95.6
92.5
94.6
96.2

98.0
96.0
97.3
97.5

103.5
104.9
104.0
101 .9

106.1
111 .3
107.9
105.8

109.0
117.9
110.9
109.7

110.1
124.5
114.7
114.8

109.5
129.6
115.7
121.1

77.3
107.6
91.0
59 .6

90.3
100.4
95.8
83.5

91.4
97.0
94.8
82.5

94.8
98.2
96.7
85.5

95.3
99 .0
97.2
88.4

96.5
100.4
98.2
92.6

97.5
100.0
98.6
95.8

102.0
100.0
101 .0
105.1

104.7
99.0
102.2
110.5

107.1
97.6
102.9
115.7

110.3
95.9
104.4
120.2

111.6
92 .0
103.3
120.1

70.7
55.4
65.5
71 .8

89.2
83.2
87.2
89.9

87.9
85.1
87.0
94.3

89.0
87.0
88.4
96.5

91.8
89.4
91 .0
96.3

95.4
92.2
94.3
96 .1

97 .8
95.8
97.2
97.6

103.6
105.1
104.1
101.9

106.4
111 .7
108.1
105.8

109.5
118.5
112.4
109.7

110.6
125.4
115.2
115.0

110.1
130.5
116.3
121 .3

62.0
97.2
81.2
64.3

82.2
97.5
93.3
83.2

84.1
93.6
92.4
81 .5

88.6
95.9
94.0
85.5

90.2
96.9
95.1
88.3

93.0
99.7
97.3
92.9

96.5
100.6
99.2
96 .9

103.8
101.4
103.1
105.6

108.9
101 .7
105.7
110.5

114.0
101 .7
108.7
114.7

118.3
101 .0
111 .3
117.4

119.7
95.1
110.3
112.1

103.7
66.1
86.1
63.9
65.8
79.2

101.1
85.3
93.1
77.5
84.7
89.1

96.9
87.1
93.2
78.5
84.6
88.3

96.5
89.1
93.1
83.5
92.0
90 .9

97.8
91 .1
96.6
86.5
92 .9
92.8

99.9
93.2
99.9
90 .3
96.0
95.5

100.4
96.4
102.3
93.1
100.4
97.7

101 .7
104.1
97.5
101.9
103.9
102.4

101 .5
108.7
100.6
107.5
103.1
104.6

100.7
112.8
102.9
107.9
105.4
105.5

99.2
116.2
104.3
106.9
106.5
105.5

99.6
117.9
98.9
105.5
97.7
101 .6

Private nonfarm business
Productivity:
Output per hour of all persons .............. .. ............ ....
Output per unit of capital services .... .. .............. .. .. ..
Multifactor productivity ... .. ...... ... .... .. ... ... ..... .... .... ..
Output. ................................. ........ .. ............. ...... .. ....
Inputs:
Labor input. ... .... ... ... ... ... ... .... .. ......... ..... ................ .. .... .
Capital services ......... .................... .. .. .... .. ... .. ..... .. .
Combined units of labor and capital input.. .... ...... ....
Capital per hour of all persons .. .................. .. ........ ...
Manufacturing
Productivity:
Output per hour of all persons .............................. .
Output per unit of capital services .. .............. .. .. .. ....
Multifactor productivity .................. ......... ... .... .... ...
Output. ............. ................ ...... ... .. ... .. ....... ................
Inputs:
Hours of all persons .................................................. .. .
Capital services .... ....... ..... ... .... .. .. ........ .... ... ... .. ... ..
Energy ................ ............... ............................ .. .......
Nonenergy materials .. ... ..............................................
Purchasf'd business services ... ........ .... .. .....................
Combined units of all factor inputs .. .. .. .... .. .. .. .. .. .......


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Federal Reserve Bank of St. Louis

Monthly Labor Review

February

2005

139

Current Labor Statistics: Productivity Data

50. Annual indexes of productivity, hourly compensation, unit costs, and prices, selected years
[1992 = 100]

Item

1960

1970

1980

1990

1996

1997

1998

1999

2000

2001

2002

2003

2004

Business
Output per hour of all persons .. ....... .. .. ... ... .. ... ... .. ... ... ....
Compensation per hour ..... ... ......... .. .... .. .. ... ....... ... .
Real compensation per hour ....... ... ... .. .... .... ....... .....
Unit labor costs ................... .. .... ...... .. .... .... .... ... .........
Unit non labor 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
109.5
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
117.1
116.2
119.6
117.4

134.7
156.9
118.9
116.5
124.3
119.4

Nonfarm business
Output per hour of all persons .............. ..... ........... .... .....
Compensation per hour ........ ... .... ...... ... .. ..... .. ...... ..
Real compensation per hour .. ... ... .. ............ ... ..........
Unit labor costs ........... ........... ............. ...... ........ ... .....
Unit nonlabor payments ... .... .... ...... ... .... ......... .. ....... .
Implicit price deflator .. .... ... ... ..... ... .. ........... ....... .. ...

51 .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

129.0
149.7
116.5
116.1
121.1
117.9

134.2
156.0
118.2
116.2
125.8
119.8

Nonfinancial corporations
Output per hour of all employees .... .............. .. ..............
Compensation per hour .... ..... .... ........ ...... .. .... ... ..... .
Real compensation per hour .... ... .... .. ... .. ....... ... .... ...
Total unit costs ........ ......... ........ ....... .... ........... ....... ....
Unit labor costs .................... .. ............. ................ .........
Unit nonlabor costs ...................... ... .. ............. .. ...... ... ...
Unit profits ......... ........................... .. ... .......... ................. ..
Unit non labor payments ..... ..... .. ... ... .... ... .. ........ ....... .
lmplii::-it !)rice deflator ... .. ... .. ... .. .. ....... ...... ........ .... ...

56.2
16.2
70.8
27.3
28.8
23.3
50 .2
30.5
29.4

69.8
25.7
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
111.1
111 .6
111.2
112.6
82.2
104.5
108.9

128.7
141.5
112.7
111 .0
110.3
113.0
95.4
108.3
109.6

133.7
147.3
114.6
110.8
110.1
112.9
114.6
113.3
111.2

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
147.1
117.1
99.8

154.7
159.6
124.2
103.2

-

-

-

-

-

-

Manufacturing
Output per hour of all persons .... ... ..... ....... ........ ............
Compensation per hour ......... ... ..... ... ...... ... .. ... .......
Real compensation per hour ......... ..... ... ... .... .. ...... .. .
Unit labor costs ........................ .. .... ..... ... ....... .. ....... ...
Unit non labor payments ...... .... .. ........... ... ........ ..... ....
Implicit price deflator .. .. .... .. .... .... ...... ... ... .. ... .. .... ....
Dash indicates data not available.

140

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February

2005

162.4
165.4
125.4
101 .9

-

51. Annual indexes of output per hour for selected NAICS Industries, 1990-2002
[1997=100)
NAICS

Industry

21
21 1
212
2121
2122
2123

Mining ...... ............................................... ..
Oil and gas extraction ............................. ... .....
Mining, except oil and gas ........... .. ... .. ........ .. ....
Coal mining ........ .... ... ....... .. ...... .....................
Metal ore mining .......... .. .... .......... ...... .... ...... ..
Nonmetallic mineral mining and quarrying ...........

2211
2212

Power generation and supply ... ................ .... ....
Natural gas distribution .................................. .

3111
311 2
3113
3114
3115

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Mining

2001

2002

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
11 7.4
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 confectionery products .....................
Fruit and vegetable preserving and specialty .......
Dairy products ...... ........... .. .... ... .. .... .. ..... .... ....

90.1
89.0
91 .0
86 .4
90.8

89.3
91.2
93.8
89.7
92 .1

90.2
91.1
90.5
90.7
95.4

90.2
93.8
92.5
93.8
93.9

87.3
94.7
94.0
94.9
95.4

94.0
99.1
94.3
97.1
98.7

87.5
91 .3
98.2
98.2
98.0

100.0
100.0
100.0
100.0
100.0

109.4
107.5
104.0
106.8
99.1

109.5
114.2
107.1
108.4
94.5

109.7
112.5
111 .9
109.8
96.0

127.2
117.3
109.9
117.0
96.2

-

3116
3117
3118
3119
3121

Animal slaughtering and processing ........ ...........
Seafood product preparation and packaging ........
Bakeries and tortilla manufacturing ... ... ..... ..... ....
Other food products .... .. .... ... .. .... ...... ... .... .... .. ..
Beverages .... .... . .... ....... .... ... ........................

94.5
117.5
92.6
91 .9
86 .5

96.8
112.0
92 .3
93.5
90.1

101.5
115.3
95.6
95.9
93.8

100.9
113.9
96.0
102.8
93.2

97.4
114.1
96.7
100.3
97.7

98.5
108.4
99.7
101.3
99.6

94.3
116.2
97.7
103.0
101 .1

100.0
100.0
100.0
100.0
100.0

99.9
117.0
103.8
106.9
98.5

100.3
130.2
105.4
108.8
92.4

101 .9
137.6
105.3
110.2
90.6

102.7
147.3
106.3
103.2
91 .7

-

3122
3131
3132
3133
3141

Tobacco and tobacco products ................. ...... ..
Fiber, yarn , and thread mills ............... .......... .. ..
Fabric mills ..... ... ........ ... ... .. ... ..... ....... .. .... ..... .
Textile and fabric finishing mills .. ..... ..... .. ...........
Textile furnishings mills ....... .. ... ................. ......

81.4
73.9
75.0
81 .7
88.2

77.3
74.7
77.7
80.4
88.6

79.6
80.1
81.5
83.7
93.0

73.7
84.6
85.0
86.0
93.7

89.8
87.2
91 .9
87.8
90.1

97.5
92.0
95.8
84.5
92.5

99.4
98.7
98.0
85.0
93.3

100.0
100.0
100.0
100.0
100.0

98.1
102.2
103.9
100.6
99.9

92.1
104.6
109.8
101 .7
101.2

98.0
102.6
110.2
104.0
106.8

100.0
110.5
109.1
109.7
106.9

-

3149
3151
3152
3159
3161

Other textile product millsv
Apparel knitting mills ........ ...............................
Cut and sew apparel. ........... ............. ...... ........
Accessories and other apparel. ..... ............. ..... ..
Leather and hide tann ing 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
11 4.8
109.7

-

3162
3169
3211
3212
3219

Footwear ..... ......... ... ... ... ... .. .... ............ ..........
Other leather products .... ... ... ... .... ... ..... .. .... .....
Sawmills and wood preservation ..................... ..
Plywood and engineered wood products .............
Other wood products .............................. ........

77.1
102.5
79.2
102.3
105.4

74.?
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

Pulp, paper, and paperboard mills .....................
Converted paper products ... .. .... .... ... ....... ........
Printing and related support activities .................
Petroleum and coal products ............. ..... ... ... ....
Basic chemicals ......................... .. .... ... ... ........

88.5
90.5
96 .6
76.7
91.4

88.1
93.5
95.4
75.8
90.1

92 .3
93.7
101 .3
78.9
89.4

92.9
96.3
100.1
84.5
89.9

97 .6
97.6
98.3
85.6
95.1

102.0
97.2
98.8
90 .1
92 .3

97.6
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
104.9
113.2
118.4

117.5
101 .0
105.6
112.2
111.0

-

3252
3253
3254
3255
3256

Resin, rubber, and artificial fibers ... ... .... ....... .... .
Agricultural chemicals ... ... ... .... ........................
Pharmaceuticals and medicines .................... . .. .
Paints, coatings, and adhesives ........................
Soap, cleaning 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

111 .3
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 ferroalloy 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
95.5
106.5
94.2

100.4
97.0
95.6
108.5
96.4

97 .1
100.1
96.8
106.7
97.1

-

3313
3314
3315
3321
3322

Alumina and aluminum produ ction .. .. ......... ... ... ..
Other nonferrous metal production ....................
Foundries ........ ...... .......................................
Forging and stamping .. ... .. . .. . .. . ... .... . . .. ... .. ... ...
Cutlery and hand tools ...................... .... .... ......

91.9
95.6
85.3
88.6
85.1

93.3
95.8
84.5
86.5
85.4

96.8
98.8
85.8
91 .7
87.2

96.0
101.8
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 .......... .. .........
Boilers, tanks , and shipping containers ... ... .... .....
Hardware ......... .............. .. .......... ... ...............
Spring and wire products .. ..... ... ..... ......... .. .... ...
Machine shops and threaded products ...... .... .... .

87.8
90.4
84.4
85.2
78.8

89.1
:!L.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

Utllltles

Manufacturing


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

Monthly Labor Review

February

2005

-

-

-

-

-

-

141

Current Labor Statistics:

Productivity Data

51. Continued-Annual indexes of output per hour for selected NAICS Industries, 1990-2002
[1997=100]
NAICS

Industry

1992

1993

1994

1996

1997

1998

1999

3328
3329
3331
3332
3333

Coating, engraving, and heat treating metals ... ...
Other fabricated metal products ........... ........ .. ...
Agriculture, construction, and mining machinery
Industrial machinery .. ... ···· ·· ······
··· ·· ········ ········
Commercial and service industry machinery .... .. .

81 .6
86.7
82.8
80.6
91.4

78.1
85.9
77.2
81.1
89.6

86.9
90 .6
79.6
79.5
96.5

91.9
92.1
84.1
84.9
101.7

96.5
95.0
91.0
90.0
101.2

102.8
97.1
95.6
97 .9
103.0

102.9
98.9
95.9
98.8
106.3

100.0
100.0
100.0
100.0
100.0

101.7
102.3
104.2
94.4
107.5

101 .5
100.2
95.0
105.2
111 .2

105.9
100.8
101 .0
129.7
101.4

105.1
98.2
99.5
104.6
94.4

3334
3335
3336
3339

HVAC and commercial refrigeration equipment
Metalworking machinery ... .. ..... .. ................ ......
Turbine and power transmission equipment.. .... .. .
Other general purpose machinery ............ .........

88.8
85.3
85.1
85.9

88.2
82.3
84 .6
85.2

90.8
89.3
81.2
85.1

93.8
89.3
84.8
89.8

97.3
94.0
93.3
91.5

96.6
99.1
92.1
94.6

97.8
98.1
97.9
95.1

100.0
100.0
100.0
100.0

106.6
99.1
106.4
103.2

110.4
100.5
113.3
105.6

108.3
106.4
117.1
113.0

110.8
102.0
130.2
109.4

1990

1991

1995

2000

2001

2002

3341

Computer and peripheral equipment.. ............. ...

14.3

15.8

20.6

27.9

35.9

51.3

72.6

100.0

138.6

190.3

225.4

237.0

3342
3343
3344
3345
3346

Communications equipment.. ......... ............ ......
Audio and video equipment. .. .. . ....... .......... .. ...
Semiconductors and electconic 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

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
10/.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
425 '12

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.U

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 stures ......... .. .. .... ........ .... ....... ....... ..
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

42
42~

142

Monthly Labor Review


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Federal Reserve Bank of St. Louis

February

2005

51. Contlnued--Annual Indexes of output per hour for selected NAICS Industries, 1990-2002
[1997=100]

1990

1991

1992

1993

1994

1995

1996

1997

1998

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
103.0
101 .1

97.6
97.1
100.3
100.8
95.5

100.0
100.0
100.0
100.0
100.0

107.6
101.2
99.9
100.3
95.0

4453

Beer, wine and liquor stores .. .... .. ... ... ...... ........

100.1

100.2

101.0

94.4

92.9

96.2

103.1

100.0

446
447
448

Health and personal care stores ... .. ... .. ...... ...... ..
Gasoline stations .. ... ... ............. ....... .. .. ... ...... ..
Clothing and clothing accessories stores .. .. .... ... ..

92 .0
84.8
69.5

91 .6
85.7
70.5

90.7
88.5
75.3

91.9
92.8
78.9

91.8
96.8
83.3

93.0
99.7
91.2

95.7
99.4
97.9

100.0
100.0
100.0

4481

Clothing stores ...... .... .. ... .... ... ..... .. .... .. ..... ... ...

68.9

71.4

77.1

79.2

81.9

90.1

97.1

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

452
4521
4529
453
4531

General merchandise stores ... .... ... ... ..... ...... ... .
Department stores ..... .. .... ... .. .... ..... ... .... .. ... .. ...
Other general merchandise stores ......... ..... .... .. .
Miscellaneous store retailers ..... .. ............... .... ..
Florists ..... .. ... ........................... ... .. ..... .. ....... .

75.3
84 .0
61 .4
70 .6
75.1

79.0
88.3
64.8
68.0
75.9

83.0
91.6
69.7
74.2
85.1

88.5
95.0
77.8
79.1
91.4

90.6
95.1
82.6
87.0
85.4

4532
4533
4539
454
4541
4542
4543

Office supplies, stationery and gift stores ..... ..... ..
Used merchandise stores .... ... ... ... .. .... .. ...... .....
Other miscellaneous store retailers ............. .. ... ..
Non store retailers ......... ...... ..... .. .. ... ........... ... .
Electronic shopping and mail-order houses ... .. ... .
Vending machine operators ..................... ..... ...
Direct selling establishments ... .... ... .. ........ ....... .

64.6
84.9
79.6
54 .4
43.5
97.1
70.0

66.3
83.1
69.2
55.0
46.7
95.4
67.6

71.5
89.7
74.7
63.4
50.6
95.1
82.1

75.8
88.9
80.5
66.7
58.3
92.8
79.7

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

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

52211

Finance and insurance
Commercial banking ... ....... ... .. ... .. ... .. ... ...........

80.5

532111
53212

Real estate and rental and leasing
Passenger car rental. .. ..... ... ..... .............. ..... ... .
Truck, trailer and RV rental and leasing ........... ....

541213
54181

1999

2000

2001

2002

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

105.8

99.8

111 .1

110.4

111 .8

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

100.0

106.7

113.3

120.9

125.2

132.7

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

92.2
94.7
87.6
89.5
83.5

96.9
98.4
94.3
95.0
96.1

100.0
100.0
100.0
100.0
100.0

105.0
100.6
113.4
108.3
101.2

113.1
104.5
129.8
109.8
117.3

119.9
106.3
145.9
111 .3
116.0

124.2
104.0
162.1
108.4
108.6

130.5
104.7
177.5
115.6
120.7

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
110.4
98.7

140.3
121.4
92.8
169.6
209.8
113.3
110.2

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

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

83.2

83.3

90.3

92 .9

96.0

99.3

100.0

98.0

101 .5

104.2

101.6

103.8

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

Professional, scientific, and technical services
Tax preparation services ..... ..... .. .. .. .......... ....... .
Advert1s1ng 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./

92.1
123.9

7211
122
7221
7222
7223
7224

Accomodatlon and food services
Traveler accommodations ... .. .. ··· ····· ·· ··· ··· ··· ·····
Food services and drinking places .. ... ...... ..... .. ...
Full-service restaurants .... .... ...... ... ... ..... ..... ... ..
Limited-service eating places ... ...... ... .. .. ....... ....
t;pec1a1 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
9/.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./
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

17/.b

NOTE: Dash indicates data are not available.


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

Monthly Labor Review

February

2005

143

Current Labor Statistics:

International Comparison

52. Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data
seasonally adjusted
Annual average
Country

2002

United States ... .. ...
Canada ... ............ .
Australia .. ..... .... ....

5.8
7.0
6.4

2003
6.0
6.9
6.1

2002

Ill

2003

IV

II

I

2004

Ill

IV

5.7
7.0
6.3

5.9
6.9
6.2

5.8
6.7
6.2

6.1
6.9
6.2

6.1
7.2
6.1

II

I
5.9
6.8
5.8

5.6
6.7
5.7

Ill
5.6
6.6
5.6

6.4
5.6

Japan ....... .. ... ... ...

5.4

5.3

5.5

5.4

5.4

5.4

5.2

5.1

5.0

4.7

France .................

8.7

9.3

8.7

8.9

9.0

9.2

9.4

9.4

9.4

9.4

9.4

Germany ...... ...... ..

8.6

9.3

8.7

8.9

9.2

9.4

9.4

9.3

9.2

9.9

10.0

9.1
5.1
5.2

8.8
5.8
5.0

9.1
5.1
5.2

9.0

9.0

5.2
5.1

5.~

8.8
5.6
5.0

8.7
5.8
5.0

8.6
6.2
4.9

8.6
6.6
4.8

ltaly

1

••• •••••••• ••••••••

Sweden 2 • ••••••••••••••
United Kinadom ... ..
1

5.1

4.8

-

6.8
4.8

6.6
4.7

Quarterly rates are for the first month of the quarter.

"Notes on the data" for information on breaks in series. For further

Preliminary data for 2003.

qualifications and historical data, see Comparative Civilian Labor

Force Statistics. Ten Countries, 1959-2003 (Bureau of Labor
NOTE:

Quarterly figures for France and Germany are calculated

Statistics, June 23, 2004), on the Internet at

by applying annual adjustment factors to current published data,

http://www.bls.gov/flsJhome.htm.

and therefore should be viewed as less precise indicators of

Monthly and quarterly unemployment rates, updated monthly, are

unemployment under U.S. concepts than the annual figures. See

also on this site.

144

Monthly Labor Review


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Federal Reserve Bank of St. Louis

February

2005

53. Annual data: employment status of the working-age population, approximating U.S. concepts, 1O countries
[Numbers in thousands]

Emolovment status and countrv

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

131,056
14,400

132,304
14,517

133,943
14,669

136,297
14,958

137,673
15,237

139,368
15,536

142,583
15,789

143,734

144,863

16,027

146,510
16,819

Clvlllan labor force
United States .. .. .. ... ... ... .... ... ........ .. ...... ..... .. .... .. .
Canada ............ .. ...... ..... .... ................. ............ .
Australia ............. ... .. .. ................. .. ... ... .. ... .. ..... .
Japan ............ ............... ................ .... .. .. ... .... ... .

129,200
14,308
8,613

8,770

8,995

9,115

9,204

9,339

9,414

9,590

9,752

16,475
9,907

65,470

65,780

65,990

66,450

67,200

67,240

67,090

66,990

66,870

66,240

66,010

France .......... .. ........ ............................... ... .. ... .

24,480

24,670

24,760

25,010

25,130

25,460

25,790

39,102

39,074

38,980

39,142

39,415

39,754

26,350
39,459

26,590
39,413

22,570

22,450

22,460

22,570

22,680

22,960

39,375
23,130

26,070
39,302

26,730

Germany ........ ... .............................................. .
Italy .... .. .. ....................................................... .
Netherlands .......... ... ......... .. ...... ..... ....... ... .... .... .

23,340

23,540

23,750

23,880
8,330

Sweden ... ....... .............. ...... .... .. ... .................. .
'.J,,it'ld Kingdom ...... ....... .................. .... ... .. ... ..... .

Participation rate

10,092

39,276

7,010

7,150

7,210

7,300

7,540

7,620

7,850

8,150

8,340

8,300

4,444

4,418

4,460

4,459

4,418

4,402

4,430

4,489

4,530

4,544

4,567

28,165

28,149

28,157

28,260

28,417

28,479

28,769

28,930

29,053

29,288

29,490

1

United States ................................................... .
Canada ....... .... .... ......... ... .. .... .. . .... .. .. .. ...... ...... .

66.3

66.6

66.6

66.8

67.1

67.1

67.1

67.1

66.8

66.6

66.2

65.5

65.2

64.9

64.7

65.0

65.4

65.8

65.9

66.0

66.8

67.3

Australia ................ .. ... .......................... ... .... ... .
Japan ............ ..... .. .. .... ............. ........... .... ... ... .. .
France ......... .. .................................... ............ .

63.5

63.9

64.5

64.6

64.3

64.3

64.4

64.4

64.4

64.6

63.3

63.1

62.9

63.0

63.2

62.8

64.0
62.4

62.0

61.6

60 .8

60.3

55.4

55.5

55.4

55.6

55.5

55.9

56.3

56.6

56.8

57.0

57.0

Germany .... .... ....... .. .... ... ...... .. .. ... ... .. ..... ... .... .. .
Italy ..... .. ...... ... ....... .... ........ ... .... .... ............... .. .
Netherlands... ... ... ....... . . . . . . . . . . . . . . . . . . . . . . . . . ........ .

57 .8

57.4

57.1

57.1

57.3

57.7

56.8

56.6

56.6

56.3

56.1

47.9

47.3

47.1

47.1

47.2

47.8

48.1

48.3

48.6

48.8

57.9
64.5

58.6

58.8
64.1

59.2
64.0

60 .8
63.3

62.6

63.7

47 .6
61.1
62.8

64.5
63.8

65.8
63.7

65.0
64.0

64.6
64.0

62.7

62 .6

62.4

62.4

62.6

62.5

62.8
62 .9

62.9

62 .7

62.9

62 .9

Sweden ................... ...... ... ....... ....................... .
United Kingdom .......... .......................... ........... .

Employed
United States .. .. .. .. ... ....... ........ . .. ... .. ... .............. .

120,259

123,060

124,900

126,708

129,558

131,463

133,488

136,891

136,933

136,485

137,736

Canada .... .. ... ... ..................... ...... ..... .. .. ... ... .... .
Australia .... .. .. .. ........................................ .... ... .

12,770

13,027

13,271

13,380

13,705

14,068

14,456

14,827

14,997

15,325

15,660

7,699

7,942

8,256

8,364

8,444

8,618

8,762

8,989

9,091

9,271

9,481

Japan ......... ... ... .. .. ... ... ....... ..... .... ...... .............. .
France ................................................... ..

63,810

63,860

63,890

64,200

64,900

64,450

63,920

63,790

63,470

62,650

62,510

21,710

21 ,750

21,960

22,040

22,170

22,600

23,050

23,690

24,140

24,280

24,250

Germany ...... .. .. ...... .. ... .. ........ .. ........ ... ... .. .. ...... .
Italy .. ... ... .. .......... .. .... .... .. ... .. ... ..... .. .
Netherlands ................................................ ..... .

35,989

35,756

35,637

35,508

36,061

36,042

36,236

36,350

20,270

19,940

35,780
19,820

19,920

19,990

20,210

20,460

20,840

21,270

36,018
21,580

21,790

35,615

6,570

6,660

6,730

6,860

7,160

7,320

7,600

7,910

8,130

8,070

8,010

4,028

3,992

4,056

4,019

3,973

4,034

4,117

4,229

4,303

4,310

25,242

25,429

25,718

25,964

26,433

26,696

27,048

27,350

27,570

27 ,768

4,303
28,011

United States .. ... ... .. ...... ... ........................ .... .... .

61 .7

62 .5

62.9

63.2

63.8

64.1

64.3

64.4

63.7

62.7

62.3

Canada .............................. .... ........................ .
Australia ... .... ....... ....... .... ........ ... ... ... ............... .

58.5

59.0
57.8

59.4
59.2

59.1

59.7

60.4

61 .3

62.1

61 .9

62.4

59.3

59.0
61 .0

59.3
60.2

59.6

60.3

60.1

63.0
60.7

59.4

49.0

49.7

50 .3

59.0
51.4

58.4
52 .0

60 .3
57 .5

Sweden .... ........................... ......... .................. .
United Kingdom ... ... .... .. ........... ..... ................... ..

Employment-population ratio

2

56.8
61 .7

61.3

60.9

49.1

49.0

49.1

60 .9
49.0

53.2

52.6

52.4

52.0

51 .6

52 .3

52.0

52.2

Italy .............. ... .............. .. ..... .. .. ...... .. .. ...... .... .
"'etherlands ... .................... ... ....... . .... ..... ........ ..
Sweden .......................................................... .

43.0

42.0

41 .5

41.6

41.6

41.9

42.3

54.2

54 .6

54.9

55.7

57.8

58.7

58.5

57.6

58.3

57 .7

56.9

United Kingdom ......... ... .............. .............. ..... . .

56.2

56.5

57.0

57.4

58.2

United States ......... .. ........... ............... .. ... .. ....... .
Canada ..................... ......... .. .............. .. .... .. .

8,940

7,996

7,404

7,236

1,539

1,373

1,246

1,289

Australia ................... ... ..... .............. . ............ ... .
Japan .................... ...... ...... ... ... .. .. .... .............. .

914

829

739

1,660

1,920

2,770
3,113
2,300

Japan ................................................. .
France .................. ...... .... .... ... ... .. ... .... ... .. ....... .
Germany ........ ....... .... ..... .... .... .. .. .... .. ... ... .. ...... .

57.1

52.0

51 .7

52.2

51.5

50.9

42.9

43.6

44.1

44.6

62.6

64 .2

60.1

60.5

63.2
60.7

62 .1

57.6

60.6
58.4

58.6

59.1

59.4

59.5

59.6

59.8

6,739

6,210

5,880

5,692

6,801

8,378

8,774

1,252

1,169

1,080

962

1,031

1,150

1,159

751

759

721

652

602

661

636

611

2,100

2,250

2,300

2,790

3,170

3,200

3,400

3,590

3,500

2,920
3,318
2,510

2,800
3,200
2,640

2,970
3,505
2,650

2,960
3,907
2,690

2,870
3,693
2,750

2,740
3,333
2,670

2,380
3,065
2,500

2,210
3,110
2,270

2,310
3,396
2,160

2,480
3,661
2,100

440

490
426
2,716

480
404

370
445
1,985

300
368
1,783

250
313
1,721

240
260
1,580

210
227
1,483

230
234

2,439

440
440
2,297

320

416
2,916

1,520

4.7
6.4

5.8
7.0

6.0

60.3

Unemployed

France .. .. ...... ... ... ... ...... ........ ... .. ......... .. ...... ... .. .
Germany ... ....... .... .... .. ....... .... ..... .. .... .
Italy ............. ....... ... ............................... .. .. .. ... .
Netherlands ..................................................... .
Sweden ............... .. ..... .. ..... ...... ...... .. .
United Kingdom ..................................... .

264
1,479

Unemployment rate
United States .......................................... .
Canada .......................................................... .
Australia ... .. ....... .... .......... ... .. .............. ....... ..... .
Japan ... ..... ........ ... ..... .. .... .... ................. ....... ... .
France ................................................. .... .. ..... .
Germany .. ..... .... .. ............................................ .
Italy .... .. .. .... .. .... ....... ... ........... ... ... .. ...... .......... .
Netherlands ... .... .. ........................... ....... .......... .
Sweden ....... ....... ..... ... ........ .... .. ...... ................ .
United Kingdom .................................... .
1

6.9

6.1

5.6

5.4

4.9

4.5

4.2

4.0

10.8

9.5

8.6

8.8

8.4

7.7

7.0

6.1

10.6

9.4

8.2

8.2

8.3

7.7

6.9

6.8

6.4

6.1

2.5

2.9

3.4

4.7

5.1

5.4

5.3

11 .8

3.4
11.8

4.1

11 .3

3.2
11 .3

6.3
4.8

11.3

10.6

9.1

8.4

8.7

9.3

8.0

8.5

8.2

10.2

11.2

6.3

9.9

9.3

8.5

7.8

7.9

8.6

9.3

11 .8

9.0
11 .7

11 .9

12.0

11 .5

10.7

9.6

9.1

8.8

6.9

6.7

6.0

4.9

3.9

3.2

2.9

2.5

2.8

3.8

9.4

9.6

9.1

9.9

10.1

8.4

7.1

5.8

5.0

5.1

5.8

10.4

9.6

8.7

8.1

7.0

6.3

6.0

5.5

5.1

5.2

5.0

Labor force as a percent of the working-age population .

2

Employment as a percent .of the working-age population.
NOTE: See "Notes on the data" for information on breaks in series.


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

11 .9

6.9

For further qualifications and historical data, see Comparative Civilian Labor Force Statistics,

Ten Countries, 1959-2003 (Bureau of Labor Statistics, June 23, 2004) , on the Internet at:
http://www.bls.gov/fls/home.htm.

Monthly Labor Review

February

2005

145

Current Labor Statistics:

International Comparison

Table 54. Annual indexes of manufacturing productivity and related measures, 15 economies
[1992

= 100]

Measure and economy

1960

1970

1980

1990

1991

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Output per hour
United States .. .. .. .... ...... ..... ......
Canada .. . ..... ................ ........ ...

37.8

0.0
54.9

70.5
72.9
69.5
63.6

97.9
95.3
96.4
99.0
91 .6
96.5
99.1
100.3
97.0
98.3
96.1
99.0
98.2
95.5
93.9

102.1
105.8
106.1
101 .7
108.5
102.8
102.5
100.2
101.0
101.8
101.2
102.0
99.6
107.3
103.8

107.3
110.8
104.9
103.3
118.2
106.7
108.4
112.6
108.9
109.6
104.8
113.1
99.6
117.8
108.0

113.8
112.4
105.8
111 .0
129.3
115.1
113.2
112.5
114.4
112.3
107.9
117.3
100.7
124.5
106.2

117.0
109.7
113.6
116.1
142.3
123.1
116.3
109.8
114.7
114.7
108.3
119.3
102.5
129.5
105.4

121 .3
113.5
115.2
121.0
160.4
129.3
125.5
118.0
121.7
120.4
110.3
121.4
102.0
141.0
106.9

126.5
115.5
118.5
121 .2
178.8
135.9
126.9
117.4
127.9
122.0
110.8
124.1
99 .9
149.5
108.4

132.8
122.1
119.9
126.7
198.9
143.4
125.5
123.1
133.0
121.4
110.6
127.0
103.6
162.7
113.6

143.5
129.3
128.0
135.9
215.8
151.0
130.8
126.6
142.5
127.0
113.5
132.7
106.6
175.5
121 .0

145.2
127.0
132.4
135.9
214 .3
160.8
132.6
127.2
148.0
127.8
114.0
132.5
109.8
170.3
125.1

160.0
130.5
136.2
139.9
235.2
170.9
141.7
131.3
155.1
131.0
112.1
135.4
111.7
185.6
127.7

171 .0
132.1
140.7
146.2
256.4
177.2
146.2
136.9
158.0
134.4
110.9

1(13.5
105.9
103.8
96.3
105.4
102.4
97.0
97.0
95.7
92.4
96.5
97 .7
101.7
101.9
101.5

111.1
114.1
109.1
94.9
116.8
108.5
101.4
107.3
100.3
95.1
102.4
104.5
104.6
117.0
106.2

118.4
119.6
108.7
98.9
129.9
114.9
104.2
112.6
104.9
95.2
107.2
108.2
107.3
131.9
107.8

121.3
119.6
112.6
103.0
138.3
120.3
105.9
107.7
104.6
92.5
105.4
108.9
110.3
136.4
108.6

127.9
127.7
115.1
106.5
145.0
128.3
112.7
115.9
109.7
95.7
108.8
111 .6
114.2
146.5
110.7

133.1
133.9
118.6
100.2
133.5
132.6
114.4
116.7
115.0
97 .7
110.7
114.9
113.7
158.3
111 .3

138.9
144.9
118.3
101.9
162.6
141.5
114.4
117.9
118.7
95.8
110.3
117.6
113.6
172.5
112.1

147.6
159.2
123.8
109.2
190.2
151 .8
119.9
121.9
124.3
100.1
113.6
122.8
112.8
188.3
115.0

139.6
153.6
123.8
105.5
194.3
143.1
120.4
121.6
128.0
99.9
113.0
121.9
112.3
183.1
113.4

142.9
158.0
128.7
103.4
209. 1
152.1
121.6
120.8
129.1
99.6
111.7
121 .0
111.5
190.6
109.9

145.4
157.3
130.2
106.7
219.1
160.9
120.9
121.4
128.5
99.8
110.2
117.6
107.3
194.4
110.3

Australia .. .... .... .... ...... ...... ....... .
Japan .... .... ..... .. .. .. ....... ... ........
Korea ............... ......................
Taiwan ............. .. ..... . ..... .... .....
Belgium ...... .. .... .... ... ......... .... ..
Denmark .... ........ ...... .... ... .. ... .. .
France ....... ..... ... ............... ......
Germany ........ .... .... ...... ... .. .. ....
Italy .............. ......... ... ... .. .. .... ...
Netherlands ........... .... ..... ........ .
Norway ........... .... ................... .
Sweden ........... ...... ... .... ... .. .. ...
United Kingdom ... .. ...................
Output
United States .. . ................. ..... ..
Canada ..... .. .. .. ...... ..... .... .... ... ..
Australia .... ... ........... ...... ......... .
Japan .... .......... ......... .... .. ........
Korea ......... ...... ... ....... .. ........ ..
Taiwan .... .. ..... ....... .... ..... ........
Belgium .. .......... .. ... .... .......... ...
Denmark .. ...... .. .. ...... ... ..... ..... ..
Fran ce ... .. ............ .... ...............
Germany ............... .. . ......... ......
Italy ...... ..... .. ...... .......... .... ......
N&mer1a; •ds ... .. ... .....................
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 ..... ........... ...... .. .... .. ... ....
Korea .. ... ...... ... . ........... .... ... ....
Taiwan .................... ........... .....
Belgium ...... .... ............ .. .... ..... .
De;-; ~:J,i<.. .... .... .. ..... .. ..... ..... ... .
France ........ .... ... .. ... ........ .. ..... .
Germany ...... .......................... .
Italy ...... .... ..... ... .. ... ........ ..... ...
Netherlands ... ........ ...... .. .. ..... ...
Norway .... ... .. .. .. .. ... ..... ........... .
Sweden .................. ........... .....
United Kingdom ........... .............
See notes at end of table.

-

-

13.9

37.7

-

-

-

-

18.0
25.2
19.9
29.2
24 .6
18.8
37.6
27 .3
30.0

32.S
46.3
39.0
52.0
46 .2
38.5
59.1
52.2
43.2

47.6
65.4
83.2
61 .6
77.2
78 .6
69 .1
77.9
73 .1
54.3

96.9
93.4
91.6
94.4
81 .5
88.8
96.8
98.4
93.9
99.0
96.6
98 .7
98.1
94.6
89.2

75.8
83.6
89.8
60.8
29.9
44.0
78.2
94.3
81 .6
85.3
84.4
76.9
104.9
90.7
87 .2

101.6
106.0
104.1
97.1
86.7
90.0
101.0
101.7
99.1
99.1
99.4
99.0
101 .4
110.1
105.3

98 .3
99.C
100.7
102.0
95.0
96.1
100.7
100.7
99.8
102.3
99.3
99.8
99.0
104.1
100.1

107.5
114.6
129.2
95.5

104.8
113.5
113.6
102.9
106.5
101.4
104.3
103.3
105.6
100.1
102.9
100.3
103.4
116.4
118.1

100.4
103.9
104.4
103.1
103.7
99.6
101 .5
100.5
102.9
104.1
103.3
100.8
100.8
109.0 I

101.4
100.1
97.8
94 .7
97.1
99.6
94 .7
96.7
94.7
90.8
95.4
95.8
102.1
94.9
106.6 1 97.7

103.6
103.0
103.9
91 .9
98.8
101 .7
93.6
95.2
92.1
86.8
97.7
92.4
105.0
99.4
98.4

104.0
106.4
102.8
89.1
100.4
99.8
92.0
100.1
91.7
84.8
99.4
92.3
106.6
105.9
101.5

103.6
109.0
99.1
88.7
97.2
97.7
91 .0
98.1
91.2
80.6
97.3
91.2
107.6
105.3
103.1

105.4
112.4
100.0
88.0
90.4
99.2
89.8
98.2
90.2
79.5
98.6
91.9
112.0
103.9
103.5

105.2
115.9
100.1
82 .7
74.7
97 .6
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

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

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

85.0
119.1
92 .5
73 .0
85.4
90.8
82.7
88.7
81 .3
74.3
99 .3

90.8
88 .3
86.3
90.6
68.6
85.2
90.1
93.5
90.9
89.4
87.6
89.8
92 .3
87.8
82 .9

95.6
95.0
94.0
96.5
86 .2
93.5
97.3
97.9
96.4
91.5
94.2
94.8
97.5
95.5
93.8

105.6
103.7
104.3
104.7
129.8
111.1
106.1
106.0
106.5
111 .8
106.8
109.0
104.4
99.8
107.3

107.9
106.0
113.2
108.3
158.3
120.2
109.2
108.1
110.4
117.6
111.3
112.1
109.2
106.8
108.8

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

111 .5
109.3
124.6
112.6
200.3
132.4
115.2
116.6
111 .8
125.7
123.0
117.2
118.7
121 .0
115.7

117.4
111.7
128.2
115.4
218.2
140.3
117.0
119.6
112.7
127.6
122.2
122.0
125.7
125.6
123.0

122.0
115.8
133.0
114.8
219 .4
144.3
118.5
127.3
116.6
130.6
124.2
126.0
133.0
130.3
129.9

133.2
119.6
140.0
113.7
234.2
146.6
120.6
130.2
122.8
137.4
127.8
132.0
140.5
136.8
137.6

136.3
123.7
149.5
114.6
241 .7
150.0
127.2
136.5
128.3
142.0
132.5
138.2
148.9
143.8
144.3

145.4
126.8
154.7
122.8
266.1
145.8
136.5
143.2
135.2
145.5
135.7
147.3
157.9
148.8
152.2

157.8
131.4

-

-

33.4

58.9

-

-

10.8

30.7
42.0
27.9
41 .5
23.0
31 .9
57.7
45.9
67.5

39.4
7.0
12.7
57.6
72.7
57.7
70.9
48.1
59.8
91.0
80.7
90.2

92.1
88.3

104.4
107.1

-

-

·-

77.8

1(}1.3

-

-

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

92.4
119.7
113.4
132.5
110.5
107.4
111.2
134.7
124.0
160.5

14.9
10.0

23.7
17.1

55.6
47.5

-

-

-

-

4.3

16.4

58.6

-

-

5.4
3.9
4.3
8.1
1.8
6.2
4.7
4.1
2.9

13.7
11.1
10.5
20.7
5.3
19.4
11 .8
10.7
6.1

146 Monthly Labor Review

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

29.6
52.5
45.1
41 .2
53 .6
30.4
60.5
39.0
37.3
32.0

February

2005

102.7
102.0
105.9
102.7
114.3
105.9
104.8
102.4
103.1
106.4
105.7
104.5
101.5
97.4
104.5

113.5
196.5
134.8

94 .5
98.9
81.9

123.8
290.9
146.7

150.0
139.1
148.9
140.0

164.6
154.3
160.3

Table 54. Continued-- Annual indc-xes of manufacturing productivity and related measures, 15 economies
1960 1970 1980 1990 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001
Measure and economy
Unit labor costs
(national currency basis)
United States .. .........................
Canada ..... .. .... ....... ................
Australia ...... ... ... ..... ........ ........
Japan .....................................
Korea ................................. ....
Taiwan .......... .... ... ... ....... ..... ... .
Belgium ........... .... ..... ........ ......
Denmc1rk ................ ....... .. ... .... .
France ............................ ........
Germany ................ .... ....... .. .. ..
Italy ...................... .... .... ..........
Netherlands .............................
Norway .. .................... .............
Sweden ................... .......... .. ...
United Kingdom .. ......................
Unit labor costs
(U.S. dollar basis)
United States ................. ..........
Canada ...................................
Australia ................... ............ ...
Japan .....................................
Korea ...... ... ............ ..... .. .........
Taiwan .................. ............. ....

-

-

26.4

31 .1

-

-

-

31 .1

43.6

92.1

-

-

23.8
41.7
23.9
26.8
39.8
11.4
50.4
20.0
20.6
14.1

62.2
80 .3
54.2
67.0
69.4
38.7
87.6
50.0
51 .0
59.0

-

-

32 .9

36.0

78.8
67.4

30 .1
15.3
21.7
27.8
7.2
32.9
12.6
15.0
9.8

-

-

-

11.0

15.4

51.5

-

-

14.9
27.0
Belgium .................................. 19.4
19.~
Denmark ...... ........... ................ 13.4
25.7
France .............. ...................... 23.4
17.1
Germany ..... ..... ................. .... .. 10.4
22.3
Italy ........ ............ ........ .... .. .. ... . 14.3
24.5
Netherlands ......... .. .............. .... 15.3
17.4
Norway ................... ...... .......... 11.0
23.1
Sweden .................................. 16.9
19.1
United Kingdom ...................... .. 15.6
NOTE: Data for Germany for years before 1991 are


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

78.8
65.2

43.4
88.3
58.1
83.9
59.6
55.7
77.5
62.9
70.2
77.6
for the

93.7
94.6
94.2
95.9
84.2
95.9
93.0
95.0
96.8
90 .3
90 .7
91.1
94.2
92 .9
93.0

100.6
97.6
96.4
99.6
99.8
97.5
101 .0
97.5
105.4
94.1
103.0
96.8
i
102.3
98.1
102.2
97.6
102.0
99.3
104.5
93.1
104.5
98.0
102.4
95.7
101.9
99.2
90.8
100.0
100.7
100.0

98.5
93.6
99.4
101 .4
109.8
104.1
97.9
94.2
97.8
102.0
101.9
96.4
104.8
84.7
99.4

98.5
100.6
97.6
93.7
82.8
105.1
90.3
98.0
98.9
92.3
103.3
100.1
125.8
115.3
91.8
83.9
106.8
102.6
100.3
93.0
99.0
98.1
91.1
89.7
94.2
95.1
92.3
89.5
95.1
89.4
92.0
92.7
93.4
95.3
94.1
93.1
98.2
98.7
87.5
87.3
77.9
81.8
97.3
93.3
93.2
96.9
90.0
87.9
92.3
89.2
95.0
93.6
64.0
67.8
91.3
96.3
85.6
86.2
100.0
93.9
former West Germany. Data for 1991

94.8
94.3
107.0
97.5
122.4
104.5
96.4
96.1
96.5
104.7
103.2
95.6
108.4
85.8
102.5

93.5
97.5
108.1
94.0
129.6
104.1
95.5
102.8
97.8
107.5
109.8
95.9
110.8
89.0
105.7

91.9
96.2
108.2
93.0
124.9
102.3
91.8
98.8
91 .9
104.5
111.4
96.5
116.4
85.8
108.2

92.8
96.7
108.2
95.2
122.0
103.2
92.2
101.9
88.1
104.6
110.3
98.3
125.7
84.0
113.5

91.9
94.9
110.9
90.6
110.3
100.7
94.4
103.4
87.6
107.6
112.3
99.1
128.4
80.1
114.3

91 .9
92.8
91 .9
93.5
94.8
77.2
78.8
84.0
86.4
83.0
97.3
92.6
109.4
115.1
107.8
101 .0
97.4
92.2
109.5
131.6
68.4
72.7
103.4
126.3
124.3
78.3
77.4
89.5
95.4
99.2
81 .6
80.2
82 .4
99.1
105.2
89.3
91 .7
90.2
107.0
103.6
75.3
79.1
83.3
101.2
102.5
91 .5
92.9
94.0
111 .6
114.2
78.2
76.2
80.6
87.7
78.0
84.3
87.2
87.0
100.0
104.8
102.2
103.5
102.1
106.6
106.4
56.4
61 .5
65.4
77.3
70.0
106.5
104.7
100.4
93.4
91.6
onward are for unified Germany. Dash indicates

2002

2003

90.9
97.2
113.5
87.8
113.1
85.3
96.4
109.0
87.2
111 .1
121 .1

92 .3
99.4

92.8
92.5
109.4
83.6
108.5
97.1
92.2
102.8
86.2
108.1
112.6
99.5
131.9
77.9
113.7

93.9
97.4
112.9
84.4
112.8
93.3
95.9
107.3
86.6
111 .2
116.2
104.3
135.6
84.4
115.4

92.8
75.2
86.5
98.4
75.3
78.1
67.8
76.7
64.2
79.7
66.2
73.3
93.0
49.5
97.6
data not

90.9
93.9
74.8
76.0
84.0
79.4
88.9
88.0
71 .0
68.5
62 .1
69.4
72 .6
68.4
83.5
77.8
66.5
62.6
79.5
83.9
72.9
66.2
82.1
74.5
110.0
93.7
48.1
47.6
101 .4
94.0
available

Monthly Labor Review

108.8
141 .3
80.2
119.2

February

84.7
113.5
82.7

109.6
88.0
110.8
126.2
112.6
144.9
78.6
118.9

2005

92 .3
85.8

92.6
74.7
60.5

100.6
80.4
100.1
90.9
101.7
127.2
56.6
110.0

147

Current Labor Statistics:

Injury and Illness

55. Occupational injury and Illness rates by Industry, 1 United States
Incidence rates per 100 full-time workers3

Industry and type of case2

1989

1

1990

1991

1992

1993

4

1994

4

1995

4

1996

4

1997

4

1998

4

1999

4

2000

4

2001

4

PRIVATE SECTORS

Total cases .. ... ... ... .... ... ... ... .... . . . . . . . . . . . . . . . . ..... ....... ...... ... ....... .
Lost workday cases ......... ................ ....................... ...... .. .............. .
Lost workdays ............................................................................. .

8.6
4.0
78.7

8.8
4.1
84.0

8.4
3.9
86.5

8.9
3.9
93.8

8.5
3.8

8.4
3.8

8.1
3.6

7.4
3.4

7.1
3.3

6.7
3.1

6.3
3.0

6.1
3.0

5.7
2.8

Agriculture, forestry, and fishings
Total cases ........ ... ................... .................. ... ...................... .
Lost workday cases ...................................................................... .
Lost workdays ...... ................................. ... ........ ........... ..... ........... .

10.9
5.7
100.9

11 .6
5.9
112.2

10.8
5.4
108.3

11.6
5.4
126.9

11 .2
5.0

10.0
4.7

9.7
4 .3

8.7
3.9

8.4
4.1

7.9
3.9

7.3
3.4

7.1
3.6

7.3
3.6

Mining
Total cases ......................................................................... .
Lost workday cases ......................... ....................... ......... ... .......... .
Lost workdays ....... .................... ....................... .. ......................... .

8.5
4.8
137.2

8.3
5.0
119.5

7.4
4.5
129.6

7.3
4.1
204.7

6.8
3.9

6.3
3.9

6.2
3.9

5.4
3.2

5.9
3.7

4.9
2.9

4.4
2.7

4.7
3.0

4.0
2.4

Construction
Total cases ........ ............ ......... ........ ... ....... ... ..................... .. .
Lost workday cases ...................................................................... .
Lost workdays ... ... .. ... ...... .. ................ ...... ................................ .. .. .

14.3
6.8
143.3

14.2
6.7
147.9

13.0
6.1
148.1

13.1
5.8
161.9

12.2
5.5

11.8
5.5

10.6
4.9

9.9
4.5

9.5
4.4

8.8
4.0

8.6
4.2

8.3
4.1

7.9
4.0

General building contractors:
Total cases .... ......................................................... .... .... .... .
Lost workday cases ...... ......... ............... ..... ...... .... ..... .. ...... ............ .
Lost workdays ............................................................................. .

13.9
6.5
137.3

13.4
6.4
137.6

12.0
5.5
132.0

12.2
5.4
142.7

11.5
5.1

10.9
5.1

9.8
4.4

9.0
4.0

8.5
3.7

8.4
3.9

8.0
3.7

7.8
3.9

6.9
3.5

Heavv construction . except buildina:
Total cases .... ....... .... ...... ...... ..... .. ..... ....................... ... .... .... .
Lost workday cases ........... .... ......... .............................................. .
Lost workdays .............. ......................... ... ... ... ........................ ......

13.8
6.5
147.11

13.8
6.3
144.6

12.8
6.0
160.1

12.1
5.4
165.8

11.1
5.1

10.2
5.0

9.9
4.8

9.0
4.3

8.7
4.3

8.2
4.1

7.8
3.8

7.6
3.7

7.8
4.0

Special trades contractors:
Total cases ......................................................................... .
Lost workday cases .......... ........... ...................... ........................... .
Lost workdays ............ ... .................................... ... .. .... ......... ........ .

14.6
6.9
144.9

14.7
6.9
153.1

13.5
6.3
151 .3

13.8
6.1
168.3

12.8
5.8

12.5
5.8

11.1
5.0

10.4
4.8

10.0
4.7

9.1
4.1

8.9
4.4

8.6
4.3

8.2
4.1

Manufacturing
Total cases .. ................ .......... .......... ...... ... ............... .. .. .... ... .
Lost workday cases ... .......... ...... ............... .................................... .
Lost workdays .. .... .. ..... ............................... ......................... .........

13.1
5.8
113.0

13.2
5.8
120.7

12.7
5.6
121.5

12.5
5.4
124.6

12.1
5.3

12.2
5.5

11.6
5.3

10.6
4.9

10.3
4.8

9.7
4.7

9.2
4.6

9.0
4.5

8.1
4.1

Total cases ......................................................................... .
Lost workday cases ..... ... .................. ............... .. ........................... .
Lost workdays .......... .... .......... ............. ............................... ....... ...

14.1
6.0
116.5

14.2
6.0
123.3

13.6
5.7
122.9

13.4
5.5
126.7

13.1
5.4

13.5
5.7

12.8
5.6

11 .6
5.1

11 .3
5.1

10.7
5.0

10.1
4.8

Lumber and wood products:
Total cases ....................................................................... .
Lost workday cases .... ............................... ............................. ... .
Lost workdays .......................................................................... .

18.4
9.4
177.5

18.1
8.8
172.5

16.8
8.3
172.0

16.3
7.6
165.8

15.9
7.6

15.7
7.7

14.9
7.0

14.2
6.8

13.5
6.5

13.2
6.8

13.0
6.7

12.1
6.1

10.6
5.5

16.1
7.2

16.9
7.8

15.9
7.2

14.8
6.6
128.4

14.6
6.5

15.0
7.0

13.9
6.4

12.2
5.4

12.0
5.8

11.4
5.7

11.5
5.9

11.2
5.9

11.0
5.7

Stone. clav. and alass 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

Primarv 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
7.9
155.7

17.4
7.1
146.6

16.8
6.6
144.0

16.2
6.7

16.4
6.7

15.8
6.9

14.4
6.2

14.2
6.4

13.9
6.5

12.6
6.0

11 .9
5.5

11.1
5.3

Industrial machinery and equipment:
Total cases ....... .................. .... ... .... ... .. ... ... ..... .... ..... .......... .
Lost workday cases .......................... ..................... .... ...... .... ...... .
Lost workdays .......... .. ............................... .... ..... ... ... ................ .

12.1
4.8
86.8

12.0
4.7
88.9

11 .2
4.4
86.6

11 .1
4.2
87.7

11 .1
4.2

11 .6
4.4

11 .2
4.4

9.9
4.0

10.0
4.1

9.5
4.0

8.5
3.7

8.2
3.6

11.0
6.0

Electronic and other electrical eauipment:
Total cases ....................................................................... .
Lost workday cases ......... ........... ....... .......... ......... ...... .......... ..... .
Lost workdays .......... ... ...... .................. .. ................... .... ............ .

9.1
3.9
77.5

9.1
3.8
79.4

8.6
3.7
83.0

8.4
3.6
81 .2

8.3
3.5

8.3
3.6

7.6
3.3

6.8
3.1

6.6
3.1

5.9
2.8

5.7
2.8

5.7
2.9

5.0
2.5

Transportation eauipment:
Total cases ..... ............. ........... .... ................ ... .... .......... .. ... .
Lost workday cases ................................................................... .
L"S! workdays ................................................. ......... ..... ........... .

17.7
6.8
138.6

17.8
6.9
153.7

18.3
7.0
166.1

18.7
7.1
186.6

18.5
7.1

19.6
7.8

18.6
7.9

16.3
7.0

15.4
6.6

14.6
6.6

13.7
6.4

13.7
6.3

12.6
6.0

Instruments and related Products:
Total cases ....................................................................... .
Lost workday cases ..... ...... .................... .... ... .... ......................... .
Lost workdays ...... ......... ............................................. ........... ... .

5.6
2.5
55.4

5.9
2.7
57.8

6.0
2.7
64.4

5.9
2.7
65.3

5.6
2 .5

5.9
2.7

5.3
2.4

5.1
2.3

4.8
2.3

4.0
1.9

4.0
1.8

4.5
2.2

4.0
2.0

Miscellaneous manufacturina industries:
Total cases ................................. ... ...... ... .... .. .. .. ....... .. .. ... .. .
Lost workday cases ....................... ..... .... ................ ... ................ .
Lost workdays .......................................................................... .

11.1
5.1
97.6

11 .3
5.1
113.1

11 .3
5.1
104.0

10.7
5.0
108.2

10.0
4.6

9.9
4.5

9.1
4.3

9.5
4.4

8.9
4.2

8.1
3.9

8.~ I
4.0

7.2
3.6

6.4
3.2

Durat-ie goods:

Furniture and fixtures:
Total cases .. ........ ...................... .. ..... ...... .... ... ... ................ .
Lost workday cases ............ ... ......... ......... ........ ......................... .
Lost workdays ............. ... .................. ........... ..............................

1

8.8
4 .3

~--~--~--~--~--~--~--~---~--~--~--~--~---

See footnotes at end of table.

148

Monthly Labor Review


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February 2005

1
55. Continued-Occupational inju!'}' and illness rates by industry, United States

Industry and type of case

Incidence rates per 100 workers

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

4

1999

2000 4

2001

4

11 .6
5.5
107.8

11.7
5.6
116.9

11 .5
5.5
119.7

11 .3
5.3
121.8

10.7
5.0

10.5
5.1

9.9
4.9

9.2
4.6

8.8
4.4

8.2
4 .3

7.8
4.2

7.8
4.2

6.8
3.8

18.5
9.3
174.7

20.0
9.9
202.6

19.5
9.9
207.2

18.8
9.5
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

Tobacco oroducts:
Total cases .. ........... ...... ............ .. ........ .. .
l ost workday cases .
..... ..... .... ..... ....... ..
Lost workdays.. .. .... . . . . .. . . ........

8.7
3.4
64.2

7.7
3.2
62.3

6.4
2.8
52.0

6.0
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

Textile mill oroducts:
Total cases ..
Lost workday cases ..
Lost workdays ............. ....... .

10.3
4.2
81.4

9.6
4.0
85.1

10.1
4.4
88.3

9 .9
4. 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

Aooarel and other textile oroducts:
Total cases .
Lost workday cases ..
Lost workdays ........... .

8.6
3.8
80.5

8.8
3.9
92.1

9.2
4.2
99.9

9 .5
4 .0
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
5.8
132.9

12.1
5.5
124.8

11.2
5.0
122.7

11.0
5.0
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 oublishma:
Total cases ..
Lost workday cases
Lost workdays.

6.9
3.3
63.8

6.9
3.3
69.8

6.7
3.2
74.5

7.3
3 .2
74.8

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

7.0
3.2
63.4

6.5
3.1
61.6

6.4
3.1
62.4

6 .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 elastics oroducts:
Total cases ....... .. ..... .
Lost workday cases ............ ..... ..... ....... .... .. .... ..
Lost workdays ... .... .. .

16.2
8.0
147.2

16.2
7.8
151 .3

15.1
7.2
150.9

14.5
6.8
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 workday cases ........... ......... ..... .
Lost workdays ...... .... ........... ... ...... ..... .. .. ..... .... ......... .

13.6
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

9.G

5.5
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

Wholesale and retail trade
Total cases ................................. .. ................. .... ......... .. .. .. .
Lost workday cases
Lost workdays

8.0
3.6
63.5

7.9
3.5
65.6

7.6
3.4
72 .0

8.4
3.5
80.1

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

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:
--:-~!al <.,;ases ..
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

Finance, Insurance, and real estate
... ...... ..... .... ... ... .. ..... ...... ... .
Total cases .......
Lost workday cases
Lost workdays ..

2.0
.9
17.6

2.4
1.1
27.3

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
.9

.7
.5

1.8
.8

1.9
.8

1.8

.9

Services
Total cases .... ..
Lost workday cases.
Lost workdays ... .. .... .... .. .... ... . . ... ... .

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

Paoer and allied oroducts:
-:-01a· :::ases ... ........................ .. .
Lost workday cases
Lost workdays ....... ..... ........ ............ .. ... ........ ... ...

1

Data for 1989 and subsequent years are based on the Standard Industrial Class-

N s number of injuries and illnesses or lost workdays;
EH - total hours worked by all employees during the calendar year; and

ification Manual, 1987 Edition . For this reason, they are not strictly comparable with data
for the years 1985-88, which were based on the Standard Industrial Classification

200,000 - base for 100 full-time equivalent workers (working 40 hours per week, 50 weeks

Manual , 1972 Edition, 1977 Supplement.

per year).

2

Beginning with the 1992 survey, the annual survey measures only nonfatal injuries and

.7

4

Beginning with the 1993 survey, lost workday estimates will not be generated. As of 1992,

illnesses, while past surveys covered both fatal and nonfatal incidents. To better address

BLS began generating percent distributions and the 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


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and

were

calculated

as

(N/EH)

X

200,000,

where :

NOTE: Dash indicates data not available.

Monthly Labor Review

February 2005

149

Current Labor Statistics: Injury and Illness

56. Fatal occupational injuries by event or exposure, 1997-2002
Fatalities
Event or exposure

1

average

Number

Percent

5,915

5,524

100

Transportation Incidents.............................................................. .
Highway incident. ... ... .... ... ....... ... .. .... .. .... .... ...... .. .... ....... ... ............
Collision between vehicles, mobile equipment.. .......................
Moving in same direction .......... ....... ...................... .. .. .... ....... .
Moving in opposite directions, oncoming ....... . ..... .. .. ... ........ ..
Moving in intersection .. .. . ... .... ... ... ... ... ...... .. .. .... .. .......... .... .... .
Vehicle struck stationary object or equipment.. ...... .............. .. .
Noncollision incident. ..... .. .... ... ... .................................................
Jackknifed or overturned-no collision ..... .. .. .. .. ... ... .. .... ... ... . .
Nonhighway (farm, industrial premises) incident... .... ... .. ... .. .... ... .. .
Overturned .. .. .. . .. .... .. ............... .. ........... ...................... ..... .. ... .. ..
Aircraft. ..... .. .......... ..... ... .. .... ... .... ...... .. .. .. ........ .. .... ..... ..... .
Worker struck by a vehicle .. ................ ..... ... .. ..... ... ............. .
Water vehicle .. .. ... .... ... ....... .. .... .. .. .. .... ..... ... .. ... ... .. .............. .. ..... .. .
Rail vehicle .. .. .... .. . ..... .... .. ................................................. .

2,593
1,421
697
126
254
148
300
369
300
368
202
248
382
99
68

2,524
1,409
727
142
257
138
297
339
273
326
158
247
383
90
62

2,381
1,372
635
155
202
145
326
373
312
322

43
25
11
3
4
3
6
7
6
6
3
3
6

Assaults and violent acts ............................................................. .
Homicides ................ .... ..... ... .. ... ... .. ... ................ .. ... ... ..... .. .. ... ..... .
Shooting ......... ....... . .... . ... .. .. .... .. ........ ... .. ... .. .. .... ... ... ..... .
Stabbing ....... .. . .... ... ... .... .............................................. .
Other, including bombing .............................. ... .... ... . ... ... . .
Self-inflicted injuries ... ..... .. .... .. .... .. ........ ............. .. ... .... ..... ........ ....

964
709
567
64
78
221

908
643
509
58
76
230

840
609
469
58
82
199

Contact with objects and equipment... ..................................... .
Struck by object. .. .................... ...... ...... .. .. .... ... .......... .... ... ... ... ... ..
Struck by falling object... ................ .. .. ... ... ... .... .... ... ....... ... .. .... . .
Struck by flying object. .... .... .. ... ... .. .... .... ... .. ... .... .... ....... .... ... ... .
Caught in or compressed by equipment or objects ... ........... .. .. .. .
Caught in running equipment or machinery ..... ... .. .................. .
Caught in or crusherl in collapsing materials ....................... .... .. .

995
562
352
58
290
156
126

962
553
343
60
266
144
122

873
506
303
38
231
110
116

737
111
155
91
61

810
700
123
159
91
84

714
634
126
143
87
63

Exposure to harmful substances or environments........ ........ .
Contact with electric current. ........... ..... .. ..................... ...... .... ..... .
Contact with overhead power lines .. ....................................... .
Contact with temperature extremes .... .. ... ......... .. ..... .. ... ........ ..... .
Exposure to caustic, noxious, or allergenic substances .............. .
Inhalation of substances ..... .. .......... ...... .. ............... .. ............... .
Oxygen deficiency .. ... ... ... .. .... ............... .......... ... .. ... .. ..... ... ... ........
Drowning, submersion .. .. .... ... ...... ..... ... .. .... .. ... .. ... ........ .. ......... .

529
291
134
41
106
52
89
71

499
285
124
35
96
49
83
59

538
289
122
60
98
49
90
60

10
5
2

Fires and explosions ........ .......................................................

197

188

165

3

Other events or exposures3 •••• • ••• •• •• • •• • •• •• ••• ••• •• • •.• . •• ...•..•.• •• ..••.......•.

21

24

13

1

Based on the 1992 BLS Occupational Injury and Illness

Classification Structures.
2

The BLS news release issued Sept. 25, 2002, reported a

total of 5,900 fatal work injuries for calendar year 2001 . Since

654

3

164
192
356
71
64

15

11
8

1
4
16
9
5
4
2
2

13

11
2
3
2

1
2
1
2

Totals for 2001 exclude fatalities from the September 11

terrorist attacks.
3

Includes the category "Bodily reaction and exertion."
NOTE:

Totals

for

major categories

may include sub-

then, an additional 15 job-related fatalities were identified,

categories not shown separately. Percentages may not add

bringing the total job-related fatality count for 2001 to 5,915.

to totals because of rounding. Dash indicates less than 0.5
percent.

Monthly Labor Review

Number

6,036

Falls....................... .. ...................................................................
Fall to lower level. ...... ... .... ... ... ..... .... .................... ...................... .
Fall from ladder .. ... .. .... ............ .......... ... .... ...... ... ... ... ... .. .. .... .. ....
Fall from roof ..... ... .. . ..... .. ...................................... .......... ..... ... .
Fall from scaffold, staging .......... .. . ....... ......... ... .. ... .... ... ... ... ... ..
Fall on same level. ............ ........ .. .. .. ... ... ..... .... .... .. .......................

150

2002

2001

Total. ... ... .. . ............. ........................................................... .


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date

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Productivity and costs

February 3

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