Full text of Monthly Labor Review : August 2005
The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
August 2005
---...
',:-
,,
,,
M
0
T
N
U.S. Department of Labor
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
H
L
y
L
A
B
0
'
R
U.S. Bureau of Labor Statistics
G AND OUTSOURCING
OFFSHORI N
--
U.S. Department of Labor
Elaine L. Chao, Secretary
U.S. Bureau of Labor Statistics
Kathleen P. Utgoff, Commissioner
The Monthly Labor Review (USPS 987- 800) is published
monthly by the Bureau o f Labor Stati sti cs of the U.S.
Departme nt of Labor. The Review welcomes articles on the
labo r fo rce , labo r- ma n age me nt re la t io ns, bus ine ss
co nd itio ns , indu s tr y pro duc ti v it y. co mpe nsati o n,
occ upational safe ty and health, de mographic tre nds, a nd
othe r economic deve lopme nts. Pape rs sho uld be fact ual
and analytical, no1 polemical in to ne . Pote ntial articles, as
we ll as communications on ed itorial matters, sho uld be
submitted 10:
Ed i,v, -111-Chief
Monthly Labor Rei•iew
U.S. Bureau of Labor Statistics
Was hington, oc 202 12
Telephone : (202) 691 - 5900
Fax: (202) 69 1-5899
E-mail : ml r@ bls.gov
Inq uiries on subscriptions and circulation, including address
changes , should be sent 10: S uperi ntendent o l Doc uments,
Gove rnme nt Pri nt ing O ffice , Was hing to n, DC 20402 .
Telephone: (202) 5 12- 1800.
Subscrip tion price per year- $49 do mestic; $68.60 foreign.
Single copy- $ 15 domestic ; $2 1 foreign . Make c hec ks
paya ble 10 the Superintendent of Documents.
Subscription prices and distribution policies for the Monthly
Labor Review (1ssN 0098- 18 I 8) a nd other governme nt
publications are se1 by the Government Printing Office, an
age ncy of the U. S. Congress.
The Secretary of Labor has dete rmined that the publication of
this periodical is necessary in the 1ransac1ion of the public
business required by law of th is Department. Period icals
postage paid at Washington, oc, and at additional mailing
addresses.
U nl e ss stat ed o t he rw ise , a rti cles appea rin g in th is
publication are in the public domai n and may be repri nted
wit hout express perm ission from the Editor-in-Chief. Please
ci te the speci fic issue of the Monthly Labor Review as the
source.
Informati on is available to se nsory impaired individuals
upon request:
Voice phone: (202) 69 1- 5200
Federal Relay Service: 1- 800-877- 8339.
POSTMASTER: Se nd address changes 10 Monthly Labor
Review, U.S. Gove rnment Printi ng Office, Washington, oc
20402-0001.
Cover designed by Bruce Boyd
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
MONTHLY LABOR
REVIEW _ _ _ _ _ _ _ __
Volume 128, Number 8
August 2005
Mass layoff data indicate outsourcing and offshoring work
3
Most relocations involved movement of work within the same company;
but work was moved out of the country in more than a quarter of the cases
Sharon P Brown and Lewis B. Siegel
Restructuring information technology: is offshoring a concern?
11
Employment trends by industry and occupation suggest that offshoring
in the information technogy sector occurs, but not to a great extent
Robert W Bednarzik
Manufacturing earnings and compensation in China
22
China's manufacturing employees averaged about 57 cents an hour,
based on published earnings data and estimates of hours
Judith Banister
Prevalence of weekend work among women in 16 countries
41
Women's dispropcrtionate share of weekend work is evident in the service sector;
in the industrial sector, women are underrepresented among weekend workers
Harriet B. Presser and Janet C. Gornick
Departments
Labor month in review
Precis
Publications received
Current labor statistics
2
54
55
57
Editor-in-Chief: William Parks II • Executive Editor: Richard M. Devens • Managing Editor: Anna Huffman Hill • Editors: Brian
I. Baker, Kristy S. Christiansen, Richard Hamilton, Leslie Brown Joyner • Book Reviews: Richard Hamilton • Design and Layout:
Catherine D . Bowman, Edith W. Peters
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
The August Review
The spatial dimension of employment
dynamics is becoming more important
as the economy becomes more
international and more mobile. Sharon
P. Brown and Lewis B. Siegel map out
the use of the Mass Layoff Statistics
program to follow at least some of the
movement of work. They find that most
relocations occurred within a single
company and that in cases where work
was located outside the United States,
the most common destinations were in
Mexico and China.
Robert W. Bednarzik examines one of
the most visible cases of work relocation,
the movement of jobs for information
technology workers. While he finds that
the volume of work in the field that has
been relocated may not yet be substantial,
job security will still be a concern for
information technology workers.
Judith Banister finishes her two-part
series on employment and wages in
China with a close examination of the
available wage data. After sketching the
analytical difficulties involved, Banister
finds that wages in China's manufacturing sector average somewhere
around 57 cents per hour and that there
are large variations between wages in
urban factories and the town and village
enterprises in more rural areas.
Harriet B. Presser and Janet C. Gornick
explore the ways that the shift toward
service employment and the increasing
labor force participation of women has led
to an increasing share of weekend
employment being carried out by women.
a flexible schedule, only about I in I 0
are enrolled in a formal, employersponsored flexitime program. Workers
in management, professional, and
related occupations were among the
most likely to have a formal flexitime
program. Those in production, transportation, and material moving occupations were the least likely to have
such a program.
Almost 15 percent of full-time wage
and salary workers usually worked an
alternative shift, including: 4.7 percent
on evening shifts, 3.2 percent on night
shifts, 3.1 percent working irregular
schedules, and 2.5 percent working
rotating shifts. The prevalence of shift
work was greatest among workers in
service occupations, such as protective
service and food preparation and
serving. Alternative shifts were least
common among management, professional, and related occupations. See
"Workers on Flexible and Shift
·Schedules in May 2004," USDL news
release 05-1198.
Flexible work and
working shifts
In May 2004, more than 27 million fullti me wage and salary workers had
flexible work schedules that allowed
them to vary the time they began or
ended work. These workers were 27.5
percent of all full-time wage and salary
workers.
Although more than 1 in 4 full-time
wage and salary workers can thus work
2 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
Consumer spending
in 2003
Among the major components of
consumer spending, only apparel and
services, with a 6.2-percent decrease, saw
statistically significant change in 2003.
The trend in the share of total expenditures for apparel and services has been
downward over the last several years,
possibly due to the competition from
cheaper imported clothing as well as a
shift to more casual, less expensive styles.
In contrast, consumer healthcare
spending showed little change in 2003,
rising 2.8 percent, following increases
of7.7 percent in 2002 and 5.6 percent in
2001. Among the components of
healthcare expenditures, spending on
health insurance continued to increase
significantly, with a 7.2-percent rise in
2003, following increases of I 0.1 percent
in 2002 and 7. 9 percent in 2001.
The increase in health insurance
spending in 2003 was offset somewhat
by a 4.2-percent drop in spending on
drugs. The decrease in drug spending
in 2003 followed relatively large
increases of 8.6 percent in 2002, 7 .8 percent in 2001, and 12.6 percent in 2000.
The other major components of
consumer expenditure were little
changed in 2003. Spending on food
declined by less than I percent, transportation spending rose 0.3 percent,
and housing expenditures were up 1.1
percent. See "Consumer Expenditures
in 2003," BLS Report 986.
Contingent workers
Contingent workers are persons who do
not expect their jobs to last or who
report that their jobs are temporary.
Using the broadest definition of
contingency, 5.7 million workers were
classified as contingent in February
2005, accounting for about 4 percent of
total employment. More than half of
contingent workers (55 percent) would
have preferred a permanent job.
In addition to contingent workers,
those workers who have alternative
work arrangements were identified. In
February 2005, there were I 0.3 million
independent contractors (7.4 percent of
employment), 2.5 million on-call workers
( 1.8 percent of employment), 1.2 million
temporary help agency workers (0.9
percent of employment), and 813,000
workers provided by contract firms (0.6
percent of employment).
An employment arrangement may be
defined as both contingent and alternative, but this is not automatically the
case. For example, there were I 0.3 million
people working as independent contractors in February 2005, accounting for
7.4 percent of the employed. Only about 3
percent of independent contractors
considered themselves contingent
workers and fewer than IO percent of
freelancers reported that they would
prefer a traditional job. See "Contingent
and Alternative Employment Arrangements, February 2005," USDL news
release 05-1433.
□
Mass layoff data indicate
outsourcing and offshoring work
Employer interviews revealed that most of the relocations
were domestic, involving the movement of work
within the same company, but work was moved
out of the country in more than a quarter of the cases
Sharon P. Brown
and
Lewis B. Siegel
Sharon P. Brown Is
chief of the Division
of Local Area
Unemployment
Statistics,
Bureau of Labor
Statistics
and Lewis B. Siegel Is a
senior economist In the
same division.
E-mail:
brown.sharon@bls.gov
slegel.lewis@bls.gov
A shorter version was
presented at the
EU-US Seminar
on Offshoring of
Services in ICT and
Related Services,
Brussels, Belgium,
December 13-14, 2004.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
M
ss layoff statistics provide important
nd detailed information on a subset
f establishments experiencing major
job cutbacks and of workers experiencing layoffs
and dislocation. In cooperation with State agencies,
the Bureau of Labor Statistics Mass Layoff
Statistics (MLS) program identifies establishments
that employ 50 or more workers and have at least 50
initial claims for unemployment insurance. State
analysts conduct interviews with employers of
those establishments to identify mass layoff events
that last more than 30 days and to augment the
administrative data with information on the nature
of the layoff itself, including the reason for
separation.
The MLS program provides aggregate data
nationally and by State and selected areas. The
statistics are among the most timely economic
measures issued by BLS. Monthly data on mass
layoff events and laid-off workers (without regard
to duration of the layoff) by State and industry of
the establishment are issued about 3 weeks after
the end of the reference month. Data on extended
mass layoffs (those lasting more than 30 days) are
issued about 7 weeks after the end of the reference
quarter. In addition to providing timely labor market
information, the MLS data are used to identify the
need for employment and training services to
workers and to indicate available labor supply.
BLS has operated the MLS program since 1995.
During this period, the program has been able to
examine the effects of current economic events in a
timely manner through the employer interview. For
example, after the terrorist events of 9/ l 1, the MLS
program added "nonnatural disaster" as a reason
for separation, allowing analysts to identify and
track job loss directly and indirectly associated
with 9/11. Another example is the increased use of
offshoring and outsourcing of work. The MLS
program, particularly the employer interview
component, was determined to be an appropriate
vehicle for collecting information on this economic
phenomenon. After an intensive development
period, questions were added to the MLS employer
interview in January 2004 that identify job loss
associated with movement of work from within a
company to another company, and from the United
States to another country. Beginning in June 2004,
the results of these questions have been published.
MLS program description
The MLS is a Federal-State cooperative program.
BLS is responsible for certain tasks and the States
are responsible for others. For instance, BLS
provides specifications for the program, maintains
quality assurance, reviews and accepts the data,
and publishes monthly and quarterly BLS news
releases. State analysts collect administrative data,
interview employers, develop the data, and publish
State publications.
The MLS program identifies, describes, and
tracks the effects of major job cutbacks. To define
the MLS population, the program uses administrative statistics on establishments covered by
unemployment insurance: laws and on unemployment insurance claimants who previously
worked in these establishments. Data are retrieved
from records created as part of the administration
of the Unemployment Insurance program. These
Monthly Labor Review
August 2005
3
Mass Layoff Data
statistics are augmented by information obtained through the
employer interview.
Administrative data. Administrative data are available in
every State, and provide important socioeconomic information.
For an establishment identified as having conducted a mass
layoff event, administrative data include the State in which the
establishment is located and its detailed industry code. For the
workers who file for unemployment compensation, administrative data include their age, race, gender, location of residence,
and status in the unemployment insurance system. The program
yields information on the individual's entire spell of insured
unemployment, up to the point at which regular unemployment
insurance benefits are exhausted.
The MLS establishment data are the universe of establishments meeting program specifications, and the claimant data are
all claims filed against these establishments. MLS specifications
concerning the size of establishment, number of claims, and timing of filing refine the administrative data to represent an economic event. However, they also limit the scope of the program.
Size specification. Relatively large and concentrated
layoffs are identified through the MLS size limitation on
establishments and the requirement that at least 50 initial
claims for unemployment insurance were filed against the
establishment in a consecutive 5-week period.
Focusing on the subset of establishments employing 50
or more workers means that, according to 2004 data, 4.6
percent of all covered employers and 56.2 percent of covered
employment are in program scope. The size criterion was
determined more than two decades ago, when 5 percent of
establishments and 61 percent of employment were reported
in establishments of 50 or more workers. Since then, smaller
establishments have accounted for a greater share of covered
employment. Layoff activity in these establishments may be
significant, but such actions are not in the scope of the MLS
program.
Reference period for filing. The MLS program specifies that
at least 50 initial claims must be filed in a 5-week period. The
5-week period is used to approximate a "mass" layoff. Once
50 claims are reached, the event is triggered and claims are
allowed to aggregate against the establishment. However, if a
large layoff occurs gradually, the requirement of 50 claims in
a 5-week period may not be reached and the event not identified in the MLS program.
Minimum duration of layoff. The requirement that the layoff
last more than 30 days to be included in the MLS program allows
analysts to focus on more permanent job dislocation, and
significantly reduces program coverage of job loss.
The following tabulation provides the number of mass
layoff events and initial claims for unemployment insurance
4
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
from the private non farm sector, for 2001--04. Note that private
nonfarm mass layoff events are those in which 50 or more
initial claims for unemployment insurance benefits were filed
against an establishment during a 5-week period, regardless
of duration. Extended mass layoff events reflect the
constraint that the layoff had to last more than 30 days.
2001
2002
2003
2004
19,449
7,375
3 7. 9
18,212
6,337
34.8
16,821
6,181
36.7
14,207
5,010
35.3
Mass layoff
initial claimants:
Total............ 2,346,584
Extended ..... 1,457,512
Percent
62.1
of total ......
2,069,713
1,218,143
1,721,985
1,200,81 I
1,464,164
902,365
58.9
69.7
61.6
Mass layoff
events:
Total............
Extended .....
Percent of total
The tabulation shows that most layoff events involving 50 or
more workers last for 30 days or less. On the one hand, by
excluding such layoffs, more than 500,000 workers in 2003 were
out of program scope. On the other hand, more than 1,200,000
initial claimants were identified in extended mass layoffs in 2003.
In 2004, more than 900,000 initial claimants were identified in
extended mass layoffs and about 560,000 were excluded because
the layoff lasted 30 days or less.
Employer interviews. All employers in establishments
meeting the MLS layoff event trigger of 50 initial claims in a
consecutive 5-week period are interviewed. The employer is
first asked whether the separations are of at least 31 days
duration and, if so, information is obtained on the total number
of affected workers, the economic reason for the layoff, the open/
closed status of the worksite, and recall expectations. (See the
appendix for more information on the structure of the MLS
employer interview, including questions asked about the
movement of work.)
The employer interview is conducted via telephone and
largely in an unstructured manner, by trained State employment
security agency analysts. Employer participation in the MLS
interview is voluntary, with a 95-percent response rate in 2004.
The employer is not provided with a copy of the questionnaire
or response options in advance of the interview. From responses
provided by the employer, the analyst codes the information
into standard categories The MLS contained 25 reasons for
separation in 2003; among them were separation for "domestic
relocation" and "overseas relocation."
Movement of work
decided to use the MLS as the vehicle for collecting
additional information on outsourcing and offshoring because
BLS
the employer interview component collects specific information
on the nature of the layoff event, including reason for separation.
In doing so, the following definitions were used.
•
Outsourcing is the movement of work that was formerly
conducted in-house by employees paid directly by a
company to a different company. The different company
can be located inside or outside of the United States. The
work can occur at a different geographic location or remain
onsite.
• Offshoring is the movement of work from within the United
States to locations outside of the United States.
"Offshoring" can occur within the same company and
involve movement of work to a different location of that
company outside of the United States, or to a different
company altogether (offshoring/outsourcing).
Recognizing that the terms "offshoring" and "outsourcing" may
be open to interpretation, BLS chose to approach the data collection by defining these economic actions in terms of "movement
of work." A BLS group, which included members from the BLS
Behavioral Sciences Research Laboratory, crafted the following
two basic questions on movement of work associated with the
layoff event. One pertains to movement within the company and
the other pertains to movement of work to another company
under contractual arrangements:
I. "Did this layoff include your company moving work from
this location(s) to a different geographic location(s) within
your company?"
2. "Did this layoff include your company moving work that
was performed in-house by your employees to a different
company, through contractual arrangements?"
If an employer responded " yes" to either of those basic
questions, then the respondent was asked to indicate the
specific geographic area to which work was moved and the
number of separated workers associated with that action.
Those questions were to be asked when the employer-provided reason for layoff was other than seasonal or vacation,
because such reasons would not have a movement of work
component. (See the appendix for the employer interview.)
Analysts then related the responses to the two questions to
the terms "offshoring" and "outsourcing." Offshoring is measured by an affirmative response to either question I or question
2, when the work moved out of the United States, and outsourcing is measured by an affirmative response to question 2,
when the work moved domestically, out of the United States, or
remained on-site.
As part of the development and implementation of the
movement-of-work questions, BLS conducted a review of the
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
reasons for separation used by the program. In this evaluation,
Bureau analysts recognized that, although "domestic relocation"
and "overseas relocation" were accepted as reasons for separation, these fell short of the requirement that the reason for
separation be an economic one. "Domestic relocation" and
"overseas relocation" actually provide information on the effect
of the economic reason on the establishment, rather than the
reason itself. Economic reasons for these actions can include reorganizing staff to be more efficient, saving costs, or moving
closer to customers. Additionally, before the offshoring and
outsourcing terms were used, respondents volunteered those
reasons, but such responses could not be viewed as representative of the experiences of all MLS-identified layoff events
with movement of work. Therefore, effective with the
implementation of the movement-of-work questions in 2004,
"domestic relocation" and "overseas relocation" were no longer
to be used as economic reasons for separation. Analysts were
directed to probe employers who cite these actions and obtain
the underlying economic reasons for moving work.
Through the expanded employer interview, direct job loss
from offshoring, as well as outsourcing, both domestically and
outside of the United States, can be measured when these job
losses fall within the scope of the MLS program.
It is important to recognize, however, those components
of offshoring that are beyond the scope of the MLS program.
The MLS program does not co11ect statistics from sma11
establishments-those employing fewer than 50 workers . In
establishments employing 50 or more, MLS does not collect
statistics on small layoffs-those of less than 50 workers in a
5-week period. Also, MLS does not collect information when
there is no direct job loss-where employers initiate or
transfer work elsewhere without laying off workers.
Findings
Overview. MLS data have been collected since the second
quarter of 1995. Statistics from the program identified an annual
total of nearly I 7,000 layoff events of 50 or more workers,
affecting more than 1.8 million initial claimants who were
identified each year. Private nonfarm layoff events totaled nearly
15,000 per year, with more than 1.6 million initial claimants.
Considering those events that lasted more than 30 days, the
MLS identified an annual total of 5,400 extended mass layoff
events and more than I million workers from private nonfarm
industries. Mass layoff and plant closing activity peaked in 200 I,
when the MLS identified 7,375 extended mass layoff events
affecting more 1.5 million workers.
In 2004, the program identified 5,010 layoff events from private
nonfarm industries, affecting 993,511 workers. Manufacturing
establishments accounted for more than one-fourth of MLS
activity during the year. Fifteen percent of extended layoff
events in 2004 were permanent closures, accounting for
Monthly Labor Review
August 2005
5
Mass Layoff Data
159,856 workers, and were due to mainly internal company
restructuring. Permanent closures were most numerous in
manufacturing, primarily in food, transportation equipment,
computer and electronic products, and furniture. Reorganization
within the company was most often cited as the reason for
closures in manufacturing.
Employers expected to recall workers in 51 percent of the
mass layoff actions in 2004, which is higher than the 43-percent
recall rate in 2003, and about the ~ame as the 50-percent recall
rate since the data collection began.
Seasonal work continued to be most often cited as the reason
for layoff. Internal company restructuring (bankruptcy, business
ownership change, financial difficulty, and reorganization)
accounted for 20 percent of layoff events and resulted in the
separation of nearly 200,000 workers in 2004.
Movement of work in 2004. The questions on movement
of work were implemented in the employer interview
beginning with layoff events identified in January 2004. Thus
far, quarterly reports on the job loss associated with
movement of work have been issued from first quarter 2004
through second quarter 2005.
As the following tabulation shows, in 2004, employers took
5,010 mass layoff actions that resulted in the separation of
993,.311 workers from their jobs for at least 31 days. Extended
mass layoffs that involve the movement of work within the same
company or to a different company, domestically or out of the
United States, occurred in 366 of all private nonfarm events
excluding those for seasonal or vacation reasons. The events
involving movement of work were associated with the separation
of73,2 l 7 workers-about 11 percent of all separations resulting
from nonseasonal and nonvacation mass layoff events.
Action
Total, private nonfarm sector ........ .
Total, excluding seasonal and
vacation events ........................... .
Total with movement work .......... .
Movement of work actions ....... .
With separations reported ....... .
With separations unknown ..... .
Layoff events
Separations
5,010
993,511
3,222
366
480
382
98
641,519
73,217
55,122
As part of the 366 layoff events, 480 movement-of-work
actions were taken by employers. (The number of movement-ofwork actions exceeds the number of layoff events because
individual mass layoff events may involve more than one
movement of work action. For example, an employer may shut
down a worksite and move the work previously performed there
to two or more other sites.) Employers were able to provide
information on the specific separations associated with the
movement of work component of the layoff in 382 actions, 80
percent of the total for 2004.
6
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
More than 55,000 separations were associated with these
382 layoff actions. (In the remaining 98 movement-of-work
actions, the employer could not provide the number of
separations associated with these actions.) Thus, a range of
55,122 (separations in movement of work actions for which
the employer was able to provide specific detail) to 73,217
(total separations in all layoff events that included movement
of work) is established for separations due to movement of
work in 2004.
Of the broadest measure of layoffs events-the 366 layoff
events that involve some movement of work--63 percent were
permanent closures of worksites that affected 50,348 workers.
This compares with a 15-percent closure rate for all 5,010 layoff
events in 2004.
Internal company restructuring (bankruptcy, business
ownership change, financial difficulty, and reorganization)
accounted for 68 percent of layoff events involving relocation
of work, and resulted in 50,022 separations. (See table 1.) Most
of these were due to reorganization within the company. In
contrast, about 20 percent of all layoff events in 2004 were
attributed to internal company restructuring.
Of the layoffs involving movement of work, about twothirds of the events and separations were from manufacturing
industries in 2004. (See table 2.) Among all private nonfarm
extended layoffs, manufacturing accounted for 29 percent of
events and 26 percent of separations.
The information technology-producing industries (communication equipment, communication services, computer hardware, and software and computer services) accounted for 235
layoff events affecting 40,409 workers in 2004. (See table 3.)
Movement of work was reported in 42 events in these industries,
affecting 10,347 workers. Although these industries accounted
for a relatively greater proportion of movement-of-work events
and separations than for the total, layoff activity in these
industries is markedly lower than in the recent past. Closings
and layoffs within the computer hardware industry peaked in
200 I (503 layoff events and I 02,587 separations). Annual highs
in 2001 were also recorded for software and computer services
(242 events and 36,016 separations) and for communications
equipment ( 140 events and 34,874 workers). Layoff activity for
communications services reached a high in 2002 (176 events
and 32,134separations).
Of the 382 movement-of-work actions reported in 2004 for
which complete information is available, more than 7 in 10 of the
relocations were domestic-270 out of 382-and more than 8 in
10 of those involved moving work within the company. (See
table 4.) More than 1 out of 4 of the relocations were out of the
United States, and. again, most (74 percent) involved the
movement of work within the company. When work was moved
out of the United States, Mexico and China were cited 52 percent
of the time. When work was moved to another company under
contractual arrangements, in nearly 4 out of 10 instances, the
work was moved outside of the United States.
Extended mass layoff events and separations associated with the movement of work by reason for layoff, 2004
Separations
Layoff events
Reason for layoff
Total
Total, private nonfarm .......................... .
Automation .............. ... ...... ........ ..... .... .. .... ...
Bankruptcy ... ................................. ............ .
Business ownership change ..... ................ .
Contract cancellation ................ ........... .... ..
Contract completed .. ...... ........................... .
Energy-related ... ... .... .. .... ........................... .
Environment-related ... ....... .... ... ..... .. ....... ... .
Financial difficulty ...... .... ......... ... ...... ......... .
Import competition .................. .................. .
Labor dispute ............... ............... ........ ....... .
Material shortage .... ......................... .... ..... .
Model changeover ....... ..................... ... .... .. .
5,010
(1)
90
128
111
Natural disaster ......................................... .
Non-natural disaster ..... ..... ... ....... ........... ... .
Plant or machine repair .. .. ......................... .
Product line discontinued ...... ....... ..... .... ....
Reorganization within company ....... .. ....... .
Seasonal work ... .. ..................... ...... ........... .
Slack work ...... ... ..... ... .. .... .... .. .... ...... ........ ...
Vacation period ... .... .... ...................... ... ..... .
Weather-related .. .. .......... .. ...... ... ... ..... ...... ...
Other ................. ........ ........................... .... .. .
Not reported ........... .. .... ....... .. ... ..... .. ..... .... ..
(1)
(1)
19
Movement of work
772
(1)
219
51
31
5
9
35
552
1,678
579
110
62
173
375
' Data do not meet BLS or State agency disclosure standards.
The questions on movement of work were not asked of employers
2
The separation of 16,197 workers were associated with out-ofcountry relocations, 29 percent of all separations related to
movement of work and about 2.5 percent of all extended layoff
separations excluding seasonal and vacation. Domestic relocation of work-both within the company and to other companies-affected 36,246 workers.
Data comparisons
Did some industries experience more layoff events or lay off
more workers than others? Are the characteristics of the workers
laid off from their jobs in establishments that made decisions to
move work any different from those whose employers did not?
Are there geographical differences in layoff events, amount of
separations, and movement of work? The MLS has some data
available to answer these questions.
For the following analysis, the baseline data are from those
employers in extended mass layoff events. Those employers
were asked about the movement-of-work activities. The total
of 3,222 such events in 2004 was split between 366 events ( 11
percent) in which the employer engaged in at least some
movement of work and 2,856 events (89 percent) in which the
employer did not. The total number of workers laid off as a
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
366
(1)
24
9
5
Total
Movement of work
993 ,511
(1)
20,119
30,376
18,398
170,192
73,217
(1)
3,805
1,362
621
25
17
(1)
43,220
8,064
29,935
(1)
2,417
(1)
10
200
(2)
17
(2)
(1)
(1)
2,811
7,143
105,482
334,380
76,643
17,612
7,626
37,513
78,816
1,766
39,700
(2)
3,476
(2)
6,517
3,149
384
56
11,642
when the reason for layoff was either seasonal work or vacation period .
NoTE : Dash represents zero.
result of these events, 641,519, was similarly divided-73,217
or 11 percent in movement-of-work situations and 568,302 (89
percent) without them.
Industry. About two-thirds of the layoff events and worker
separations associated with the movement of work occurred in
manufacturing, particular in transportation equipment, computer
and electronic products, food, and electrical equipment and
appliances. Layoff activity among those employers who did not
engage in any movement of work was also concentrated in
manufacturing, but at substantially lower proportions-about
one-third of the events and one-fourth of the separations.
Transportation equipment and food manufacturing were the most
numerous among total manufacturing separations.
Layoffs in retail trade and in information ranked second and
third, respectively, among movement-of work-related layoffs. In
contrast, establishments in administrative and waste services
(largely in temporary help) and retail trade reported the next
largest layoff activity (after manufacturing) among employers
who had layoffs in which there was no movement of work.
Reason for layoff. Reorganization within the company was
by far the most frequently reported reason for layoff among
Monthly Labor Review
August 2005
7
Mass Layoff Data
Extended mass layoff events and separations associated with the movement of work by industry
distribution, 2004
Separations
Layoff events
Industry
Total
Movement of work
366
993,511
73,217
(1)
246
19
3
9
7
16
3
3
14
8
6,123
2,964
254,427
64,050
4,505
6,140
4,546
11,583
1,873
4,587
5,750
5,764
2,781
63
95
49
189
73
39
9
19
3
5
12
13
27
16
27
21
12
6,566
10,336
11,269
8,217
13,549
9,195
14,979
11,395
40,634
10,761
5,947
1,248
3,501
467
623
2,097
2,035
6,481
4,224
6,223
3,473
2,481
Wholesale trade ........................................ .
Retail trade ........... ................... ....... ............
Transportation and warehousing .. ............. .
Information ................................................ .
Finance and insurance .............................. .
Real estate and rental and leasing .... ... .. ...
Professional and technical services ........ .
Management of companies and
enterprises ...................................... ... .... .
Administrative and waste services .. ........ ..
Edul,dtional services ...... .... .... .. ................. .
Health care and social assistance .. ......... .
Arts, entertainment, and recreation .......... .
Accommodation and food services .......... .
Other services, except public
administration ....................... ........ .......... .
94
344
278
170
158
13
151
15
24
10
17
20
(1)
7
15,908
143,660
59,098
36,593
34,026
3,889
33,199
2,096
5,298
2,090
4,605
3,180
(1)
1,244
21
545
16
284
138
314
(1)
14
(1)
2,832
(1)
3,688
113,288
1,429
44,212
37,687
68,711
88
3
14,906
311
Unknown ............ ....... ...... ... ...... ............. .... ..
6
Total
Total, private nonfarm ......................... .
5,010
Mining ...... ..... ..................... ... ... ..... ............. .
Utilities .......................... ........ ..... ................ .
Manufacturing ............... ...... ................ .. ..... .
Food ............................................ ............ .
Beverage and tobacco products ........... .
Textile mills ................... ... .... ...... .. ...... .. ... .
Textile product mills .............. ....... ........ .. .
Apparel ...................... ...... .. ................. .... .
Leather and allied products .... ............ ... .
Wood products ............................. ......... .
Paper ...................... ..... ...................... .... ..
Printing and related support activities .. .
Petroleum and coal products ...... ........... .
40
13
1,467
310
21
40
26
69
11
38
43
41
21
Chemicals .......................................... ..... .
Plastics and rubber products ............... ..
Nonmetallic mineral products ................. .
Primary metal ......................................... .
Fabricated metal products .. ..... .............. .
Machinery ... ............... ............................. .
Computer and electronic products .... ... ..
Electrical equipment and appliance ....... .
Transportation equipment ....... ... .... .... .. ... .
Furniture and related products .......... .. ... .
Miscellaneous manufacturing ..... ... ... ... .. .
48
78
70
49
Movement of work
94
3
(1)
48,183
4,233
314
1,522
1,129
4,102
444
224
1,889
1,473
621
(1)
748
:
1
Data do not meet
BLS
or State agency disclosure standards.
employers having movement of work-about 54 percent of
both events and separations. In contrast, about 12 percent of
the events and separations among employers who did not
move work were attributed to reorganization. Rather, those
employers were more likely to cite contract completion (27
percent of events and 30 percent of separations) or slack
work (20 percent of events and 13 percent of separations) .
Worker characteristics. With respect to gender and age, the
characteristics of the workers in the two groups were not very
different. In both groups, men made up more than half of the laidoff workers, but the share was even larger for cases in which no
movement of work took place (58 percent, versus 53 percent).
8
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
NOTE:
Dash represents zero.
Those workers also tended to be somewhat younger (57 percent
under age 45, compared with 52 percent).
Geography. Across the four census regions, almost two-thirds
of the mass layoff events and separations among "movementof-work employers" took place in the Midwest and the South,
more than one-fifth in the West, and about one-seventh in the
Northeast. In contrast, slightly more than half of the movementof-work events and separations were in the Midwest and South
and a little less than half were in the Northeast and West.
Forty-four percent of movement-of-work-related layoff
activity occurred in California, 111inois, North Carolina, and
New Jersey in 2004. In mass layoffs in which there was no
Extended mass layoff events and separations in information technology-producing industries, private
nonfarm sector, 1996-2004
Information technology-producing industries'
Total extended
mass layoffs
Computer
hardware2
Year
Layoff
events
Layoff
events
Separations
4,760
4,671
4,859
4,556
4,591
7,375
6,337
6,181
5,010
948,122
947,843
991,245
901,451
915,962
1,524,832
1,272,331
1,216,886
993,511
503
303
196
76
17,884
11,934
36,069
22,557
18,805
102,587
59,653
32,689
11,524
366
73,217
18
4,618
Separations
Software and
computer services3
Communications
equipmen~
Layoff
events
Layoff
events
Separations
Separations
Communications
services 5
Layoff
events
Separations
Total
1996 .......................
1997 .......................
1998 .......................
1999 ............... ... .....
2000 .......................
2001 ... ... .... .... ........ .
2002 .......................
2003 .......................
20046 ••••.••• . •• . •••. ...•..
100
64
166
103
66
62
10,724
3,206
4,056
5,194
16,774
36 ,016
22,382
16,230
9,732
9
2,626
20
25
23
29
70
242
162
100
33
27
25
140
112
62
16
5,323
2,515
6,971
4,344
4,618
34,874
23 ,236
10,408
1,887
18
25
18
24
136
176
113
81
6,612
3,237
4,150
3,930
4,048
30,084
32,134
21,721
17,266
5
608
10
2,495
32
23
33
Movement of work
20046
•••••.•••• •. ••••• .••.•
1
Information technology-producing industries are defined in Digital
Economy 2003, Economics and Statistics Administration, U.S. Department
of Commerce.
2
The industries included in this grouping, based on the 2002 North
American Industry Classification System (NA1cs}, are: semiconductor
machinery manufacturing; office machinery manufacturing; electronic
computer manufacturing; computer storage device manufacturing; computer
terminal manufacturing; other computer peripheral equipment manufacturing;
electron tube manufacturing; bare printed circuit board manufacturing;
semiconductors and related device manufacturing; electronic capacitor
manufacturing; electronic resistor manufacturing; electronic coils,
transformers , and inductors; electronic connector manufacturing; printed
circuit assembly manufacturing; other electronic component manufacturing;
industrial process variable instruments; electricity and signal testing
instruments; analytical laboratory instrument manufacturing; computer and
software merchant wholesalers; and computer and software stores.
3
The industries included in this grouping, based on the 2002 North
American Industry Classification System (NA1cs), are: software publishers;
movement of work, 45 percent of the events and 50 percent of
the worker separations were in businesses that were located
in California, Florida, Pennsylvania, and New York.
Data collection continues
MLS data collection, including the specific movement of work
questions for employers, continues. As we, at BLS, receive
additional quarters of information on extended mass layoffs
with domestic and out-of-country relocations, we will be able
to learn more about this activity and provide more information
to the public.
During the first year of movement-of-work data collection,
employers could not provide specific information on job loss
associated with the movement of work in 98 instances-about
20 percent of all actions. BLS is continuing to explore ways to
obtain the actual numbers for this question.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Internet service providers ; Web search portals; data processing and related
services; computer and software merchant wholesalers; computer and
software stores; custom computer programming services; computer systems
design services ; computer facilities management services; other computer
related services; office equipment rental and leasing ; and computer and
office machine repair.
4
The industries included in this grouping, based on the 2002 North
American ndustry Classification System (NA1cs) , are: telephone apparatus
manufacturing; audio and video equipment manufacturing; broadcast and
wireless communication equipment ; fiber optic cable manufacturing; software
reproducing; and magnetic and magnetic and optical recording media
manufacturing.
5
The industries included in this grouping, based on the 2002 North
American Industry Classification System (NA1cs), are: wired telecommunications carriers ; cellular and other wireless carriers ; telecommunications
resellers; cable and other program distribution; satellite telecommunications;
other telecommunications; and communication equipment repair.
6
Preliminary data.
First, BLS conducted a cognitive reinterview of a sample of
establishments, not only with the events identified with
movement of work, but also from the general MLS population
as well. The purpose of the reinterviews was to gauge whether
or not the respondents understood the movement-of-work
questions as they were intended. The results have indicated
that respondents do understand the questions and this allows
us to be confident about the data that are being collected on
layoff events.
Second, these reinterviews have led us to conclude that the
typical respondent who may be the best source to provide
information on other aspects of the layoff, may not be the best
person to answer the questions relating to the movement of
work. Rather, a management official higher in an organization's
chain-of-command would be more likely to know the details
of the business decisions to outsource or offshore jobs (or
both). Thus, we have instructed our State partners to ask the
MLS
Monthly Labor Review
August 2005
9
Mass Layoff Data
• 1 • • • 11
=---••
Relocations of work actions by employers, 2004
Action
Layoff actions
Separations
Total, private nonfarm sector, excluding seasonal
and vacation events, with movement of work .....
By location:
Out of country .......... ................................. .. ....... .
Within company ..... .... ............... .. ... .. .. ....... .. ....
Different company .. .... ....... ....... ... ... .. ..... ... ......
Domestic relocations .. ..... .... ... .. .... ... ........... ..... ... .
Within company ... .............. ............................ .
Different company ........ ........... ............... ....... .
Unable to assign ..... .. ......... .. ..... .... .... ..... ...... .. .... ..
382
55,122
103
76
27
270
228
42
9
16,197
12,905
3,292
36,246
30,769
5,477
2,679
By company:
Within company ..... ......... .. .......... .... ........... ... ... ... .
Domestic .............................. ....... ..... ..... .... ... ...
Out of country ..... .. .. ......... ..... ........................ .
Unable to assign .......... ................................ ..
Different company ... ...... .. ..... .... .... ............... ........ .
Domestic ....... .... .. ... .... .. ..... .... .. ..... ... .... ........... .
Out of country .. ... .... .... ........ .... ... .................. ..
Unable to assign ....... ..... ........ ..... .... ....... .. ..... .
312
228
76
8
70
42
27
1
45,700
30,769
12,905
2,026
9,422
5,477
3,292
653
movement-of-work questions of someone else in the
establishments that are having extended mass layoffs.
And third, BLS will undertake an in-depth review of the
reasons for separation used in the MLS program. Are they
Appendix:
MLS employer interview including offshoring and outsourcing questions
The analyst has the following information on a potential layoff event:
Establishment name
Establishment address
Industry of the company
Number of initial claims filed against the company, weeks in
which the claims were filed , and week in which the event
triggered
Prior layoff history of the establishment
Using the telephone number and contact person, the analyst calls and
asks the following:
Did a layoff in fact occur?
Did the layoff last more than 30 days?
How many people were involved in the layoff?
When did the layoff begin?
What was the (economic) reason for the layoff?
For all reasons other than seasonal and vacation:
I .a. Did this layoff include your company moving
work from this location(s) to a different
geographic location( s) within your
company?
Yes, go to I b.
No, skip to question 2a.
Don't know or refusal , go to question 2a.
b. Is the other location inside or outside of the
U.S.?
l0
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
appropriate as descriptors of economic activity today? Are
we anticipating the reasons why employers take certain
actions? The major thrust will be to ensure that we are focusing on economic reasons for layoffs .
□
August 2005
Inside U.S .: Which State(s)?
Outside U.S.: Which Country(s)
c. How many of the layoffs were a result of
this reduction?
Number inside U.S .?
Number outside U.S.?
2.a. Did this layoff include your company
moving work that was conducted in-house
by your employees to a different
company, through contractual
arrangements?
Yes , go to 2b.
No, proceed with employer interview.
Don ' t know or refusal, proceed with
employer interview.
b. Is that company located inside or outside
of the U.S. ?
Inside U.S.: Which State(s)?
Outside U.S.: Which Country(s)?
c. How many of the layoffs were a result of
moving the work to the different
company ?
Number inside U.S.?
Number outside U.S.?
Is a recall expected?
Will the recall be total or partial (percentage)?
What is the timeframe for possible recall?
Open/closed status of the worksite?
Restructuring information technology:
is offshoring a concern?
Employment trends by industry
and occupation suggest that offshoring
in the information technology sector
occurs, but not to a great extent
Robert W. Bednarzik
Robert W. Bednarzik
Is a visiting professor
at the Georgetown
Public Policy Institute,
Georgetown
University.
E-mail :
bednarzr@
georgetown .edu
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
he immunity from global competition that
U.S. white-collar workers have enjoyed
for so long has seemingly started to vanish. There is an increasing concern the next great
wave of globalization will come in services-in
particular, white-collar services. Numerous articles have described the concerns of computer
programmers, software engineers, and other
workers in the information technology (IT) fieldabout losing their jobs as companies move service jobs overseas to take advantage of lower labor costs. This article discusses restructuring in
the IT sector in the United States and the number
and likelihood of IT jobs moving offshore.
Historically, the U.S. economy and labor market have been marked by change. In the latter
part of the 17th and into the 18th centuries, many
workers began moving off farms to factories as
the 'industrial revolution' began to take hold.
Factory pay was higher, and farming techniques
were improving and getting more mechanized.
Buoyed by an increasing standard of living,
growing labor force participation of women, and
expanding technology, the U.S. economy and
labor force continued to evolve in the 20th century. In terms of job growth, jobs producing
goods were continually outpaced by jobs providing a service. This trend continued, even in many
factory jobs. Often referred to as economic restructuring, these shifts reflect the continued
pressures on farms, factories, and companies to
remain competitive.
Much like these past shifts, the U.S. economy
and labor market seem to be reinventing them-
T
selves again. Service-based companies are hiring workers in other countries to do work previously done by their domestic staff, and manufacturers have been locating plants offshore for the
past 25 years. 1 Now, companies in the IT sector,
typically thought of as a high-wage sector, are
relocating jobs to other countries. Declining
communication costs has opened up the path for
them to take increased advantage of lower wages
abroad in countries such as India and China. This
has raised the issue's visibility because of the apparent shift in 'job losers ' from international
trade: from blue to white collar. For example, a
recent article explored this phenomenon-listing computer programmers, call-center operators, and travel agents as examples of professionals whose jobs might be performed in India or
other countries with large numbers of highly educated workers but with relatively low labor
costs. 2 However, no one has been able to pinpoint precisely how many white-collar jobs have
moved overseas. What is fact and what is fiction
with regard to offshoring? What do we know
and what do we need to know to get a firm grasp
of this phenomenon? This article reviews and
examines the evidence, including recent trends
in the labor market, to answer these questions.
Because there are several definitions of
offshoring and outsourcing, a quick review of
them is provided to distinguish what
offshoring means in this article. This review
includes the composition of the IT sector, another definition that varies widely in the literature. What industries and occupations are
Monthly Labor Review
August 2005
11
Offshoring Information Technology
included? It is also important to establish perspective.
How large is the U.S. IT sector? What is its share of all
jobs and is it getting bigger? That is, what is the base
level of IT jobs ? Employment and unemployment trends
in individual IT industries and occupations are also examined. Several studies have estimated and forecasted the
number of IT-sector jobs that have moved offshore. A synthesis of them is provided.
Definitions-offshoring and IT
Because this article examines the effects of offshoring on
the U.S. IT sector, we must define both what is meant by
offshoring and what exactly the IT sector encompasses.
Perhaps due to the emerging nature of the concept, no commonly accepted definition of offshoring exists. It is often
used interchangeably with outsourcing. Outsourcing typically refers to the practice of one company hiring another
company to perform tasks that used to be done in-house.
If that task is located in another country, it is sometimes
referred to as international outsourcing. For example, if a
car manufacturer buys tires from another domestic firm
(domestic outsourcing) or a firm in another country (international outsourcing) instead of making the tires itself.
The intention here is for the product to be shipped to the
manufacturer for assembly.
Offshoring is a little different. Principally, it refers to
the practice of replacing domestically supplied services
with imported services. Foreign workers are substituted
for American workers while remaining in their country.
However, not all the service these foreign workers produce may be imported back to the United States. They may
also produce services for foreign markets. The key question is to what extent offshoring leads to displacement of
U.S. workers. However, there could be other adverse labor market effects. As output grows abroad, U.S. firms
could recruit workers in the foreign country, which could
lead to decreased domestic hiring. Moreover, market
shares could shrink for U.S.-based companies, as their affiliates in other countries capture more of the market. This
could lead to a negative employment impact on U.S. export industries.
The dynamic aspects of the U.S. labor market are an
important factor. New firms are born, others go out of
business, and existing firms expand and contract on a regular basis. That is, restructuring can be commonplace.
Further impetus to restructure comes from companies trying to become or remain competitive by increasing productivity through the introduction of new technology or
by reorganizing work at home as well as overseas. Finally, we have the natural ebb and flow of the business
12 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
cycle. The recent recession devastated the dot-com and
other high-paying IT jobs. Many of the jobs identified in
the popular press as being offshored are prevalent here.
How can we sort this out to get a reasonable estimate of
offshoring's impact on the labor market? Offshoring of IT
services can lead to job losses due to imports of services
in the United States from foreign suppliers and foreign
affiliates; increased foreign market share by affiliates leading to a decline in U.S. service exports; and decreased
domestic hiring. To quantify these effects, they must be
separated from domestic labor market restructuring, productivity growth, and recessionary impacts.
There are several definitions of the IT sector, ranging
from narrow to broad. The Organization for Economic
Cooperation and Development (OECD), 3 the U.S. Department of Commerce, 4 and the Information Technology Association of America (ITAA) 5 all provide a broad categorization of the IT sector. Other organizations and agencies,
such as the U.S. Bureau of Labor Statistics (BLS) 6 and Global Insight 7 use narrower definitions.
Defining the IT sector presents a challenge because most
IT workers are in non-IT companies. 8 Moreover, there have
IT-sector occupational and
industry definitions
Occupation or industry
Standard Occupation Classification (soc)
Code
1
Computer and information systems managers ....................... .
Computer programmers .......................................................... .
Computer and information scientists ..................................... .
Computer systems analysts ..................................................... .
11 - 3021
15-1021
15-1011
15-1051
Computer hardware engineers ................................................ .
17-2061
Computer software engineers, applications ........................... .
Computer software engineers, systems software ................... .
Computer support specialists .................................................. .
Database administrators .......................................................... .
Network and computer systems administrators ... ................ ...
Network systems and data communications
analysts .................................................................................. .
Computer operators ................................................................. .
Date entry keyers ................................................................ ..... .
Computer, auto-teller and office machine repairers ............. ..
15-1031
15-1032
15-1041
15-1061
15-1071
15-1081
43-9011
43-9021
49-2011
North American Industry Classification
System (NAICS)
Software publishing .......... ............... ..... ........... ... .... ................ .
Computer systems design and related services ...................... .
Internet service providers and web search portals ................ ..
Data processing, hosting and related services ...................... ..
Computer and electronic product manufacturing .................. .
Communications equipment manufacturing ......................... ..
5112
5415
5181
5182
3341
3342
1
2002 Census Bureau classification system introduced into the Current Population
Survey (CPS) in January 2003. Derived from the 2000 soc system.
Employment and hourly average wages in the economy and IT sector by industry, selected years, 1994-2004
[In thousands]
2004
2000
1994
Industry
Jobs
Wages
$14.00
131,481
3,253
$15.67
1,820
248
14.73
14.39
1,326
151
17.28
16.86
261
1,254
194
314
28.48
27.13
25.60
16.97
239
1,148
118
271
36.90
30.14
21 .58
19.95
Wages
Jobs
Wages
114,291
2,805
$11 .32
131 ,785
4,093
1,651
218
12.19
12.13
Software publishing ............................... ....... ......... .
Computer services ... ............... .... .. ..... ................... .
Internet services .................. .. ............ ................... .
Data processing ..... .... ........................................... .
139
531
41
227
20.50
20.39
23.39
13.32
Non-IT .. .................. ...................... ................ ..........
111,486
Total ....................................... .... .. ......... ...... ......... .. .
IT ........................................................................... .
Jobs
Manufacturing IT
Computer equipment manufacturing ..... .... ........ ... .
Communications equipment manufacturing .... ... ...
Services IT
NOTE:
128,228
127,692
Dash indicates data not available.
been major changes in the Government's statistical occupation and industry classification series, making historical comparisons difficult. For these reasons, two definitions of the IT sector are adopted: an occupation-based
one because of the wide spread of IT workers across companies , and an industry-based definition to obtain a longer
historical series. BLS uses an occupational-based definition of the IT sector, which includes the core computerrelated occupations. 9 Global Insight adopts a very similar
definition, citing modeling and also commenting that
"most of the IT software and service occupations that are
offshored tend to fall into the core group definition." 10
Discussions with BLS led to the adoption of the industrybased definition used here. 11 Exhibit 1 on page 12 provides a list of the occupations and the industries encompassed in these two IT-sector definitions. Although both
the occupation and industry classification systems have
recently been revised, BLS has restored the historical series for occupations back to 2000 and for industries back
to 1994. As noted earlier, the reason for having an industry IT definition is to have a slightly longer time series to
examine trends.
The number of jobs in the IT sector now stands at
around 3.3 million, or 2.5 percent of the total number of
jobs. (See table 1.) Prior to the recession in 2001, the IT
sector had more than 4 million jobs and accounted for
more than 3 percent of all jobs. How much of this loss is
due to the business cycle downturn and how much to
offshoring is not really known. Nonetheless, some clues
are provided by digging deeper into the data available.
Because business cycles are more likely to affect manufacturing jobs, while offshoring in the IT sector is more
likely to affect service-sector jobs, the IT sector will be
divided into manufacturing and service jobs. Over the
1994-2004 period, the share of service jobs in the IT sector jumped from 33 percent in 1994 to 50 percent in 2000
and 55 percent in 2004, indicating perhaps that extensive
offshoring is not occurring. Table 2 shows a steady,
gradual shift within the IT sector from manufacturing to
service jobs. Moreover, the lower paying manufacturing
Percent distribution of IT-sector employment in
manufacturing and services, 1994-2004
Year
Manufacturing
Services
1994 ..... ... ..... ..... .... ................... ....... .. .
1995 ·· ·· ················· ····· ············· ··· ····· ····
1996 ··· ·· ·· ············································
1997 ............ ... .. ... ...................... .. ...... .
1998 .................................................. .
1999 ··· ·· ·· ·· ··· ····· ······· ·· ···· ·················· ·· ·
66.6
64.5
62.6
60.1
57.1
52 .7
33.4
35.5
37.4
39 .9
42 .9
47.3
···················································
···················································
........ .. ..... ............ ........ .. ........... .. .
............................... .... .. .... ... ...... .
.. ......... ... ................ ..... .. ..... ........ .
50.5
49 .0
47.8
46.4
45.4
49 .5
51.0
52.2
53.6
54.6
Employment in the IT sector
Technology has contributed to long-term economic growth
in the United States. Information technology's (IT) share
of the U.S. economy doubled between the late 1970s and
the turn of the century. 12 Gaining momentum in the 1990s,
digital technologies and the transformation to a knowledge-based economy led to a robust demand for highly
skilled workers. IT job growth was strong in the 1990s
before tapering off when the 2001 recession took hold.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
2000
2001
2002
2003
2004
Monthly Labor Review
August 2005
13
Offshoring Information Technology
IT-sector employment in manufacturing and services, 1994-2004
Employment
(in thousands)
Employment
(in thousands)
4,500
4,500
Total
4,000
IT
3,500
sector
\
-.
-. -·. .
3,000
-. - .
.
.
. -. -.
-.
_____
.-
. _. _. _ ....
...
4,000
...
3,500
3,000
2,500
2,500
2,000
2,000
1,500
1,500
1,000
1,000
Services
IT
500
500
0
95
1994
0
97
96
98
component accounted for a disproportionate 70 percent
of the job losses from 2000 to 2004. Chart 1 illustrates
the continued downturn in IT-sector employment since the
recession hit, especially in IT manufacturing.
Of course, not all jobs in the industries identified as IT
industries are IT jobs. For this reason, the primary focus
is on our occupational-based definition of the IT sector.
Table 3 confirms the relative magnitude of the IT sector of
just more than 3 percent of the U.S. workforce and its dip
during the recent recession.
The total number of workers employed in IT occupations
was 4.5 million, on average, in 2004. This is somewhat higher
and perhaps more accurate than the estimate based on the
industry-based . definition. More importantly, from an
Percent distribution of employment by IT sector,
2000-04
Sector
2000
2001
IT sector .... .........
Non-IT sector ..... .
3.2
96.8
3.5
96.5
NOTE:
14
2002
3.3
96 .7
Based on occupations in exhibit 1 .
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
2003
2004
3.3
96.7
3.2
96.8
99
2000
01
02
03
04
offshoring standpoint, what is the trend? Are any of the detailed occupational group's employment levels trending
downward? Since peaking in 2001, the total number of workers employed in the IT sector declined through 2003, but held
steady between 2003 and 2004. Losses in the following occupations are mainly responsible: computer programmers;
system analysts; hardware engineers; computer support; network administrators and analysts; computer operators; and
data entry keyers. All of these illustrate continuous employment declines or have not bounced back much from the recent recession. (See table 4.)
Dividing the IT sector into high- and low-wage occupations is revealing. It shows a gradual shift away from lowwage jobs that appears to have started prior to the recent
recession. (See table 5.) Recall that the industry-based
definition of the IT sector showed the same shift. This is
consistent with Mary Amiti and Shang-Jin Wei's findings
that U.S. service outsourcing reduced manufacturing employment by about 0.5 percent a year over the 1992-2001
period, 13-and with the trade theorists' contention that
jobs lost in the United States from offshoring would be
mainly low skilled and low paid. 14 Moreover, the 4.8-percent unemployment rate for IT workers in 2004 was 6.1
■ 1•1•11:..••
Employment in the IT sector, by occupation, 200CH>4
Occupation
Total - IT sector ......................... ..... ...... .................... ..... .... .. ..
Computer and information system managers ....... ... ....... .. .
Computer programmers ..... .......... .............. .. .. ... ... .. ...... ..... .
Computer and information scientist and systems
analysts ....................................... ........ ..... ... ..... .... ............
Computer hardware engineers ..... ..... ........ ........................
Computer software engineers ............................... .............
Computer support special ists ............................ .. ..............
Database adm inistrators .......... .. ...... .. ........... .. ...... .............
Network and computer systems admin istrators ..... .. .. .... ...
Network systems and data communication analysts ... .....
Computer operators .. .... .... ........................................ ... ......
Data entry keyers .. .................................... ............. .... ....... .
Computer auto-teller and office machine repairers ...........
2001
4,718
228
745
4,795
316
689
4,510
323
630
4,494
347
563
4,495
337
564
835
83
739
350
54
154
305
313
632
280
734
100
745
355
66
185
353
324
623
305
682
76
715
353
84
179
328
283
542
315
722
99
758
330
72
176
359
191
581
296
700
96
813
325
94
190
312
191
504
369
percent for those in low-wage occupations and only 3.6
percent for those in high-wage occupations.
Trends in unemployment support the employment figures . This is not always the case because of the dynamism
of labor markets. The employment change between two
time periods is a net figure made up of new employment
entrants as well as workers who lost their job or just quit.
Not all employment losers or leavers become unemployed;
some may retire or leave the labor force for other reasons,
such as to return to school. In the IT sector it does appear,
however, that employment cutbacks have led to increased
jobless ness . The unemployment rate in the IT sector had
climbed to 6 percent in 2003 , before showing improvement in 2004. Moreover, five of the IT occupations that
experienced employment reductions also showed steady
ri sing jobless ness over the 2000- 03 period and only little
or no improvement in 2004--computer programmers, systems analysts, computer support, network analysts, and
data entry keyers. (See table 6.) This could be consid■ 1•1•11=---
Percent distribution of IT-sector by high- and
low-wage occupations, 200CHl4
Year
2000
2001
2002
2003
2004
······ ····· ······························· ·········
..... ..... .... .... ..... .. .... ....... ...... ..... .. ..
.......... ................ ..................... .. ..
....... ................... .........................
.......... .........................................
High-wage 1
Low-wage 2
64.4
66.5
66.9
68.9
69.1
35.6
33.5
33.1
31 .1
30.9
'Computer and information systems managers, computer programmers,
computer systems analysts, computer hardware and software engineers,
network computer system administrators and analysts.
2
Computer support specialists, computer operatives, data entry keyers.
Computer auto-teller and office machinery repairers.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
2004
2000
2002
2003
ered light evidence of offshoring , at least to some extent,
in these specific IT-sector occupations-certainly it rai ses
suspicions. To put the magnitude of this in perspective,
adding the number unemployed in each of the five occupations together yields 149,000 workers. If they were all
employed, it would have reduced total unemployment
from 5.5 to 5.4 percent in 2004.
How can we sort out the recessionary job losse s from
those due to offshoring in the 2000--04 period? Examining
a few of the underlying dynamics of labor market behavior
by looking at labor force flows might be revealing.
Job growth is a combination of new companies opening
for business (births) plus existing companies hiring additional
workers (expansion); this is offset by companies going out of
business (deaths) and companies losing workers through layoffs, quits, retirements, and so forth (contractions). The rate
of gross job creation is the sum of births and expansions as a
percentage of total employment. The rate of gross job destruction is analogously the sum of deaths and contractions
as a percentage of total employment. Over the U.S. postwar
period, gross job creation has exceeded gross job destruction except during recessions. As expected, in the recent
business cycle the rate of job destruction increased during
the recession and then declined during the recovery to its
pre-recession rate. However, the pattern for job creation has
been unusual, or off the typical trend. (See chart 2.) It began
to fall well before the recession and continued to fall during
the economic recovery until turning upward in 2004. That
is, the unusually low rate of job growth in the current expansion stems from a lack of job creation, not from a high rate of
job destruction. Has offshoring played a role in this atypical
trend? To help figure this out, it is possible to examine gross
job creation and destruction rates in the professional and
business services industry, where many jobs are thought to
Monthly Labor Review
August 2005
15
Offshoring Information Technology
Unemployment rates in the IT sector, by occupation, 2000~4
[In percent]
Occupation
Total, IT sector .......................................................................
Computer and information system managers ................... .
Computer programmers ......... ........................................... .
Computer and information scientist and systems analysts .. .
Computer hardware engineers ........................................ ..
Computer software engineers ........................................... .
Computer support specialists ...... ..................... ............... ..
Database administrators ................................................... .
Network and computer systems administrators ............... .
Network systems and data communication analysts ....... .
Computer operators .......................................................... .
Data entry keyers .................................. ................ .............
Computer auto-teller and office machine repairers .......... .
1!!111'
2000
2001
2002
2003
2004
2.7
1.6
2.0
2.3
1.8
1.7
3.4
3.0
1.3
2.8
3.2
5.5
2.6
4.0
3.3
4.0
2.8
2.9
4.2
4.2
2.6
2.1
4.6
4.2
5.8
3.8
5.5
5.6
6.1
4.4
6.5
4.7
5.4
2.9
6.0
4.3
4.9
7.9
5.0
6.0
5.0
6.4
5.2
7.0
5.2
5.4
6.6
5.3
6.5
5.0
7.6
8.3
4.8
4.0
5.8
3.9
2.1
3.3
4.6
2.0
3.4
5.8
3.1
9.0
4.7
Average employment and gross domestic product (GDP) growth in postwar recoveries in the United States
Dates
October 1945 to November 1948 ............................................................... .
October 1949 to July 1953 ........................... .......................... .................... .
May 1954 to August 1957 ...........................................................................
April 1958 to April 1960 ............................... .. ...................... ................ ...... .
February 1961 to Dececember 1969 ......................................................... .
November 1970 to November 1973 .......................................................... .
March 1975 to January 1980 ... ...... ...................... ... .................................. .
July 1980 to July 1981 ... ... ... .......... .. ........................... .... .. ......... ... ............. .
November 1982 to July 1990 .....................................................................
March 1991 to March 2001 ........................................................................ .
November 2001 to February 2005 ............................................... .............. .
Length
(months)
Average
errpoymerl
growth
37
45
39
24
106
36
58
12
92
120
39
178,000
169,000
107,000
158,000
167,000
208,000
244,000
147,000
229,000
200,000
50,000
Average
GDP
growth
(percent)
6.3
3.7
5.4
4.8
4.5
3.9
3.4
4.1
3.5
3.3
Average
productivity
growth
(percent) 1
3.1
1.5
3.9
3.0
2.6
1.7
2.2
2.1
2.2
4.1
1
Average change in each quarter at an annual rate in output per hour in nonfarm business.
be offshored. The same unusual trend prevails. (See chart
3.) Gross job creation in the professional and business services industry also began falling prior to the recession-and
continued to do so until turning upward recently. Thus, jobs
are no longer being lost, but they are also not largely being
created. Several studies have noted the possibility of decreased domestic hiring as an outcome of offshoring. 15 Thus,
it could be assumed that offshoring services contributed modestly to poor employment recovery in the United States.
What is the driving force behind the anemic U.S. recovery? It is instructive to compare the recent recoveries
with past recoveries to see what differences, if any, may
be revealed. Table 7 illustrates the average employment,
gross national product (GDP), and productivity growth in
U.S. postwar recoveries. The number that stands out is
the very weak employment growth in the current recovery
to date, even though GDP growth is only a little below average compared with past recoveries. This requires an explanation-and high productivity growth appears to be
16
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
standing out as part of the answer. Productivity has grown
at an annual rate of 4.1 percent in the current recovery, the
highest ever recorded in a postwar recovery. Why have
firms chosen to respond to higher demand almost entirely
through higher productivity rather than increasing employment? A good analysis of this question is provided by the
Federal Reserve Bank of Boston 16-which believes that
firms are uncertain about current economic growth and
the demand for their products, especially in the short run;
thus, they are reluctant to hire workers. 17 Companies view
further productivity gains as a safer, less costly strategy to
the recent economic growth spawned mainly by monetary
and fiscal policy. 18 Conceivably viewing this growth as
transitory, they meet it with transitory increases in productivity.19 Whether offshoring is also playing a role in
this through reorganizing work by sending it offshore is
unknown. However, an examination of trade flows in services should provide some insights into the involvement
of offshoring in this scenario.
Total private gross job gains and losses, 1992-2004, quarterly, seasonally adjusted
Percent
Percent
9
9
8.8
Gross job gains
8.8
8.6
...
••
8.4
8
••
••. --••
•
•
•••
7.8
7.6
·-·
7.4
7.2
8.4
••
••
••
••
•
•••
•
••
-
8.2
8.6
••
8.2
8
7.8
7 .6
7.4
7.2
6.8
6.6
7
·--,'
7
Gross job losses
6.8
6.6
6.4
6.4
6.2
6.2
6
6
1992
93
94
96
95
98
97
99
2000
02
01
04
03
Professional and business services gross job gains and losses, 1992-2004,
quarterly, seasonally adjusted
Percent
Percent
11
11
••••
•
,• •
•• ••
• •
••• •••
••
•
••
•••
•-,
.
10.5
10
9.5
9
8.5
.
.... . •'. ...:
.
:,..
.... ... . :•:
. ........ .. ·..' ..: '....: -..:.·..
.. ... /' -; ..·
.~:,.. .·l•-:f
•
• • •
•
I
••
•• •:
10.5
10
9.5
•
9
:
...
•
8
8.5
••
•• ••• •
•
Gross job losses
8
7.5
7.5
1992
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
93
94
95
96
97
98
99
2000
01
02
03
Monthly Labor Review
04
August 2005
17
Oftshoring Information Technology
Services that are offshored to other countries could return to the United States as imports. For example, a company hires software engineers in India to develop a new
program to combat Internet viruses. When the project is
complete, the company uses the new program in all its U.S.
domestic facilities. This would be recorded as imports of
services to the United States. Indeed , imports and exports
of private services have been growing. (See table 8.) The
main interest here is the trend in imports of business professional and technical services, which includes computer,
data processing, and other information services. Imports
of business services are rising as a share of total private
services~ this trend is also visible for India and China.
Although the magnitudes of the imports are not large, the
upward trend, especially from India, seems to support the
notion that some offshoring of IT work is occurring.
In summary, offshoring in the IT sector appears to be occurring but not to a great extent. 20 A review of the U.S. literature describes where the offshoring issue has been examined extensively in recent years.
offshoring, due primarily to the new revenue it generates
that flows back in the Nation. 23 Forrester provided the
most widely cited job impact number from offshoring3.3 million jobs lost by 2015. 24 This estimate is consistent with the sentiment in the literature that service
outsourcing, although now very low, has been steadily increasing.25 The focus of this literature review is primarily
on studies exploring the impact of offshoring on U.S. employment and, to a lesser extent, U.S. productivity.
A recent report by U.S. Government Accountability
Office (GAO) concluded that data on offshoring are extremely weak; there is just not much available. 26 With the
exception of BLS data from the Mass Layoff Survey, which
directly measures the magnitude and reasons companies
move work offshore, most of the studies of the employment impact of offshoring use an indirect approach. When
pulling the findings of these studies together, offshoring
appears to have a small employment impact in the aggregate, but certain occupations and industries are hard hit.
BLS surveys companies undergoing large layoffs-50 or
more in a 30-day period-to determine the reason(s) for
the layoffs. Although the survey has been around for a
number of years, BLS only added questions pertaining to
outsourcing and offshoring in 2004. If the reason companies give for the layoffs is other than seasonal or vacation,
BLS asks whether the layoff was due to the company moving work geographically (but keeping it in the same company), and/or moving it to a different company. If work
was indeed moved, a follow-up question is asked: Where
was the work moved? Between January and September
2004, there were only 40,727 separations, of which 26
percent were due to overseas relocations-19 percent
within the same company and 7 percent to a different company. Amiti and Wei found that service offshoring reduced
manufacturing employment by a small amount, but when
What the literature shows
Economic theory suggests that offshoring is likely to provide overall gains to the U.S. economy, but some workers
could suffer negative effects from job losses and/or wage
reductions. The literature appears to bear this out.
Offshoring has generated a number of studies on a wide
range of topics such as its impact on GDP, inflation, trade,
consumers, productivity, wages , and employment. Studies have also addressed the underlying reasons for
offshoring, such as companies seeking cost savings and
revenue growth. Much of the early effort has come from
management consulting firms, most notably McKinsey
Consulting 2 1 and Forrester Research. 22 McKinsey concluded that the United States gets more than it gives from
■ 1• 1 • 1 (~:■
Business professional and technical services share of total private services for selected year and country
[In millions of dollars]
Exports
Country
1998
2000
Imports
2003
1998
2000
2003
All countries - total private services ................ .. .. ..
Percent - business professional and technical
services .............. ... .. ............. .... ....... .. ... ...... .. ..... ..
$244 ,748
18.6
19.4
23.7
13.6
14.7
18.1
India - total private services .... .. ...... .. ...... ... ... ...... ..
Percent - business professional and technical
services .... ............... .. .... ................ .... ..................
$1,880
$2,535
$3,720
$1,542
$1,896
$2,184
10.6
8.6
9.5
8.6
10.9
19.2
China - total private services ..................... ....... ....
Percent - business professional and technical
services .............. ... ..... ... .... ........ ... .................... .. .
$3,958
$5,201
$5,916
$2 .302
$3.268
$3,869
16.0
15.1
12.1
3.1
3.4
3.5
SouRCE:
18
$284,410
$294,080
$166,226
U.S. Department of Commerce, Bureau of Economic Analysis, Survey of Current Business, October 2004.
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
$208,560
$225,216
Estimated employment impact on the IT sector of offshoring
from the literature
Author
Forrester
Global Insight, Inc.
Schultze
Bardhand and Kroll
Bhagwati and others
Estimated annual
employment losses
50,000
34,000
52,000-72,000
500,000
65,000
they aggregated their 45O-industry sample to only 100 industries, the effect disappears. 27 They conclude that increased demand in other industries offset the small declines in manufacturing. 28
A number of papers examined the IT sector. (See exhibit 2.) Despite their varied methodologies and definitions of outsourcing, the overall findings still indicate a
small employment impact. Part of the reason there is an
employment effect at all results from outsourcing's positive effect on productivity, which in turn lowers the employment level needed to produce the same amount of
goods or services. The GAO report, for instance, concluded
that offshore outsourcing could hurt IT employment growth
in the next decade. 29 Using a survey-based approach,
Forrester Research released a follow-up report saying
outsourcing overseas was accelerating, and forecasting
that 542,000 IT-sector jobs could be lost by 2015; this is
about 50,000 per year. 30 Using a micro-simulation approach, Global Insight Inc. estimated the IT sector would
lose (or never create) 34,000 jobs per year as a result of
offshoring. 31 Using import flows in business and professional services, Charles L. Schultze forecasted an aggregate job loss from offshoring of between 52,000-72,000
per year for 2000-03. 32 Using a direct approach, Ashok
Bardhan and Cynthia Kroll developed a list of industries
they felt were "at risk" of outsourcing to India and East
Asia based upon how often they were noted in the media.33 In 2001, the "at risk" group accounted for just more
than 5 percent of total U.S. employment; moreover, they
suffered disproportionate job losses between 2001 and
2003. 34 However, the authors did not acknowledge the
importance of separating the 500,000 per-year employment decline in "at risk" industries into its cyclical and
secular components, given the economic downturn in most
of 2001.
A second strand of literature recently developed in the
offshoring debate. It features a discussion among very
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Methodology
Survey
Micro-simulation
Import flows
At risk
Job growth in India, Ireland, Philippines
well-known economists about whether offshoring between
the United States and countries such as India has changed
our terms of trade. 35 This can be seen when viewing the
role of outsourcing as vertical integration, whereby the
production process is broken into steps , each located in a
different geographical area depending on where it can be
produced at the lowest cost. 36 That is, each step is produced where there is a comparative advantage for that step.
This appears to be happening in IT-sector service functions. Paul Samuelson argues, for example, that tasks such
as computer programming done increasingly in India and
other low-wage countries for U.S.-based companies have
the potential to change the terms of trade by raising the
trading partner's productivity in products they export. 37
Some of the services would be imported back into the
United States. When asked in an interview if importing
offshore services back into the United States would allow
U.S. prices to drop generally to the benefit of consumers,
as does the trade in goods, Samuelson rep}ied, "being able
to purchase groceries 20 percent cheaper at Wal-Mart does
not necessarily make up for the wage losses." 38 In other
words, trade does not always work to a11 parties' advantage, according to Samuelson. 39 Jagdish Bhagwati and
others counter this argument by saying that the domestic
impact of services trade does not apply broadly across the
U.S. economy. 40 They agree with Samuelson that
offshoring can enhance productivity growth, but emphasize, as does Catherine L. Mann, 41 that it will lead to faster
U.S. GDP growth. Moreover, further gains wi11 be garnered
from increases in "intra-industry" trade. 42 Results from a
2001 study concluded that intra-industry trade in the service sector is probably of similar magnitude as intra-industry trade in goods. 43
The trade theorist view of offshoring-as just another
way of doing international trade-predicts job losses in
lower skilled, lower-paid jobs. This appears to be borne
out somewhat by the data presented earlier, although some
Monthly Labor Review
August 2005
19
Offshoring Information Technology
higher-paid service occupations are also suffering losses.
Using data from India, Ireland, and the Philippines,
Bhagwati and others estimate service offshoring to have
cost the United States approximately 65,000 jobs per year,
not far above the previous estimates presented. 44 The debate now turns to whether those service-sector workers
who are displaced by outsourcing will be bumped down to
lower-paying jobs. The conventional view is that trade
replaces bad jobs with good jobs, but does this view hold
for services where some good jobs are indeed being displaced? Some job losers have higher skills that help them
get a new job, but they also demand higher wages that
limit their re-employment possibilities. If service
offshoring does create good jobs, while eliminating others, it would enhance the transition process. There is a
lack of knowledge here. Bhagwati and others think that
service offshoring will create services not previously
available-when using cheaper workers abroad makes an
act1v1ty that uses higher-skilled workers in the United
States financially feasible. 45 On the other hand, Lori
Kletzer concludes that trade does dump some displaced
workers into lower-wage jobs . 46 From 1979 to 1999,
roughly 30 percent of the people who were unemployed
as a result of cheap imports in sectors other than manufacturing had not found jobs a year later.
In summary, most studies find the extent of job losses from
services offshoring relatively small in the aggregate, but
somewhat concentrated in a few industries and occupations.
The job losses stem from both a direct impact of offshoring,
which displaces some workers, plus an indirect impact
through the productivity enhancements that it provides. However, there are still unanswered empirical questions, including the just-mentioned productivity effect. Indeed, offshoring
could raise productivity directly or indirectly by displacing
low-wage jobs and creating high-wage ones, but it could also
do just the opposite.
D
Notes
ACKNOWLEDGMENT: This article is adapted from a paper presented at
the European Union-United States seminar "Offs"horing of services in
ICT and related services" in Brussels on December 13-14, 2004, under
a contract from DTI Associates for the U.S. Department of Labor. However, the author is solely responsible for the statements and conclusions. The author thanks Karen Lynch , Master 's of Public Policy student at the Georgetown Public Policy Institute, for her help in the preparation of this article.
1
An estimated 5 million factory jobs were lost. See Griff Witte, ·'As
Income Gap Widen s, Uncertainty Spreads," The Washington Post, Sept. 20,
2004, p. AO I .
2
Ibid.
3
Measuring the Information Economy (Paris, Organization for Economic Cooperation and Development I0ECD I, 2002).
4
Digital Economy 2003 (Washington, DC, U.S. Department of Commerce, December 2003).
5
ITAA Quarterly Workforce Survey (Arlington, YA, Information Technology Association of America IITAAI, Dec. 18, 2002).
6
See Roger Moncarz, .. Preparing for careers in information technology
is a function of multiple subroutines. Which algorithm will you choose?"
Occupational Outlook Quarterly (Washington, DC, fall 2002); Daniel E.
Hecker, .. High-technology employment: a NAICS-based update," Monthly
Labm Review, July 2005, pp. 57-72; and William Luker, Jr. and Donald
Lyons, ·'Employment shifts in high-technology industries, 1988-96,"
Monthly Labor Re view, June 1997, pp. 12-25.
7
Global Insight, Inc., .. The Impact of Offshore IT Software and Services
Outsourcing on the U.S. Economy and the IT Industry," (Lexington, MA,
March 2004).
8
Moncarz, .. Preparing for careers ... "
9
Ibid.
10
Global Insight, Inc., "The Impact of Offshore IT Software ... "
11
E-mail correspondence with Roger Moncarz, BLS, on Sept. 15, 2004.
20 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
12
Carol Ann Meares and others, The Digital Work Force: Building
lnfotech Skills at the Speed of Innovation (U.S. Department of Commerce,
June 1999), figure I, p. 5.
1.1 Mary Amiti and Shang-Jin Wei, ··service Outsourcing, Productivity
and Employment: Evidence from the US," (International Monetary Fund
[IMF], First Draft, May 2004). Because their sample of 450 industries included only 5 service industries, they did not separate out an impact just on
them.
14
See, for example, Jagdish Bhagwati, Arvind Panagariya, and T.N.
Srinivasan, "The Muddles over Outsourcing," Journal of Economic Perspectives (forthcoming).
15
See, for example, Government Accountability Office (GAO), ..International
Trade: Current Government Data Provide Limited Insight into Offshoring of Services" (Washington, DC, Government Printing Office, September 2004); and Global Insight, Inc., ·1ne Impact of Offshore IT Software ... "
16
Federal Reserve Bank (FRB) of Boston, .. Understanding the ·Job-Loss'
Recovery," Public Policy Brief~· No. 04-1, June 2004.
17
Ibid.
18
Ibid.
19
Ibid.
20
The OECD, using a broader occupational-based definition of the IT sector which represented about 19 percent of total employment, reached a similar conclusion. They concluded that the number of jobs lost to offshoring was
relatively small compared with general job turnover in OECD countries. This
was further supported by a European Union (EU) study that concluded jobs
lost due to offshoring seldom resulted in redundancies. See OECD, Potential
Offshoring of !CT-Intensive Using Occupations, DSTI/ICCP/IE (2004) 19
(Paris, December 2004); and EU, Outsourcing of !CT and related services in
the EU, (Luxembourg, European Foundation for the Improvement of Living
and Working Conditions, 2004).
21
McKinsey Consulting, '•Offshoring: Is It a Win-Win Game?" (San
Francisco, CA, August 2003).
22
John McCarthy, "3.3 Million U.S. Service Jobs to Go Offshore,"
(Forrester Research, November 11, 2002).
23
McKinsey Consulting, "Offshoring: Is It. .. "
24
John McCarthy, "3.3 Million U.S. Service Jobs ... "
25
See, for example, Mary Amiti and Shang-Jin Wei, "Fear of Service
Outsourcing: Is It Justified?" NBER Working Paper No. 10808 (Cambridge,
MA, September 2004).
26
Government Accountability Office (GAO), "International Trade: Current Government Data Provide ... "
27
See Mary Amiti and Shang-Jin Wei, "Service Outsourcing Productivity ... "
28
ibid.
29
Government Accountability Office (GAO), "International Trade: Current Government Data Provide ... "
30
Estimates were determined from a survey of I 00 companies specializing in business process outsourcing plus 1,800 leading IT companies in
the United States and India. See John McCarthy, "Near-Term Growth of
Offshoring Accelerating," Forrester Research, May 2004.
31
Global Insight Inc., "Executive Summary: The Comprehensive Impact of Offshore IT Software and Services Outsourcing on the U.S. Economy
and the IT Industry," sponsored by Information Technology Association of
America (ITAA), March 2004. Model forecasts the economy for 2004--08
with and without outsourcing; assumption is a 40-percent cost savings for
companies using outsourcing.
32
Charles L. Schultze, "Offshoring, Import Competition and the Jobless
Recovery," Policy Brief #136 (Brookings Institution, August 2004).
33
Ashok D. Bardhan and Cynthia Kroll, "The New Wave of Outsourcing,
Institute of Business and Economic Research, Fisher Center for Real Estate
& Urban Economics" (Berkeley, CA, University of California, 2003).
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
34
Ibid.
35
Terms of trade are typically defined as the prices of ex ports divided by
the prices of imports.
36
Robert C. Shelburne, 'Trade and inequality: the role of vertical specialization and outsourcing." Paper presented to International Trade and
Finance Association, San Antonio, TX, May 2004.
37
Paul Samuelson, "Where Ricardo and Mill Rebut and Confirm Arguments of Mainstream Economists Supporting Globalization," Journal of
Economic Perspectives, (forthcoming).
38
"Ten Myths about Jobs and Outsourcing," Economic Watch, on the
Internet at http://www.heritage.org/research/features/economywatch/
outsourcing.cfm, visited November I, 2004.
39
·'Samuelson Strikes Again: The Debate Over Outsourcing," Exploit
the Worker, on the Internet at http://exploittheworker.com/exploit/archives/000061.html, visited November I, 2004.
40
Jagdish Bhagwati, Arvind Panagariya, and T.N. Srinivasan, ··Toe
Muddles over Outsourcing ... "
41
Catherine L. Mann, '"Globalization of IT Services and White Collar
Jobs: The Next Wave of Productivity Growth," International Economic.,·
Policy Briefs, December 2003; Bhagwati ...
42
Bhagwati ...
43
Robert C. Shelburne and Jorge G. Gonzalez, "The Role of Intra-Industry Trade in the Service Sector." Paper presented at the Annual Conference
of the International Trade and Finance Association, Washington, DC, May
2001.
44
Bhagwati ...
45
Ibid.
46
Lori Kietzer, "Job Losses from Imports: Measuring the Costs" (Washington, DC, Institute for International Economics, 200 I).
Monthly Labor Review
August 2005
21
Manufacturing earnings
and compensation in China
On the basis of published earnings data,
estimated compensation ratios, and estimated hours,
China's manufacturing employees averaged
about 57 cents compensation per hour worked in 2002
Judith Banister
Judith Banister is a
consultant working
with Javelin
Investments in Beijing,
China. She Is former
head of the
International
Programs Center at
the U.S. Census
Bureau. E-mail:
Judlth_Banister
@yahoo.com
th by far the world's largest manuacturing workforce, at more than 100
million, 1 China is widely known to have
low labor costs. Statistics available for the
first time for the entire country for 2002 now permit
the estimation of those costs with some degree of
precision. Employees in China's city manufacturing enterprises received a total compensation of $0.95 per hour, while their noncity
counterparts, about whom such estimates had
not previously been generally available, averaged less than half that: $0.41 per hour. Altogether, with a large majority of manufacturing
employees working outside the cities, the average hourly manufacturing compensation estimated for China in 2002 was $0.57, about 3 percent
of the average hourly compensation of manufacturing production workers in the United States
and of many developed countries of the world.
Equally as striking, regional competitors in the
newly industrialized economies of Asia had, on
average, labor costs more than 10 times those for
China's manufacturing workers; and Mexico and
Brazil had labor costs about 4 times those for China's
manufacturing employees.
This article evaluates the quality and usability of
China's statistics on manufacturing earnings and
labor compensation for 2002-the most recent year
for which adequate data are available-and for the
period since 1990. The analysis demonstrates that
China has released just enough relevant data on
average annual earnings and labor-related employer
costs to derive 2002 estimates of annual labor
compensation for 30 milli0n city manufacturing
employees2 and 71 million noncity manufacturing
employees-those working in town and village
W:
22
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
enterprises (TVE's). 3 Combining the published
earnings figures and adjusted labor compensation
figures for these two groups results in a reasonable
approximation of average 2002 labor compensation
per manufacturing employee in China. A national
time series on compensation for China could not be
developed due to the lack of earnings data for the
country's noncity manufacturing workers prior to
2002; however, data on trends in real (priceadjusted) earnings for city manufacturing employees from 1990 onward are available and show a
sharp upward trend since 1998.
Because China has not systematically collected and reported adequate data on actual hours
worked by manufacturing employees for the whole
year 2002 or, indeed, for any full year, this article
uses published partial labor force survey information and a set of hypotheses to estimate annual
hours worked by city and noncity manufacturing
employees, thus calculating approximations of
average 2002 hourly labor compensation in manufacturing for these two categories of manufacturing
employees and for China as a whole. Labor compensation estimates are converted into U.S. dollars at
the official exchange rate for 2002.
The article also assesses the probable biases in
China's statistics on manufacturing earnings and
total labor compensation. The analysis that follows
argues that city manufacturing enterprises in particular have powerful incentives to underreport earnings and other elements of the compensation provided to their employees. The main purposes of
underreporting employee compensation are to
avoid taxes and to minimize required employer and
employee payments to social insurance and employee housing funds administered by urban
authorities.
There is, however, a competing bias in city manufacturing
employment and earnings data. Indirect evidence indicates that
many city manufacturing workers are not included in these
numbers at all. In particular, the lower paid migrant manufacturing
workers seem to be considerably underrepresented in the
reported urban employment d~ta for cities, and the earnings of
most of the comparatively poorly paid migrant workers in general
also appear to be excluded from urban manufacturing earnings
data. Whether the net result of these competing biases is to
underreport or overreport earnings of the average urban manufacturing employee for 2002 is unclear; however, it is likely that
the exclusion of the more stagnant earnings of the rural-to-urban
migrants leads to some exaggeration of the trend of rising average
earnings in city manufacturing for the 1990--2002 period.
The analysis that follows discusses the cost to employers of
employee compensation and the competitiveness of Chinese
manufacturing in the global economy. For comparative purposes, official exchange rates were used to convert compensation costs to U.S. dollars. The official exchange rate is the
appropriate conversion rate for compensation cost comparisons,
because it reflects the cost in U.S. dollars that employers must
actually pay for Chinese labor. Compensation costs converted
with the use of commercial exchange rates do not, however,
indicate relative living standards of workers or the purchasing
power of their income, for at least two reasons. First, because
they include costs that are not paid directly to the worker, compensation costs do not provide an accurate portrayal of worker
income. Second, prices of goods and services vary greatly
The Bureau of Labor Statistics has been a leader in compiling
international comparisons of hourly compensation of manufacturing workers over a wide range of countries. Despite its large and
growing importance in world manufacturing, China has not been
included in the comparisons because of difficulties in obtaining and
interpreting that country's data and because of concerns about the
quality of the data. Although the two Monthly wbor Review articles
by Judith Banister have greatly facilitated understanding of Chinese
employment and compensation statistics, many problems with
data availability, coverage, and reliability remain, as described in
the articles. Therefore, the Bureau does not plan to include China in
its regular comparisons of hourly compensation costs at this time.
These articles and the associated report on the BLS Web site, which
have been funded by the Bureau, are intended as first steps toward
developing the measures necessary to include China in the regular
comparisons series that currently includes 31 countries. Because
of the widespread interest in expanded country coverage, the Bureau
is indeed considering providing data on China, along with data on
some other countries, the quality of whose data is problematic, but
in a separate format with appropriate annotations. As better data
become available, China and other countries could be moved into
the regular comparisons series.
Division of Foreign Labor Statistics, Bureau of Labor Statistics
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
among countries, and the official exchange rate is not a reliable
indicator of the relative difference in prices between China and
other countries. 4
As will be demonstrated in the analysis, the numbers frequently published in the global and U.S. popular media on the
low compensation of China's manufacturing workers ($0.40-$1 .50 per hour) are within the realm of reasonable estimates.
China is indeed a relatively low wage manufacturing environment, and the country also enjoys other advantages that give it
a competitive edge over many other manufacturing locations
around the world.
This article is the second of a two-part series on manufacturing
labor statistics in the People's Republic of China (hereinafter,
"China"). 5 The earlier article 6 focused on levels and trends of
manufacturing employment; this one estimates average hourly
labor compensation for China's manufacturing employees. A
more detailed exposition of the analysis in the two articles is
found on the Bureau of Labor Statistics (BLS) Web site. 7 Occasionally, that report refers to terminology in Chinese because
the standard English translations of the terms are misleading or
ambiguous and, in some cases, because there is no succinct
and accurate English translation of the term. A complete glossary
of Chinese terms used in this and the earlier article can be found
at the end of the report on the BLS Web site.
Background
The Bureau of Labor Statistics publishes estimates of hourly
compensation costs for production workers in manufacturing
for 31 economies on its Web site. 8 Although most of the countries are developed countries with high-quality data, some
developing countries with adequate data also are included. The
Bureau is working to add countries, including China, to the
published list, but BLS standards for the quality of statistics are
high. Data for China are not yet in accord with BLS comparability
definitions. (See box, this page.) This article assesses the quality
and completeness of those statistics which are available on
manufacturing earnings and compensation in China.
The subsequent analysis is based as much as possible on
information published by China's official statistical organizations. Most statistics for China are collected under the central
guidance of the National Bureau of Statistics (NBS) and often
are published jointly with the Ministry of Labor and Social
Security (hereinafter, Ministry of Labor). Collecting data on
manufacturing employment and earnings in TVE's, however, is
the responsibility of the Ministry of Agriculture, and data on the
earnings of noncity manufacturing employees were first published for the year 2002. 9
Focusing on 2002, the most recent year for which adequate
data are available, the upcoming discussion tabulates information on earnings, required social benefit payments, and other
Monthly Labor Review
August 2005
23
Manufacturing Compensation in China
labor compensation and derives annual, monthly, and estimated
hourly manufacturing labor compensation, in Chinese yuan, for
urban, TVE, and all-China manufacturing employees. These estimates are then calculated in U.S. dollars at the official exchange
rate.
The annual data on labor compensation in manufacturing
used in this article come from the annual yearend statistical
reporting system. (China's censuses do not ask for earnings
data.) In China's cities and, to a lesser extent, outside the cities,
each enterprise, economic unit, small business, or self-employed
individual or group is required to report employment and earnings data each year according to the group's "labor situation"
the previous year and at the previous yearend. The data are
then compiled upward in a statistical reporting chain to the
national government. Accountants or those who report employment and earnings figures on behalf of their enterprises or other
work units (at least, those in urban areas) are given detailed
instructions on how to report monthly, quarterly, yearend, and
average annual figures on employment and earnings. The
instructions are based on regulations released by the NBS,
especially those released in 1990, with further clarifications in
1998 and 2002. 10
In reporting annual statistics on employment and earnings,
China's NBS and Ministry of Labor use an administrative reporting system that ignores the progress China has made in the
statistical definitions of "urban" and "rural" during the last
several decades. As mentioned in the earlier companion piece to
this article, in statistical publications on China's labor force,
employment and earnings data labeled "urban" actually refer to
cities and exclude employees working outside narrowly defined
city boundaries. Even factories located in suburbs, large
industrial parks, and towns that have been officially established
as urban places since the 1980s are excluded from the so-called
urban statistics on employment and earnings. In the tables and
charts of the current article, statistics are faithfully shown as
they were reported in official publications. In the text, the word
"city" often is used to describe the "urban" data, simply because
those data actually refer to city employees and their earnings.
By contrast, the term "town and village enterprises" (TVE's)
seems to cover not only rural areas, but also factories in
urbanized places outside narrow city boundaries. Accordingly,
the text uses the word "noncity" to refer to TVE data.
The concept of compensation
The BLS measures of hourly compensation costs include both
data on hourly direct pay (which includes pay for time worked,
pay for vacations and holidays, bonuses, in-kind pay, and other
premiums) and data on employer social insurance expenditures
and other labor taxes (which include employer expenditures for
legally required insurance programs and contractual and private
benefit plans, as well as other taxes on payrolls or employment).
24
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
China's statistical authorities at the NBS also try to use an
internationally recognizable definition of employee compensation in the calculation of China's gross domestic product.
The NBS defines what it variously translates as "compensation
of employees" or "laborers' remuneration" (laodongzhe baochou) as follows:
Laodongzhe baochou refers to the whole payment of
various forms earned by the laborers from the productive
activities they are engaged in. It includes wages, bonuses,
and allowances the laborers earned in monetary form and in
kind. It also includes the free medical services provided to
the laborers and the medicine expenses, transport subsidies, social insurance, and housing fund paid by the
employers.''
This passage suggests that China's government either collects
data on these various components of worker compensation or
at least estimates them for its calculations of China's gross
domestic product.
The subsequent analysis begins with a description of Chinese
earnings statistics on manufacturing workers and then describes
the sources and methods of estimating the nonearnings portions
of compensation-that is, the social insurance expenditures that
employers must pay on behalf of employees. Two issues that
are relevant to the estimation of social insurance expenditures,
namely, the difference by city in mandatory social insurance
contribution rates and the likely underreporting of earnings to
minimize tax and social insurance contributions, are discussed.
The article then examines the difficult issue of estimating working
time in manufacturing in order to construct estimates of compensation on a per hour basis. Following an analysis of the compensation of manufacturing employees in export-oriented industries
and of migrant workers, the discussion touches on how
manufacturing earnings in China have changed over time and
how the compensation estimates in this article compare with
those published in other venues. Finally, the implications of the
current research results for China's competitiveness are explored.
Throughout the analysis, separate estimates are made for
urban workers and TVE workers, because the data sources and
the working situations that relate to each group are different.
Where possible, national estimates combining the two groups
are made as well.
Reported manufacturing earnings in
Chinese currency
Earnings and other compensation data for manufacturing
workers in China are poorly and partially reported. The available
data on "wages" or "earnings" come from the annual yearend
reporting system, and the fragmentary figures are published in
the China Labor Statistical Yearbook and, for TVE employees,
China Village and Town Enterprise Yearbook 2003. 12 Average
annual remuneration for manufacturing workers is called
"wages" (gongzi) when referring to staff and workers, but is
called "earnings" or remuneration (laodong baochou) when
referring to the other employees of urban manufacturing units.
The two terms appear to me~n the same thing, and both are
defined as follows:
The total wages and total earnings are calculated this way:
They include whatever is paid to or for the workers in money
or in kind according to relevant regulations, including
salaries paid for a certain time period or payments based on
piece work, bonuses, allowances, subsidies, overtime pay,
and pay for dangerous or challenging duty. 13
In this article, the term "earnings" designates the wages or
earnings of both urban and TYE manufacturing employees in
cash and in kind, as reported to statistical and tax authorities.
The term does not include the social insurance payments that
employers are required to pay to city or county authorities on
behalf of their employees or the welfare fund payments given to
employees in the enterprises. The terms "compensation" and
"total compensation" include earnings plus these other elements
of total labor compensation in manufacturing. These definitions
correspond to the definitions used by the Bureau of Labor
Statistics in its international report on hourly compensation
costs.
Table 1 shows that the 30 million on-post employees of
manufacturing enterprises in China's cities had average reported
earnings of 11,152 yuan for the year 2002. 14 Of these employees,
95 percent were on-post (not laid-off or unemployed) "staff
and workers" whose earnings that year averaged 11,001 yuan,
and 5 percent were the 740,000 "other" city manufacturing
workers, who averaged much higher earnings of 17,237 yuan in
2002 (in part because this category includes foreign employees
of China's manufacturing companjes and reemployed or still
employed retirement-age workers with high seniority, and both
these groups probably get higher earnings than the average for
"staff and workers").
The 11,152-yuan average annual earnings figure of the 30
million workers in manufacturing urban units masks a wide
range of earnings in different urban manufacturing subsectors, as shown in table 2. For example, the lowest-paid group
of city manufacturing workers is the 3 million textile industry
workers, whose earnings average 7,268 yuan per year. The 5
million city manufacturing workers in the subsectors of timber
and bamboo products, food processing, nonmetal mineral
products, paper products, furniture manufacturing, and "other"
manufacturing also earn less than the average urban worker:
their reported average annual earnings are less than 9,000 yuan.
At the other end of the pay spectrum, the 7.5 million city
manufacturing workers in tobacco processing, electronics and
telecommunications, petroleum processing, ferrous metal
smelting, transport equipment manufacturing, and medical and
pharmaceutical products all have average annual earnings of
13,000 yuan or higher.
The recorded 9 million laid-off manufacturing workers still
nominally connected to their manufacturing units averaged a
small annual living subsidy of 2,213 yuan. (See table I.) This
kind of payment might be considered similar to payments of
unemployment compensation for laid-off or unemployed
workers in developed countries.
Published earnings of manufacturing employees in China, 2002
Category of manufacturing
workers
Manufacturing in urban units .. .. .................
On-post urban manufacturing staff and
workers .............. ..... ..... ........................
Other urban manufacturing employment
Laid-off urban manufacturing staff and
workers ... ..... .... .......... ....... ........ ...............
Manufacturing TvE's 1 • • ••••• ••• ••••• •• • •• • •• • • •• •• •• • ••
Large-scale manufacturing TVE's 1 •• •• • • • •• • •
Total earnings
paid
(billions of yuan)
Average number
of employees
(millions)
Number of
employees
(yearend, millions)
Average earnings
per employee
Average living
subsidy
(yuan)
(yuan)
334.39
29.81
229.98
11,152
-
321 .90
29.07
.74
29.26
11,001
17,237
-
489.22
168.94
70.62
2 18.98
9.13
70.87
19.05
2
2
6,927
2
8,899
2,213
-
I
1
2
TVE's are town and village enterprises.
Derived from other numbers reported in the table or in the sources.
NOTES: Dash indicates data are not available or not applicable. In the
sources, remuneration for workers in urban manufacturing units and for other
urban manufacturing employees is called "earnings" (laodong baochou), whereas
remuneration for on-post urban manufacturing staff and workers is called
''wages"(gongz1). For manufacturing TVE's, only the total 2002 expenditure for
earnings (laodongzhe baochou) is reported; the average per employee is not
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
directly reported. All figures for manufacturing in urban units exclude selfemployed individuals and small privately owned firms.
SouRcEs: China National Bureau of Statistics and China Ministry of
Labor, compilers, China Labor Statistical Yearbook 2003 (Beijing , China
Statistics Press, 2003), pp. 29, 34, 46, 169, 171, 179, 230, 243, 249, 473;
China Ministry of Agriculture, TVE Yearbook Editorial Committee, ed. , China
Village and Town Enterprise Yearbook 2003 [in Chinese] (Beijing, China
Agriculture Publishing House, 2003), pp . 130-31.
Monthly Labor Review
August 2005
25
Manufacturing Compensation in China
Urban manufacturing employment and
earnings by subsector in China, 2002
Average
earnings
per employee
(yuan)
Urban manufacturing subsector
Urban
employees
(yearend)
Total manufacturing in urban units ...
29,984,619
11,152
Tobacco processing ..... ....... .... ... ...... .
Electronics and telecommunications ..
Petroleum processing and coking
products ... ...... ... ....... ............. .. .......
Smelting and pressing of ferrous
metals .. .... ...... ................................
Transportation equipment
manufacturing .. ......... ... ..................
Medical and pharmaceutical
products .........................................
Instruments and office machinery ....
Smelting and pressing of nonferrous
metals ............. .... .... ...... ... .. ... .... ... ..
Electric equipment and machinery ...
Chemical fibers manufacturing ..........
Printing and record medium
reproduction ...................................
Ordinary machinery manufacturing ...
Special-purpose equipment
manufacturing ........ .... ................ ....
Cultural, educational, and sport
products ......... .. ..............................
233,485
1,623,783
23,744
17,636
565,505
17,357
1,900,648
15,032
2,319,421
14,409
844,857
464,762
13,207
12,720
755,646
1,441,399
263,378
12,491
12,405
11,404
493,497
1,921,315
10,863
10,668
1,400,594
10,406
294,636
10,390
2,213,256
606,800
897,455
621,757
377,633
740,250
10,359
10,131
10,075
10,064
10,055
9,619
578,590
1,336,191
180,484
601,416
592,400
2,116,034
977,439
9,108
9,066
8,881
8,781
8,668
8,123
7,965
267,666
2,841,565
7,339
7,268
Chemical raw materials and
products .............. ..... .. .... .............. ..
Plastic products ................................
Metal products ................ ... ... ............
Food products manufacturing ...........
Rubber products ................................
Beverage manufacturing ...................
Leather, furs, down , and related
products .... ... .... ....... .... .. ...... ...........
Garments and other fiber products .....
Furniture manufacturing ....................
Other manufacturing .. ... ... .................
Papermaking and paper products .... .
Nonmetal mineral products .............. .
Food processing ... .... ...... .. ..... ... ........ .
Timuer, bamboo, natural fiber and
straw products ...................... ... ......
Textile industry ..................................
NoTEs: These data refer only to urban manufacturing employment and
earnings.The subsectors listed here refer to 29.47 million of China's urban
manufacturing workers. Rural manufacturing workers in each subsector
undoubtedly have lower earnings than those displayed here. The earnings
figures shown do not include required empl0yer social insurance payments
or other nonwage labor costs.
SouRcEs: China National Bureau of Statistics and China Ministry of
Labor, compilers, China Labor Statistical Yearbook 2003 (Beijing, China
Statistics Press, 2003), pp. 179 and 218-25.
In years prior to 2002, earnings data were not published for
manufacturing workers outside the cities. For the reported 71
million manufacturing TYE employees in 2002, the Ministry of
Agriculture published, for the first time, the total earnings
( laodongzhe baochou) paid out for that entire year in all
manufacturing TYE's. 15 Average annual earnings per worker are
derived in table 1 in the same way that the average annual
earnings are calculated for urban manufacturing workers. TYE
manufacturing workers averaged 6,927 yuan in reported earnings
26
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
in 2002, 62 percent of the average earnings that year for employees of urban manufacturing units. Workers in large-scale
manufacturing TYE's had higher average 2002 earnings of 8,899
yuan, 80 percent of the average reported earnings for employees
of urban manufacturing units.
What forms of remuneration are included in the average annual
earnings figures for China's manufacturing employees? Exhibit 1
lists all the items whose value is required to be included in
earnings data reported by enterprises in urban China for their onpost manufacturing staff and workers, based on written instructions to enterprise accountants and statistical personnel.
Most forms of income, benefits, and subsidies in cash and in
kind are on this list. Cash salary and earnings payments, housing
and transportation provided to workers, meals given to them,
and the value of income tax and social insurance payments
deducted from earnings and remitted to the government on behalf
of employees are all required to be included in the "total earnings"
figure, based on relevant reporting regulations.
One group of benefits that is provided by some of China's
manufacturing enterprises to employees, but that is specifically
excluded from the earnings figures, is the use of a company
medical clinic or the payment of some employee hospital costs. 16
It would seem that this is an important group of benefits which,
conceptually, ought to be included in earnings data. But many
countries share this shortcoming in earnings statistics, with the
result that the Bureau of Labor Statistics specifically excludes
the costs of medical clinics in plant facilities from its comparative
international data on labor compensation in manufacturing. 17
This article does not include any estimation of these particular
medical benefits which are missing from China's earnings data.
One important difference between China's earnings data
shown in table 1 and the data used by the Bureau in its international comparisons is that the Bureau data relate only to
production workers, while the Chinese data relate to all
employees-that is, both production and nonproduction
workers. Because production workers typically have lower wages
than those of nonproduction workers, it is likely that the inclusion
of both types of workers in the Chinese data leads to higher
earnings levels. However, the production worker data necessary
to match the BLS concept are not available for China, so it is
unclear how much lower Chinese earnings for production workers
would be.
The earnings data do not include figures for the comparatively
small privately owned manufacturing groupings and the selfemployed manufacturing workers in China's cities. These two
categories of workers together totaled 8.2 million (22 percent of
China's reported total of urban manufacturing workers) in 2002,
according to China's State Administration for Industry and
Commerce. 18 This feature of China's earnings data parallels the
same dearth in manufacturing earnings data from many countries.
For reasons of practicality, if a country does not include earnings
for employees in small manufacturing units in its earnings data,
Components of Chinese urban earnings statistics
The statistical concept of wage (gongzi) or earnings for on-post urban "staff and workers" includes the following components, whether the employees receive the earnings or benefits in money or in kind and whether the earnings or benefits are or
are not taxable items:
Monthly or annual salary income (including base earnings
and additions based on position, seniority, wage scale,
and so on)
Earnings during on-the-job training, probationary period
Employee income paid on an irregular basis
Hourly payment for work performed
Piecework payment for work performed
Bonus payments
Incentive, performance-based payments
Overtime pay
Hardship, danger pay
All kinds of subsidies in cash or in kind
Festival, holiday subsidy
Travel money, food allowance while traveling
Transport subsidy (car or shuttle bus provided, cash for bus
or taxi, and so on)
Personal services such as baths, haircuts
Books, newspapers, magazines provided for employees
Meals provided, food allowance
Housing subsidy (dormitory provided, or directly subsidized
rent or purchase of housing)
Individual income tax deducted from earnings and paid
directly by enterprise to government
Social insurance funds (pension, medical, unemployment
insurance funds, and housing purchase fund) deducted
from the employee's wage and paid by the work unit to
government on behalf of the employee
Money for rent, and utilities (electricity, water)
Money given for fixed line or mobile phone
Clothing subsidy
Subsidy compensating workers for lack of vacation time
Earnings during approved leaves of absence, pay for time
not worked (regular vacation, compassionate leave, to visit
relatives, family-planning operation, national or societal
duty, study leave, leave due to sickness or injury)
Anything that has the nature or spirit of labor earnings, even
if it is not spelled out in the regulations
SOURCE: Laodong gongzi; tongji taizhang [Labor wages; statistical accounts] (Beijing, Beijing Municipality Statistical Bureau, 2004), pp. 2-1 to 2-5.
the Bureau also excludes the employees and compensation for
these units from its estimates of hourly labor compensation in
manufacturing. 19 Self-employed workers in manufacturing also
are excluded from the Bureau's estimates. Using data from
manufacturing censuses, the Bureau has researched the effect
of excluding such earnings and found it to be small.
Estimating total 2002 compensation in
manufacturing
To estimate total compensation for China's manufacturing
employees, it is necessary to add to the reported earnings the
other components of total compensation, including social
insurance payments paid by employers on behalf of employees,
as well as other payments to or for employees that are not
included in the earnings data.
In the urban areas, employers pay considerable sums for
social welfare benefits on behalf of their employees, above
and beyond the employees' earnings. China's cities today
have built, or are in the process of building, municipal social
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
insurance funds and housing funds to which both employers
and employees are required to contribute each month. 20 There
are six kinds of funds: an old-age pension fund, a medical
insurance fund, an unemployment insurance fund, a workers'
compensation fund, a maternity leave fund, and a fund in
which money is set aside for each worker by name-money
that the worker can use to help buy an apartment. These
monthly payments by employers to city governments are
mandatory, and stiff penalties are specified for noncompliance,21but noncompliance is rampant and penalties are
rarely enforced.
The payments deducted from employee earnings for the six
public funds and remitted to city governments are included in
the reported earnings data (see exhibit 1), but the part paid by
employers is excluded. 22 Legally required payments to government social insurance and employee benefit programs are
included in the BLS concept of compensation, 23 so, in order to
adjust the reported manufacturing earnings to include legally
required employer social insurance payments and other labor
compensation costs, one needs to know the overall perMonthly Labor Review
August 2005
27
Manufacturing Compensation in China
centage of the total earnings bill that urban manufacturing
employers paid in 2002 for social insurance and required
housing fund payments, as well as other employee benefit
payments. China's Ministry of Labor conducted a survey of
11,704 urban enterprises in 51 large and medium-sized cities
throughout the country and collected all relevant worker
compensation data from these organizations for the year
2002. 24 This article uses the results of that large survey to
estimate average labor compensation costs in urban manufacturing above and beyond the reported earnings data for
2002 given in table I. On the basis of the results of this Labor
Ministry survey, the reported 2002 annual earnings should be
increased by an amount equivalent to 53.8 percent of earnings
to estimate the following labor compensation costs (expressed
as a percentage of urban earnings) actually paid by employers :25
Percent
Cost
Required employer social insurance
payments to the government .............................. .
Required housing fund payments ........................... .
Additional employee welfare costs
not included in earnings ...................................... .
Other labor-related costs
not specified in detail .... .... ..... ............................. .
28
4
12
10
In table 3, therefore, average 2002 total compensation for employees of urban manufacturing enterprises is estimated to
be 17,152yuan.
Note that the amount China's urban employers are required
by law to remit to the government every month as the employer
■ re1e1 r---.-
Total for manufacturing urban units
and Tv1:'s 1 • •••• ••• ••••• ••• •• • • •• •• • • • •••••• • •••
Manufacturing urban units ..... ........
On-post urban manufacturing
staff and workers .... .. ... ... ........
Other urban manufacturing
employment ...... ..... ...... ....... ... ...
Manufacturing TVE's 1 ••• • •••• • •••• •• • •••• ••
Large-scale manufacturing TvE's 1
Average
number of
employees
(millions)
Average
earnings
per
employee
(yuan)
Old-age pension fund ........
Medical insurance fund .....
Unemployment insurance.
Workers' compensation
insurance ........................
Maternity leave insurance.
Employee housing fund .....
16.5
8.0
2.0
22.0
8.0
2.0
.6-.8
1.0
20.0
9.0
1.5
1.0
8.0
Not only do the required employer contributions vary by municipality and city, but also, the amounts have been increasing over
time. Therefore, it is likely that the legally required employer
contribution to the social insurance funds for the average manufacturing employee has increased since 2002.
The inclusion in total labor compensation of the amorphous,
vaguely reported categories of welfare costs and other unspecified labor-related costs just discussed may help offset some
of the likely downward biases in the basic earnings data. To
minimize individual and corporate taxes and required social
insurance payments, urban employers tend to underreport
earnings to the extent possible, neglecting to include some inkind benefits in the reported earnings and offloading as many
employee subsidies and benefits as possible into the welfare
Annual compensation
per employee
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Hourly compensation
per employee
U.S.
dollars
Yua,
U.S.
dollars
Yua,
U.S.
dollars
100.61
29.98
8,186
11,152
10,363
17,152
$1,252
2,071
864
1,429
$104
173
4.73
7.87
$0.57
.95
29.26
11,001
16,920
2,043
1,410
170
7.76
.94
.72
70.62
18.98
17,237
6,927
8,899
26,511
7,481
9,611
3,202
904
1,161
2,209
623
801
267
75
97
12.17
3.40
4.37
1.47
.41
.53
TvE's are town and village enterprises.
Monthly Labor Review
Monthly compensation
per employee
Yua,
NOTES: Total labor compensation for urban workers is 1.538 times
earnings and for TVE workers is 1.08 times earnings. U.S. dollars are calculated at the 2002 prevailing commercial exchange rate: 8.28 yuan= U.S.$1.
Hourly compensation is calculated under the assumption that urban
manufacturing employees perform 2 ,179 actual hours of work per year and
28
Contribution
Changshu City, Wuxi City,
Beijing
Jiangsu
Jiangsu
Province
Province Municipality
Estimated labor compensation of manufacturing employees in China, 2002
Category of manufacturing
workers
1
contribution to the social insurance system and, in some cities,
the home purchase fund varies from city to city. 26 For example,
the following tabulation shows the additional amount, expressed as a percentage of earnings, that manufacturing employers in three cities are required to contribute: 27
August 2005
that TVE workers perform 2,200 hours per year. (See text for details.)
SouRcEs: Table 1; China National Bureau of Statistics and China Ministry
of Labor, compilers, China Labor Statistical Yearbook 2003 (Beijing, China
Statistics Press, 2003), pp. 29, 34, 46, 169, 171, 179, 230, 249, 473; China
Ministry of Agriculture, TVE Yearbook Editorial Committee, ed., China Village
and Town EnterpriseYearbook 2003 [in Chinese] (Beijing, China Agriculture
Publishing House, 2003), pp. 130-31.
fund category or "other" labor compensation category. (Underreporting of urban manufacturing employment and earnings is
discussed shortly.)
For TYE manufacturing employees, there is ample evidence
that the reported earnings total may capture almost all of their
total compensation, because TYE workers do not have many of
the social insurance and other welfare benefits that urban
employees often get. For example, by the end of 2002, the number
of rural and smalltown workers with any rural social pension
insurance was minuscule. 28 China's urban towns and rural areas
have very weak or nonexistent social benefit systems for pensions, medical insurance, unemployment insurance, workers'
compensation, and the like. Pension and medical insurance
systems paid into by employers and employees essentially do
not exist in China outside of cities today. 29 A survey of large
manufacturing enterprises in Nanjing Municipality, the capital
of Jiangsu Province on the country's east coast, found that welfare benefits for workers, above and beyond earnings, for the
years 1994-2001 averaged 36 percent of the earnings in urban
state-owned manufacturing enterprises, but only 16 percent of
the earnings in unusually large manufacturing TYE's in counties
under Nanjing 's administration. 30 Now, on the one hand, these
TYE's surely had an exceptionally high level of welfare benefits
compared with those offered by all manufacturing TYE's in
China during those years, both because TYE's in counties
near major cities have better social welfare benefits than TYE's
elsewhere and because large TYE 's have better benefits than
avei"ag~ -sized TYE 's. On the other hand, average manufacturing
TYE worker welfare benefits in 2002 were very likely a higher
percentage of those workers' total compensation than in earlier
years. Therefore, pending the discovery of better data for 2002,
the average total of social insurance and other welfare benefits
for China's manufacturing TYE employees can be tentatively
estimated to be in the range from Opercent to 16 percent of their
total earnings. A reasonable estimate of such employee benefits
for the average TYE employee in 2002 is 8 percent, the midpoint
of the range. Table 3 estimates average annual total compensation
for TYE employees at 7,481 yuan.
Underreporting of urban manufacturing
employment and earnings
China's people and work units were unaccustomed to paying
income taxes, value-added taxes, corporate income taxes, or high
payments for social insurance during the Maoist decades from
1949 to 1978. The government extracted the money for its budget
in other ways, but not so visibly as the way taxes are taken out
now. Individuals got benefits in both urban and rural areas, while
earnings were kept very low. Today, during the post-Mao economic reform era, employers appear to have developed a culture
of tax avoidance. For example, when foreign and multinational
companies come to China and attempt to acquire, or set up a
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
joint venture or merger with, a (usually state-owned) Chinese
company, the foreign company insists on engaging in a due
diligence process to determine whether the joint venture, merger,
or acquisition is in the interests of its owners and shareholders.
The auditors and accounting companies frequently discover that
the target company has two sets of books: "Most domestic
enterprises keep separate sets of 'management accounts' and
'tax accounts. "'31 The "tax ledger" is the set of employee and
financial data reported to the tax and other authorities, and
the "administrative ledger" records a more accurate picture of
the numbers of employees, their actual earnings, the true costs
and income of the company, its actual profits, and more. The
tax ledger is designed to minimize tax exposure, particularly
corporate income taxes, value-added taxes, personal income
taxes for employer and employees, and required social benefit
payments. It is believed that non-public-sector domestic
Chinese enterprises avoid taxation and social benefit payments to an even greater extent than the state-owned and
collective-owned enterprises.
Such tax avoidance in the manufacturing sector probably
has a number of implications. 32 First, many urban employees,
especially those who are in-migrants and do not have city
residence permits or those who are temporary or part-time
workers, may be left off the books entirely, at least with regard
to what is reported to authorities. When they are, their employment is kept informal, and neither the employee nor his or
her earnings, which are paid in cash, are reported. This means
that the employee can avoid paying income tax and any required
social insurance deductions, while the employer can avoid
paying the required social insurance payments for the employee. As a result, actual manufacturing employment may be
underreported in China's statistics, especially in the urban
figures. 33
Second, even when employment is reported to authorities,
both employer and employees tend to collude to minimize
reported earnings. Employers in urban areas are required to remit
to the city government social insurance and other payments that
are calculated as a percentage of the unit's reported total earnings.
These required payments are high by international standards
and have been increasing rapidly: "high contribution rates are
leading to high rates of evasion in the basic pension system," as
well as evasion of other required social welfare payments. 34 Many
employers might perceive that the required payments are
squeezing their profits and are burdensome; they would
therefore have an incentive to underreport employee earnings.
Some of the money actually given to employees (as bonuses,
overtime pay, or financial subsidies of various kinds) may not
be reported as earnings, instead getting shifted to the welfare
fund category or other unspecified labor-related cost category; thus, it is important to include these labor cost categories in a realistic estimate of urban manufacturing labor
compensation in China. It is also likely that many urban enter-
Monthly Labor Review
August 2005
29
Manufacturing Compensation in China
prises underreport or leave out of reported earnings the value of
some benefits provided in kind to employees (for example, meals,
housing, transportation, and food distributions). Therefore, it is
likely that even the earnings of urhan manufacturing workers
whose employment is reported to authorities are systematically
underreported.
Those employees whose employment is not reported to the
authorities at all, whether in urban or rural areas, are usually paid
lower wages than other employees. According to anecdotal
evidence, the going rate for an unskilled rural or migrant worker
in nonagricultural work in China today is about 500-600 yuan
per month, plus whatever benefits it is essential to provide,
such as simple meals, dormitories, and emergency medical
assistance. Some rural workers are paid as little as 300 yuan
per month, while more desirable workers might get as much as
800 yuan monthly. If unreported workers in the manufacturing
sector average cash pay of 550 yuan per month, and if their
simple accommodations and food cost another 200 yuan per
month. then their earnings total 750 yuan, or U.S.$91, per month,
but only when they are actually working. Thus, if, for 3 months of
the year, they are not engaged in paid employment while planting
and harvesting and while taking time off for holidays, illnesses,
and personal business, then their annual take-home cash plus
in-kind benefits would be 6,750 yuan per year. This estimate is
close to the reported data that yield earnings of 6,927 yuan for
TYE manufacturing workers in 2002.
Annual dollar compensation for
manufacturing workers
To translate reported average annual earnings for China's
manufacturing workers into dollars (see table 3), the analysis
that follows uses official nominal exchange rates between U.S.
dollars and Chinese yuan. The Chinese yuan was pegged to
the U.S. dollar at 8.28 yuan per dollar for a decade from
1994 to August 2005; this exchange rate is the correct one for
2002 data. 35
On the basis of reported earnings data only, China's 30
million employees of urban manufacturing units had average
2002 earnings of 11,152 yuan, or U.S.$1,347, at the official exchange rate. China's manufacturing workers in TYE's averaged
6,927 yuan, or U.S.$837, in reported annual earnings in 2002.
(See tables I and 3.) After adjusting reported earnings to
account for additional indirect and direct remuneration for
employees, table 3 estimates that China's urban manufacturing employees received an average of about U.S.$2,071 in
annual labor compensation for 2002, while TYE manufacturing
employees got approximately U.S.$904. It is important to note,
however, that TYE employment is highly desirable to China's
rural workers because their TYE earnings are higher than the
earnings they can derive from agriculture. 36
30
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
Monthly labor compensation in
manufacturing
To calculate the monthly compensation of TYE manufacturing
workers from their average annual labor compensation, it would
be helpful to know whether all or even most of the reported 71
million TYE manufacturing employees work most of the year and
what proportion are part-year or part-time workers. As noted
earlier, it is likely that many unreported workers do not work year
round. If the assumption is made that these 71 million reported
workers represent year-round workers, then their average
monthly total compensation was about U.S.$75. (See table 3.)
Urban manufacturing employees are, generally speaking, yearround, full-time employees. Monthly urban manufacturing labor
compensation was U.S.$173.
Annual hours worked in manufacturing
To calculate the hourly labor compensation of China's manufacturing employees in 2002 would require data on the average
number of hours actually worked per employee during that year.
Some data have been published on China's urban manufacturing
employees' average hours worked in 2002. Specifically, China's
NBS and Labor Ministry have been conducting a labor force
survey for some years. Most results of this survey have not
been published, but data on hours worked by urban manufacturing workers during 2 reference weeks of 2002 have been
published. According to the survey, urban manufacturing employees in China actually worked an average of 44.86 hours
during the 7-day period from May 9 to May 15, 2002, and 46.0
hours during the reference week of September 24-30, 2002. 37
Averaging those two figures results in the estimate that, during 2002, in the weeks when urban manufacturing employees
actually worked at all, they averaged 45.4 hours of work per
week.
The remaining problem is to estimate the average number of
weeks actually worked by urban manufacturing employees in
China during 2002. Because urban employees are supposed to
receive a total of 10 days of statutory holidays per year, it is
reasonable to assume that urban manufacturing employees get 2
weeks of public holidays per year. It is also reasonable to assume
that urban manufacturing employees, on average, missed I week
per year for some combination of illness, injury leave, and maternity leave and I week per year for personal leave plus work stoppages and downtime due to equipment repair and shortages of
electricity and manufacturing inputs. On the assumption that
China's urban manufacturing workers actually worked 48 weeks
during 2002, averaging 45.4 hours per week, the average annual
hours worked are estimated to be 2,179 hours.
No data have been published or released on average hours
worked per week by rural or TYE manufacturing employees, even
though such data were collected for September 24-30, 2002, in
China's October 2002 labor force survey. 38 All of the calculations
that follow are therefore strictly hypothetical. Because labor laws
are more explicit and more enforced in cities than outside the
cities, it is likely that, during each week that manufacturing
employees actually are working, those in cities work fewer hours
than those outside the cities. Therefore, it is in this case reasonable to assume that TYE manufacturing workers averaged 50
hours of work per week in 2002 during those weeks that they
were working. Also, assuming that TYE manufacturing employees took 2 weeks off for Chinese New Year and stopped
work for another 2 weeks for reasons such as illness, injury, family emergencies, personal leave, and factory downtime due to
shortages and breakdowns, this would leave 48 weeks of actual
work per year. In addition, some TYE manufacturing employees
who work in the same county as their home village also may be
involved in agriculture during peak seasons. This assumption is made because most TYE workers come from rural
households that still grow crops, and farm households tend
to need all the labor they can get for planting and harvesting.
However, migrant manufacturing workers would not be able
to get home to participate in agriculture, and some manufacturing workers who live close to their family homes have left
agriculture altogether. It is therefore reasonable to assume that,
say, one-half of TYE manufacturing workers take leave from their
manufacturing jobs for 2 weeks for peak planting time twice a
year (assuming double-cropping, on average) and 2 weeks for
each of two peak harvest seasons, thus working 40 weeks per
year in manufacturing, but that the other half of TYE manufacturing workers do not do agricultural work and, as a consequence, work 48 weeks in manufacturing each year. Under these
assumptions, TYE manufacturing workers would have averaged
44 weeks of actual factory work in 2002 at 50 hours per week,
totaling 2,200 hours for the year.
It is possible that the estimate for the numbers of hours
worked, on average, per year by manufacturing employees in
city and noncity factories is too low. Some investigations in
China's export zones in Guangdong and other coastal provinces
have discovered many factories in which the employees typically
work the entire year, with a 2-week holiday at Chinese New Year.
In many such export-oriented factories, employees usually work
6 or 7 days each week, totaling 60 to RO hours per week in whatever
period constitutes the peak season for that manufacturing
subsector. 39 This season can last up to 8 months a year. Average
yearly hours actually worked per employee might be as high as
4,000 hours in some China manufacturing enterprises. Suppose
that, in those hardworking Guangdong factories, the average
urban wage in 2002 was 14,958 yuan, as discussed shortly and
as reported in table 4, and suppose also that urban earnings
must be increased by 53.8 percent to include all employer
social insurance payments, welfare costs, and other labor
costs, 40 giving an average annual labor compensation of
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
23,005 yuan, or $2,778. Then, if some city manufacturing
employees worked 4,000 hours in 2002 for that income, hourly
compensation was $0.69 per hour. Outside Guangdong's cities
in Guangdong Province, reported 2002 average earnings in
industry were 8,345 yuan. (See table 4 and the discussion that
follows.) Increasing this figure by 8 percent to adjust for social
insurance payments on the part of employers results in a total
average labor compensation of 9,013 yuan, or $1,088, in 2002.
For those factories whose workers put in 4,000 hours of
production work that year, per hour average labor compensation was $0.27. This illustration emphasizes why it is
important to determine the actual average number of hours
worked in each year for both city and TYE manufacturing
employees.
Data from China's 2000 census confirm that, generally
speaking, manufacturing employees in China work a lengthy
week; at least, they did during the last week of October 2000.
The census indicated that 58 percent of manufacturing workers had worked 6 or 7 days the previous week; however, the
census may have classified tens of millions of part-year,
seasonal manufacturing workers from rural areas and small
towns as farmers. 41 Such rural (probably called TYE) manufacturing workers would put in far fewer hours in manufacturing per year than those counted in the census or those
working year round in coastal-zone factories. Thus, the
percentage of workers who worked 6 or 7 days probably was
lower than 58 percent.
It is not known whether manufacturing employees whose
factories sell only to China's domestic market work about the
same number of hours per week, month, or year as does the
average employee of export-oriented factories. Of China's
reported 70.9 million TYE manufacturing employees in 2002, for
example, only 13.4 million were reported to be producing for
export, while 57.5 million were apparently producing only for the
domestic market. 42 An adequate estimate of average annual hours
worked must take into account both of these categories of
manufacturing workers-those who produce for export and
those who produce for domestic sale.
For China, legal limits on working hours or overtime hours are
not likely to yield realistic estimates of actual hours worked.
Factories routinely report that they are abiding by the regulations
when, in fact, employees are working more hours per day, and
many more hours per week or month, than the statutory limits.
One purpose of the double bookkeeping in China's factories is
to report compliance with laws on minimum wages and maximum
permissible overtime hours when, in reality, the factory routinely
violates the laws. Generally speaking, grassroots investigators
report that the factories do not claim that they paid more total
earnings per month or per day to the employees than they
actually paid; rather, they underreport the actual hours worked
to earn the reported monthly or daily income.
Monthly Labor Review
August 2005
31
Manufacturing Compensation in China
Hourly labor compensation in
manufacturing
Despite the limitations on estimates of annual hours worked,
it is possible to produce reasonable estimates of hourly compensation costs for manufacturing workers in China, as is
shown in table 3. According to these estimates, compensation
for employee s of urban manufacturing units was about
U .S.$0.95 per hour of work and for TYE manufacturing
employees was about U.S.$0.41 per hour.
The analysis presented herein combines labor compensation estimates for the reported 71 million TYE manufacturing employees and the 30 million manufacturing employees
of urban units to derive estimates for annual, monthly, and hourly
labor compensation in China's manufacturing sector. As
shown in table 3, these 101 million Chinese manufacturing
employees received an average of approximately U.S.$1,252
in labor compensation in 2002, a figure that works out to about
U.S.$ I 04 in monthly labor compensation and implies an hourly
labor compensation of around U.S.$0.57 for China's manufacturing employees.43
How does that U.S.$0.57 compare internationally? Chart 1
shows manufacturing hourly compensation costs in China in
relation to the same costs in several other countries. Chinese
costs are 3 percent of those in the United States, according to
data from the BLS series. Even compared with some of the lower
cost countries in the series, Chinese costs are low: a quarter of
the cost level in Brazil and Mexico and less than a tenth of the
average of Hong Kong, Korea, Singapore, and Taiwan. 44
Manufacturing labor compensation in key
export regions
China's urban manufacturing earnings statistics are reported
by province, which facilitates estimating urban manufacturing
labor compensation for the leading export centers. Using the
same ratio of additional compensation to earnings, namely,
53.8 percent, as in table 3, table 4 adjusts the earnings of
urban manufacturing workers to derive annual, monthly, and
hourly labor compensation for the city manufacturing workers
of four leading provinces in China's manufacturing import
and export trade. (Actual levels of additional compensation
as a percentage of earnings vary by province and by municipality, but data are not available to adjust earnings by using
different multipliers for the urban manufacturing workers in
different provinces.)
The three provinces of the Yangtze River Delta have a wide
range of urban manufacturing earnings and labor compensation.
As shown in table 4, Shanghai's 1.3 million city manufacturing
Average hourly compensation costs of manufacturing workers, selected economies
and regions, 2002
U.S.= 100
($21.11)
U.S.= 100
($21.11)
120 , - - - - -- - - -- - - - - - - - - - - - - - - · - - - - - - - - - - - - - - - - - 120
100
100
100
80
80
60
60
40
40
20
20
3
0
United States
1
China
Brazil
Mexico
EU(15)
1
Japan
Asian NIE'S 2
0
EU(15) are the European Union member countries prior to the expansion to 25 countries on May 1, 2004.
2
Asian NI E's are the newly industrialized economies of Hong Kong, Korea, Singapore, and Taiwan.
SOURCE: Bureau of Labor Statistics, "International comparisons of hourly compensation costs for production workers in
manufacturing, 1975-2003," Nov. 18, 2004; on the Internet at http://www.bls.gov/fls/home.htm. For China, data are from this article and
not from the BLS series. The data for China refer to all employees rather than just production workers.
Monthly Labor Review
32
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
■ 1•I•ir~•-
Compensation of urban manufacturing employees and TVE 1 industry employees, Yangtze Delta provinces
and Guangdong, China, 2002
Province
Annual
earnings
(yuan)
Adjusted annual labor
compensation
Yua,
Adjusted monthly labor
compensation
U.S.
dollars
Yua,
U.S.
dollars
Adjusted hourly labor
compensation
Yua,
U.S.
dollars
Urban manufacturing employees:
National average ..........................
Shanghai municipality ........... ..........
Zhejiang province ............................
Jiangsu province .............................
Guangdong province .......................
11,152
21,957
13,435
11,731
14,958
17,152
33,770
20,663
18,042
23,005
$2,071
4,078
2,496
2,179
2,778
1,429
2,814
1,722
1,504
1,917
$173
340
208
182
232
7.87
15.50
9.48
8.28
10.56
$0.95
1.87
1.15
1.00
1.28
TVE' industry employees:
National average ..........................
Shanghai municipality ................ .. ...
Zhejiang province ............................
Jiangsu province ........ .............. .. .....
Guangdong province .......................
6,891
11,939
10,188
8,143
8,345
7,442
12,894
11,003
8,794
9,013
$899
1,557
1,329
1,062
1,088
574
1,075
917
733
751
$69
130
111
89
91
3.13
5.86
5.00
4.00
4.10
$0.38
.71
.60
.48
.49
'TvE's are town and village enterprises.
NoTEs: U.S. dollars are calculated at the 2002 prevailing commercial exchange rate : 8.28 yuan= U.S.$1. Hourly wage estimates for urban workers
are calculated under the assumption that urban manufacturing employees
perform 2,179 actual hours of work per year and that TVE workers perform
2,200 hours per year. (See text for details.)
workers are comparatively highly paid in the Chinese context.
Their 2002 labor compensation averaged about U.S.$4,078, and
hourly compensation was approximately U.S.$1.87. Manufacturing workers in Zhejiang, Jiangsu, and Guangdong had lower
labor compensation than Shanghai, but still higher than the
national average.
These city manufacturing earnings statistics for China's
leading export-manufacturing regions do not yield a true picture
of the earnings paid by manufacturing enterprises in those
provinces. In the first place, it is not certain that the earnings of
most migrant manufacturing workers in the cities of the
aforementioned provinces are included in the urban manufacturing earnings data. Second, no wage data are reported for
the so-called rural manufacturing workers by province, nor
are TYE manufacturing earnings figures reported by province.
However, reported earnings statistics are available by province for TYE industry (gongye) employees. Nationally, 92.4
percent of TYE industry workers are manufacturing employees, and wages of these manufacturing workers are
similar to those of other industry workers. Therefore, TYE
industry earnings by province can be used to estimate manufacturing earnings.
Table 4 also reports 2002 TYE industry earnings and
derives labor compensation for the same regions. Like their
urban counterparts, TYE industry workers in these regions
have higher earnings than the national average. Shanghai
and Zhejiang TYE industry employees were the highest paid,
earning U .S .$0. 71 per hour in the Shanghai suburban and rural
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
SouRcEs: Table 3; China National Bureau of Statistics and China Ministry
of Labor, compilers, China Labor Statistical Yearbook 2003 (Beijing, China
Statistics Press, 2003), pp. 179, 473; China Ministry of Agriculture, TVE
Yearbook Editorial Committee, ed., China Village and Town Enterprise
Yearbook 2003 [in Chinese] (Beijing, China Agriculture Publishing House,
2003), pp. 156, 174.
areas and U.S.$0.60 an hour in Zhejiang Province's rural and
industrial zones outside of its cities. Noncity industry workers
in Jiangsu and Guangdong Provinces were not as well paid,
receiving U.S.$0.48 and U.S.$0.49 per hour, respectively.
Earnings of migrant manufacturing workers
In theory, if a worker has migrated from a village to a city and
is employed in a manufacturing enterprise, the employer
should report the migrant's job and earnings in the "manufacturing staff and worker" category. But in practice, in most
cities of China, migrants who do not possess permanentresident documents are apparently not eligible for urban social
insurance and housing benefits:
Contracted rural migrant laborers are supposed to be
covered [in the social basic pension system] as well.
While the inclusion of rural migrant labor in urban areas
would also reduce the dependency ratio because of the
concentration of migrant laborers in the young working
age groups, present weaknesses in administrative capacity make it questionable whether these workers will ever
draw benefits, especially if they return to rural areas or
move on to other urban areas. In some cases, the pension
contribution is simply an added tax from which the
migrant will derive no benefits. 45
There is increasing informal evidence that published urban
earnings data exclude the pay of most migrant workers. 46 The
Monthly Labor Review
August 2005
33
Manufacturing Compensation in China
earlier companion piece to this article 47 referred to published
2002 statistics on manufacturing employment in urban units,
totaling 29.81 million, that included 4.59 million rural-to-urban
migrants whose household registration was still in rural areas.
Probably, their reported earnings were part of the published
average earnings data for urban manufacturing staff and workers,
but very likely, many millions more rural-to-city migrant manufacturing workers were not in the reported urban manufacturing
employment or earnings data. There are many possible reasons
for such exclusion, including the fact that many cities and
municipalities in China do not consider rural-to-urban migrants
to be real urban or municipal employees. 48 It is not known
whether these migrant manufacturing workers and their earnings
get picked up in the TYE manufacturing data.
It is reasonable to assume that TYE manufacturing employment and earnings data usually include the migrant manufacturing workers in towns and rural areas. The reason is that,
because of the much lower rati0 of social insurance costs in
towns and rural areas, there is almost no incentive to leave these
workers out of the data in those areas, in contrast to the situation
in cities, where the higher ratio of social insurance costs affords
a financial incentive to exclude migrant workers. There is no
separate reporting of the earnings of migrant manufacturing
workers either in the cities or outside urban areas.
Manufacturing earnings over time
Most of the data in this article relate to the year 2002 only.
Although it would be revealing to analyze trends in manu-
•••l•H=---•
facturing earnings over several years, the data required to
construct such series over time are sparse. Published data on
earnings trends for the manufacturing sector are available
solely for urban manufacturing staff and workers. Table 5
presents published information on annual percent changes in
average real earnings for this subset of city manufacturing
employees. Real living standards have been rising in China's
cities, and real earnings have been rising for urban staff and
workers in manufacturing. 49 The --staff and worker" component
of urban manufacturing workers is supposed to include manufacturing workers who migrated into cities from rural areas, but
the rising wages indicated in table 5 probably exclude data on the
earnings of most rural-to-urban migrant manufacturing workers. 50
Reported urban manufacturing earnings rose rapidly in the early
1990s, slowly in the mid-I 990s, and very rapidly at the end of the
1990s and on into the early 21st century. Tables 5 and 6 and chart
2 show that these generalizations about city manufacturing
earnings trends also hold for manufacturing employees in stateowned units, collective-owned units, and "other" ownership
units (joint ventures, foreign-owned firms, multinational companies, and the like).
Table 6 and chart 2 present trends in real annual earnings (not
including required employer payments for social insurance plans
or other nonwage labor costs) for urban manufacturing staff and
workers in China. In 1990, the 53 million urban manufacturing
staff and workers earned an average of 5,058 yuan (in constant
2002 yuan). As the number of urban manufacturing staff and
workers shrank to 29 million in 2002, the earnings of those
Annual percent change in average real (price-adjusted) earnings of urban manufacturing staff and workers
in China, selected years, 1979-2002
Year
Total
Urban stateowned units
1979 ... .. .... .. ..... .... .... ... ............. ········· ··········
1980 ...................................... ··· ······ ······ ··· ···
9.1
5.4
7.4
5.2
4.4
7.5
1985 ........................................................... .
1986 ........................................................... .
1987 ···· ····················· ··· ··························· ... .
1988 ................................ ,.... ..... ............... ...
1989 ···· ····· ···· ··· ···· ·· ········ ········ ····· ······ ······ ·····
1990 ·············· ··· ··· ···· ···· ····················· ·· ·········
1991 ···················· ······· ··········· ········ ·· ····· ··· ····
1992 ..... .... ..... ........ ... ................ ........... ....... .
1993 .. ................... ..... ................................. .
4.1
7.1
2.2
-.1
-4.5
7.7
5.1
6.0
9.4
3.4
8.6
2.6
.5
-4.4
8.6
4.1
6.2
6.2
6.9
4.3
.8
-2.5
-5.7
5.2
5.4
3.3
5.4
17.9
7.5
7.6
14.0
.9
4.4
12.9
5.5
1.1
1994 ........................... ........ ..... ... ....... .. ... .... .
1995 ····························································
1996 ........................................................... .
1997 .......................................................... ..
1998 ........................................................... .
1999 ...... ... ............................. .. ............ ..... .. .
2000 .. ........... .............................................. .
2001 ... ....... ...... ........ ....... .. ......................... .
2002 ········· ··· ···· ···· ······ ···· ········· ········ ····· ··· ·····
2.3
3.3
.3
2.0
5.1
11 .8
11.4
10.9
13.7
1.2
1.6
-.4
.5
2.3
10.5
11.5
11.3
14.6
- .3
3.5
-.9
-.3
2.4
7.6
6.6
5.7
12.0
.1
1.8
NorE: Dash indicates data are not available.
SouRcE: China National Bureau of Statistics and China Ministry of Labor,
34
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
Urban collectiveowned units
Other urban
ownership units
.8
2.3
-1.8
10.3
8.5
7.9
9.7
compilers, China Labor Statistical Yearbook 2003 (Beijing, China Statistics
Press, 2003), pp. 36, 39, 42, 45.
• 1 • 1• 11
=--••
Average annual real earnings of urban manufacturing staff and workers in China, 1990-2002
[In constant 2002 yuan and constant 2002 dollars]
Urban state-owned
units
Total
Year
1990 .......... .... ............... ..................... .
1991 ············ ····· ···· ················ ··············
1992 .................................................. .
1993 .................................................. .
1994 ···················································
1995 .................................................. .
1996 ···················································
1997 .. ... .... ............................ ........... .. .
1998 ... ... .... ..................... .. ..... ............ .
1999 ...................... .. ... .............. ....... .. .
2000 ................. ..... .. .......................... .
2001 ···················································
2002 ···················································
Yua,
U.S.
dollars
5,058
5,316
5,635
6,165
6,307
6,515
6,534
$611
642
681
745
762
787
789
5,599
5,828
6,189
6,573
6,652
6,759
6,731
$676
704
748
794
803
816
813
4,149
4,373
4,517
4,761
4,746
4,913
4,868
$501
528
546
575
573
593
6,665
7,005
7,832
8,724
9,675
11,001
805
6,765
6,921
7,647
8,527
9,490
10,876
817
836
924
1,030
1,146
1,314
4,854
4,970
5,348
5,701
6,026
6,749
846
946
1,054
1,169
1,329
Yua,
NOTE: This table presents only the reported annual earnings, which have
not been adjusted to include other labor compensation costs, such as
required employer payments to municipal social insurance systems.
remaining averaged 11,001 yuan, more than double the 1990
average earnings. There was a shift in the composition of the
"urban manufacturing staff and workers" category over that 13year period. 51 In 1990, the lowest-paid subgroup, urban collective
manufacturing workers, was large ( 18 million) and held down
average real earnings, while the highest-paid subgroup, privatesector enterprises, was minuscule. By 2002, the highest-paid
subgroup constituted more than half of urban manufacturing
staff and workers. This trend toward the better paid private sector
raised average earnings among urban staff and workers in manufacturing.
Estimates of manufacturing employee
compensation
Many media and other sources around the world have published
very rough estimates of hourly or monthly earnings or total
compensation for manufacturing workers in China. A comparison
of their estimates with those in this article is instructive. For
example, one journal stated that manufacturing wages in China
average about 60 cents an hour, 52 very close to the 57 cents
estimated here for total compensation. One newspaper wrote,
"A Chinese factory worker earns the equivalent of less than
$1 per hour," 53 a statement supported by the preceding
analysis, and one that holds true even for urban manufacturing workers, who are better paid than their counterparts
outside the cities.
Regarding particular manufacturing sectors, a newspaper
article said that, in China, employees of auto-parts suppliers have
average wage costs of 90 cents an hour. 54 Another author said
that employees of big global automakers in China "make the
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Urban collectiveowned units
U.S.
dollars
Yua,
Other urban ownership
units
U.S.
dollars
Yua,
U.S.
dollars
588
6,833
7,714
8,138
8,228
8,236
8,384
8,452
$825
932
983
994
995
1,013
1,021
586
600
646
689
728
815
8,646
8,490
9,365
10,161
10,964
12,027
1,044
1,025
1,131
1,227
1,324
1,453
SouRcE : China National Bureau of Statistics and China Ministry of
Labor, compilers, China Labor Statistical Yearbook 2003 (Beijing , China
Statistics Press, 2003), pp. 34-45.
equivalent of $1.50 per hour in wages and benefits. " 55 Table 2
indicates that China's urban transportation equipment manufacturing workers had average 2002 earnings of 14,409 yuan,
which would translate into about 80 cents an hour for earnings alone and $1.23 per hour for total compensation. Therefore, the overseas reports of the compensation of auto workers in China are compatible with the data presented in this
article.
One journal wrote, "China is already by far the biggest
garment exporter in the world, with average wages in the
industry of 40 cents an hour. " 56 That figure is close to the 41
cents an hour that the foregoing analysis has posited for the
compensation of China's TYE manufacturing employees.
Garment workers outside the cities are paid less than that,
because they are among the lower paid manufacturing employees in China. Table 2 indicated that urban garment workers
average 9,066 yuan per year, or approximately 50 cents per
hour, in earnings; their total compensation might be about 77
cents an hour. If so, then the estimate of 40 cents per hour is
too low for China's urban garment workers, but correct for
noncity employees in garment manufacturing.
In general, global media-published estimates of manufacturing earnings or compensation in China are in the ballpark
of reasonable estimates.
Labor compensation costs and China's
competitiveness
It is widely agreed 57 that low earnings and low total labor
compensation costs make manufacturing production in China
competitive in the international market. One of the leading
Monthly Labor Review
August 2005
35
Manufacturing Compensation in China
Average real earnings of urban manufacturing staff and workers in China, 1990-2002
Yuan
Yuan
14,000 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 14,000
12,000
12,000
~r:,;r,'
10 000
'
""#;~-
10,000
.,.,.,:(,'
_/;r-::9··
Other urban ownership units, real earnings
,,,...
. _.,.;,.,_,4.r:'::-.:.:!,:.';,;,•
8,000
Urban state-owned units, real earnings
fl
Ill'* ,. •• '#
4t <Ir Qf, $
1'I . . . .
~ ~ ::;,o.:><-»:Q,o/h:;,;,.,:,:,,:<;~·:«,;.l(~-:,.
.
. .. • .
->:<«:-,:.;.:,,;.)),¾❖;❖Y.<,',;v~•~
8,000
... -1'!)1¥<
6,000 ···················::::.,,,.""'""'-cfrb~otal manufacturing staff and workers, real earnings__ .. - -· - - -· ·
~;,,;«..,,J,W>»'U/,,1->~"~~
.,. • ,., ' -
-
6,000
•
Urban collective-owned units, real earnings
4,000 ..
4,000
2,000
2,000
0
'-------'-------L-------'------'----'------'------'-------'-------'------'----'----
1990
SOURCE:
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
0
2002
Table 6,
reasons that some of China's own domestic manufacturing
industries can sell their products at home and abroad, and
that multinational and other foreign companies are moving
their manufacturing operations to China, is the low cost of
employing manufacturing workers there.
The low cost of labor makes China particularly competitive
in a number of manufacturing industries, including laborintensive, assembly, and reprocessing industries; industries
with low value added; those with simple repetitive steps in
the manufacturing process; and food-processing industries.
As one source puts it, "China has become an essential link in
the global production chain for many labor-intensive products ... a manufacturing hub for the rest of the world in low-end
labor-intensive goods." 58 Labor productivity (output per
employee) is low by world standards in these kinds of Chinese
factories, and earnings are correspondingly low. 59 In the 1990s
and beyond, China's employees experienced widening earnings
inequality, as earnings rose for city workers, but basically
stagnated for the least skilled and least educated workers. 60
China is not particularly competitive in capital-intensive or
materials-intensive industries.
However, China is beginning to compete successfully in some
kinds of moderately skills intensive kinds of manufacturing. Large
proportions of China's young adults now have at least a lower
36
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
middle school education and therefore are basica11y literate and
numerate. Also, mi11ions of young and middle-aged workers from
rural areas are eager to get out of the countryside and therefore
willing to work hard in a disciplined manner for pay that is low by
international standards, but higher than they can earn in agriculture. China also has many millions ofuniversity-educated young
adults who are especia11y competitive because they are good in
engineering and technical fields, are hard working and motivated,
and work for a fraction of the salaries received by equally capable
young adults in developed countries. China now produces at
least half of the world's cameras and photocopiers and onequarter of the world's television sets and washing machines. 61
Indeed, China "is the new workshop of the world, producing
two-thirds of a11 photocopiers, microwave ovens, DVD players,
and shoes, over half of all digital cameras, and around two-fifths
of personal computers."62
Labor compensation in China's manufacturing sector is
higher than it was a decade or two ago. This means that some
other developing countries are now able to compete with
China purely on the basis of earnings per manufacturing
worker. Real living standards have been rising in China's cities,
and real earnings have been rising for urban staff and workers
in manufacturing, as shown in tables 5 and 6 and chart 2. 63
Why are urban manufacturing earnings rising rapidly in
China? Some scholars argue that because labor productivity is
rising rapidly in China's city factories , we would expect city
manufacturing earnings also to rise. 64 Among the forces driving
the increase in urban manufacturing earnings are a sustained
rise in the returns to education and skill, as well as a wage
premium for Communist Party members and others remaining in
protected state-owned enterprises. 65 Rigidities in urban labor
markets also have forced earnings upward and impeded competition.66 Other experts contend that the huge supply of surplus
urban and rural workers ought to keep their earnings down:
"The coincidence of rising mass unemployment and rapid
increases in real wages in the late 1990s appears contrary to the
predictions of competitive labour markets." 67 The range of
earnings in Chinese manufacturing has indeed widened, and
the least educated unskilled workers have experienced near
stagnation in their real earnings "under the twin pressures of
heavy migration from China's villages and [the] intense pursuit of cost advantage from overseas buyers of labor-intensive
goods." 68
In addition to the earnings bill, required payments for other
urban employee benefits have increased. 69 China is trying to
build a viable system of pensions, medical benefits, unemployment benefits, workers' compensation, and housing benefits, at
least for its city population, as discussed previously. One source
argues that required employer payments for these urban social
safety net programs in China are now higher than they need to
be-for example, substantially higher than in Malaysia, South
Korea, Taiwan, and Singapore. 70 In some cities, the mandated
payments are still rising rapidly. For example,
Average labor costs in Shanghai rose by 15% last year due
to increases in welfare payments, healthcare subsidies, and
housing subsidies. On average local companies paid 10,849
yuan in fixed and optional welfare fees, up 22.4% [from the
year before]. This rise was significantly higher than in cities
such as Kunshan, Nanjing, Hangzhou, Suzhou, or Ningbo.71
As earnings and mandated social insurance payments increase, urban China becomes less competitive in the global
context and even in the domestic Chinese context. Shanghai,
for example, is beginning to become too expensive for many
manufacturing concerns. 72 Some businesses are moving from
the city to the poorer inland province of Anhui. 73 Cities
throughout China are much more expensive for manufacturing
than even their nearby suburbs. Factories can save a third in
power costs and half in wage bills just by relocating a factory
half an hour's drive outside of Guangdong's capital city of
Guangzhou. 74 Indeed, many manufacturing companies are
now choosing to move their production operations from
developed countries or from China to other developing
countries with lower labor costs. For instance, India, Pakistan,
and Vietnam are becoming competitive as textile and apparel
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
producing and exporting countries because the cost of textile
production is generally lower there than in China. 75 Of course,
China remains highly competitive globally because of its
relatively low labor costs and many other favorable factors, 76
but rising labor compensation in China has begun to erode
the country's manufacturing price advantage.
THIS ARTICLE HAS COMBINED EMPLOYMENT AND EARNINGS
DATA for China's urban manufacturing workers and for the
noncity TYE manufacturing workers in order to derive approximations of annual, monthly, and hourly labor compensation for
urban, noncity, and all-China manufacturing employees. Reported
earnings and labor compensation data have been adjusted
separately to yield urban data and TYE data. As of 2002, the
latest year for which adequate earnings data are available, average
labor compensation for 30 million of China's urban manufacturing
employees was approximately U.S.$0.95 per hour, while the
reported 71 million manufacturing employees in TYE's outside
the cities averaged about U.S.$0.41 in labor compensation per
hour of work. Combining the labor compensation of manufacturing workers in cities and in TYE's to derive an all-China
estimate results in average labor compensation of approximately U.S.$0.57 per hour of work for 101 million manufacturing workers in China.
The following items should have high priority for future
data collection in China and future research on hourly labor
compensation in China 's manufacturing sector:
1.
Data on hours worked. For the important goal of
calculating average hourly labor compensation in
manufacturing in China, a high priority is to get better
data on actual hours worked by employees in the
manufacturing sector. China's government could itself
gather and publish more systematic data on this important measure, and scholars should also emphasize
gathering information on it.
2.
National economic census. During the year 2005, with
reference year 2004, China conducted its first national
census of the economy. This undertaking is expected
to refine, correct, and update data on labor compensation received in manufacturing. When results of the
economic census become available starting in late 2005,
the new information should be used to update the
estimates in this article.
3.
Noncity manufacturing labor compensation. Much
more data collection and analytical research are needed
to fill in some of the missing information on rural and
town manufacturing earnings and total compensation.
Monthly Labor Review
August 2005
37
Manufacturing Compensation in China
Labor force surveys. China needs to design, carry
out, and publish results of labor force surveys using
international standards and definitions. Such surveys
should cover the whole country and should collect
and publish data on earnings and total compensation.
China reportedly will begin a regular labor force survey
in 2006, the results of which will subsequently be
published.
O
ACKNOWLEDGM ENTS:
This article was written under contract to the
U.S. Department of Labor, Bureau of Labor Statistics, in order to further
the knowledge of China's manufacturing earnings and labor compensation
statistics. The views expressed are those of the author and do not reflect
the views of the Bureau. This research project has benefited from the
valuable feedback of colleagues in China and in other countries on China's
economy and Chinese business practices. In particular, economists Loraine
A. West and Nicholas R. Lardy served as expert discussants at a November
2004 BLS seminar on an early draft of the full report on the BLS Web site.
Official statistical organizations in China have helped to correct some
errors and point toward missing pieces of information. BLS economistsin particular, Constance Sorrentino, Chris Sparks, Elizabeth Taylor, Aaron
Cobet, Susan Fleck, Marie Claire Guillard, Gary Martin, Ann Neff, and
Erin Lett-have provided their expertise and support. Patricia Capdevielle,
formerly of the Bureau of Labor Statistics, provided expert advice and
comments. I would especially like to thank Xing Yan (LeLe), Xing Shuo,
Song Jintao, Xing Shuqin, Wang Jianping, Li Fang, Xue Jianwen, and
Robert Boyer for their dedicated research assistance. The opinions,
anaiysis, and conclusions expressed in this report are solely mine, and any
mistakes or errors remain my responsibility.
example, the purchasing power parities used may not accurately reflect
the actual purchasing patterns of manufacturing workers, and the price
data used to construct the parities may not correctly approximate the
relative prices of many goods and services. For a discussion of the
purchasing power of Chinese manufacturing worker incomes, see Judith
Banister, .. Manufacturing Employment and Compensation in China,"
on the Internet at http://www.bls.go,·/fls/#publications.
4.
Notes
1
The companion piece to this article, .. Manufacturing employment
in China" (Monthly Labor Review, July 2005, pp. 11 - 29), noted that
China's official statistics reported 83 million manufacturing employees
at yearend 2002, but a variety of other available statistics strongly
indicated that the actual number was more than I 00 million.
2
Banister , '·Manufacturing employment in China," noted that
China's official statistics reported 38 million city manufacturing
employees at yearend 2002. Data on earnings are not available for 8.2
million manufacturin g workers in the cities; of these workers, 2.6
million are self-employed. The Bureau of Labor Statistics does not
include the self-employed in its comparative estimates of hourly
compensation costs, which relate only to production workers. China's
data cover both production and nonproduction workers.
3
TYE 's originally were established as collective economic units run by
local governments in rural areas and towns. The purpose of TYF's was, and
still is, to employ small farmers and rural laborers in industrial or serv ice
occupations in locations not far from their family homes. This effort
allows China's vast countryside to become modernized without necessitating massive migration from the villages to cities. In the 1980s, and
especially from the 1990s to today, TYE's shifted from public toward
private ownership, and many foreign-funded enterprises became classified
as TYE's. Nowadays , in addition to including small local enterprises, the
TYE category can include very large factories in industrial parks outside
cities, as well as suburban, town, and rural factories. Companies have
incentives to have their factories classified as TYE's because required social
insuran,e payments are low, statistical reporting requirements are minimal,
and the companies receive many legal and tax benefits.
4
To more closely approximate the purchasing power of Chinese
manufacturing worker incomes in U.S. dollars, some type of purchasing
power parity (that is, the amount of yuan required to purchase the
equivalent of $1 of goods and services in China) would be needed. Although
purchasing power parities provide a better measure of differences in relative
price levels than do commercial exchange rates, there are still important
limitations in using them to construct comparisons of worker income. For
38
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
5
The analysis presented herein applies to the mainland of the
People's Republic of China and excludes statistics for Hong Kong,
Macao, and Taiwan .
6
Banister, .. Manufacturing employment in China."
7
Banister, .. Manufacturing Employment and Compensation in China."
x .. International
Comparisons of Hourly Compensation Costs for
Production Workers in Manufacturing," on the Internet at http://
www.bis.gov/news.release/ichcc.toc.htm.
9
See Banister, -- ~ anufacturing employment in China," for further
background information about China 's statistical system.
10
Examples are available of statistical reporting forms and instructions
issued to city enterprises to use to report employment and earnings data
for the calendar year 2003. A '·labor situation form" [Laodong qingkuang
biao] was to be submitted to authorities by the end of February 2004.
Wage-reporting instructions were in the publication Laodong gongzi;
tongji taizhang [Labor wages; statistical accounts] (Beijing, Beijing
Municipality Statistical Bureau, 2004), especially p. 2-1.
11
China National Bureau of Statistics, China Statistical Yearbook
2003 (Beijing, China Statistics Press, 2003), pp. 66, 84, 87, 90.
12
China National Bureau of Statistics and China Ministry of Labor,
compilers, China Labor Statistical Yearbook (Beijing, China Statistics
Press, published annually); China Ministry of Agriculture, TYE Yearbook
Editorial Committee, ed., China Village and Town Enterprise Yearbook
2003 [in Chinese] (Beijing, China Agriculture Publishing House, 2003),
pp. 130-31.
13
China Labor Statistical Yearbook 2003 (Beijing, China National
Bureau of Statistics and China Ministry of Labor, compilers; China
Statistics Press, 2003), pp. 630, 638 .
14
Chinese sources did not report earnings data for another 8 million
urban manufacturing employees: self-employed individual manufacturing
workers and the investors and workers in relatively small private manufacturing concerns. It is not known whether this group of city manufacturing employees earns more or less than the " manufacturing employees in urban units." However, some of the employers of these 8
million workers pay lower social insurance payments or none at all to city
governments.
15
China Village and Town Enterprise Yearbook 2003, pp. 130-3 1.
16
Wage-reporting instructions, 2004, p. 2-4.
17
See BLS Handbook of Methods (Bureau of Labor Statistics, 1997),
Chapter 12, " Foreign labor statistics," pp. 114-15; and Chris Sparks,
Theo Bikoi, and Lisa Moglia, "A perspective on U.S. and foreign
compensation costs in manufacturing," Monthly Labor Review, June
2002, pp. 36- 50, especially p . 49 .
18
See Banister, " Manufacturing employment in China."
Inequality, Labor Market and Welfare Reform in China , Au s trali a n
National University, Canberra, Australia, August 2004, Table I, pp.
28-29; on the Internet at http://econrsss.anu.edu.au/pdf/china-
a bstract-pdfmongpa per. pd f.
19
Sparks, Bikoi, and Moglia, ·'U.S. and foreign compensation costs,"
p. 49 .
20
Xiaochun Qiao, China 's Aging and Social Security of the Elderly:
With Ref eren ce to Japan ' s Ex p eri en ces , Japan External Trade
Organization, ID E- JETRO Visiting Research Fellow Monograph Series
No. 388 (Chiba, Japan, Institute of Developing Economies, 2004) .
31
Kim Woodard and Anita Qingli Wang, " Acquisitions in China: A
View of the Field," China Business Review, November-December 2004,
pp. 34-38 , and " Acquisitions in China : Closing the Deal ," China
Business Review, January-February 2005 , p. 35 .
32
See Fox and Zhao, " China 's labor market reform ."
33
21
" Sh ehui baoxianfei zheng jia o zanx ing tiaoli" (" Provisional
regulations for payment of social insurance fees " ), in Laodong he
shehui baox ian zhengce xuan chuan ca iliao (Materials on social
insurance policy announcements) , Beijing, Haidian District Labor and
Social Security Office, regulation number 259, promulgated Jan. 22,
1999 .
22
23
Wage -reporting instructions, p. 2-5 .
Handbook, pp. 114-15; Sparks, Bikoi, and Moglia, "U .S. and
foreign compen sation costs ," p. 37 .
BL S
24
All data in this paragraph are from China Ministry of Labor,
Zhongguo laodongli shi chang g ongz i zhidao jiawei (2003 nian)
[China Labor Force Market Wa ge Guide 2003] (Beijing, China Labor
Social Security Press , 2004), p. 379.
25
Ibid. , p. 379.
26
Loraine A. West, " Pension reform in China: Preparing for the future," Journal of Development Studies, February 1999, p. 165. In some
cities, the social benefit payment that the enterprise is required to pay the
government is not strictly a percentage of whatever the total gross salary
bill is. For example, in Shanghai for 2003, enterprises had to pay 43 .5
percent of the total wage bill, subject to the following constraints: if the
reported total wage bill divided by the reported number of employees
averaged less than 60 percent of Shanghai 's average monthly salary for
the first half of 2003, the enterprise still had to pay 43.5 percent of that
minimum salary threshold; the maximum payment the enterprise was
required to remit was 43 .5 percent of the total wage bill that would
represent 3 times the average 2003 Shanghai wage. (See Lulu Zhang,
"Shanghai region: Updates on Shanghai social benefit affecting FIE monthly
overheads," China Briefing; The Practical Application of China Business,
June 2004, p. I 0.) This procedure is supposed to be applied nationwide,
based on State Council Document Number 6. See also Loraine A. West and
Daniel Goodkind , Pension Mana gement and Reform in China, NBR
Executive Insight Series No. 15 (Seattle, National Bureau of Asian Research,
1999), p. 3.
27
Data for Changshu City are from Qiye shenbao shehu1 baoxian
jiaofei yewu zhinan (Busine ss guide to enterprises on social insurance
payments) , Jan . 15, 2004; on the Internet at http://www.changshu.
gov.cn/H/content/HQA0000000000002837 .htm. Data for Wuxi City
are from Shehui baoxianfei jiaofei bili mingxi biao (Table of detailed
comparisons of required so cial insurance payments), 2858 fuwuwang
(2858 service Internet site) at http://www.wx2858.com/XCBST/jyzn/
shehuibaoxian.asp.
28
China Labor Statistical Yearbook 2003, pp. 471, 575-81. China had
21.3 million TYE'S of all kinds in 2002, but only 85,000 of them had any
rural old-age pension insurance. By yearend 2002, a cumulative total of
54.6 million people had ever contributed to any rural social pension
insurance scheme, but during 2002, only 4.1 million contributed to such
a system.
29
Louise Fox and Yaohui Zhao , " China 's labor market reform:
Performance and prospects, " background paper for the China 2002
Country E conomi c Memorandum (Washington , DC, World Bank,
2002); Xiaochun Qiao, China 's Aging and Social Security .
30
Xiao-yuan Dong, ·'The Changing Wage-Structures in the 1990s:
A Comparison between Rural and Urban Enterprises in China," paper
presented at the International Research Conference on Poverty,
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
See Judith Banister, " Manufacturing employment in China, "
Monthly Labor Review, July 2005, pp. 11-29, for a further explanation
of the underreporting of manufacturing employment and its
consequences .
34
Richard Jackson and Neil Howe, The Graying of the Middle Kingdom (Washington , oc, Center for Strategic and International Studies
and Prudential Foundation , 2004), p. 14.
35
David Hale and Lyric Hughes Hale, "China takes off," Foreign Affairs,
November-December 2003, p. 46; Nicholas R . Lardy, " United StatesChina ties: reassessing the economic relationship," testimony presented
before the House Committee on International Relations , U.S. House of
Representatives, Oct. 21, 2003; on the Internet at http://www.iie.com/
publications/papers/lardy1003.htm; and Henny Sender, " Self-interest
may lead China to revalue yuan ," Wall Street Journal , Apr. 19, 2004, p.
A2.
36
John Knight and Linda Yueh, "Urban Insiders Versus Rural Outsiders:
Complementarity or Competition in China 's Urban Labour Market?"
paper presented at the International Research Conference on Pove rty,
Inequality, Labour Market and Welfare Reform in China , Au stralian
National University, Canberra, Australia, August 2004; on the Internet at
http ://econ rsss.anu .ed u .au/ch inaco nfa bstracts .h tm.
37
Jianchun Yang , " China Working Time Statistics," on the Internet
at http ://www.insee.fr/en/nom _def_ met/colloques/citygroup/pdf/
China-general.pdf; China Labor Statistical Yearbook 2004 , p. 111 ;
personal communication with N BS official s.
38
Yang , " China Working Time Statistics," p. I .
39
See "Excessive Overtime in Chinese Supplier Factories : Causes,
Impacts, and Recommendations for Action," Verite Research Paper,
September 2004, on the Internet at http://www.verite.org/Excessive
% 20Overtime % 20in % 20Chinese % 20Factories.pdf; and Les I ie T.
Chang, "At 18, Min finds a path to success in migration wave," Wall Street
Journal , Nov. 8, 2004, p. A I.
40
See earlier in this article, pp . 27- 29.
41
Banister, " Manufacturing employment in China. "
42
China Village and Town Enterprise Yearbook 2003, p. 219.
43
Employment weights are used to calculate an estimate of national
total labor compensation in manufacturing.
44
Note again that the data for China refer to all employees, while
the figures for the United States and other countries refer to production
workers. Employees have higher compensation than production work ers, so the data for China are overstated to an unknown degree for
these comparisons.
45
West, " Pension reform in China," p. 172.
46
Thomas G. Rawski, personal communication , May 28 , 2004.
47
Banister, " Manufacturing employment in China," p. 23 .
48
Shanghai municipality, for example, excludes from its employment statistics data on in -migrant workers from other provinces . (See
Banister, "Manufacturing employment in China .")
Monthly Labor Review
August 2005
39
Manufacturing Compensation in China
49
See Nicholas R. Lardy, "Do China's Abusive Labor Practices
Encourage Outsourcing and Drive Down American Wages?" testimony
presented before the Senate Democratic Policy Committee Hearing,
Mar. 29, 2004; on the Internet at http://democrats.senate.gov/dpc/
hearings/hearing14/Iardy.pdf.
50
Rawski, personal communication, May 28, 2004; Fox and Zhao,
"China's labor market reform," pp. 3, 22.
51
Banister, "Manufacturing employment in China," table I.
52
"Is the wakening giant a monster?" The Economist, Feb . 15,
2003, pp. 63-65.
53
George Stalk and Dave Young, "How China gets our business,"
Washington Post, Mar. 7, 2004, p. B3 .
54
Norihiko Shirouzu, " China drives auto-parts shift," Asian Wall
Street Journal, June 10, 2004, p. AS.
55
Joseph Szczesny, "China an exporter by 2007? Will too many
cars force Chinese automakers to begin selling outside the Middle
Kingdom?"; on the Internet at http://www.thecarconnection.com/
index.asp?article=7233.
56
"Is the wakening giant a monster?" p. 63 .
57
For a few examples, see Stalk and Young, "How China gets our
business"; Szczesny, "China an exporter by 2007?"; and Chinese Academy
of Social Sciences, Industry Economic Research Institute, Zhongguo
gongye fazhan baogao [China's Industrial Development Report] (Beijing,
Economic Management Press, 2001 ), pp. I 09, 547.
58
Hale and Hale, ·'China takes off," p. 46.
59
Lardy, ·'China's Abusive Labor Practices."
° Fox
6
and Zhao, "China's labor market reform."
61
Matt Forney, "Tug-of-war over trade: As China becomes the
world's factory, U.S. and European manufacturers are hurting," Time
International (Europe Edition), Feb. 23, 2004, p. 34.
62
"The dragon and the eagle," The Economist, Sept. 30, 2004.
'' Sec also Lardy, "China's Abusive Labor Practices."
64
Nicholas R. Lardy, discussant,
8, 2004.
40
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
BLS
seminar, Washington, oc, Nov.
August 2005
65
See Fox and Zhao, "China's labor market reform."
66
Knight and Yueh, ·'Urban insiders versus rural outsiders."
67
Simon Appleton and Lina Song, "The evolution of wage structure in
urban China during reform and retrenchment," paper presented at the
International Research Conference on Poverty, Inequality, Labor Market
and Welfare Reform in China, Australian National University, Canberra,
August 2004, p. 2; on the Internet at http://econrsss.anu.edu.au/
chinaconfababstractshtm.
68
Thomas G. Rawski, " Recent developments in China's labour
economy," revised November 2003 from a report prepared for the
International Labor Office in January 2002, p. 17; see also Fox and Zhao,
"China's labor market reform," pp. 3, 22; and the entire Rawski article.
69
Jianchun Yang, " 2002 nian zhongguo jiuye qingkuang" ["China
2002 employment situation"], Zhongguo renkou tongji nianjian 2003
[China Population Statistics Yearbook 2003] (Beijing, China Statistics
Press, 2003).
10
Rawski, "Recent developments," p. 27; see also Bureau of Labor
Statistics, "International comparisons of hourly compensation costs
for production workers in manufacturing, 1975-2003"; on the Internet at http://bls.gov/fls/home.htm.
71
Paul French, "Welcome to bubble town," Asian Wall Street Journal,
May 27, 2004, p. A7.
72
Iain McDaniels, "A critical eye on Shanghai: Will the city's
extraordinary growth continue?" China Business Review, JanuaryFebruary 2004, pp. 8-9, especially p. 8.
7
'
French, "Welcome to bubble town."
74
·'String of pearls : China's development," The Economist, Nov.
20, 2004, p. 44.
75
Mu Xin and Zhenpeng Liang, "Mei caigou shang xuejian Zhongguo
fangzhi dingdan" ["U.S. purchasers have cut textile orders from China"],
Xin kuai bao [New Express], Apr. 28, 2004; on the Internet at http://
www.ycwb.com/gb/content/2004-04/28/content _ 683077 .htm.
76
See Banister, "Manufacturing Employment and Compensation,"
for further information on China's many competitive advantages in
manufacturing .
Female Weekend Empioim.-·.
~i{ ..
,
~
The female share of weekend
employment: a study of 16 countries
Along with the increase in women's employment
in many European countries has been a rise in their share
of weekend employment, particularly on Sundays;
women's disproportionate share in weekend work is most evident
in the service sector; in the industrial sector,
women are underrepresented among weekend workers
Harriet B. Presser
and
Janet C. Gornick
Harriet B. Presser is
distinguished
university professor
in the Department
of Sociology at the
University of
Maryland . Janet
C. Gornick is
associate professor
in the Department
of Political Science
at Baruch College
and at The
Graduate Center,
City University of
New York.
E-mails:
presser@
socy.umd .edu
and
janet_gornick@
baruch.cuny.edu
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
e postindustrial era has brought with it
hanges in the temporal nature of labor
orce activity in highly industrialized countries, including a growing diversity in employees'
work schedules. How many hours a week people
are employed and which hours in the day they are
employed are becoming more varied-not just
within countries, but across countries; so, too, are
which days of the week people are employed.'
Researchers have long studied the number of
hours per week that people work and now are focusing some attention to workers' shifts, whether
they work mostly days, evenings, nights, or weekends, or have a rotating schedule; however, there
is considerably less research about what is happening to employment during the weekend, both
Saturdays and Sundays. Yet weekend employment
is a phenomenon of considerable interest as the
service sectors of many advanced economies
grow, responding to the growing demands of
consumers for "24/7" access to certain services. 2
Also, because women are disproportionately employed in the service sector in virtually all highly
industrialized countries, it is expected that a growing share of weekend employment will be female.
It is important to consider the gendered nature
of weekend employment, both in terms of trends
T:
and variations. This article documents, for the first
time, the share of women working weekends, focusing on 15 contemporary European countries,
and to a lesser extent (limited by problems of comparability), the United States. 3 This comparative
analysis shows considerable variation among European countries that call for contextual factors as
part of the explanation, such as differences among
countries in public policies and collective agreements bearing on work-hour regulations, pay premia and/or compensatory time, and childcare.
These differences will be analyzed in more detail
in future work; this article lays the groundwork for
further exploration.
Data sources
Data are from the Labour Force Surveys (LFS) of
15 European countries, obtained from Eurostat, the
statistical office of the European Union (Eu). 4 The
trend analyses presented cover the 1992-2001 period, or the most recent year when reliable data on
work schedules are available. The total sample
sizes of these surveys range from approximately
12,500 (Finland) to 380,000 (Germany). The
countries are ordered in the analysis according to
region: Nordic countries, including Sweden, Fin-
Monthly Labor Review
August
2005
41
Female Weekend Employment
land , Denmark, and Norway; British Isles, including the
United Kingdom and Ireland; Western/Central European
countries, including France, Germany, Switzerland, Austria,
the Netherlands, Belgium, and Luxembourg; and Southern
European countries, including Italy and Spain. These were
the countries for which reliable LFS data on work schedules
were obtained from Eurostat. 5
This regional breakdown was adopted largely because
much comparative literature on European policies and employment outcomes, especially women's employment, has
shown a substantial degree of homogeneity within these
groupings. The Nordic countries, for example, tend to have
high rates of female employment, sizable service sectors, and
large redistributive welfare policies. The Western/Central
European countries typically have lower rates of female employment, smaller service sectors, and less redistributive social policies. The British Isles, like the United States, generally have moderate rates of female employment, and much
more market-oriented regulatory and social welfare systems.
The Southern European countries generally have both low
female employment and less developed social policies.
Eurostat does not provide to outside scholars the individual records for these countries. Rather, it is possible to
purchase from them only cross-classification tables, which
present weighted clusters of individuals with identical sets
of characteristics. 6 The samples drawn for this study are restricted to those aged 25-64, to wage and salary earners, and
to those working in nonagricultural occupations (farmers and
farm laborers are excluded). 7
This article's main variables of interest, whether respondents worked Saturday and whether they worked Sunday,
were available in all the countries reported. The responses
were "usually," "sometimes," and "never." This article focuses on usual employment (typically defined by countries
as at least half of the weekends during the reference period
of I month), and both Saturday and Sunday usual employment have been dichotomized accordingly (yes/no). To
assess the percent female working Saturdays and Sundays,
the base is all employees (including men) with the same
restrictions as noted above.
The first chart in this article on female employment trends
includes data for the United States obtained from the May
1997 and May 2001 U.S. Current Population Surveys (CPS).
Both surveys ask respondents, in addition to employment status, which days of the week they usually work. 8 However,
the 2001 CPS (unlike the May 1997 CPS) expanded the options to allow for "days vary" without determining whether
these variable days included Saturday or Sunday, and this
"days vary" category is substantial in size. Given this
change, data on weekend work are reported for the United
States only for 1997. The CPS data are based on approximately
50,000 households.
42 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
Trends in female share of employment
Over the 1992-2001 period, the 15 European countries under study experienced either an upward trend in the percent
of all those employed aged 25-64 who are female, or sustained the high levels achieved earlier. Sustained high levels
are characteristic of the Nordic countries, the United Kingdom, and France, with the percent female ranging between
47.5 and 50.7. (See chart I.) All of the other countries start
from lower positions and show patterns of increasing "feminization" in employment-that is, a growing female share of
all those employed-achieving levels in 2001 ranging from
38.8 percent female (Spain) to 46.8 percent female (Ireland).
The high levels in all four Nordic countries (ranging from
50. 7 percent to 48 .4 percent in 2001) exceed the female share
in the United States as of 2001 ( 48.3 percent), based on CPS
data.
Trends in weekend employment
Along with an increase in the female share of all workers,
some European countries, but not all, have experienced an
increase in employment on Saturdays and/or Sundays. Before considering the extent to which the female share of weekend employment has increased, it is of interest to examine
what the overall trend in weekend employment has been for
all those employed aged 25-64.
The 15 countries are highly variable in whether they show
an upward, downward, or fairly stable level of Saturday employment from 1992 to 2001. (See chart 2.) (Some of the
countries have missing data for certain years.) For most countries, about one-fifth of those employed work Saturdays, with
minor fluctuations over the years. The lowest levels are for
two Western/Central European countries: Belgium, which
shows an upward trend (from 9.2 percent in 1992 to 11.5
percent in 1998, latest reliable year); and Luxembourg, which
is fairly stable over the decade ( 14.2 percent in 1992 and
14.0 percent in 2001 ). In contrast, the two Southern European countries, Italy and Spain, are the countries with relatively high levels of Saturday employment: Italy with its peak
of 36.1 percent in 1993, but declining notably to 29.4 percent in 2001; and Spain, peaking at 29.1 percent in 1995 and
declining to 26.3 percent in 2001.
Sunday employment is less common than Saturday employment. Countries that are relatively high in Saturday
employment are not always relatively high in Sunday employment. Three of the Nordic countries, Sweden, Finland,
and Denmark, along with the Netherlands and Spain, show
the highest levels ofusual Sunday employment, with close to
one-sixth of all those employed. (See chart 3.) The lowest
levels are for some of the Western/Central European countries: France, Belgium, and Luxembourg, plus Italy (which
Female employment trends: percent of employees aged 25-64 who are female, 15
European countries, 1992-200 l where comparable data are available, and the United
States, 1997 and 2001
10
0
20
40
30
60
50
I
Sweden
,.,,,0,,,,,,,,,,,,,,,,,,.,,..,,,,,0,/lii,,,J; MF"'F""i'«'" "'"·"'v,,tw&\4)½7(,.,,-·
V>7£"Y--½'"»·""· " ' - = 7 . v
(1995-2001 )
Fin land
Nordic
countries
50.7
~~;·=ffJi ·- •'
h'.;:.;.~;;;:,.;._,Hd%::~ ·.;,,.,...,,,;,.:-',¥$t""'J,,-:¼-L,ili}WM®xd~--fo:..-~A:h;,),m,·»~----t{"'mf:im}~~~""W1r4%?f~~~W:t~"«~~~"::'~~~l-»:u~~~~
50.4
(1995-2001)
Denmark · - - - - - - - - - - - - - - - - - - - - - - - - - (1992-2001)
49.0
Norway ~~~~~~~'~l"<l~=~=-::>~~:-m:,1A~U~mn~,:r.c~m:,.~:,~~::.~:-:-r.=:-:-i,:;,,,:-m':·~~~~--;•· -.,.,... · :. ~~~™-'~~~~~
(1995-2001) ""
-,
48.4
United Kingdom
British
Isles
(1992-2001)
Ireland
•;;:.
(1992-2001)
»
France
"
,.-,,,,,,
(1992-2001)
''(•: --~= ...,,-,.,.,.,
~"'"'•Ti·,
·., .. '· :,.,
> '
46.8
-:-,;:
· i:-,:-;-;,:
' ' ~~'-:-~.
-.-,,.,.-,.<'-,·.·
~
• . . .·.··--
'°"'
• "''
47.7
Germany
(1992-2001)
45.5
Switzerland ''""'"1'"'"'""''''""""""'~"'·''""'""'"""-"'""""""'"'"''''""''"''"""'""""''''""'""'''"'"'"''"''""''"'""'"""'"""'"'.
45.3
(1996-2001)
Western/
Central
European
countries
Austria
(1995-2001) ,
44.7
Netherlands
(1992-2001)
43.8
I
I
Belgium
.. ,
(1992-2001)
43.7
Luxembourg
(1992-2001)
40.3
Italy
Southern
European
countries
(1992-2001)
41.3
Spain
(1992-2001)
38.8
United States
(1997.2001) - - - - - - - - - - - - - - - - - - - -- - - 1
0
NorE :
10
20
30
40
r
4 3
60
50
Values shown indicate percent female in 2001. Some countries have missing data for certain years.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
43
Female Weekend Employment
Saturday work: percent of employees aged 25 to 64 who usually work Saturdays, 15
European countries, 1992-2001 where comparable data are available, and the United
States, 1997
0
5
10
25
20
15
30
35
40
35
40
Sweden
17.1
(1995-2001 )
Finland
Nordic
countries
(1995-2001)
17.9
Denmark
(1992-2001)
19.3
Norway
(1995-2001)
15.4
United Kingdom
British
Isles
(1992-2001)
20.9
Ireland
(1992-97, 2001)
17.5
France
(1992-2001)
19.4
Germany
(1992-97)
Switzerland
Western/
Central
European
countries
1,s,m@,<W@1''«<<c-,,,ssssm""''W~-.,,,,,,,"~"""'"'"'"""""'"'""""""""''""'""""'"'"'"'-" "'' ,,,
(1996-2001)
Austria
(1995-2001)
22.0
Netherlands
(1992-99)
k~.m--.~,---="""'------."<ill1.1M®""""'
Belgium
20.1
11.5
(1992-98)
Luxembourg
14.0
(1992-98, 2001) - - - - - - - - - - - -
Southern
European
countries
Italy
(1992-2001)
Spain
(1992-98,2001) - - - - - - - - - - - - - - - - - - - - -
26.3
23.3
United States
(1997)
0
NorE:
5
10
15
20
25
30
Values shown indicate percent Saturdays for most recent year. Some countries have missing data for certain years.
44 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
Sunday work: percent of employees aged 25 to 64 who usually work Sundays, 15 European
countries, 1992-200 l where comparable data are available, and the United States, 1997
0
2
4
6
18
16
14
12
10
8
20
Sweden
(1995-2001)·------------------------,-
16.0
. ----~,_-,,~~
....,"
Finland
(1995-2001)
Nordic
countries
Denmark
(1992-2001)
15.6
Norway
(1995-2001)
9.2
United Kingdom
British
Isles
(1992-2001)
11.5
Ireland
(1992-97, 2001)
France
(1992-2001)
6.4
Germany
9.6
(1992-97)
Switzerland
8.0
(1996-2001)
Western/
Central
European
countries
Austria
12.1
(1995-2001)
Netherlands
13.5
(1992-99)
Belgium
6.3
(1992-98)
Luxembourg
(1992-98, 2001)
5.3
Italy ,,.
Southern
European
countries
(1992-2001)
6.3
Spain
; :.:•:><;.,«~
(1992-98, 2001)
10.9
14.3
United States
(1997)
0
NorE:
2
4
6
8
10
12
14
16
18
20
Values shown indicate percent Sundays for most recent year. Some countries have missing data for certain years.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
45
Female Weekend Employment
has the highest level of Saturday employment). Among all
the countries, the only one to show a clear declining trend in
usual Sunday employment is Finland, from 15.8 percent in
1995 (earliest year available) to 13.3 percent in 2001. The
more general change seems to be a trend toward more Sunday employment, most evident for France, Germany, Austria, the Netherlands, and Spain.
People who are employed Sundays are highly likely to be
employed Saturdays. Thus, the trends for those who usually
work both Saturday and Sunday (not shown) are similar to
trends for those who usually work Sundays, shown in chart
3, except the levels are lower. As of 2001, the percent who
worked both Saturday and Sunday was highest in Sweden
(15.0 percent) and lowest in Luxembourg (5.2 percent).
Female share of weekend employment
Women are increasingly becoming employed in most of these
countries, and sustaining their high levels in others. In many
countries, there has been an increase in weekend employment, particularly on Sundays, but what is the extent to which
weekend work has become "feminized"? In other words,
what is the trend in the female share of all workers usually
employed on Saturdays and/or Sundays?
As noted earlier, the growth of women's employment is
linked to the growth of the service economy, and-in all of
the countries in this study-the service sector has higher rates
of weekend employment than does the industrial sector (results not shown). 9 Thus, an increase over time is expected in
the percent of weekend employees who are women for many
of these countries.
Interestingly, 7 of the 15 European countries that had relatively low female shares among all employees aged 25-64
working Saturdays in 1992 show notable increases by 2001:
the United Kingdom, Ireland, Germany, Austria, the Netherlands, Luxembourg, and Spain. (See chart 4.)
A similar pattern is evident in Sunday employment. (See
chart 5.) In addition to the six countries noted above (where
Saturday female shares rose), the percent female usually
working Sundays increased in Finland, Norway, Belgium, and
Italy during this time period (with minor fluctuations over
the decade). Only two countries showed no clear pattern of
change in the female share of weekend employment (both
Saturday and Sunday): Sweden and France-both with relatively high levels to begin with. Denmark is unique in that
the female share of both Saturday and Sunday employment
declined.
Although trend data are not available for the United States,
the percent female of those working Saturdays and Sundays
in 1997 was about midway along the continuum for the European countries that year ( 41 .2 percent for Saturdays and
45.0 percent for Sundays).
46 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
Detailed comparisons
The remainder of this article makes some detailed comparisons among countries in the percent female of all those working weekends, focusing on the year 2001 or the most recent
year for which comparable data are available, and considering economic sector and weekly hours worked.
Disproportionate female share on weekends. Allowing for
the fact that different countries have different levels of female employment, to what extent does the percent female of
those working weekends exceed the percent female of all employed? Relative to their share of the employed population,
are women disproportionately working weekends?
In most of these European countries, they are. (See chart
6.) Ratios of the percent female in weekend employment to
the percent female in all employment are computed for Saturday and Sunday separately. Ratios of more than 1.00 represent disproportionate female employment on these weekend days, meaning female shares in weekend employment
are larger than female shares in the workforce more generally. Regarding Saturday employment, the only European
countries showing less than 1.00 are the United Kingdom
and Ireland; regarding Sunday employment, only Norway,
Ireland, Austria, Italy, and Spain have an underrepresentation
of women. It is notable that weekend employment in the
United States is not disproportionately female, either with
regard to Saturday or Sunday, with ratios of less than 1.00.
The "feminization" of weekend employment is most notable in Sweden (ratios of 1.29 and 1.27 for Saturday and
Sunday, respectively) and Luxembourg (a ratio of 1.31 for
Saturday).
Contrasts within economic sectors. Cross-national variation in the share of females among weekend workers may
be because of multiple factors, including variation in the
percent of females among the employed and variation in
the size of countries' service sectors. Both factors are taken
into account by assessing the service and industrial sectors
separately. 10
Regardless of which days are worked, in all countries
women are more concentrated in the service sectors than in
the industrial sectors. (See table 1.) However, allowing for
this fact, the service sector also disproportionately draws
women into weekend work. For most of the European countries considered, but not the United States, the female share
among service sector workers is higher for those working
weekends than for service sector workers overall. The exceptions to greater disproportionate female share on weekends among the European countries are the United Kingdom,
Ireland, Germany, Italy, and Spain. 11
The reverse is true with regard to the industrial sector.
Female share of Saturday work: percent of Saturday employees aged 25 to 64 who are
female, 15 European countries, 1992-2001 where comparable data are available, and the
United States, 1997
10
0
20
40
30
50
70
60
->~------~
,.!Ir$,"·~
Sweden ,_,,_.-_,"%~"""'~ew~~W%1'.~®!!,
(1995-2001) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Finland
~~~'!'!:""
65.5
1- ·
(1995-2001)
Nordic
countries
53.6
Denmark
(1992-2001)
Norway - - - - - - - - - - · , ~ - - · - - - - - - - - - 50.6
(1995-2001 )
United Kingdom
British
Isles
(1992-2001 )
Ireland
(1992-97, 2001) - - - - - - - - - - - - - - - - - - -
39.8
France
(1992-2001)
55.1
Germany
(1992-97)
Switzerland
(1996-2001 )
Western/
Central
European
countries
54.0
Austria
(1995-2001 )
"""'""' 50.6
Netherlands
Belgium
48.9
(1992-98)
Luxembourg
(1992-98, 2001)
i,----------------------- - 53.0
Italy
Southern
European
countries
(1992-2001)
43.3
Spain
(1992-98, 2001) - - - - - - - - - - - - - - - - - - -
40.6
41.2
United States
(1997)
0
NorE:
10
20
30
40
50
70
60
Values shown indicate percent female for most iecent year. Some countries have missing data for certain years.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
47
Female Weekend Employment
Female share of Sunday work: percent of Sunday employees aged 25 to 64 who are female,
15 European countries, 1992- 200 l where comparable data are available, and the United
States, 1997
10
0
20
50
40
30
60
l
I
(1995-2001) · ''"""""""'
Nordic
countries
70
53.7
Denmark
(1992-2001) ,,_,._
Norway r,•_,,,,,,,,,w-
~
»-~, •
54.6
·-,n:. ....,,.,.,, ... ,_._.,. -
-~~~~~~~~~~~'(~«~~'(,X,,:'(«:;•:~,i~~~""
;-;
(1995-2001)
""''"""'''<"'>
:,.-.:
44.1
United Kingdom
British
Isles
(1992-2001)
Ireland
(1992-97, 2001)
48.3
1i-------------------
42.6
France
(1992-2001)
49 .6
Germany
45.1
(1992-97)
Switzerland
(1996-2001)
Western/
Central
European
countries
Au stria
52.4
tm;fflW1r%'i'!'WWlZMMtMW11r-~~''trtw~~f!1i!W/';~~$'!Wi!ll"''?i"t~·•w,N1,
42.6
(1995-2001)
Netherlands
7.....""""''---'f·1@>:s,_,._,_,,,_ _,,...,,_'S.S;;%.~..@·;:::-~-1t,"S,_-.,,,,.,,
(1992-99) """'"'''"""""'"'''''''"'"'''"'""'
48.6
Belgium
(1992-98)
Luxembourg
(1992-98, 2001)
1i--------------------
44.9
Italy
Southern
European
countries
(1992- 2001) ' ·
35.4
Spain
(1992-98, 2001) ..,..
_ _ _ _ _ _ _ _ _ _ _ _ _ _ __
37.4
45.2
United States
(1997)
I
0
NmE:
10
20
30
40
50
60
Values shown indicate percent female for most recent year. Som e countries have missing data for certain years .
48 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
70
Ratio of percent female in weekend employment to percent female in all employment, 15
European countries and the United States, 2001 or most recent year comparable data are
available
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
I
I
I
I
I
I
I
Sweden
1.4
I
I
Finland
Nordic
countries
,
/y
Denmark
I
Norway
British
I
·...
I
I
United Kingdom
Isles
•
Saturday
D
Sunday
I
Ireland
I
France
I
Germany
I
(1997)
Switzerland
Western/
Central
European
countries
I
Austria
..
.: : +, .:•
:>:\.
·)\
I
I
Netherlands
.
(1999)
I
I
Belgium
I
(1998)
Luxembourg
Southern
European
countries
I
Italy
I
•:
I
Spain
I
United States
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
I
(1997)
0.6
I
I
I
I
I
I
0.7
0.8
0.9
1.0
1.1
1.2
Monthly Labor Review
I
1.3
August
1.4
2005
49
Female Weekend Employment
Percent female of all wage and salary earners aged 25 to 64 by weekend work schedule and economic sector in
15 European countries and the United States, 2001 or most recent year comparable data are available
Nordic countries
Economic
sector
Sweden Finland
British Isles
DenIreNorway United
mark
Kingdom land
Western/Central European countries
France
Germany
( 1997)
NetherSwitzer- Austria
lands
land
(1999)
Belgium Luxem(1998) bourg
Southern
European
countries
United
States
( 1997)
Italy Spain
Service
sector
Total ..... ....
60.1
60.8
57.6
56.8
56.5
57.1
56.1
56.1
53.1
55.2
50.1
52.3
48.2
49 .6
50.4
55.3
Saturdays .....
70.7
61.6
59 .1
57.4
52.2
49.3
58.6
55.8
57.3
57.2
52 .5
53.8
56.0
49.2
47.4
46.8
Sundays .......
70.4
63.1
60.2
51.2
54.2
48.9
55.0
51.8
55.1
48.7
55.2
51.3
48.7
39.1
42.3
49.8
Both Saturday
and
Sunday .....
71.5
63.3
60.6
52 .6
53.3
48.2
55.1
52.0
55.0
49.1
55.2
50.9
48.3
39.1
42.2
48.7
Weekdays
only ...........
57.7
60 .6
57.1
56.8
58.9
58.8
55.4
56.2
51 .9
54.6
49.4
52.0
46.7
49.9
51.9
58.5
Total .... .....
23.3
25.6
26.2
20.8
22 .2
24.1
25.3
24.1
22.7
21.6
17.2
19.9
12.2
25.8
17.5
27.0
Saturdays .....
17.5
19.0
11 .7
14.8
9.3
11 .9
27.0
24.0
27.6
18.7
11.1
12.1
27.4
15.6
11 .6
17.0
Sundays .......
21.1
16.8
14.7
15.2
13.3
17.9
23.3
11.4
19.2
16.5
7.5
7.0
17.8
12.3
10.9
18.3
Both Saturday
and
Sunday .....
17.2
15.7
10.5
14.4
11.7
17.7
23.4
10.6
18.3
17.0
6.2
7.0
17.8
12.2
10.7
16.6
Weekdays
only ..... ......
23.7
26.4
27.1
21.4
25.1
25.6
25.1
24.1
22.4
22.1
27.4
20.3
11.1
27.4
18.4
29.2
Industrial
sector
NorE:
"Satu rdays" and "Sundays" include those who may also work the other weekend day; these two categories are not mutually exclusive.
For almost all of the European countries and the United
States, women are underrepresented among the weekend
workforce. The exceptions in this regard are France, Switzerland, and Luxembourg, where female workers are more
highly represented among weekend workers than among indu strial workers overall.
Contrasts within hours worked. These surveys generally
do not ask how many hours women and men are employed
during the weekend, and there may be gender differences in
this regard. However, the total number of weekly hours
worked helps to illuminate variation in the female share of
weekend employment among those working fewer than 30
hours per week versus more than 30 hours per week (the distinction most often used in Europe for part- and full-time
work, respectively). 12
There is a much larger percent female in part-time work
than in full-time work. (See table 2.) At the same time,
among those who work fewer than 30 hours a week, women
are about equally likely to work weekends as weekdays only;
there are some differences (mostly with regard to Sundays)
50 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
but, overall, they are small. The most marked contrast for
both Saturdays and Sundays is for Sweden, in which the female share exceeds that for all part-timers by about 7 percentage points.
Among those working 30 hours or more, women's disproportionate employment on the weekends is more evident. In
most of our study countries, full-time working women are
more likely to be overrepresented on weekends. The only
exceptions are Norway, Ireland , and the United States.
Economic sector contrasts for full-timers. Does the female overrepresentation in weekend employment among
those working 30 hours or more appear in both economic
sectors, service and industry? The answer is consistent with
what was found without regard to the number of hours
worked: full-time employed women in the service sector in
most of the countries are disproportionately in weekend employment, but the reverse is true in the industrial sectors,
where women are typically underrepresented among weekend workers. (See table 3.) Luxembourg's industrial sector
is a notable exception; while fewer than 1 in 10 weekday
Percent female of all wage and salary earners aged 25 to 64 by weekend schedule and number of hours worked,
15 European countries and the United States, 2001 or most recent year comparable data are available
--.,.--
Number of
hours
worked
IreUnited
DenNorway
Sweden Finland
Kingdom land
mark
Fewer than
30 hours
Southern
European
countries
Western/Central European countries
British Isles
Nordic countries
United
States
GerNether- BelLuxemSwitzergium
( 1997)
Austria lands
France maoy
bourg
land
Italy I Spain
(1999) (1998)
(1997)
i--- -
I
Total .... ... ... .
79.9
73.1
75.3
Saturdays ......
86.5
75.7
75.7
Sundays ... .... .
86.9
74.4
76.2
I
86.7
89 .7
86.2
82.6
89 .1
86.8
92.4
88.8
83.8
92.8
77.4
85.9 1 68.7
85.7
88.5
88.6
81.5
89 .2
86.9
92.7
89 .1
85.8
90.1
75.4
81 .2
63.1
78.6
88.4
90.5
71.4
83.3
84.7
89.0
90.3
80.9
93.8
63.1
67 .8
64.8
83.9
84.3
89.2
90.5
80.3
93.8
64 .8
68.0 I 60.2
89.2
86.8
92.3
88.8
83.4
93.4
78.6
87.4 1 70.0
I
Both Saturday
and
Sunday .. .... ..
87.0
72.1
78.5
80.6
88.2
90.1
70.9
Weekdays
only .. ....... ... .
76.9
72.2
75.8
87.3
90.6
86.0
83.1
I
30 hours
or more 1
I
I
I
Total .. ... .... ..
46.9
48.6
46.2
40.4
36.9
139.1
43.1
34.9
31 .8
37.2
25.7
34.1
31 .7
35.4
35.1
44 .7
Saturdays .... ..
59.7
51.4
50.4
38.8
32 .8
33.9
50.6
41.8
41 .0
43.5
28.7
38.7
47.5
37.1
37.8
37.4
Sundays ...... ..
58.6
52.0
51 .2
33.6
37.4
37.3
46.4
39.8
40.6
37.7
30.0
35.6
40.6
32.7
35.3 1 41 .3
Both Saturday
and
Sunday ... .....
60.2
51.9
51.5
34.5
37.5
37.3
47.0
40.5
41.3
38.4
30.4
35.4
40.0
32.9
35.3
Weekdays
only .............
45.0
48.0
45.3
40.7
38.3
40.1
41.5
33.3
29.9
35.5
25 .1
33.6
29.2
34.7
I
' Thirty hours or more is considered full time in European
countries.
47 .1
34.0
j
1
NoTE: "Saturdays" and "Sundays" include those who may also work the
other weekend day; these two categories are not mutually exclusive.
workers are women, women constitute about a fifth of weekend workers.
Summary and discussion
As noted at the outset, this article examines women's share
of employment, with a focus on weekend work for 15 European countries and the United States. For all European countries considered, the data show an upward trend over the decade, or sustained high levels, in the percent female among
all wage and salary earners . Along with the increase in the
female share of all earners, some countries have experienced
an increase in weekend employment. It is interesting that the
"popular wisdom" is that weekend employment is on the rise
throughout Europe, because of a loosening of restrictions on
weekend commerce, increasing rationalization in production,
and the spread of "American-style" consumer preferences.
In fact, the picture of change in Europe is more complicated.
In the last decade, there has been no uniform increase in Saturday employment, and some countries show a decline. How-
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
I
41 .3
ever, Sunday employment, which is less common, is ri sing in
more countries than not, especially in the Western/Central
European countries and in Spain.
Many European countries have also experienced an increasing share of females among those working weekends.
However, it is not necessarily the countries with higher shares
female of those employed that have higher shares female
working weekends. Moreover, it matters whether one is considering Saturday or Sunday employment, as some countries
relatively high on one day are not on the other.
Comparisons of these countries for the most recent year
by economic sector show that women's greater likelihood of
being in the service rather than industrial sector (relative to
men) helps generate the disproportionate share of female
weekend employment. However, even among men and women
within the service sector, weekend employment is disproportionately female in several countries; the reverse is true for
the industrial sector.
Women are more likely than men in these countries to
work part time, and part-time work has a much higher share
Monthly Labor Review
August
2005
51
Female Weekend Employment
■ l•lell=..--
Percent female of all wage and salary earners aged 25 to 64, employed 30 hours or more, by weekend schedule
and economic sector, 15 European countries and the United States, 2001 or most recent year comparable data
are available
Nordic countries
British Isles
Sector
Sweden Finland Denmark
Norway
United
Kingdom
Southern
European
countries United
States
Nether- Bel( 1997)
Switzer- Austria
lands
gium Luxem- Italy Spain
land
(1999) (1998) bourg
Western/Central European countries
GerIre- France many
land
(1997)
Service
sector
Saturdays .......
65.6
60.1
56.1
45.6
40.1
43.5
54.1
48.1
44.5
50.1
34.0
43.2
50.7
42.9
44.4
42.7
Sundays .........
65.0
62.1
57.3
40.4
43.2
43.9
51.9
46.3
43.3
43.6
35.7
42 .0
44.3
36.4
40.2
45.8
Both Saturday
and
Sunday .. ... ...
66.3
62 .6
57.6
41.7
43.1
43.9
52.3
46.7
43.5
43.9
36.0
41.7
43.6
36.5
40.1
45.5
Weekdays
only ..... .. ......
54.9
59.2
54.6
49.3
47.1
50.4
51.2
45.4
36 .8
46.4
31.8
42.4
36.9
42.7
46.9
55.4
Saturdays .......
15.9
16.8
12.1
11.5
6.8
10.0
24.3
18.4
14.9
15.5
6.9
9.6
24.2
14.0
10.6
16.1
Sundays ....... ..
20.2
15.8
15.2
12.2
10.4
14.5
20.8
10.3
14.7
14.7
6.3
4.5
19.2
11.1
9.8
17.5
Both Saturday
and
Sunday ..... ...
16.3
14.4
10.8
12.3
8.4
14.1
21.3
9.8
14.6
15.5
5.3
4.3
19.2
10.8
9.6
15.2
Weekdays
only ........... ..
22.2
26.1
26.5
17.9
21 .1
23.4
24.1
19.3
16.7
17.9
11.2
18.6
8.7
25.1
17.5
28.1
Industrial
sector
NorE: "Saturdays" and "Sundays" include those who may also work the other weekend day; these two categories are not mutually exclusive.
of female employees than does full-time work. However,
among part-timers , weekend employment is not much more
"feminized" than weekday work; the difference i5 more
marked for full-timers. Among full-timers in the service sector, women are disproportionately in weekend employment,
whereas for full-timers in the industrial sector, women disproportionately work weekdays only.
This article's findings raise some important analytical
questions. A key question is: Does the overall pattern of high
and rising weekend employment among women advance
women economically, or does this pattern indicate another
form of labor market disadvantage among women? Weekend employment may be viewed as an important part of the
general erosion of the standard work week, regarded by some
as "one of the major achievements of the working class." 13
This perspective suggests that weekend work, when mandated by employers, may not be in the interest of most employees and could potentially affect morale and productivity.
It changes the temporal structure of family life, often reducing spouse interaction and parental time with children. It
also adds to the complexity of childcare arrangements, par52 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
ticularly in single-parent families. 14 In addition, many other
forms of social interaction may be constrained because one
is unavailable when friends and family who are not employed
on weekends engage in leisure activities.
In some countries and/or sectors, weekend employment
commands relatively high pay premia, whereas in others it
does not. In the former cases, employees would presumably
compete for weekend shifts, whereas, in the latter cases, those
with less seniority or less bargaining power may be assigned
those shifts. It may be, for example, that in the service sector
weekend workers receive little in the way of compensatory
pay and thus women's disproportionate share of weekend
service work reflects their disadvantage in the labor market.
If the opposite tends to be true in the industrial sector for
some or a11 countries, then the fact that this sector has a higher
percent of women working weekdays only, compared with
weekends, might be a sign of women's disadvantage vis-avis male workers (or possibly a bias by the unions that represent them).
Responding to these issues would require data on a number of variables in addition to gender and weekend employ-
ment, variables not available in the European Labour Force
Survey data. To fully understand the extent to which women,
and men, prefer weekend shifts, and the advantages and disadvantages associated with working those shifts, one would
need microdata that include workers' wages, scheduling preferences, and union member~hip, as well as other variables.
This line of analysis is probably best approached using country case studies, supplemented by country-specific datasets.
Another key issue concerns the institutional factors that
shape the prevalence, and the quality, of weekend employment. Regions, or country clusters, are generally not very
homogeneous with respect to weekend employment-that is,
its prevalence, growth, or degree to which workers are
women. This suggests that the sources of country-level varia-
tion are not clearly rooted in overarching labor market characteristics or welfare-state designs. To the extent that public
policies matter, the factors have yet to be identified. 15 Moving forward in this regard entails consideration of such factors as the extent to which countries restrict production or
operation at nonstandard times, including weekends, the extent to which public services (such as childcare) are available on a 7-day basis to accommodate workers scheduled at
nonstandard times, and the extent to which weekend workers
are compensated for such employment in the form of pay
premia and/or compensatory time.
To conclude, the share of women working weekends is an
important social and economic phenomenon that merits more
□
attention and needs further exploration.
Notes
ACKNOWLEDGMENTS: The authors thank Sangeeta Parashar and Lijuan
Wu , graduate students at the University of Maryland supported by the
William and Flora Hewlett Foundation, for their programming assistance
for this article. We also gratefully acknowledge financial support from the
Russell Sage Foundation to conduct this research.
1
"Working Hours: Latest Trends and Policy Initiatives," OECD Employment Outlook (Organisation for Economic Co-operation and Development, 1998), pp. 153- 88; John M. Evans, Douglas C. Lippoldt, and
Pascal Marianna, " Labour Market and Social Policy: Trends in Working
Hours in 0ECD Countries," Occasional paper 45 (Paris, Organisation for
Economic Co-operation and Development, Employment, Labor, and Social Affairs Committee, 2000).
2
Harriet B . Presser, Working in a 2417 Economy: Challenges for
American Families (New York, Russell Sage Foundation, 2003).
3
In another paper in preparation, we assess employment during nonday
hours , that is evening, night, and rotating hours, in these same countries.
4
All of the European countries in this article are EU members, with the
exception of Switzerland and Norway. Eurostat gathers data on a limited
number of nonmember European countries.
~ For reasons of confidentiality, Eurostat would not provide the precise sample sizes for each of these countries after the subsample was selected with the restrictions noted, although weights were provided and
used to generate the national estimates.
6
Eurostat's distribution policy changed in July 2005. As of that date;
Eurostat will make anonymized microdata files available to researchers from
qualifying institutions for a fee.
7
The restriction to wage and salary workers is based on our interest in
workers who are subject to employer demands and have less control over
working weekends than the self-employed. The prevalence of weekend
employment would be higher if the self-employed were included.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
8
In the 1997 CPS, no reference period was specified in the question
concerning which days of the week people worked (neither '·usually" nor
"last week"); however, this question was asked after other questions relating to the usual week.
9
Harriet B. Presser and Janet C. Gornick, ··weekend Employment in
High -Income Countries: A Comparative Analysis," paper presented at the
2004 annual meeting of the Population Association of America, Boston,
MA, April 1, 2004; Presser, Working in a 2417 Economy.
10
The European labor force surveys include a variable called '"economic activity of local unit." Eurostat uses the Standard Classification of
Industries (NACEIRev 1) to classify all workers into one of three sectorsagriculture, industry, or services. In this analysis, we excluded the agricultural sector and contrasted the other two.
11
To make this comparison precisely, we compare the female share in
Saturday work, and in Sunday work, with the female share of the total
service sector workforce. If the female share on either Saturday or Sunday
exceeds the female share of the total, we consider that to be a case of
female overrepresentation on the weekend. We use this same comparison
rule in our analyses of tables I and 2.
12
In the United States, 35 hours or more per week is considered fulltime employment.
13
Karl Hinrichs, " Working Time Development in West Germany: Departure to a New Stage," in Karl Hinrichs, William Roche, and Carmen
Sirianni, eds., Working Time in Transition: The Political Economy of
Working Hours in Industriali zed Nations (Philadelphia, Temple University Press, 1991 ), p. 30.
14
Presser, Working in a 2417 Economy.
A study using crude indicators of regulation around the year 1990
examined public policies' relation to weekend employment in several European countries, and did not find a connection. See David Grubb and
William Wells , ··Employment Regulation and Patterns of Work in EC Countries," OECD Economic Studies, 1993, Vol. 21 (winter), pp. 7- 58.
15
Monthly Labor Review
August
2005
53
Immigrants of New York
Although Ellis Island is today just a
national park, New York is still a city very
much affected by immigration. In "New
York City Immigrants: The 1990s Wave,"
the June 2005 title in the Federal Reserve
Bank of New York series of Current
Issues in Economics and Finance, Rae
Rosen, Susan Wieler, and Joseph Pereira
outline the impact immigration has had
on the City's population and labor force
in the 2000 census.
In the decade just preceding the
decennial census, 1.2 million foreign
immigrants very nearly replaced the 1.3
million residents who left New York for
nearby counties or other States. Over
the years, that "cycling" of migration
resulted in foreign-born persons making
up fully 45 percent of New York City's
adult population. Obviously, such a
large group has a significant impact on
the characteristics of the population and
labor force; that impact reflects a remarkable diversity in the characteristics ofrecent immigrants .
Rosen, Wieler, and Pereira find, for
example, that "although the 1990s adult
immigrants are on the whole better
educated than foreign - born city
residents who arrived in earlier decades,
they tend to cluster at opposite ends of
the education spectrum." New arrivals
from Latin America, the Caribbean, or
Mexico may often have limited English
or be without a high school diploma. At
the other end of the scale, recent immigrants from many parts of Asia have a
higher proportion of college graduates
among them than the proportion of
degree holders among native-born
residents. Within the Asian immigrants,
new arrivals were the exception with
relatively low rates of both college
graduation and English fluency.
Labor force participation and labor
ma1 ket outcomes also vary widely
among recent immigrant groups. Recent
immigrants from China have relatively
high labor force participation rates,
54
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
while those from various segments of
the former Soviet Union have a
participation rate lower than 50 percent.
"Contrary to what one might expect,
however," write Rosen, Wieler, and
Pereira, "it is not always the least
educated or least English fluent groups
that have the highest unemployment or
public assistance rates. As noted earlier,
immigrants from China have relatively
low levels of education, English fluency,
and income, yet their public assistance
rate ( 1.8 percent) is one of the lowest
reported. Their unemployment rate (5.9
percent) is also among the lowest
reported, and their labor force participation rate (62.6 percent) one of the
highest."
Productivity and
business cycles
An often-noted common feature of the
business cycle recoveries of 1991 and
2001 has been relatively slow employment growth in the early years of the
upturn. In the July/ August issue of the
Federal Reserve Bank of St. Louis
Review, Kathryn Koenders and Richard
Rogerson examine this phenomenon
from the perspective of organizational
dynamics. Their article, "Organizational
Dynamics Over the Business Cycle: A
View on Jobless Recoveries," starts by
noting that the two most recent recoveries share another, less-frequently
noted, common feature: both followed
the recession that ended an unusually
long expansion.
Using that feature as a starting point,
Koenders and Rogerson developed a
model by which the dynamics of
reorganizing production to eliminate
unneeded labor could be the link
between the speed of net job growth
during recovery and the duration of the
previous expansion. In their model,
labor utilization inefficiencies emerge
over time, but the effort to reorganize
2005
might be postponed during a long
period of expansion. "Because," say
Koenders and Rogerson, "reorganization leads to the shedding of
unnecessary labor and takes time, this
gives rise to an extended period in
which the economy sheds labor, thereby
delaying the date at which aggregate
employment begins to increase during
the recovery."
One very useful aspect of Koenders
and Rogerson 's research is an extension of the long-expansion-delayedemployment-growth observation beyond the past two business cycles. In
their analysis of the eight post- 1950
recessions, they found not two but
three that followed exceptionally long
expansions: the recovery from the 196970 recession followed an expansion that
ranked second in duration between the
two most recent. Using the perspective
of their model, Koenders and Rogerson
found that "the behavior of employment
in the 1970 recovery is in fact very similar
to the behavior of employment in the
recoveries of 1991 and 2001 and is
qualitatively different from the behavior
of employment in the other post-World
War II recoveries."
Specifically, Koenders and Rogerson
examined the change in employment
relative to trend after the 1970 trough
and found that the "cyclical" component of employment continued to
fall for a year after the business cycle
trough, as did cyclical employment
in 1991 and 2001. Although trendadjusted employment in the earlier
episode started to recover after only a
year's delay, Koenders and Rogerson
suggest that the period of reorganization should be dated from four
quarters before the 1969 peak, rather
than the typical two quarters.
Koenders and Rogerson suggest that,
with these adjustments, one could
consider that all three recoveries from
recessions ending long expansions
were characterized by delayed recoveries in employment.
□
Agriculture and
natural resources
Folmer, Henk and Tom Tietenberg, eds., The
International Yearbook of Environmental
and Resource Economics 2003/2004: A
Survey of Current Issues. Northampton,
MA, Edward Elgar Publishing, Inc., 2003,
386 pp., $55/softcover.
Economic and social statistics
Bergin, Paul, Reuven Glick, and Alan M.
Taylor, Productivity, Tradability, and the
Long-Run Price Puule. Cambridge, MA,
National Bureau of Economic Re search,
Inc., 2004, 54 pp. (Working Paper 10569)
$10 per copy, plus $10 for postage and
handling outside the United States.
Bergoeing, Raphael, Norman Loayza, and Andrea Repetto, Slow Recoveries. Cambridge,
MA, National Bureau of Economic Research, Inc., 2004, 37 pp. (Working Paper
10584) $10 per copy, plus $10 for postage and handling outside the United States.
Pakes, Ariel, Michael O strovsky, and Steve
Berry, Simple Estimators for the Parameters of Discrete Dynamic Games (With
Entry/Exit Examples). Cambridge, MA,
National Bureau of Economic Research,
Inc., 2004, 47 pp. (Working Paper I 0506)
$10 per copy, plus $10 for postage and
handling outside the United States.
Economic growth
and development
Annual Report to Parliament on Immigration 2004. Ottawa, Minister of Public
Works and Government Services Canada,
39 pp., softcover.
Arora, Ashish and Alfonso Gambardella, The
Globalimtion ofthe Software Industry: Perspectives and Opportunities for Developed
and Developing Countries. Cambridge, MA,
National Bureau of Economic Research, Inc.,
2004, 40 pp. (Working Paper l 0538) $10 per
copy, plus $10 for postage and handling outside the United States.
Pagan, Jose A., Worker Displacement in the US!
Mexico Border Region: Issues and Challenges. Northampton, MA, Edward Elgar
Publishing, Inc., 2004, 127 pp., $65/cloth.
Postlewaite, Andrew, Lany Samuelson, and Dan
Silverman, Consumption Commitments and
Preferences for Risk. Cambridge, MA, National Bureau of Economic Research, Inc.,
2004, 41 pp. (Working Paper l 0527) $10 per
copy, plus $10 for postage and handling outside the United States.
Sciarra, Silvana, Paul Davies, and Mark
Freedland, Employment Policy and the
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Regulation of Part-time Work in the European Union: A Comparative Analysis.
New York, Cambridge University Press,
2004, 368 pp. , $95/hardback.
Education
Freeman, Richard B., Emily Jin, and Chia-Yu
Shen, Where Do New U.S.-Trained ScienceEngineering PhDs Come From? Cambridge,
MA, National Bureau of Economic Research,
Inc., 2004, 31 pp. (Working Paper I0554)
$10 per copy, plus $10 for postage and handling outside the United States.
Lavy, Victor, Targeted Remedial Education
for Under-Performing Teenagers: Costs
and Benefits. Cambridge, MA, National
Bureau of Economic Research, Inc., 2004,
43 pp. (Working Paper 10575) $10 per
copy, plus $10 for postage and handling
outside the United States.
Nicholson, Sean, How Much Do Medical Students Know About Physician Income? Cambridge, MA, National Bureau of Economic
Research, Inc., 2004, 23 pp. (Working Paper
10542) $10 per copy, plus $10 for postage
and handling outside the United States.
Industrial relations
Kohl, Heribert and Hans-Wolfgang Platzer, eds.,
Industrial relations in Central and Eastern
Europe: Transformation and integration. A
comparison ofthe eight new EU member states.
Brussels, European Trade Union Institute,
2004, 422 pp., softcover.
Lipset, Seymour Martin, Noah M. Meltz, Rafael
Gomez, and Ivan Katchanovski, The Paradox ofAmerican Unionism: Why Americans Like Unions More Than Canadians Do
But Join Much Less. Ithaca, NY, Cornell University Press, 2004, 208 pp., $32.5Q/cloth.
Morris, Charles J., The Blue Eagle at Work:
Reclaiming Democratic Rights in the American Workplace. Ithaca, NY, Cornell University Press, 2005, 328 pp., $35/cloth.
Industry and government
organization
Jolls, Christine, Identifying the Effects ofthe Americans with Disabilities Act Using State-Law
Variation: Preliminary Evidence on Educational Participation Effects. Cambridge, MA,
National Bureau of Economic Research, Inc.,
2004, 16 pp. (Working Paper I 0528) $10 per
copy, plus $10 for postage and handling outside the United States.
Mulligan, Casey and Andrei Shleifer, Conscription as Regulation. Cambridge, MA,
National Bureau of Economic Research,
Inc., 2004, 25 pp. (Working Paper 10558)
$10 per copy, plus $10 for postage and
handling outside the United States.
International economics
Hatton, Timothy J. and Jeffrey G Williamson,
International Migration in the Long-Run:
Positive Selection, Negative Selection and
Policy. Cambridge, MA, National Bureau
of Economic Research, Inc., 2004, 37 pp.
(Working Paper I 0529) $10 per copy, plus
$10 for postage and handling outside the
United States.
Labor and economic history
Bender, Daniel E. and Richard A. Greenwald,
Sweatshop USA: The American Sweatshop
in Historical and Global Perspective. New
York, Routledge/Taylor and Francis Books,
Inc., 2003, 300 pp. , $24.95/softcover.
Cappelli, Peter and Monika Hamori, The Path to
the Top: Changes in the Attributes and Careers of Corporate Executives, 1980-2001.
Cambridge, MA, National Bureau of Economic
Research, Inc., 2004, 53 pp. (Working Paper
10507) $10 per copy, plus $10 for postage
and handling outside the United States.
Linder, Marc, "Time and a Halfs the American
Way": A History ofthe Exclusion ofWhiteCollar Workers from Overtime Regulation,
1868-2004. Iowa City, Fanplhua Press, 2004,
1,342 pp., paperback.
Olwell, Russell B.,At Work in the Atomic City: A
Labor and Social History of Oakridge, Tennessee. Knoxville, TN, The University ofTennessee Press, 2004, 176 pp., $29/cloth.
Roscigno, VincentJ. and William F. Danaher, The
Voice ofSouthern Labor: Radio, Music, and
Textile Strikes, 1929-1934. Minneapolis,
University of Minnesota Press, 2004, 2 I 6
pp., $59.95/cloth; $19.95/paperback.
Sterling, Dorothy, Close to My Heart: An Autobiography. New York, The Quantuck
Lane Press, 2004, 120 pp., $23/cloth.
Von Drehle, David, Triangle: The Fire That
Changed America. New York, Grove
Press, 2003, 340 pp., $14/cloth.
Labor force
Vickrey, William S., and Mathew Forstater
and Pavlina R. Tchemeva, eds., Full Employment and Price Stability: The Macroeconomic Vision of William S. Vickrey.
Northampton, MA, Edward Elgar Publishing, Inc., 2004, 141 pp., $85/cloth.
Labor organizations
Gifford, Court, ed., Directory of U.S. Labor
Organizations 2004 Edition. Washington, DC, The Bureau of National Affairs,
Inc., 2004, 292 pp., $105/softcover.
Monthly Labor Review
August 2005
55
Publications Received
Management and organization
theory
Bergstresser, Daniel, Mihir A. Desai, and Joshua
Rauh, Earnings Manipulation and Managerial Investment Decisions: Evidence from
Sponsored Pension Plans. Cambridge, MA,
National Bureau of&onomic Research, Inc.,
2004, 48 pp. (Working Paper 10543) $10 per
copy, plus $10 for postage and handling outside the United States.
Downs, Cal W. and Allyson D. Adrian, Assessing Organizational Communication:
Strategic Communication Audits. New
York, The Guilford Press, 2004, 292 pp.,
$40/paperback.
Stone, Katherine V. W., From Widgets to
Digits: Employment Regulation/or the
Changing Workplace. New York, Cambridge University Press, 2004, 300 pp.,
$75/hardback; $29.99/paperback.
Monetary and fiscal policy
Davis, Steven J. and Magnus Henrekson, Tax
Effects on Work Activity, Industry Mix and
Shadow Economy Size: Evidence from
Rich-Country Comparisons. Cambridge,
MA, National Bureau of Economic Research, Inc., 2004, 64 pp. (Working Paper
10509) $10 per copy, plus $IO for postage and handling outside the United States.
Prices and living conditions
Jovanovic, Boyan, Asymmetric Cycles. Cambridge, MA, National Bureau of &anomic
Research, Inc., 2004, 25 pp. (Working Paper
10573) $10 per copy, plus $IO for postage
and handling outside the United States.
Productivity and technological
change
Adams, James D.,R&D Sourcing,Joint Ventures and Innovation: A Multiple Indicators Approach. Cambridge, MA, National
Bureau of Economic Research, Inc., 2004,
34 pp. (Working Paper 10474) $10 per
copy, plus $10 for postage and handling
outside the United States.
Galenson, David W., A Portrait ofthe Artist as a
Very Young or Very Old Innovator: Creativity at the Extremes of the Life Cycle. Cambridge, MA, National Bureau of &anomic
Research, Inc., 2004, 86 pp. (Working Paper
10515) $IO per copy, plus $10 for postage
and handling outside the United States.
Maurer, Stephen M. and Suzanne Scotchmer,
Profit Neutrality in Licensing: The Boundary
BetweenAntitrust Law and Patent Law. Cambridge, MA, National Bureau of &anomic
Research, Inc., 2004, 46 pp. (Working Paper
56
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
l 0546) $10 per copy, plus $10 for postage
and handling outside the United States.
Schor, Adriana, Heterogeneous Productivity Response to Tariff Reduction: Evidence from
Brazilian Manufacturing Firms. Cambridge,
MA, National Bureau of&onomic Research,
Inc., 2004, 29 pp. (Working Paper 10544)
$10 per copy, plus $10 for postage and handling outside the United States.
Syverson, Chad, Market Structure and Productivity: A Concrete Example. Cambridge,
MA, National Bureau of Economic Research, Inc., 2004, 45 pp. (Working Paper
10501) $10 per copy, plus $10 for postage and handling outside the United States.
Social institutions and
social change
Hunt, Jennifer, Trust and Bribery: The Role of
the Quid Pro Quo and the Link with Crime.
Cambridge, MA, National Bureau of&onomic
Research, Inc., 2004, 38 pp. (Working Paper
10510) $10 per copy, plus $10 for postage
and handling outside the United States.
Paul, Annie Murphy, The Cult ofPersonality:
How Personality Tests Are Leading Us to
Mis educate Our Children, Mismanage Our
Companies, and Misunderstand Ourselves. New York, Free Press/Simon &
Schuster, Inc., 2004, 302 pp., $26/cloth.
Persico, Nicola, Andrew Postlewaite, and
Dan Silverman, The Effect ofAdolescent
Experience on Labor Market Outcomes:
The Case of Height. Cambridge, MA,
National Bureau of &anomic Research,
Inc., 2004, 40 pp. (Working Paper 10522)
$10 per copy, plus $10 for postage and
handling outside the United States.
Urban affairs
Collins, Wliliam J. and Robert A. Margo, The
EconomicAftermathofthe 1960sRiots: Evidence from Property Values. Cambridge, MA,
National Bureau of&onomic Research, Inc.,
2004, 30 pp. (Working Paper I0493) $10 per
copy, plus $10 for postage and handling outside the United States.
Lustig, Hanno and Stijn Van Nieuwerburgh,
Housing Collateral and Consumption Insurance Across U.S. Regions. Cambridge,
MA, National Bureau of Economic Research, Inc., 2004, 47 pp. (Working Paper
10505) $10 per copy, plus $10 for postage and handling outside the United States.
Wages and compensation
Adams, Scott and David Newmark, The Economic Effects of Living Wage Laws: A
Provisional Review. Cambridge, MA,
August 2005
National Bureau of Economic Research,
Inc., 2004, 38 pp. (Working Paper 10562)
$10 per copy, plus $10 for postage and
handling outside the United States.
Adams, Scott and David Neumark, When Do
Living Wages Bite? Cambridge, MA, National Bureau of Economic Research, Inc.,
2004, 36 pp. (Working Paper I 0561) $10
per copy, plus $10 for postage and handling outside the United States.
Allen, Steven G, The Value of Phased Retirement. Cambridge, MA, National Bureau of Economic Research, Inc. , 2004,
39 pp. (Working Paper 10531) $ 10 per
copy, plus $10 for postage and handling
outside the United States.
Hunt, H. Allan, ed., Adequacy ofEarnings Replacement in Workers ' Compensation Programs: A Report of the Study Panel on
Benefit Adequacy ofthe Workers' Compensation Steering Committee. Kalamazoo,
MI, W.E. Upjohn Institute for Employment
Research, 2004, 177 pp., $16/paperback.
Welfare programs
and social insurance
Acs, Gregory and Pamela Loprest, Leaving Welfare: Employment and Well-Being ofFamities that Left Welfare in the Post-Entitlement
Era. Kalamazoo, MI, W.E. Upjohn Institute
for Employment Research, 2004, 120 pp.,
$40/cloth; $15/paperback.
Cawley, John and Sheldon Danziger, Obesity as a Barrier to the Transition from
Welfare to Work. Cambridge, MA, National Bureau of Economic Research, Inc.,
2004, 34 pp. (Working Paper 10508) $10
per copy, plus $10 for postage and handling outside the United States.
Chetty, Raj, Optimal Unemployment Insurance
When Income Effects Are Large. Cambridge,
MA, National Bureau of Economic Research,
Inc., 2004, 56 pp. (Working Paper 10500)
$10 per copy, plus $10 for postage and handling outside the United States.
Currie, Janet, The Take Up of Social Benefits.
Cambridge, MA, National Bureau ofEconomic
Research, Inc., 2004, 51 pp. (Working Paper
10488) $10 per copy, plus $10 for postage
and handling outside the United States.
Lemieux, Thomas and Kevin Milligan, Incentive
Effects of Social Assistance: A Regression
Discontinuity Approach. Cambridge, MA,
National Bureau of Economic Research, Inc.,
2004, 55 pp. (Working Paper 10541) $10 per
copy, plus $10 for postage and handling outside the United States.
D
Notes on labor statistics ..............................
Comparative indicators
58
1. T.abor market indicators .... ..... ..... .. .. .. .... .. .. .. ... ... .... .. .. .. .... .. 71
2. Annual and quarterly percent changes in
compensation, prices, and productivity ....................... 72
3. Alternative measures of wages and
compensation changes................................................... 72
Labor force data
4. Employment status of the population,
seasonally adjusted .......................................................
5. Selected employment indicators,
seasonally adjusted .......................................................
6. Selected unemployment indicators,
seasonally adjusted .......................................................
7. Duration of unemployment,
seasonally adjusted .......................................................
8. Unemployed persons by reason for unemployment,
seasonally adjusted .......................................................
9. Unemployment rates by sex and age,
seasonally adjusted ..................................................... ..
I 0. Unemployment rates by State,
seasonally adjusted .......................................................
11. Employment of workers by State,
seasonally adjusted .......................................................
12. Employment of workers by industry,
seasonally adjusted ........ ...............................................
13. Average weekly hours by industry,
seasonally adjusted .......................................................
14. Average hourly earnings by industry,
seasonally adjusted ........................................................
15. Average hourly earnings by industry ................................
16. Average weekly earnings by industry ...............................
17. Diffusion indexes of employment change,
seasonally adjusted ........................................... ............
18. Job openings levels and rates, by industry and regions,
seasonally adjusted.........................................................
19. Hires levels and rates by industry and region,
seasonally adjusted..........................................................
20. Separations levels and rates by industry and region,
seasonally adjusted..........................................................
21. Quits levels and rates by industry and region,
seasonally adjusted..........................................................
22. Quarterly Census of Employment and Wages,
IO largest counties .... ...... .... .. .. .. ...... .... .... ... .... ... .... .. .. .... .
23. Quarterly Census of Employment and Wages, by State..
24. Annual data: Quarterly Census of Employment
and Wages, by ownership .............................................
25. Annual data: Quarterly Census of Employment and Wages,
establishment size and employment, by supersector ...
26. Annual data: Quarterly Census of Employment and
Wages, by metropolitan area .........................................
27. Annual data: Employment status of the population ........
28. Annual data: Employment levels by industry ..................
29. Annual.data:. Average hours and earnings level,
by industry .....................................................................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
73
74
75
75
76
77
78
79
80
83
84
85
86
87
88
88
89
89
90
92
93
94
95
100
I 00
101
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.
I 02
I 04
I 06
I 07
I08
I 09
11 O
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 .... .. .... .. .. .. .... .. .... .. .... ...... .. .... ...... ...
111
114
1 I5
116
117
I 18
119
120
121
121
121
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 ....................................................
51. Annual indexes of output per hour for select
industries ................................ .................................... ...
122
123
124
125
International comparisons data
52. Unemployment rates in nine countries,
seasonally adjusted....................................................... 128
53. Annual data: Employment status of the civilian
working-age population, IO countries............................ 129
54. Annual indexes of productivity and related measures,
15 economies.................................................................. 130
Injury and Illness data
55. Annual data: Occupational injury and illness ................... 132
56. Fatal occupational injuries by event or exposure .............. 134
Monthly Labor Review
August
2005
57
Notes on Current Labor Statistics
This section of the Review presents the principal statistical series collected and calculated by the Bureau of Labor Statistics:
series on labor force; employment; unemployment; labor compensation; consumer,
producer, and international prices; productivity; international comparisons; and injury
and illness statistics. In the notes that follow, the data in each group of tables are
briefly described; key definitions are given;
notes on the data are set forth; and sources
of additional information are cited.
General notes
The following notes apply to several tables
in this section:
Seasonal adjustment. Certain monthly
and quarterly data are adjusted to eliminate
the effect on the data of such factors as climatic conditions, industry production
schedules, opening and closing of schools,
holiday buying periods, and vacation practices, which might prevent short-term evaluation of the statistical series. Tables containing data that have been adjusted are identified as "seasonally adjusted." (All other
data are not seasonally adjusted.) Seasonal
effects are estimated on the basis of current
and past experiences. When new seasonal
factors are computed each year, revisions
may affect seasonally adjusted data for several preceding years.
Seasonally adjusted data appear in tables
1-14, 17-21, 48, and 52. Seasonally adjusted labor force data in tables I and 4-9
were revised in the February 2005 issue of
the Review. Seasonally adjusted establishment survey data shown in tables 1, 12-14,
and 17 were revised in the March 2005 Review. A brief explanation of the seasonal
adjustment methodology appears in '·Notes
on the data."
Revisions in the productivity data in
table 54 are usually introduced in the September issue. Seasonally adjusted indexes
and percent changes from month-to-month
and quarter-to-quarter are published for numerous Consumer and Producer Price Index series. However, seasonally adjusted indexes are not published for the U.S. average All-Items CPI. Only seasonally adjusted
percent changes are available for this series.
Adjustments for price changes. Some
data-such as the "real" earnings shown in
table 14--are adjusted to eliminate the effect of changes in price. These adjustments
are made by dividing current-dollar values
by the Consumer Price Index or the appropriate component of the index, then multiplying by I 00. For example, given a current
hourly wage rate of $3 and a current price
58 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
index number of 150, where 1982 = I 00,
the hourly rate expressed in 1982 dollars is
$2 ($3/150 x 100 = $2). The $2 (or any other
resulting values) are described as "real,"
"constant," or" 1982" dollars.
Sources of information
Data that supplement the tables in this section are published by the Bureau in a variety of sources. Definitions of each series and
notes on the data are contained in later sections of these Notes describing each set of
data. For detailed descriptions of each data
series, see BLS Handbook of Methods, Bulletin 2490. Users also may wish to consult
Major Programs of the Bureau of Labor Statistics, Report 919. News releases provide
the latest statistical information published
by the Bureau; the major recurring releases
are published according to the schedule appearing on the back cover of this issue.
More information about labor force, employment, and unemployment data and the
household and establishment surveys underlying the data are available in the Bureau's
monthly publication, Employment and
Earnings. Historical unadjusted and seasonally adjusted data from the household survey are available on the Internet:
www.bls.gov/cps/
Historically comparable unadjusted and seasonally adjusted data from the establishment
survey also are available on the Internet:
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:
www.bls.gov/lpd
For additional information on interna-
August 2005
tional comparisons data, see International
Comparisons of Unemployment, Bulletin
1979.
Detailed data on the occupational injury
and illness series are published in Occupational Injuries and Illnesses in the United
States, by Industry, a BLS annual bulletin.
Finally, the Monthly Labor Review carries analytical articles on annual and longer
term developments in labor force, employment, and unemployment; employee compensation and collective bargaining; prices;
productivity; international comparisons;
and injury and illness data.
Symbols
n.e.c. = not elsewhere classified.
n.e.s. = not elsewhere specified.
p = preliminary. To increase the timeliness of some series, preliminary
figures are issued based on representative but incomplete returns.
r
revised. Generally, this revision
reflects the availability of later
data, but also may reflect other
adjustments.
Comparative Indicators
(Tables 1-3)
Comparative indicators tables provide an
overview and comparison of major BLS statistical series. Consequently, although many
of the included series are available monthly,
all measures in these comparative tables are
presented quarterly and annually.
Labor market indicators include employment measures from two major surveys
and information on rates of change in compensation provided by the Employment
Cost Index (ECI) program. The labor force
participation rate, the employment-population ratio, and unemployment rates for major demographic groups based on the Current Population ("household") Survey are
presented, while measures of employment
and average weekly hours by major industry sector are given using nonfarm payroll
data. The Employment Cost Index (compensation), by major sector and by bargaining
status, is chosen from a variety of BLS
compensation and wage measures because
it provides a comprehensive measure of
employer costs for hiring labor, not just
outlays for wages, and it is not affected
by employment shifts among occupations
and industries.
Data on changes in compensation,
prices, and productivity are presented in
table 2. Measures of rates of change of compensation and wages from the Employment
Cost Index program are provided for all civilian nonfarm workers (excluding Federal
and household workers) and for all private
nonfarm workers . Measures of changes in
consumer prices for all urban consumers;
producer prices by stage of processing; overall prices by stage of processing; and overall export and import price indexes are
given. Measures of productivity (output per
hour of all persons) are provided for major
sectors.
Alternative measures of wage and
compensation rates of change, which reflect the overall trend in labor costs, are summarized in table 3. Differences in concepts
and scope, related to the specific purposes
of the series, contribute to the variation in
changes among the individual measures.
Notes on the data
Definitions of each series and notes on the
data are contained in later sections of these
notes describing each set of data.
Employment and
Unemployment Data
(Tables I; 4-29)
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.
Household survey data
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
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 www.bls.gov/cps/
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.
Establishment survey data
Description of the series
Employment, hours, and earnings data in
this section are compiled from payroll
records reported monthly on a voluntary basis to the Bureau of Labor Statistics and its
cooperating State agencies by about
160,000 businesses and government agencies, which represent approximately
400,000 individual worksites and represent
all industries except agriculture. The active
CES sample covers approximately one-third
of all nonfarm payroll workers. Industries
are classified in accordance with the 2002
North American Industry Classification System. In most industries , the sampling probabilities are based on the size of the establishment; most large establishments are
therefore in the sample. (An establishment
is not necessarily a firm; it may be a branch
plant, for example, or warehouse.) Self-employed persons and others not on a regular
civilian payroll are outside the scope of the
survey because they are excluded from establishment records. This largely accounts for
the difference in employment figures between
the household and establishment surveys.
Employed persons include (I) all those
rvcps03.pdf).
Definitions
who worked for pay any time during the
week which includes the 12th day of the
month or who worked unpaid for 15 hours
or more in a family-operated enterprise and
(2) those who were temporarily absent from
their regular jobs because of illness, vacation, industrial dispute, or similar reasons.
A person working at more than 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
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-1 I 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 www.bls.gov/cps/cpsrs.pdf) for a discussion of the introduction of the use of X- 12
An establishment is an economic unit
which produces goods or services (such as
a factory or store) at a single location and is
engaged in one type of economic activity.
Employed persons are all persons who
received pay (including holiday and sick
pay) for any part of the payroll period including the 12th day of the month. Persons
holding more than one job (about 5 percent
of all persons in the labor force) are counted
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
59
Current Labor Statistics
in each establishment which reports them.
Production workers in the goods-produc 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 deflater for this series is derived from the Consumer Price Index for Urban Wage Earners and Clerical
Workers (CPI-W).
Hours represent the average weekly
hours of production or nonsupervisory
workers for which pay was received, and are
different from standard or scheduled hours.
Overtime hours represent the portion of average weekly hours which was in excess of
regular hours and for which overtime premiums were paid.
The Diffusion Index represents the percent of industries in which employment was
rising over the indicated period, plus onehalf of the industries with unchanged employment; 50 percent indicates an equal balance between industries with increasing and
decreasing employment. In line with Bureau
practice, data for the 1-, 3-, and 6-month
spans are seasonally adjusted, while those
for the 12-month span are unadjusted. Table
17 provides an index on private nonfarm
employment based on 278 industries, and a
manufacturing index based on 84 industries.
These indexes are useful for measuring the
dispersion of economic gains or losses and
are also economic indicators.
Notes on the data
Establishment survey data are annually adjusted to comprehensive counts of employment (called "benchmarks"). The March
2003 benchmark was introduced in February 2004 with the release of data for January 2004, published in the March 2004 is-
60 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
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
third month of their appearance. Thus, December data are published as preliminary in
January and February and as final in March.
For the same reasons, quarterly establishment data (table I) are preliminary for the
first 2 months of pub! ication 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 CLAUS) program, which is conducted in
cooperation with State employment security
agencies.
Monthly estimates of the labor force,
employment, and unemployment for States
and sub-State areas are a key indicator of
local economic conditions, and form the basis for determining the eligibility of an area
for benefits under Federal economic assistance programs such as the Job Training
Partnership Act. Seasonally adjusted unemployment rates are presented in table I 0.
Insofar as possible, the concepts and definitions underlying these data are those
used in the national estimates obtained
from the CPS.
Notes on the data
Data refer to State of residence. Monthly
data for all States and the District of Columbia are derived using standardized procedures established by BLS. Once a year,
estimates are revised to new population controls, usually with publication of January
estimates, and benchmarked to annual average CPS levels.
FOR ADDITIONAL INFORMATION on data in
this series, call (202) 691-6392 (table 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 ( uC FE) 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
ofnonprofit employers, such as religious organizations, are given a choice of coverage
or exclusion in a number of States. Workers
in these organizations are, therefore, reported to a limited degree.
Persons on paid sick leave, paid holiday,
paid vacation , and the like, are included. Persons on the payroll of more than one firm
during the period are counted by each u1subject employer if they meet the employment definition noted earlier. The employment count excludes workers who earned no
wages during the entire applicable pay period because of work stoppages, temporary
layoffs, illness, or unpaid vacations.
Federal employment data are based on
reports of monthly employment and quarterly wages submitted each quarter to State
agencies for all Federal installations with
employees covered by the Unemployment
Compensation for Federal Employees (ucFE)
program, except for certain national security agencies, which are omitted for security
reasons. Employment for all Federal agencies for any given month is based on the
number of persons who worked during or
received pay for the pay period that included
the 12th of the month.
An establishment is an economic unit,
such as a farm, mine , factory, or store, that
produces goods or provides services. It is
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
typically at a single 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 u1 report. The Multiple Worksite Report is used to collect separate employment
and wage data for each of the employer's
establishments, which are not detailed on the
u1 report. Some very small multi-establishment employers do not file a Multiple
Worksite Report. When the total employment in an employer's secondary establishments (all establishments other than the largest) is IO 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 l0orfewer
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 40 I (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 we! I 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
August
2005
61
Current Labor Statistics
show average wage levels appreciably less
than the weekly pay levels of regular fulltime employees in these industries. The opposite effect characterizes industries with
low proportions of part-time workers, or industries that typically schedule heavy weekend and overtime work. Average wage data
also may be influenced by work stoppages,
labor turnover rates, retroactive payments,
seasonal factors, bonus payments, and so on.
Notes on the data
Beginning with the release of data for 2001,
publications presenting data from the Covered Employment and Wages program have
switched to the 2002 version of the North
American Industry Classification System
(NAICS) as the basis for the assignment and
tabulation of economic data by industry.
NAICS is the product of a cooperative effort
on the part of the statistical agencies of the
United States, Canada, and Mexico. Due to
difference in NAICS and Standard Industrial
Classification (SIC) structures, industry data
for 2001 is not comparable to the SIC-based
data for earlier years.
Effective January 200 I, the program began assigning Indian Tribal Councils and related establishments to local government
ownership. This BLS action was in response
to a change in Federal law dealing with the
way Indian Tribes are treated under the Federal Unemployment Tax Act. This law requires federally recognized Indian Tribes to
be treated similarly to State and local governments. In the past, the Covered Employment and Wage (CEW) program coded Indian
Tribal Councils and related establishments
in the private sector. As a result of the new
law, CEW data reflects significant shifts in
employment and wages between the private
sector and local government from 2000 to
200 I. Data also reflect industry changes.
Those accounts previously assigned to civic
and social organizations were assigned to
tribal governments. There were no required
industry changes for related establishments
owned by these Tribal Councils. These tribal
business establishments continued to be
coded according to the economic activity of
that entity.
To insure the highest possible quality
of data, State employment security agencies verify with employers and update, if
necessary, the industry, location, and ownership classification of all establishments
on a 3-year cycle. Changes in establishment classification codes resulting from the
verification process are introduced with the
data reported for the first quarter of the year.
62 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Changes resulting from improved employer
reporting also are introduced in the first
quarter. For these reasons, some data, especially at more detailed geographic levels, may not be strictly comparable with
earlier years.
County definitions are assigned according to Federal Information Processing Standards Publications as issued by the National
Institute of Standards and Technology. Areas shown as counties include those designated as independent cities in some jurisdictions and, in Alaska, those areas designated by the Census Bureau where counties
have not been created. County data also are
presented for the New England States for
comparative purposes, even though townships are the more common designation used
in New England (and New Jersey).
The Office of Management and Budget
(0MB) defines metropolitan areas for use in
Federal statistical activities and updates
these definitions as needed. Data in this table
use metropolitan area criteria established by
0MB in definitions issued June 30, 1999
(0MB Bulletin No. 99-04). These definitions
reflect information obtained from the 1990
Decennial Census and the 1998 U.S. Census Bureau population estimate. A complete
list of metropolitan area definitions is available from the National Technical Information Service (NTIS), Document Sales, 5205
Port Royal Road, Springfield, Va. 22161,
telephone l-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: ( l) 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.
August 2005
Job Openings and Labor
Turnover Survey
Description of the series
Data for the Job Openings and Labor Turnover Survey (JOLTS) are collected and compiled from a sample of 16,000 business establishments. Each month, data are collected
for total employment, job openings, hires,
quits, layoffs and discharges, and other separations. The JOLTS program covers all private
nonfarm establishments such as factories,
offices, and stores, as well as Federal, State,
and local government entities in the 50 States
and the District of Columbia. The JOLTS
sample design is a random sample drawn from
a universe of more than eight million establishments compiled as part of the operations
of the Quarterly Census of Employment and
Wages, or QCEW, program. This program includes all employers subject to State unemployment insurance (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 ( l)
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 recail 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 I 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 I 00.
Separations are the total number of terminations of employment occurring at any time
during the reference month, and are reported
by type of separation--quits, layoffs and discharges, and other separations. Quits are voluntary separations by employees (except for
retirements, which are reported as other separations). Layoffs and discharges are involuntary
separations initiated by the employer and include layoffs with no intent to rehire, formal
layoffs lasting or expected to last more than 7
days, discharges resulting from mergers,
downsizing, or closings, firings or other discharges for cause, terminations of permanent
or short-term employees, and terminations of
seasonal employees. Other separations include
retirements, transfers to other locations, deaths,
and separations due to disability. Separations
do not include transfers within the same location or employees on strike.
The separations rate is computed by dividing the number of separations by employment, and multiplying that quotient by I 00.
The quits, layoffs and discharges, and other
separations rates are computed similarly,
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
dividing the number by employment and
multiplying by I 00.
Notes on the data
The JOLTS data series on job openings, hires,
and separations are relatively new. The full
sample is divided into panels, with one panel
enrolled each month. A full complement of
panels for the original data series based on
the 1987 Standard Industrial Classification
(SIC) system was not completely enrolled in
the survey until January 2002. The supplemental panels of establishments needed to
create NAICS estimates were not completely
enrolled until May 2003. The data collected
up until those points are from less than a
full sample. Therefore, estimates from earlier months should be used with caution, as
fewer sampled units were reporting data at
that time.
In March 2002, BLS procedures for collecting hires and separations data were revised
to address possible underreporting. As a result, JOLTS hires and separations estimates for
months prior to March 2002 may not be comparable with estimates for March 2002 and
later.
The Federal Government reorganization
that involved transferring approximately
180,000 employees to the new Department
of Homeland Security is not reflected in the
JOLTS hires and separations estimates for the
Federal Government. The Office of Personnel Management's record shows these transfers were completed in March 2003. The
inclusion of transfers in the JOLTS definitions
of hires and separations is intended to cover
ongoing movements of workers between establishments. The Department of Homeland
Security reorganization was a massive onetime event, and the inclusion of these intergovernmental transfers would distort the
Federal Government time series.
Data users should note that seasonal adjustment of the JOLTS series is conducted with
fewer data observations than is customary.
The historical data, therefore, may be subject to larger than normal revisions. Because
the seasonal patterns in economic data series
typically emerge over time, the standard use
of moving averages as seasonal filters to capture these effects requires longer series than
are currently available. As a result, the stable
seasonal filter option is used in the seasonal
adjustment of the JOLTS data. When calculating seasonal factors, this filter takes an average for each calendar month after detrending
the series. The stable seasonal filter assumes
that the seasonal factors are fixed; a necessary assumption until sufficient data are avail-
able. When the stable seasonal filter is no
longer needed, other program features also
may be introduced, such as outlier adjustment
and extended diagnostic testing. Additionally,
it is expected that more series, such as layoffs and discharges and additional industries,
may be seasonally adjusted when more data
are available.
JOLTS hires and separations estimates cannot be used to exactly explain net changes in
payroll employment. Some reasons why it is
problematic to compare changes in payroll
employment with JOLTS hires and separations,
especially on a monthly basis, are: (I) the
reference period for payroll employment is
the pay period including the 12th of the
month, while the reference period for hires
and separations is the calendar month; and
(2) payroll employment can vary from month
to month simply because part-time and oncall workers may not always work during the
pay period that includes the 12th of the
month. Additionally, research has found that
some reporters systematically underreport
separations relative to hires due to a number of factors, including the nature of their
payroll systems and practices. The shortfall
appears to be about 2 percent or less over a
12-month period.
FOR ADDITIONAL INFORMATION on the Job
Openings and Labor Turnover Survey, contact the Division of Administrative Statistics
and Labor Turnover at (202) 961-5870.
Compensation and
Wage Data
(Tables 1-3; 30-36)
Compensation and waged data are gathered
by the Bureau from business establishments,
State and local governments, labor unions,
collective bargaining agreements on file
with the Bureau, and secondary sources.
Employment Cost Index
Description of the series
The Employment Cost Index (ECI) is a
quarterly measure of the rate of change in
compensation per hour worked and includes
wages, salaries, and employer costs of employee benefits. It uses a fixed market
basket of labor-similar in concept to the
Consumer Price Index's fixed market basket of goods and services-to measure
change over time in employer costs of employing labor.
Statistical series on total compensation
Monthly Labor Review
August
2005
63
Current Labor Statistics
costs, on wages and salaries, and on benefit costs are available for private non farm
workers excluding proprietors, the self-employed, and household workers. The total
compensation costs and wages and salaries
series are also available for State and local
government workers and for the civilian
nonfarm economy, which consists of private industry and State and local government workers combined. Federal workers
are excluded.
The Employment Cost Index probability
sample consists of about 4,400 private nonfarm establishments providing about 23,000
occupational observations and 1,000 State
and local government establishments providing 6,000 occupational observations selected to represent total employment in each
sector. On average, each reporting unit provides wage and compensation information
on five well-specified occupations. Data are
collected each quarter for the pay period including the 12th day of March, June, September, and December.
Beginning with June 1986 data, fixed
employment weights from the 1980 Census
of Population are used each quarter to
calculate the civilian and private indexes
and the index for State and local governments. (Prior to June 1986, the employment
weights are from the 1970 Census of Population.) These fixed weights , also used to
derive all of the industry and occupation
series indexes, ensure that changes in these
indexes reflect only changes in compensation, not employment shifts among industries or occupations with different levels of
wages and compensation. For the bargaining status, region, and metropolitan/ nonmetropolitan area series, however, employment data by industry and occupation are
not available from the census. Instead, the
1980 employment weights are reallocated
within these series each quarter based on the
current sample. Therefore, these indexes are
not strictly comparable to those for the aggregate, industry, and occupation series.
Definitions
Total compensation costs include wages ,
salaries, and the employer's costs for employee benefits.
Wages and salaries consist of earnings
before payroll deductions, including production bonuses, incentive earnings , commissions, and cost-of-living adjustments.
Benefits include the cost to employers
for paid leave, supplemental pay (including nonproduction bonuses), insurance, retirement and savings plans, and legally required
64 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
benefits (such as Social Security, workers'
compensation, and unemployment insurance).
Exel uded 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:
www.bls.gov/ect/
FOR ADDITION AL INFORM ATIO N on the
Employment Cost Index , contact the Office
of Compensation Levels and Trends: (202)
691-6199.
Employee Benefits Survey
Description of the series
Employee benefits data are obtained from
the Employee Benefits Survey, an annual
survey of the incidence and provisions of
selected benefits provided by employers.
The survey collects data from a sample of
approximately 9,000 private sector and State
and local government establishments. The
data are presented as a percentage of employees who participate in a certain benefit,
or as an average benefit provision (for example, the average number of paid holidays
provided to employees per year). Selected
data from the survey are presented in table
34 for medium and large private establishments and in table 35 for small private establishments and State and local government.
The survey covers paid leave benefits
such as holidays and vacations , and personal,
funeral, jury duty, military, family, and sick
leave; short-term disability, long-term disability, and life insurance; medical, dental,
and vision care plans; defined benefit and
defined contribution plans; flexible benefits
plans; reimbursement accounts; and unpaid
family leave.
Also, data are tabulated on the incidence of several other benefits, such as
severance pay, child-care assistance, wellness programs, and employee assistance
programs.
August 2005
Definitions
Employer-provided benefits are benefits
that are financed either wholly or partly by
the employer. They may be sponsored by a
union or other third party, as long as there is
some employer financing. However, some
benefits that are fully paid for by the employee also are included. For example, longterm care insurance and postretirement life
insurance paid entirely by the employee are
included because the guarantee of insurability and availability at group premium rates
are considered a benefit.
Participants are workers who are covered by a benefit, whether or not they use
that benefit. If the benefit plan is financed
wholly by employers and requires employees to complete a minimum length of service for eligibility, the workers are considered participants whether or not they have
met the requirement. If workers are required to contribute towards the cost of a
plan, they are considered participants only
if they elect the plan and agree to make the
required contributions.
Defined benefit pension plans use predetermined formulas to calculate a retirement benefit (if any), and obligate the employer to provide those benefits. Benefits
are generally based on salary, years of service, or both.
Defined contribution plans generally
specify the level of employer and employee
contributions to a plan, but not the formula
for determining eventual benefits. Instead,
individual accounts are set up for participants, and benefits are based on amounts
credited to these accounts.
Tax-deferred savings plans are a type
of defined contribution plan that allow participants to contribute a portion of their salary to an employer-sponsored plan and defer income taxes until withdrawal.
Flexible benefit plans allow employees
to choose among several benefits, such as
life insurance, medical care, and vacation
days, and among several levels of coverage
within a given benefit.
Notes on the data
Surveys of employees in medium and large
establishments conducted over the 197986 period included establishments that employed at least 50, 100, or 250 workers,
depending on the industry (most service
industries were excluded). The survey conducted in 1987 covered only State and local governments with 50 or more employ-
ees. The surveys conducted in 1988 and
1989 included medium and large establishments with 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.
FOR ADDITIONAL INFORMATION on the
Employee Benefits Survey, contact the Office of Compensation Levels and Trends on
the Internet:
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:
www.bls.gov/cba/
Price Data
(Tables 2; 37-47)
Price data are gathered by the Bureau
of Labor Statistics from retail and primary markets in the United States. Price indexes are given in relation to a base periodDecember 2003 = 100 for many Producer
Price Indexes (unless otherwise noted), 198284 = I00 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.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
tween major revisions so that only price
changes will be measured. All taxes directly
associated with the purchase and use of
items are included in the index.
Data collected from more than 23,000 retail establishments and 5,800 housing units
in 87 urban areas across the country are used
to develop the "U.S. city average." Separate
estimates for 14 major urban centers are presented in table 38. The areas listed are as indicated in footnote I to the table. The area
indexes measure only the average change in
prices for each area since the base period,
and do not indicate differences in the level
of prices among cities.
Notes on the data
In January 1983, the Bureau changed the
way in which homeownership costs are
meaured for the CPI-U. A rental equivalence
method replaced the asset-price approach to
homeownership costs for that series. In
January 1985, the same change was made in
the CPI-W. The central purpose of the change
was to separate shelter costs from the investment component of homeownership so that
the index would reflect only the cost of shelter services provided by owner-occupied
homes. An updated CPI-U and CPI-W were
introduced with release of the January 1987
and January 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 PPI organizes data in
accordance with the 2002 North American Industry Classification System and product
codes developed by the U.S. Census Bureau.
Monthly Labor Review
August
2005
65
Current Labor Statistics
To the extent possible, prices used in calculating Producer Price Indexes apply to the
first significant commercial transaction in
the United States from the production or
central marketing point. Price data are generally collected monthly, primarily by mail
questionnaire. Most prices are obtained directly from producing companies on a voluntary and confidential basis. Prices generally are reported for the Tuesday of the week
containing the 13th day of the month.
Since January 1992, price changes for
the various commodities have been averaged
together with implicit quantity weights representing their importance in the total net
selling value of all commodities as of 1987.
The detailed data are aggregated to obtain
indexes for stage-of-processing groupings,
commodity groupings, durability-of-product groupings, and a number of special composite groups. All Producer Price Index data
are subject to revision 4 months after original publication.
FOR ADDITIONAL INFORMATION, contact
the Division of Industrial Prices and Price
Indexes: (202) 691-7705.
International Price Indexes
Description of the series
The International Price Program produces
monthly and quarterly export and import
price indexes for nonmilitary goods and services traded between the United States and
the rest of the world. The export price index provides a measure of price change
for all products sold by U.S. residents to
foreign buyers. ("Residents" is defined as
in the national income accounts; it includes corporations, businesses, and individuals, but does not require the organizations to be U.S. owned nor the individuals to have U.S. citizenship.) The import
price index prov ides 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-
66
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
pleted during the first week of the month.
Survey respondents are asked to indicate
all discounts, allowances, and rebates applicable to the reported prices, so that the
price used in the calculation of the indexes
is the actual price for which the product
was bought or sold.
In addition to general indexes of prices for
U.S. exports and imports, indexes are also
published for detailed product categories of
exports and imports. These categories are defined according to the five-digit level of detail
for the Bureau of Economic Analysis End-use
Classification, the three-digit level for the Standard International Trade Classification (SITC),
and the four-digit level of detail for the Harmonized System. Aggregate import indexes by
country or region of origin are also available.
BLS publishes indexes for selected categories of internationally traded services,
calculated on an international basis and on
a balance-of-payments basis.
Notes on the data
The export and import price indexes are
weighted indexes of the Laspeyres type. The
trade weights currently used to compute
both indexes relate to 2000.
Because a price index depends on the
same items being priced from period to period, it is necessary to recognize when a
product's specifications or terms of transaction have been modified. For this reason, the
Bureau 's questionnaire requests detailed descriptions of the physical and functional
characteristics of the products being priced,
as well as information on the number of units
bought or sold, discounts, credit terms, packaging, class of buyer or seller, and so forth.
When there are changes in either the specifications or terms of transaction of a product,
the dollar value of each change is deleted from
the total price change to obtain the "pure"
change. Once this value is determined, a linking procedure is employed which allows for
the continued repricing of the item.
FOR ADDITIONAL INFORMATION , contact
the Division of International Prices: (202)
691-7155.
Productivity Data
(Tables 2; 48-51)
Business and major sectors
Description of the series
The productivity measures relate real out-
August 2005
put to real input. As such, they encompass a
family of measures which include singlefactor input measures, such as output per
hour, output per unit of labor input, or output per unit of capital input, as well as measures of multi factor productivity (output per
unit of combined labor and capital inputs).
The Bureau indexes show the change in output relative to changes in the various inputs.
The measures cover the business, nonfarm
business , manufacturing , and nonfinancial
corporate sectors.
Corresponding indexes of hourly compensation, unit labor costs, unit nonlabor
payments , and prices are also provided.
Definitions
Output per hour of all persons (labor productivity) is the quantity of goods and services produced per hour of labor input. Output per unit of capital services (capital productivity) is the quantity of goods and services produced per unit of capital services
input. 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-equipmentt structures,
land, and inventories-weighted by rental
prices for each type of asset.
Combined units of labor and capital
inputs are derived by combining changes in
labor and capital input with weights which
represent each component 's share of total
cost. Combined units of labor, capital, energy,
materials, and purchased business services are
similarly derived by combining changes in
each input with weights that represent each
input's share of total costs. The indexes for
each input and for combined units are based
on changing weights which are averages of the
shares in the current and preceding year (the
Tornquist index-number formula).
Notes on the data
Business sector output is an annuallyweighted index constructed by excluding
from real gross domestic product (GDP) the
following outputs: general government, nonprofit institutions, paid employees of private
households, and the rental value of owneroccupied dwellings. Nonfarm business also
excludes farming. Private business and private nonfarm business further exclude government enterprises. The measures are supplied by the U.S. Department of Commerce's
Bureau of Economic Analysis. Annual estimates of manufacturing sectoral output are
produced by the Bureau of Labor Statistics.
Quarterly manufacturing output indexes
from the Federal Reserve Board are adjusted
to these annual output measures by the BLS.
Compensation data are developed from data
of the Bureau of Economic Analysis and the
Bureau of Labor Statistics. Hours data are
developed from data of the Bureau of Labor
Stali:-.Lics.
The productivity and associated cost
measures in tables 48-5 l describe the relationship between output in real terms and
the labor and capital inputs involved in its
production. They show the changes from period to period in the amount of goods and
services produced per unit of input.
Although these measures relate output to
hours and capital services, they do not measure the contributions of labor, capital, or
any other specific factor of production.
Rather, they reflect the joint effect of many
influences, including changes in technology; shifts in the composition of the labor
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
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.
Industry productivity
measures
ducing that output. Combined inputs include capital, labor, and intermediate purchases. The measure of capital input represents the flow of services from the capital
stock used in production. It is developed
from measures of the net stock of physical
assets-equipment, structures, land , and inventories. The measure of intermediate
purchases is a combination of purchased
materials, services, fuels , and electricity.
Notes on the data
Description of the series
The BLS industry productivity indexes measure the relationship between output and
inputs for selected industries and industry
groups, and thus reflect trends in industry
efficiency over time. Industry measures include labor productivity, multifactor productivity, compensation, and unit labor
costs.
The industry measures differ in methodology and data sources from the productivity measures for the major sectors because the industry measures are developed
independently of the National Income and
Product Accounts framework used for the
major sector measures.
The industry measures are compiled from data
produced by the Bureau of Labor Statistics and
the Census Bureau, with additional data supplied by other government agencies, trade associations, and other sources.
FOR ADDITIONALINFORMATION on this series , contact the Division of Industry Productivity Studies: (202) 691-56 I 8, or vi sit
the Website at: www.bls.gov/lpc/home.htm
International Comparisons
(Tables 52-54)
Labor force and
unemployment
Description of the series
Definitions
Output per hour is derived by dividing an
index of industry output by an index of labor input. For most industries, output indexes are derived from data on the value of
industry output adjusted for price change.
For the remaining industries, output indexes
are derived from data on the physical quantity of production.
The labor input series is based on the
hours of all workers or, in the case of some
transportation industries, on the number of
employees. For most industries, the series
consists of the hours of all employees. For
some trade and services industries, the series also includes the hours of partners, proprietors, and unpaid family workers.
Unit labor costs represent the labor
compensation costs per unit of output produced, and are derived by dividing an index
of labor compensation by an index of output. Labor compensation includes payroll
as well as supplemental payments, including both legally required expenditures and
payments for voluntary programs.
Multifactor productivity is derived by
dividing an index of industry output by an
index of combined inputs consumed in pro-
Tables 52 and 53 present comparative measures of the labor force, employment, and
unemployment approximating U.S. concepts for the United States, Canada, Australia, Japan , and six European countries. The
labor force statistics published by other industrial countries are not, in most cases, comparable to U.S. concepts. Therefore, the Bureau
adjusts the figures for selected countries, for
all known major definitional differences, to the
extent that data to prepare adjustments are
available. Although precise comparability may
r.ot be achieved, these adjusted figures provide a better basis for international comparisons than the figures regularly published by
each country. For further information on adjustments and comparability issues , see
Constance Sorrentino, "International unemployment rates: how comparable are they?"
Monthly Labor Review, June 2000, pp. 3- 20
(available on the BLS Web site at:
www.bls.gov/opu b/ml r/2000/06/
artlfull.pdf).
Definitions
For the principal U.S. definitions of the labor force , employment, and unemployment,
see the Notes section on Employment and
Monthly Labor Review
August
2005
67
Current Labor Statistics
Unemployment Data: Household survey
data.
Notes on the data
The foreign country data are adjusted as
closely as possible to U.S. concepts, with the
exception of lower age limits and the treatment
of layoffs. These adjustments include, but are
not limited to: including older persons in the
labor force by imposing no upper age limit,
adding unemployed students to the
unemployed, excluding the military and family
workers working fewer than 15 hours from the
employed, and excluding persons engaged in
passive job search from the unemployed.
Data for the United States relate to the
population 16 years of age and older. The U.S.
concept of the working age population has
no upper age limit. The adjusted to U.S.
concepts statistics have been adapted, insofar
as possible, to the age at which compulsory
schooling ends in each country, and the
Swedish statistics have been adjusted to
include persons older than the Swedish upper
age limit of 64 years. The adjusted statistics
presented here relate to the population 16
years of age and older in France, Sweden,
and the United Kingdom; 15 years of age and
older in Australia, Japan, Germany, Italy, and
the Netherlands. An exception to this rule is
that the Canadian statistics are adjusted to
cover the population 16 years of age and
older, whereas the age at which compulsory
schooling ends remains at 15 years. In the labor
force participation rates and employmentpopulation ratios, the denominator is the
civilian noninstitutionalized working age
population, except that the institutionalized
working age population is included in Japan
and Germany.
In the United States, the unemployed
include persons who are not employed and
who were actively seeking work during the
reference period, as well as persons on layoff.
Persons waiting to start a new job who were
actively seeking work during the reference
period are counted as unemployed under U.S .
concepts; if they were not actively seeking
work, they are not counted in the labor force.
In some countries, persons on layoff are
classified as employed due to their strong job
attachment. No adjustment is made for the
countries that classify those on layoff as
employed. In the United States, as in Australia
and Japan, passive job seekers are not in the
labor force; job search must be active, such
as placing or answering advertisements,
contacting employers directly,or registering
with an employment agency (simply reading
ads is not enough to qualify as active search).
Canada and the European countries classify
68
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
passive jobseekers as unemployed. An
adjustment is made to exclude them in Canada,
but not in the European countries where the
phenomenon is less prevalent. Persons waiting
to start a new job are counted among the
unemployed for all other countries, whether
or not they were actively seeking work.
The figures for one or more recent years
for France, Germany, and the Netherlands are
calculated using adjustment factors based on
labor force surveys for earlier years and are
considered preliminary. The recent year
measures for these countries are therefore
subject to revision whenever more current
labor force surveys become available.
There are breaks in series for the United
States ( 1994, 1997, 1998, 1999, 2000, 2003),
Australia (200 I), 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 www.bls.gov/cps/
eetech_methods.pdf).
For Australia, the 200 l break reflects the
introduction in April 200 l of a redesigned
labor force survey that allowed for a closer
application of International Labor Office
guidelines for the definitions of labor force
statistics. The Australian Bureau of Statistics
revised their data so there is no break in the
employment series. However, the reclassification of persons who had not actively
looked for work because they were waiting to
begin a new job from "not in the labor force"
to " unemployed" could only be incorporated
for April 200 l forward. This reclassification
diverges from the U.S. definition where
persons waiting to start a new job but not
actively seeking work are not counted in the
labor force. The impact of the reclassification
was an increase in the unemployment rate by
0.1 percentage point in 200 I.
For Germany, the 1999 break reflects the
incorporation of an improved method of data
calculation and a change in coverage to
persons living in private households only.
For further qualifications and historical
data, see Comparative Civilian La,bor Force
Statistics, Ten Countries, on the BLS Web site
at www.bls.gov/fls/flslforc.pdf
August 2005
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
of Belguim 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 I 998 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 l O 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, compensation is increased to account for other significant taxes on payroll or employment. For
the United Kingdom, compensation is reduced between 1967 and 199 l 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 l. 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 l l employees, employers regulated
by other Federal safety and health laws, and
Federal, State, and local government agencies
are excluded from the survey.
The survey is a Federal-State cooperative program with an independent sample
selected for each participating State. A stratified random sample with a Neyman allocation is selected to represent all private industries in the State. The survey is stratified
by Standard Industrial Classification and
size of employment.
Definitions
Notes on the data
In general, the measures relate to total manufacturing as defined by the International
Standard Industrial Classification. However,
the measures for France include parts of
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Under the Occupational Safety and Health
Act, employers 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 l 00 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
August
2005
69
Current Labor Statistics
nized and reported. These long-term latent illnesses are believed to be understated in the
survey's illness measure. In contrast, the overwhelming majority of the reported new illnesses are those which are easier to directly
relate to workplace activity (for example, contact dermatitis and carpal tunnel syndrome).
Most of the estimates are in the form of
incidence rates, defined as the number of injuries and illnesses per 100 equivalent fulltime workers. For this purpose, 200,000 employee hours represent 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 illnesses (nature of the disabling condition, part of body
affected, event and exposure, and the source
directly producing the condition). In general,
70
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
these data are available nationwide for detailed
industries and for individual States at more
aggregated industry levels.
FOR ADDITIONAL INFORMATION on occupational injuries and illnesses, contact the Office of Occupational Safety, Health and
Working Conditions at (202) 691-6180, or
access the Internet at: http://www.bls.gov/tlf/
Census of Fatal
Occupational Injuries
The Census of Fatal Occupational Injuries
compiles a complete roster of fatal job-related injuries, including detailed data about
the fatally injured workers and the fatal
events. The program collects and cross
checks fatality information from multiple
sources, including death certificates, State
and Federal workers' compensation reports,
Occupational Safety and Health Administration and Mine Safety and Health Administration records, medical examiner and autopsy reports, media accounts, State motor
vehicle fatality records, and follow-up questionnaires to employers.
In addition to private wage and salary
workers, the self-employed, family members, and Federal, State, and local government workers are covered by the program. To be included in the fatality census, the decedent must have been employed (that is working for pay, compensation, or profit) at the time of the event,
engaged in a legal work activity, or
present at the site of the incident as a requirement of his or her job.
August 2005
Definition
A fatal work injury is any intentional or unintentional wound or damage to the body resulting in death from acute exposure to energy, such as heat or electricity, or kinetic
energy from a crash, or from the absence of
such essentials as heat or oxygen caused by
a specific event or incident or series of events
within a single workday or shift. Fatalities
that occur during a person's commute to
or from work are excluded from the 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: www.bls.gov/iif/
1. Labor market indicators
Selected indicators
2003
2003
2004
II
2004
IV
Ill
II
2005
Ill
IV
II
Employment data
Employment status of the civilian noninstitutional
population (household survey):
1
Labor f0<ce participation rate ......................... .......... .
66.2
66.0
66.4
66.2
66.1
66.0
66.0
66.0
66.0
65.8
66.0
Employment-population ratio ..................................... .
62.3
62.3
62.3
62.1
62.2
62.2
62.3
62.4
62.4
62.3
62.7
Unemployment rate . ......... ............. ...... .................... ....... .. .
6.0
5.5
6.1
6.1
5.9
5.6
5.6
5.5
5.4
5.3
5.1
Men ......................... ................. .. ..... ... ......................... .
6.3
5.6
6.5
6.4
6.1
5.7
5.7
5.6
5.6
5.4
5.1
12.6
16 to 24 years .................... ... .................................. ..
13.4
12.6
13.9
13.7
13.0
12.6
12.9
12.5
12.6
13.2
25 years and older. ......... ........................................................ .
5.0
4.4
5.2
5.1
4.9
4.5
4.5
4.4
4.3
4.1
3.8
Women ....................... ... ..................... ........ ......... ........ .
5.7
5.4
5.7
5.8
5.6
5.6
5.4
5.3
5.2
5.1
5.1
11.4
11.0
11 .8
11 .5
10.9
11.1
10.9
10.9
10.9
10.4
10.5
4.6
4.4
4.6
4.7
4.6
4.5
4.4
4.3
4.2
4.1
4.2
16 to 24 years .................................... .. ..... ......... ..... .
25 years and older. .... ................................ .
Employment, nonfarm (payroll data) . in thousands:
1
Total nonfarm . .... .. ........................... .. ..................................... .
129,931
131,480
129,845
130,168
130,541
131,125
131,731
132,302
132,814
133.405
Total private ... .. .... .. ............. .......... ... .. .............................. .
108,356
109,862
108,253
108,320
108,614
108,986
109,737
110,095
110,600
111 ,089
111,655
21,817
21 ,884
21,828
21,700
21,684
21,725
21,868
21,932
22,000
22,054
22,134
14,525
14,329
14,555
14,377
14,313
14,285
14,338
14,353
14,338
14,314
14,288
108,114
109,596
108,017
108,190
108,483
108,816
109,457
109,799
110,302
110,759
111,271
Goods-producing
Manufacturing ....... . ... ... ....... .............. ... ..... .......... .. .... .
Service-providing
129,890
Average hours:
Total private ........ ........ .................................................. .... ...... .
33.7
33.7
33.6
33.6
33.7
33.8
33.7
33.7
33.7
33.7
33.7
Manufacturing ...................... ...... .............. ......... ...... ..... .
40.4
40.8
40.2
40.3
40.7
41.0
40.8
40.8
40.6
40.6
40.4
Overtime ........................... .......... .. ....................... .... .
4.2
4.6
4.0
4.1
4.4
4.5
4.5
4.6
4.5
4.5
4.4
Employment Cost lndex2
Percent change in the ECI, compensation:
All w0<kers (exduding farm, household and Federal workers) .....
Private industry w0<kers .. .................. ... ................. ........ .
Goods-producing
3
Service-providing
3
..
State and local government w0<kers ................... ................. .
3.8
3.7
.8
1.1
.5
1.4
.9
1.0
.5
1.1
.6
4.0
3.8
.8
1.0
.4
1.5
.9
.8
.5
1.1
.7
4.0
4.7
.9
.7
.5
2.3
.9
.9
.6
1.5
.9
4.0
3.3
.8
1.1
.5
1.1
1.0
.8
.3
1.0
.6
3.3
3.5
.4
1.7
.5
.7
.4
1.7
.6
.9
.3
Workers by bargaining status {private industry):
1
2
Union . .... ................................................................ ....... .
4.6
5.6
1.2
1.0
.7
2.8
1.5
.8
.5
.7
.8
Nonunion ................................... .... ....... ......... ................. .
3.9
3.4
.8
1.0
.4
1.3
.8
.9
.4
1.3
.7
Quarterly data seasonally adjusted.
NOTE: Beginning in January 2003, household survey data reflect revised population
Annual changes are December-to-December changes. Quarterly changes are calculated
controls. Nonfarm data r eflect the conversion to the 2002 version of the North
using the last month of each quarter.
American Industry Classification System (NAICS), replacing the Standard Industrial
3
Classification (SIC) system. NAJCS-based data by industry are not comparable with sic-
Goods-producing industries include mining, construction, and manufacturing. Service-
providing industries include all other private sect0< industries.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
based data.
Monthly Labor Review
August 2005
71
Current Labor Statistics:
Comparative Indicators
2. Annual and quarterly percent changes in compensation, prices, and productivity
Selected measures
2003
2004
2003
II
2004
Ill
2005
Ill
II
IV
II
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
3.7
3.8
0.8
.8
1.1
1.0
0.5
.4
1.4
1.5
0.9
.9
1.0
.8
0.5
.5
1.1
1.1
0.6
.7
2.9
3.0
2.4
2.4
.6
.7
.9
.8
.3
.4
.6
.7
.6
.7
.9
.9
.3
.2
.7
.7
.5
.6
2.3
3.3
- .3
-.2
-.2
1.2
1.2
.2
.2
1.0
.5
3.2
4.2
.4
4.6
25.2
4.1
4.6
2.4
9.1
18.0
-.8
1.8
- .6
-2.1
-10.6
.3
.3
- .1
-. 1
3.4
.0
.0
.0
.0
14.4
1.2
1.5
.6
2.5
6.0
1.2
1.4
.5
3.0
7.6
.0
-1 .7
.4
1.9
-5.1
1.1
.9
1.6
.9
8.3
2.0
-2.6
2.1
3.5
9.7
.3
1.4
- .2
.8
-2.5
3.9
3.8
4.1
3.4
3.4
3.9
7.6
6.6
7.3
8.4
9.6
7.3
.3
.8
2.4
3.4
2.1
3.4
4.5
2.3
1.4
1.3
7.4
3.1
2.5
8.5
2.9
3.2
3.6
2.2
1
Consumer Price Index (All Urban Consumers) : All Items..... .
Producer Price Index:
Finished goods ... ........ .. .. .. ............................. .. ..... .............. .
Finished consumer goods ........... .................. ................ ..
Capital equipment.. ... ... ... .. ..... .......... ... ... ...... .......... .
Intermediate materials, supplies, and components ... ....... . .
Crude materials ......... ... ... ..... ... ......... .. ...... .. ... ..................... ..
Productivity data
3
Output per hour of all persons:
Business sector .. .................... .............. ............................... .
Nonfarm business sector .. ....................................................
Nonfinancial coroorations
4
..
' Annual changes are December-to- December changes.
3
Quarterly changes are
.8
Annual rates of change are computed
by comparing annual averages.
calculated using the last month of each quarter. Compensation and price data are not
Quarterly percent changes reflect annual rates of change in quarterly indexes.
seasonally adjusted, and the price data are not compounded.
The data are seasonally adjusted.
2
4
Excludes Federal and private household workers.
Output per hour of all employees.
3. Alternative measures of wage and compensation changes
Quarterly change
II
Four quarters ending-
2005
2004
Components
Ill
IV
2004
II
II
Ill
2005
IV
II
1
Average hourly compensation:
All persons, business sector. .......... ... ..........................................
All persons, nonfarm business sector. .............. .. ........... ..... .... .. ..
3.3 1
3.7
6.5
6.1
11.3
10.2
6.2
6.9
2.5
3.5
3.6
3.7
4.3
4.0
4.8
5.8
6.8
6.7
6.6
6.7
i
Employment Cost Index-compensation :
2
Civilian nonfarm ..
Private nonfarm ... ............................................ ........
Union .......................................... ... ...... ... ..... ... .. .. ... ................ .
Nonunion ........... ............... ........ .. ........................................... .
State and local governments .. ............................ ............ .. ....... ..
.9
.9
1.5
.8
.4
1.0
.8
.8
.9
1.7
.5
.5
.5
.4
.6
1.1
1.1
.7
1.3
.9
.6
.7
.8
.7
.3
3.9
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
3.5
3.4
3.6
3.4
3.6
3.2
3.2
2.9
3.2
3.6
.6
.7
1.0
.6
.2
.9
.9
.8
.8
1.0
.3
.2
.4
.2
.5
.7
.7
.1
.8
.6
.5
.6
.8
.6
.2
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
2.4
2.4
2.3
2.4
2.3
2.4
2.4
2.1
2.4
2.4
Employment Cost Index-wages and salaries:
2
Civilian nonfarm ..
Private nonfarm ............... ............. ................... .
Union ..... ................... ............. .. .. .. ..... ... .......... ..... .
Nonunion ..... .. ........... .............. ......... ........ .. ............................ .
State and local governments ................ ... ............... .... .. ... .. ....... .
1
Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate.
2
Excludes Federal and household workers.
72
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
1.2
4. Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Employment status
2005
2004
Annual average
2003
2004
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
221,168
146,51 0
66.2
137,736
223,357
147,401
. 66.0
139,252
223,196
147,386
66.0
139,158
223,422
147,823
66.2
139,639
223,677
147,676
66.0
139,658
223,941
147,531
65.9
139,527
224,192
147,893
66.0
139,827
224,422
148,313
66.1
140,293
224,640
148,203
66.0
140,156
224,837
147,979
65.8
140,241
225,041
148.132
65.8
140,144
225,236
148,157
65.8
140,501
225,441
148,762
66.0
141,099
225,670
149,122
66.1
141,475
225,911
149,123
66.0
141,638
62.3
62.3
8,149
5.5
75,956
62.3
62.5
62.4
62.5
62.4
62.4
62.3
62.4
62.6
8,228
5.6
75,809
8,184
5.5
75,599
62.4
8,018
5.4
76,001
62.3
8,774
6.0
74,658
8,005
5.5
76,410
8,066
5.4
76,299
8,020
5.5
76,109
8,047
5.4
76,437
7,737
5.2
76,858
7,988
5.4
76,909
7,656
5.2
77,079
7,663
5.2
76,679
62.7
7,647
5.1
76,547
7,486
5.0
76,787
population ..
Civilian labor force .............
Participation rate ........ .
Employed .......................
Employment-pop-
98,272
99,476
99,396
99,512
99,642
99,776
99,904
100,017
99,476
100,219
100,321
100,419
100,520
100,634
100,754
74,623
75.9
70,415
75,364
75.8
71,572
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
75,594
75.4
72,029
75,816
75.6
72,131
75,921
75.6
72,429
76,173
75.8
72,817
76,439
76.0
73,100
76,462
75.9
73,174
ulation ratio 2 . . ... .. . .. .. .
Unemployed ...................
Unemployment rate ....
Not in the labor force .. .....
71.7
4,209
5.6
23,649
71.9
3,791
5.0
24,113
72.0
3,786
5.0
24,035
72.2
3,737
4.9
23,945
72.1
3,768
5.0
24,026
71.9
3,761
5.0
24,314
72.0
3,736
4.9
24,272
72.1
3,733
4.9
24,151
71 .9
3,733
4.9
24,372
71 .9
71.9
72.1
72.4
72.6
72.6
3,565
4.7
24,625
3,685
4.9
24,505
3,492
4.6
24,498
3,356
4.4
24,347
3,339
4.4
24,195
3,288
4.3
24,292
106,800
107,658
107,586
107,687
107,801
107,920
108,032
108,129
107,658
108,316
108,403
108,486
108,573
108,672
108,776
64,716
60.6
61,402
64,923
60.3
61,773
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
65,318
60.3
62,295
65,270
60.2
62,202
65,051
60.0
62,099
65,420
60.3
62,384
65,479
60.3
62,464
65,470
60.2
62,451
TOTAL
Civilian noninstitutional
1
population .................. ......
Civilian labor force .............
Participation rate .........
Employed .................. .....
Employment-population ratio 2 .. .
Unemployed .......... .........
Unemployment rate ....
Not in the labor force ... .....
62.7
Men, 20 years and over
Civilian noninstitutional
1
Women, 20 years and over
Civilian noninstitutional
1
population ..
Civilian labor force .............
Participation rate ...... ...
Employed ... ... .................
Employment-pop2
57.5
57.4
57.4
57.5
57.4
57.4
57.5
57.5
57.4
57.2
57.5
3,150
4.9
42,735
3,259
5.0
42,597
3,183
4.9
42,603
3,032
4.7
42,892
3,069
4.7
42,912
57.4
3,102
4.8
42,906
57.5
3,314
5.1
42,083
3,099
4.7
42,885
3,051
4.7
42,961
3,023
4.6
42,998
3,068
4.7
43,133
2,952
4.5
43,435
3,036
4.6
43,153
57.5
3,015
4.6
43,192
57.4
3,019
4.6
43,306
16,096
16,222
16,214
16,222
16,234
16,246
16,257
16,293
16,222
16,302
16,317
16,332
16,347
16,364
16,381
7,170
44.5
5,919
7,114
43.9
5,907
7,036
43.4
5,853
7,172
44.2
5,907
7,152
44.1
5,934
7,062
43.5
5,887
7,165
43.9
5,908
7,202
44.2
6,014
7,189
44.1
5,927
7,066
43.3
5,917
7,046
43.2
5,811
7,185
44.0
5,973
7,168
43.9
5,897
7,204
44.0
5,911
7,192
43.9
6,013
36.8
1,251
17.5
8,926
36.4
1,208
17.0
9,108
36.1
1,184
16.8
9,178
36.4
1,265
17.6
9,051
36.6
1,217
17.0
9,082
36.2
1,175
16.6
9,184
36.3
1,227
17.2
9,122
36.9
1,188
16.5
9,074
36.4
1,262
17.6
9,104
36.3
1,150
16.3
9,235
35.6
1,235
17.5
9,271
36.6
1,212
16.9
9,147
36.1
1,271
17.7
9,179
36.1
1,293
17.9
9,160
36.7
1,178
16.4
9,190
181,292
182,643
182,531
183,022
184,015
184,167
184,328
120,995
66.1
115,318
183,483
121,509
66.2
115,910
183,888
121,278
66.3
115,526
183,340
121,606
66.3
115,966
183,767
121,212
66.4
115,199
183,188
121,273
66.2
115,618
183,640
121,686
66.3
115,239
182,676
121,383
66.4
115,610
182,846
120,546
66.5
114,235
121,553
66.2
116,158
121,621
66.2
116,022
121,484
66.1
116,135
121,961
66.3
116,574
122,177
66.3
116,791
121,985
66.2
116,778
63.0
6,311
5.2
60,746
63.1
5,847
4.8
61,558
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
63.3
63.2
63.3
63.1
63.2
63.4
63.4
63.4
6,013
5.0
61,319
5,655
4.7
61,915
5,640
4.6
61,735
5,600
4.6
61,973
5,395
4.4
62,088
5,598
4.6
62,146
5,349
4.4
62,403
5,387
4.4
62,054
5,386
4.4
61,989
5,206
4.3
62,343
population . .
Civilian labor force .............
Participation rate .........
Employed ..................... ..
Employment-pop-
25,686
26,065
26,040
26,078
26,120
26,163
26,204
26,239
26,273
26,306
26,342
26,377
26,413
26,450
26,448
16,526
64.3
14,739
16,638
63.8
14,909
16,521
63.4
14,825
16,775
64.3
14,937
16,721
64.0
14,972
16,711
63.9
14,981
16,820
62.4
15,012
16,728
63.8
14,913
16,713
63.6
14,907
16,721
63.6
14,946
16,708
63.4
14,890
16,741
63.5
15,025
16,940
64.1
15,184
17,050
64.5
15,329
17,147
64.7
15,378
ulation ratio 2 .. . . ........
Unemployed ........ ...........
Unemployment rate ....
Not in the labor force .......
57.4
1,787
10.8
9,161
57.2
1,729
10.4
9,428
56.9
1,696
10.3
9,520
57.3
1,838
11.0
9,303
57.3
1,749
10.5
9,399
57.3
1,730
10.4
9,452
57.3
1,808
10.7
9,384
56.8
1,814
10.8
9,512
56.7
1,806
10.8
9,559
56.8
1,775
10.6
9,585
56.5
1,818
10.9
9,634
57.0
1,716
10.3
9,636
57.5
1,756
10.4
9,473
58.0
1,721
10.1
9,400
58.1
1,769
10.3
9,341
ulation ratio . . .. . ... .. .
Unemployed ...................
Unemployment rate ....
Not in the labor force .. .....
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 .... . ..
Whites
Civilian noninstitutional
1
population . . . . .... . .. .............
Civilian labor force ....... ......
Participation rate .. ... ....
Employed .... ...................
Employment-pop2
ulation ratio .... . .. . . . . ..
Unemployed ... ..... ...... .. ...
Unemployment rate ....
Not in the labor force .......
63.1
Black or African Americans
Civilian noninstitutional
1
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August 2005
73
Current Labor Statistics:
Labor Force Data
4. Continued-Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Annual average
Employment status
2004
2005
2003
2004
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
27,551
18,813
68.3
17,372
28,109
19,272
68.6
17,930
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
28,642
19,379
67.7
18,198
28,729
19,458
67.7
18,211
28,815
19,541
67.8
18,425
28,902
19,665
68.0
18,412
28,989
19,761
68.2
18,578
29,079
19,777
68.0
18,623
63.1
1,441
7.7
8,738
63.8
1,342
7.0
8,837
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
63.5
1,181
6.1
9,263
63.4
1,248
6.4
9,270
63.9
1,117
5.7
9,273
63.7
1,252
6.4
9,237
64.1
1,183
6.0
9,228
64.0
1,154
5.8
9,302
Hispanic or Latino
ethnicity
Civilian noninstitutional
1
oooulation ............. ...........
Civilian labor force .. ....... .. .. .
Participation rate........ .
Employed ........ ...... .. .......
Employment-population ratio2 ... ....
Unemployed ...................
Unemployment rate ....
Not in the labor force ..
1
2
The population figures are not seasonally adjusted.
Civilian employment as a percent of the ci vilian noninstitutional populati on.
3
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.
NOTE: Estimates for the above race groups (white and black or African American) do not sum
to totals becau se data are not prese nted 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 refl ect revised population controls used in the
household survey.
5. Selected el'llJloymerl irdcators, monthly ctaa seasooally c:qusted
[In thousands]
Selected categories
Olaracteristic
EJTµOJed, 16 years and dder.
l'v1en ......................................
Wxren .......................... .. ...
Married men, spouse
present.......... .....................
Married v.omen, spouse
p-esent........ .. .....................
2004
Anooal average
2005
2003
2004
June
Juy
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
Ju,e
137,736
73,332
64,404
139,252
74,524
64,728
139,158
74,501
64,658
139,639
74,811
64,828
139,658
74,824
64,834
139,527
74,629
64,898
139,827
74,852
64,975
140,293
75,188
65,104
140,156
74,938
65,218
140,241
74,934
65.~7
140,144
74,964
65,180
140,501
75,375
65,127
141,099
75,735
65,364
141,475
75,985
65,490
141,638
76,092
65,545
44,653
45,084
44,958
44,948
45,099
45,093
45,127
45,462
45,315
45,171
45,351
45,382
45,482
45,725
45,357
34,695
34,600
34,487
34,607
34,494
34,704
34,808
34,961
34,878
34,739
34,601
34.~7
34,539
34,747
34,622
4,701
4,567
4,504
4,488
4,509
4,476
4,762
4,533
4,474
4,395
4,269
4,344
4,293
4,361
4,465
3,118
2,841
2,801
2,642
2,816
2,805
3,052
2,761
2,735
2,768
2,629
2,643
2,613
2,741
2,668
1,403
1,312
1,385
1,440
1,329
1,296
1,419
1,363
1,346
1,420
19,502
19,089
19,555
19,458
19,584
19,435
19,021
Petsons at work part time1
All industries:
Part time for ecoromc
reasons ........... .. . ········Siad< wo1< or busiress
corditions ....................
Could only find part-time
WOO< .. ..... ...... ... ..........
Part time for nonecoraric
nonecoraric reasons .... .
Noncgirutural industries:
Part time for ecoromc
reasons ..... _... . .. ... ... ... . - .
Slack wo1< or busiress
corditions.......................
Could only find J)c:¥1-time
woi< ..... - .......... .. . ......
Part time for nonecoraric
reasons .................. ... .... ..
1,279
1,409
1,400
1,472
19,014
19,380
19,564
19,737
19,657
19,410
19,704
19,499
4,596
4,469
4,423
4,390
4,408
4,400
4,656
4,404
4,382
4.~
4,153
4,268
4,186
4,280
4,386
3,052
2,773
2,753
2,500
2,722
2,750
2,971
2,685
2,682
2,702
2,572
2,592
2,540
2,705
2,616
1,264
1,399
1,382
1,484
1,388
1,320
1,363
1,396
1,397
1.~
1,268
1,411
1,351
1,331
1,416
18,658
19,026
19,123
19,327
19,204
19,061
19,288
19,141
19,176
18,765
19,254
19,182
19,226
19,160
18,633
'·"" i
' Exdudes persons 'Wth a job but not at v.ak'' during the survey paiod for such reasons as vacation, illness, or industrial dsp..1tes.
N.:>TE Begrning in January 2003, data reflect revised pq:,ulation controls used in the l"o.Jsehdd survey.
74
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
6. Selected unemployment indicators, monthly data seasonally adjusted
[Unemployment rates]
2003
2004
2005
2004
Annual average
Selected categories
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
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
5.6
16.8
5.0
5.0
5.5
17.6
4.9
4.9
5.4
17.0
5.0
4.7
5.4
16.6
5.0
4.7
5.5
17.2
4.9
4.8
5.4
16.5
4.9
4.7
5.4
17.6
4.9
4.7
5.2
16.3
4.7
4.6
5.4
17.5
4.9
4.7
5.2
16.9
4.6
4.5
5.2
17.7
4.4
4.6
5.1
17.9
4.4
4.6
5.0
16.4
4.3
4 .6
White. total ' ...................................
Both sexes, 16 to 19 years ...............
Men , 16 to 19 years ...... .. .. .............
Women, 16 to 19 years ..................
Men, 20 years and older ............... ....
Women, 20 years and older ..............
5.2
15.2
17.1
13.3
5.0
4.4
4.8
15.0
16.3
13.6
4.4
4.2
5.0
14.8
16.2
13.3
4.5
4.4
4.8
14.9
15.5
14.2
4.3
4.2
4.7
15.4
15.8
15.0
4.4
4.0
4.7
14.7
15.9
13.5
4.3
4.0
4.7
15.1
17.4
12.6
4.2
4.0
4.6
14.4
15.5
13.2
4.2
4.1
4.6
15.7
17.9
13.4
4.2
3.9
4.4
14.0
16.3
11.8
4.0
3.9
4.6
15.5
18.1
12.9
4.1
3.9
4.4
14.5
17.7
11.0
4.0
3.8
4.4
15.3
17.8
12.8
3.8
4.0
4.4
15.4
17.8
13.0
3.8
3.9
4.3
14.2
16.0
12.3
3.6
3.9
Black or African American, totai1. ........
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
10.3
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
10.6
30 .2
30.0
30 .5
10.4
8.9
10.9
31.5
34.1
28.6
10.9
9.1
10.3
32.6
35.8
29 .2
9.2
8.9
10.4
35.5
37.8
32 .8
9.3
8.8
10.1
35.8
36.3
35 .3
9.2
8.4
10.3
32.4
37.6
26.9
9.6
8.8
Hispanic or Latino ethnicity .. ........ .. ....
Married men, spouse present... ............
Married women, spouse present.. ........
Full-time workers ... ............... ....... .. .......
Part-time workers ..................................
7.7
7.0
6.8
3.2
3.5
5.6
5.2
6.9
3.1
3.5
5.5
5.2
6.7
6.1
3.1
3.2
5.2
5.3
6.4
3.0
3.1
5.4
5.5
6.6
3.1
3.4
5.4
5.4
5.7
3.0
3.1
5.5
5.0
6.7
3.1
3.4
5.4
5.4
6.4
3.1
3.5
5.6
5.3
6.7
3.2
3.7
5.6
5.5
7.0
3.8
3.7
6.1
5.5
3.0
3.2
5.4
5.4
3.0
3.0
5.1
5.4
2.7
3.3
5.1
5.3
6.0
2.7
3.1
5.0
5.6
5.8
2.6
3.3
4.9
5.4
8.8
8.5
8.7
8.3
8.2
8.9
8.2
8.0
8.3
7.5
7.8
7.8
8.4
I
7.8
7.0
5.5
4.8
5.0
4.2
5.1
4.2
5.0
4.2
4.9
4.1
4.8
4.0
4.9
4.2
4.9
4.3
4.9
4.3
4.7
4.1
4.9
4.2
4.7
4.0
,. I
3.9
4.5
3.9
4.7
3.9
3.1
2.7
2.7
2.7
2.7
2.6
2.5
2.5
2.5
2.4
2.4
2.4
2.5
2.4
2.3
Educational attainment2
Less than a high school diploma...............
3
High school graduates, no college .........
Some college or associate degree ...........
Bachelor's degree and higher
4
................
1
Includes high school diploma or equivalent.
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 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
2003
2004
2005
2004
Annual average
June
July
Aug.
Sept.
June
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
2,611
2,361
3,012
1,294
1,718
2,865
2,264
2,961
1,325
1,636
2,599
2,343
2,824
1,201
1,623
2,755
2,317
2,888
1,255
1,633
2,531
2,319
2,817
1,165
1,652
2,666
2,268
2,698
1,093
1,615
2,699
2,262
2,667
1,133
1,534
2,666
2,342
2,350
1,041
1,310
19.8
9.8
19.3
9.5
19.3
9.4
19.1
9.3
19.5
9.3
19.6
8.9
18.8
9.1
17.1
9.1
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
2,715
2,397
3,051
1,294
1,757
2,803
2,458
2,885
1,198
1,686
2,605
2,521
2,924
1,243
1,681
2,796
2,251
2,971
1,227
1,744
2,753
2,290
3,032
1,261
Mean duration, in weeks .. .. ... .. ........
Median duration, in weeks .. .. ..........
19.2
10.1
19.6
9.8
19.8
10.8
18.5
8.9
19.2
9.5
19.6
9.5
19.7
9.5
,.771 I
NOTE : Beginning in January 2003, data reflect revised population controls used in the household survey.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August 2005
75
Current Labor Statistics:
Labor Force Data
8. Unemployed persons by reason for unemployment, monthly data seasonally adjusted
[Numbers in thousands]
Reason for
Annual average
unemployment
1
Job losers . . . ............. . . . . . ........... .
On temporary layoff .... .... ..... ..... .. .
Not on temporary layoff... .... .... ... ..
Job leavers ..... ..... ... ..... .. .. .. ............ ..
Reentrants ............... ... ..... ........... ... ..
New entrants ..... .............. .............. .
2003
2004
2004
2005
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
4,197
998
3,199
858
2,408
686
4,117
1,009
3,108
909
2,426
642
4,228
1,068
3,160
3,978
971
3,007
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
4,048
966
3,082
819
2,324
624
3 ,980
965
3 ,015
965
2,405
745
3,784
961
2,823
855
2,364
711
3,675
3 ,646
864
2 ,782
942
2,353
728
3,680
975
2,705
844
2,219
661
55.1
51 .5
50 .9
51.9
49.7
50.4
50.5
5.1
50.9
51 .8
49 .2
49.1
47.9
47.5
49.7
12.8
42.4
9.3
28.2
7.3
12.2
39.3
10.5
29.5
8.4
12.5
38.4
11 .2
30 .0
7.9
13.1
38.8
11 .0
28.6
8.4
12.1
37.6
11 .1
30.5
8.7
11 .6
38.9
10.4
30.4
11 .7
38.8
10.9
29.6
9.0
11 .9
38.9
11.1
29.2
8.8
11 .8
38.8
10.3
29.9
9.3
8.8
12.4
39.4
10.5
29.7
8.0
11 .9
37.2
11 .9
29.7
9.2
12.5
36.6
11 .1
30.6
9.2
10.9
37.0
11 .7
30.7
9.7
11 .3
36.3
12.3
30 .7
9.5
13.2
36.5
11.4
30.0
8.9
3.3
2.8
2.8
2.9
2.7
2.7
2.8
2.7
2.8
2.7
2.7
2.6
2.5
2.4
2.5
.6
.6
.6
.6
.6
.6
.6
.6
.6
.6
.7
.6
.6
.6
1.7
1.6
1.7
.5
1.6
1.6
.5
1.6
1.6
1.6
1.6
1.6
.6
1.5
.5
.5
1.6
.4
1.6
.5
1.6
.4
1.6
.4
.5
.5
.5
.5
.4
4,838
1,121
3,717
818
2,477
641
896
885
2 ,333
686
2,440
699
838
2 ,837
897
2 ,356
747
Percent of unemployed
1
Job losers . . . . . . . . . . . . . . . . . . . .......... .
On temporary layoff .. ... ............... .
Not on temporary layoff .. ... ...........
Job leavers .......................... ........... .
Ree ntrants ............ .......................... .
New entrants .. .. ........ .... ........... ... .
Percent of civilian
labor force
1
Job losers . . . . . . . . . . . . . . ....... .
Job leavers ........................... ... ...... .
Reentrants ... .... .............................. .
New entrants .. ... .... . ... ............... ... . .
.5
.5
' Includes persons who completed temporary jobs.
NOT E: Beginning in January 2003, data reflect revised population controls used in the household survey.
76
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
9. Unemployment rates by sex and age, monthly data seasonally adjusted
[Civilian workers)
2004
Annual average
Sex and age
2003
2004
June
July
Aug.
Sept.
2005
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
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
5.6
12.0
16.8
20.5
14.4
9.7
4.5
4.5
3.9
5.5
11.9
17.6
20.3
16.1
9.2
4.4
4.6
3.7
5.4
11 .6
17.0
20.7
14.9
9.0
4.3
4.4
3.7
5.4
11.8
16.6
19.6
14.9
9.5
4.3
4.4
3.7
5.5
12.2
17.2
20.6
15.2
9.8
4.3
4.4
3.8
5.4
11 .5
16.5
21.2
13.5
9.2
4.3
4.4
3.7
5.4
11.7
17.6
20.6
15.4
8.9
4.3
4.5
3.5
5.2
11 .7
16.3
19.3
14.4
9.5
4.1
4.2
3.5
5.4
12.4
17.5
20.6
15.5
10.0
4.2
4.3
3.6
5.2
11 .6
16.9
19.4
15.0
9.0
4.0
4.2
3.5
5.2
11.8
17.7
19.9
16.9
8.9
4.0
4.1
3.5
5.1
11.8
17.9
20.0
16.3
8.8
4.0
4.2
3.2
5.0
11 .2
16.4
18.3
15.2
8.8
3.9
4 .1
3.1
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
5.6
12.7
18.0
22.3
15.9
10.4
4.4
4.4
4.3
5.5
12.2
17.8
21.2
15.9
9.7
4.4
4.5
3.8
5.6
12.5
18.1
21 .9
16.1
10.0
4.4
4.5
4.0
5.6
12.9
18.2
20.6
16.8
10.5
4.3
4.4
3.9
5.6
13.0
19.2
22 .1
17.7
10.2
4.3
4.4
4.1
5.5
12.4
18.2
23.0
14.8
9.8
4.3
4.4
3.7
5.6
12.5
20.3
24.3
17.8
9.0
4.4
4.6
3.5
5.3
12.7
18.2
22.0
16.1
10.2
4.0
4.1
3.9
5.6
14.1
20.4
25.0
17.7
11.3
4.1
4.2
3.7
5.3
12.9
19.9
22 .9
17.5
9.7
4.0
4.1
3.6
5.1
13.0
20.4
22.2
19.9
9.5
3.8
3.9
3.5
5.1
12.5
20.0
22.5
18.4
9.2
3.8
4.0
3.0
5.0
12.3
19.0
21.7
17.5
9.3
3.7
3.9
3.1
Women, 16 years and older .... .......
16 to 24 years ............................
16 to 19 years .........................
16 to 17 years .................. .
18 tO 19 years ...................
20 to 24 years ........ .................
25 years and older .....................
25 to 54 years ......................
5.7
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
11 .2
15.6
18.9
12.7
9.0
4.5
4.7
5.5
11.6
17.5
19.5
16.4
8.7
4.4
4.7
5.2
10.6
15.9
19.7
13.5
7.9
4.3
4.4
5.2
10.6
15.0
18.6
12.8
8.4
4.3
4.4
5.3
11 .3
15.1
19.0
12.5
9.4
4.2
4.4
5.2
10.5
14.6
19.3
12.1
8.5
4.3
4.4
5.2
10.8
14.8
17.2
12.9
8.9
4.2
4.4
5.1
10.5
14.3
16.8
12.7
8.7
4.1
4.4
5.2
10.6
14.6
16.5
13.2
8.6
4.2
4.4
5.0
10.1
13.7
15.8
12.2
8.3
4.0
4.2
5.2
10.4
14.9
17.5
13.9
8.2
4.2
4.4
5.2
10.9
15.8
17.7
14.2
8.4
4.1
4.3
5.1
10.0
13.8
15.1
12.8
8.1
4.2
4.4
3.7
3.6
3.8
3.8
3.9
3.5
3.3
3.6
3.2
3.3
3.5
3.2
3.2
3.2
3.3
55 years and older
1
1
.. ..........
Data are not seasonally adjusted.
NOTE: Beginning in January 2003, data reflect revised population controls used in the household survey.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August 2005
77
Current Labor Statistics:
Labor Force Data
10. Unemployment rdes by Stde, seasondly cx:Uusted
State
May
Apr.
May
2004
2005
2005P
Alabama ................ .................... .
Alaska ... ............................ ........ .. ............. .
Arizona ............................. .. .... .......... .... .
Arkansas ....................................... .
California ................................ .. .
5.7
7.4
5.0
5.8
6.3
4.4
6.7
5.0
4.9
5.4
4.4
6.4
4.8
5.0
5.3
Colorado .. .............................. ....... .
Connecticut... ......... ................... .. .
Delaware ...................... ............................ .
District of Columbia.. ...... .. ....... ........... .. ... .
Florida ...... ............ .................................... .
5.5
5.0
4.1
8.0
4.8
5.3
4.9
3.9
7.7
4.2
5.3
5.3
4.1
7.9
4.1
Georgia .... ... ...... ... ....... ........... .... ...... .... .
Hawaii ...................................................... .
Idaho .................................. ............ ..... .
Illinois .................. ..................................... .
Indiana ................................................. .
4.6
3.3
4.8
6.2
5.1
5.0
2.9
4.0
5.9
5.4
Iowa .................................................... .
Kansas ..................................................... .
Kentucky .......................... .. ... .... ............ .
Louisiana ..... .. .................... .................... ...
Maine .................................. .. ..... ....... .. .
4.8
5.6
5.5
5.8
4.4
Maryland .............................................. .
Massachusetts ......................................... .
Michigan .. ..... ....................... ...... ........... .
Minnesota ............ ... ... .. ................. ............
Mississippi ............................... ........... .. .
4.2
5.2
7.0
4.6
6.0
P
78
Monthly Labor Review
May
Apr.
May
2004
2005
2005P
Missouri ....... .......... .. .. .. ... ........ ........ . .
Montana .................... .. ............................. .
Nebraska .. ....... .. ........ ..................... .. ... ..
Nevada .................................................... .
New Hampshire ................................ ... .. .
5.6
4.4
3.8
4.4
3.9
5.6
4.4
3.9
4.0
3.4
5.6
4.5
4.0
4.0
3.6
New Jersey........ .. ............................ ........ .
New York ................................ ................. .
North Carolina ... .................... ............ .... .
North Dakota ............................................ .
4.9
5.7
5.8
5.6
3.3
4.2
6.0
4.9
5.3
3.2
3.9
6.0
5.0
5.1
3.5
5.2
2.7
3.9
5.8
4.8
Ohio .......................................... ...... .. .. .
Oklahoma ................................................ .
Oregon ... .. ......... ................ .. ... .............. .
Pennsylvania ... .. ...................................... .
Rhode Island ......................... ...... ...... .... .
6.1
4.9
7.4
5.5
5.3
6.1
4.5
6.5
4.9
4.7
6.1
4.5
6.4
4.8
4.5
4.5
5.2
5.6
5.1
4.7
4.8
5.3
5.7
5.4
5.0
South Carolina ............ ........................... .
South Dakota .. ............ .. .. ... .......... .............
Tennessee ... ......................................... .
Texas ... ............ ... ............................. ........ .
Utah ......... .......... .. ... ..... .............. ..... .... .
6.7
3.5
5.4
6.1
5.3
6.5
3.7
5.8
5.5
4.9
6.3
4.0
6.2
5.5
4.9
4.3
4.7
7.0
4.0
6.8
4.3
4.8
7.1
4.3
7.1
Vermont... ............. .. .... .. ........ .............. . .
Virginia ......................................................
Washington ....................................... .... .
West Virginia .................. .......................... .
Wisconsin ... .. .... .. ... ... ... ... .... ....... ..... .. .. .. .
Wyoming ...................... ......................... ... .
3.6
3.7
6.2
5.4
5.0
3.8
3.3
3.6
5.5
5.1
4.5
3.5
3.1
3.6
5.6
4.5
4.7
4.1
= preliminary
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
State
August 2005
New Mexico ......................... ... .... .. ... ..... .
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
11. Employment of workers on nonfam oavrolls bv Stae, seasondlv cxiiusted
State
May
Apr.
May
2004
2005
2005P
Alabama ...................................... . 2,147,632
331 ,810
Alaska .. .............. .................... ....... .
Arizona ..
2,765,804
1,303,212
Arkansas ... ..... ..... .. ....... ................ .
California ....................... ... ... ... ... .. . 17,514,163
State
May
Apr.
May
2004
2005
2005P
2,143,531
339,688
2,834,853
1,338,943
17,746,916
2,143,048
338 ,854
2,816,286
1,345,629
17,783,775
Missouri .... ........ ..... ..... ............ .
Montana ............... .. .. .......... ..... ......
Nebraska ............................ .... .... .
Nevada ......................................... .
New Hampshire ............................ .
3,032,682
482 ,510
984,945
1,174,422
722,649
3,023,591
490,597
988,902
1,217,259
733,778
3,031,278
491,261
986,876
1,2 12,923
734,690
Colorado ... :.. ................................. .
Connecticut... ..... ............. ............ .
Delaware ....................................... .
District of Columbia ........................
Florida .. . ... .. .................. ...... ......... .
2,515,412
1,799,035
422 ,677
297,487
8,378,936
2,559 ,003
1,807,993
429,449
303,233
8,622,259
2,560,398
1,812,919
432,201
298,768
8,653,301
New Jersey ... ... .. .......... ... ... ...... ..... .
New Mexico ........ ...................... .. ..
New York .................. .............. .. .... .
North Carolina ........... ........ ..
North Dakota .......... ... ..... ...
4,384.485
910,838
9,339,303
4,250,170
353,531
4,413,481
942,006
9,410,201
4,301,942
355,964
4,406,372
940 ,008
9,423,714
4,308,337
355,364
Georgia .......... .. ........ .... .
Hawaii ..... ......... ..... ........
Idaho ..... .
Illinois ...................... .. ... ... ... ........... .
Indiana .. ........ ....... ........ ........ ... .. . .
4,383,178
615,293
702 ,405
6,391,383
3,165,476
4,469,954
630,913
728,573
6,495,078
3,217,082
4,487,063
625,173
728,370
6,479,643
3,200,411
Ohio ............................... .
Oklahoma .. .... ....................... ..... .. ..
Oregon .............. .. ....................... .
Pennsylvania .. ....... ................... .. .. .
Rhode Island ............................... .
5,881,084
1,708,861
1,854,661
6,266,860
563,379
5,947,936
1,725,450
1,873,284
6,329,209
567 ,637
5,930,253
1,722,874
1,865,148
6,350,018
570 ,690
Iowa .. ... ............ ............... .. .. ... ... .
Kansas .......................................... .
Kentucky ........... .. ....................... .
Louisiana ................. ..
Maine ....... .
1,623,982
1,463,365
1,977,561
2,054,535
698,294
1,645,255
1,471,560
1,993,718
2,101,000
705,740
1,639,877
1,472,267
1,991,855
2,110,625
708,850
2,040,302
South Carolina .......... .. ...... .......... ..
427,471
South Dakota ................................ .
Tennessee ......... .. ...... .. ... ........ ..... . 2,910,691
Texas ... ... ... .... ... ..... ....... ..
11 ,016,016
1,201 ,852
Utah ......................... .. ...... ..... ... .. .
2,072,512
430,352
2,907,118
11,208,51 1
1,233,673
2,068,652
428,280
2,907,197
11,216,988
1,235,731
Maryland .. ........ ........................... .
Massachusetts
Michigan .......... .
Minnesota ....... ........ .. ... ..
Mississippi ................. ............... ... .
2,881,577
3,395,294
5,077,529
2,955,962
1,328,002
2,915,228
3,377,480
5,142,355
2,970,541
1,343,322
2,935,738
3,373,772
5,129,447
2,975,345
1,349,625
Vermont... .......... .. ....................... .
Virginia .......................................... .
Washington ................................. .
West Virginia .. ............... ... ... .. ....... .
Wisconsin ................. ... ................ .
Wyoming .... .. .......... ................. .. ... ..
352,288
3,897,576
3,270,470
798,117
3,058,501
283,805
351,495
3,907,947
3,269,472
791,437
3,049,673
285,537
352,921
3,811,152
3,226,235
789,390
3,070,022
280,988
NOTE: Some data in this table may differ from data published elsewhere because of the continual updating of the database.
Monthly Labor Review
August 2005
79
Current Labor Statistics:
Labor Force Data
12. Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Industry
Annual average
2003
TOT AL NONFARM ...............
TOT AL PRIVATE......................
2004
2004
June
July
Aug.
Sept.
2005
Oct.
Nov.
Dec.
Jan.
Feb
Mar.
Apr.
May"
JuneP
129,999
131,480
131,479
131,562
131 ,750
131,880
132,162
132,294
132,449
132,573
132,873
132,995
133,287
133,391
133,537
108.416
21,816
109.862
21,884
109.908
21,890
109.976
21,902
110.105
21,946
110.203
21,947
110.462
21 ,982
110.588
21,996
110.749
22,022
110.863
22,004
111 .140
22,066
111.264
22,093
11.542
22,130
111.639
22,138
111.783
22,134
mining ............ .. ..... ... .............
Logging ........ ....... ..................
Mining ......... .. ........... ............. .. ...
Oil and gas extraction ....... ....
572
69.4
502.7
120.2
591
67.8
523.2
123.1
591
67.6
523.8
123.2
596
67.4
528.9
123.2
595
67.5
527.8
123.8
597
68.0
528.5
124.0
595
67.0
527.7
123.6
599
66.9
532.5
124.4
602
67.9
534.4
124.1
607
68.0
538.7
123.4
602
67.3
545.0
122.5
619
68.7
549.8
124.0
623
65.2
558.0
124.3
625
64.6
558.5
125.0
627
64.6
562.8
125.2
Minina. exceot oil and oas' ..
Coal minina ................ ...... .
Support activities for mining ..
202.7
70.0
179.8
207.1
71.7
193.1
208.1
72.0
192.5
211.8
73.5
193.9
209.1
73.1
194.9
208.5
72.9
196.0
208.4
72.7
195.7
210.7
73.7
197.4
211 .3
73.9
199.0
212.9
75.4
202.4
215.5
76.1
207.0
215.7
76.1
210.1
218.5
76.9
215.2
219.6
76.6
215.4
221.4
77.4
216.2
GOODS-PRODUCING .. ..... .........
Natural resources and
Construction ..............................
6,735
6,964
6,955
6,965
6,985
6,998
7,043
7,060
7,086
7,090
7,133
7,159
7,207
7,219
7,237
Construction of buildinas ...
Heavv and civil enaineerina ..
Soecialitv trade contractors .....
Manufacturing............................
1.575.8
903.1
4.255.7
14,510
1.632.2
902.5
4.429.7
14,329
1.626.7
899.8
4.428.6
14,344
1.632 .2
899.7
4.433.1
14,341
1.636.3
901.1
4.447.6
14,366
1.647.8
902 .1
4.447.8
14,352
1.663.0
904.1
4.476.1
14,344
1.668.3
906.4
4.484.8
14,337
1.678.9
907.8
4.499.2
14,334
1.682.4
908.2
4.499.6
14,307
1.689.2
911.7
4.531 .8
14,321
1.692.5
915.7
4.550.9
14,315
1.693.4
926.6
4.586.5
14,300
1.694.6
932.2
4.592.2
14,294
Production workers ..............
Durable goods.........................
10.190
8,963
10.083
8,923
10.095
8,931
10.102
8,926
10.131
8,965
10.117
8,957
10.111
8,960
10.104
8,954
10.097
8,957
10.082
8,942
10.085
8,962
10.091
8,957
10.086
8,954
10.090
8,957
1.699.1
945.1
4.593.1
14,270
10,075
8,945
Production workers ...
Wood oroducts ........
Nonmetallic mineral products
Primary metals .........
Fabricated metal oroducts ...
Machinerv ..... . .. .. ..... ......
Comouter and electronic
6.152
537.6
494 .2
477.4
1.506.8
1.149.4
6.137
548.4
504.8
465.9
1.470.3
1.141 .5
6.147
549
507.4
467.4
1.498.3
1.142.7
6.144
550
507.9
468.4
1.502.6
1.146.8
6.180
551.7
507.6
467.4
1.506.8
1.151.5
6.172
550.1
508.8
466.4
1.508.5
1.148.7
6.172
554.5
509.1
466.0
1.511.5
1.147.3
6.166
553.3
507.9
465.8
1.510.9
1.147.4
6.170
555.2
506.5
465.2
1.512.8
1.146.0
6.166
554.7
504.5
465.5
1.514.3
1.145.9
6.178
553.6
504.0
466.9
1.514.1
1.148.0
6.182
555.2
502.0
466.6
1.517.3
1.151.7
6.188
551 .8
504.7
466.0
1.517.5
1.153.7
6.196
549.5
501.6
465.8
1.520.1
1.156.1
6.189
550.6
501 .4
464.6
1.519.8
1.155.1
oroducts' ................... . . . ..
Computer and oerioheral
equipment... ... .... ... .............
Communications equipment..
Semiconductors and
electronic components ........
Electronic instruments .....
Electrical equipment and
appliances ......... .......... ........
Transportation equipment... ....
Furniture and related
products ............ · ···· ··· ····
Miscellaneous manufacturing
1,355.2
1,326.2
1,327.4
1,332.8
1,334.0
1,332.5
1,329.8
1,327.1
1,325.8
1,327.0
1,327.5
1,326.0
1,329.0
1,329.6
1,337.0
224.0
154.9
212.1
150.5
212.2
150.1
211.4
151.3
212 .4
151.6
211.9
151 .0
209.7
150.7
209.3
152.7
210.4
153.7
210.2
155.1
211.2
154.5
211.3
153.7
212.5
153.9
213.2
153.8
215.5
154.1
461 .1
429.7
452.8
431 .8
455.2
431.2
457 .9
433.9
457.4
434.2
457.0
434.6
454.9
437.0
451 .9
435.6
448.0
435.7
447.4
436.4
447.1
436.4
446.7
436.2
446.7
437.5
446.5
437.6
448.1
441 .1
459.6
1,774.1
446.8
1,763.5
446.8
1,762.2
447.3
1,739.1
447 .7
1,769.5
447.0
1,768.5
445.1
1,771 .0
447.4
1,767.2
445.8
1,771.9
445.1
1,760.1
445.3
1,781 .8
444.5
1,776.7
442.8
1,775.7
443.4
1,779.0
441.2
1,764.7
572.9
663.3
572 .7
655.5
573.6
656.4
574.0
656.8
573.3
655.2
572.1
654.5
571 .3
654.1
572.2
654.7
571.7
656.4
570.3
654.3
567.5
653.5
565.9
651.3
562.8
650.3
560.9
651.4
558.9
651.8
Nondurable goods ...................
Production workers. .. ... .......
5,547
4,038
5,406
3,945
5,413
3,948
5,415
3,958
5,401
3,951
5,395
3,945
5,384
3,939
5,383
3,938
5,377
3,927
5,365
3,916
5,359
3,907
5,358
3,909
5,346
3,898
5,337
3,894
5,325
3,886
Food manufacturing ....... .. ... ...
Beverages and tobacco
products ................. .... .. .
Textile mills .. ... . . . . . . . . . . . . . .
Textile product mills .................
Apparel .. .... . .... .. ........
Leather and allied products ...
Paper and paper products .......
Printing and related support
activities .... .................
Petroleum and coal products ...
Chemicals... ....... .. ... .... .... .. ......
1,517.5
1,497.4
1,498.6
1,504.6
1,497.0
1,494.3
1,493.5
1,493.6
1,498.8
1,494.3
1,493.2
1,495.2
1,489.6
1,489.0
1,486.8
199.6
261.3
179.3
312 .3
44 .5
516.2
194.3
238.5
177.7
284.8
42.9
499.1
194.4
239.3
178.5
285.9
42.6
496.7
194.2
238.8
178.2
283.2
42 .5
499.2
193.4
238.1
177.6
282 .6
42.5
500.6
194.9
237 .3
177.8
281.0
42 .7
499.3
192.9
236.5
178.1
276.1
42 .8
499.4
195.1
235.0
178.4
273.4
43.4
498.1
193.0
233.2
178.0
271.9
43.1
497.9
192.2
231.5
178.1
269.3
43.1
499.9
192.5
230.1
177.9
267.2
43.2
500.2
191.6
228.7
177.9
262.8
42.9
502.0
191.1
225.5
177.7
262.2
42.8
499.3
191.4
225.4
178.3
258.5
42.4
498.2
190.6
224.7
176.7
256.0
42.4
495.8
680.5
114.3
906.1
668.3
112.9
888.8
807.1
665.2
112.8
887.7
661.6
113.2
885.5
807.1
661.0
113.3
884 .5
806.3
661.3
113.6
882 .4
808.6
660.8
113.8
880.5
659.6
114.5
877.1
659.2
115.1
876.4
658.7
116.4
878.4
657 .2
117.1
877.6
656.4
116.8
878.3
815.4
665.0
112.8
887.0
806.6
808.9
663.9
113.2
885.8
806.6
806.2
804.9
804.1
658.8
115.0
877.5
805.8
804.3
801.7
800.2
SERVICE-PROVIDING ..................
108,182
109,596
109,589
109,660
109,804
109,933
110,180
110,298
110,427
110,569
110,807
110,902
111,157
111,253
111,403
PRIVATE SERVICEPROVIDING .. .. .. ........ ...........
86,599
87,978
88,018
88,074
88,159
88,256
88,480
88,592
88,727
88,859
89,074
89,171
89,412
89,501
89,649
25,287
5,607.5
2,940.6
2,004.6
25,510
5,654.9
2,949.1
2,007.1
25,536
5,653.4
2,948.4
2006.6
25,536
5,660.2
2,955.3
2004 .0
25,537
5,662 .9
2,957.8
2004.0
25,555
5,672.4
2,960.2
2008.1
25,581
5,674.7
2,962.3
2009.1
25,621
5,680.0
2,960.4
2012.6
25,620
5,683.6
2,964.5
2009.9
25,652
5,679.9
2,965.6
2,005.4
25,714
5,688.7
2,968.7
2,006.9
25,743
5,702.2
2,975.6
2,011.2
25,797
5,707.7
2,976.8
2,012.6
25,831
5,716.9
2,981.7
2,013.0
25,834
5,717.4
2,983.0
2,012.5
Plastics and rubber products ..
Trade, transportation,
and utilities..............................
Wholesale trade .......................
Durable goods ........ .............
Nondurable goods ......... .....
Electronic markets and
agents and brokers ..... . . .. ...
662.2
Retail trade ............................... 14.917.3
Motor vehicles and parts
dealers' ....... .. .. ...... . ........
Automobile dealers ..... ........ ..
Furniture and home
furnishings stores ........... .. ......
Electronics and appliance
stores ..... ........... ...... ...............
698.8
698.4
700.9
701.1
704.1
703.3
707.0
708.9
709.2
713.1
715.4
718.3
722.2
721 .9
15,034.7 15.060.5 15.048.2 15.043.3 15,037.7 15,056.5 15,081.4 15,077.0 15.081.2 15.125.4 15,128.7 15.157.5 15.172.7 15,174.8
1,882.9
1,254.4
1,901.2
1,254.2
1,904.1
1257.1
1,904 .4
1254.1
1,899.8
1251 .2
1,898.4
1247.3
1,896.4
1245.0
1,901.2
1247.6
1,905.9
1249.1
1,907.4
1247.9
1,911 .2
1248.8
1,912.6
1250.2
1,914.2
1252.2
1,915.4
1253.6
1,912.0
1250.7
547.3
560.2
559.1
559.8
561.6
561.9
562.3
565.6
563.7
562.1
562.6
562.3
565.5
568.9
565.2
512 .2
514.4
514 .1
513.4
512 .0
513.6
520.2
520.3
516.5
516.1
515.1
518.4
518.4
521.0
523.2
See notes at end of table.
80
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands)
Annual average
Industry
2003
2004
2005
2004
June
July
Aug.
Sept.
Oct.
I
Building material and garden
supply stores ...
Food and beverage stores ....
Health and personal care
stores .... .... . . ......... ......... .
Gasoline stations . . . . . . . . . . . . .. .
Clothing and clothing
accessories stores .............
Sporting goods, hobby,
book, and music stores .. ....
General merchandise stores1.
Department stores ..............
Miscellaneous store retailers ..
Nonstore retailers .... .......... .
Transportation and
warehousing ..........................
Air transportation ........... .... ..
Rail transportation ..... .... . ···· ·
Water transportation ......
Truck transportation ......... ....
Transit and ground passenger
transportation ..........
Pipeline transportation .... ..... .
Scenic and sightseeing
transportation .. ...... ..... .....
Support activities for
transportation ....... ··········· ·
Couriers and messengers .....
Warehousing and storage
Utillties ............................ ·- ········
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 ......................
1,224.7
2,828.5
1,228.1
2,826.2
1,232.5
2,827.1
1,236.3
2,830.2
. ...... .. ..•
intermediation' .... . ...........
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 nonfinandal
intangible assets .... ...... .. ...
Professional and business
services...............................
Apr.
I
1.248.o l
2,826.0
1,240.4 1 1,243.5
2,819.8
2,822.7
June"
MayP
I
I
1,264.8
2,826.6
1,263.7
2,826.8
1,264.5
2,834 .9
1,267.2
2,833.6
1,271.6
2,836.2
949.7
874.6
949.2
874.5
955.0
875.0
959.1
875.1
957.6
871.8
1,396.0
1,185.0
2,383.4
1,226.0
2,826.3
1,223.8
2,832.6
938.1
882.0
941.7
877.1
941.3
877.5
941.0
876.6
941.0
876.5
942.1
878.0
941 .6
877.0
944.5
873.7
946.6
871.3
1,304.5
1,361.8
1,367.6
1,369.5
1,374.4
1,371.9
1,376.0
1,377.9
1,381.3
1,375.5 1 1,380.5
1,384.0
1,387.0
1,390.8
646.5
2,822.4
1,620.6
930.7
427.3
639.2
2,843.5
1,612.5
918.6
424.8
639.4
2,856.4
1,618.0
919.2
425.4
638.9
2,848.0
1,616.1
918.8
424.6
639.0
2,842.5
1,611.4
918.9
423.3
638.7
2,832.9
1,603.3
917.0
423.6
638.0
2,835.2
1,604.2
920.5
422.8
639.0
2,854.9
1,619.1
917.4
423.8
636.2
637.7
635.8
2,864.1
2,852.9 1 2,853.5
1,619.3
1,619.1 1 1,625.7
919.9
918.7
918.2
420.1
421 .5
418.5 1
638.3
2,862.0
1,624.2
919.4
417.5
638.0
2,864.7
1,625.3
921.6
418.7
634.6
636.7
2,864.0 1 2,862.4
1,620.2
1,624.3
926.6
923.4
417.6
417.5
4,185.4
528.3
217.7
54.5
1,325.6
4,250.0
514.8
224.1
57.2
1,350.7
4,250.9
517.0
224.7
58.2
1,352.2
4,257.0
516.3
225.0
58.1
1,352.5
4,260.4
515.0
224.6
56.7
1,352.5
4,274.1
513.8
225.5
57.2
1,358.5
4,279.6
514.2
225.4
57.7
1,356.0
4,289.6
514.6
224.6
57.8
1,358.9
4,288.0
512.3
224.0
58.6
1,366.5
4,324.1
4,31601
507.9
509.4
223.9
60.0
1,378.0
4,336.6
508.0
223.7
61.6
1,383.2
4,355.8
508.8
223.7
61.3
1,389.8
4,365.5
508.2
224 .3
61 .5
1,394.4
4,365.7
504.8
224.0
61.3
1,397.3
382.2
40.2
385.5
38.8
381.6
38.9
383.2
39.0
386.2
38.9
388.3
39.0
389.3
38.9
389.4
39.0
391.0
38.7
391.0
39.4
388.7
39.3
393.3
39.5
391.2
39.3
390.9
39.2
26.6
26.7
27.4
26.3
27.7
27.8
24 .2
24.9
26.7
27.2
27.6
27.9
520.3
561 .7
528.3
535.6
560.5
556.0
534.3
562.1
554.5
535.5
563.1
558.0
536.9
562.6
559.3
537.7
563.8
562.5
539.9
564.4
568.2
544.6
568.7
565.9
547.0
556.4
566.9
549.3 1
577.5
567.8
551.5
577.6
569.9
553.4
579.3
572.7
554.2
581.8
576.2
556.7
582.3
580.0
553.4
580.9
586.0
576.0
575.2
575.6
575.6
576.2
3,127
3,134
3,152
3,150
3,152
25.6 1
26.1
944 .8 1
872.9
224.4
59.8
1,372.6
391.7 1
39.3 1
26.6 1
577.0
570.2
570.8
570.9
570.1
571.1
570.3
570.2
571.3
3,188
3,138
3,151
3,144
3,135
3,127
3,131
3,133
3,127
574.7 1
3,123
924.8
909.8
911.9
909.6
909.3
909.2
90091
905.7
905.0
905.6
906.8
905.7
395.3
329.5
390.6
329.7
384.8 1
329.7
380.3 1
331.3
380.9
330.4
386.9
330.7
399.3
330.7
396.6
330.6
396.6
331.6
34.6
34 .8 1
34.0 1
1,032.2
1,030.8
1,031 .5
35.0
1,029.9
35.3
1,037.3
35.4
1,036.7
35.8
1,036.5
392.6
50.9
393.7
50.7
393.9
50.1
396.2
50.2
395.9
50.6
8,165
8,150
6,030.9 1 6,037.6
8,167
6,039.8
8,182
6,048.0
8,186
6,053.2
8,202
6,061.3
20.4
20.4
20.3
20.4
20.3
2,906.8
008
'I
905.3
904.5 1
389.3
327.8
389.7
328.1
31.7
31.4 1
1,037.1
1,041.9
32.0
1,028.4
33.0
1,024.8
33.6
1,030.0
388.6
51.3
387.6
51.7
387.6
51.5
389.2
50.9
389.5
50.7
390.4
50.7
8,043
5,958.6
8,058
5,970.2
8,083
5,982.1
8,093
5,994.1
8,107
6,001.3
8,128
6,014.5
21.6
21.5
21.3
2,829.2 i
2,833.4
2,841.0
2,847.9
2,859.2
2,871.9
1,762.1
1,760.6
1,763.0
1,765.1
1,768.1
1,773.3
1.286.3
1,283.9
1,283.5
1,286.4
1.288.3
1.293.1
765.1
766.3
769.9
772.3
777.3
776.9
779.7
782.5
784.8
786.9
787.6
2,259.6
2,256.7
2,250.9
2,253.9
84.7
84.8
83.6
2,127.2
1,443.8
658.3
2,126.8
1,444.0
657.8
2,134.3
1,449.7
659.0
2,132.7
1,451.7
655.1
2,140.7
1,457.3
658.2
376.2
324.3
389.0
326.6
395.5
326.5
29.2
1,082.3
31 .3
1,042.5
31.5
1,044.0
402.4
48.7
388.1
50.9
389.9
51.6
7,977
5,922.6
8,052
5,965.6
8,051
5,965.6
22.6
21.6
21.6
2,792.4
2,832.3
2,833.7
1,748.5
1,761 .2
1,280.1
1,285.3
757.7
766.8
394.4
327.2
21.5 1
20.9 1
389.9
51 .0
20.5
20.6
Credit intermediation and
related activities' ..
Deoositorv credit
Mar.
Feb.
Jan.
Dec.
Nov.
I
2,882.7 I
2,891 .0
2,896.8 1 2,902.6
1,778.8
1,785.6
1,790.3
1,794.0
1,795.9
1.296.8
1.301.6
1.305.5
1.308.0
1,308.3
I
2,915.8
1,797.8 1 1,801.6
1.310.9
1,308.8
787.7 1
2,253.7
785.8
2,253.9
2,266.0
2,260.3
2,260.9
2,257.0
2,261.0
2,263.3
2,264.1
2,260.4
2,258.1
83.9
84.7
84.3
84.6
84.3
84.0
83.5
83.9
84.2
2,053.9
1,383.6
643.1
2,086.2
1,417.0
643.9
2,085.7
1,415.7
645.0
2,084.6
1,416.7
643.0
2,088.2
1,420.0
643.3
2,101.3
1,429.1
647.6
2,099.2
1,428.6
646.3
2,105.5
1,434.7
646.0
2,113.6
1,437.8
650.9
27.3
25.4
25.0
24.9
24.9
24.6
24.3
24.8
24.9
25.2 1
25.1
25.0
25.6
25.9
25.2
15,987
16,414
16,415
16,453
16,470
16,514
16,614
16,611
16,674
16,694 1
16,775
16,796
16,843
16,853
16,909
6,629.5
1,142.1
6,762.0
1,161.8
6,754.0
1,163.5
6,765.1
1,1 65.0
6,779.7
1,1 63.6
6,805.4
1,166.8
6,835.3
1,167.4
6,882.11 6,902.7
1,161.2
1,160.8
6,907.3
1,161.5
6,928.5
1,161 .8
6,932.3
1,163.5
6,959.6
1,164. 1
815.3
816.0
810.5
813.9
814.2
816.1
821 .5
862.7
853.9
862.3
1,226.9
1,260.8
1,258.7
1,262.0
1,264.4
1,270.5
1,280.5
~·I
2,119.0
,
. ., ,
,
654.1
1
84.6 1
85.5
Professional and technical
. .... ..... .... ...
services' .. ..
Legal services .. .... ... .... ....
Accounting and bookkeeping
services ...... · ·· ··· · ····· ··· ·····
Architectural and engineering
services ..........................
6,834.4 1 6,869.9
1,164.4
1,163.1
816.6 1
840.8 1
1,284.9 1 1,289.5
858.11
1,286.9
8581 1
1,292.0
856.6 1
1,295.7 1 1,300.8
1,304.6 1 1,314.0
See notes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August 2005
81
Current Labor Statistics:
Labor Force Data
12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Industry
Computer systems design
and related services ..........
Management and technical
consulting services .... .......
Management of companies
and enterprises .....................
Administrative and waste
Annual average
2004
2005
2003
2004
June
July
Aug.
Sept
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May"
1,116.6
1,147.4
1,142.3
1,145.9
1,155.0
1,161.1
1,167.3
1,174.1
1,174.3
1,171.8
1,174.2
1,175.5
1,178.5
1,178.5
1,183.7
JuneP
744.9
779.0
783.6
784.7
786.9
787.9
790.5
787.8
789.9
789.3
793.7
795.5
798.8
801.0
804.5
1,687.2
1,718.0
1,722.6
1,723.7
1,720.7
1,715.0
1,715.3
1,722.5
1,725.6
1,730.7
1,731.3
1,731.5
1,733.4
1,734.5
1,737.4
7,669.8
7,934.0
7,938.3
7,964.0
7,969.7
7,993.2
8,063.1
8,054.3
8,078.0
8,081.6
8,140.9
8,156.7
8,181.1
8,186.4
8,212.0
7,347.7
7,608.7
7,611.2
7,637.2
7,643.1
7,667.3
7,736.4
7,728.2
7,751.4
7,755.2
7,813.6
7,831.8
7,858.1
7,865.4
7,889.4
3,299.5
3,470.3
3,449.5
3,477.5
3,480.0
3,513.5
3,572.9
3,570.5
3,584.5
3,595.9
3,633.8
3,645.7
3,666.0
3,668.7
3,683.8
2.224.2
2.393.2
754.5
749.7 1
2.383.9
760.3
2.398.6
758.1
2.411.8
757.9
2.438.7
752.6
2.486.5
755.9
2.484.7
754.6
2.479.4
757.0
2.479.1
752.8
2.508.0
755.7
2.506.1
754.1
2.520.7
754.9
2.520.2
753.7
2.529.0
751.9
1.636.1
1.694.2
1.707.7
1.705.2
1.706.6
1.706.4
1.708.6
1.707.2
1.706. 1
1.701.4
1.711.2
1.712.6
1.715.9
1.718.6
1.725.3
322.1
325.3
327.1
326.8
326.6
325.9
326.7
326.1
326.6
326.4
327.1
324.9
323.0
321.0
322.6
16,588
16,954
Educational services ...............
2,695.1
2,766.4
Health care and social
assistance ..................... ..... 13,892.6 14,187.3
16,936
2,755.1
16,963
2,765.6
17,010
2,772.3
17,019
2,773.2
17,081
2,794.0
17,108
2,797.2
17,142
2,805.5
17,178
2,825.0
17,186
2,810.3
17,210
2,814.0
17,243
2,814.0
17,289
2,819.9
17,327
2,823.6
14,1 80.7
14,197.8
14,237.8
14,246.1
14,287.2 14,310.7
14,336.1
14,353.2
14,375.4
14,396.0
14,429.1
14,468.9
14,503.4
services ....... . ..... ............ .... .
Administrative and suooort
services' ....
·············· ······
1
...... .
Employment services .
Temoorarv helo services ....
Business suooort services .. ..
Services to buildinas
and dwellinas ...................
Waste management and
remediation services ...........
Educational and health
services ........ .......................
1
Ambulatorv health care
services' .........................
Offices of physicians .......... .
Outpatient care centers ........
Home health care services ...
Hospitals .......... ......... ........
4,786.4
2,002.5
426.8
732.6
4,946.4
2,053.9
446.2
773.2
4,941.9
2,051.1
446.6
771.7
4,956.2
2,054.5
448.4
775.4
4,969.2
2,059.1
449.7
778.0
4,975.0
2,064.5
448.7
779.5
4,996.9
2,074.2
449.5
782.7
5,006.7
2,077.7
449.8
789.2
5,017.0
2,084.3
450.3
790.7
5,027.0
2,085.3
451.5
796.6
5,035.0
2,090.9
451.1
796.8
5,041.6
2,093.2
452.6
798.8
5,054.2
2,103.6
453.6
797.9
5,069.8
2,114.2
455.2
799.8
5,080.5
2,118.5
455.8
804.0
4,244.6
4,293.6
4,292.2
4,296.2
4,305.0
4,306.0
4,311.2
4,319.7
4,323.5
4,329.6
4,337.8
4,344.6
4,354.2
4,362.3
4,373.9
2,786.2
2,814.8
2,814.4
2,818.0
2,819.8
2,825.0
2,827.2
2,827.2
2,827.9
2,832.5
2,839.8
2,842.6
1.575.3
2,132.5
1.576.3
2,132.2
1.576.9
2,127.4
1.576.7
2,143.8
1.576.6
2,140.1
1.576.8
2,151.9
1.576.4
2,157.1
1.574.5
2,167.7
2,827.0 1 2,830.0
1.571.5
1.571.6
2,169.6
2,172.6
2,830.0
1.579.8
2,075.4
1.572.3
2,179.8
1.571.4
2,188.2
1.572.6
2,197.0
1.573.9
2,206.4
755.3
12,173
767.1
12,479
767.4
12,486
770.4
12,497
776.1
12,508
767.9
12,522
772.8
12,546
775.3
12,571
780.4
12,589
780.5
12,611
782.5
12,650
785.1
12,662
788.6
12,723
790.0
12,723
798.4
12,742
1,811.0
1,805.4
1,808.4
1,805.8
1,823.9
1,822.4
1,828.2
357.9
355.6
357.0
357.8
361.1
359.0
357.4
115.8
116.8
117.5
117.8
Nursina and residential
r.:arpf:,rilitiP<: 1
Nursina care facilities ..........
Social assist;mai 1........ ...
Child day care services ........
Leisure and hospitality ...........
Arts, entertainment,
and recreation ......................
1,812.9
1,834.8
1,833.0
1,830.9
1,831.0
1,836.2
1,834.4
1,826.4
Performing arts and
spectator sports ..................
371.7
363.6
364.8
359.2
358.4
363.6
364.4
362.5
Museums, historical sites,
zoos, and parks ..................
117.1
114.7
117.8
118.6
118.8
118.3
118.2
116.9
Amusements. gambling, and
recreation .......................... 1,326.5
1,351.1
1,353.4
1,353.1
1,353.8
1,354.3
1,351.8
1,347.0
Accommodations and
food services ....................... 10,359.8 10,646.0 10,650.7 10,666.1 10,676.5 10,685.3 10,712.0 10,744.1
Accommodations .................. 1,775.4
1,795.9
1,797.3
1,798.0
1,801.3
1,801.5
1,800.6
1,814.7
Food services and drinking
places ............................ ... 8,584.4
8,850.1
8,852.7
8,868.8
8,875.2
8,883.8
8,911.4
8,929.4
Other services ........................
5,401
5,431
5,443
5,441
5,438
5,436
5,434
5,441
Repair and maintenance ........
1,233.6
1,227.6
1,226.5
1,227.4
1,225.9
1,226.9
1,227.9
1,227.1
Personal and laundry services
1,263.5
1,274.1
1,283.4
1,278.0
1,271 .5
1,276.9
1,267.8
1,271.6
Membership associations and
organizations ....... ... .........•
2,903.6
2,929.1
2,932.7
2,932.8
2,937.9
2,937.9
2,938.1
2,942.3
Government. ...............................
Federal ......................................
Federal, except U.S. Postal
Service ...................................
U.S. Postal Service ................
State .........................................
Education ............. ... ............. ..
Other State government... .....
Local .........................................
Education ...............................
Other local government.. .......
1
1,346.0
1,345.9
1,353.0
10,856.0
10,899.0
10,900.1
10,913.3
1,824.6
1,825.9
1,830.3
1,826.6
1,830.1
1,827.7
1,823.3
8,953.8
5,447
1,229.9
1,276.8
8,979.2
5,451
1,229.4
1,280.4
9,010.8
5,457
1,233.7
1,280.5
9,029.4
5,459
1,235.6
1,282.2
9,068.9
5,472
1,239.9
1,286.9
9,072.4
5,469
1,241.6
1,284.7
9,090.0
5,483
1,245.6
1,283.7
2,940.6
2,941.4
2,942.9
2,940.8
2,945.6
2,942.9
2,953.4
2.728
21,677
2,730
21,700
2,723
21,706
2,728
21,700
2,706
21,710
2,717
21,733
2,720
21,731
2,724
21,745
2,718
21,752
2,720
21,754
2,713
1,943.4
784.1
4,985
2,249.2
2,736.2
13,905
7,762.5
6,143.0
1,946.3
785.1
4,963
2,228.2
2,734.4
13,877
7,742.5
6,134.5
1,939.2
1,945.5
784.3
4,987
2,249.4
2,737.8
13,928
7,785.7
6,142.2
1,946.8
1,940.1
782.5
5,007
2,268.4
2,738.2
13,970
7,810.8
6,159.3
1,946.4
781.4
5,015
2,271.3
2,743.4
13,963
7,806.3
6,156.7
1,939.5
766.4
5,020
2,277.9
2,741.9
13,974
7,810.8
6,163.1
1,937.2
780.2
5,025
2,280.4
2,744.4
1,939.8
780.1
5,027
2,283.0
2,744.4
13,968
7,808.8
6,159.2
13,986
7,820.7
6,165.1
1,943.2
780.8
5,024
2,280.8
2,743.2
13,983
7,813.5
6,169.0
1,937.1
780.7
5,026
2,281.2
2,745.1
14,001
7,823.9
·5177.4
1,938.1
781.4
5,024
2,279.4
2,744.2
14,008
7,824.7
6,183.1
1,932.5
780.7
5,026
2,282.5
2,743.5
14,015
7,830.3
6,184.3
786.4
4,976
2,241.4
2,734.4
13,884
7,757.8
6,126.6
See "Notes on the data" for a description of the most recent benchmark revision.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
1,332.2
10,805.1 10,841.1
10,778.4
21,645
2,730
1,952.4
808.6
5,002
2,254.7
2,747.6
13,820
7,709.4
6,110.2
Monthly Labor Review
113.6
1,337.8
21,586
2,726
21,618
p = preliminary.
82
114.5
1,335.3
21,571
2,731
21,583
2,761
Includes other industries not shown separately.
NOTE:
114.8
1,338.3
August 2005
783.4
5,000
2,263.7
2,736.4
13,947
7,793.2
6,153.4
1
13. Average weekly hours of production or nonsupervisory workers on private nonfarm payrolls, by industry, monthly
data seasonally adjusted
Industry
--- -
2003
2004
2005
2004
Annual average
June
July
Aug.
Oct.
Sept.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
MayP
JuneP
I
33.7
TOT AL PRIVATE .............................
33.7
33.7
33.6
33.7
33.7
33.8
33.8
33.7
33.7
33.7
33.7
33.7
33.8
33.7
GOODS-PRODUCING ..........................
39.8
40.0
39.9
40.1
40.0
40.1
39.9
39.9
40.0
39.8
39.9
39.8
40.1
39.9
39.9
Natural resources and mining ............
43.6
44.5
43.9
44.2
44 .4
44 .5
44 .8
45.0
45.4
45.5
45.1
45.3
45.7
45.8
45.2
Construction ....................................
38.4
38.3
38.0
38.3
38.1
38.1
38.2
38.3
38.4
37.6
38.2
38.3
39.0
38.5
38.5
Manufacturing ... .. ...............................
Overtime hours .......... ··············· ······
40.4
4.2
40.8
4.6
40.7
4.5
40.8
4.6
40.9
4.6
40.8
4.6
40.7
4.5
40.5
4.5
40.5
4.5
40 .7
4.5
40.6
4.6
40.4
4.5
40.5
4.4
40.4
4.4
40.4
4.4
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
42.3
43.1
41.1
41.9
40.4
40.7
42 .5
39.5
38.5
41.2
4.6
40 .6
41.8
43.4
41.0
42.0
40.4
40.8
42 .2
39.6
38.4
41 .3
4.7
40.7
42.2
43.2
41 .2
42.1
40.7
40.8
42.4
39.3
38.6
41.3
4.7
40.8
42 .3
43.2
41 .2
42.1
40.4
40 .9
42.5
39.3
38.5
41 .2
4.7
40.4
42.4
43.1
41 .2
42.3
40.3
40.6
42.4
39.3
38.4
41.2
4.7
40 .3
42.4
43.0
41 .1
42 .2
40.1
40.6
42.3
39.2
38.4
40 .9
4.6
40.0
42 .1
42.9
40.9
42 .0
39.6
40.1
42.2
39.2
38.2
40.3
42 .3
42.8
40.9
42.0
39.8
40.0
42.4
39.5
38.3
41 .1
4.6
40 .6
41.9
43.1
40 .9
42.0
40.0
40 .1
42.4
39.5
38.5
41 .0
4.7
39.9
42 .1
43.0
40.8
42.0
39.6
40.0
42.4
39.4
38.6
40.8
4.5
39.5
41.7
42.9
40.7
42 .0
39.5
40 .0
42.0
39.4
38.7
40.9
4.5
39.5
41.9
42.6
40 .8
42.0
39.8
40.1
42.1
39.2
38.8
40.8 1
4.4
39.5
41.9
40.8
4.4
39.5
41 .9
42.5
40 .6
41.7
39.8
40.1
42.1
39.3
38.8
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
41.5
40.0
4.4
39.3
39.2
40.1
38.9
36.0
38.4
42.1
40.1
4.4
39.4
38.6
40.3
38.9
35.9
38.3
41 .9
40 .1
4.4
39.3
38.9
40.5
38.6
36.0
37.8
42.4
40.2
4.5
39.3
39.4
40.5
38.8
36.2
38.1
42.5
40.1
4.4
39.3
39.2
40.2
39.1
36.2
38.2
42.2
39.9
4.3
39.0
38.6
40.1
39.1
36.0
38.4
42.1
39.8
4.3
39.1
39.0
40 .0
39.1
35.7
38.2
42.1
39.8
4.3
38.8
39.6
39.8
39.0
35.9
37.6
42.0
40.0
4.4
39.0
40.5
40.2
39.5
35.9
37.1
42.5
40.0
4.5
39.3
40.2
39.7
39.5
35.9
37.2
42.1
39 .7
4.4
38.8
40 .1
40.0
39.4
35.9
37.3
41 .9
39.8
4.3
39.0
40.4
40.2
38.8
35.7
37.8
42.2
39.7
4.3
38.9
38.9
40.4
38.6
35.0
38.3
42.3
39.6
4.3
38.9
39.8
40.6
37.1
34.9
38.4
42.4
38.2
44.5
42.4
40.4
38.4
44.9
42.8
40.4
38.5
44.9
42.6
40.8
38.6
45.0
42.8
40.5
38.5
45.9
42.9
40.5
38.3
46.0
42.8
40 .3
38.3
45.0
42.7
40.1
38.3
45.5
42.4
39.4
38.5
44 .6·
42.6
39.8
38.6
44 .5
42.8
40 .0
38.5
44.7
42.3
40.1
38.3
45.1
42.2
39.8
38.3
46 .0
42.4
39.7
38.4
45.6 i
42.2
39.6
38.2
45.3
42.1
39.5
32.4
32.3
32.2
32.4
32.4
32.5
32.4
32.3
32.4
32.4
32.4
32.4
32.5
32.4
32.4
33.6
37.9
30.9
36.8
41 .1
36.2
35.5
33.5
37.8
30.7
37.2
40.9
36.3
35.5
33.2
37.6
30.4
36.9
41.1
36.5
35.5
33.4
37.8
30.6
37.2
40.9
36.3
35.6
33.5
37.7
30.7
37.2
40.9
36.4
35.5
33.6
37.8
30.8
37.5
41.4
36.3
35.5
33.6
37.7
30.8
37.5
40.8
36.3
35.7
33.5
37.7
30.6
37.5
40.4
36.2
35.6
33.6
37.6
30.8
37.4
40.7
36.4
35.7
33.6
37.7
30.7
37.5
41.0
36.3
35.9
33.6
37.8
30.8
37.3
40.5
36.4
35.8
33.5
37.7
30.7
37.2
40.3
36.5
35.9
33.5
37.7
30.7
37.3
41 .1
36.5
36.0
33.4
37.6
30.6
37.2
40 .9
36.6
36.0
33.4
37.6
30.5
37.1
41.1
36.3
36.0
34.1
32.3
25.6
31.4
34.2
32.4
25.7
31.0
34.0
32.4
25.7
30.9
34.2
32.6
25.6
31.0
34.3
32.5
25.6
31 .0
34.7
34.3 1
32.5 I
34.2
32.4
25.6
30.9
34.2
32.5
25.7
30.8
34.1
32.6
25.6
30.9
34.0
32.6
25.7
30.9
34.0
32.6
25.7
30.9
34.2
32.6
25.8
31.1
34.1
32.6
25.8
31.0
, I
"4.6
42.4
40.7
41 .9
39.9
40.1
41.9
39.2
I
I
38.7 1
PRIVATE SERVICEPROVIDING ..................... .............
Trade, transportation, and
utilities ................. .... ..........................
Wholesale trade ................... .............
Retail trade .... .............. ..... .... . ..... .
Transportation and warehousing ........
Utilities ............. ........ .. ... ... .. .... .. ....
Information ................................. ......
Financial activities ............................
Professional and business
services .... ......................................
Education and health services ............
Leisure and hospitality ......................
Other services ..... .. .. ..... ........................
1
Data relate to production workers in natural resources and mining and
manufacturing, construction workers in construction, and nonsupervisory workers in
the service-providing industries.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
32.5
J
::>5.6 1
31.0
25.7 1
30.9
I
I
34.1
32.5
25.8
31.0
NOTE: See "Notes on the data" for a description of the most recent benchmark
revision.
p = preliminary.
Monthly Labor Review
August 2005
83
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
Industry
2004
Annual average
2005
2003
2004
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
MayP
JuneP
Current dollars ....................... ..
Constant (1982) dollars . ..... .... ... .
$ 15.35
$15.67
$15.64
$15.70
$15.74
$15.77
$15.81
$15.82
$ 16.06
8.23
8.25
8.25
8.22
8.21
$15.91
8.22
$16.03
8.20
$15.90
8.24
$16.00
8.23
$15.85
8.23
$15.95
8.27
8.19
8.16
8.19
8.20
GOODS-PRODUCING ....... ....... ... ...... .......
16.80
17.19
17.16
17.19
17.24
17.30
17.32
17.33
17.36
17.35
17.43
17.45
17.51
17.54
17.58
Natural resources and mining .............
Construction ....... ......... ...................... ....
Manufacturing ...................... .... .............
Excluding overtime ....... .. .. ..............
17.56
18.95
18.08
19.23
18.16
19.19
18.27
19.34
18.57
19.36
15.74
14.96
16.14
15.29
16.12
15.28
18.55
19.38
16.47
15.62
16.54
15.69
18.60
19.42
16.56
15.70
TOTAL PRIVATE
18.08
18.05
18.06
18.10
18.22
18.37
18.43
18.40
19.21
16.16
15.30
19.25
16.22
15.36
19.27
16.29
15.42
19.34
16.27
15.42
19.31
16.29
15.43
19.29
16.34
15.48
19.24
16.37
15.51
19.31
16.42
15.54
17.18
16.43
15.56
17.17
17.23
17.29
17.32
15. 19
15.23
15.23
15.32
15.31
15.51
15.56
15.60
15.63
15.66
.......................
16.45
16.82
16.77
16.83
16.90
16.98
16.97
Nondurable goods ........... ...............
14.63
15.05
15.07
15.09
15.14
15.18
15.15
16.99
15.16
17.06
15.16
17.10
15.18
14.96
15.26
15.24
15.30
15.34
15.36
15.40
15.42
15.45
15.51
Durable goods ..
PRIVATE SERVICEPROVIDING ........................................ .
Trade,transportation, and
utilities ............. ................. ........ ..
Wholesale trade........................... ... ... .
1
14.34
14.59
14.59
14.63
14.65
14.66
14.69
14.70
14.72
14.82
14.79
14.83
14.88
14.90
14.89
17.36
17.66
17.66
17.71
17.69
17.73
17.78
17.80
17.87
17.91
17.95
17.97
18.05
18.02
18.07
Retail trade .. .............................. ..........
11 .90
12.08
12.07
12.10
12.13
12.16
12.16
12.20
12.21
12.32
12.29
12.31
12.35
12.38
12.34
Transportation and warehousing .......
16.25
16.53
16.54
16.58
16.65
16.53
16.61
16.54
16.54
16.58
16.52
16.62
16.62
16.67
16.68
Utiliti es ...................... ............ .. .. .
Information .. ........ ........................... .... ...
Financial activities ..... ................ ...........
24.77
25.62
25.48
25.60
25.66
25.82
26.11
26.23
26.04
26.32
26.38
21.42
21 .28
21.52
17.57
21.62
17.64
26.00
21.59
25.77
21.01
21.58
21 .70
17.71
21 .79
17.78
21.98
17.85
26.34
22.03
17.65
21.67
17.74
26.46
21.94
17.71
21.80
17.71
17.83
17.84
17.14
17.53
17.49
21.42
17.55
Professional and business
services................................................
17.21
17.46
17.43
17.48
17.59
17.54
17.63
17.66
17.69
17.79
17.80
17.82
17.89
17.93
17.98
Education and health
services ............................................... .
15.64
16.16
16.15
16.24
16.24
16.28
16.31
16.34
16.37
16.40
16.45
16.53
16.55
16.61
16.67
Leisure and hospitality ................. ..... ...
8.76
8.91
8.86
8.89
8.91
8.95
8.99
9.02
9.01
9.03
9.05
9.05
9.08
9.09
9.10
Other services.......................................
13.84
13.98
13.97
13.98
14.00
14.05
14.08
14.12
14.13
14.15
14.17
14.18
14.16
14.19
14.20
Data relate to produ ction workers in natural resources and mining and manufac-
turing. constructi on workers in co nstruction, and nonsupervisory workers in the
service-provid ing industries.
84
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
NOTE: See "Notes on the data" for a description of the most recent benchmark revision.
p
= preliminary.
1
15. Average hourly earnings of production or nonsupervisory workers on private nonfarm payrolls, by industry
2004
Annual average
Industry
2003
2004
2005
June
July
Aug.
Sept.
$15.79 $15.82
15.77
15.81
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
MayP
JuneP
$ 15.84
15.82
$ 15.88
15.85
$16.00
15.90
$15.96
15.91
$1 5.95
15.95
$1 6.01
16.00
$16.04
16.03
$15.96
16.06
TOTAL PRIVATE .. .........................
Seasonally adjusted .......... .. ... ...
$15.35
15.47
$15.67
-
$15.56
15.64
$15.59
15.70
$15.66
15.74
GOODS-PRODUCING .............................
16.80
17.19
17.14
17.18
17.28
17.37
17.43
17.31
17.34
17.37
17.56
18.08
18.12
18.02
17.95
18.07
18.21
18.46
18.53
18.45
18.36
17.48
18.67
17.51
17.56
17.40
17.97
17.39
Natural resources and mining ...........
18.57
18.55
Construction ....................................... .
18.95
19.23
19.12
19.24
19.33
19.42
19.47
19.35
19.31
19.12
19.20
19.25
19.35
19.31
19.37
Manufacturing .................................
15.74
16.14
16.08
16.03
16.16
16.35
16.26
16.32
16.46
16.42
16.43
16.41
16.45
16.50
16.52
Durable goods ... ........ ... ...... ... ... .. .. ..
Wood products ...... ... ....... .. ...............
Nonmetallic mineral products .... .....
Primary metals ...... ...........................
Fabricated metal products .. .............
Machinery .... .. .... ........ .. .. .. .... ..... .
Computer and electronic products .. .
Electrical equipment and appliances
Transportation equipment ..... .. ...... ...
Furniture and related products .........
Miscellaneous manufacturing ........ ..
16.45
12.71
15.76
18.13
15.01
16.30
16.69
14.36
21.23
12.98
13.30
16.82
13.03
16.25
18.57
15.31
16.68
17.28
14.90
21.49
13.16
13.85
16.73
12.99
16.22
18.50
15.23
16.56
17.22
14.92
21.31
13.11
13.82
16.60
16.84
13.04 1302.00
16.37
16.28
18.65
18.57
15.27
15.27
16.68
16.72
17.30
17.38
14.92
15.04
20.73
21.49
13.12
13.28
13.90
13.88
17.06
13.14
16.51
18.89
15.43
16.85
17.48
15.08
21 .91
13.39
13.97
16.98
13.03
16.38
18.73
15.38
16.84
17.52
15.05
21.78
13.27
13.92
17.04
13.13
16.45
18.66
15.43
16.85
17.65
15.10
21.91
13.29
13.96
17.22
13.17
16.36
18.75
15.59
16.99
17.92
15.12
22.17
13.46
14.05
17.15
13.13
16.27
18.84
15.55
17.03
18.04
15.07
21.90
13.42
14.07
17.20
13.04
16.20
18.78
15.67
17.02
18.04
15.15
21.97
13.34
14.04
17.16
13.11
16.28
18.76
15.62
17.02
18.00
15.10
21 .84
13.37
14.05
17.20
13.13
16.68
18.80
15.62
16.98
18.26
15.07
21.78
13.46
14.02
17.24
13.23
16.58
18.81
15.67
16.89
18.43
15.03
21.89
13.45
14.06
17.28
13.11
16.82
18.68
15.77
16.92
18.35
15.09
22.05
13.52
14.03
Nondurable goods .... .... ... ..... ..... .....
Food manufacturing ...... .. .................
Beverages and tobacco products ....
14.63
12.80
17.96
15.05
12.98
19.12
15.03
13.01
19.37
15.13
13.07
19.26
15.08
13.00
19.08
15.23
13.09
19.17
15.11
12.94
19.18
15.16
12.99
18.80
15.21
13.03
18.82
15.24
13.07
18.44
15.17
13.07
18.65
15.19
13.02
18.94
15.22
12.98
19.32
15.28
13.05
19.02
15.26
13.04
18.59
Textile mills ............. ............ ....... .. ... .
Textile product mills ............. .. ..........
Apparel ....... ..... .............. ........ ......... .
Leather and allied products ... .. ..... .
Paper and paper products ...... ..... ..
Printing and related support activitie,
Petroleum and coal products .... .. ...
Chemicals .. ..... ......... .. .. ........ .... ..
Plastics and rubber products .... .. .....
11 .99
11.23
9.56
11 .66
17.33
15.37
23.63
18.50
14.18
12.13
11 .39
9.75
11 .63
17.90
15.72
24.38
19.16
14.58
12.14
11 .27
9.60
11 .58
17.91
15.56
24.22
19.16
14.59
12.06
11 .45
9.73
11 .67
17.96
15.73
24.32
19.31
14.69
12.08
11.43
9.72
11 .67
17.89
15.88
24.05
19.24
14.66
12.25
11 .49
9 .93
11 .56
18.21
15.96
24 .44
19.44
14.75
12.11
11 .42
9.97
11 .58
17.93
15.95
24.33
19.42
14.55
12.09
11.44
10.00
11.62
18.09
15.93
24.71
19.44
14.58
12.25
11.43
10.00
11 .51
18.07
15.80
24.48
19.59
14.76
12.33
11 .31
10.15
11 .60
18.00
15.77
24.75
19.52
14.81
12.25
11.48
10.19
11.42
17.86
15.79
24.74
19.32
14.65
12.26
11.56
10.05
11.48
17.93
15.70
24.78
19.47
14.70
12.35
11.70
10.08
11.43
17.91
15.62
24.06
19.61
14.75
12.41
11 .54
10.10
11.42
18.00
15.56
24.54
19.72
17.88
12.49
11.77
10.19
11.43
18.10
15.62
24.60
19.38
17.90
PRIVATE SERVICEPROVIDING ..... ...............................
14.96
15.26
15.13
15.16
15.22
15.35
15.40
15.43
15.46
15.66
15.60
15.59
15.62
15.65
15.53
14.34
17.36
11 .90
16.25
24.77
14.59
17.66
12.08
16.53
25.62
14.55
17.57
12.07
16.53
25.34
14.56
17.65
12.05
16.58
25.45
14.58
17.68
12.07
16.62
25.36
14.69
17.71
12.21
16.51
25.89
14.69
17.75
12.17
16.59
26.02
14.67
17.82
12. 16
16.56
26.01
14.61
17.87
12.10
16.59
26.00
14.88
18.03
12.34
16.59
26.14
14.86
17.99
12.35
16.57
25.98
14.86
17.91
12.35
16.60
26.34
14.94
18.06
12.42
16.60
26 .52
14.93
18.07
12.4 1
16.61
26.54
14.86
17.99
12.32
16.67
26.22
2 1.01
21 .42
21.16
21.29
21.43
21.73
21.69
21 .70
21 .74
21 .83
21 .67
21 .68
21.92
21 .90
21.77
17.14
17.53
17.40
17.46
17.59
17.62
17.68
17.61
17.67
17.83
17.73
17.76
17.86
17.99
17.73
17.21
17.46
17.31
17.35
17.50
17.47
17.54
17.62
17.73
18.06
17.91
17.83
17.86
18.02
17.85
16.60
Trade, transportation, and
utilities ..................................... ..........
Wholesale trade ..... ... .. .. ..... .... ... .. ..
Retail trade ......... ....... ... ...... .. .. .... .
Transportation and warehousing ......
Utilities .... ... .. ... .. ... ... ........ ...... .... ...
Financial activities .............................
Professional and business
services ........................................
Education and health
services .......................................
15.64
16.16
16.10
16.23
16.20
16.30
16.30
16.33
16.44
16.47
16.46
16.51
16.53
16.55
Leisure and hospitality ....................
8.76
8.91
8.79
8.79
8.81
8.94
9.02
9.06
9 .11
9.11
9.09
9.07
9.07
9.08
9.01
Other services .................................. .
13.84
13.98
13.92
13.88
13.93
14.06
14.06
14.12
14.17
14.23
14.23
14.18
14.19
14.25
14.14
1
Data relate to production workers in natural resources and mining and
NOTE:
See "Notes on the data" for a description of the most recent benchmark
manufacturing, construction workers in construction , and nonsupervisory workers in
revision.
the service-providing industries.
p = preliminary.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August 2005
85
Current Labor Statistics: Labor Force Data
16. Average weekly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry
Industry
Annual average
2003
2004
2005
2004
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May"
June"
$528.56
-
$524.37
525.50
$528.50
529.09
$535.57
530.44
$530.54
533.03
$534.72
534.38
$532.22
533.13
$536.74
534.15
$537.60
535.83
$534.66
536.17
$534.33
537.52
$537.94
540.80
$543.76
540.21
$539.45
541 .22
669.13
688.03
689.03
687.20
696.38
690.78
697.34
694.80
702.43
683.75
683.20
689.59
697.45
700.40
705.91
765.94
Construction .......................... . 726.83
804.03
735.70
806.34
736.12
801 .89
752.28
804.16
755.80
796.07
730.19
820.38
753.49
824.91
739.17
836.24
737.64
833.85
703.62
822.87
712.32
826.20
727.65
847.62
748.85
854.22
751.16
842.17
757.37
Manufacturing ........ ......... ........ 635.99
658.53
659.28
646.01
660.94
663.81
661.78
665.86
678.15
666.65
663.77
662.96
662.94
666.60
669.06
Durable goods ..... .............. .... . 671.21
Wood products ............ ............. 514.10
Nonmetallic mineral products.... 664.92
Primary metals .. .................... 767.60
Fabricated metal products ........ 610.37
Machinery .......... .. . .. . ... .. ... .. 664.79
Computer and electronic
products ........ ......... ..... .. ........ 674.72
Electrical equipment and
appliances............... ................ 583.23
Transportation equipment. ... ... 889.48
Furniture and related
products .. .. .... .... ··· ··· · ·· ..... 505.30
Miscellaneous
manufacturing ............ . . .... .. . ... 510.82
694.16
529.46
688.05
799.77
628.80
699.51
694.30
535.19
689.35
808.45
627.48
698.83
673.96
697.75
521 .66
709.93
808.49
628.00
699.28
699.58
526.41
701.06
702.05
718.07
703.15
532.03
694.09
788.90
621 .49
692.22
695.49
539.03
700.04
796.65
627.60
697.22
526.51
694.19
802.38
634.17
711 .07
532.07
688.76
813.75
648.54
727.17
527.83
665.44
815.77
637.55
718.67
703.48
511 .17
667.44
807.54
637.77
716.54
701 .84
512.60
669.11
806.68
634.17
718.24
700.04
516.01
697.22
799.00
634.17
713.16
705.12
527.88
698.02
797.54
639.34
709.38
708.48
525.71
713.17
795.77
641 .84
707.26
698.28
699.13
695.46
700.41
700.95
704.30
706.00
723.97
716.19
712.58
711 .00
719.44
735.36
730.33
606.64
912.97
613.21
907.81
602.77
839.57
613.63
909.03
603.20
926.79
614.04
923.47
613.06
926.79
616.90
962.18
605.81
926.37
601 .46
933.73
602.49
921 .65
599.79
914.76
601.20
919.38
606.62
937.13
519.78
521 .78
515.62
529.87
519.53
516.20
523.63
546.48
528.75
522.93
526.78
526.29
521 .86
532.69
533.47
530.69
528.20
534.38
530.86
534.53
536.06
545.14
543.10
543.35
547.95
543.98
544.12
547.17
Nondurable goods ........ ... .. .. .. ... 582.61
607.92
515.70
612.96
608.08
600.73
601 .52
601 .19
605.09
605.82
513.38
505.81
505.81
497.36
497.13
506.34
509.86
731.32
483.60
735.76
498.13
445.61
361.34
429.20
768.60
738.54
485.10
450.02
363.78
425.97
744.76
757.60
494.08
457.78
363.81
431.65
745.89
792.12
495.24
451 .62
361.87
436.63
750.43
743.68
502.61
444.29
353.50
439.67
757.80
749.18
505.85
436.67
354.61
442.34
767.44
607.15
604.76
604.45
593.56
591 .28
592.00
TOTAL PRIVATE. ................... $517.30
Seasonally adjusted ......... .
GOODS-PRODUCING ................
Natural resources
and mining .... ...... ... ... .............
Food manufacturing .. .. .. ...........
Beverages and tobacco
products ....................... ··········
Textile mills ................ .. . .. . .. .
Textile product mills .... ....... ... .
Apparel. ... ...... ...... ....... .........
Leather and allied products.... ...
Paper and paper products .... ...
Printing and related
support activities ........... .......
Petroleum and coal
products ............. .......... ......
Chemicals ....... .... ..... ...... .... ..
Plastics and rubber
products .. ...... .............. ......
801 .64
633.66
707.28
602.48
604.21
602.17
606.22
610.72
602.89
502.92
509.66
512.59
513.65
514.80
520.98
508.54
702.45
469.33
444.70
340.12
457.83
719.73
750.51
486.69
443.01
351 .28
446.73
753.89
759.30
490.46
444.04
348.48
442.36
750.43
758.84
481.19
433.96
348.33
422.45
752.52
761 .29
489.24
442.34
352.84
441 .13
756.75
762.97
488.78
444.66
352.52
430.03
772.10
734.59
481.98
447.66
357.92
445.83
756.65
360.00
445.05
768.83
737.74
491 .23
451 .49
364.00
437.38
775.20
587.58
604.32
594.39
600.89
611 .38
612.86
614.08
618.08
616.20
448.45
1,052.32 1,094.83 1,094.74 1,118.72 1,096.68 1,119.35 1,097.28 1,131 .72 1,099.15 1,096.43 1,100.93 1,105.19 1,085.11
783.95
818.13
819.59
814.88
821.55
830.09
825.35
830.09
844.33
835.46
817.24
821.63
827.54
1,119.02 1,107.00
830.21
813.96
872.26
589.70
599.65
583.19
590.80
591 .48
583.46
578.83
596.30
592.40
586.00
585.06
585.58
590.74
591.53
PRIVATE SERVICEPROVIDING ....... ......................... 483.89
493.67
488.70
492.70
499.22
495.81
498.96
496.85
500.90
507.38
502.32
500.44
504.53
510.19
503.17
481 .14
488.58
487.43
492.13
495.72
493.58
492.12
488.51
490.90
494.02
493.35
493.35
497.50
657.29
367.15
666.93
371.15
660.63
371.76
665.41
375.96
673.61
377.79
665.90
377.29
669.18
373.62
671 .81
368.45
670.13
375.10
681.53
372.67
674.25
374.21
671 .63
374.21
679.06
377.57
501 .65
686.66
380.99
676.42
379.46
Trade, transportation,
and utilities...................... .....
Wholesale trade .... ......... ... .......
Retail trade .... ...... ... ......... .....
Transportation and
warehousing .............. .... ......
Utilities ..... . .. .. .. . .. . ... .. . .. •• •. . • ..
Information ..... ....... ..... .. ...... ...
598.41
614.90
611.61
1,017.27 1.048.82 1.044.01
616.78
628.24
617.47
622.13
622.66
1,033.27 1,032.15 1,074.44 1,066.82 1,061.21
620.47
625.44
1,053.00 1,066.51
497.81
608.12
610.88
612.54
619.55
618.46
1,052.19 1,056.23 1,087.32 1,088.14 1,080.26
760.81
777.42
774.46
772.83
788.62
786.63
787.35
787.71
791.34
798.98
786.62
782.65
793.50
803.73
792.43
Financial activities................... 609.08
622.99
614.22
618.08
635.00
620.22
627.64
625.16
627.29
649.01
632.96
632.26
637.60
656.64
636.51
Professional and
business services.... ...... ... ....
596.96
590.27
591 .64
607.25
593.98
599.87
602.60
604.59
614.04
607.15
604.44
609.03
621 .69
610.47
587.02
Education and
health services..... .. .... .. .........
505.69
523.83
520.03
529.10
531 .36
528.12
528.12
529.09
534.30
541 .86
534.95
534.92
535.57
541 .19
537.84
Leisure and hospitality .. ........ ..
224.30
228.63
227.66
231 .18
234.35
226.18
230.91
229.22
231.39
230.48
231 .80
230.38
231 .29
236.08
235.16
Other services........................
434.41
433.04
430.13
431 .67
436.01
433.05
434.45
434.90
436.44
439.71
438.28
435.33
438.47
441 .75
439.75
1
Data relate to production workers in natural resources and mining and manufacturing,
construction workers in construction, and nonsupervisory workers in the service--
NOTE: See "Notes on the data" for a description of the most recent benchmark revision.
Dash indicates data not available.
providing industries.
p = preliminary.
86
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 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
I
Over 1-month span:
2001 ............................................. .
2002 ............................................. .
2003 ............................................. .
49.5
41.0
44.4
2004 .. ............................ ...... ... .... .. .
2005 ...................................... .
50.9
54 .1
47.7
35.6
38.7
53.4
61 .2
48.6
39.7
35.3
66.0
53.1
32 .7
39.2
41.4
42.4
40.5
39.4
40.8
47.7
36.7
42.8
39.0
43.0
39.9
42.1
67.3
61.7
64.6
57.0
59.7
55.0
55.4
39.4
53.8
37.6
42.1
50.4
33.6
39.0
48.9
57.6
58.6
36.9
41.5
37.1
35.1
50.5
54.3
50.0
54.7
I
Over 3-month span:
49.8
53.2
35.3
37.9
49.8
36.5
42.3
34.2
38.1
34.4
38.3
52.5
58.5
35.4
53.8
60 .3
33.3
56.7
63.7
33.5
69.4
62.4
36.5
75.4
57.6
53.1
50.9
29.9
52.0
32.0
45.5
31.7
43.0
29.5
32.7
47.3
60.3
32.2
50.4
62.8
31.3
31.3
54.9
63.7
62.6
62.2
59.5
31.7
53.4
49.3
30.4
2003 ................................. .
59.5
33.6
34.5
2004 ............................................. .
2005 ...... ... ...... .... ... .... .
40.3
61.2
2001 ......................... ····················
2002 ............................................. .
2003 ............................................. .
2004 ............................................ .
2005 ... ... .... .... .................... ... .
Over 6-month span:
2001 ............................................. .
2002 ............................................. .
2003 ............................................. .
2004 ............................................. .
2005 .................................. .
Over 12-month span:
2001 .............................. .
2002 ... .................. .... .. ....... .
31.5
42.1
64.7
30.2
32.9
44.8
64.2
33.5
48.7
65.8
34.2
37.8
40.6
37.6
44.1
37.8
63.5
37.4
56.8
39.7
37.4
38.5
33.6
37.1
38.7
33.1
64.4
62.6
37.6
33.6
67.3
32.2
48.6
30.2
34.2
45.0
29.1
43.3
43.9
31.3
30.9
39.4
41.7
71.2
57.9
69.6
60.1
35.1
56.7
52.0
63.7
32.0
32.7
57.4
68.9
33.1
57.6
34.7
37.8
43.2
57.4
33.5
35.3
40.3
64.6
39.9
30.0
37.1
60.3
35.4
37.1
46.4
59.9
30.8
35.8
48.6
59.7
34.2
33.6
36.0
43.7
37.9
46.4
62.2
59.7
37.8
29.5
37.1
36.7
32.9
37.2
62.1
64.6
32.0
36.7
50.2
56.3
30.9
35.1
49.3
55.9
34.9
34.7
39.2
64.0
59.9
Manufacturing payrolls, 84 industries
Over 1-month span:
2001 .......................... .
22.0
17.3
22.0
17.9
16.1
22.6
13.1
15.5
18.5
17.3
14.9
11.9
19.0
35.1
19.6
19.0
31.0
20.8
51.8
35.7
35.7
22.6
23.2
24.4
48.8
28.6
32.7
42.9
15.5
49.4
44.6
32.1
11 .9
65.5
47.6
26.2
19.6
39.3
42 .3
22.0
19.0
50 .0
41.1
35.1
42.3
18.5
39.9
46.4
16.7
42.9
44.6
20.8
11.9
14.3
11 .3
9.5
9.5
35.1
35.7
2005 ......................... .
32.7
10.7
16.1
42.3
45.2
Over 6-month span:
2001 .............................. .
2002 .................. ·· ·········· ······· ·· ······
2003 ............................................. .
2004 ............................... .
2005 .... ........................ .
Over 3-month span:
2001 ... .......................................... .
2002 ....... ....... ...................... .... ..... .
2003 .................... .. ....................... .
2004 ........... ........ ............... ........... .
2002 .............................. .
2003 ............................................. .
2004 ............................................. .
2005 .. .............. ..... ..... ... ... ... ... .
o~~~~ ~~~~~'.~.~~.~.~i........................
2002 ... .......................................... .
2003 .............................. .. ............. .
2004 .. .. ......................................... .
2005 ............................................. .
60 .1
44.6
60.7
16.7
14.3
14.3
11.9
11.9
9.5
7.7
12.5
17.9
8.9
58.3
46.4
14.9
10.7
69.0
20.2
10.7
69.6
36.3
25.6
14.3
23.8
15.5
20.2
18.5
13.7
27.4
43.5
42 .9
11.3
12.5
42.9
52.4
62.5
53.6 1
52.4
44.6
22.6
24.4
21.4
10.7
14.3
13.1
58.9
7.1
8.3
16.7
50.6
8.3
19.6
45.2
7.7
26.8
42.9
43.5
8.3
7.1
33.3
42.3
14.3
7.1
11.3
52.4
5.4
8.3
10.1
29.8
44.0
19.6
9.5
8.3
47.0
39 .3
7.7
6.0
12.5
27.4
29.8
7.1
10.7
13.1
45.2
32 .1
6.0
6.0
14.3
45.8
20.8
6.0
6.5
13.1
47.6
19.0
6.5
6.0
19.0
44.6
13.1
7.1
8.3
25.6
41 .1
11.9
4.8
10.7
45.8
10.1
7.1
10.7
48.2
8.3
4.8
9.5
49.4
6.0
8.3
10.7
46.4
39.9
11.9
13.1
38.7 1
13.1
11 .3
12.5
11.3
10.7
57.1
35.1
4.8
60.1
10.1
58.9
12.5
3.6
10.7
4.8
11.9
6.0
7.1
34.5
36.9
7.1
43.5
8.3
40.5
''I
31.5
45.2
I
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 months are preliminary.
Monthly Labor Review
August 2005
87
Current Labor Statistics:
Labor Force Data
18. Job openings levels and rates by industry and region, seasonally adjusted
1
Levels (in thousands)
2004
Industry and region
Dec.
Totat2 ....................................................
Percent
2005
Jan.
Feb.
Mar.
2004
Apr.
May
Dec
JuneP
2005
Jan.
Feb.
Mar.
3,507
3,385
3,569
3,598
3,576
3,416
3,541
2.6
2.5
2.6
Total private 2 ••••••••• • •••• ••• ••• • •••••••••••••••••
3,106
3,020
3,160
3,212
3,178
3,050
3,165
2.7
2.7
Construction ............. ...... ... .... .... .....
132
127
133
170
113
107
111
1.8
1.8
Manufacturing ............ .. .......... .. ......
266
252
252
258
259
240
259
1.8
Apr.
May
JuneP
2.6
2.6
2.5
2.6
2.8
2.8
2.8
2.7
2.8
1.8
2.3
1.5
1.5
1.5
1.7
1.7
1.8
1.8
1.6
1.8
2.4
Industry
Trade, transportation, and utilities .. .... .
561
564
668
624
627
597
624
2.1
2.2
2.5
2.4
2.4
2.3
Professional and business services ....
699
682
607
646
691
659
634
4.0
3.9
3.5
3.7
3.9
3.8
3.6
Education and health services ...........
557
603
3.1
3.5
3.4
3.4
3.4
440
440
3.4
3.2
3.3
3.4
440
608
457
611
450
602
447
616
Leisure and hospitality ...... ............ ...
560
434
3.4
3.4
3.5
3.3
3.5
Government. .................. ..... .... .. .... ... ..
396
346
404
383
396
378
381
1.8
1.6
1.8
1.7
1.8
1.7
1.7
620
1,329
602
1,342
606
1,399
615
1,447
602
1,414
563
1,303
584
2.4
2.3
2.3
2.4
2.3
2.2
2.2
South ...... .............. ..... ... .... ... ..... ...
1,290
2.8
2.9
2.7
2.6
740
716
745
737
742
786
755
2.3
2.3
3.0
2.3
2.9
Midwest. .............. .... ... .... .. . ........ . ..
2.8
2.2
2.3
2.4
2.3
West.. ..... ... .. .... ............. .............. .
792
718
823
806
818
799
872
2.7
2.4
2.8
2.7
2.7
2.7
2.9
Reglon
3
Northeast. .. ..... ......... ............. .. ..... .
1
West Virginia;
Detail will not necessarily add to totals because of the independent seasonal
Illinois,
Midwest:
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,
adjustment of the various series.
Includes natural resources and mining, information , financial activities, and other
Washington, Wyoming.
services, not shown separately.
NOTE: The job openings level is the number of job openings on the last business day of
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.
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 ,
P
Mississippi, North Carolina, Oklahoma, South Carolina. Tennessee. Texas, Virginia,
= preliminary.
19. Hires levels and rates by industry and region, seasonally adjusted
1
Levels (in thousands)
Industry and region
2004
Dec
Totat2 .. .. ..... ..... ... ... ... ..... ... .. ............. ... ..
Percent
2005
Jan.
Feb.
Mar.
2004
Apr.
May
JuneP
Dec
2005
Jan.
4,639
4,709
4,760
4,841
4,538
4,740
4,635
3.5
3.6
Total private 2 •• ••••••••••••••.••••••.••••••••••••••
4,337
4,374
4,430
4,497
368
339
430
414
4,212
412
4,398
420
4,309
Construction ..................................
399
3.9
5.2
336
1,055
334
1,047
319
342
1,042
1,030
330
1,040
895
472
792
887
826
5.3
487
466
453
Feb.
Mar.
Apr.
May
JuneP
3.6
3.6
3.4
3.6
3.5
3.9
4.0
4.8
6.0
4.0
5.8
3.8
5.7
3.9
5.8
3.9
5.4
2.3
2.1
2.3
4.1
2.2
4.0
2.3
4.1
2.3
4.1
2.4
3.8
4.0
4.0
5.3
2.6
5.1
5.3
4.7
5.3
4.9
2.6
2.9
2.7
2.8
2.7
2.6
Industry
Manufacturing ................................
324
307
Trade, transportation, and utilities .......
1,056
Professional and business services ....
986
878
882
853
Education and health services ...........
452
445
500
Leisure and hospitality ................ .... .
834
826
771
798
742
750
863
6.6
6.6
6.1
6.3
5.8
5.9
6.8
Government. ........ ..... ... ... ... ..... .. ....... ..
307
341
329
336
329
339
331
1.4
1.6
1.5
1.5
1.5
1.6
1.5
Region
3
Northeast .............. ....... ... ..............
858
762
820
856
825
764
763
3.4
3.0
3.2
3.4
3.3
3.0
3.0
South ... .. ............ .. ............... .........
1,770
1,880
1,867
1,922
1,701
1,816
1,763
3.8
4.0
4.0
4.1
3.6
3.8
3.7
Midwest. .......................................
1,043
1,092
1,081
1,034
1,020
1,129
1,056
3.3
3.5
3.5
3.3
3.3
3.6
3.4
West.. ..........................................
970
959
1,069
1,036
1,037
1,048
1,070
3.4
3.3
3.7
3.6
3.6
3.6
3.7
Detail will not nE:cessarily 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,
Midwest:
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Washington, Wyoming.
3
Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New
York, Pennsylvania, Rhode Island, Vermont; South: Alabama, Arkansas, Delaware ,
NOTE: The hires level is the number of hires during the entire month; the hires rate is
District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
the number of hires during the entire month as a percent of total employment.
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;
88 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
P
= oreliminarv.
20. Total separations levels and rates by industry and region, seasonally adjusted
1
Levels (in thousands)
Industry and region
2004
Dec.
Total 2 ••••.••••••.. . •..•••••••• . • .. ..•.• •• •••••• ... •...•..
Percent
2005
Jan.
Feb.
Mar.
2005
2004
Apr.
May
JuneP
4,435
4,352
4,295
4,502
4,562
4,504
4,362
Total private 2 . . •.• .• •.••. •. . . •. . . . • . ••• . •.. . •..• ...
4,146
4,091
4,035
4,237
4,306
4,256
4,111
Construction .. .... ....... ..... ....... ........ .
355
417
3
303
421
408
370
Dec.
Feb.
Jan.
3.3
Mar.
Apr.
May
JuneP
3.3
3.2
3.4
3.4
3.7
3.7
3.6
3.8
3.9
3.8
3.7
5.0
5.9
5.7
4.2
5.8
5.6
5.1
3.4
3.3
Industry
Manufacturing ....... ... ............ ... ... ....
353
361
341
360
369
369
344
2.5
2.5
2.4
2.5
2.6
2.6
2.4
Trade, transportation, and utilities .......
1,062
882
940
980
1,018
989
950
4.1
3.4
3.7
3.8
3.9
3.8
3.7
Professional and business services ....
833
836
772
924
869
851
795
5.0
5.0
4.6
5.5
5.2
5.1
4.7
Education and health services .. ...... .. .
375
356
389
445
433
405
389
2.2
2.1
2.3
2.6
2.5
2.3
2.2
Leisure and hospitality .... ...... .. ...... .. .
758
832
790
743
709
750
745
6.0
6.6
6.3
5.9
5.6
5.9
5.8
Government. .. ...... ...... .... ... .... .. .......... .
274
258
260
267
256
254
255
1.3
1.2
1.2
1.2
1.2
1.2
1.2
Reglon
1
3
Northeast. ...... ........ ...... ...... ..... ......
773
773
732
802
807
717
688
3.0
3.1
2.9
3.2
3.2
2.8
2.7
South ... ................... .......... ...........
1,707
1,747
1,647
1,763
1,766
1,743
1,664
3.6
3.7
3.5
3.7
3.7
3.7
3.5
Midwest. ... ..... .... .... ..... ... ... ...... ....
986
981
937
1,051
982
976
909
3.1
3.1
3.0
3.4
3.1
3.1
2.9
West. .... ....... .... .... .. ... ... .... ...... ...... j
953
964
961
926
1,006
1,034
1,034
3.3
3.3
3.3
3.2
3.4
3.5
3.5
Detail will not necessarily add to totals because of the independent seasonal adjustment Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska,
of the various series.
North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona, California,
2
Includes natural resources and mining, information, financial activities, and other Colorado, Hawaii , Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington,
services, not shown separately.
Wyoming.
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, Louisiana, Maryland, Mississippi , month; the total separations rate is the number of total separations during the entire
p = preliminary.
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;
month as a percent of total employment.
21. Quits levels and rates by industry and region, seasonally adjusted
1
Percent
Levels (in thousands)
Industry and region
2004
Dec.
Totai2 ................ .. ......... ............ .. . . . . . . . . . .
2005
Jan.
Feb.
Mar.
2004
Apr.
May
JuneP
2,495
2,530
2,307
2,516
2,520
2,514
2,498
Total private 2 •.• ••• • •• • •• •• ••••• • ••••••• . ••• ... ...
2,366
2,412
2,192
2,383
2,395
2,391
Construction ..... .. .... .. ... .... .. ...... .... ..
162
171
139
150
146
168
Manufacturing ..... ... .. .... ..... ... ... ... ....
194
185
181
186
178
Trade, transportation, and utilities ......
570
563
512
583
577
Dec.
2005
Jan.
Feb.
1.7
Mar.
Apr.
May
JuneP
1.9
1.9
2,369
2.1
2.2
2.0
2.1
139
2.3
2.4
2.0
2.1
183
194
1.4
1.3
1.3
1.3
1.2
1.3
1.4
589
575
2.2
2.2
2.0
2.3
2.2
2.3
2.2
2.4
1.9
1.9
1.9
2.1
2.1
2.1
2.0
2.3
1.9
1.9
Industry
Professional and business services .. .
415
417
410
424
417
420
401
2.5
2.5
2.4
2.5
2.5
2.5
Education and health services ...........
232
230
259
280
277
249
260
1.4
1.3
1.5
1.6
1.6
1.4
1.5
Leisure and hospitality ............ .. .......
506
516
474
458
506
488
500
4.0
4.1
3.8
3.6
4.0
3.8
3.9
Government. .. ... .... .... ... ......... ........ .....
129
124
117
124
125
123
125
.6
.6
.5
.6
.6
.6
.6
Reglon 3
1
Northeast. ...... .......... ...... ......... ... .. .
392
424
340
410
446
373
349
1.5
1.7
1.3
1.6
1.8
1.5
1.4
South .. .. ... ....... ........ .... ........ .. .......
1,021
1,053
914
1,003
992
1,020
977
2.2
2.2
1.9
2.1
2.1
2.2
2.1
Midwest. ... .......... ..... .... .. ...... ..... ... .
544
539
509
561
540
554
540
1.7
1.7
1.6
1.8
1.7
1.8
1.7
West. ........... .... .... .... ...... .... ...... ....
536
530
550
562
573
562
633
1.9
1.8
1.9
1.9
2.0
1.9
2.2
Detail will not necessarily add to totals because of the independent seasonal adjustment
of the various series.
Includes natural resources and mining, information, financial activities, and other
services, not shown separately.
3
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.
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,
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
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;
employment.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
P
= preliminary.
Monthly Labor Review
August 2005
89
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)
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 ..
Average weekly wage 1
Employment
December
2003
(thousands)
Percent change,
December
2002-03 2
Fourth
quarter
2003
Percent change,
fourth quarter
2002-03 2
8,314.1
8,048.7
123.7
804.9
376.8
1,853.6
145.2
767.0
1,329.4
732.2
669.9
1,080.6
265.3
129,341.5
108,215.1
1,557.8
6,689.5
14,307.8
25,957.3
3,165.9
7,874.7
16,113.2
15,974.0
12,042.8
4,274.1
21,126.3
0.0
.0
.1
1.2
-4.2
-.3
-4.0
1.2
.6
2.1
1.7
-.1
-.2
$767
769
703
837
943
665
1,139
1,138
945
731
335
494
757
3.6
3.9
4.9
2.3
6.7
3.4
3.9
5.9
3.8
3.8
3.4
3.1
2.4
Los Angeles, CA .... ... ..... .. .... ......... ..... ...
Private industry
Natural resources and mining
Construction ... ....... ... ..... .. .... ... .......... ... .
Manufacturing ................................... .
Trade, transportation, and utilities
Information ........... .................... .... ....... ..
Financial activities
Professional and business services
Education and health services
Leisure and hospitality .................... .
Other services ....
Government .
356.0
352.2
.6
12.9
17.8
53.9
9.2
23.0
40.1
26.6
25.6
142.1
3.8
4,075.3
3,486.3
11 .0
133.9
485.2
794.6
194.9
237.9
575.0
456.5
375 .9
220 .7
589.0
-.5
-.2
.7
-1.1
-7.1
-1.2
-2.0
.9
1.6
1.9
5.6
3.5
-2.3
903
898
955
883
900
735
1,627
1,258
1,043
820
766
422
930
4.2
4.2
16.9
1.7
6.5
2.7
5.2
7.0
3.7
3.9
6.5
5.0
3.3
Cook, IL
................. ............................ ...
Private industry ........... ........ .... ...... .... ...... ...
Natural resources and mining
Construction ...................... ... ....... ... ....... ... .. ....... ... ..
Manufacturing ..... ........... .. ...... ...... .... ...... .. .
Trade, transportation, and utilities
Information ................... ....... ... ........................... .
Financial activities .
.............. .............. ...... .
Professional and business services
Education and health services
Leisure and hospitality
Other services .... .. ... ... ...... .. ....
Government
....................... .
126.7
125.5
.1
10.5
7.9
26.7
2.5
13.8
26.1
12.3
10.5
12.6
1.2
2,539.8
2,221.9
1.3
96.7
265.7
499.4
66.1
219.4
405.5
350 .8
217.7
95.1
317.9
-1.2
-.9
-3.6
.0
-5.1
-.8
-4.1
-.8
-1.3
1.0
2.8
-2.0
-3.1
922
929
1,037
1,169
975
753
1,164
1,471
1,206
791
375
655
871
3.0
3.2
3.2
-.8
6.3
.4
.1
8.1
4.1
3.7
-.3
3.0
.9
New York, NY ... .................... .
Private industry
Natural resources and mining .. .
Construction ..... ... ..... ... ...... .. .. ............... .. ..... .
Manufacturing ..................... .
Trade, transportation, and utilities
Information .. ...
.......... ...... ... ............ .
Financial activities
............ .... .... .... .
Professional and business services
Education and health services .
Leisure and hospitality ..... .......... .... .
Other services . ... ... ... .. .. ... ...
.................... .
Government .
111.9
111 .7
.0
2.2
3.5
22.1
4 .3
16.7
22.6
7.8
10.1
16.0
.2
2,253.6
1,800.4
.1
30.0
46.6
247.6
130.6
352.0
439.7
273.8
188.2
82.9
453.2
-1.0
-.6
.0
-4.5
-4.9
-1.2
-5.1
-2.0
.5
2.4
.4
-1 .1
-2.2
1,480
1,623
1,197
1,567
1,290
1,164
1,751
3,034
1,702
918
787
871
912
7.2
8.1
-6.5
3.4
6.4
5.5
7.9
16.1
2.6
7.6
6.1
6.1
.1
Harris, TX .
.................. .
Private industry
... ... .... ..... .... ........ .
Natural resources and mining
Construction .
Manufacturing
..... ...................... .
Trade, transportation, and utilities
Information ............... ....................................... .
Financial activities .......... ... .... ... .. ... ... ...... .... ...
Professional and business services
Education and health services ......... .......... ....... ..... ... ....... .. . .
Leisure and hospitality .
Other services
Government
89.4
89.0
1.2
6.3
4.7
21.1
1.4
9.7
17.0
8.8
6.5
10.3
.4
1,841 .5
1,595.2
62.5
135.5
164.0
403.2
33.8
113.1
279.0
188.3
155.2
56.3
246.3
-.9
-1.2
8.7
-5.0
-4.9
-2.1
-3.9
1.7
-1 .7
1.5
.7
-3.1
1.1
906
929
2,185
919
1,106
821
1,098
1,181
1,073
812
335
539
759
2.1
2.1
-.9
2.6
2.3
1.0
.4
4.9
3.2
1.8
-.9
.4
3.1
Maricopa, A2. ....................................... .
Private industry
........ .. ... ..... .
Natural resources and mining
Construction .
. ....... .. ........... .. ........... .
Manufacturing ...
Trade, transportation, and utilities .. ..
Information .............. .. ...... ... ..... .. ................... .
Financial activities
.... ......... .... .... .
Professional and business servi ces
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.
90
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
22. Continued-Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003.
County by NAICS supersector
Average weekly wage 1
Employment
Establishments,
fourth quarter
December
2003
(thousands)
2003
Percent change,
December
Fourth
quarter
Percent change,
fourth quarter
(thousands)
2002-03 2
2003
2002-03 2
Dallas. TX .. ...... .......................................................... .. ................ .
Private industry ... ............................................. ... ............. ........
Natural resources and mining ................ ............................. .
Construction ............ .. .:... ... .................. ................ ................ .
Manufacturing ............ ........ ....................... ... .. ..................... .
Trade, transportation, and utilities ...... ....... .. .......... .. ......... .. ..
Information ........ ...... ..... ..... .................................................. .
Financial activities ..... ..... .... ..... ... .............. ........................ ...
Professional and business services .................................... .
Education and health services ........................... .. ............... .
Leisure and hospitality ......... ............................................... .
Other services ...... ..... ..... ........ ........ ....... ......... ........... .. ...... .. .
Government ..... ........... ...... .. .. ... ....... ... ..... ........... ....... .. .. .. ........ .
68.6
68.2
.5
4.5
3.5
15.8
1.9
8.6
14.0
6.3
5.2
6.7
.4
1,450.8
1,294.6
6.8
73.0
144.9
326.1
64.0
140.0
237.7
131.4
127.5
40 .5
156.2
-1.4
-1.4
-20 .5
-2 .2
-3.1
-3.3
-5 .1
1.2
.0
2.4
.0
-3.4
-1 .8
$952
970
2,680
909
1,075
898
1,272
1,2 15
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 .................................... ............. .. .... .. .............
Trad e, 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
81 5
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
42 1
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 reclassifications. See Notes on Current Labor
Statistics.
3
Totals for the United States do not include data for Puerto Rico or the
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Virgin Islands.
4
Data do not meet BLS or State agency disclosure standards.
NOTE: Includes workers covered by Unemployment Insurance (UI) and
Unemployment Compensation for Federal Employees (UCFE) programs. Data are
preliminary.
Monthly Labor Review
August 2005
91
Current Labor Statistics:
Labor Force Data
23. Quarterly Census of Employment and Wages: by State, fourth quarter 2003.
State
Establishments,
fourth quarter
2003
(thousands)
United States 2
Average weekly wage 1
Employment
December
2003
Percent change,
December
Fourth
quarter
Percent change,
fourth quarter
(thousands)
2002-03
2003
2002-03
8 ,314.1
129 ,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
3.8
4.1
3.8
2.0
3.8
5.0
3.9
3.8
Georgia ........ .. ...................... .. ... ....... .
Hawaii ........... ........... ...................... ...
Idaho ... .... .. .. ..................................... .
Illinois .. ...................... ... ..... ............... .
Indiana .. .... ...... ................................. .
Iowa .............. ... .. ... ......... ..... ..... ....... ..
Kansas .............. ...................... .. ....... .
Kentucky ............ ......... .... .. ... .... .... .... .
Louisiana ........ ... .. ... ............. ... .. ........ .
Maine ..................................... ......... .
245.6
37.4
48.5
325.7
152.1
90.6
82.2
105.7
114.0
47.4
3,845.6
583.0
577.5
5,738.7
2,852.2
1,418.5
1,298.3
1,740.6
1,870.9
595.8
.2
1.3
.6
-1.2
-.3
.0
-.9
.3
.5
.7
734
678
579
827
675
626
631
645
628
631
2.8
3.7
1.8
3.2
3.5
4.7
2.8
3.5
2.4
4.6
Maryland .. ..................... ................... .
Massachusetts ................. .. .......... .. .. .
Michigan ................. .. ............. ........... .
Minnesota ............ .. ......................... ..
2,466.4
3,154.6
4,365.8
2,591 .9
1,108.1
2,633.6
396.6
884.4
1,111 .2
614.9
.7
-1.9
Montana .. .. .......... ... .. .......... .............. .
Nebraska ........ ... ......... ...................... .
Nevada .. ........ ... .. ............... ... ........... . .
New Hampshire .......... .. ................... .
150.4
206.6
251 .3
159.0
65.6
165.4
42.0
55.3
60.3
47.0
-.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
3,91 2.8
757. 1
8 ,379.2
3,759.6
31 7.6
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
................. .... .
~:::ii:F:.'..:::::::::::::::::::::::::::::::::::::::::
-1.1
1.1
Wyoming .. ........ ................ .
22.0
241 .6
1.7
616
4.1
Puerto Rico ... ...... ..... .. ................... .. ..
Virgin Islands ... ........... .. .......... .. ....... .
50.2
3.2
1,074.1
42.5
3.5
-.2
450
629
4.7
2.4
1
Average weekly wages were calculated using unrounded data.
2 Totals for the United States do not include data for Pu erto Ri co
or the Virgin Islands.
92
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
NOTE: Includes workers covered by Unemployment Insurance (UI)
and Unemployment Compensation for Federal Employees (UCFE)
programs. Data are preliminary.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
24. Annual data: Quarterly Census of Employment and Wages, by ownership
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
29,5 18
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 ······· ··· ·· ····· ························· ....... .
1999 .... .... ... .. ... .... .............. ... ........ .... .
2000 ................................................. .
2001 ............ ... ... .......... .................... .
2002 ···· ··· ···· ················· ..................... .
6,454,381
6,596,158
6,803,454
6,946,858
7,121,182
7,381,518
7,560,567
7,622,274
7,724,965
7,839,903
91,202,971
94,146,344
96,894,844
99,268,446
102,175,1 61
105,082,368
107,619,457
110,015,333
109,304,802
107,577,281
$2,365,301,493
2,494,458,555
2,658,927,216
2,837,334,217
3,071,807,287
3,337,621,699
3,577,738,557
3,887,626,769
3,952,152,155
3,930,767,025
State government covered
1993 ................................................. .
1994 ................................................. .
1995 ··················································
1996 ....................... ... .. ..... ................ .
1997 ·· ········· ···························· ··"·•·····
1998 .. ................. ........... ................... .
1999 ...................... ... ... ..................... .
2000 ......... ... ..................................... .
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
140,093
141,491
143,989
146,767
11,059,500
11,278,080
11,442,238
11,621,074
11,844,330
12,077,513
12,339,584
12,620,081
13,126,143
13,412,941
$288,594,697
301,315,857
315,252,346
329,105,269
345,069,166
365,359,945
385,419,781
408,721,690
440,000,795
464,153,701
Federal Government covered (UCFE)
1993 ...
·································· ·· ·
1994 ................................................. .
1995 ................................................ ..
1996 ........................ ......................... .
1997 ................................................. .
1998 ........... ...................................... .
1999 ................................................. .
2000 ................................................. .
2001 ............. .............. .. .... ................ .
2002 ···· ····· ············· ·· ·"···················"··
47,714
48,377
50,083
51,524
52,110
47,252
49,661
50,256
50,993
50,755
3,071,140
3,023,098
2,948,046
2,881,887
2,810,489
2,782,888
2,786,567
2,871,489
2,752,619
2,758,627
$113,448,871
114,992,550
113,567,881
116,469,523
120,097,833
121 ,578,334
123,409,672
132,741,760
134,713,843
143,587,523
NOTE: Detail may not add to totals due to rounding. Data reflect the movement of Indian Tribal Council establishments from private industry to
the public sector. See Notes on Current Labor Statistics.
Monthly Labor Review
August 2005
93
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
Fewer than
5 workers 1
5 to 9
workers
10 to 19
workers
20 to 49
workers
50 to 99
workers
100 to 249
workers
250 to 499
workers
500 to 999
workers
1,000 or
more
workers
Total all industries 2
Establishments, first quarter ......... ... ..... .
Employment, March ·······························
7,933,974
105,583,548
4,768,812
7,095,128
1,331 ,834
8,810,097
872,241
11,763,253
597,662
18,025,655
203,030
13,970,194
115,598
17,299,058
28,856
9,864,934
10,454
7,090,739
5,487
11,664,490
Natural resources and mining
Establishments, first quarter .... .... ..........
Employment, March . . . . . . ... . . . . . .. . . . . .. . . . . . . . .
124,527
1,526,176
72 ,088
110,155
23,248
153,629
14,773
198,895
9,226
275 ,811
2,893
198,122
1,593
241 ,559
501
171 ,063
161
108,563
44
68,379
Construction
Establishments, first quarter ..................
Employment, March ...............................
795,029
6,285,841
523,747
746,296
129,201
846,521
76,215
1,021 ,722
46,096
1,371 ,071
12,837
872,274
5,604
823,846
1,006
338,107
262
172,944
61
93,060
Manufacturing
Establishments, first quarter ......... .... .....
Employment, March ··············· ··· ·············
381 ,159
14,606,928
148,469
252,443
65,027
436 ,028
57,354
788,581
54 ,261
1,685,563
25 ,927
1,815,385
19,813
3,043,444
6,506
2,245,183
2,565
1,732,368
1,237
2,607,933
Trade, transportation, and utilities
Establishments, first quarter ........ ...... ....
Employment, March ..... ....... .. .. ... . . . .. ... . . .
1,851,662
24,683,356
992,180
1,646 ,304
378,157
2,514,548
239,637
3,204,840
149,960
4,527,709
51,507
3,564,316
31,351
4,661,898
6,681
2,277 ,121
1,619
1,070,141
570
1,216,479
Information
Establishments, first quarter ··················
Employment, March .............. ...... ... .......
147,062
3,208,667
84,906
11 2,409
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
Establi shments, 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
.
' Includes establi shments that reported no workers in March 2003.
2
94
Includes data for unclassified establishments, not shown separately.
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 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 area 1
2001
2002
Percent
change,
2001-02
Metropolitan areas3 ............ ......... ..... ...... .. .. .......... .............. .
$37,908
$38,423
1.4
Abilene, TX ..................... .. .... ...... ... .............. .. .... ............ .. ... .. .
Akro n, OH ......... ............ ...... .................. ... ..................... .. ..... .
Albany, GA ........................ .............................................. ..... .
Albany-Schenectady-Troy, NY ................................. ............ .
Albuquerque, NM ........................... .. ................ .. .... .. .......... .
Alexandria, LA ................... ... ......... ......................... ...... .. ... .
Allentown-B ethl ehem-Easton, PA .. ... .. ..... .. ...... .......... ... ... ... .. .
Altoona, PA .... ............................................................. .. .... .. .. .
Amarill o, 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
An n Arbor, Ml
.. ................................... .. ......... .. ... ..... .
Anniston, AL .......................................................... .............. ..
Appleton-Oshkosh-Neenah, WI .. .............................. ... ......... ..
Asheville , NC .. ... ..... .. ..................... ............... ....................... ...
Athens, GA ............... ......................... ........... ........................ ..
Atlanta, GA ... ... .. ........ ........... .. .. .. .... ........ ...... ............... ... ... .. ...
Atlantic-Cape May, NJ ................................ .. ........ .. .... .. ........ ..
Auburn-Opelika, AL ................... .................... .. ... ... .... ........... .
Au gusta-Aiken, GA-SC .......................................................... .
Au stin-San Marcos, TX .............................. .... .. ... .................. ..
37,582
26,486
32,652
28,511
28,966
40 ,559
31 ,268
25,753
30,626
40 ,831
39,220
27,547
33,020
28,771
29,942
41 ,123
32 ,201
26,405
31 ,743
39,540
4.4
4.0
1.1
.9
3.4
1.4
3.0
2.5
3.6
-3.2
Bakersfield , CA ................................................................... ...
Baltimore, MD .................................................. ................ ..... ..
Bangor, ME ... ... .. ...... ..................................................... .. ...... ..
Barnstable-Yarm outh , MA ............ .... ....... ............................. .
Baton Rouge, LA ................................................................. ..
Beaumont-Port Arthur, TX .................. .......... ..................... . ..
Bellingham, WA ............. ... .. ......... .... .... ............ .. .... .. ............ ..
Benton Harbor, Ml .............................. ................. ........... .. .... .
Bergen-Passaic, NJ ..
.. .......... ..... .. ... ... ................... .. .. .
Billings, MT ...................... .. ........... ............ .... .. . .. ........... ..... ..
30 ,106
37,495
27,850
31 ,025
30 ,321
31 ,798
27 ,724
31 ,140
44,701
27,889
31 ,192
38,718
28,446
32 ,028
31,366
32 ,577
28,284
32,627
45,185
28,553
3.6
3.3
2.1
3.2
3.4
2.4
2.0
4.8
2.4
Biloxi-Gulfport-Pascagoula, MS ............................................ .
Binghamton, NY ........................................... ........ .. .............. ..
Birmingham, AL ...................
.. ... .............. .... .. ..... .. .... .... .
Bismarck, ND ...... ..... ............. .. ......... ..... ........................... .... ..
Bloomington, IN ........... .. ................ ........ ............................... ..
Bloomington-Normal, IL ... ........................................ .............. .
Boise City, ID ... ........ ................ ........................ .. .... ... ............ ..
Boston-Worcester-Lawrence- Lowell-Brockton , MA-NH ... .... ..
Boulder-Longmont, CO .................. .... .. .... .......... ... ............. ..
Brazoria, TX ......................... .... ......... ........ .. ..... ..... .......... ..... ..
28,351
31,187
34,519
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
.6
2.1
4.1
3.2
3.0
2.9
1.0
-.2
-.6
1.7
Bre merton, WA .... ....................................................... ...... .. ..
Brownsville-Harlingen-San Benito. TX
.................. ..
Bryan-College Station, TX ........................ .. .. ................. ..... .
Buffalo-Niagara Falls, NY ................................. ..................... .
Burlington, VT .. ............................. ....................... ................. ..
Canton-Massillon, OH ........ .......... ......... ... .. ..... .. ........... .. ...... ..
Casper, WY ................ ..... .... .... .... ............ ............. ................ ..
Cedar Rapids, IA ............................................. .................... ..
Ch ampaign-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
7.1
3.4
1.1
2.3
2.3
2.3
2.2
.2
4.9
3.8
Ch arleston , WV .......... .................................... ............. ......... ..
Charlotte-Ga stonia-Rock Hill , NC-SC ............ .. ............... .. ..... .
Ch arlottesville, 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
1.9
3.1
2.8
2.2
4.5
1.3
2.6
3.1
5.4
1.7
Colorado Springs, CO ..... ...................................................... .
Columbia, MO ........ ... ........................ ..... ... ............................. .
Columbia, SC ........................................ .. ... ........ .......... .. ....... .
Columbus, GA-AL ..... ........ ......... ..... .. ..................................... .
Columbus, OH .
.. ......... .. ............................................. .. ..
Corpu s Christi , TX .... ............................... ... ... .. .. ... .... .... .. ....... .
Corvallis , OR .................... ............... ............... ............. .......... .
Cumberland , MD-WV ..... ........................... ................... .. ... .. .. .
Dallas. TX .................. .. .... ........................... ..... ............ .. .. ...... .
Danville, VA ................ ........ ... ...................................... ... ...... .
34,391
28,490
29,904
28,412
35,028
29,361
35,525
25,504
42 ,706
25,465
34,681
29,135
30.721
29,207
36,144
30,168
36 ,766
26,704
43,000
26,116
.8
2.3
2.7
2.8
3.2
2.7
3.5
4.7
.7
2.6
1.1
See footnotes at end of table.
Monthly Labor Review
August 2005
95
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,
2001
2002
Percent
change,
2001-02
Davenport-Moline-Rock Island, IA-IL ..................................... .
Dayton-Springfield, OH .......................................................... .
Daytona Beach, FL ............................................................... .
............................... .
Decatur, AL.........................
Decatur, IL .......................................... .................. ................. .
Denver, CO ............................................................................
Des Moines, IA ..................................................................... .
Detroit, Ml ............................................................................ .
................................ .
Dothan, AL............................
... ............................. .
Dover, DE ...........................
$31,275
33,619
25,953
30,891
33,354
42 ,351
34,303
42,704
28,026
27,754
$32,118
34,327
26,898
30 ,370
33,215
42 ,133
35,641
43,224
29,270
29,818
2.7
2.1
3.6
-1.7
-.4
-.5
3.9
1.2
4.4
7.4
...................... ... ....... .
Dubuqu e, 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, NC ....................................................................... .
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
3.4
3.5
3.3
2.0
. ................................ .
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
2.7
2.5
3.5
2.0
3.3
1.2
3.6
2.5
4.4
1.4
.. .................... ..... ..
Houston, TX ............. .. ..............
Huntington-Ashland, WV-KY-OH ........................................... .
Huntsville, AL .......................... ................................................
....... ... .. ............... ........ .
Indianapolis, IN ......................
.. ... ... ... ...... ... ......... ...
Iowa City, IA .. ... . ..... ... ..
.................................. ..
Jackson, Ml ..........................
.................................... .
Jackson , MS .....................
Jackson, TN ............... .......................................................... ..
Jacksonville, FL ..................................................................... .
Jacksonville, NC .................................................................. .
42,784
27,478
36,727
35,989
31,663
32,454
29,813
29,414
32,367
21,395
42,712
28,321
38,571
36,608
32,567
33,251
30,537
30,443
33,722
22,269
-.2
3.1
5.0
1.7
2.9
2.5
2.4
3.5
4.2
4.1
See footnotes at end of tabl e.
96
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area 1
2001
2002
Percent
change,
2001-02
Jamestown, NY ............ .... .... ................. .. .... .... ........... ... ........ .
Janesville-Beloit, WI ................................................. .... .... ..... .
Jersey City, NJ ......... .. .. .............................................. ........ ... .
Johnson City-Kingsport-Bristol , TN-VA ................................. .
Johnstown, PA ...... ...... .................................. ....... ................. ..
Jonesboro, AR ........ ............................... ......................... ...... ..
Joplin, MO ......... .. ................................. .. .... ........ .. ... ... ....... ... . .
Kalamazoo-Battle Creek, Ml ....... ........ ... .. ............ .................. .
Kankakee , IL ................................. ,......... .... .......................... . .
Kansas City, MO-KS ... ..... ...................................................... .
$25,913
31 ,482
47,638
28,543
25,569
25,337
26,011
32,905
29,104
35,794
$26,430
32,837
49,562
29,076
26,161
26,165
26,594
34,237
30,015
36,731
2.0
4.3
4.0
1.9
2.3
3.3
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 Charl es, LA ................ .. ... ..... ......................................... .
Lakeland-Winter Haven , FL ..... .............................................. .
Lancaster, PA ...... ... .. ................................................ .. ...... ..... .
31 ,562
26,193
30,422
39,599
27 ,774
29,693
31 ,484
29,782
28,890
31 ,493
32,473
27,299
31 ,338
40,778
28,719
30,104
31 ,700
30,346
29,505
32 ,197
2.9
4.2
3.0
3.0
3.4
1.4
1.9
2.1
2.2
Lansing- East Lansing , Ml .......... ............................................ .
Laredo, TX .................................. .......... .... ... ................ ... ...... ..
Las Cruces, NM ........................... ...... ..... .. ..... ... ............... ..... ..
Las Vegas , NV-AZ ............ .......................... .................... ....... .
Lawrence, KS ... ... ... .................... ........................................... .
Lawton , OK ... .... .............. ............. ... ...... .... .... ......................... .
Lewiston-Auburn , ME .. ................................. ...... ...... ...... ....... .
Lexington , KY .. ... .................................... .. .... ......................... .
Lima, OH ................ .... ................................................. .... .... ...
Lincoln, NE ......................... .............. ... ..... ...... ... ... .. .. .. .. ..... .... .
34,724
24,128
24,310
32,239
25,923
24,812
27,092
31 ,593
29,644
29,352
35,785
24,739
25,256
33,280
26,621
25,392
28,435
32,776
30,379
30,614
3.1
2.5
3.9
3.2
2.7
2.3
5.0
3.7
2.5
4.3
Littl e 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
2.0
2.6
3.9
2.0
4.2
3.9
4.5
3.9
Medford-Ashland, OR ....... ... .. ...... .......................................... .
Melbourne-Titusville-Palm Bay, FL ....... ....... ............... .. ...... ... .
Memphis, TN-AR-MS .................. ...................... .... ... .... .. ....... .
Merced, CA ................... ...................... ... .. .. ... .. ..... .... ... ........... .
Miami, FL .................... ........................................................... .
Middlesex-Somerset-Hunterdon, NJ ...................... ............... .
Milwaukee-Waukesha, WI .... ......................... .. ............... ....... .
Minneapolis-St. Paul, MN-WI ................................................ .
Missoula, MT .............................. .... ..... ... ................ .......... ..... .
Mobile, AL ........................... ....................................... ... ..........
27,224
32,798
34,603
25,479
34 ,524
49,950
35,617
40,868
26,181
28,129
28,098
33,913
35,922
26,771
35,694
50,457
36,523
41 ,722
27,249
28,742
3.2
3.4
3.8
5.1
3.4
1.0
2.5
2.1
4.1
2.2
Modesto, CA .. ...... .. ..................... .. ..... ... ............. .................... .
Monmouth-Ocean, NJ .. ..... ...... ............... .. ............................. .
Monroe, LA .. ... ... ... ................... ... .. ..... .... .... ..................... ....... .
Montgomery , AL ..................................... .. ............................. .
Muncie, IN ....... .. ... .... ...... ... .... ... ... ...... .................................... .
Myrtle Beach , SC ..................... .............................................. .
Naples, FL ....... ... ......... ................................. .. ....................... .
Nashville, TN ............................................... ..... .. ... ...... .......... .
Nassau-Suffolk, NY ....... ....... .. ..... .......................................... .
New Haven-Bridgeport-Stamford-Waterbury-Danbury, CT ... .
29,591
37 ,056
26,578
29,150
28,374
24,029
30,839
33,989
39,662
52,198
30,769
37,710
27,614
30,525
29,017
24,672
31 ,507
35,036
40,396
51,170
4.0
1.8
3.9
4.7
2.3
2.7
2.2
3.1
1.9
-2.0
New London- Norwich, CT ................................ ..................... .
New Orleans, LA ...................... ...... ............ ........ ................... .
New York , NY .... ..... ... .. ....... ... .. .......... .. ..... ........................... . ..
Newark, NJ ............................................................................ .
Newburgh, NY-PA ..... .. .................. ........................................ .
Norfolk-Virginia Beach-Newport News, VA-NC ..................... .
Oakland, CA ... ........... ....... ..................................................... .
Ocala, FL .... ... .......................... ...... ........ .... ............................ .
Odessa-Midland, TX ... ....... ... .. ....................................... ........ .
Oklahoma City, OK ................................. ..... ... .... .. ........... ..... ..
38,505
31 ,089
59,097
47,715
29,827
29,875
45,920
26,012
31 ,278
28,915
38,650
32,407
57,708
48,781
30,920
30,823
46,877
26,628
31,295
29,850
.7
2.5
.5
.4
4.2
-2.4
2.2
3.7
3.2
2.1
2.4
.1
3.2
See footnotes at end of table.
Monthly Labor Review
August 2005
97
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 area1
2002
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
3.0
3.9
2.4
3.8
3.3
3.8
5.8
Phoenix-Mesa, AZ ................... ................ .. ........ ............... ... .. .
Pine Bluff, AR ........................................ .. ........................... ... .
Pittsburgh, PA ... ....... .. ... .... ..................... .. ..... .. .. .... .... ... .......... .
Pittsfield, MA .......................................................................... .
Pocatello, ID ........... .. ............. ..................... ............................
Portland, ME ..... ......... .... .... ................... ... ....... .. .. .... ............... .
Portland-Vancouver, OR-WA ... ... .... .... ....................... ..... ...... .
Providence-Warwick-Pawtucket, RI ...................... .......... ...... .
Provo-Orem , UT .................................................................... .
Pueblo, CO .......... ... ......................................................... ...... .
35,514
27,561
35,024
31,561
24,621
32,327
37,285
33,403
28,266
27,097
36,045
28,698
35,625
32,707
25,219
33,309
37,650
34,610
28,416
27,763
1.5
4.1
1.7
3.6
2.4
3.0
1.0
3.6
Punta Gorda, FL .. ... ... .................. .... ..... .. .. ... .... ...... ............... ..
Racine, WI ....... .. ........ ........ ... ........... ............ ... ....................... .
Raleigh-Durham-Chapel Hill, NC ... .... ........... ... ...................... .
Rapid City, SD ....................................................................... .
Reading , PA .. ...... ... ... .... .. ........... .. ............... ...... ........ ...... .. ... ..
Redding, CA ....................... ........ ..... .... ............ .. .... .... ... .... ......
Reno, NV ..... .... ... ..... ........... .... .. .... .. .... ..... .............. ................ .
Richland-Kennewick-Pasco, WA ...................................... ..... .
Richmond-Petersburg, VA ..................................................... .
Riverside-San Bernardino, CA ................................. ............. .
25,404
33,319
38,691
25,508
32,807
28,129
34,231
33,370
35,879
30,510
26,119
34,368
39,056
26,434
33,912
28,961
34,744
35,174
36,751
31 ,591
2.8
3.1
Roanoke, VA .. ... .......................... ... .......... .......................... ... .
Rochester, MN ... .............. ..... .... ... ... ... ... ............. .... ... .. ..... .. .... .
Rochester, NY ... ...... ........... ......................... .. ..... ... .... ..... .. .... ..
Rockford, IL ................................. .. ... .. ............................... .. .. .
Rocky Mount, NC .................................................................. .
Sacramento, CA ..... ................. ... .............. .. .............. .. ... ........ .
Saginaw-Bay City-Midland, Ml .......... ................ ... .. ................
St. Cloud, MN .... ... ... .... .. .. ............ .. ....... .. .......... .. ...... ........ .... . .
St. Joseph, MO ...................................................................... .
St. Louis, MO-IL ............. .. .... .. .................... ........ ... ... ...... .. ... ... .
30,330
37 ,753
34,327
32 ,104
28,770
38,016
35,429
28,263
27 ,734
35 ,928
31,775
39,036
34,827
32,827
28,893
39,354
35,444
29,535
28,507
36,712
4.8
3.4
1.5
2.3
Salem, OR ............................................................................. .
Salinas, CA .... .. ...... .... .. ............ ..... ......... ................................ .
Salt Lake City-Ogden, UT ................ ...... .. ... ..... .................... .. .
San Angelo, TX ..................................................................... .
San Antonio, TX .................................................................... .
San Diego, CA .................. ...... ..... .. ........................ .. .. ... ...... .. ..
San Francisco, CA ... ..... ............. ............................................ .
San Jose, CA .............................. ... ................ .............. ......... ..
San Luis Obispo-Atascadero-Paso Robles, CA .................... .
Santa Barbara-Santa Maria-Lompoc, CA .............................. .
28,336
31,735
31 ,965
26,147
30 ,650
38,418
59,654
65,931
29,092
33,626
29,210
32,463
32,600
26,321
31 ,336
39,305
56,602
63,056
29,981
34,382
3.1
2.3
2.0
2.2
2.3
-5.1
-4.4
3.1
2.2
Santa Cruz-Watsonville, CA ................. .............. .... ...... ......... .
Santa Fe, NM .. ....... ..... ... ..... ..... .... ................ ........................ ..
Santa Rosa, CA ..................................................................... .
Sarasota-Bradenton, FL ........................................................ .
Savannah, GA .... .............. .. .... .... .. .. .. ..... ............ ... .... ............. .
Scranton-Wilkes-Barre-Hazleton, PA ... ....... .. ...................... .
Seattle-Bellevue-Everett, WA ..... ... ... ... .................. ............ ... . .
Sharon, PA ... .. .. ... .. ... ... ... ...... ............... .. ... ... .... ... ....... .. .. .. ... ... .
Sheboygan, WI .... ... ......... .. ... ... ............. ..... .. ........... ................
Sherman-Denison, TX ...... ..... ..... .. .. ............................. ... ....... .
35,022
30 ,671
36,145
27,958
30,176
28,642
45,299
26,707
30,840
30,397
35,721
32,269
36,494
28,950
30,796
29,336
46,093
27,872
32,148
30,085
2.0
5.2
1.0
3.5
2.1
2.4
1.8
4.4
4.2
-1.0
Shreveport-Bossier City, LA .............................................. ... ..
Sioux City, IA-NE ......... .. ...... .... .............. .. ................... ... .. .... ...
Sioux Falls, SD ...................................................................... .
South Bend, IN .......... .. .. ................ .. ...................... ................ .
Spokane, WA .... ........................................ ... ................. .. ... ... . .
Springfield , IL .................. ............. .. ............................ .. .......... .
Springfield, MO ............. ....... .. ...... .... ........ .. .. ................. ......... .
Springfield, MA .... .... .......... ... ....... ... .... ........................ ... .. ...... .
State College, PA .................... .... .............. .. ......... ................. .
Steubenville-Weirton, OH-WV .... .. .. ........ ... .............................
27,856
26,755
28,962
30,769
29 ,310
36,061
27,338
32,801
29,939
28,483
28,769
27,543
29,975
31,821
30,037
37,336
27,987
33,972
30,910
29,129
3.3
2.9
3.5
3.4
2.5
3.5
2.4
3.6
3.2
2.3
See footnotes at end of table .
98
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Percent
change,
2001-02
2001
August 2005
.5
2.9
2.2
.5
2.5
.9
3.6
3.4
3.0
1.5
5.4
2.4
3.5
.4
3.5
.0
4.5
2.8
2.2
.7
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area'
Percent
change,
2001-02
2001
2002
Stockton-Lodi, CA ................................................................. ..
Sumter, SC ........................................................................... ..
Syracuse, NY ...................................................... ................... .
Tacoma, WA ....... .. .............................. ............. ............. ......... .
Tallahassee, FL .............................. ........ .. ..............................
Tampa-St. Petersburg-Clearwater, FL .................................. .
Terre Haute, IN ...................................................................... .
Texarkana, TX-Texarkana, AR ............... ............... .. .... .......... .
Toledo, OH .......... ..... ......... ... ........ ... .... ... ............................... .
Topeka, KS ............................................................................ .
$30,818
24,450
32,254
31,261
29,708
31,678
27,334
26,492
32,299
30,513
$31 ,958
24,982
33,752
32,507
30,895
32,458
28,415
27,717
33,513
31,707
3.7
2.2
4.6
4.0
4.0
2.5
4.0
4.6
3.8
3.9
Trenton , NJ ............................................................................ .
Tucson, AZ ..... .............. .................................... ..................... .
Tulsa, OK ............................................................................... .
Tuscaloosa, AL ...................................................................... .
Tyler, TX ..... .. ............................... ..................... .... .......... .. ..... .
Utica-Rome, NY ..................................................................... .
Vallejo-Fairfield-Napa, CA ................................ ................... ...
Ventura, CA .......................................................................... ..
Victoria, TX .............................................................................
Vineland-Millville-Bridgeton, NJ ............................................ ..
46,831
30,690
31,904
29,972
30,551
27 ,777
33,903
37,783
29,068
32,571
47,969
31,673
32,241
30,745
31,050
28,500
34,543
38,195
29,168
33,625
2.4
3.2
1.1
2.6
1.6
2.6
1.9
1.1
Visalia-Tulare-Porterville, CA ................................................ .
Waco, TX ................. .... .. ................ ................... ..... .. ............. . .
Washington, DC-MD-VA-WV ................................................. .
Waterloo-Cedar Falls, IA ............ ............ .. .... .. ..................... .. .
Wausau , WI .................................... ......... .................... .... ...... .
West Palm Beach-Boca Raton, FL ....................... ................ ..
Wheeling, WV-OH ............................................................... ..
Wichita, KS ................... ......... .. ...............................................
Wichita Falls, TX .................................................................... .
Williamsport, PA .................................................................... .
24,732
28,245
47,589
29,119
29,402
35,957
26,282
32,983
25,557
27,801
25,650
28,885
48,430
29,916
30,292
36,550
26,693
33,429
26,387
27,988
3.7
2.3
1.8
2.7
3.0
1.6
1.6
Wilmington-Newark, DE-MD ................................................. ..
Wilmington, NC ............ .... ......................... ...... .. ..................... .
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
2.1
3.5
4.3
4.5
Aguadilla, PR ......................................................................... .
Arecibo, PR .............................................. .... ........... .... .......... .
Caguas, PR ..... .. ................. ......................... ... .............. .. ..... ...
Mayaguez, PR ....... .. ......... .... ........................ ... ................. ..... .
Ponce, PR .. .......................... ......... ......... ......................... ..... ..
San Juan-Bayamon , PR ........................................................ .
18,061
16,600
18,655
17,101
17,397
20,948
19,283
18,063
19,706
17,500
18,187
21,930
6.8
8.8
5.6
2.3
4.5
4.7
Yakima, WA ..... .... ...... ........... ........ ... .. ........ ..... .......... .......... ... .
.3
3.2
1.4
3.2
.7
2.9
-.4
3.0
.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
August 2005
99
Current Labor Statistics:
Labor Force Data
27. Annual data: Employment status of the population
[Numbers in thousands]
Employment status
1994 1
1995
1996
199i
19981
1999 1
2000 1
2001
2002
2003
2004
196,814
198,584
132,304
200,591
133,943
203,133
136,297
205,220
137,673
207,753
215,092
143,734
217,570
221,168
139,368
212,577
142,583
144,863
146,510
223,357
147,401
66.6
124,900
66.8
126,708
67.1
67.1
67.1
67.1
66.8
66.6
66 .2
66.0
129,558
131,463
133,488
136,891
136,933
136,485
137,736
139,252
62.3
8,149
Civilian noninstitutional population. .........
Civilian labor force .. .. ......................... .. .
131 ,056
Labor force participation rate .. .... ... ... ..
66.6
Employed ............................. . . . ... .. .
123,060
Employment-population ratio .........
Unemployed .. ... ............... ...............
62.5
62.9
63.2
63.8
64.1
64.3
64.4
63.7
62.7
62 .3
7,996
7,404
7,236
6,739
6,21 0
5,880
5,692
6,801
8,378
8,774
1
Unemployment rate .......... ....... .. .. ...
6.1
5.6
5.4
4.9
4.5
4.2
4.0
4.7
5.8
6.0
5.5
Not in the labor force ........... .... ............ ..
65,758
66,280
66,647
66,836
67 ,547
68,385
69,994
71 ,359
72,707
74,658
75,956
Not strictly comparable with prior years.
28. Annual data: Employment levels by industry
[In thousands]
1995
1996
1997
1998
1999
2001
2002
....
95,016
97,866
100,169
103,113
106,021
108,686
110,996
110,707
108,828
108,416
109,862
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
129,999
583
6,716
15,259
21 ,816
572
6,735
14,510
131,480
21,884
591
6,964
14,329
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 .. .. .......... ..... . ..... . .. .....
72,242
23,128
5,247.3
13,490.8
3,701 .0
689.3
2,738
6,867
12,174
12,807
10,100
4,428
74,710
23,834
5,433.1
13,896.7
3,837.8
666.2
2,843
6,827
12,844
13,289
10,501
4,572
76,759
24,239
5,522 .0
14,142.5
3,935.3
639.6
2,940
6,969
13,462
13,683
10,777
4,690
79,227
24,700
5,663.9
14,388.9
4,026.5
620.9
3,084
7,178
14,335
14,087
11,018
4,825
81,667
25,186
5,795.2
14,609.3
4,168.0
613.4
3,218
7,462
15,147
14,446
11 ,232
4,976
84,221
25,771
5,892.5
14,970.1
4,300 .3
608.5
3,419
7,648
15,957
14,798
11 ,543
5,087
86,346
26,225
5,933.2
15,279.8
4,410.3
601 .3
3,631
7,687
16,666
15,109
11,862
5,168
86,834
25,983
5,772.7
15,238.6
4,372 .0
599.4
3,629
7,807
16,476
15,645
12,036
5,258
86,271
25,497
5,652 .3
15,025.1
4,223.6
596.2
3,395
7,847
15,976
16,199
11 ,986
5,372
86,599
25,287
5,607 .5
14,917.3
4,185.4
577.0
3,188
7,977
15,987
16,588
12,173
5,401
87,978
25,510
5,654.9
15,034.7
4,250 .0
570.2
3,138
8,052
16,414
16,954
12,479
5,431
19,275
19,432
19,539
19,664
19,909
20,307
20,790
21,118
21 ,513
21 ,583
21,618
1994
Industry
Total private employment... ................... ..
Government. ... ... . ... .......... ... ... . .......... .. .
l 00
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
2000
2003
2004
29. Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm
payrolls, by industry
1994
Industry
1995
1998
1999
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
517.30
33.7
15.67
528.56
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
669.1 3
40.0
17.19
688.03
45.3
14.41
653.14
45.3
14.78
670.32
46.0
15.10
695.07
46.2
15.57
720.11
44.9
16.20
727.28
44.2
16.33
721.74
44.4
16.55
734.92
44.6
17.00
757.92
17.19 1
741.97
43.6
17.56
765.94 1
44.5
18.08
804.03
38.8
39.2
17.48
685.78
38.7
18.00
695.89
38.4
18.52
711.82
38.4
18.95
726.83
38.3
19.23
735.70
34.5
11.32
390.73
34 .3
11.64
399.53
Goods-producing:
Average weekly hours .... .... ................... .................
Average hourly earnings (iri dollars) ... ... ................
Average weekly earnings (in dollars) .....................
41 .1
12.63
519.58
• • • • • • • • • • • • • • I
1996
1997
Private sector:
Average weekly hours ............. ........ .. ... ...... ... .. ..•..
Average hourly earnings (in dollars) .. ... ..
Average weekly earnings (in dollars) ... ...... ..............
2000
2001
2002
2003
2004
I
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) ... ... ...............
38.8
14.38
558.53
38.8
14.73
571.57
38.9
15.11
588.48
38.9
15.67
609.48
16.23 1
629 .75
39 .0
16.80
655.11
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
635.99
40.8
16.14
658.53
Private service-providing:
Average weekly hours ..........................................
Average hourly earnings (in dollars) ......... .. .... ..... ...
Average weekly earnings (in dollars) ....................
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
483.89
32.3
15.26
493.67
34.3
10.80
370.38
34 .1
11.10
378.79
34.1
11.46
390.64
34.3
11.90
407.57
34.2
12.39
423.30
12.82
434 .31
33.8
13.31
449.88
33.5
13.70
459.53
33.6
14.02
471.27
14.34
481 .14
33.5
14.59
488.58
38.8
12.93
501.17
38.6
13.34
515.14
38.6
13.80
533.29
38.8
14.41
559.39
38.6
15.07
582.21
38.8
15.62
602.77
38.4
16.77
643.45
38.0
16.98
644 .38
37.9
17.36
657.29
37.8
17.66
666.93
30 .9
8.61
501.17
30.8
8.85
515.14
30.7
9.21
533.29
30.9
9.59
559.39
30.9
10.05
582 .21
10.45
602.77
30 .7
10.86
631 .40
11.29
643.45
30 .9
11.67
644.38
30.9
11 .90
657.29
30.7
12.08
666 .93
39.5
12.84
507.27
38.9
13.18
513.37
39.1
13.45
525.60
39.4
13.78
542.55
38.7
14.12
546 .86
37 .6
14.55
547 .97
37 .4
15.05
562.31
36.7
15.33
562 .70
36.8
15.76
579.75
36.8
16.25
598.41
37.2
16.53
614.90
42.3
18.66
789.98
42.3
19.19
811.52
42.0
19.78
830.74
42.0
20.59
865.26
42.0
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
24.77
1,017 .27
40.9
25.62
1,048.82
36.0
15.32
551 .28
36.0
15.68
564.98
36.4
16.30
592.68
36.3
17.14
622.40
36.6
17.67
646.52
36.7
18.40
675.32
36.8
19.07
700.89
36.9
19.80
731 .11
36.5
20.20
738.17
36.2
21 .01
760.81
36.3
21.42
777.42
35.5
11 .82
419.20
35.5
12.28
436.12
35.5
12.71
451.49
35.7
13.22
472.37
36.0
13.93
500.95
35.8
14.47
517.57
35.9
14.98
537.37
35.8
15.59
558.02
35.6
16.17
575.51
35.5
17.14
609.08
35.5
17.53
622 .99
34.1
12.15
414.16
34.0
12.53
426.44
34 .1
13.00
442.81
34.3
13.57
465.51
34.3
14.27
490.00
34 .4
14.85
510 .99
34 .5
15.52
535.07
34 .2
16.33
557.84
34 .2
16.81
574.66
34 .1
17.21
587.02
34.2
17.46
596.96
32.0
11 .50
368.14
32.0
11 .80
377.73
31 .9
12.17
388.27
32.2
12.56
404 .65
32.2
13.00
418.82
32.1
13.44
431.35
32.2
13.95
449.29
32.3
14.64
473.39
32.4
15.21
492 .74
32.3
15.64
505.69
32.4
16.16
523.83
26.0
6.46
168.00
25.9
6.62
171.43
25.9
6.82
176.48
26.0
7.13
185.81
26.2
7.48
195.82
26.1
7.76
202.87
26.1
8.11
211 .79
25.8
8.35
215.19
25.8
8.58
221 .26
25.6
8.76
224.30
25.7
8.91
228.63
32.7
10.18
332.44
32.6
10.51
342.36
32.5
10.85
352.62
32.7
11 .29
368.63
32.6
11 .79
384 .25
32.5
12.26
398.77
32.5
12.73
413.41
32.3
13.27
428.64
32.0
13.72
439 .76
31.4
13.84
434 .41
31.0
13.98
433.04
Trade, transportation, and utilities:
Average weekly hours ........... ... ..... ...... ...................
Average hourly earnings (in dollars) ......................
Average weekly earnings (in dollars) ............... .....
Wholesale trade:
Average weekly hours ........................................
Average hourly earnings (in dollars) .. .... .. ...........
Average weekly earnings (in dollars) .... ... ..........
Retail trade:
Average weekly hours ................... .. ... ... .............
Average hourly earnings (in dollars) ......... .........
Average weekly earnings (in dollars) .................
Transportation and warehousing:
Average weekly hours ........................................
Average hourly earnings (in dollars) ..................
Average weekly earnings (in dollars) .................
Utilities:
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) ..... ... ... ......
Leisure and hospitality:
Average weekly hours ........... .............................
Average hourly earnings (in dollars) ... ......... .. .. ..
Average weekly earnings (in dollars) ....... ...........
Other services:
Average weekly hours ............... ........... ..............
Average hourly earnings (in dollars) ................. .
Average weekly earnings (in dollars) .................
3391
16.28
~·1 631.40
3081
'''I
43.2
I
3361
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
August 2005
701
Current Labor Statistics:
Compensation & Industrial Relations
30. Employment Cost Index, compensation, 1 by occupation and industry group
[June 1989
= 100]
2003
June
Series
2004
Sept.
Dec.
Mar.
June
2005
Sept.
Dec.
Mar.
Percent change
June
3 months
12 months
ended
ended
June 2005
2
Civilian workers ... ..................... .... .... ... ... .. .... .
165.8
167.6
168.4
170.7
172.2
173.9
174.7
176.6
177.7
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
J70.2
175.8
175.3
166.9
169.7
174.0
171.2
177.1
177.2
168.8
170.9
175.8
173.6
178.2
178.7
170.1
172.7
176.6
174.7
179.4
180.0
170.9
173.6
178.8
176.8
182.0
182.0
172.4
174.9
179.9
177.6
183.1
183.3
173.8
175.9
Public administration ................................. .
Nonmanufacturing ............................................................. .
164.6
165.4
166.2
166.3
167.6
170.8
164.2
164.3
165.8
165.8
166.5
168.2
168.5
169.3
173.1
166.9
167.3
167.8
166.8
167.1
169.1
169.5
170.7
174.8
167.6
168.1
168.6
170.4
171 .7
170.8
171 .2
173.0
176.8
168.5
170.1
170.4
171.9
173.2
172.3
172.3
174.4
178.2
168.9
171.4
171.8
173.4
174.9
174.0
174.5
176.7
180.5
171.8
174.1
173.5
174.4
175.4
174.7
175.5
177.7
181 .8
172.9
175.4
174.4
177.0
178.2
176.5
177.0
179.9
184.3
173.9
177.6
176.1
178.5
179.6
177.4
177.8
181.1
185.5
174.5
178.3
177.1
Private Industry workers ... .... .. .................... ... ......... .... .
Excluding sales occupations ... ... .... ............................... .
166.4
166.6
168.1
168.1
168.8
169.0
171 .4
171.6
173.0
173.2
174.4
174.6
175.2
175.6
177.2
177.7
178.5
178.9
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 ... .
169.4
170.4
167.7
173.1
165.1
170.9
161.4
162.0
161 .1
155.1
166.8
171.2
172.1
169.4
175.0
167.2
172.3
162.8
163.1
162.6
156.7
168.6
172.0
173.0
170.5
175.9
167.1
173.2
163.6
164.2
163.2
156.9
169.5
174.2
175.3
173.4
176.8
169.2
176.1
166.9
167.1
168.7
158.5
171 .7
175.7
176.7
174.7
178.1
171 .2
178.1
168.8
169.1
170.5
160.6
173.2
177.3
178.3
176.8
179.2
173.1
179.4
170.1
170.2
172.2
161.8
174.3
178.1
179.5
178.1
180.2
171.4
180.7
170.8
171.2
172.5
162.3
175.3
180.4
182.0
180.8
183.0
173.1
182.8
172.3
173.1
173.3
163.7
176.9
181.6
183.2
181 .6
184.2
174.4
184.3
173.7
174.9
173.8
165.7
177.9
0.6
3.2
Workers, by occupational group:
White-collar workers .......................................................... .
Professional specialty and technical. ..................... ... ...... .
Executive, adminitrative, and managerial. ..................... .
Administrative support, including clerical. ..................... .
Blue-collar workers ......................................... .. .... ............ .
Service occupations .......................................................... .
.6
3.4
.5
.6
3.7
.7
.8
.6
3.4
3.4
3.0
2 .9
Workers, by industry division :
Goods-producing .. ................... .. .................... .................... .
Manufacturing ........... ...................................................... .
Service-producing ....................................... .. ....... .............. .
Services ...........................................................................
Health services .............................................................. .
Hospitals ............... ............. ......................................... .
Educational services .................................... ... ... ... ... .. .... .
3
Service occupations .. .... ... ............................... ... ..... ... ... .
3.8
3.7
3.0
3.2
3.8
4.1
3.3
4 .0
3.1
3.2
3.3
.8
.8
.8
1.0
.3
1.2
.6
3.4
3.7
3.9
3.4
1.9
3.5
2.9
3.4
1.9
3.2
2 .7
.7
.7
.4
.7
162.6
163.8
164.3
166.9
168.2
168.9
169.7
170.9
171.9
.6
2.2
164.1
165.7
166.6
169.3
171 .0
172.4
173.0
174.6
175.8
.7
2.8
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 ............................................. .. ...... .... .. ....... .
164.5
163.8
169.2
167.5
161.5
161 .1
165.4
168.7
166.4
162.8
165.5
164.9
165.7
165.0
170.1
168.5
162.9
162.3
166.5
169.5
167.4
164.1
166.6
166.0
166.5
165.9
170.5
169.2
163.9
163.3
167.1
169.6
167.8
165.1
167.3
166.6
170.3
169.8
173.5
172.2
168.1
164.6
171.7
173.2
171.3
170.4
172.4
170.4
171.8
171.2
174.7
173.3
169.8
165.9
173.2
174.6
172.6
172.0
174.0
171.7
173.3
172.5
176.4
174.5
171.3
167.0
174.9
176.4
174.1
173.7
175.8
173.1
174.3
173.7
177.8
176.4
172.0
167.3
175.4
176.7
174.7
174.3
176.3
173.6
176.9
176.3
182.2
180.9
173.4
169.1
178.2
181.4
179.4
175.8
179.5
175.8
178.5
177.9
184.2
183.0
174.7
171.0
179.6
183.4
181 .5
176.7
181.2
176.8
.9
.9
1.1
1.2
3.9
3.9
5.4
5.6
2 .9
3.1
3.7
5.0
5.2
2 .7
4.1
3.0
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 ............................................................... .
167.0
168.0
169.2
171 .3
160.8
162.0
165.4
158.9
174.2
175.5
172.6
162.5
162.7
171.3
169.9
157.4
159.2
158.6
168.8
169.7
171.2
173.1
162.2
163.2
166.5
159.4
176.4
178.4
173.8
164.3
165.0
172.0
171.2
159.9
161.2
159.3
169.7
170.6
172.0
174.2
162.6
164.3
167.0
159.6
177.0
179.0
174.6
165.0
165.9
172.0
171.3
161 .0
165.6
160.3
171.6
172.5
174.1
176.2
164.1
166.1
169.8
162.0
180.4
182.2
178.2
166.3
167.4
173.8
173.7
162.1
165.8
162.1
173.3
174.2
175.7
177.8
166.4
167.4
172.5
164.7
183.1
183.6
182.4
168.1
168.6
175.9
174.0
163.7
166.2
163.5
174.7
175.6
177.3
179.4
167.4
168.1
173.6
166.2
183.6
183.6
183.3
169.1
169.6
177.8
175.3
164.2
168.8
163.5
175.3
176.5
177.8
180.4
168.1
168.9
173.5
166.2
183.4
183.5
183.3
169.1
170.4
176.6
176.3
164.7
169.5
164.0
177.1
178.4
179.7
182.4
169.9
170.1
174.5
165.5
186.9
186.0
188.0
170.9
172.3
179.1
179.2
166.2
172.3
165.0
178.1
179.4
180.7
183.2
171.5
171.1
175.8
166.1
189.2
188.4
190.2
171 .7
173.1
179.3
179.5
167.3
172.1
165.9
Production and nonsupervisory occupations
4
................ .
1
.7
1.1
.8
1.1
1.2
.5
.9
.6
.6
.6
.6
.4
.9
.6
.7
.4
1.2
1.3
1.2
.5
.5
.1
.2
.7
-.1
.5
2.8
3.0
2.8
3.0
3.1
2.2
1.9
.9
3.3
2 .6
4.3
2 .1
2.7
1.9
3.2
2.2
3.5
1.5
L-------'--------'---__J-----'--------'---__j_---'--------'--------'---------'---------
See footnotes at end of table.
102 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
I
30. Continued-Employment Cost Index, compensation, by occupation and industry group
[June 1989
= 100]
2003
Series
June
Sept.
2004
Dec.
Mar.
June
Percent change
2005
Sept.
Dec.
Mar.
June
3 months
12 months
ended
ended
June 2005
Finance, insurance, and real estate ...............................
Excluding sales occupations:.. ..................................
Banking , savings and loan, and other credit agencies.
Insurance ................. .................... ... ..............................
Services ......... ..... ..........................................................
Business services .......... ...... ................... .......... ...........
Health services .... ......... ............... .. ... .. ..... .. . . . . . .. . . . . . .. . . . .
Hospitals ....................................................................
Educational services .....................................................
Colleges and universities ...........................................
178.3
180.2
184.0 1,853.0
206.3
207.6
173.9
175.1
168.4
170.4
169.2
171.9
167.9
169.4
171.9
173.9
177.1
180.2
175.4
178.4
180.9
186.1
209.0
176.2
171 .4
172.6
170.8
175.9
181.3
179.4
182.5
186.6
207.2
177.8
173.5
174.8
173.3
178.1
183.1
181.2
183.6
188.7
208.9
180.5
175.1
176.9
174.8
179.7
184.2
182.5
184.8
190.9
210.5
182.1
176.9
178.5
177.0
181.8
187.0
185.2
186.0
191.2
212.3
183.6
177.9
179.1
178.0
183.2
188.5
186.2
188.9
194.3
213.7
186.3
179.7
180.1
180.3
185.8
190.0
187.6
190.9
196.1
217.3
188.8
180.6
181.0
181.5
187.3
190.9
188.6
1.1
.9
1.7
1.3
.5
.5
.7
.8
.5
.5
4.0
3.9
4.0
4.6
3.1
2.3
3.8
4.2
3.6
3.3
Nonmanufacturing ................... ............ .......... .................
166.4 !
White-collar workers .. .................................... ... ...... ......
Excluding sales occupations ....................................
Blue-collar occupations ................................................
Service occupations .. .. ..... .. ........................................
169.3 1
171.4
159.7
162.0
168.1
171.2
173.2
161.1
1R3.2
169.0
172.1
174.2
161.7
162.4
170.9
174.1
176.2
163.4
166.0
172.5
175.7
177.7
165.5
167.3
173.9
177.2
179.3
166.4
168.0
174.7
178.0
180.6
167.3
168.9
176.5
180.0
182.7
168.8
170.1
177.6
181.0
183.6
170.6
171.0
.6
.6
.5
1.1
.5
3.0
3.0
3.3
3.1
2.2
163.2
165.9
166.8
168.0
168.7
171.5
172.6
174.1
174.7
.3
3.6
162.2
160.8
165.7
164.4
161 .7
164.9
163.4
168.0
167.9
163.6
165.7
164.1
169.1
168.5
165.2
166.8
165.1
170.1
170.4
166.7
167.5
165.6
171.0
171.8
167.5
170.0
168.4
172.1
174.3
169.9
171.2
169.4
174.3
175.5
171.0
172.6
170.4
176.7
177.2
172.6
173.1
171.1
176.5
177.7
173.8
.3
.4
-.1
.3
.7
3.3
3.3
3.2
3.4
3.8
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
171.8
175.6
178.9
179.1
170.9
171.2
169.8
175.1
177.6
172.4
176.4
179.6
179.8
171 .4
171 .7
170.3
175.6
178.3
.3
.5
.4
3.4
3.7
3.9
3.8
3.3
3.2
3.5
2.7
4.0
State and local government workers ...................................
Workers, by occupational group:
White-collar workers ............................................ .. .............
Professional specialty and technical. ........................... .. ..
Executive, administrative, and managerial. ....................
Administrative support, including clerical. ....... ., .............
Blue-collar workers ................................... .. ........... ...... .. ....
Workers, by industry division :
Services ....... ......... ................... ...................... ,.. ,..... ,... ......
5
Services excluding schools ..
......... .. ......... .. ... ....
Health services .............................................................
Hospitals .............................. .,........ ,...........................
Educational services ......................... ················ ······ ·····
Schools ............. ................................ ,........................
Elementary and secondary ......... .... .. ...
Colleges and universities .................. .. ............. .....
3
Public administration ..... .....................................
Cost (cents per hour worked) measured in the Employment Cost Index consists of
wages, salaries, and employer cost of employee benefits.
2
Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
3
.4
.3
.3
.3
.3
.4
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
August
2005
103
Current Labor Statistics:
Compensation & Industrial Relations
31. Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989 = 100)
2003
2004
Percent change
2005
Series
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
3 months
12 months
ended
ended
June 2005
1
160.3
161 .8
162.3
163.3
164.3
165. 7
166.2
167.3
168.2
0.5
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 .......................... ... ............................. .
162.9
160.1
169.0
163.1
154.8
158.7
164.5
161.8
170.5
164.3
155.8
159.8
165.1
162.5
171.2
164.9
156.3
160.6
166.1
163.8
171.4
166.3
157.3
161.2
167.1
164.4
172.4
167.5
158.4
161.9
168.7
166.5
173.4
168.8
159.7
162.8
169.1
167.0
174.4
169.7
160.0
163.6
170.3
168.1
175.9
170.9
161.0
164.4
171.1
168.7
176.9
172.0
162.2
165.3
.5
.4
.6
.6
.7
.5
2.4
2.6
2.6
2.7
2.4
2.1
Workers, by industry division:
Goods-producing ............................................ .... ............... .
Manufacturing ............................................... .. .
Service-producing ....................................... ...................... .
Services .......................................................................... .
Health services .............................................................. .
Hospitals ..................................................................... .
Educational services ....................... .. ............................. .
157.5
159.0
161.4
162.8
163.2
164.4
160.7
158.3
159.7
163.0
164.7
164.7
166.3
162.7
160.6
160.1
163.6
165.4
165.9
167.7
163.2
159.9
161.3
164.6
166.5
167.7
169.0
163.6
161.0
162.4
165.5
167.4
168.6
169.9
163.8
162.3
163.8
167.0
167.3
170.8
171.8
166.0
162.4
164.0
167.5
170.1
171.7
173.2
166.8
163.8
165.3
168.6
171.2
173.2
174.7
167.5
164.9
166.4
169.5
171.9
174.3
175.7
167.9
2.4
2.5
2.4
2.7
3.4
3.4
2.5
Public administration ...... .. ........ ... .. ........... .
Nonmanufacturing ........................... .......................... ... .. ... .
158.0
160.5
159.4
162.1
160.0
162.7
161.1
163.7
161.4
164.6
162.6
166.0
163.5
166.5
165.0
167.6
165.6
168.5
2.6
2.4
Private industry workers .... .................. ......... ..... ......... .
Excluding sales occupations ......................................... .
160.4
160.5
161.7
161.7
162.3
162.4
163.4
163.5
164.5
164.5
165.9
165.8
166.2
166.5
167.4
167.6
168.4
168.7
2.4
2.6
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 ... .
163.8
164.8
160.5
170.3
159.3
164.0
154.6
154.7
155.3
149.0
159.0
165.3
166.2
162.1
171.8
161 .6
165.1
155.6
155.5
156.8
149.8
159.9
165.9
167.0
163.0
172.5
161 .1
165.7
156.1
156.2
156.9
149.8
160.6
167.1
168.1
164.7
172.7
162.6
167.2
157.2
157.1
158.6
150.4
161.8
168.2
169.2
165.5
173.9
163.9
168.6
158.3
158.3
159.8
151.8
162.7
169.7
170.6
167.6
174.9
165.9
169.7
159.5
159.3
161.6
152.9
163.6
170.0
171.4
168.0
175.7
164.0
170.8
159.9
159.7
161.6
153.3
164.5
171.3
172.7
169.4
177.2
164.9
172.0
160.8
160.4
162.6
154.4
165.6
172.3
173.7
170.0
178.4
166.0
173.3
162.1
162.0
163.7
156.0
165.9
.6
.6
.4
.7
.7
.8
.8
1.0
.7
1.0
.2
Service occupations ........... .. ................................. ........ .
156.1
157.1
157.8
158.4
159.3
159.8
160.6
161.4
162.3
.6
1.9
157.4
158.8
159.4
160.7
161.7
163.1
163.4
164.5
165.5
.6
2.4
157.4
156.5
161.4
159.2
154.8
152.4
159.0
161.6
158.9
156.9
159.7
157.8
158.3
157.4
161.9
159.9
155.9
153.6
159.7
162.0
159.5
157.9
160.6
158.3
158.7
158.0
162.1
160.4
156.4
154.0
160.1
162.1
160.0
158.5
160.9
158.7
159.9
159.2
163.2
161.5
157.7
155.1
161.3
163.3
161.2
159.8
161.9
160.4
160.9
160.2
164.5
162.7
158.6
155.9
162.4
164.7
162.5
160.6
162.9
161.6
162.3
161.2
166.0
163.6
159.8
157.1
163.8
166.1
163.5
162.1
164.5
162.8
162.4
161.6
165.9
164.1
160.1
157.0
164.0
166.1
163.9
162.4
164.7
162.9
163.6
162.8
167.3
165.3
161.2
157.7
165.3
167.6
165.1
163.6
165.9
164.5
164.8
164.0
168.5
166.7
162.4
159.2
166.4
168.7
166.5
164.7
167.1
165.3
.7
.7
.7
.8
2.4
2.4
2.4
2.5
2.4
2.1
2.5
2.4
2.5
2.6
2.6
2.3
Civilian workers
.............. .
2
Production and nonsupervisory occupations3
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 .................................................................. .
.7
1.0
.7
.7
.8
.7
.7
.5
2.4
2.7
2.7
2.6
1.3
2.8
2.4
2.3
2.4
2.8
2.0
167.9
167.5
170.0
166.1
2.3
165.0
169.0
163.3
163.9
161 .7
.6
Service-producing ....................................................... .... .
169.3
168.5
167.1
171.4
170.4
164.2
2.6
.6
166.0
165.0
Excluding sales occupations .................................... .
162.8
170.8
170.4
173.0
168.9
172.1
.5
167.8
166.0
166.6
2.4
White-collar occupations .............................................. .
164.1
173.6
172.8
175.9
171.2
2.7
170.2
175.0
168.2
169.0
Excluding sales occupations ... .................................. .
166.5
.5
159.4
158.9
157.8
160.1
161 .5
.9
2.3
156.2
155.1
155.4
154.3
Blue-collar occupations ............................................... .
160.2
159.4
158.8
160.9
161.8
.6
1.9
158.0
156.6
157.4
155.6
Service occupations .................... ...... .. ......... ............... .
160.5
160.4
159.1
1.3
161 .1
.8
157.6
159.8
156.0
156.5
155.6
Transportation and public utilities ................................. .
155.1
155.0
153.4
153.4
.8
154.6
.8
151.7
150.4
150.8
150.6
Transportation ............................................................. .
167.5
167.5
166.4
2.1
165.3
168.2
163.4
164.1
162.1
169.9
1.0
Public utilities ......... ........................ .. ..... .. ... .................. .
168.3
168.8
167.5
1.7
168.4
170.3
1.1
167.0
165.4
165.9
163.4
Communications ....................................................... .
166.6
165.9
165.1
169.2
2.5
163.3
.8
167.9
161.0
161.8
160.4
Electric, gas, and sanitary services .......................... .
162.1
162.5
161.6
1.5
160.3
.4
163.4
159.5
164.1
159.2
157.5
Wholesale and retail trade .... ............... .. ........... ... .. ....... .
167.5
169.7
167.8
169.4
1.0
-.1
169.5
165.3
164.8
166.2
164.7
Wholesale trade .......................................................... .
168.9
168.6
167.6
2.3
167.8
171 .5
.0
171.5
166.3
165.7
165.2
Excluding sales occupations .................. ..... ............. .
159.3
158.7
158.4
1.9
157.3
161.4
.7
160.3
156.5
156.3
Retail trade ................................................................. .
153.8
158.1
157.5
154.9
-.2
159.3
2.6
154.1
159.0
153.6
153.1
General merchandise stores ..................................... .
152.0
155.0
154.5
154.3
155.8
1.6
153.8
156.7
.6
152.2
152.8
Food stores ................................................................
151.6
See footnotes at end of table.
'--------'--------_j_-------'-----'-------....J._------'----____.J'--------'--------_j_--- - _ _ _ _ . J - - - - -
104
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
31. Continued-Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989 = 100]
2003
Percent change
2005
2004
3 months
12 months
ended
ended
Series
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
June 2005
Finance, insurance, and real estate ........ ... ................. ...
Excluding sales occupations:........ .... ....................... .
Banking, savings and loan, and other credit agencies.
Insurance ......................................................................
Services ................. ....... .................. .. ..............................
Business services ..... ...................................................
Health services ........ ... .. .......... ... ....... .......... ..................
Hospitals ........... ............................. .. .... ............. .........
Educational services ........................................... .. .......
Colleges and universities ...... ..................... ......... ..... ..
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
179.2
184.6
210.7
171 .7
173.4
175.5
173.4
175.4
177.9
174.6
181.2
186.5
215.4
173.7
174.2
176.5
174.6
176.7
178.6
175.5
1.1
1.0
2.2
1.2
.5
.6
.7
.7
.4
.5
3.4
3.3
3.8
3.9
2.9
2.2
3.4
3.6
3.5
3.2
Nonmanufacturing ... .. .............. .......................................
White-collar workers ....... ....... ..................... ..................
Excluding sales occupations ....... ............. .. ... ...........
Blue-collar occupations .......................................... .. ... .
Service occupations .................................... ........ ...... .
160.5
163.9
166.1
152.4
155.5
162.1
165.7
167.7
153.4
156.5
162.6
166.3
168.5
153.8
157.3
163.7
167.5
169.7
154.7
157.9
164.8
168.6
170.7
156.1
158.7
166.2
170.1
172.3
157.1
159.2
166.6
170.5
173.1
157.5
160.1
167.7
171.7
174.4
158.2
160.8
168.7
172.7
175.4
159.7
161 .7
.6
.6
.6
.9
.6
2.4
2.4
2.8
2.3
1.9
State and local government workers ............ ...................
163.2
165.9
166.8
168.0
168.7
171.5
172.6
174.1
174.7
.2
2.4
Workers, by occupational group:
White-collar workers .. ..................................... .... .. ... ... ........
Professional specialty and technical. ...................... .. ... .. ..
Executive, administrative, and managerial ........ .. ......... ..
Administrative support, including clerical .. .......... ...........
Blue-collar workers .. .............................. ....... ....................
159.2
159.1
161 .0
157.2
156.5
161.0
161.0
162.5
159.1
157.6
161 .5
161.4
163.3
159.5
158.3
162.1
162.1
163.5
160.4
158.9
162.4
162.3
163.8
160.8
159.2
164.1
164.4
164.3
162.6
160.7
164.9
165.0
166.1
163.0
161.4
165.9
165.7
168.2
163.9
162.4
166.2
166.2
168.0
164.0
163.2
.2
.3
-.1
.1
.5
2.3
2.4
2.6
2.0
2.5
Workers, by industry division:
Services ................. ......... .. .... .......... .................. ............. .. .
159.8
161.6
162.1
162.6
162.7
164.8
165.5
166.2
166.6
.2
2.4
161.8
163.5
163.8
159.3
159.5
158.5
162.1
163.2
165.1
165.5
161.2
161.4
160.6
163.5
164.5
166.7
166.7
161.6
161.8
160.9
164.0
165.1
167.4
167.4
162.0
162.1
161 .3
164.3
165.6
167.8
167.9
162.1
162.3
161 .5
164.4
167.5
169.6
169.9
164.2
164.3
163.8
165.4
168.3
170.7
171.0
164.9
165.0
164.5
166.3
169.4
171 .9
172.0
165.5
165.6
164.8
167.9
170.1
172.6
172.5
165.8
166.0
165.1
168.2
.4
.4
.3
.2
.2
.2
.2
2.7
2.9
2.7
2.3
2.3
2.2
2.3
158.0
159.4
160.0
161 .1
161.4
162.6
163.5
165.0
165.6
.4
2.6
4
. ...... ... ....... .. .....
Services excluding schools ..
Health services ............. ..... .. ......................... ........ ..... ...
Hospitals ........................ ............ ........ ... .. ........ .... .......
Educational services ............... ..... ..... .. ..................... ....
Schools ................ ... ... ................................. .............. .
Elementary and secondary ... .. ................ ... ... ... .......
Colleges and universities .... .. ......... .. ......... ...... .. ... .. .
Public administration
2
.. ... .... ... .. .... ... .. ..... ... ... .
Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
2
Consists of legislative, judicial, administrative, and regulatory activities.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
3
This seri es has the same industry and occupational coverage as the Hourly
Earnings index, which was discontinued in January 1989.
4
Includes, for example, library, social , and health services.
Monthly Labor Review
August
2005
105
Current Labor Statistics:
Compensation & Industrial Relations
32. Employment Cost Index, benefits, private industry workers by occupation and industry group
[June 1989 = 100]
2003
2004
Percent change
2005
Series
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
3 months
12 months
ended
ended
June 2005
Private industry workers ................... ...................................
182.0
184.3
185.8
192.2
195.3
196.9
198.7
203.3
204.9
0.8
4.9
Workers, by occupational group:
White-collar workers ...........................................................
Blue-collar workers ............
. ...................... ..
185.5
176.1
187.7
178.4
189.2
179.9
194.4
188.3
197.4
191 .8
199.1
193.3
201.1
194.9
206.8
197.8
208.5
199.4
.8
.8
5.6
4.0
Workers, by industry division:
Goods-producing ................................. . ................ ............ .
Service-producing ......... .. ............. ......................................
Manufacturing ......... .. ................ .................. .......... ..............
Nonmanufacturing ................................................
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
207.0
200.5
206.7
201.6
209.4
201.6
208.8
203.0
1.2
.5
1.0
.7
6.7
3 .9
6 .0
4 .5
l 06
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
33. Employment Cost Index, private industry workers by bargaining status, region, and area size
[June 1989 = 100]
2004
2003
Percent change
2005
Series
June
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
3 months
12 months
ended
ended
June 2005
COMPENSATION
Workers, by bargaining status
1
Union ............................ .......................................................... .
Goods-producing ................................................................ .
Service-producing .. ..... .......... ............... ........................ ...... .
Manufacturing ..................................................................... .
Nonmanufacturing .......... ........................ ........................... .
164.1
163.4
164.6
163.8
163.7
165.7
164.7
166.5
165.0
165.5
166.8
165.9
167.5
166.3
166.5
171.4
172.3
170.2
175.0
168.8
173.9
174.6
172.9
177.0
171 .6
175.3
176.0
174.4
178.4
173.0
176.2
176.7
175.4
178.9
174.1
177.5
178.2
176.6
180.6
175.2
179.0
179.8
177.9
181.7
176.9
0.8
.9
.7
.6
1.0
2.9
3.0
2.9
2.7
3.1
Nonunion ............... ......... ... ....................................... .. ............ .
Goods-producing ...................... ............ ................ .. ............ .
Service-producing .............................................................. .
Manufacturing .. ............. ...................................................... .
Non manufacturing .......................... .. ................................ ..
166.8
164.9
167.2
165.8
166.7
168.4
166.1
169.0
166.9
168.5
169.1
166.7
169.8
167.3
139.3
171.3
169.7
171 .6
170.6
171.1
172.7
170.9
173.2
172.0
172.6
174.2
172.4
174.6
173.8
174.0
174.9
173.5
175.1
174.3
174.7
177.1
176.5
177.0
177.5
176.6
178.3
178.0
178.0
179.0
177.7
.7
.8
.6
.8
.6
3.2
4.2
2.8
4.1
3.0
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
176.1
172.5
180.0
181.4
177.6
173.4
180.9
183.3
.9
.5
.5
1.0
3.1
3.3
2.7
3.7
166.6
165.0
168.3
166.1
169.1
166.9
171.5
170.2
173.1
172.1
174.6
173.3
175.3
174.3
177.4
176.4
178.6
177.3
.7
.5
3.2
3.0
Union ........ ... ..................................... .......... ........................... .
Goods-producing ................................................................ .
Service-producing .............................................................. .
Manufacturing .................... .............. ................................... .
Nonmanufacturing .... .. .... .. .... .. ................ ........................... .
154.3
153.9
155.1
155.9
153.5
155.3
154.8
156.3
156.7
154.6
156.2
155.4
157.3
157.1
155.6
157.2
156.3
158.5
158.1
156.6
158.7
157.5
160.3
159.2
158.4
160.0
158.7
161.7
160.5
159.6
160.6
158.9
162.6
160.7
160.4
160.8
159.6
162.3
161.5
160.3
162.1
161 .1
163.6
162.8
161.7
.8
.9
.8
.8
.9
2.1
2.3
2.1
2.3
2.1
Nonunion .. ...................................................................... .. ...... .
Goods-producing ................................................................ .
Service-producing ............. ................................................ ..
Manufacturing .. .. ...... .......... .... .... ...... .. ..................................
Nonmanufacturing ........................................................... ...
161 .5
158.9
162.3
160.2
161.5
163.0
159.7
164.0
160.9
163.1
163.4
160.1
164.5
161 .3
163.7
164.6
161.4
165.6
162.6
164.7
165.6
162.4
166.6
163.7
165.7
167.0
163.8
168.0
165.2
167.1
167.3
163.9
168.4
165.3
167.5
168.6
165.2
169.7
166.8
168.7
169.6
166.4
170.7
167.8
169.7
.6
.7
.6
.6
.6
2.4
2.5
2.5
2.5
2.4
158.4
156.1
165.0
163.1
160.0
157.4
166.1
164.7
160.9
157.9
166.5
165.2
162.0
159.1
166.9
166.8
163.6
160.1
167.7
167.9
164.9
161.6
169.2
169.1
165.0
162.3
169.2
169.5
166.0
163.6
170.6
170.3
167.3
164.4
171 .3
171 .9
.8
.5
.4
.9
2.3
2.7
2.1
2.4
160.7
158.0
162.2
158.9
162.7
159.5
163.8
160.8
164.9
162.1
163.3
162.1
166.6
163.8
167.7
165.1
168.8
166.3
.7
.7
2.4
2.6
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
Workers, by region
1
1
Northeast. .. ........................................................................... .
South ..................................................................................... .
Midwest (formerly North Central) .. ........................................ .
West. .......... ... ................... ............................ .. ............ ........... .
Workers, by area size
1
Metropolitan areas ................................................................. .
Other areas .. ............................ ... ......... .. ............ ................... .
1
The indexes are calculated differently from those for the occupation and industry groups. For a detailed description of the index calculation, see the Monthly Labor Review
Technical Note, "Estimation procedures for the Employment Cost Index," May 1982.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
107
Current Labor Statistics:
Compensation & Industrial Relations
34. Percent of full-time employees participating in employer-provid ed benefit plans, and in selected features within plans,
medium and large private establishments, selected years, 1980-97
Item
1980
Scope of survey (in OOO's) .. ..
Number of employees (in OOO's):
With medical care
With life insurance ...
With defined benefit plan
Time-off plans
Participants with:
Paid lunch time . ... ..... .. .. . .... ..... .... . .. . .... ..... .
Average minutes per day ....... . .... .... .. .... ..... .. .. . .
Paid rest time .. ... .. .. .. ....... ...... ... . .. .......... ... ..... .
Average minutes per day ..
Paid funeral leave ..... ..
Average days per occurrence
.. ..... ... ....... .
Paid holidays . ... . .... .. ... .. .... ... ...... ..... ...... .. .. .. ... . .
Average days per year ..
Paid personal leave.
Average days per year.
Paid vacations .. .. ..... ... . .. .
Paid sick leave ' .. .... ... .. .. ....... .. .. ... ... .. .. ....... .. .. .
Unpaid maternity leave ........ ... ..... . .... ... .. ... .... .. ..
Unpaid paternity leave . .... .. ..... .. .... . .
Unpaid family leave . . ..
.. ..... ........... . .
Insurance plans
Parti cipants in medical care plans
Percent of parti cipants with coverag e for:
Home health care ...... ..
Extended care facilities .......... ..... ...... .. .
Physical exam .. .. ... . .. . .. .. .. ... ....... .. ... . .. ... . .... .
Percent of participants with employee
contribution required for:
Sell coverage ...... . .. ... ... ..... ... ... ... ..
Average monthly contribution .... .. .... .. . .. ... .... .. .
Family coverage . ... . ... .. .... .. . ..... . .. ..... ... .. . ..... .
Average monthly contnbutIon ..... . ...... . .... .... ... .
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 ........ ...... ....... ... .. ... ...... .. ...... ..... .
1982
1984
1986
1988
1989
1991
1993
1995
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
11
29
72
26
85
3.2
96
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
21
3.3
21
3.1
22
3.3
20
3.5
10
9
9
75
25
76
25
26
73
26
99
99
99
10.1
10.0
9.8
99
10.0
9.4
10
26
71
26
84
3.3
97
9.2
20
24
3.8
23
3.6
25
3.7
24
3.3
22
3.1
88
-1
3.2
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
97
97
97
58
62
46
62
8
36
$11.93
58
$35.93
26
27
46
51
90
92
83
82
77
76
66
70
18
76
79
28
75
80
28
81
80
30
86
82
42
78
73
56
85
78
63
43
$12.80
63
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
$41.40
44
$19.29
64
$60.07
$130.07
80
96
96
96
96
92
94
94
91
87
87
69
72
74
71
7
42
76
64
78
8
49
71
6
64
72
10
59
44
41
77
7
37
74
6
33
40
43
47
48
42
45
40
41
42
43
54
51
51
49
46
43
45
44
53
55
5
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 . ..... ... .... .
1997
21,352
84
55
98
58
97
53
45
52
45
82
76
63
63
59
56
52
50
63
97
47
64
98
35
57
62
59
98
26
55
98
7
56
54
52
95
62
62
97
22
64
63
61
48
52
96
4
58
51
52
95
10
56
49
60
45
48
48
49
55
57
33
36
41
44
43
54
55
54
56
Participants in defined contribution plans ........ ....... .
Participants in plans with tax-deferred savings
arrangements ..... .. ... . .
55
6
Other benefits
Employees eligible for :
Flexible benefits plans ..... .. ..... ........ ... ....... .. ...... .
2
Reimbursement accounts .. .. ... . .... . . .. ............... .
Premium conversion olans .... .. .. ........ . .... . .......... .
I
]
1
The definitions for paid sick leave and short-term disability (previously sickness and
accident insurance) were changed for the 1995 survey. Paid sick leave now includes only
plans that specify either a maximum number of days per year or unlimited days. Short-
2
5
9
10
12
12
13
5
12
23
36
52
38
32
5
7
fits at less than lull pay.
2
Prior to 1995, reimbursement accounts included premium conversion plans, which
specifically allow medical plan participants to pay required plan premiums with pretax
dollars. Also, reimbursement accounts that were part of flexible benefit plans were
terms disability now includes all insured, self-insured, and State-mandated plans available
on a per-disability basis, as well as the unfunded per-disability plans previously reported as
tabulated separately.
sick leave. Sickness and accident insurance, reported in years prior to this survey, included
only insured, sell-insured, and State-mandated plans providing per-disability bene-
NOTE: Dash indicates data not available.
108
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
35. Percent of full-time employees participating in employer-provided benefit plans, and in selected features
within plans, small private establishments and State and local governments, 1987, 1990, 1992, 1994, and 1996
Small private establishments
Item
1990
1992
1994
State and local governments
1987
1996
1990
1992
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 employ ees (in OOO 's):
With medical care
With life insurance .............................. .... .
With defined ben efit 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
8
9
37
49
26
50
3.0
82
17
34
58
29
56
10
34
53
29
65
3.7
75
62
3.7
73
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 ................................ ..... .. ... ...... . .
Averaoe davs per vear ' .. .
. ........ .. .. . .
Paid personal leave .. .... ... .. . ... .
Average days per ye ar ..
Paid vacations ..
Paid sick leave
2
..
Unpaid leave ..
Unpaid paternity leav e.
Unpaid family leave ..
50
3.1
82
51
3.0
80
37
81
11
36
56
29
63
3.7
74
7.5
13
2.6
88
9.2
12
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
37
48
27
47
2.9
84
9.5
11
2.8
8
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 contributi on .. .. .. .... .. ... . .
Family coverage .... . .. .. .... ....... .... .
Average monthly contribution
Participants in life insurance plan s
Percent of participants with:
Accidental death and dismemberm ent
insurance ..
Survivor income benefits .......... .. ... ..... .... .. .
Retiree protection available ... .. ... .. ... ... ...... ... . . ..
Participants in long-term disability
insurance plans ........... ... .. ............... ... .
Participants in sickn ess and acc ident
insurance plans ............ .. .... ............ ........ .. . .
Participants in short-term disability plans
71
69
79
83
26
47
48
66
64
80
84 1
28
Participants in defined contribution plans .. .
Participants in plans with tax-deferred savings
arrangements ..
93
93
93
90
87
76
78
36
82
79
36
87
84
47
84
81
55
42
$25 .13
67
47
$36.51
73
52
$40 .97
76
52
$42 .63
75
35
$15 .74
71
38
$25 .53
65
43
$28 .97
72
47
$30.20
71
$109.34
$150 .54
$159.63
$181.53
$71.89
$117 .59
$139 .23
$149 .70
64
64
61
62
85
88
89
87
78
76
79
77
67
67
74
64
1
1
2
1
1
1
1
2
19
25
20
13
55
45
46
46
19
23
20
22
31
27
28
30
26
26
14
21
22
21
15
93
90
87
91
47
92
92
90
89
88
53
44
100
18
92
89
10
100
10
92
87
13
99
49
38
9
29
2
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 .. .
33
20
22
54
95
7
58
49
50
95
31
33
17
15
33
4
54
46
34
24
16
100
28
8
9
9
9
45
45
24
23
28
4
5
5
5
19
12
31
50
64
Other benefits
Employees eligible for:
Flexible benefits plans
2
3
Reimbursement account s
Premium conversion plans
14
7
' Methods used to calculate the average number of paid holidays were revised
Sickness and accident insurance, reported in years prior to this survey,
in 1994 to count partial days more precisely. Average holidays for 1994 are
included only insured, self-insured, and State-mandated plans providing per-
not comparable with those reported in 1990 and 1992 .
disability benefits at less than full pay.
2
3
The definitions for paid sick leave and short-term disability (previously
Prior to 1996, reimbursement accounts included premium conversion plans ,
sickness and accident insurance) were changed for the 1996 survey. Paid sick
which specifically allow medical plan participants to pay required plan
leave now includes only plans that specify either a maximum number of days
premiums with pretax dollars. Also , reimbursement accounts that were part of
per year or unlimited days. Short-term disability now includes all 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.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
NOTE: Dash indicates data not available.
Monthly Labor Review
August
2005
109
Current Labor Statistics:
Compensation & Industrial Relations
36. Work stoppages involving 1,000 workers or more
Annual totals
Measure
2003
2004
2004
June
July
Aug.
Sept.
2005
Nov.
Oct.
Dec.
Jan.
Feb.
Mar.
Apr.
May
JuneP
Number of stoppages:
Beginning in period .............................
In effect during period ........................
14
15
17
18
3
4
0
1
2
2
2
3
1
3
2
4
3
4
0
2
0
2
2
4
3
5
1
2
0
4
Workers involved:
Beginning in period (in thousands) .. ..
In effect during period (in thousands).
129.2
130.5
170.7
316.5
27.6
28.6
.0
1.6
3.7
3.7
4.5
6.5
10.0
16.1
3.2
16.1
9.8
8.5
.0
2.5
.0
2.6
4.7
7.3
11 .0
14.0
1.9
3.2
.0
6.3
Days idle:
Number (in thousands) ......................
4,091 .2
3,344.1
94.0
3.2
52.5
57.0
300.0
114.9
97.5
50.0
49.4
86.0
48.5
38.7
57.8
.01
.01
(2)
(2)
(2)
(2)
.01
(2)
(2)
(2)
(2)
(2)
{2)
(2)
(2)
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 explanation of the measurement of idleness as a percentage of
the total time
110
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
worked is found in "Total economy measures of strike idleness," Monthly Labor Review , October
1968, pp. 54-56.
2
Less than 0.005.
NOTE:
P = preliminary.
37. Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city average,
by expenditure category and commodity or service group
[1982-84
= 100, unless otherwise indicated]
Annual average
Series
2003
2004
2004
June
July
Aug.
Sept.
2005
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
CONSUMER PRICE INDEX
FOR ALL URBAN CONSUMERS
All items
........... . ... . ........ . ........... .... ... .. .. .. ... . ..... . .... •. .
184 .0
. .. .. ....
551 .1
All items (1967 = 100) ..... .... ... . ..... .
. ... .. . .. ..
. . . . . . . ... . . . ... . .. .. .. ... . ..... . . .... . . ... .
.. ... .... .. . . .... .. .... ..• ... .. .... .. ... .. ....
Food and beverages
Food ······ ··••·
Food at home ....
188.9
565.8
189.7
189.4
189.5
189.9
190.9
191.0
190.3
190.7
191 .8
193.3
194.6
194.4
194.5
568.2
567.5
567.6
568.7
571 .9
572.2
570.1
571 .2
574 .5
579 .0
582.9
582.4
582. 6
180.5
186.6
186.8
187.2
187.3
187.2
188.4
188.6
188.9
189.5
189.3
189.6
190.7
191 .1
190.9
180.0
186.2
186.3
186.8
186.8
186.7
187.9
188.2
188.5
189.1
188.8
189.1
190.2
190.6
190.4
188.0
188.1
190.3
189.4
208.4
208.5
189.8
209 .1
209 .7
209.4
...... ·· ··· · . .......... ...
Cereals and bakery products ... ..... .. ···· ····· ······ . ...
179.4
186.2
186.8
187.1
186.7
186.1
187.9
188.1
188.5
202.8
206.0
206.8
207.2
207 .2
206 .4
207.0
206.8
206.4
188.9
207.6
Meats, poultry , fish , and eggs
..... .. ..... .
169.3
181.7
182.3
183.7
183.7
183.4
182.9
182.4
183.1
183.4
183.9
184.3
184 .7
185.0
185.2
Dairy and related products
Fruit s and vegetables ..... .... .. ... .. .. ... ... ..... .. ... .. .. .. .
Non alcoholic beverages and beverage
167.9
180.2
232.7
188.8
226.7
187.7
224 .5
184.9
224.0
181 .6
226.0
182.1
240.0
180.9
248.3
180.1
250.8
183.3
242.9
181 .8
234.8
181.4
233.7
182.2
240.1
183.3
244.7
181 .0
238.4
143.6
165.7
144.8
144.3
144 .0
167.5
166.9
164.9
169.4
166.3
163.3
167.8
183.0
182.0
182.9
. . ..... ••..... •
1
materials
... . . . . . ... . ........... . .. .. ..• . .. . ....... . .....• •. .
Oth er loods at home .. . .......... • ·· ··· ··· ..... ...... ....
Sugar and sweets .............. ······ .... .. .... .. ..... ... ..
Fats and oi ls ..
·•··· ···
Oth er foods ..
Oth er miscellaneous foods
Food away lrom hom e
12
•
··· ········ ····· ···
1
12
home •
Oth er lood away from
Alcoholic beverages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ........
Housing .... . ....... ... ....... ....... . ..... . .... ... .... ... . ... . . .... ...
.
Sh elter ...
·····• ··
···· ····· ··•····
Rent ol primary resid ence ...... ... ........
Lodging away fr om home ..
Owners' equivalent rent of primary residence
3
..
12
Tenants' and household insurance •
Fuels and utilities ...... ...... .... .. ........ ..... .. .....
Fuels
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .
. .... .... ... ...
Fu el oil and other fuels . . ..... . ... ..... .
... ····· ····
Gas (piped) and electricity .... ... ..... .. . . . .. .. . .. . • .. ..
Household furnishings and operations ···· ····· ··· ...
Apparel ..... .... .... ............... ... .. .. ...... ...... ......... .... .... .....
Men's and boys' apparel ..... ··············· . ... .........
Women's and girls' apparel. ... .. ...... .... .. , ···· · .....
225.9
139.8
140.4
139.8
140.5
140.3
140.3
1406
142.2
142.5
164.9
165.8
166.0
165.2
165.4
163.6
165.6
165.3
162.0
157.4
178.8
163.2
167.8
179.7
162.8
171.3
169.7
180.9
163.5
170.4
179.4
162.6
170.2
163.1
167.8
164 .2
169.3
180.1
178.9
161 .3
167.4
178.3
163.0
170.4
180.5
163.8
171 .9
180.3
166.2
164.4
139.6
164.4
140.4
162.6
180.3
179.7
162.6
167.0
181 .3
110.3
110.4
110.9
109.4
111.5
110.5
109.9
110.5
110.8
110.1
110.3
111 .9
110.8
110.8
110.2
182.1
187.5
187.0
187.8
188.4
188.9
189.4
189.6
189.9
190.8
191 .4
191 .7
192.8
192.6
193.2
121.3
187.2
125.3
192.1
124.8
192.4
125.1
192.2
125.4
192.5
125.9
193.4
126.8
193.6
126.7
194.0
127.0
193.9
127.5
194.3
128.7
195.2
129.4
195.7
129.6
195.9
130.3
195. 5
131 .6
195.9
184 .8
213.1
189.5
218.8
190.3
219.2
190.9
191.2
191 .0
220.0
220.3
220 .2
191.0
220.6
190.8
219.9
190.7
219.8
191.8
221 .0
192.7
222 .5
194 .1
224 .4
194.4
224.4
194.5
224 .0
224.5
205.5
211.0
210.7
211.2
211.9
212.4
212.8
213.2
213.9
214.5
215.0
215.5
216.0
216.4
216 .8
119.3
125.9
129.1
132.2
130.6
127.2
128.0
121.9
118.7
122.6
128.9
138.3
136.2
131 .7
132. 8
219.9
224.9
224.7
225.1
225.7
226.1
226.5
226.8
227.2
227.8
228.4
228.7
229.0
229.4
229 .7
165.7
164.5
195.5
114.8
154.5
116.2
161.9
116.2
165.5
116.1
166.6
116.3
167.7
116.6
166.7
116.3
162.8
117.7
165.6
118.7
165.7
118.5
166.9
118.7
166.4
119.0
166.7
118.2
169.6
118.0
171 .7
118.0
177.4
138.2
144.4
148.5
149.5
150.5
149.3
144.9
147.8
148.0
149.0
148.1
148.4
151 .5
153.7
159.9
139.5
160.5
150.7
151 .1
157.4
161 .6
177.3
186.6
183.7
181 .2
188.5
195.5
199.5
193.9
145.0
126.1
150.6
155.8
157.6
156.0
150.0
152.7
153.0
154.3
152.9
152.7
155.9
158.7
195.0
165.6
125.5
125.6
156.9
125.2
124.8
125.0
126.1
125.8
125.5
126.1
126.1
126.1
126.3
126.7
126.0
120.9
118.0
120.4
117.5
120.1
115.9
116.5
121.2
124.1
123.0
118.8
116.1
118.7
123.5
123.7
122.4
118.3
115.2
106.1
113.8
107.5
116.2
114.4
118.3
119.2
118.9
116.8
116.3
110.0
115.0
105.1
116.3
109.3
119.6
117.1
120.4
116.6
119.7
114.2
115.3
109.1
113.1
113.0
117.7
112.3
122.1
118.5
116.2
114.5
115.0
119.5
120.6
120.3
118.6
117.5
118.1
119.0
121 .3
119.8
116.4
119.6
119.3
118.4
115.1
117.3
121 .7
122.1
121.8
120.3
119.4
121 .1
122.8
123.8
123.2
121.7
157.6
163.1
165.7
164.0
162.9
162.9
166.4
167.2
164.8
164.0
166.1
168.8
173.2
172. 1
171 .8
Private transportation .. .. . . . . . . . . . . . . . . . . . . . . . .... ...
153.6
159.4
161 .9
160.0
159.1
159.4
162.9
163.6
161 .3
160.5
162.6
165.2
169.6
168.3
167.7
New and used motor vehicles2 . ... .. ..... .
New vehicles . . .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . .......
96.5
94.2
93.6
93.5
93.4
93.9
94.3
95.2
95.4
95.8
95.9
95.6
95.6
95.7
95.6
137.9
137.1
137.2
135.9
134.9
134.9
135.9
137.9
138.8
139.8
139.9
139.1
138.8
138.7
138.1
.. ... ..
142.9
135.8
133.3
160.4
130.6
173.3
132.1
165.2
133.8
162.0
136.5
161.2
136.8
173.1
136.7
171.9
137.3
161 .2
137.5
156.4
137.6
164.3
137.7
175.9
138.1
193.9
138.8
188.2
139.9
185.5
.
135.1
159.7
172.7
164.5
161.2
160.5
172.2
171.0
160.4
155.6
163.4
175.0
193.9
187.3
184 .6
107.8
195.6
108.7
200.2
108.2
199.7
108.8
200 .3
109.0
200.8
109.3
200.7
109.5
201.7
109.9
202.9
109.9
203.3
110.6
204.0
110.9
203.9
110.9
204 .7
110.8
205.0
111 .0
205.6
111.2
206.1
....
209.3
209.1
212.3
214.4
209.7
205.3
206.5
208.6
205.4
204.4
205.9
210 .1
2 15.0
218.0
222.4
........... ... .. ....... ... ........
297 .1
310.1
310.0
311.0
311.6
312.3
313.3
314 .1
314.9
316.8
319 .3
320 .7
32 1.5
322.2
322. 9
1
Infants' and toddlers' apparel
Footwear ··· ··· ·· ···· ·· ··
···•···· .... ...........
Tran sportation.
.. . . . . . . . .. . . . . . . . . . . . . . . ... .. . ......
... .. ..... .. ..
.
.
Used cars and trucks
Motor fu el. ..
Gasolin e (all types)
1
...
..... .. .. . ... .. .
. . .. . . .. .. .. .. .. . . ... . .. . .. .. ..
Motor vehicle parts and equipment
........
Motor vehicle maintenance and repair ...
Public transportation ..
... ....
Medical care .. . ....... ....
Medical care commodities ....... ... .. . .. , ..... . ...... ...
.
Medical care services . . . . . . . . . . . . . . . . . .. .. . ...... .... .. ...
Professional services . . . . . . . . . .. . . . . . . . .. .. ..............
Hospital and related services ..
Recreation
.
...... .......... ...........
2
Vir1 P.o ;,,nn i'lllr1 io
12
•
2
Education and communication
2
Education
Educational books and supplies ..
....
Tuition , oth er school fees, and child care ....... .
Comm ,inir.;,,tion
12
•
Information and information
Telephone services
12
processinq •
....
12
•
262. 8
269 .3
269 .6
269 .9
270.0
270.9
271.7
271.2
270.8
271.6
272 .8
273.2
2735
274 .6
275 .6
306 .0
261.2
394.8
321.3
271 .5
417.9
321 .0
271.6
416.9
322 .3
272.3
419.1
323.1
273.3
418.8
323.7
273.3
420.3
324.8
273.7
422.5
326.0
274.2
425.0
327.3
274.6
428.0
329.5
276.2
431.0
332.5
278.6
434.7
334 .3
279.7
437.3
335.2
281.0
437.1
335.9
281 .6
437.3
336.3
281 .9
437.9
107.5
108.6
108.9
108.7
108.5
108.6
108.7
108.7
108.5
108.9
1090
109.0
109.2
109.5
109.1
103.6
104.2
104.4
104.4
104.1
104 .0
104.2
104.0
103.9
104.2
104.3
104.6
104 .8
104 .6
103.1
109.8
111.6
110.8
110.9
111 .7
112.9
112.5
112.7
112.6
112.7
112.8
112.7
112.9
11 2. 7
11 2. 8
134.4
335.4
143.7
351.0
141 .6
350.6
142.1
349.5
145.1
353.3
147.9
352.8
148.3
353.8
148.4
354.4
148.5
355.9
148.8
357.4
149.2
359.9
149.3
360.6
149.5
361.3
149.9
362.3
150.5
363.4
362.1
414.3
86.7
407.6
409.4
418.3
427.4
430.6
86.1
86.2
85.4
85.4
85.4
430.9
85.2
431.4
86.5
428.7
85.6
429.7
86.8
428.2
85.5
428.9
89.7
85.4
432.7
84 .9
434.4
84 .6
87.8
84.6
84.7
84 .5
84 .0
84.1
83.4
83.5
83.3
83.2
83.3
83.1
83.2
82 .7
82.4
98.3
95.8
95.8
95.6
95.0
95.3
94.6
94.5
94 .8
94 .8
95.1
95.0
95.3
94 .8
94 .6
16.1
14.8
14.9
14.8
14.7
14.7
14.5
14.3
14.2
14.2
14.0
14.0
13.9
13.8
13.6
Information and information processing
1
othP.r th;,,n IP.IP.nhonP. sP.rvir.es .4
Personal computers and peripheral
12
equipment ·
Other goods and services ... ................... .... .... ..... ... .
Tobacco and smoking products ... ..... ..
Personal care
1
Personal care products
Personal care services
. . ...
.. . . . . .. .
..... ...
..
1
1
.
....
·•
..
17.6
15.3
15.5
15.3
15.1
15.0
14.6
14.2
13.9
14.0
13.5
13.4
13.4
13.2
13.0
298.7
304.7
304.1
305.1
305.5
306.3
306.8
307.0
307.8
309.3
310.8
311.2
311 .5
312.5
312.5
469 .0
478.0
476.0
480.5
481.6
482.9
482.3
481.7
484 .8
493.9
496.1
496 .6
497.0
498.0
497 .8
178.0
181 .7
181.4
181.7
181.9
182.3
182.8
83.0
183.3
183.5
184.4
184.7
184.9
185.5
185.5
153.5
153.9
153.8
153.4
152.8
153.5
154.0
153.8
153.4
153.1
153.9
153.0
153.4
154 .4
154.3
193.2
197.6
196.9
197.5
198.9
199.1
199.4
200.0
201.2
201.9
202.9
203.3
203.3
202 .8
203.0
August
2005
See lootnotes at end of table .
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
111
Current Labor Statistics:
Price Data
37. Continued-Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city
average, by expenditure category and commodity or service group
[1982-84 = 100, unless otherwise indicated]
Annual average
Serles
2003
Miscellaneous personal services... ..... .... ..
....
2004
2004
June
July
Aug.
Sept.
2005
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
283.5
293.9
293.6
294.4
295.2
295.9
296.3
296.9
297 .7
298.5
299.8
300.8
154.7
154.2
154.9
155.8
155.4
187.2
136.1
187.3
135.6
187.2
136.7
188.6
139.4
188.9
137.2
189.5
136.4
156.5
189.3
138.1
149.7
120.9
157.2
120.4
160.5
120.1
156.7
115.9
156.1
116.5
157.8
121 .2
157.1
188.4
139.4
162.6
157.2
186.6
136.7
155.8
186.8
138.2
154.5
Food and beverages .... . ···· ··········· ........... ... ...
Commodities less food and beverages .... . .. . . . .
Nondurables less food and beverages ............
Apparel ... ............ .......................................
151 .2
180.5
134.5
124.1
162.0
123.0
157.4
118.8
155.2
116.1
Nondurables less food, beverages,
and apparel ..... ......... ... ......... ..... ... .........
Durables .. .. .... ......... .. . ..... ... ..... .. ........ .. .. ..
171.5
117.5
183.9
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
185.2
115.5
183.3
116.0
Commodity and service group:
Commodities ········· .......... ..............................
.
Apr.
May
June
301 .4
302 .8
302.9
158.2
160.3
189.6
140.4
190.7
142.9
159.8
191 .1
142.0
190.9
140.8
158.6
118.7
163.7
123.5
168.9
123.7
167.0
122.4
164.7
118.3
187.3
116.0
192.7
115.7
201 .0
115.6
198.6
115.7
197.5
115.4
158.9
........... .......
216.5
222.8
223.3
224.1
224.5
224.5
224 .5
224.6
224.6
225.6
226.8
228.0
228.6
228.8
229.8
Rent of shelter .. .... ..... .... .............• .... .... ...
Transporatation services ..... ... .... .......... ...... ......
Other services. .. ........•.•.. ··· ········•·················
221.9
216.3
254.4
227.9
220.6
261 .3
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
230 .1
221 .7
265.1
231.7
222.4
265.8
233.7
223.3
266.1
233.7
224.4
266.7
233.2
225.1
266.9
233.8
226.0
266.7
190.9
180.9
184.2
192.3
194.0
195.3
195.1
195.2
181 .9
185.3
183.2
186.8
185.1
188.1
185.0
187.9
184.9
187.9
144.0
168.7
197.5
166.6
196.5
Services ............... .. .. .. .... .. ..... . .. .....
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. ..........
Nondurables. ..... ...... .. .. ... . ....... .... .. ... ... ... ...
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 ... ........... ..........
184.7
189.4
190.3
189.9
189.9
190.4
191.4
191.5
190.6
174.6
178.1
179.3
182.7
180.2
183.5
179.6
183.2
179.5
183.2
180.1
183.6
181.9
184.7
180.9
183.9
136.5
151 .9
172.1
138.8
159.3
183.8
140.3
138.2
141.4
163.9
189.7
138.6
140.2
142.5
144.9
158.8
185.6
138.8
159.9
184.4
139.3
162.4
189.0
137.7
158.2
184.3
181.4
184.6
141 .1
159.5
185.1
157.5
183.5
160.8
187.2
165.6
192.1
170.6
199.7
164.2
190.0
142.8
165.3
172.2
174.0
172.2
171 .9
172.8
175.8
175.6
173.3
172.5
174.2
177.0
180.3
179.4
178.2
226.4
233.5
234.2
235.0
235.6
235.9
235.1
236.4
236.5
237.4
238.0
238.5
239.8
240.7
242.4
208.7
136.5
214.5
151.4
215.0
159.7
215.8
156.3
216.2
155.3
216.1
154.3
216.0
157.7
216.1
158.6
216.0
153.7
217.0
151.9
218.0
155.2
219.2
160.8
219.7
170.9
219.9
169.4
220.9
171.4
190.6
193.2
194.4
196.6
194.4
196.6
194.5
196.6
194.7
196.8
195.2
197.4
196.0
198.2
1196.0
198.1
195.8
196.4
198.4
197.3
199.5
198.3
200.7
198.6
200.9
198.6
198.5
200.8
200.6
140.9
136.7
197.8
139.6
139.4
138.2
138.1
139.4
140.5
140.6
139.8
139.7
140.3
141 .1
141 .2
141 .1
140.0
223.8
161 .2
230.2
172.8
230.2
165.1
231.0
162.5
231.4
162.0
231 .6
174.2
232.1
173.6
231.9
163.4
231.9
158.7
232.9
166.6
234.3
178.0
235.7
195.2
236.0
189.4
235.9
187.0
236.4
179.8
535.6
184.5
549.5
185.3
551 .9
184.9
550.8
185.0
551 .0
185.4
552.4
186.5
555.7
186.8
556.3
186.0
554.2
186.3
554 .9
187.3
557.9
188.6
561 .9
190.2
566.4
190.0
566.0
190.1
566.2
CONSUMER PRICE INDEX FOR URBAN
WAGE EARNERS AND CLERICAL WORKERS
All items .................................................................
.
All items (1967 - 100) .. . ..... ···· ··· . . . . . . . . . . . .. ... .. ..
Food and beverages .... .. .... ················· ........ .......
Food ................... .. .... ..... . . .................................
Food at home ... ... .. . . .. . . . . . . . . . . . . . . .. . . . . . . . .. ....... ... .
Cereals and bakery products ...... ......... ......
.
Meats, poultry, fish , and eggs .. ..... .. .. .. ........ .
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 1•2 .. ..................
Food away from home
1
..
12
home ·
Other food away from
........ ......
Alcoholic beverages .. .......... ............ ....... ........ . ..
Housing ............................ .... .... ............................
179.9
186.2
186.4
186.8
186.9
186.8
187.9
188.1
188.4
189.0
188.8
189.1
190.1
190.4
190.3
179.4
178.5
202.8
185.7
185.4
206.0
185.9
186.3
186.3
207.2
183.7
186.4
186.1
207.0
183.7
186.2
187.6
187.9
190.0
189.8
187.2
208.5
183.9
188.5
187.4
208.5
189.6
187.6
206.3
183.2
188.5
188.0
207 .6
183.4
188.2
185.5
206.3
183.4
187.4
187.1
206.9
188.9
209.0
188.6
209.5
185.2
169.2
181 .8
186.1
206.7
182.4
183.0
187.3
206.8
182.4
184.3
184.5
189.4
209.7
184.9
167.6
224 .3
180.0
230.4
189.0
224.3
187.8
222 .3
184.9
222.2
181.4
223.9
181.8
238.0
180.8
246.4
179.9
248.6
183.2
240 .1
181 .6
232.2
181.3
231.3
182.1
237.5
183.1
242.2
180.9
235.9
139.1
162.2
161.6
157.4
179.2
139.7
164.5
162.5
167.8
180.1
139.3
165.5
162.2
171.4
180.8
139.8
165.6
162.9
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
141.6
165.3
162.2
141 .8
165.0
163.6
143.0
165.3
161 .8
172.0
180.7
139.6
165.8
163.8
169.9
181.4
170.4
180.8
169.1
180.2
167.2
181 .7
144.1
167.0
163.9
169.4
183.4
143.7
165.8
162.3
168.0
182.3
143.4
166.3
164.8
164.5
183.1
110.8
110.9
111.4
109.7
112.0
111 .0
110.3
111 .1
111.3
110.7
110.9
112.5
111 .1
111.3
110.5
182.0
187.4
186.8
187.6
188.2
188.8
189.3
189.5
189.7
190.6
191 .2
191 .6
192.0
192.4
193.0
121 .5
187.1
125.1
192.4
124.7
192.7
124.9
192.2
125.2
192.8
125.8
194.0
126.8
193.9
126.8
194.2
127.0
194.2
127.3
194.4
128.4
195.2
129.1
196.0
129.2
196.2
129.6
195.3
131.5
195.7
180.4
186.2
186.2
213.8
187.3
190.9
213.5
214.4
188.9
216.8
189.7
213.4
188.1
215.7
189.4
213.0
186.5
213.4
186.4
212.2
186.6
213.4
186.4
206.9
185.0
212.2
185.6
. ...... ... ...•..........
216.9
216.8
217.3
Rent of primary residence ...............................
204.7
210.2
209.9
210.3
211 .0
211.6
212.0
212.4
213.0
213.7
214.2
214 .6
215.2
215.5
215.9
119.8
126.4
128.8
133.0
131.6
127.7
128.3
121 .8
118.6
122.2
129.1
137.1
135.2
131.1
132.9
199.7
204.1
203.9
204.2
204.7
205.1
205.5
205.8
206.1
206.6
207.2
207.4
207.7
208.0
208.4
114.7
153.9
116.4
161.2
143.2
116.5
165.0
116.3
166.1
148.4
116.5
167.2
116.8
166.2
116.5
161 .9
118.8
166.0
118.9
165.4
118.3
170.7
118.3
176.7
143.5
146.4
147.4
146.6
119.4
165.7
146.8
118.5
168.6
148.2
118.1
164.5
146.2
118.9
164.7
149.3
149.8
152.1
158.5
186.5
183.4
180.9
187.7
195.3
199.2
193.6
194.8
151 .7
152.0
121 .3
118.6
115.7
153.3
121.9
116.1
152.0
121 .9
151 .8
118.6
116.1
155.0
122.1
123.2
119.9
157.7
122.5
121 .9
114.6
121 .9
123.0
119.6
Shelter.. .........
·····-- -· -········· --
LodQin!l away from home
2
...
Owners' eQuivalent rent of primary residence 3
12
Tenants' and household insurance · ..
Fuels and utilities .................................. .........
···--
137.0
Fuel oil and other fuels .. .. ... ..... .. .... . .... .. ....
Gas (piped) and electricity ... ... . --··· ··· ··· ···· ···
Household furnishings and operations ............
Apparel .... ............................... ....... .... ....... ... .. .......
Men's and boys' apparel ...........................
138.7
144.1
Women's and girls' apparel .............................
Fuels .............................. ...... ........ ....... ....
1
Infants' and toddlers' apparel . ·········-·--····-··Footwear ... ..... ..... ... ............. ..... .......................
Transportation .. .. .. ....... ........................................
Private transportation ............... .. ......... .... .. .
New and used motor vehicles2 .....
..........
160.0
149.8
150.2
156.8
161 .1
177.2
155.1
121 .3
156.2
121 .9
149.8
121.1
149.1
121.7
120.0
117.3
119.6
117.8
120.7
115.6
115.2
155.3
120.6
120.0
117.5
156.8
120.4
115.9
113.3
120.6
115.6
123.5
117.8
121.5
122.6
118.6
112.1
112.8
112.2
106.0
106.9
114.0
119.3
116.9
110.2
105.3
109.3
116.8
124.1
113.9
108.7
124.1
121 .3
117.6
122.3
121.4
122.5
118.9
116.3
161.4
120.4
161 .6
159.1
120.6
165.8
163.2
119.4
163.4
160.9
121.7
167.6
164.9
122.7
172.2
158.6
121 .0
120.6
164.7
162.2
122.7
114.4
162.2
120.5
118.8
1632.6
160.0
121 .9
159.3
123.3
120.6
165.3
162.7
123.1
118.2
161.5
158.8
118.8
117.0
164.0
161.3
117.0
119.1
156.3
153.5
169.5
122.4
171.0
168.2
121.3
170.6
167.7
96.0
92.8
92.1
92.1
92.2
92.3
93.3
94.0
94.3
94.6
94.7
94 .5
94.5
94.7
94.8
See footnotes at end of table.
112
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
147.4
August 2005
119.2
164.8
121.9
117.9
114.9
37. Continued--Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and aerical Workers: U.S. city
average, by expenditure category and commodity or service group
(1982-84 = 100, unless otherwise indicated]
2003
2004
2005
2004
Annual average
Series
June
July
Aug.
Sept. I Oct.
I
Dec.
Nov.
Jan.
Feb. I Mar.
Apr.
May
June
New vehicles ............... .... ............ .. ...... .....
139.0
138.1
138.2
137.0
136.0
136.0
136.9
138.9
139.8
140.7
140.7
140.0 1
139.7
139.6
1
143.7
134.1
131.4
133.0
134.6
137.3
137.6
137.5
138.1
138.3
138.4
138.5 1
138.9
139.6
140.7
136.1
135.5
107.3
197.3
206.0
160.9
160.2
108.2
202.0
207.1
173.8
173.2
107.8
201 .5
162.4
161.7
108.4
202.7
208.0
161.7
161.0
108.7
202.7
203.1
173.6
172.9
108.9
203.8
204.2
172.3
171 .6
109.4
204.9
207.1
161.7
160.9
109.3
205.3
204.2
156.9
156.1
110.1
206.0
203.4
164.9
164.1
110.4
206.1
204.9
176.5
175.7
110.5
206.9
209.0
194.5
193.7
210.0
165.6
165.0
108.2
202.1
212.1
110.4 J
207.2
213.3
188.7
187.9
110.5
207.9
2 15.8
186.1
185.3
110.8
208.4
219.8
296.3
257.4
305.9
263.4
391.2
309.5
263.2
321 .5
310.4
263.7
322.4
274.8
415.2 '
311.0
263.8
323.2
275.8
414.9
311 .7 1
264.8
323.9
312.7 1
265.4
325.0
276.3
31361
264.9
314.4
418.5 1
326.3
276.9
421 .0
266.3
333.0
281.2
430.9
320.3
266.6
334.8
282.3
433.6
321.1 I
266.9
335.8
283.6
433.4
3219:
264.4 1
327.7
277.2
424.2
316.3
265.2
330.0
278.9
427.4
31891
274.0
414.0
309.4
263.8
321.2
274.1
413.0
105.5
106.3
106.7
106.3
106.1
106.2
106.2
106.3
106.1
106.5
106.5
106.5
106.8
433.7 1
107.0
322.5
268.8
337.0
284.6
434.3
102.9
103.4
103.7
103.3
103.5
103.3
104.0
103.9
102.5
110.0
109.4
109.9
110.8
110.6
110.6
110.7
110.8
142.5
352.2
140.6
351 .5
141.0
350.4
143.6
354.7
146.3
354.8
146.8
356.1
147.0
357.6
147.3
359.0
147.7
361.5
147.8
362.4
148.0
363.1
110.6 1
148.5
364.0
110.7
133.8
336.5
110.51
146.7
355.6
10351
110.7
103.9
109.0
10321
110.5
103.4
109.4
103.71
103.4
149.1
365.1
377.3
91 .2
402.5
88.3
396.7
88.4
398.1
88.1
405.8
87.6
414.0
415.2
87. 1
415.6
87.2
415.8
87.0
416.8
87.0
417.6
87.0
418.0
86.8
418.5
87.0
419.8 1
86.5
421.6
86.3
89.9
86.8
86.9
86.7
86.2
863
85.6
85.7
85.5
85.5
85.5
85.3
85.0
84.8
98.5
96.0
96.1
95.8
95.2
95.5
94.8
95.1
95.0
94.9
95.3
95.1
94.9
94.8
16.7
15.3
15.4
15.3
15.3
15.2
15.0
14.9
14.8
14.8
14.6
14.5
14.5
14.3
14.2
14.9 1
313.5
14.81
314.4
14.31
314.7
13.9
13.7
13.7
314.9
315.9
13.2 1
319.6
13.2 ;
319.9
320.8
320.9
Tobacco and smoking products ........................ .
481 .6
483.0
482.5
485.7
496.9
497.4
497.8
498.7
498.9
177.0
180.4 1
180.3
48261
180.5
483.9
Personal care .... .....................................
476.9 1
180.0
318.0 1
494.9
13.3 1
319.4
13.0
311 .8 1
15.0
313.2 1
12.7
15.0 !
312.6
478.8
15.2
307.o l
470.5 1
180.9
181.4
181 .7
181.9
182.1
182.9
183.2
183.8
183.8
1
154.2
154.4 1
154.3
154.3
154.3
153.8
153.3
154.2
153.6
193.9
198.2
197.5
153.1
199.5
154.0
1
153.9 1
198.1
183.0 1
153.3
199.7
199.9
200.6
201.8
202.4
203.3
203.6
203.6
154.5
203.1
154.5
203.3
283.3
294.0
293.5
294.7
295.4
29621 ~61
298.4 1
299.2 1
299.8
300.8
301 .5 1
W321
303.2
151.8
179.9
135.8
152.1
120.0
155.4
186.2
138.1
160.6
120.0
156.6
186.4
139.6
164.4
119.6
155.2
186.8
137.5
160.4
115.6
154.9
186.9
137.1
159.5
115.9
155.7
186.8
138.2
161 .2
156.3
189.0
138.0
158.8
116.1
15921
161 .5
190.1
145.0
12351
156.6
188.4
138.8
160.9
118.6
157.4 1
188.8
139.8
162.5 1
120.6
158.1
188.1
141 .0
165.9
122.6
17361
123.2
160.9
190.4
144.0
171.5
121 .9
160.1
190.3
142.8
169.2
117.9
and apparel. .................................................
Durables .......................................... ..............
175.6
117.4
189.6
114.0
196.0
113.5
191.8
113.2
190.2
113.1
190.1
113.7
196.9
114.3
196.5
114.8
115.1
10081
188.8
115.5
193.3
115.5 1
199.4
115.3
208.9
115.3
206.0
115.5
204.7
115.3
Used cars and trucks .. ........ . . .... .... . ' ..
Motor fuel. ......................... .. ................... .........
Gasoline (all types) ........................................
Motor vehicle parts and equipment. ................
Motor vehicle maintenance and repair ............
Public transportation ... ........... ........... .................
1
M:::car::·::::;.ti~~::::::::::.::.:.:.:.::.::::.:::::.:::::·:::1
Professional services ..... ........ ....................... ,
Hospital and related services .. .. ····················
RP.r.rP.:ition
2
Vitioo ;mti
12
;111tiio ·
2
Education and communication ..
.. ... ·.
2
Education ....
····· ···········
Educational books and supplies ..
Tuition, other school fees, and child care ......
12
C:omm mir.:ition ·
2
Information and information processin!l 1· ....
12
services ' ...
Telephone
Information and information processing
1
othP.r th;rn tP.IP.nhonP. SP.rvir.P.s .4
Personal computers and penpheral
Other goode:~~dms:::~ .............::.·... ·.:·.·..·..
:.::.::.::j
Personal care products ........ ............. .. . .... .
Personal care services .. _. .... . . .. . . . . .. . . . . ........
Miscellaneous personal services ............... .. ...
Commodity and service group:
Commodities......................... ......... ............•......
Food and beverages ................... .... ...... .... ....•.
Commodities less food and beverages .. ...........
Nondurables less food and beverages .. .... .. .. ..
Apparel ........................ ... ........ ..... ... ...... ..•.. ..
Nondurables less food, beverages,
17.3
I
275.9 1
416.4
87.8 1
158.0
187.9
141 .0 1
166.5
297.5
118.6 1
189.1
142.2
167.8
123.0
::
1
267.9
336.5
284.3
139.0
106.6
. ..............................
212.6
218.6
219.0
219.7
220.2
220.3
220.0
220.4
220.5
221.5
222.3
223.2
223.8
224.2
225.3
Rent of shelter ................. .... .... ........
Transporatation services ..... ....... .....................
Other services ....................... ..........................
199.2
216.2
248.5
204.3
220.9
254.1
204.4
220.7
253.3
205.1 1
221.6
253.5
205.5
221.0
254.4
205.5
220.5
256.0
205.9
205.6
222.0 1
255.9
205.5
223.4
256.3
206.5
222.8
257.2
207.7
223.4
257.8
208.8
224.0
258.1
208.9
224.8
258.7
208.8
225.3
258.9
209.3
226.0
258.6
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 .... .............
Nondurables ...................... .. . .... ... ..... .. ... .. ......
179.7
171.9
174.8
137.7
154.2
175.9
166.4
184.1
176.4
179.1
140.0
162.6
189.0
173.9
185.0
177.5
180.0
141 .5
166.2
194.8
175.9
184.5
176.7
179.6
139.4
162.3
191.0
174.0
184.5
176.6
179.6
139.0
161.5
189.6
173.6
185.1
177.3
180.0
140.2
163.2
189.7
174.5
186.2
178.6
181 .1
142.2
168.2
195.6
177.7
186.4
179.1
181 .3
142.9
167.6
195.4
177.5
185.5 1
178.0
187.0
179.0
181 .7
141.7
164.4
192.7
176.1
188.5
180.4
183.1
144.1
169.5
198.3
179.0
190.1
182.4
184.6
146.8
175.1
206.9
182.5
189.9
182.3
184.4
145.9
173.0
204.2
190.0
182.2
184.5
144.7
170.8
203.0
180.3
201.3
207.4
210.6
151.3
189.5
190.6
139.4
161.5
226.2
208.9
211.8
156.2
189.3
190.3
138.0
165.5
226.7
209.3
212.2
155.1
189.5
190.5
138.0
162.8
227.1
209.5
205.2
135.9
186.1
187.9
141 .1
136.8
220.2
208.2
211 .1
159.9
189.3
190.3
139.0
173.3
226.0
208.6
212.0
157.8
191 .0
192.1
140.5
174.5
227.9
209.8
212.3
158.5
191 .1
192.2
140.6
173.7
228.0
175.1 1
209.9
212.4
153.3
191.0
192.0
139.9
185.7
178.0
180.8
140.0
160.9
188.5
174.3
210.8
213.2
151 .4
191 .5
192.4
139.9
211.6
214.7
160.9
192.9
194.2
141.3
212.7
215.4
171.4
193.3
194.5
141.4
163.4 1
228.1
158.7 1
229.0
211 .2
214.0
155.0
192.2
193.4
140.5
166.6
178.11
231.1
195.~ I
231.4
Services ........ ...... ....
. . . . .. .. . . .
3
3
Services less rent of shelter .................. . ... . ...•
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 ......... ...... ....... ......... .....
1
Not seasonally adjusted.
2
Indexes on a December 1997 = 100 base.
3
Indexes on a December 1982 = 100 base.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
212.3
154.2
190.2
191.4
139.5
162.3
227.4
4
222.7 1
256.5
18061
140.7
162.9
190.3
230.1 I
181 .5 1
213.6
215.7
169.6
193.4
194.5
141.3
189.7 1
231.5
215.3
216.8
171 .5
193.2
194.3
140.4
187.3
231.9
Indexes on a Decerroer 1988 = 100 base.
NOTE: Index applied to a month as a whole, not to any specific date.
Monthly Labor Review
August
2005
113
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-
2005
ule
U.S. city average .. ... . ····· ······· ·· ······
······ .. ...... ...
1
Jan.
M
190.7
Feb.
191 .8
Mar.
193.3
Urban Wage Earners
2005
Apr.
194.6
May
June
194.4
194.5
Jan.
Feb.
186.3
187.3
Mar.
188.6
Apr.
190.2
May
190.0
June
190.1
Region and area size 2
Northeast urban · ··· ······ ··· ·· ······ ·· · ··· ·· ··· ·· ·········· · ·· ·· •··•·· · ··· •···
Size A-More than 1,500,000 ....... ................ ........... .......
Size B/C-50,000 to 1,500,000
3
. . . . . . . .. .
. ............
.
.
...
.. .
4
Midwest urban . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ·• · •• · ·· .. .. . . . . . . . . . . . . . .
Size A-More than 1,500 ,000 ..... ..... ... .. ... ....... ... ····· ··· ··
3
Size B/C-50 ,000 to 1,500,000
Size D-Nonmetropolitan (l ess than 50 ,000) . .. .. .. . . . .... .
South urban ....... ... ... ..... ... ..... ... .. ... ...... . ...... ... . .... .. ... . ... .
Size A-More th an 1,500,000 .... .... ... .. ..... . ...... . .. ..... .... ..
3
Size B/C-50,000 to 1,500,000 . .
Size D-Nonmetropolitan (less than 50,000) .... ..... . ... .. .
West urban ... ·· ·· ·· ············· ············ ····· ···· ·· ············· ............
M
202 .6
203.6
206.0
206.9
206.2
206.2
199.0
200.0
201 .8
202 .9
202.5
202.5
M
205.0
206 .0
208.6
209.3
208.6
208.5
200.1
201 .1
202.8
203.8
203.5
203.4
M
119.4
120.1
121.3
122.0
121 .6
121.8
119.6
120.1
121.2
122.1
121.6
121 .8
M
184.1
185.2
186.3
187.7
187.4
187.8
179.1
180.2
181.2
182.8
182.4
182.9
M
185.9
187. 1
188.3
189.6
189.4
189.8
180.4
181.3
182.5
184.1
183.8
184.0
M
11 7.3
118.1
118.7
119.6
119.3
119.6
116.4
117.2
117.8
118.8
118.5
119.0
M
178.2
179.2
179.9
181.7
181 .6
182.3
175.7
176.5
177.3
179.1
178.8
179.6
M
183.6
184.7
185.9
187. 3
187.3
187.8
180.5
181 .5
182.7
184.3
184.2
184.7
M
185.2
186.6
187.9
189.9
189.2
189.7
182.6
184.0
185.3
186.7
186.8
187.3
M
117.1
11 7. 7
118.4
119.3
119.4
119.7
115.7
116.3
117.0
117.9
117.9
118.2
186.7
M
182.3
183.1
184.5
187.2
186.6
186.9
181 .9
182.7
184.1
186.7
186.2
M
194.5
195.7
197.1
198.6
198.8
198.0
189.5
190.5
192.0
193.7
193.9
193.1
M
196.7
198.3
199.8
201 .3
201.5
200 .5
190.1
191.6
193.2
194.9
195.2
194.1
M
119.5
119.6
120.4
121.4
121 .3
121 .1
118.9
119.0
119.8
120.8
120.8
120.6
M
M
M
174.3
117.9
183.0
175.5
118.5
183. 7
177.0
119.2
184 .8
178.1
120.1
186.9
178.0
120.0
186.9
177.9
120.2
186.9
172.6
117.0
181.0
173.7
117.5
181 .7
175.0
118.3
182.9
176.3
119.2
185.1
176.3
119.1
185.0
176.2
119.3
185.1
Chicago-Gary-Kenosha, IL-IN-W I. .. ... ........ ... .... .... .... .
Los Angeles-Riverside-Orange County , CA. ..... .... . ....... .
M
M
189.9
195.4
190.5
197.4
191 .3
199.2
193.2
201 .1
193.3
201.5
194.0
200.7
183.5
188.5
184.2
190.3
184.8
192.1
186.9
194.2
186.8
194.6
187.1
193.7
New York, NY-Northern NJ-Long Island, NY-NJ-CT- PA ..
M
208.1
208.9
212.4
212.5
211.4
210.7
202.6
203.3
205.5
206.0
205.6
205.1
Boston-Brockton-Nashua, MA-N H- ME-CT ........... . .......
Cleveland-Akron, OH .. . ···· ······ ······ ··· ·· ·· ··· ·· ······· •·· ... ..
1
211 .3
214.2
214.6
-
210 .3
-
214 .0
183.3
186.8
174.5
177.2
177.9
181 .6
-
123.6
-
-
213.1
1
-
122.3
-
123.2
-
183.4
186.0
-
Size A-More than 1,500,000 ...... ···· ·········· ···· ··············· ··
3
Si ze B/C-50,000 to 1.500.000 . .
Size classes:
As. ··········· · ·· ··· ····· ······· . . . . . . . . . . . . . . . . . . . . . .. .
3
B/C .. . ······· •·· •····· · · ······•··· · ·· · .... ..... , .... .. ..... .... . .. ... ...... .
D ...... . . .. . .. . . . . . . . .. .. ...... ·· ············ ····· ·.. ........ ... ... ... ... .
Selected local areas 6
Dallas-Ft Worth, TX ... . ....... .. ...
.. . ....
.. ... . .. . . . . ······ ·--·· ··
7
Was hi noton-Balti more, DC-M D-VA-WV .. ........ .. .. ....... ....
Atlanta, GA ... ... . ·· · ····· . . . . . . . . ... . . .. . . .. .. .. . ...... . . .. . . . . .. .
Detroi t-Ann Arbor-Flint, Ml. .. .... ..... .. ·········· . .... .. .. . ..
1
180.0
-
181.3
1
121 .3
-
122.7
-
2
-
185.3
-
188.0
-
189.6
2
-
187.8
-
189.8
-
189.6
-
175.0
-
174.2
193.2
-
192.6
-
203.3
-
204.8
-
200.0
202.5
-
201.2
-
199.8
-
197.3
201 .3
Houston-Galveston-Brazoria, TX .. ···· ····· ·· ···· ·· " ·•·· ..... ..
Miami-Ft. Lauderdal e, FL. .. ... .... ... .. ... ....... . ...... ..
2
-
174 .6
2
190.6
Philadelphia- Wilmington-Atlantic City, PA-NJ- DE-MD .....
2
-
San Francisco-Oakland- San Jose, CA .. ..... ... ............ .. .
2
Seattle-Tacoma-Bremerton, WA. .... .. .... .... ......... .. .... ....
2
200. 1
201 .2
197.6
186.3
183.5
180.3
120.7
182.6
-
171 .8
-
172.8
188.3
-
191 .2
192.4
185.2
202.9
184.1
187.5
184.7
172.7
190.7
204 .0
199.3
-
197.5
196.2
-
194.8
1
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.
Report: Anchorage, AK; Cincinnatti, OH-KY- IN ; Kansas City, MO-KS; Milwaukee-Racine,
WI ; Minneapoli s-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 .
2-February, April , June, August, October, and December.
2
Regi ons defined as th e four Census regions.
NOTE: Local area CPI indexes are byproducts of the national CPI program. Each local
3
Indexes on a December 1996 = 100 base.
index has a sm aller sample size and is, therefore, subject to substantially more sampling
4
The "North Central" region has been renamed the "Midwest" region by the
Census Bureau. It is composed of the same geographic entities.
6
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
Indexes on a December 1986 = 100 base.
Labor Statistics strongly urges users to consider adopting the national average CPI for use
In addition, the following metropo litan areas are published semiannually and
in their escalator clauses. Index applies to a month as a whole, not to any specific date.
appear in tables 34 and 39 of the January and July issues of the
114
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
CPI
Detailed
Dash indicates data not available.
39. Annual data: Consumer Price Index, U.S. city average, all items and major groups
[1982-84 = 100]
Series
Consumer Price Index for All Urban Consumers:
All items:
Index .. .......... ···· ············ ······ ········· ··· ··················
Percent 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 Pri ce Index for Urban Wage Earners
and Cleri cal 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
2003
2004
148.2
2.6
152.4
2.8
156.9
3.0
160.5
2.3
163.0
1.6
166.6
2.2
172.2
3.4
177.1
2.8
179.9
1.6
184.0
2.3
188.9
2.7
144.9
2.3
148.9 1
2.8
153.7
3.2
157.7
2.6
161 .1
2.2
164.6
2.2
168.4
2.3
173.6
3.1
176.8
1.8
180.5
2.1
186.6
3.3
144.8
2.5
148.5
2.6
152.8
2.9
156.8
2.6
160.4
2.3
163.9
2.2
169.6
3 .5
176.4
4.0
180.3
2.2
184.8
2.5
189.5
2.5
133.4
- .2
132.0
-1 .0
131 .7
-.2
132.9
.9
133.0
.1
131.3
-1 .3
129.6
-1 .3
127.3
- 1.8
124.0
-2 .6
120.9
- 2. 5
120.4
-.4
134.3
3.0
139.1
3.6
143.0
2.8
144.3
0.9
141 .6
-1 .9
144.4
2.0
153.3
6.2
154.3
0.7
152.9
-.9
157.6
3.1
163. 1
3.5
211 .0
4.8
220.5
4.5
228.2
3.5
234.6
2.8
242 .1
3.2
250.6
3.5
260.8
4.1
272. 8
4.6
285.6
4.7
297. 1
4.0
310. 1
4.4
198.5
2.9
206 .9
4.2
215.4
4.1
224.8
4.4
237.7
5.7
258.3
8.7
271 .1
5.0
282.6
4.2
293.2
3.8
298.7
1.9
304.7
2.0
145.6
2.5
149.8
2.9
154.1
2.9
157.6
2.3
159.7
1.3
163.2
2.2
168.9
3.5
173.5
2.7
175.9
1.4
179.8
2.2
188.9
5.1
Monthly Labor Review
August
2005
115
Current Labor Statistics:
Price Data
40. Producer Price Indexes, by stage of processing
[1982
= 100]
Annual average
Grouping
2003
2004
2004
2005
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.P
Apr.P
MayP
JuneP
Finished goods....................................
Finished consumer goods ..... .. .... .............
Finished consumer foods ...... .................
143.3
145.3
145.9
148.5
151.6
152.6
148.7
152.0
155.0
148.5
151.9
152.3
148.5
151 .8
152.2
148.7
152.1
152.7
152.0
155.7
155.1
151.7
155.4
154.7
150.6
153.8
154.9
151 .4
154.8
154.2
152.1
155.7
155.4
153.5
157.5
156.2
154.4
158.7
156.5
154.1
158.3
156.8
154.0
158.4
155.1
Finshed consumer goods
excluding foods .................. ... ... ............
Nondurable goods less food ............... ..
Durable goods ......................................
Capital equipment.. .................. ..... ... .... ..
144.7
148.4
133.1
139.5
150.9
156.6
135.1
141 .5
150.5
156.0
134.9
141.1
151.4
158.0
133.6
140.7
151.3
157.9
133.6
141.2
151.5
158.2
133.5
141.2
155.6
162.1
137.8
143.4
155.3
161 .8
137.4
143.4
153.0
158.5
137.2
143.6
154.6
160.7
137.8
144.1
155.5
162.4
137.0
143.9
157.7
165.5
137.0
144.3
159.3
167.9
137.0
144.5
158.6
167.1
136.7
144.4
159.2
168.6
135.6
144.0
Intermediate materials,
supplies, and components ....................
133.7
142.5
142.8
143.5
144.8
145.3
146.5
147.7
146.9
148.0
148.8
150.4
151.7
151 .0
151.6
Materials and components
for manufacturing ....................................
Materials for food manufacturing ..............
Materials for nondurable manufacturing .. .
Materials for durable manufacturing .........
Components for manufacturing ................
129.7
134.4
137.2
127.9
125.9
137.9
145.0
147.6
146.6
127.4
137.7
152.0
145.9
145.8
127.6
138.1
147.3
147.3
147.2
127.4
139.4
144.9
149.8
150.3
127.7
140.6
144.3
152.6
152.1
128.0
141.5
144.2
154.4
153.0
128.2
142.0
143.9
155.5
153.6
128.3
142.8
145.2
156.8
155.2
128.5
143.9
145.7
157.9
157.3
129.2
144.4
145.6
158.1
159.1
129.5
145.2
146.6
160.7
158.7
129.5
145.3
146.6
160.4
158.9
129.9
144.9
147.6
160.4
156.7
129.7
144.3
145.0
159.8
155.8
129.6
Materials and components
for construction ...................................... ...
Processed fuels and lubricants ..................
Containers ................... ..............................
Supplies ..................... ·······························
153.6
112.6
153.7
141 .5
166.4
124.1
159.2
146.7
166.9
124.9
158.9
147.3
167.5
126.4
159.7
148.0
169.8
128.5
162.0
147.6
170.9
126.9
162.5
147.9
170.8
130.8
164.6
147.9
170.7
134.0
164.9
147.9
171 .3
128.9
165.2
148.5
173.1
129.5
165.5
149.6
174.7
130.9
166.1
150.0
175.2
135.8
166.8
150.6
175.3
141.1
167.0
151.2
174.9
139.3
167.1
151.4
175.4
142.5
167.7
151.7
Crude materials for further
processing ...........................................
Foodstuffs and feedstuffs ...........................
Crude nonfood materials ....................... ....
135.3
113.5
148.2
159.0
126.9
179.2
163.0
137.4
178.0
162.5
130.9
182.2
162.2
124.8
186.6
154.4
122.0
174.9
160.5
120.1
187.3
171.5
119.5
207.1
165.7
121.5
195.3
163.0
123.8
188.7
162.5
121.5
189.7
169.4
127.6
197.0
174.1
125.0
207.3
171.7
126.2
202.1
165.7
122.1
194.8
150.9
121 .1
154.5
159.3
154.7
150.7
120.1
154.4
159.2
154.7
149.2
114.5
154.6
159.4
154.9
150.5
116.4
155.1
159.9
155.8
151.0
118.6
155.3
160.4
155.7
152.6
123.4
155.7
160.7
156.0
153.7
126.9
155.9
160.9
156.1
153.2
125.2
156.0
161.1
156.1
153.5
127.3
155.3
160.3
155.7
Special groupings:
Finished goods, excluding foods ..... ... ... .. ...
Finished energy goods ...............................
Finished goods less energy ........................
Finished consumer goods less energy. .....
Finished goods less food and energy .........
Finished consumer goods less food
and energy .. .................. ... .......................
142.4
102.0
149.0
153.1
150.5
147.2
113.0
152.4
157.2
152.7
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
115.1
152.1
156.9
152.3
157.9
160.3
160.0
159.4
159.6
159.7
162.2
162.3
162.5
163.8
163.7
163.8
164.0
164.1
163.7
eo;~~~~;,;"'"<able ooad''·ss ''""·· ·
I
177.9
180.7
180.2
180.3
180.8
181.2
181.7
182.2
182.8
184.8
185.4
185.7
186.1
186.6
187.0
Intermediate materials less foods
and feeds .................................................
Intermediate foods and feeds .....................
Intermediate energy goods ........................
Intermediate goods less energy ............. ....
134.2
125.9
111.9
137.7
142.9
137.0
123.1
145.8
142.8
144.9
123.7
146.0
143.7
142.3
125.1
146.4
145.3
136.3
127.1
147.5
145.9
134.4
125.8
148.5
147.3
131.2
129.9
149.0
148.3
130.7
132.7
149.4
147.8
131 .0
128.4
149.9
148.9
132.0
129.0
151 .1
149.7
131 .7
130.0
151.8
151.3
133.3
134.7
152.5
152.6
134.2
139.4
152.9
151.9
135.2
138.2
152.4
152.5
134.3
141.9
152.1
Intermediate materials less foods
and energy ....... ... .. ....... .... ... ...... ...............
138.5
146.5
146.2
146.8
148.3
149.5
150.1
150.6
151.1
152.3
153.1
153.8
154.1
153.6
153.3
Crude energy materials ..............................
Crude materials less energy ......................
Crude nonfood materials less energy .........
147.2
123.4
152.5
174.7
143.9
192.8
180.0
147.0
176.3
177.9
147.5
195.4
181.9
144.6
200.8
166.6
141.6
197.4
181.8
141.9
203.5
208.3
142.7
207.9
192.7
143.3
204.9
183.9
144.5
203.3
186.6
142.0
200.2
196.5
146.8
201.6
210.6
145.3
203.1
206.7
144.0
194.7
200.2
138.5
185.5
116
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
41. Producer Price Indexes for the net output of major industry groups
[December 2003
= 100, unless otherwise indicated]
2005
2004
NAICS
Industry
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.P Apr.P
155.5
155.6
159.3
149.6
160.6
179.1
169.2
163.3
166.2
173.4
183.0
179.1
175.8
Oi I and gas extraction ( December 1985= 100) ............ .. .................. 198.0
108.1
102.2
196.6
110.2
103.7
202.7
110.4
105.3
184.0
112.3
106.4
203.0
112.8
109.2
234.8
114.0
111 .4
214.7
116.4
114.9
202.5
120.2
115.5
205.3
121 .0
122.2
217.4
121 .8
125.2
234.0
122.3
126.9
227.0
122.8
126.9
219.7
123.3
131.4
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
144.2
143.8
100.6
101.4
100.2
146.5
143.5
101.2
101.6
100.3
146.1
143.3
101 .2
101 .7
100.4
145.0
144.2
101.5
101 .5
100.5
146.2
144.7
104.1
102.3
100.4
147.0
145.0
104.0
102.4
100.2
148.9
146.0
104.7
103.0
100.3
149.7
146.6
104.4
103.2
100.2
149.3
147.2
104.6
103.7
99.9
149.4
145.9
105.0
103.4
99.9
143.5
108.3
102.3
101.0
143.7
106.8
103.2
101.3
143.6
109.8
104.4
101.3
143.6
110.7
105.0
101.8
143.5
107.6
105.5
101.8
143.8
105.1
105.7
102.0
143.9
105.9
105.8
102.0
143.8
106.9
106.1
102.5
144.2
108.8
106.5
102.4
144.6
109.5
106.8
102.7
144.5
108.8
107.1
102.5
144.5
107.5
107.1
102.4
144.3
109.4
107.1
103.2
144.1
171.6
152.3
172.2
155.6
173.8
158.9
175.5
176.7
177.2
170.4
179.3
150.3
180.5
155.9
182.7
163.6
183.4
182.5
185.2
189.3
186.5
183.3
186.4
189.1
185.4
Total mining industries (December 1984=100)...............................
211
212
213
311
312
313
315
316
321
322
323
324
Mining, except oil and gas ..... .. .. ...... .. .............. .... .. ...... .. ........
Mining support activities . ... . ................ ... . ... .. ..... ........ ............
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 ..... .... ...... ...............................1
MayP Jurnf
331
332
333
334
335
336
337
Petroleum and coal products manufacturing
I
(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 ..... .. .... .. ... .... .. ........ ... ....... ... ........ ...
Comouter and electronic oroducts manufacturina . ............ ..........
Electrical equipment, appliance, and components manufacturing . .
Transportation equipment manufacturing .... .......... .. ... .... .. ...... ..
Furniture and related product manufacturing
130.8
142.3
141.9
101.8
99.1
103.5
100.6
131.2
144.7
142.5
102.1
98.9
103.6
99.7
131.7
148.3
143.4
102.3
98.9
103.8
99.8
133.1
150.8
144.2
102.5
98.7
104.2
99.9
134.3
152.9
144.9
102.9
98.6
104.7
103.2
135.3
154.2
145.4
103.2
98.4
104.6
102.7
136.1
155.5
145.7
103.4
98.5
104.9
102.9
137.4
158.6
146.9
104.1
98.3
106.0
103.2
138.4
159.5
148.2
104.5
98.2
106.6
102.6
139.0
158.1
147.9
105.1
98.1
107.0
102.5
139.4
157.9
148.9
105.2
97.9
107.5
102.6
139.8
156.0
149.0
105.6
97.4
107.4
102.3
140.1
153.6
149.4
105.6
97.5
107.5
101.4
339
(December 1984=100) .... ... ... ... ............ ............................ . 151.7
Miscellaneous manufacturing ... . ... .. .. . .. ······· ·· ··· ······ ······ ··· ···· ·· 101.2
152.0
101.2
152.7
101.4
152.8
101.8
153.4
101 .3
154.6
101 .3
155.1
101 .6
155.5
102.2
156.2
102.5
155.9
102.7
156.8
102.7
157.1
102.8
157.4
102.8
325
326
454
Retail trade
Motor vehide and parts dealers .. .. ... ... ... ... .... .. ... ...... . ..............
Furniture and home furnishings stores .... .... ...... .. .. .... ...... .... .. ...
Electronics and appliance stores ... ....... ... ................ .. .. ... .. ... ...
Health and personal care stores .. .............. ... ...................... .. ..
Gasoline stations (June 2001=100) . .. .............. .. ..... .. .. .... .. .......
Nonstore retailers .... .. .................. ... .... ..... ................... ..... ...
103.7
102.8
98.9
98.7
48.3
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
104.4
103.4
99.2
105.1
46.3
105.6
104.2
103.8
98.4
104.1
43.1
104.7
104.2
103.7
97.9
106.8
53.3
111.5
104.2
104.6
93.6
107.2
59.8
117.4
106.2
105.6
98.3
106.5
49.0
117.5
106.7
106.6
100.2
105.6
49.8
122.6
105.7
106.9
102.3
107.9
48.3
119.6
107.2
107.0
101.1
106.2
49.5
121 .6
108.3
108.2
102.9
107.6
51.9
123.2
108.3
109.7
99.9
107.4
38.9
120.2
481
483
491
Transoortation and warehousina
Air transportation (December 1992=100). ·············· ············· .. . ,.
Water transportation ........ ... .. .... .. ... ...... .... ..... . .. . ..... ...............
Postal service (June 1989= 100) .......... ... .... ........... .... .. ..... ......
162.8
100.3
155.0
163.9
101.5
155.0
163.4
102.1
155.0
159.8
103.2
155.0
160.9
103.8
155.0
162.2
103.7
155.0
161.4
103.5
155.0
164.9
104.0
155.0
164.5
104.3
155.0
171 .1
104.4
155.0
169.6
105.0
155.0
167.0
105.7
155.0
173.6
105.1
155.0
221
Utilities
Utilities .... .... .. ... ...... ... . ... ........ ... ... ... .... .. .............................
106.9
107.1
107.4
105.2
104.3
108.8
108.9
108.3
107.5
107.9
110.2
111 .1
111 .3
Health care and social assistance
Office of physicians (December 1996=100) ... ... .. ... .. . .. ... ... ... .... .. 114.3
Medical and diagnostic laboratories .... . .. . ........ . ........................ 100.0
Home health care services (December 1996=100) .. ................... 119.7
Hospitals (December 1992=100) .. .. ........................................ 140.9
Nursing care facilities ... .. .... . .. .. .. ..... .. .. . .. ... .... .. ... .... ........ .... ... 102.0
Residential mental retardation facilities . ... ... ...... ... ....... ... .......... 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
114.4
100.1
119.8
141 .7
103.2
102.5
114.4
100.1
120.1
143.3
103.7
102.5
114.4
100.1
120.2
143.5
103.9
102.5
114.5
100.1
120.3
143.8
103.9
102.5
115.7
102.4
120.9
144.8
105.3
103.8
115.9
104.2
121 .0
145.6
105.4
103.7
115.1
104.4
120.6
145.3
104.9
103.7
115.2
104.3
120.9
145.5
105.1
103.7
115.6
104.3
120.9
145.8
105.7
103.8
115.8
104.2
120.9
145.9
105.7
103.7
100.4
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
101.4
100.8
99.6
98.7
104.5
101.8
104.3
99.4
98.7
104.3
102.1
103.2
99.2
98.6
105.8
101 .9
100.8
99.9
98.6
106.0
103.0
100.2
99.0
98.7
108.0
103.4
100.5
98.1
98.8
109.8
103.2
100.8
97.8
98.6
109.8
103.6
102.4
98.4
98.7
110.1
103.7
104.2
98.4
98.6
111.4
104.1
104.3
98.1
99.0
112.0
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
101.0
101.0
110.8
131.5
101.4
103.9
104.0
99.8
108.0
131 .8
101.4
104.6
103.1
101 .5
107.8
132.0
101.6
103.0
103.1
101 .2
107.7
132.0
101.7
104.2
105.9
102.3
108.1
132.0
101.3
104.2
106.0
103.2
105.2
136.8
101.8
103.5
106.0
102.0
106.9
137.1
102.8
103.4
106.0
101.0
109.1
136.9
102.0
105.2
106.0
102.6
104.8
137.3
101 .9
104.2
105.9
101.6
106.0
137.7
104.3
103.6
105.6
103.9
108.4
138.9
104.1
126.5
99.9
114.0
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
127.3
100.4
114.2
94.5
100.9
101.4
127.2
127.3
100.3
115.2
95.8
101.4
101.5
127.0
127.3
100.5
115.2
95.2
101.4
101.5
125.1
127.7
100.5
114.4
96.1
101 .4
101.5
123.8
128.2
100.8
115.1
94.5
101 .7
101.5
125.7
128.6
101.0
115.7
93.7
101 .8
101.5
129.1
128.8
101 .0
115.2
96.2
101 .9
101.5
127.9
129.2
101.1
114.9
97.1
102.0
103.8
127.8
129.2
101 .0
115.6
95.9
102.1
103.1
129.1
129.4
101.9
115.8
95.3
101 .9
102.7
133.7
441
442
443
446
447
6211
6215
6216
622
6231
62321
511
515
517
5182
523
53112
5312
5313
5321
5411
541211
5413
54181
5613
56151
56172
5621
721
Other services industries
Publishing industries, except Internet ............................... .....
Broadcasting, except Internet. ... .. ........ ... .. . .... .... .... .... .... ... .....
Telecommunications .... . ... .. .... .. .... ............ ..... . .. . ...................
Data processing and related services .................... .. ............ .. ..
Securitv. commoditv 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 leasinq (June 2001=100) .... ... ..
Legal services (December 1996=100) .......... ...........................
Offices of certified public aocountants ..... .. ....... ........... ... ..........
Architectural, engineering, and related services
(December 1996= 100) .... .... .. ... .................. . ... ..... ...... ... .....
Advertising agencies .............. ... .......................... . ................
Employment services (December 1996= 100) .. .. ............. ........ .. .
Travel agencies. ... ... . ... ... .. .... .. . ... ....... .... ........ ............ ...... ..
Janitorial services .... . ............ .. .............................................
Waste collection ........ ... ... ... .. ......... ... ... ...... ... ... ... .......... ... .. ..
Aocommodation !December 1996= 1OOl ...................................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
117
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
145.9
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
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
Crude materials for further processing
Total. .... ... ............................................................... .......
Foods .............................................. .................... .
Energy .. ............................................. ................ .
Other .............. .... .............................................. .... .
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
118
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
84.3
43. U.S. export price indexes by Standard International Trade Classification
(2000 = 100)
SITC
2004
Industry
Rev. 3
June
July
0 Food and live animals..... .. .. ..... ... ...... ... .. .. .... ... .... .....
01
Meat and meat preparations ... .............. ...... ..... ........... ... .
04
Cereals and cereal preparations. .................................. .
Vegetables , fruit, and nuts, prepared fresh or dry ......... ..
05
123.9
127.3
141 .2
111 .1
119.8
123.0
128.0
110.0
116.4
126.1
120.6
113.2
2 Crude materials, inedible, except fuels. .........................
Oilseeds and oleaginous fruits ......... ......... ... ..... .... ...... ... .
22
24
Cork and wood .... ......... ............. .................... ....... ..........
25
Pulp and waste paper .. ........ ....... ........... .... ........... .... ......
Textile fibers and their waste .. ........ ........ .. ... ........ ..... ......
26
Metalliferous ores and metal scrap .. ... ..................... .......
28
125.7
168.5
98.3
100.8
108.7
167.5
132.1
184.5
98.9
100.1
102.9
190.2
3 Mineral fuels, lubricants, and related products.............
Petroleum, petroleum products, and related materials .. .
33
131.8
129.7
5 Chemicals and related products, n.e.s. .........................
Medicinal and pharmaceutical products .... ....... .. .............
54
Essential oils; polishing and cleaning preparations .........
55
57
Plastics in primary forms .... ..... .......... ..... .......... ..............
Plastics in nonprimary forms .. ... ......... ..... ..... ......... ..........
58
Chemical materials and products, n.e.s ... ............ ..........
59
6 Manufactured goods classified chiefly by materials.....
62
Rubber manufactures. n.e.s .............. ...... ..... .... ..............
2005
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
117.6
124.8
122.0
119.8
118.3
126.9
115.6
130.6
118.7
125.4
113.1
137.2
118.1
124.6
116.4
129.9
118.2
121 .3
119.2
127.4
118.3
125.1
116.2
128.1
120.1
128.5
121.4
125.2
121.2
132.9
116.9
130.4
123.9
139.3
116.1
137.4
124.7
142.4
118.7
133.6
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
119.5
110.3
98.4
98.2
97.5
197.0
119.4
111.1
98.8
98.8
96.4
195.0
123.1
115.2
98.7
100.0
98.4
205.8
122.1
109.7
98.9
100.7
98.7
206.0
127.5
128.9
98.9
103.0
104.1
206.4
129.4
124.6
98.6
101.8
104.8
223.4
128.5
127.7
98.1
101 .6
103.3
213.6
130.8
136.5
98.0
101.1
101 .7
217.1
137.5
134.5
139.6
136.2
141.2
138.0
156.0
156.4
151 .1
151 .0
146.5
144.6
148.5
147.3
154.2
155.7
169.3
174.9
181 .5
189.9
175.1
178.5
178.7
184.8
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 .6
106.7
106.6
113.2
98.1
105.2
112.9
106.9
107.5
117.2
98.7
105.3
114.0
107.2
109.1
118.9
99.9
105.8
116.1
108.3
109.8
126.6
101 .5
106.5
116.3
107.9
111 .1
127.5
102.1
106.4
117.0
107.9
111 .3
128.3
103.2
106.0
117.8
108.3
112.8
128.5
103.6
106.7
116.7
107.9
113.1
124.8
104.2
106.6
114.5
107.4
113.2
122.9
104.4
106.3
107.0
108.5
109.6
110.5
111 .3
111 .8
112.2
113.0
113.5
113.7
114.3
114.1
113.8
111 .2
111 .8
112.0
111.4
111.6
112.4
112.9
113.8
114.2
114.4
115.0
115.4
115.4
99.2
99.9
95.4
101.2
99.9
95.4
101 .9
100.2
96.5
102.7
100.4
99.0
104.0
101.1
99.1
103.7
101 .3
100.6
104.2
101 .6
101 .5
104.1
101 .9
103.4
104.1
102.0
105.6
103.8
102.2
107.2
103.8
102.5
109.3
103.7
102.5
108.5
103.1
103.5
105.9
7 Machinery and transport equipment...............................
71
Power generating machinery and equipment... ...... ... ... ...
72
Machinery specialized for particular industries .... ... ........
74
General industrial machines and parts, n.e.s.,
98.2
108.7
105.4
98.2
108.9
105.7
98.2
109.0
105.9
98.2
109.0
106.1
98.4
109.4
107.3
98.4
110.3
107.6
98.5
110.4
108.0
98.7
111.4
109.3
98.7
111.4
109.2
98.7
111.5
109.4
98.7
111.4
110.6
98.7
111.4
110.6
98.7
111.4
110.6
and machine parts ..... ...... ...... ... ... .. ....... ......... ........ ...... .
Computer equipment and office machines ........ ........ .. ...
Telecommunications and sound recording and
reproducing apparatus and equipment... ........ ..... .........
Electrical machinery and equipment... ........... ......... ..... .. .
Road vehicles ..... .................... .......................................
104.9
87.2
105.2
86.6
105.3
86.4
105.3
86.0
106.2
85.1
106.4
84.4
106.6
83.8
107.6
83.0
108.2
82.9
108.3
82.3
108.9
81 .5
109.2
81.3
109.3
81 .0
91 .8
88.2
102.4
91 .5
88.3
102.5
90.7
88.2
102.5
90.7
88.1
102.4
90.5
87.9
102.8
90.5
87.7
102.8
90.4
87.9
103.0
90.5
87.8
103.0
90.5
87.6
103.0
90.5
87.7
103.0
90.0
87.5
102.9
90.1
87.4
103.1
89.8
87.5
103.1
102.0
101 .7
101.9
101 .8
102.2
102.3
102.6
103.4
103.4
103.4
103.5
103.1
103.1
64
66
68
75
76
77 '
78
Paoer. oaoerboard. and articles of oaoer. oulo.
and oaoerboard .. ... .... ......... .... ..... . .......... ..... .........
Nonmetallic mineral manufactures. n.e.s . .. ... ······ ··········
Nonferrous metals ................ .. .... ...... ........ ... .. . ····· ··· .. ······
Aug.
Sept.
87 Professional, scientific, and controlling
instruments and apparatus.....................................
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
119
Current Labor Statistics:
Price Data
44. U.S. import price indexes by Standard International Trade Classification
(2000= 100]
SITC
Rev. 3
2004
Industry
June
July
Aug.
Sept.
0 Food and live animals..............................................
01
Meat and meat preparations ............ ........................ .......
Fish and crustaceans, mollusks, and other
03
106.9
128.9
107.4
133.7
107.4
134.2
aquatic invertebrates .......... ... . .. ...............................
Vegetables, fruit, and nuts, prepared fresh or dry ......... ..
Coffee, tea, cocoa, spices, and manufactures
thereof. .... ... .......................... .... ..........................
84.1
105.9
86.1
102.1
86.9
100.6
107.0
102.7
103.4
105.6
1 Beverages and tobacco...........................................
11
Beverages .. .... ..... .. ........ ...... ... .... ... ...............................
105.3
105.6
105.9
106.4
106.1
106.6
106.2
106.7
2 Crude materials, inedible, except fuels..........................
24
Cork and wood .. .... ............... ....... .. ................... ....... ........
Pulp and waste paper.....................................................
25
Metalliferous ores and metal scrap ..... ............................
28
Crude animal and vegetable materials, n.e.s . ....... .........
29
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
3 Mineral fuels, lubricants, and related products .............
Petroleum, petroleum products, and related materials ...
33
Gas, natural and manufactured ...... ... ................... ......... .
34
131 .5
130.0
140.0
133.9
133.0
134.8
144.2
144.8
136.3
5 Chemicals and related products, n.e.s. .........................
Inorganic chemicals .. ..... .. .......... ... ... ........... ... .. ...............
52
Dying, tanning, and coloring materials............................
53
Medicinal and pharmaceutical products..... .. ......... ... .......
Essential oils; polishing and cleaning preparations .........
Plastics in primary forms ................ ...... ...........................
Plastics in nonprimary forms .. .... ...... ........ .......................
Chemical materials and products, n.e.s ........ ............... ..
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
6 Manufactured goods classified chiefly by materials.....
Rubber manufactures, n.e.s ...........................................
62
106.1
100.5
Paper, paperboard, and articles of paper, pulp,
and paperboard . ...... ... ... . .. ... ....... .. .... .....................
Nonmetallic mineral manufactures, n.e.s .......................
Nonferrous metals .................................... ..... ........ ..........
Manufactures of metals, n.e.s . .............. .........................
2005
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
109.2
134.9
111 .1
134.2
111 .0
131 .8
111 .9
133.0
110.9
134.5
112.6
134.8
117.5
135.9
116.4
136.5
116.4
139.0
112.7
139.1
86.0
109.2
85.6
114.5
84.7
116.3
85.0
112.2
86.0
107.0
87.0
107.5
88.5
121 .6
88.3
117.6
88.0
116.9
87.8
103.2
104.5
108.9
114.4
118.9
122.8
130.2
128.9
125.3
126.9
106.5
106.9
106.7
107.1
107.1
107.6
107.5
107.9
107.7
108.1
107.8
108.2
107.9
108.4
108.1
108.6
108.1
108.5
125.1
126.3
99.8
166.2
96.3
121 .7
117.1
98.0
167.0
96.5
125.5
124.7
100.3
167.3
98.3
129.6
127.0
103.6
170.8
110.1
135.7
132.0
107.2
169.6
137.5
135.0
136.9
108.7
176.9
109.9
134.9
132.5
109.6
186.3
110.3
132.0
121 .9
107.8
184.5
123.5
131 .2
126.8
104.3
180.1
112.6
146.8
149.5
121 .9
161.2
165.7
124.1
157.2
155.3
166.2
140.6
137.0
163.5
142.2
140.4
150.8
148.3
148.6
143.3
166.5
169.0
145.8
173.5
174.5
161.4
165.8
166.3
158.2
176.2
179.4
149.4
105.1
123.8
98.4
107.3
93.4
108.4
103.2
94.1
106.7
124.1
98.4
106.6
93.4
109.6
103.8
94.4
108.4
125.5
98.5
106.4
93.6
109.9
104.4
95.3
108.9
126.8
98.7
107.4
93.7
113.2
105.1
95.8
109.6
126.7
98.7
108.9
94.4
116.1
105.7
96.1
110.2
127.6
97.9
110.5
94.9
123.0
106.7
96.2
111.8
128.9
98.6
110.1
95.2
124.2
106.4
97.7
112.2
130.2
98.6
110.2
95.5
125.9
106.4
99.2
114.0
133.0
99.8
110.8
95.5
107.0
'"'
101.9
112.8
132.6
101 .0
110.4
94.2
127.0
106.9
103.1
111 .6
132.5
101.0
110.3
94.3
125.9
107.1
102.5
106.1
100.5
107.7
100.8
108.9
100.8
108.9
101.0
109.4
101 .3
110.4
101 .9
111.4
102.2
111 .8
102.6
112.8
103.5
113.1
104.2
112.7
104.0
112.7
104.3
95.5
99.4
101 .6
102.4
96.4
99.3
102.3
102.7
96.9
100.2
105.6
103.3
97.9
100.4
106.3
103.9
99.2
100.5
106.6
104.4
99.4
100.5
108.6
105.3
99.0
100.7
111.0
106.7
100.0
100.9
112.1
108.1
99.9
100.8
114.1
108.4
100.3
100.9
116.1
108.7
101.5
101.0
118.5
108.9
101 .5
101 .1
118.8
108.8
101 .6
101.4
116.9
108.5
7 Machinery and transport equipment. ........................... ...
Machinery specialized for particular industries ...............
72
74
General industrial machines and parts, n.e.s.,
95.1
106.6
95.0
107.2
95.0
107.6
95.0
107.4
94.9
107.8
95.1
108.5
95.2
109.5
95.3
110.5
95.2
110.6
95.1
110.8
95.0
111.2
95.0
111.4
95.0
111 .2
103.5
75.5
104.0
74.9
104.1
74.3
104.3
73.9
104.6
73.2
104.9
73.0
105.3
72.8
106.2
72.4
106.6
71.9
106.8
71 .2
107.3
70.1
107.2
70.0
107.3
70.0
77
78
and machine parts .... .. ... ................ .. ..... .... ....... .. ...... .....
Computer equipment and office machines .... ............ .....
Telecommunications and sound recording and
reproducing apparatus and equipment.. ..... ........ ..........
Electrical machinery and equipment... .... ............. ....... ... .
Road vehicles ............. ...... .... ............. .. ....... .. .......... ... ......
84.7
94.7
102.4
84.3
94.6
102.6
84.0
94.7
102.8
83.8
94.6
103.1
83.4
94.3
103.4
83.4
94.4
103.6
83.1
94.6
103.7
83.0
94.6
103.6
82.8
94.4
103.7
82.7
94.5
103.7
82.2
94.5
103.8
82.4
94.4
103.8
82.4
94.4
103.8
85
Footwear .. ...... ... ......... ....... ....... .... ..... ... .. .......... ... ....... ...
100.4
100.4
100.1
100.5
100.5
100.5
100.5
100.3
100.3
100.3
100.3
100.4
100.4
88
Photographic apparatus, equipment, and suppli es,
and optical ooods n.e.s ... ... ....... ....... .... ....... .......... .....
99.0
98.2
98.2
98.2
98.2
98.3
98.6
99.1
99.1
99.1
99.3
99.2
99.1
05
07
54
55
57
58
59
64
66
68
69
75
76
120 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
I
45. U.S. export price indexes by end-use category
[2000
= 100)
2004
Category
2005
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
ALL COMMODITIES... .. .... .. ................... ...... ..............
103.4
103.9
103.4
103.8
104.4
104.7
104.8
105.6
105.7
106.4
106.9
106.7
106.7
Foods, feeds, and beverages .............................. .
Agricultural foods, feeds. and beverages ................
Nonagricultural (fish, bever;iges) food products .. ...
129.1
131 .1
110.7
128.0
129.9
110.1
116.5
117.0
110.9
118.7
119.3
113.0
117.5
117.8
114.4
118.3
118.5
115.5
116.9
116.6
118.4
117.1
116.7
119.7
116.4
116.0
119.7
120.9
120.7
121 .8
121.0
120.9
121 .4
123.6
123.7
121 .7
125.5
125.8
122.1
121 .8
June
Industrial supplies and materials ............................
109.9
112.0
113.1
114.0
116.6
117.4
118.0
120.1
120.7
122.3
124.1
122.5
Agricultural industrial supplies and materials ..........
110.7
109.0
108.4
109.4
109.2
108.5
109.5
112.9
112.8
115.6
116.7
116.5
115.6
Fuels and lubricants ..... ... ....... .. ......................... ....
Nonagricultural supplies and materials,
excluding fuel and building materials .......... ........
Selected building materials .....................................
114.9
118.6
120.4
121.5
132.2
128.3
125.4
128.3
133.0
143.8
152.0
145.5
147.8
110.0
103.4
112.4
102.8
113.5
103.3
114.4
104.0
116.4
103.9
117.9
104.0
118.9
104.4
121 .0
104.6
121 .0
104.8
121.4
105.3
122.5
105.5
121.4
105.8
120.2
106.3
Capital goods ....................... ...... .. .. ..... ...... ... .....
Electric and electrical generating equipment. .. .. ... ..
Nonelectrical machinery .. .. .. ................................. .
97.8
102.0
94.1
97.8
102.2
94.0
97.8
102.2
94.0
97.8
102.4
93.9
98.0
103.3
93.9
98.1
103.5
93.8
98.2
103.6
93.9
98.4
103.8
94 .0
98.5
103.5
94.0
98.4
103.9
93.9
98.4
104.0
93.8
98.4
104.0
93.7
98.5
104.1
93.8
Automotive vehicles, parts, and engines .. ...... ... .. .. ..
102.3
102.4
102.6
102.5
102.7
102.8
102.9
103.1
103.1
103.3
103.3
103.4
103.5
Consumer goods, excluding automotive ........... .. ... ..
Nondurables, manufactured .................................. .
Durables, manufactured ................ ..... ................
100.4
100.0
100.7
100.9
100.8
100.8
101 .1
101 .0
101 .0
101.0
101 .0
100.9
100.9
100.5
100.8
101 .0
100.6
101 .0
101.2
101.0
101 .1
101 .7
101.6
101.4
101 .6
101.5
101 .5
101 .6
101.5
101.5
101 .9
101.9
101 .7
101.8
101 .6
101 .6
101.6
101.2
101 .7
Agricu ltural commodities ..................................... .
Nonagricultural commodities .... ...... .... .. ... ... ...... ... ..
127.4
101.5
126.1
102.2
115.5
102.5
117.6
102.8
116.3
103.6
116.7
103.9
115.4 1 116.1
104.1
104.9
115.5
105.0
119.9
105.4
120.2
106.0
122.5
105.5
124.0
105.4
46. U.S. import price indexes by end-use category
[2000 = 100]
2004
Category
June
July
Aug.
Sept.
2005
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
ALL COMMODITIES ..... ..... .... ....................................
101 .7
102.1
103.6
104.1
105.8
105.5
104.0
104.6
105.5
107.8
108.8
107.7
108.8
Foods, feeds, and beverages .... ... .............. .. ........
Agricultural foods, feeds, and beverages ................
Nonagricultural (fish, beverages) food products .....
106.9
114.3
90 .3
107.5 1 107.3
114.5
114.1
91.8
92.3
108.7
116.4
91 .4
110.0
118.4
91 .1
110.3
119.1
90.7
111 .5
120.7
91 .0
111 .1
119.6
92.0
112.2
120.8
92 .8
115.9
125.7
94.0
115.5
125.4
93.5
115.7
125.7
93.3
113.7
122.8
93.2
I
June
Industrial supplies and materials .............. ..............
119.3
120.6
126.6
128.5
134.9
133.2
126.4
127.9
130.7
139.8
143.7
139.3
143.9
Fuels and lubricants ......................................... .... .
Petroleum and petroleum products .. ... ... ......... ..
130.9
129.7
133.2
132.7
143.4
144.4
146.2
149.2
160.8
165.8
157.0
155.9
141 .0
138.1
142.5
141 .2
148.0
148.4
165.6
168.3
173.0
174.3
165.1
165.9
174.9
178.5
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 .............
99.0
100.0
100.4
101 .1
101.4
101 .1
101.3
102.4
103.0
103.8
104.8
104.5
103.9
106.0
120.5
124.4
98.7
106.5
117.6
126.1
98.5
107.7
124.0
129.8
98.5
108.0
125.6
133.1
98.8
108.7
115.3
134.2
98.9
109.3
111 .8
136.4
99.2
109.8
115.6
138.5
99.7
111 .3
117.9
139.6
100.9
112.0
119.8
138.8
100.9
113.0
122.7
140.4
100.8
114.0
120.3
142.4
101.2
113.6
115.7
141.3
101 .0
113.2
118.1
139.3
100.8
Capital goods .. .. .... .. .... .. ... ... ...................... .. ......
Electric and electrical generating equipment.. ........
Nonelectrical machinery ................................... .....
92.2
97.0
90.1
92 .2
97.5
90.0
92.1
97.7
89.9
92.0
97.4
89.8
91 .8
97.4
89.5
91 .9
97.5
89.6
92 .2
98.0
89.9
92 .5
98.4
90.1
92.4
98.7
90.0
92.3
98.8
89.8
92 .1
98.9
89.6
92.2
98.7
89.6
92.2
98.6
89.6
Automotive vehicles, parts, and engines .............. ...
102.2
102.3
102.5
102.7
103.0
103.1
103.2
103.2
103.2
103.2
103.4
103.4
103.4
Consumer goods, excluding automotive .... ..............
Nondurables, manufactured ............. .. ....................
Durables, manufactured .. .. ... .. ...........................
Non manufactured consumer goods .....................
98.5
100.9
96.1
96.8
98.5
101.0
95.9
97.4
98.4
100.9
95.9
97.9
98.4
100.8
95.9
97.9
98.5
100.9
96.0
97.9
98.7
101 .1
96.2
98.0
99.0
101.4
96.5
98.2
99.6
102.2
96.8
100.1
100.1
102.8
96.7
105.0
99.9
102.8
96.8
100.3
99.9
102.9
96.7
100.4
100.0
102.8
96.7
103.1
99.9
102.7
96.8
101.9
47. U.S. international price Indexes for selected categories of services
[2000
= 100, unless indicated otherwise]
2003
Category
June
2004
Sept.
Dec.
Air freight (inbound) .................. ............... ................. ..
Air freight (outbound) ......................... ... .............. ...
109.4
95.4
Inbound air passenger fares (Dec. 2003 = 100) ... .......
Outbound air passenger fares (Dec. 2003 = 100)) .......
Ocean liner freight (inbound) ... .... .... ......... ................
-
-
116.1
116.2
-
Mar.
June
2005
Sept.
Dec.
Mar.
June
112.5
95.5
112.9
94.9
116.2
96.1
116.6
99.0
118.7
100.7
125.2
104 .7
126.3
103.8
125.9
107.6
-
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
105.4
122.7
114.5
105.0
121.3
116.1
120.5
128.4
NOTE: Dash indicates data not availabl e.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
121
Current Labor Statistics: Productivity Data
48. Indexes of productivity, hourly compensation, and unit costs, quarterly data seasonally adjusted
[1992
= 100]
2002
Item
2003
2004
II
Ill
IV
I
II
Ill
IV
I
II
Output per hour of all persons ... .. .. ................................
Compensation per hour .. ........ .. .. ..... ... .. ... .... ... ... ....
Real compensation per hour ........... ..... ..... .. ...... ......
Unit labor costs .. ... ... ....... ... .... .. .......... .. ... ... .. ...... .... ...
Unit nonlabor payments ............................. ... ... .... ....
Implicit price deflater ........................... ........ ......... .
123.5
145.0
11 5.7
117.7
112.9
115.9
125.0
145.7
115.7
116.9
11 5.0
116.2
124.7
145.8
115.1
116.2
116.3
116.7
125.6
147.8
115.5
117.7
116.4
117.2
127.9
150.3
11 7.3
117.5
117.2
117.4
130.5
152.0
118.0
116.4
120.3
117.9
130.6
152.8
118.4
117.0
120.5
118.3
131 .7
154.4
118.5
117.3
123.0
119.4
132.8
155.7
118.2
117.2
126.1
120.5
Nonfarm business
Output per hour of all persons .......................................
Compensation per hour ................. .............. ..... ... ..
Real compensation per hour ... .... ...... ......................
Unit labor costs .. .. ................................. .............. ... ...
Unit nonlabor payments ............... ...... .. ........ .... ...... ..
Implicit price deflater ... .. .... .... .... ... ... ............. .........
122.7
144.2
115.0
117.5
115.0
116.6
123.9
144.8
114.9
116.9
116.9
116.9
124.0
145.0
114.5
116.9
118.1
11 7.3
124.9
147.0
114.9
117.7
118.2
11 7.9
126.9
149.3
116.5
11 7.6
118.7
118.0
129.9
151.2
117.4
116.4
121 .6
118.3
130.1
152.2
117.9
116.9
121 .3
118.6
130.8
153.5
117.8
117.3
123.5
119.6
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 paym ents .......... ............ ................ .....
Implicit price deflater .... ................. ... .....................
127.9
141 .8
11 3.1
11 0.9
110.9
110.7
94.5
103.4
109.4
129.1
142.7
11 3.3
110.4
110.6
110.0
100.3
107.4
109.5
130.1
143.2
113.1
110.0
110.1
109.6
111.2
110.0
110.1
130.4
144.6
113.0
111 .0
110.9
111 .4
107 .8
110.5
110.7
132.7
147.0
114.8
110.7
110.8
110.5
111 .3
111 .4
111 .0
135.1
148.9
115.6
110.4
110.2
110.9
119.9
113.3
111.3
135.9
149.8
116.0
110.4
110.2
110.8
124.8
114.6
111.7
146.5
147.6
117.7
100.8
148.7
149.0
118.3
100.2
149.5
150.2
118.6
100.5
151.6
156.5
122 .3
103.2
152.9
159.2
124.3
104. 1
156.9
161 .5
125.4
102.9
158.1
163.2
126.5
103.2
2005
Ill
IV
I
II
133.3
158.2
119.6
118.7
124.2
120.7
134.3
162.5
121.8
121 .0
122.3
121 .5
135.3
164.9
122.9
121 .9
122.9
122.3
135.7
166.0
122.4
122.3
124.1
123.0
132.2
154.9
117.6
11 7.1
126.5
120.6
132.7
157.2
118.8
118.5
125.3
121 .0
133.5
161.0
120.7
120.7
123.7
121.8
134.5
163.8
122.0
121 .7
124.3
122.7
135.3
165.2
121.8
122.1
125.5
123.4
136.1
150.3
115.4
110.7
110.4
111 .4
130.2
116.4
112.4
136.1
151.7
115.2
111 .0
110.8
111 .5
138.6
118.7
113.4
139.4
154.0
116.5
110.5
110.5
110.3
139.7
118.2
113.1
142.3
158.0
118.4
110.5
111 .0
108.8
143.1
118.0
113.4
143.5
160.8
119.8
110.9
112.0
107.9
145.3
11 7.9
114.0
-
-
159.3
159.1
122.1
99.9
162.2
161 .1
122.3
99.3
164.0
164.9
124.7
100.6
166.5
169.3
126.9
101 .7
168.2
172.3
128.4
102.4
169.9
175.0
129.1
103.0
Business
Manufacturing
NOTE: Dash indicates data not availabl e.
122
Monthly Labor Review
-
-
I
Output per hour of all persons .................... .. .... ... .. .. .....
Compensation per hour .........................................
Real compensation per hour ....... ...... .. ....................
Unit labor costs .... .. ....... .... ............ ... .... ..... .... ............
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
-
August 2005
49. Annual indexes of multifactor productivity and related measures, selected years
[2000
= 100, unless otherwise indicated]
Item
1990
1991
1992
1993
1994
1995 1996
1997
1998
1999
2000
2001
2002
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 ..............................
81.4
102.6
90.9
68.6
82 .7
99.7
90.3
68.1
86.2
101 .7
92.7
70.9
86.5
102.6
93.1
73.2
87.5
104.5
94.1
76.9
87.7
103.6
93.8
79.1
90 .3
103.9
95.5
82.8
91 .9
104.1
96.3
87.2
94.4
102.6
97.4
91 .5
97 .2
101.8
98.7
96.2
100.0
100.0
100.0
100.0
102.7
96 .3
100.1
100.4
107.2
95.5
102.0
102.3
80.1
66.9
75.5
79.3
79 .1
68.4
75.4
83.0
80.0
69.7
76.5
84 .8
82.4
71.3
78.6
84.4
86.1
73.5
81 .7
83.7
88.5
76 .4
84 .3
84 .6
90.4
79.7
86.7
86.9
94 .0
83.8
90 .5
88.3
96.2
89.2
93.9
92.0
99.0
94.5
97.5
95.4
100.0
100.0
100.0
100.0
98.6
104.2
100.4
106.6
97.4
107.1
100.3
112.2
81.7
104.2
91 .5
68.6
83.1
101.1
91 .0
68 .1
86.5
102.8
93.2
70.8
86.9
103.8
93.6
73.2
87.9
105.4
94 .5
76 .7
88.4
104.7
94 .6
79 .3
90.8
104.7
96.0
82.9
92 .2
104.6
96.6
87 .2
94.7
103.0
97.7
91 .5
97.3
102.1
98.8
96.3
100.0
100.0
100.0
100.0
102.6
96 .3
100.0
100.5
107.2
95.4
102.0
102.4
79.8
65.8
75.0
78.4
78.7
67.4
74.8
82.3
79.6
68.8
75.9
84 .1
82.2
70.6
78.2
83.7
85.6
72 .8
81 .2
83.3
88.0
75.7
83.8
84.4
90.0
79.2
86.3
86 .7
93.7
83.3
90 .2
88.2
96.0
88.8
93.7
91 .9
99.0
94.3
97.5
95.3
100.0
100.0
100.0
100.0
98.8
104.4
100.5
106.6
97.3
107.3
100.3
112.4
82.2
97.5
93.3
83.2
84.1
93.6
92.4
81 .5
88.6
95.9
94.0
85.5
90.2
96.9
95.1
88.3
93.0
99.7
97.3
92 .9
96.5
100.6
99.2
96.9
100.0
100.0
100.0
100.0
103.8
101.4
103.1
105.6
108.9
101 .7
105.7
110.5
114.0
101.7
108.7
114.7
118.3
101 .0
111 .3
117.4
119.7
95.1
110.3
112.1
101.1
85.3
93.1
77.5
84 .7
89.1
96.9
87.1
93.2
78.5
84.6
88.3
96.5
89.1
93.1
83.5
92.0
90.9
97.8
91 .1
96.6
86.5
92.9
92.8
99.9
93.2
99.9
90.3
96.0
95.5
100.4
96.4
102.3
93.1
100.4
97.7
100.0
100.0
100.0
100.0
100.0
100.0
101 .7
104.1
97.5
101.9
103.9
102.4
101 .5
108.7
100.6
107.5
103.1
104.6
100.7
112.8
102.9
107.9
105.4
105.5
99.2
116.2
104.3
106.9
106.5
105.5
93.6
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 [1996 = 100)
Productivity:
Output per hour of all persons ... ....... ........ ....... .. ....
Output per unit of capital services .... ......................
Multifactor productivity .. .. .................... ..... ..... .......
Output. .. .................. .... .. ................. ....... ... ...............
Inputs:
Hours of all persons ............... ...... ....... .........................
Capital services .. ............... .. ............. .......... ..........
Energy ... .... ................. ... ..... ... ... ........... ................ ..
Nonenergy materials .. ........................ .. .......................
Purchased business services .. .. ......................... .........
Combined units of all factor inputs ................. .........
NOTE: Dash indicates data not available.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
123
Current Labor Statistics:
Productivity Data
50. Annual indexes of productivity, hourly compensation, unit costs, and prices, selected years
[1992
= 100]
1960
Item
1970
1980
1990
1996
I 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
60.8
28.4
24.8
27.1
66.3
23.6
78.8
35.6
31 .5
34.1
79.1
54.1
89.1
68.4
61 .3
65.8
94.5
90.6
96.3
96.0
93.8
95.1
104.7
109.6
99.6
104.7
112.0
107.4
106.7
113.1
100.6
106.1
113.9
109.0
109.7
120.0
105.3
109.4
110.1
109.7
112.9
125.8
108.1
111.4
109.5
110.7
116.1
134.5
111 .9
115.9
107.4
112.7
119.0
140.2
113.4
117.8
110.2
114.9
123.8
145.0
115.1
11 7. 1
114.4
116.1
128.6
150.7
117.3
117.2
8.6
117.7
133.0
157.7
119.5
118.6
123.9
120.6
Nonfarm business
Output per hour of all persons ............. .. ....................... .
Compensation per hour ................... .. .................. ..
Real compensation per hour ......... .... ..... ........ .........
Unit labor costs ..................................................... ....
Unit nonlabor payments ...... ... ........ ..........................
Implicit price deflator .......... .. ........ ....... .. ...... ..... .....
51 .9
14.5
63.3
27.9
24.3
26.6
68.0
23.7
79.2
34.9
31 .2
33.5
80.6
54.4
89.5
67.5
60.4
64.9
94.5
90.4
96.0
95.7
93.5
94.9
104.9
109.5
99.5
104.5
112.2
107.3
106.6
112.9
100.4
105.9
114.6
109.1
109.5
119.6
105.0
109.3
111 .1
109.9
112.6
125.2
107.5
111 .2
111.1
111.1
115.6
134.0
111 .4
115.9
108.9
113.3
118.5
139.3
112.6
11 7.5
111 .8
115.4
123.3
144.2
114.8
11 7.0
116.3
116.7
128.0
149.9
116.7
11 7. 1
120.0
118.2
132.3
156.7
118.7
118.4
124.7
120.7
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 ... .... ....... .... .... ...... ... ... ...... .....
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
98.5
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
123.6
106.2
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
128.2
142.0
113.0
110.7
110.7
110.8
95.4
107.4
109.6
133.5
147.6
114.8
110.6
110.5
110.9
116.7
112.5
111 .2
138.7
153.5
116.4
110.6
110.7
110.5
138.0
117.8
113.1
Manufacturing
Output per hour of all persons ..................... ............ ......
Compensation per hour .. ...... ....... ... .......... .......... ....
Real compensation per hour .. ... .. ....... ................. .. ..
Unit labor costs ............ .. ............ ............ .... ... ...... ......
Unit nonlabor payments ....... ..... .. .... ... ............ .. ........
Implicit price deflator ........... .... .. .. ..... .... .... ... ... .......
41 .8
14.9
65.0
35.6
26.8
30.2
54.2
23.7
79.2
43.8
29.3
35.0
70.1
55.6
91 .4
79.3
80 .2
79.9
92.9
90.5
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
147.9
117.7
100.4
154.8
160.1
124.6
102.4
163.0
163.6
124.0
100.4
-
-
-
Dash indicates data not available.
124
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
-
51. Annual indexes of output per hour for selected NAICS industries
[1997=100]
Industry
NAICS
1987
1990
1992
1994
1995
1996
1997
1998
1999
2000
2001
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
3112
3113
3114
3115
3116
3117
3118
3119
3121
2003
I
Mining
21
211
212
2121
2122
2123
2002
I
85.5
80.1
69.8
58.4
71 .2
88.5
85.1
75.7
79.3
68.1
79.9
92.3
95.0
81.6
86.8
75.3
91.7
96.1
98.5
87.5
93.0
83.9
104.1
96.9
101.7
95.3
94.0
88.2
98.5
97.3
101.3
98.1
96.0
94.9
95.3
97.1
100.0
100.0
100.0
100.0
100.0
100.0
103.6
101.2
104.6
106.5
109.5
101 .3
111.4
107.9
105.9
110.3
112.7
101 .2
111.2
119.4
106.8
115.8
124.4
96.2
109.1
121.6
109.0
114.4
131 .8
99.3
113.9 1 116.2
124.0
130.5
111.4
113.6
112.2 1 113.1
142.4
141 .0
103.6
108.6
65.6
67.8
71.1
71.4
74.5
76.1
83.1
82.3
88.5
89.0
95.2
96.0
100.0
100.0
103.7
99.0
103.5
102.7
107.0
113.2
106.4
110.1
102.9
115.4
105.1
114.3
Animal food ........................................................
Grain and oilseed milling ........................... ...........
Sugar and confectionery products .... . ........ ............
Fruit and vegetable preserving and specialty .. . . . . . . . . . .
Dairy products. ...................................... . ...........
83.6
81.1
87.6
92.4
82.7
91.5
88.6
89.5
87.6
91.1
90.5
91.1
89.2
91.9
95.2
87.4
94.3
92.7
95.5
94.9
93.8
98.7
93.2
98.3
97.6
86.1
90.0
97.8
98.8
97.8
100.0
100.0
100.0
100.0
100.0
109.0
107.5
103.5
107.1
100.0
110.9
116.1
106.5
109.5
93.6
109.7
113.1
109.8
111 .8
95.9
142.7
1314
1 123.8
119.5
140.4
122.0
112.2
121 .8
110.1
Animal slaughtering and processing ............ ...........
Seafood product preparation and packaging ... .... .....
Bakeries and tortilla manufacturing .. .. ........ ..........
Other food products ............................. .. ..............
97.4
123.1
100.9
97.5
77.1
94.3
119.7
94.5
92.4
87.6
101.8
117.8
97.1
97.6
94.9
97.4
115.5
98.6
102.2
100.5
99.0
110.3
100.7
104.0
103.2
94.2
118.0
97.3
105.0
102.0
100.0
100.0
100.0
100.0
100.0
100.0
120.2
103.8
107.8
99.0
101 .2
131.6
108.6
111.3
90.7
74.4
75.3
82.0
88.0
91.4
80.2
Tex1ile and fabric finishing mills ..............................
Tex1ile furnishings mills .. .................... . . . . . . . . . . . . . . . .. .
Other tex1ile product mills ......................... . .. . ....... ..
66.5
68.0
91 .3
91.2
92.2
83.5
92.7
91.8
87.2
91.7
87.6
90.1
94.5
91.9
95.5
84.3
92.3
95.9
98.9
98.1
85.0
93.8
97.2
100.0
100.0
100.0
100.0
100.0
102.1
104.2
101 .2
99.3
96.7
3151
3152
3211
3212
3219
Apparel knitting mills. .. ............ ............ .............. ...
Cut and seN apparel. ......... .............. .. .... ············
Sawmills and wood preservation ...... . . ............ .... .. ..
Plywood and engineered wood products ...
Other wood products ................... ······ .................
76.2
69.8
77.6
99.8
103.2
86.2
70.1
79.4
102.9
105.5
93.3
72.9
85.7
114.3
103.2
104.3
80.4
84.6
105.3
98.2
109.3
85.2
101.5
99.8
122.1
90.6
95.9
101 .1
100.5
100.0
100.0
100.0
100.0
100.0
3221
3222
3231
3241
3251
Pulp, paper, and paperboard mills. ... ..... ...... . .... .....
Converted paper products ............ .....
.....
Printing and related support activities ........... ..........
Petroleum and coal products .. . ... .. ... . .. . .... . .. . . . . . . . .
Basic chemicals ........ .. ........................................
81 .7
89.0
97.7
72.1
94.6
84.0
90.1
97.6
76.1
93.4
87.9
94.0
101 .7
79.0
90.2
94.1
97.5
98.6
83.8
94.7
98.4
97.2
98.8
89.9
91.3
95.4
97.7
99.9
93.5
89.4
3252
3253
3254
3255
3256
Resin, rubber, and artificial fibers .... .. ......... ...... .. ...
Agricultural chemicals ..... .... ...... .. . . . .....................
Pharmaceuticals and medicines .............................
Paints, coatings, and adhesives. ...... ........ ····· .....
Soap, cleaning compounds, and toiletries .......... ..... ..
77.4
80.4
87.3
89.3
84.4
76.4
85.8
91.3
87.1
84.8
80.4
82.1
87.5
89.6
85.0
93.4
86.8
93.4
93.9
90.8
95.4
89.9
95.9
92.3
96.1
3259
3261
3262
3271
3272
Other chemical products and preparations .... .... .......
Plastics products .. . ... . .... . ............
...........
Rubber products .... . . . . . . . . .. . .. ....... . ... .... . ..........
Clay products and refractories ................................
Glass and glass products ......................................
75.4
83.1
75.5
86.9
82.3
77.8
85.2
83.5
89.4
79.1
85.8
90.8
84.7
92.0
83.8
92.3
94.4
90.7
96.3
85.7
3273
3279
3311
3312
3313
Cement and concrete products ...............................
Other nonmetallic mineral products .........................
Iron and steel mills and ferroalloy production. ........ ..
Steel products from purchased steel .......................
Alumina and aluminum production ........ ·················
93.6
83.0
64.8
79.7
90.5
96.6
79.5
70.2
84.4
90.7
96.2
90.3
74.7
90.1
95.8
3314
3315
3321
3322
3323
Other nonferrous metal production ..................... .. ...
Foundries ............. ...... ... ... . ... . ..... ...... ...........
Forging and stamping ... ......... .......... ......... ........ ...
Cutlery and hand tools ....... . .. . .................... . .........
Architectural and structural metals .................... ......
96.8
81.4
85.4
86.3
88.7
96.3
86.5
89.0
85.4
87.9
3324
3326
3327
3328
3329
Boilers, tanks , and shipping containers ....................
Spring and wire products ......................................
Machine shops and threaded products · ····· ·· · ··· ·· · ·· · ·
Coating, engraving, and heat treating metals .... .......
Other fabricated metal products .............................
86.0
82.2
76.9
75.5
91.0
3331
3332
3333
3334
3335
Agriculture, construction, and mining machinery .......
Industrial machinery ........................................... ..
74.6
75.1
86.9
84.0
85.1
Utilities
Manufacturing
3131
31;jL
3133
3141
3149
I
;;:;;.;,h,;.;:;,• .• • • • • •. • .• • . • • • • •
Commercial and service industry machinery .............
HVAC and commercial refriqeration equipment.. .........
Metalworking machinery ............................... ........
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
108.6
121.4
97.1
108.2
126.7
105.0
140.5
108.3
112.7
90.8
103.7
153.0
109.9
106.2
92.7
107.8 1 107.0
170.0
177.8
110.7
110.9
113.6
118.9
105.0
103.9
110.0
102.2
99.1
107.6
101 .3
110.1
104.4
104.5
108.9
109.1
110.3
108.5
103.1
103.1
133.5
125.7
119.7
103.5
105.1
150.2
136.1
124.8
111.9
104.6
96.1
102.3
100.3
105.2
101.1
101.4
114.6
104.7
98.8
104.6
108.9
119.8
105.4
98.9
103.1
105.6
119.5
108.8
105.3
104.9
114.8
110.9
114.4
110.3
114.2
107.5
123.5
120.6
106.5
112.9
100.0
100.0
100.0
100.0
100.0
102.5
102.5
100.6
102.2
102.7
111.1
100.1
102.8
107.1
115.7
116.3
101.1
104.6
113.5
117.5
1199
100.5
105.3
112.1
108.8
93.1
91.7
100.0
99.1
97.3
100.0
100.0
100.0
100.0
100.0
106.0
98.8
93.8
100.1
98.0
109.8
87.4
95.7
100.3
93.0
109.8
92.1
95.6
100.8
102.8
106.2
90.0
99.5
105.6
106.0
93.5
94.5
92.9
97.4
87.5
94.0
96.6
94.2
102.4
94.7
100.0
100.0
100.0
100.0
100.0
99.2
104.2
99.4
101 .2
101.4
109.3
109.9
100.2
102.7
106.7
119.7
112.3
101.7
102.9
108.2
110.4
114.6
102.3
98.4
102.8
122.7
127.6
107.9 1 111 .7
99.8
103.5
107.4
115.2
95.7
89.6
87.1
99.5
99.6
99.7
91.4
90.0
100.6
95.9
102.0
96.0
94.1
100.5
95.4
100.0
100.0
100.0
100.0
100.0
105.1
99.0
101.3
100.1
101 .4
105.9
95.6
104.8
93.0
103.5
101 .6
96.6
106.0
95.5
96.5
98.0
98.6
108.5
94.3
96.0
102.4
106.7
123.8
99.7
86.4
92.2
87.4
92.7
105.1
91.8
93.4
94.1
94.7
102.7
93.1
93.9
97.2
93.3
105.9
96.0
97.4
103.8
93.9
100.0
100.0
100.0
100.0
100.0
111.3
101 .2
103.5
99.9
101.0
108.4
104.5
110.9
108.0
102.0
102.3
103.6
121.1
105.9
100.7
99.5
107.4
120.7
110.3
101 .7
108.5
117.0
125.3
90.1
85.2
79.2
81.3
86.5
95.4
90.8
87.4
86.6
90.4
100.1
91.0
91.6
95.8
94.5
97.3
99.0
98.3
102.2
96.3
100.7
102.4
99.8
101 .7
98.2
100.0
100.0
100.0
100.0
100.0
100.4
110.6
99.6
100.9
101.9
97.1
111.4
104.2
101.0
99.6
94.7
112.6
108.2
105.5
99.9
94.6
111 .9
108.8
107.3
96.7
99.7
129.1
115.6
115.2
106.5
102.0
138.8
115.8
116.9
111.2
83.3
81.6
95.6
90.6
86.5
79.0
79.9
100.1
91.5
89.2
91.0
89.5
103.1
97.1
93.5
95.4
97.1
103.6
96.4
99.2
95.7
98.5
107.2
97.2
97.5
100.0
100.0
100.0
100.0
100.0
103.3
95.1
105.9
106.2
99.1
94.3
105.8
109.8
110.2
100.3
100.3
130.0
100.9
107.9
106.1
100.3
105.8
94.3
110.8
103.3
103.7
106.0
102.0
116.6
109.0
109.7
81.4
90.4
,02,
Monthly Labor Review
I
August
~·1
I
133.1
105.5
110.0
117.9
124.0
I
123.0
98.9
96.0
109.1
124.5
I
138.0
109.3
110.7
118.9
132.0
120.9
107.2
98.6
113.5
114.6
118.9
122.7
106.9
112.4
125.8
105.2 1 101.6
125.0
127.1
,01,
106.3
I
120.5
117.5
132.9
109.0
109.1
117.6~
115.6
117.4
2005
125
Current Labor Statistics:
Productivity Data
51. Continue~Annual indexes of output per hour for selected NAICS industries
[1997=100]
1987
1990
1992
1994
1995
1996
1997
1998
1999
2000
2001
2002
NAICS
Industry
3336
3339
3341
3342
3344
Turbine and power transmission equipment.. ............
Other general purpose machinery ....... . ...................
Computer and peripheral equipment.. .................. ...
Communications equipment. ................ .. ..... . ....... ..
Semiconductors and electronic components .... .. .......
80.2
83.5
11 .0
39.8
17.0
85.9
86.8
14.7
48.4
21.9
80.9
85.4
21.4
60.6
29.8
92.7
91.3
35.3
71 .0
43.3
91.3
94.0
49.9
74.4
63.8
98.0
94.9
72.6
84.5
83.1
100.0
100.0
100.0
100.0
100.0
105.0
103.7
140.4
107.1
125.8
110.8
106.0
195.8
135.4
173.9
114.9
113.7
234.9
164.1
232.4
126.9
110.5
252.0
152.9
230.4
132.7
117.6
297.3
128.1
264.1
141.8
124.5
379.6
142.2
322.1
3345
3351
3352
3353
3359
Electronic instruments ....... .... ....................... ....... .
Electric lighting equipment... ........... ...................... .
Household appliances ... ........................ ...... .... .....
Electrical equipment. ..... .. .. ...................... .. .. ........ .
Other electrical equipment and components ...... ... .... .
70.2
91.1
73.3
68.7
78.7
78.5
88.2
76.5
73.6
76.0
85.9
94.1
82.3
79.0
82.2
90.2
94.0
94.9
88.6
89.1
97.9
91 .9
91 .8
98.0
92.0
97.6
95.8
91.9
100.4
96.3
100.0
100.0
100.0
100.0
100.0
102.3
104.4
105.3
100.2
105.7
106.7
102.7
103.9
98.7
114.6
116.7
102.0
117.2
99.4
119.6
119.3
106.7
124.7
101.0
112.9
119.3
112.3
136.0
103.2
115.6
128.5
113.1
151.6
104.9
116.9
3361
3362
3363
3364
3366
Motor vehicles ................. ........... ................... .. ...
Motor vehicle bodies and trailers .......................... ..
Motor vehicle parts ............. ...... ... .. ......... ......... ...
Aerospace products and parts ..... ..... ... .. ... .... .... ... ..
Ship and boat building .. ... .. ... .. ...... .. ........... .. ....... ..
75.4
85.0
78.7
86.5
95.5
85.6
75.9
16.0
89.1
99.6
90.8
88.4
82.3
96.8
99.4
89.9
97.8
91.4
94.4
98.9
88.5
97.4
92.3
94.9
93.1
91.0
98.5
93.0
98.9
93.5
100.0
100.0
100.0
100.0
100.0
113.4
102.9
105.0
120.2
99.3
122.6
103.1
110.0
120.0
112.0
109.7
98.8
112.3
103.2
121.9
110.0
88.7
114.8
116.7
121 .5
126.3
105.5
130.7
117.8
131.0
138.7
109.3
135.9
121.7
133.8
3371
3372
3379
3391
3399
Household and institutional furniture ...... ..................
Office furniture and fixtures .. ... .... .............. ... ..........
Other furniture-related products . ...... . ..... . .... ..... ......
Medical equipment and supplies . ... .... ....................
Other miscellaneous manufacturing ....................... .
85.2
85.8
86.3
76.3
85.4
88.2
82.2
88.9
82.9
90.5
92.5
86.4
87.6
89.2
90.3
94.1
83.9
93.8
92.1
93.6
97.2
84.9
94.8
96.6
95.9
99.8
86.3
97.6
100.5
99.7
100.0
100.0
100.0
100.0
100.0
102.2
100.0
106.9
108.7
102.0
103.1
98.2
102.0
110.4
105.0
101.9
100.2
99.5
114.6
113.6
105.5
98.0
105.0
119.3
111.7
115.7
115.2
110.4
128.6
129.5
118.2
125.3
110.5
137.1
135.3
42
423
4231
4232
4233
Wholesale trade .. .. ......... ..... ... .. .. ....... ...... ... .........
Durable goods ............. ...... .. ................. ...... ... .... .
Motor vehicles and parts . ........... .. ..... ............... . .. ..
Furniture and furnishings ..... ... .... .. ... .... .................
Lumber and construction supplies .. .. .. .... ......... ........
73.5
62.1
72.9
79.2
120.1
78.5
66.7
76.6
87.2
118.7
86.3
75.0
82.0
91 .4
119.8
91.3
84.3
94.2
93.3
112.2
93.4
88.8
93.4
96.8
102.9
96.3
93.9
95.7
96.6
102.9
100.0
100.0
100.0
100.0
100.0
104.8
105.9
105.1
98.5
104.0
112.0
116.2
120.3
100.9
105.8
115.7
120.2
113.9
106.1
102.7
118.5
121 .2
115.3
106.1
109.8
124.3
127.5
122.2
101.7
116.8
128.6
133.7
128.8
111.2
126.5
4234
4235
4236
4237
4238
Commercial equipment. .... .... ..... .. ... ... . ... ....... ....... .
Metals and minerals ........... ... .. ...... ..... ... ... ............
Electric goods ....................... ..... ... .. ...... ...... . .... ...
Hardware and plumbing .. ........ .... .... ... .. .. ....... ........
Machinery and supplies ...................... . .. ... .... ... .....
28.0
105.6
40.3
82.7
74.9
33.9
102.6
47.5
88.5
82.4
48.3
110.0
51.8
97.1
79.8
60.4
110.5
68.7
102.3
85.0
74.8
101.5
79.8
99.0
89.5
88.3
103.1
87.5
99.7
93.7
100.0
100.0
100.0
100.0
100.0
118.4
102.9
105.5
103.6
104.2
143.4
96.4
127.9
109.1
101 .1
150.6
99.8
152.4
112.0
103.9
169.5
103.1
147.4
102.9
102.1
196.3
103.5
154.6
107.1
99.7
212.4
103.7
164.7
111.4
103.9
4239
424
4241
4242
4243
Miscellaneous durable goods ......... .. ............ .. ........
Nondurable goods .. ... .... .......... . ...........................
Paper and paper products ......................... ....... .. . ..
Druggists' goods ............... ... .......... .. .. ....... .. .. . .. .. ..
Apparel and piece goods ....... . .... . .................... ... ...
86.2
93.3
86.5
70.1
88.3
87.3
97.9
82.3
80.7
101 .8
109.2
102.8
96.5
90.4
99.8
103.4
101.5
101.0
91 .9
103.7
98.0
99.6
96.4
94.7
92.0
100.2
99.0
94.8
98.4
99.1
100.0
100.0
100.0
100.0
100.0
102.3
103.5
99.7
99.9
104.8
114.5
104.9
104.1
101.4
103.2
118.4
106.3
105.8
95.7
101 .6
118.1
108.1
110.4
99.7
103.5
124.4
112.0
123.3
117.5
109.8
120.3
117.1
126.6
133.9
104.0
4244
4245
4246
4247
4248
Grocery and related products ... ... ... . .... .... .. .............
Farm product raw materials ....... ....... .. . ..................
Chemicals .. .. . ........ ..... . ................. .. ..... ... .... ... .... .
Petroleum .... . .. ............ ..... . .. ... ... .. ..... . .. . ..............
Alcoholic beverages .... . ... ... ........ . ............ ... ......... .
88.1
82.4
95.8
93.5
99.2
95.9
79.5
106.4
96.2
109.3
104.0
83.7
111 .5
117.2
105.9
104.0
79.2
110.7
114.2
106.6
103.5
86.3
102.4
108.2
103.4
99.9
88.7
100.5
104.4
104.7
100.0
100.0
100.0
100.0
100.0
102.5
101 .5
99.6
113.8
110.6
104.2
116.2
97.4
109.5
108.2
106.0
121.3
94.1
111 .1
112.0
107.3
123.4
92.3
114.6
111 .6
107.2
134.3
98.1
121.8
116.2
110.2
134.2
100.9
125.9
117.7
4249
425
42511
42512
Miscellaneous nondurable goods .. .... .............. .. ......
Electronic markets and agents and brokers ... ..... ...... .
Business to business electronic markets .... .. .............
Wholesale trade agents and brokers ........ ...............
107.9
65.7
69.2
64.2
107.3
73.2
74.8
72.6
93.6
83.4
84.0
83.5
93.5
89.6
91.2
89.6
97.0
92.6
92.9
93.1
99.0
97.0
96.6
97.3
100.0
100.0
100.0
100.0
104.1
104.9
104.1
105.0
105.8
117.3
125.8
112.7
113.0
126.5
146.1
116.9
112.3
135.4
179.1
115.6
107.2
139.7
226.5
110.9
115.5
131.0
300.4
98.2
44-45
441
4411
4412
4413
Retail trade .................... ............... ........ .. .... .. ... ..
Motor vehicle and parts dealers . .......... .. .. .. .... .... .....
Automobile dealers .. .. . ....... . ................................ .
Other motor vehicle dealers ...... .... ... ............ . ....... ..
Auto parts, accessories, and tire stores .... .. ..............
80.8
85.6
87.1
73.3
78.4
83.1
90.7
92.4
73.3
86.3
86.5
93.9
95.8
81.6
90.5
92.7
97.4
98.3
93.2
95.8
94.6
97.6
97.7
91.0
98.7
98.0
99.3
99.2
98.8
99.2
100.0
100.0
100.0
100.0
100.0
104.9
103.6
103.3
107.5
107.5
110.5
107.1
106.9
112.6
111.5
114.8
107.6
106.0
110.3
114.2
118.7
110.2
108.7
115.4
110.3
123.2
111 .1
107.2
117.5
120.0
129.8
112.9
106.4
131 .6
130.0
442
4421
4422
444
Furniture and home furnishings stores .. ...................
Furniture stores . ...... .. .... .......... .. .. ..... .. .. .. .. ..... .....
Home furnishings stores .. .. .... ... ... .. . .. ... .... .. ...........
Electronics and appliance stores ............. . ... .... ...... .
Building material and garden supply stores ..... ..........
76.7
76.3
77.0
36.9
77.4
80.1
83.3
75.8
45.9
81.5
88.3
90.5
85.3
56.9
82.7
90.9
90.8
90.8
77.7
92.8
94.7
93.5
96.1
89.4
93.1
100.2
97.9
103.0
94.8
97.4
100.0
100.0
100.0
100.0
100.0
102.6
103.3
102.0
122.9
108.0
110.0
107.9
112.8
153.0
113.9
116.3
113.8
119.5
179.7
114.4
120.3
120.3
120.4
202.5
116.4
124.8
124.3
125.6
242.6
120.8
135.3
131.4
140.4
311.0
129.3
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 ... .......... .... .. ............. ... .. . ..... .
78.2
73.1
109.6
110.6
127.0
83.0
73.8
106.6
106.5
119.3
83.3
79.3
106.1
106.7
106.3
94.0
85.8
103.9
104.7
101.4
94.2
86.8
101.9
102.8
97.6
97.5
97.1
100.5
101.0
94.4
100.0
100.0
100.0
100.0
100.0
109.2
101.0
100.5
100.5
97.9
115.6
103.4
103.6
104.6
95.4
115.7
105.9
104.5
104.5
102.0
116.3
117.5
107.8
107.8
108.8
121 .6
115.1
109.8
110.5
108.0
130.4
121.6
114.3
113.7
123.2
4453
Beer, wine and liquor stores . ..... . .. . ... .... ... .. .. .. .. .. . .. .
Health and personal care stores ....... ..... .... .... .. ...... .
Gasoline stations .. .. ................... .. ... ...... .. ..... .. . .. . ..
Clothing and clothing accessories stores ..................
Clothing stores ... .. ........................ .... . ..................
95.6
85.8
83.0
65.8
66.6
98.7
92.9
83.7
69.2
69.1
97.2
90.4
87.7
74.8
77.7
94.5
91 .6
96.1
83.2
82.3
95.1
91 .6
99.7
92.8
91.5
103.8
96.4
99.8
99.5
98.6
100.0
100.0
100.0
100.0
100.0
107.0
104.3
106.8
106.1
108.4
101 .9
105.4
110.5
113.6
113.7
112.1
110.6
107.0
123.2
124.6
113.5
113.5
112.4
126.4
129.8
112.8
119.9
121.8
130.2
134.8
127.2
129.5
117.6
138.9
141.2
2003
Wholesale trade
Retail trade
443
446
447
448
4481
126 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
51. Continued-Annual indexes of output per hour for selected NAICS industries
(1997=100]
1987
NAICS
Industry
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 ..... .................... .
65.3
63.6
73.7
69.4
84.4
71.4
67.8
81 .1
78.3
87.3
75.5
61 .9
85.0
81 .7
92.2
86.4
84.8
87.8
85.7
92.4
96.7
95.7
94.3
94.0
95.0
452
4521
4529
453
4531
General merchandise stores . ..... .. ........ ..................
Department stores ... ..... ... .. ... ............. .. .. .. ... .... .. ...
Other general merchandise stores ...... ... .... . .... .. ... ...
Miscellaneous store retailers ....................... .. .... .. ...
Florists ...... .......................... .. . .. ... .. .......... , ..... ....
73.7
87.7
54.8
65.7
77.9
75.3
84.2
61.4
69.5
73.3
82.9
91 .7
69.5
74.1
83.2
90.6
95.3
82.5
86.5
82.4
4532
4533
4539
454
4541
4542
4543
Office supplies, stationery and gift stores ........... ......
Used merchandise stores .. .. .. ... . .. . ... ......................
Other miscellaneous store retailers .................... .....
Nonstore retailers ................. .. ...................... .......
Electronic shopping and mail-order houses ...............
Vending machine operators ...... ...... ..... ..................
Direct selling establishments .. .. .... ... ..... .. ...... ..........
56.6
78.5
74.9
52.7
40.0
98.7
74.9
61 .1
82.2
81 .7
56.4
43.9
97.2
77.8
75.0
81 .8
71.8
62.6
50.5
95.0
82.1
481
482111
48412
48421
491
492
Air transportation ........... .... .. ....... .. ............... ..... ...
Line-haul railroads ...... ... ... .... .. . . · ················ ··· ·· ··
General freight trucking , long-distance ............... ... ...
Used household and office goods mov111g ................
U.S. Postal Service .. ...... ... .. . ... .. .................... .......
Couriers and messengers . .......... .. .... ......... ... ........
81.1
58.9
86.8
102.3
92.4
147.8
77.5
69.8
87.5
115.5
96.1
138.8
5111
5112
51213
515
5151
5152
5171
5172
5175
Newspaper, book, and directory publishers ......... ....
Software publishers ..... ........................... ·· ····· ·····
Motion picture and video exhibition .. .............. ,. ........
Broadcasting, except internet.. ............. ... .... ...........
Radio and television broadcasting .... .. . .. . ............... .
Cable and other subscription programming ..... ... ... ....
Wired telecommunications carriers .... ·· ····· ········ .....
Wireless telecommunications carriers .. .................. ..
Cable and other program distribution . .. ... ....... .. ....... .
104.8
10.2
90.4
99.0
97.2
105.9
56.1
79.4
105.4
52211
Commercial banking ........ .. .... .. . ... .. .. .... ... .............
1990
1992
1994
1995
1996
1997
1998
104.8
98.6
94.6
93.2
97.4
100.0
100.0
100.0
100.0
100.0
94.2
108.3
109.6
113.9
101 .7
105.0
121.5
11 6.0
122.0
105.0
111.2
128.6
122.8
129.7
110.1
92.0
94.7
87.2
88.8
82.5
96.9
98.7
93.9
94.7
92.0
100.0
100.0
100.0
100.0
100.0
105.0
100.6
113.4
107.7
102.6
11 3.3
104.7
129.8
109.6
118.7
120.1
106.7
145.9
110.7
114.8
124.5
104.5
162.1
109.6
107.9
88.2
85.0
89.4
75.2
62.5
93.9
94.4
91 .7
86.2
88.9
80.0
71 .3
88.5
94.3
93.1
95.7
97.4
92.0
84.7
97.6
102.9
100.0
100.0
100.0
100.0
100.0
100.0
100.0
111 .3
11 5.7
104.1
11 2.3
118.3
11 4.5
97.7
119.6
109.3
98.6
123.3
140.9
118.0
93.2
125.3
118.0
96.4
150. 1
158. 1
128.4
119.8
92.6
81.4
82.3
97.2
113.4
96.5
155.8
90.8
88.6
97.8
105.4
98.5
113.8
95.3
92.0
95.2
102.3
98.3
101 .5
98.8
98.4
96.7
95.4
96.7
100.2
100.0
100.0
100.0
100.0
100.0
100.0
97.6
102. 1
99.8
97.0
98.2
105.5
99.2
101.3
101 .4 1 102.4
112.5
11 7.5
96.6
28.5
109.2
97.9
97.2
100.6
65.3
72. 1
1003
96.0
43.0
104.3
102.6
103.8
96.5
71 .4
75.0
96.2
93.1
64.9
103.4
103.9
106.6
92.U
81 .7
89.7
91 .9
93.4
73.2
99.8
103.4
105.9
93.2
87.2
90.2
93.5
92.7
88.3
99.0
102.1
104.4
93.3
96.5
102.0 I
93.3
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
103.8
11 9.0
99.5
105.0
98.1
131.4
104.8
97.6
95.4
72.8 '
80.7
83.3
92.8
95.6
100.0
100.0
90.9
60.7
71 .5
88.7
69.0
92.9
103.5
67.2
99.6
107.0
79.7
117.9
100.2
88.6
115.7
109.0
97.0
101.2
100.0
100.0
100.0
100.3
95.8
11 4.6
112.7
103. 1
133.0
1052
140.6 1 137.8
89.9
94.3
104.8
91 .9
105.2
107.7
105.4
112.9
108.2
122.1
107. 1
115.7
96.9
100.7
118.7
92.6
102.8
102.0
100.0
100.0
100.0
112.2
96. 1
106.3
110.5
111.3
101.3
101.3
119.5
101.6
91.4
70.2
95.6
85.4
93.4
92 .6
94.0
86.8
93.6
90.0
100.1
96.2
100.0
100.0
107.1
107.9
111 .3
107.2
-
-
-
92.6
92.6
92.9
91 .2
91.4
90.8
94.5
94.7
94.2
100.0
100.0
100.0
11 5.7
108.6
128.8
1999
2000
127.1
114.6
2001
I
I
112.0
125.0
130.8
136.7
119.6
2002
I
2003
123.5 1 132.1
117.8
135.3
131.7
131.7
136.7
137.8
122.7
121.1
129.2
103.2
176.5
114.5
117.9
135.6
106.6
184.6
120.8
130.0
135.8
145.4
129.2
131.1
93.2
99.2
175.1
203.0
1557
176.3 1 204.8
242.2
110.7
117.8 1 128.4
127.8
110.5 I 117.2
Transportation and warehousing
Information
98.2
91.9
102.0
114.3
121 .9
131.9
101 .0
102.1
106.6
100.2
86.3 1 81.8
104.9 1 106.1
107.0
122.1
122.9
131.4
I
112.1
142.0
108.8
88.7
108.7
134.4
I
104.0
117.8
102.0
105.7
97.3
136.0
113.2
131.4
93.5
106. 1
104.3
112.2 1 1137
107.2
101.8
105.9
100.5
95.7
91.5
140.2
128.9
119.2
120.1
142.8
190.3
89.3
85.1
102.6
122.5
100.7
106.5
97.1
135.4
129.0
218.9
92.2
105.8
138.4
104.8
108.4
99.0
138.0
134.7
247.7
97.2
96.3
98.6
101.5
112.7
114.2
105.1
135.8
120.4
105.7
154.0
115.9
128.1
103.3
114.9
138.3
113.2
120.0
111 .1
114.0
130.8
105.2 1 104.4
151.9
115.9
124.2
115.8
139.6
1345 I
125. 1
153.2
138.o
127.7
156.6
142.7
126.3
173.2
136.8
117.0
172.0
106.6
101 .3
99.2
102.7
105.8
100.5
113.0
103.8
101. 1
105.4
111 .3
103.0
109.4
104.5
101 .7
107.1
107.5
102.1
I
Finance and insurance
96.7 1
98.6 1 100.8
Real estate and rental and leasing
532111
53212
53223
Passenger car rental. ......... ......... ........................ .
Truck, trailer and RV rental and leasing . ...................
Video tape and disc rental ... .. ..... ....... ..... ............ ...
112.1
105.1
I
Professional, scientific, and technical
services
541?13
54181
541921
Tax preparation services ............................. ......... .
Advertising agencies .. .. . .. .. .... .... .. . .. . ..... .. ... . ....... ...
Photography studios, portrait... ......... . ......... ....... ... .
56151
56172
Travel agencies .. ................ ........... .. . . . . . ... . .. . .. . . . .
Janitorial services ...... . .. . .. ....... .. .. ....... .. ....... ..... ....
62151
621511
621512
Medical and diagnostic laboratories. ... ... .. ....... ·· ·····
Medical laboratories .... .. .... ..... . ......
.. ............. ..
Diagnostic imaging centers ....... ... .. . .. . ... ··············
7211
722
7221
7222
7223
7224
Traveler accommodations .......... ..................... ... . ..
Food services and drinking places ..... ········ .... .......
Full-service restaurants ................... .... .. .. . .... . ..... .
Limited-service eating places ...... ... . .. . ................... .
Special food services ... ..... .. ................ ... .. ...... .... ...
Drinking places, alcoholic beverages .... ..
.. .... .....
91.2
121.6
10, 1 I
Administrative and waste management
Health care and social assistance
I
Accommodation and food services
83.8
96.5
91 .9
96.0
100.0
136.2
80.8
102.7
99.1
103.1
108.1
123.0
90.7
101 .4
97.4
102.4
106.8
119.0
95.4
100.4
97.6
103.1
101.4
100.5
97.9
100.4
96.3
104.4
98.8
104.8
99.7
99.2
96.3
102.1
97.4
102.6
100.0
100.0
100.0
100.0
100.0
100.0
100.3
101.2
100.0
102.4
101 .9
100.5
90.6
81 .5
93.1
94.2
96.4 1
100.0
110.8
89.4
85.6
104.2
94.0
115.2
95.9
88.8
106.2
95.1
116.9
102.4
92.8
100.7
99.1
106.5
99.1
97.2
97.0
101 .6
102.8
100.0
100.0
100.0
100.0
100.0
104.7 1 106.5 1 108.5
103.8
106.4
106.6
107.3
103.9
94.9
104.4
109.1
110.9
90.6
93.5
84.0
113.2
105.0
102.2
115.6
108.4
105.3
108.2 1 111.5
104.3
107.4
105.7
118.0
Other services (except public
administration)
8111
81211
81221
8123
81292
Automotive repair and maintenance .. .... ..................
Hair, nail and skin care services ...... ... .. .. . .. . .. . .. . .. . .. .
Funeral homes and funeral services ...... ... ....... . ... .. . .
Drycieaning and laundry services ......... .. ... .. ....... ... .
Photofinishing ................ ... .......... .... . .. ....... ... .......
85.9
83.3
100.2
I
109.o
114.0
91.8
115.7
82.6
I
103.5
110.0
93.1
114.0
96.0
104.3
124.8
95.5
110.1
91.6
I
NOTE: Dash indicates data are not available.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
127
Current Labor Statistics: International Comparison
52. Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data
seasonally adjusted
Annual average
Country
2003
2004
2003
II
I
2004
Ill
II
I
IV
2005
Ill
I
IV
United States ........
6.0
5.5
5.8
6.1
6.1
5.9
5.6
5.6
5.5
5.4
5.3
Canada ..... .......... .
6.9
6.4
6.7
6.9
7.1
6.8
6.6
6.5
6.4
6.3
6.2
Australia ......... ......
6.1
5.5
6.2
6.2
6.0
5.8
5.7
5.6
5.6
5.2
5.1
Japan ....... ..... ..... ..
5.3
4.8
5.4
5.5
5.2
5.1
4.9
4.7
4.8
4.6
4.6
France .............. ...
9.6
9.8
9.3
9.5
9.7
9.8
9.7
9.8
9.8
9.8
9.9
Germany ........ .. ....
9.7
9.8
9.6
9.8
9.8
9.7
9.7
9.8
10.0
10.1
11.0
Italy ....... ..............
8.5
8.1
8.7
8.4
8.6
8.4
8.3
8.1
8.1
8.1
-
Sweden .... .......... ..
5.8
6.6
5.3
5.5
5.8
6.3
6.7
6.8
6.6
6.4
6.3
United Kinqdom ... ..
5.0
4.8
5.1
5.0
5.0
4.9
4.8
4.8
4.7
4.7
-
NOTE:
Dash indicates data not available. Quarterly figures for
for further qualifications and historical data, see Comparative
Japan, France, Germany, Italy, and Sweden are calculated by
Civilian Labor Force Statistics, Ten Countries, 1960-2004 (Bureau
applying annual adjustment factors to current published data, and
of
therefore
http://www.bls.gov/fls/home.htm.
should
be
viewed
as
less
precise
indicators
of
Labor
Statistics,
May
13, 2005),
on
the
Internet
at
unemployment under U.S. concepts than the annual figures . See
Monthly and quarterly unemployment rates , updated monthly, are
"Notes on the data" for information on breaks in series.
also on this site.
128
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August
2005
53. Annual data: employment status of the working-age population, approximating U.S. concepts, 1O countries
[Numbers in th ousands]
Emolovment status and countrv
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
129,200
14,233
8,613
65,470
131 ,056
14,336
8,770
65,780
24,676
39,074
22,592
7,152
4,418
28,124
132,304
14,439
8,995
65,990
24,743
133,943
14,604
9,115
66,450
136,297
14 ,863
9,204
67,200
137,673
15,115
9,339
67,240
139,368
15,389
9,414
67 ,090
143,734
15,892
9,752
66,860
24,985
39,142
22,674
7,301
4,459
28 ,243
25,109
39,415
22 ,749
7,536
4,418
28,406
25,434
39,754
23,000
7,617
4,402
28,478
25 ,764
39,375
23,172
7,848
4,430
28,782
144,863
16,367
9,907
66,240
26,686
39,499
23,728
8,285
4,544
29,340
146,510
16,729
10,092
66,010
26,870
39,591
24,021
8,353
4,567
29,562
147,401
16,956
10,244
65,760
38,980
22,574
7,208
4,460
28,135
142,583
15,632
9,590
66,990
26,078
39,301
23,357
8,149
4,489
28,957
66.6
65.1
63.9
63.1
55 .6
57.4
47.6
58.6
63 .7
62.4
66.6
64.8
64.5
62.9
55.4
57.1
47 .3
58.8
64 .1
62.4
66.8
64 .6
64.6
63.0
55.7
57.1
47 .3
59.2
64 .0
62.4
67.1
64 .9
64.3
63.2
55 .6
57.3
47 .3
60 .8
63.3
62 .5
67 .1
65 .3
64 .3
62 .8
55 .9
57 .7
47.6
61 .1
62 .8
62.5
67 .1
65 .7
64 .0
62.4
56.3
56.9
47.9
62 .6
62 .8
62.8
67.1
65.8
64.4
62 .0
56.6
56 .7
48.1
64 .5
63.8
62 .9
66 .8
65 .9
64.4
61.6
56 .9
56.7
48 .2
65 .6
63.7
62 .7
66.6
66.7
64.4
60.8
57 .2
56.5
66 .0
67.3
64.7
60.0
48.5
64.7
64 .0
62.9
66.2
67.3
64 .6
60.3
57.4
56.4
49.1
64 .9
64 .0
63.0
124,900
13,185
8,256
63,900
21,956
35,780
20,030
6,730
4,056
129,558
13,607
8,444
64 ,900
22,169
35,508
20,165
7,163
3,973
26,418
131 ,463
13,946
8,618
64,450
22 ,597
36 ,061
20,366
7,321
4,034
26,691
133,488
14,314
8,762
63,920
23,053
36,042
20,613
7,595
4,117
27 ,056
136,891
14,676
8,989
63,790
23,693
36,236
20,969
7,912
4,229
27,373
136,933
14,866
9,091
63,460
24 ,128
36,346
21 ,356
8,130
4,303
27 ,604
136,485
15,221
9,271
62,650
24 ,293
36,061
21,665
8,059
4,310
27,817
137,736
15,579
9,481
62,510
24 ,293
35,754
21 ,973
8,035
4,303
28,079
139,252
15,864
9,677
62,630
25,696
126,708
13,309
8,364
64,200
22,039
35,637
20,120
6,858
4,019
25,945
62 .9
59.2
63 .2
59.0
64.4
61 .9
60.3
59 .0
51.5
63.7
61 .9
60.1
58.4
52 .1
62 .7
62.4
62 .3
63.0
59.3
60 .9
49 .1
52 .0
64 .1
60 .3
59.3
60 .2
49 .7
64 .3
61 .2
59.2
60.9
49.2
63 .8
59 .5
59.0
61.0
49 .1
51 .6
60.3
57 .5
52 .1
60.7
57 .1
51 .9
62 .3
63.4
61 .2
57.1
42 .0
55.6
57 .7
41.9
57 .8
56.9
52 .3
42 .2
58.7
57.6
42 .6
60.6
58.4
52.2
43.2
62 .7
60.1
52 .2
43.8
63.9
51 .6
44 .3
51.0
44 .9
62.4
60.3
57 .3
58.2
58.5
59.1
59.4
60.5
59.5
62 .9
60.7
59.6
59 .8
60.0
7,404
1,254
739
2,100
2,787
3,200
2,544
478
404
2,439
7,236
1,295
751
2,250
2,946
3,505
2,555
443
440
2,298
6,739
1,256
759
2,300
2,940
3,907
2,584
374
445
1,987
6,210
1,169
721
2,790
2,837
3,693
2,634
296
368
1,788
5,880
1,075
5,692
956
602
3,200
2,385
3,065
2,388
237
260
1,584
6,801
1,026
661
3,400
2,226
3,109
2,164
208
227
1,486
8,378
1,146
8,774
1,150
611
3,500
2,577
3,838
2,048
318
264
1,484
8,149
1,092
567
3,130
2,630
3,899
1,960
396
300
1,414
5.6
8.7
8.2
3.2
11 .3
5.4
8.9
8.2
3.4
11 .8
4.9
8.4
8.3
3.4
11.7
4.5
4.2
4.0
4.7
7.7
7.7
4.1
11.2
7.0
6.9
4.7
10.5
9.0
11 .3
6.1
9.9
11.4
5.0
10.1
9.3
11.5
3.9
8.5
11 .0
3.2
9.7
8.5
3.8
7.1
5.8
7.0
6.4
5.4
9.0
8.7
8.7
2.7
5.1
5.5
6.4
5.5
4.8
9.8
9.8
8.1
4.7
8.4
6.3
6.5
6.8
5.1
8.4
7.9
9.2
2.5
5.0
6.0
6.9
6.1
5.3
9.6
8.2
11 .3
6.6
9.1
6.1
6.3
4.8
9.1
7.8
10.2
2.9
5.8
6.0
5.5
5.1
5.2
5.8
5.0
6.6
4.8
Civilian labor force
United States
Canada
Austral ia
Japan ...... .
France.
Germany ..
Italy .....
Netherlands ..... ................... .
Sweden ..
United Kingdom .. .. ........
. ... .... ..... . ... .
Participation rate
24,490
39,102
22,771
7,014
4,444
28,094
26,354
39,456
23,520
8,338
4,530
29,090
39,698
24,065
8,457
4,576
29,748
1
United States .... ..... ........... . .. ... .. .. .
Canada .... .... ..... ... .... .......... ........... .. .
Australia ..
. ..... . ............................ . .
Japan ..... ....... ... ... .
France ...... ... .. .. .. ... ....... ......... . .... .. ... .. .
Germany ......... ... ......... ........... ... .. .
66.3
65.5
63.5
63.3
55.4
57 .8
Italy.......................... .. ...
Netherlands .... ....... . ..... .... .
Sweden
United Kingdom
. ..... . ..... .. ...... .
48.3
57.9
64 .5
62 .6
United States ...... ....... .
Canada .. .. ... ............ .............. .
Australia ... .......... . ......... ........ . .... .. ... .
Japan .. ....... .......... . .... .
Fran ce
Germany ..
Italy
Netherlands ..
Sweden
...... . ... ....... ...... . .
Un ited Kingdom ....... .. .. .... .................. .
120,259
12,694
7,699
63 ,820
21 ,714
35 ,989
20,543
6,572
4,028
25,165
123,060
12,960
7,942
61 .7
58.4
56 .8
61.7
49.2
62.5
58.9
57.8
61 .3
49.0
Sweden ................ ........ .. ... .. ... .......... ......... .
53 .2
43 .6
54.3
58 .5
52 .6
42.5
54.6
57.6
United Kingdom ..... . ..... ..... .. .. ......... ... ..... .
56.0
57.0
8,940
1,538
914
1,660
2,776
3,113
2,227
442
416
2,930
7,996
1,376
49.1
65 .5
63.7
63.0
Employed
63,860
21,750
35 ,756
20,171
6,664
3,992
25,691
35,796
22,105
8,061
4,276
28 ,334
Employment-population ratio 2
Un ited States
Canada .. .......... .. ......... .. .... .
Australia .......
. .. ........................ .
Japan ..... ................. ...... .. ... .... .. ... .... .
Fran ce ................... .... ..... ....... ......... .
Germany ... .......................... .. ..
Italy .. ............ ... .......... .... .
Netherlands ........ . ...... ...... ... .
52.4
42 .0
54.9
58.3
57 .0
59.6
59.4
50.4
52 .1
45.1
62.4
59.5
Unemployed
United ?!ates. .
. ......... .. . ...... .... ..... .
Canada .. .. ... . .... ... .. ..... .... .... .... . ... ............ .. .
Australia .... ... . ..... .... ... ...... ... ..... .. ... ............ .
Japan ................... ...... ... ... ..... ... .. . .. ........... .
France ............ .... ....... .... .. ... .. . .... ... ...... ..... .
Germany .......... . ...... .. ...... . ........ . .. .. .. .... ..... .
Italy ....... .. ... ..... .. .... .... ..... .. ....... . .... .
Netherlands ... ............... .. .............. ........... .
Sweden .... .. .. ... ..... ... . .. ........ . .. .. . .
Un ited Kingdom .. .. .... . .
829
1,920
2,926
3,318
2,421
489
426
2,433
652
3,170
2,711
3,333
2,559
253
313
1,726
636
3,590
2,393
3,438
2,062
227
234
1,524
Unemployment rate
Un ited States
..... .. ... ... ... ........ .
Canada .... . ..... ........... .......... . ... ... . .
Australia ..... ... ... ... ........ . .. . ... ... ...... .. ........... .
Japan ... .. . .. .... . .. . ..... ...... . .... ..... .. .. .. .... ... ... .
. .. . .... ... ... ..... .
France .....
Germany
................. .
Italy .............................. .
Netherlands
. .. ... ................... .
Sweden .............. .. ......
United Kingdom ...... .. ..... .. .......... .. ... ..... .. ... .. .
1
6.9
10.8
10.6
2.5
11 .3
8.0
9.8
6.3
9.4
10.4
6.1
9.6
9.4
2.9
11 .9
8.5
10.7
6.8
9.6
8.7
Labor force as a percent of the working-age population.
2
Employment as a percent of the working-age population .
NOTE : Dash indicates data not available. See "Notes on the data" for
8.7
9.9
8.1
7.0
For further qualifications and historical data, see Comparative Civilian Labor Force Statistics,
'Ten Countries, 1960-2004 (Bureau of Labor Statistics, May 13, 2005) , on the Internet at
http://www.bls.gov/fls/home.htm.
for information on breaks in series.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
August
2005
129
Current Labor Statistics:
International Comparison
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
I
Output per hour
United States ...... ...... .. .............
Canada ...... ..... ........... ·········· ··
Australia ................. ............. ...
Japan ................. .. ... ..... .... ......
Korea ......... ..... ......... ... .. .........
Taiwan ...... ... ·· ····· ·········· ..... . .
Belgium .... ..... . .......... ... ...... ... .
Denmark ......... ........................
France ........ .... ..... ..... .. ....... .... .
Germany ........................... ..... .
Italy .... ..... ..... ......... .... .. ...... ... ..
Netherlands ... .. ........................
Norway ................................ ...
Sweden ... ....... .............. .. .. ......
United Kingdom ... ...... ... .. ... ......
Output
United States .................... .. . . . .
Canada ........... ...... .............. .. ..
Australia ........... ......................
Japan ...... ............. ... ... ..... .. .....
Korea ........ ............... .. ......... ...
Taiwan ....................................
Belgium ... .......... ..... ................
Denmark ....... .. ..... ............. ......
France .. ..................................
Germany ..... ............................
Italy .... ... .... .. .... ... .... ......... .. .. ...
Netherlands .......... .. .................
Norway .... .. ... .. ......... ... ............
Sweden ..................................
United Kingdom ......... ... ............
Total hours
United States ......... ........ .. .. ......
Canada .... ....... .. ......... ........... ..
Australia ...... ..... ............ ....... .. .
Japan ...... ...... .. .................. .... .
Korea .... ..... ... ........ ........ .. .. .....
Taiwan ...................... .. .. ..... ....
Belgium ...... ..... .. ..... ...... ..........
Denmark ....... ..... ....... .......... ...
France ........... .. ..... ...... ............
Germany .... ... .. ... ......... .. ... . . ..
Italy ..... ......... ... .. ...... .. ... ... .. ... ..
Netherlands ............... .. .. .. ....... .
Norway ... .............. ...... ... .........
Sweden ..... .. ...... ........... ...... ....
United Kingdom .................... ...
Hourly compensation
(national currency basis)
United States .......... .. ... .. ... .... ...
Canada .. .. ...... .... ... .................
Australia .... ... ....... ..... ..............
-
-
13.9
37.7
-
-
-
-
-
18.0
25.2
19.9
29.2
24.6
18.8
37.6
27.3
30.0
32.9
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
99.0
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
103.5
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
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
106.6
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
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
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
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
37.8
0.0
54.9
-
-
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
-
70.5
72 .9
69.5
63.6
-
-
77 .8
104.3
-
-
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
-
-
Belgium ... .... ... ........ ..... ...........
Denmark .. .. ..... ............ ... .... .. ...
France .. ..... ... .. ....... ... ... ... ....... .
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
Norway .... ..... ....... ... ....... ... ..... .
Sweden ........ ..... ...... .... ...........
United Kingdom ..... .. .................
See notes at end of table.
130
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
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
113.5
196.5
134.8
94.5
98.9
81 .9
I
Japan ....... ............ ..................
Korea ... ......... .. .......... .. .. .. .... ...
Taiwan .............. ... ... .. .. .... ..... ...
Germany ...... .... .. .. ....... ........... .
Italy .... .... .. ...... .. ... .... ...............
Netherlands .............. ....... .. ......
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
29 .6
52.5
45.1
41 .2
53.6
30.4
60.5
39.0
37.3
32.0
August 2005
123.8
290.9
146.7
150.0
139.1
148.9
140.0
164.6
154.3
160.3
54. Continued- Annual indexes of manufacturing productivity and related measures, 15 economies
Measure and economy
Unit labor costs
(national currency basis)
United States ............... ....... .....
Canada ................. .................
Australia ........ ... ..... ..... ........ ... .
Japan .....................................
Korea ....... ..............................
Taiwan ...................................
Belgium ..... .. ................. ........ .
Denmark ........................... ......
France .. ... ... .... ........ ... ............
Germany ............. .... ... .... ..... ....
Italy .... ......... ...... ....... ......... .....
Netherlands ........... ............ ......
Norway ......... ........... ......... ..... .
Sweden ................ ..................
United Kingdom ....... ........ .... .....
Unit labor costs
(U .S. dollar basis)
United States ....... ...... .. .... .... ....
Canada ..................................
Australia .................................
Japan .....................................
Korea ................................... ..
Taiwan ... .. ...... ............. ..... ......
Belgium .......... .. ........ ...... .. .. ... .
Denmark .. ............. .............. ....
France .................. .. ... ........... ..
Germany ........... .. .... .. .. ........ ....
1970
1980
1990
1991
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
-
-
26.4
31.1
78.8
65.2
93.7
94.6
94.2
95.9
84.2
95.9
93.0
95.0
96.8
90 .3
90.7
91 .1
94.2
92 .9
93.0
97.6
99.6
97.5
97.5
94.1
96.8
98.1
97.6
99.3
93.1
98.0
95.7
99.2
100.0
100.0
100.6
96.4
99.8
101.0
105.4
103.0
102.3
102.2
102.0
104.5
104.5
102.4
101.9
90.8
100.7
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
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
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
90.9
97.2
113.5
87.8
113.1
85.3
96.4
109.0
87.2
111.1
121.1
108.8
141 .3
80.2
119.2
92.3
99.4
93.7
98.0
100.1
83.9
93.0
89.7
89.5
92 .7
94.1
87.3
93.3
87.9
93.6
91.3
93.9
97.6
105.1
103.3
91.8
100.3
91.1
92.3
92.0
93.1
87.5
97 .3
90 .0
95.0
96.3
100.0
100.6
90.3
92.3
115.3
102.6
98.1
95.1
95.1
95.3
98.7
81 .8
96.9
89.2
67.8
85.6
94.8
83.0
107.8
131 .6
124.3
99.2
105.2
103.6
102.5
114.2
78.0
104.8
106.4
70.0
91.6
93.5
86.4
115.1
109.5
126.3
95.4
99.1
107.0
101.2
111 .6
87.7
100.0
106.6
77 .3
93.4
91 .9
84.0
109.4
97.4
103.4
89.5
82.4
90.2
83.3
94.0
80.6
87.0
102.1
65.4
100.4
92.8
78.8
92.6
92.2
68.4
77.4
81.6
91.7
79.1
92.9
78.2
87 .2
103.5
61.5
106.5
91.9
77.2
97.3
101.0
72 .7
78.3
80 .2
89.3
75.3
91.5
76.2
84.3
102.2
56.4
104.7
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
93.9
76.0
79.4
88.0
68.5
69.4
68.4
77.8
62.6
79.5
66.2
74.5
93.7
47 .6
94.0
90 .9
74.8
84 .0
88.9
71.0
62.1
72.6
83.5
66.5
83.9
72.9
82 .1
110.0
48.1
101.4
1960
-
-
-
31 .1
43.6
92.1
-
-
-
23.8
41.7
;;::l.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
19.4
27.0
13.4
19.3
23.4
25.7
10.4
17.1
Italy ........................................ 14.3
22.3
Netherlands .... .. ........ ..... .......... 15.3
24.5
17.4
Norway ....... ......... ... .. .. ..... .. ..... 11.0
23.1
Sweden .................................. 16.9
United Kingdom ........................ 15.6
19.1
NOTE: Data for Germany for years before 1991 are
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
43.4
88.3
58.1
83.9
59.6
55.7
77.5
62.9
70.2
77.6
for the
98.5
82.8
98.9
125.8
106.8
99.0
94.2
89.4
93.4
98.2
77.9
93.2
92.3
64.0
86.2
former West Germany. Data for 1991
84.7
113.5
82.7
109.6
88.0
110.8
126.2
112.6
144.9
78.6
118.9
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
onward are for unified Germany. Dash 1nd1cates data not available
Monthly Labor Review
August
2005
131
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
10.9
5.7
100.9
11 .6
5.9
11 2.2
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
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
Agriculture, forestry, and fishings
Total cases . ........ .. ... ... ....... .... . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..... ....... .... .
Lost workday cases ..... ..... ........ ....... .......... ............... ...
Lost workdays .. .......... ... .. .......................... .. ............ .................. .. .
Mining
Total cases ................. .... .......................... .. .. .
Lost workday cases .................. .. ...................................... .
Lost workdays ................................ ... ....... ...........................
Construction
Total cases ................... ..... .. .... ... .. ........................... . .
Lost workday cases......................................... .......... ..... ............. .
Lost workdays .........................................
General building contractors:
Total cases ............. ... ..... ............... ... ... ....... ..... .. .......... ...... .
Lost workday cases ................................ ...........
Lost workdays ..................................... ..... ......... .. .... ........ ............ .
14.3
6.8
143.3
14.2
6.7
147.9
13.0
6.1
148.1
13.1
5.8
16 1.9
12.2
5.5
11.8
5.5
10.6
4.9
9.9
4.5
9.5
4.4
8.8
4 .0
8.6
4.2
8.3
4 .1
7.9
4.0
13.9
6.5
137.3
13.4
6.4
137.6
12.0
5.5
132.0
12.2
5.4
142.7
11 .5
5.1
10.9
5.1
9.8
4.4
9.0
4.0
8.5
3.7
8.4
3.9
8.0
3.7
7.8
3.9
6.9
3.5
Heavv construction. except buiidina:
Totai cases ....... ...... ... .......... .................... .... ... .... .. ... ... ........ .
Lost workday cases .............. ..... .
Lost workdays ............... .............. . ...... .. ... ................................... .
13.8
6.5
147.1
13.8
6.3
144.6
12.8
6.0
160.1
12.1
5.4
165.8
11 .1
5.1
10.2
5.0
9.9
4.8
9.0
4.3
8.7
4.3
8.2
4 .1
7.8
3.8
7.6
3.7
7.8
4.0
Special trades contractors:
Totai 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
Total cases ........................ ..... ....................... ... ..... ..... ... ... . .
Lost workday cases .. .. .... ... .. ...................... .
Lost workdays ... ... .... .. ... ........................... .
13.1
5.8
113.0
13.2
5.8
120.7
12.7
5.6
121 .5
12.5
5.4
124.6
12. 1
5.3
12.2
5.5
11 .6
5.3
10.6
4.9
10.3
4.8
9.7
4.7
9.2
4 .6
9.0
4.5
8.1
4.1
Durable goods:
Total cases ........................ .... .. .... ....... ........................ . .
Lost workday cases ............ .... ... ... ....................................... ........ .
Lost workdays .... ..... .. ........................ ... ...................................... .
14.1
6.0
116.5
14.2
6.0
123.3
13.6
5.7
122.9
13.4
5.5
126.7
13. 1
5.4
13.5
5.7
12.8
5.6
11.6
5.1
11 .3
5.1
10.7
5.0
10.1
4.8
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:
Totai cases ............... ....... ................... ...... .. ........ ...... ... .... . .
Lost workday cases .......... ... ...... ... ................ ............................. .
Lost workdays ............. .. ........ ............................... ............ .
17.7
6.8
138.6
17.8
6.9
153.7
18.3
7.0
166.1
18.7
7.1
186.6
18.5
7.1
19.6
7.8
18.6
7.9
16.3
7.0
15.4
6.6
14.6
6.6
13.7
6.4
13.7
6.3
12.6
6.0
Instruments and related 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
Manufacturing
Lumber and wood products:
T otai cases .... .......... .. ....
. .. ....... ......... . .
Lost workday cases...... .....
.......................... .
Lost workdays.... ...
. ..... ........................... .
Furniture and fixtures:
Total cases ............................ .. ....................... .. ........ .. .. .. .
Lost workday cases....... .. ... ......
................... .... .. ..... .
Lost workdays ... .......... ................ .................... ..........................
Miscellaneous manufacturina industries:
T otai cases .............................. ......... .. .. ...... ................ ... .. . .
Lost workday cases .......... ............ .. ...... ............. ................ ... ......
Lost workdays .......... .. .......................... ....... .............................
11 .1
11 .3
11.3
10.7
10.0
9.9
9.1
9.5
8.9
8.1
8.4
7.2
6.4
5.1
5.1
5.1
5.0
4.6
4.5
4.3
4.4
4.2
3.9
4.0
3.6
3.2
97.6
113.1
104.0
108.2
L----'----'----'------'-----1.----''-----L----'----'----'------1.-----'---
S ee footnotes at end of table.
132
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
8.8
4.3
August 2005
1
55. Continued-Occupational injury and illness rates by industry, United States
Incidence rates per 100 workers
2
Industry and type of case
1989
1
1990
Food and kindred products:
Total cases.
Lost workday cases
Lost workdays
Tobacco oroducts:
Total cases .................... ..
Lost workday cases
Lost wurkdays ....... ... ..... ... ..
1992
1993
1994
4
1995
4
1996
4
3
1997
4
1998.
2000.
1999.
2001.
-+---+---+----+----+----+---1---- +---+---+-- --l-----
- - - - -- -- -- - - - -- -- - - -- - - 4 - - - l - Nondurable goods:
Total cases ............................. ...... .......... ....... ......... . ..
Lost workday cases
Lost workdays ... ....... .. ......... .. ... ..
1991
4
11 .61
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
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
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
10.3
10.1
9.7
4.1
8.7
4.0
8.2
4.1
7.8
7)
4.2
6.8
3.8
12.7
7.3
12.4
7.3
10.9
6.3
6.4
3.4
5.5
2.2
6.2
3.1
6.7
4 .2
6 .7
3.1
7.4
3.4
6.4 1
3.2
6.0
3.2
5.2
2.7
Textile mill oroducts:
Total cases .... ... ...... ... ...... ....
Lost workday cases .. .
Lost workdays ...
4.2
81.4
9.6
4.0
85.1
88.3
9.9
4 .2
87.1
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
7.4
3.6
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 oublishina :
Total cases ...... ... .. ...... ....
Lost workday cases ..
Lost workdays .............. .. ........ ...... ...... .... .... .....
6.9
3.3
63.8
6.9
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
69.8
6.7
3.2
74.5
Chemicals and allied oroducts:
Total cases .....
Lost workday case s .. .
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
55
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
6.6
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
3.3
68.1
4.3
2.2
3 .9
1.8
4 .1
1.8
3.7
1.9
2.9
1.4
Rubber and miscellaneous olast1cs oroducts:
Total cases ........ ............... ...... ... .................. . ..
Lost workday cases ................................... .
. .................... ...... ..
Lost workdays .. . ... . . . . . . . . . ......
16.2 i
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
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
9.8
4 .5
10.3
5.0
9.0
4.3
8.7
4.3
~2
~3
121.5
~6
~5
134.1
~3
~4
140.0
~1
~1
144.0
9.5
5.4
9.3
5.5
9.1
5.2
8.7
5.1
8.2
4 .8
7.3
4 .3
7.3
4.4
6.9
4 .3
6 .9
4.3
8.0
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
6.11
2.7
5.9
2.7
6.6
63.5
7.9
3.5
65.6
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
7.8
3.7
7.7
3.8
7.5
6.6
3.4
6.5
3.2
6 .5
3.3
6.3
3.6
5.8
3.1
5.3
2.8
Retail trade :
Total cases ................ ..... .. ................. .... . ..
Lost workday cases
Lost workdays .......................... .. .. ..... ... ...... .. ....................... .
8.1
3.4
60.0
8.1
3.4
63.2
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
5.9
2.5
5.7
2.4
69.1
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
.9
2.2
.9
.7
.5
1.8
.8
1.9
.8
1.8
.7
5.5
2.7
51 .2
6.0
2.8
56.4
6.2
2.8
60.0
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
2.2
Paoer and allied oroducts:
Total cases ................ .
Lost workday cases
Lost workdays ... ... .... ....... . ..
Petroleum and coal oroducts:
Total cases ........ .... .. .. .. ......... .. ................ ..... .......... .
Lost workday cases
Lost workdays ..................... ........ ..... ... ..... .. ...... ..
4.4
3.3
3.6
2.5
6.3
2.4
4.4
Transportation and public utilities
Total cases ..
Lost workday cases ..
Lost workdays ..
I
Wholesale and retail trade
Total cases .. ....... .. ........ ...... ........ ... .. .. ........ ...... ............ .
Lost workday cases
Lost workdays
3.6
3.6
82.4
7.7
3.3
Finance, insurance, and real estate
I
................................ .
Total cases...
Lost workday cases
Lost workdays ............... .
3.3
2.5
6.1
2.5
Services
Total cases ................ ...... ..... ............ . ..... ..
Lost workday cases
Lost workdays .. ....... .......... .... .
' Data for 1989 and subsequent years are based on the Standard Industrial Class-
7.1 1
N • number of injuries and illnesses or lost workdays ;
EH. total hours worked by all employees during the calendar year; and
ification Manual. 1987 Edition. For this reason, they are not strictly comparable with data
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
4.6
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
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
and
were
calculated
as
/N/EHl
X
200.000.
where:
Monthly Labor Review
August 2005
133
Current Labor Statistics: Injury and Illness
56. Fatal occupational injuries by event or exposure, 1998-2003
Fatalities
Event or exposure
1
1998-2002
average
2002
2
3
2003
Number
Number
Percent
Total. .. .... .... ... .. ........ ..... .... .. .... ... .... .. .................. ..... .... .... .. .
6.896
S.534
S.559
100
Transportation incidents ....... .... .. ... ...................... ........... ............. .
Highway incident. ............. ................................... ............. .. ...... .. .
Collision between vehicl es. mobile equipment.. .... .... .. ... .... ... ...
Moving in same direction . ... .. .. ... ... .... ... ... ... .. .............. ... .... ... .
Moving in opposite directions. oncoming . .. .. ........ ........ ... ... ..
Moving in intersection .... .. ......... ........ ............... .. .... .... ..... .... .
2.549
1,417
696
136
249
148
2.385
1.373
636
155
202
146
2.367
1.350
648
135
269
123
42
24
12
2
5
2
Vehicle stru ck stationary obj ect or equipment in roadway .. .... .
Vehi cle stru ck stationary obj ect. or equipment
on side of road ...... .. .. ........ .. .. ........ .. .. ................ ............. .
Noncollision incident. ..... .. ... .. ..... .... ... .... .... ..... .. ............ .... .. .... ... .
Jackknifed or overturned-no collision .... ...... ....... ..... ..... ..... .
Nonhighway (farm . industrial premises) incident
............ .. .... ... .. ... .... ........... ...... .
Overturned........ ....
Worker stru ck by a vehi cle ..
Rail vehi cle
Water vehicle ....... ....................... .... .... ... .. ... ... ... ... ........... .. ... .... ...
Aircraft. .... .. ...... .... ...... ...... ... .. .. ........ . .... .. . .. ...... ... .... .. .... . .
27
33
17
(4)
281
367
303
358
192
380
63
92
235
293
373
312
323
164
356
64
71
194
324
321
252
347
186
336
43
68
208
6
6
5
6
3
6
Assaults and violent acts ..... .... .... ................ .. .... ...... ............. ... .... .
Homicides ... ..... ............... . ..... ... .... .. ... ..
Shooting .. .. .... .. .. .... .... .. .. .. ... .... .. .... .
Stabbing ... ......... ...... ....... .. .. ... . .... .. .
Self-inflicted injuries ... ..... . ... .. ... ... ...... ... .
910
659
519
61
218
840
609
469
58
199
901
631
487
58
218
16
11
9
Contact with objects and equipment. ................ ....................... .
Struck by obj ect. .. ... ............ .... ... ... .... .... .. .. .... ........ ..
...... ........ ... .. .
Struck by falling obj ect.. . . . . .. . . .. .... ... ... ....
Struck by flying object. ...... ... .. ..... ....... .... .. .. . .............. .... ..
Caught in or compressed by equipment or objects ... .
Caught in running equipment or machinery ... .... . .... .......... ..... .
Cauynt in or crushed in collapsing materials . ..... .......... .... .. .. .... . .
963
547
336
55
272
141
126
872
505
302
38
231
110
116
911
530
322
58
237
121
126
16
10
6
Falls ..................................... ............ ...................... ....... .. ........... .
... .. .. ... ... .... .. ..
Fall to lower level
Fall from ladder .... . .. ....... ... ... .... ..... .. ... ... ...... ... ... .. ..... .... .......... .
Fall from roof. ... ......... ... ............ ......... ..
Fall from scaffold. staging ......... .... ....... .... ......... .. .. .. .... .... .... ...
Fall on same level. ... ... .. .... .. ... .. .. . .. .... .... ..... ... .. ... ..... ........... ........ .
738
651
113
152
91
65
719
638 1
126
143
88
64
691
601
113
127
85
69
12
11
2
2
2
Exposure to harmful substances or environments ................ .
Contact with electric current.. .... .. .... .. .. .... ......... ... ... ..... .... ...... ... ..
Contact with overhead power lines ... ... .... .... ..... ...... .... .......... ...
Contact with temperature extremes .... ... ... ... ........ .. .... ... .... ..... .. ...
Exposure to caustic. noxious. or allergenic substances ... .... ... ... . .
Inhalation of substances .. .. ... ..... ... ........ ..... .... ...................... ....
Oxygen deficiency ........... .... ........ ..... ...... .. .. ... .......... ..... ... .. ..... ... ..
Drowning. submersion ... ... .. ... ... ..... .. .... ... .. .. ........... ................. .
526
289
130
45
102
485
246
107
42
121
65
73
52
9
4
2
1
2
89
69
539
289
122
60
99
49
90
60
Fires and explosions .... .... ...................................................... .
190
165
198
4
1
Based on the 1992 BLS Occupational Injury and Illness
so
Since then,
additional
an
Classification Manual. Includes other events and exposures,
identified. bringing
such as bodily reaction. in addition to those shown separately.
2002 to S.534.
2
3
Excludes fatalities from the Sept. 11 , 2001. terrorist attacts.
The BLS news release of September 17. 2003. reported
a total of 5,524 fatal work injuries for calendar year 2003.
4
the
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
August 2005
4
4
2
2
10 job-related fatalities were
total job-related fatality count
for
Equal to or greater than 0.5 percent.
NOTE:
Totals
for
major categories
may
include sub-
categories not shown separately. Percentages may not add to
totals because of rounding.
134
1
4
Trying to find "spin-free" data
in this global economy?
The BLS Customer Service Guide makes it
easier for you to request information. The
Guide:
• Describes the information and data that
we produce
• Provides publication sources
• Explains how to obtain data electronically
• Lists names and telephone numbers of BLS
experts by topic
Get the Bureau of Labor Statistics
Customer Service Guide
To get your
tree copy
call:
(202) 691-5200
fax:
(202)691-7890
e-mail: blsdata_staff@bls.gov
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Where are you publishing
your research?
The Monthly Labor Review welcomes articles on the
·O
labor force, labor-management relations, business
1. 1
conditions, industry productivity, compensation,
occupational safety and health, demographic trends
and other economic developments. Papers should be
factual, and analytical, not polemical in tone.
Potential articles, as well as comments on
material published in the Review, should be
submitted to:
Editor-in-Chief
Monthly Labor Review
Bureau of Labor Statistics
Washington, DC 20212
or
mlr@bls.gov
Telephone: (202) 691-5900
Need more research, facts, and analysis?
Subscribe to Monthly Labor Review today!
---------------------- ...... -----------United States Government
INFORMATION
Credit card orders are welcome!
Order Processing Code :
Fax your orders (202) 512-2250
*5551
D
Phone your orders (202) 512-1800
YES,
please send _ _ _ subscriptions to:
Monthly Labor Review (MLR) at $49 each ($68.60 foreign) per year.
The total cost of my order is$ _ _ __
For privacy protection, check the box below:
D
Price includes regular shipping & handling and is subject to change.
Do not make my name available to other mailers
Check method of payment:
Name or title
(Please type or print)
Company name
Room, floor, suite
D
State
Zipcode+4
Daytime phone including area code
Purchase order number (optional)
Mail to:
Superintendent of Documents, P.O. Box 371954,
Pittsburgh, PA 15250-7954
Important: Please include this completed order form with your
remittance.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Superintendent of Documents
□ GPO Deposit Account
Street address
City
Check payable to:
□ VISA
□ MasterCard
I I I I I I I
~
□ Discover
IIIIIIIIIIIIIIIIII
(expiration date)
I II I I
Authorizing signature
Thank you for your order!
2/04
Obtaining information from the Bureau of Labor Statistics
Office or topic
Bureau of Labor Statistics
Information services
Internet address
www.bls.gov
www.bls.gov/opub/
E-mail
blsdata_s taff@bls.gov
Employment and unemployment
Employment, hours, and earnings:
National
State and local
Labor force statistics:
National
Local
DI-covered employment, wages
Occupational employment
Mass layoffs
Longitudinal data
Job openings and labor turnover
www.bls.gov/ces/
www.bls.gov/sae/
cesinfo@bls.gov
data_sa@bls.gov
www.bls.gov/cps/
www.bls.gov/lau/
www.bls.gov/cew/
www.bls.gov/oes/
www.bls.gov/mls/
www.bls.gov/nls/
www.bls.gov/jlt/
cpsinfo@bls.gov
lausinfo@bls.gov
cewinfo@bls.gov
oesinfo@bls.gov
mlsinfo@bls.gov
nls_info@bls.gov
Joltsinfo@bls.gov
Prices and living conditions
Consumer price indexes
Producer price indexes
Import and export price indexes
Consumer expenditures
www.bls.gov/cpi/
www.bls.gov/ppi/
www.bls.gov/rnxp/
www.bls.gov/cex/
cpi_info@bls.gov
ppi-info@bls.gov
mxpinfo@bls.gov
cexinfo@bls.gov
Compensation and working conditions
National Compensation Survey:
F.mployee benefits
Employment cost trends
Occupational compensation
Occupational illnesses, injuries
Fatal occupational injuries
Collective bargaining
www.bls.gov/ncs/
www.bls.gov/ebs/
www.bls.gov/ect/
www.bls.gov/ocs/
www.bls.gov/iif/
wwwstats.bls.gov/iif/
www.bls.gov/cba/
ocltinfo@bls.gov
ocltinfo@bls.gov
ocltinfo@bls.gov
ocltinfo@bls.gov
iifstaff@bls.gov
iifstaff@bls.gov
cbainfo@bls.gov
Productivity
Labor
Industry
Multi factor
www.bls.gov/lpc/
www.bls.gov/lpc/
www.bls.gov/mfp/
dprweb@bls.gov
dipsweb@bls.gov
dprweb@bls.gov
Projections
Employment
Occupation
www.bls.gov/emp/
www.bls.gov/oco/
oohinfo@bls.gov
oohinfo@bls.gov
International
www.t-b.gov/fls/
flshelp@bls.gov
Regional centers
Atlanta
Boston
Chicago
Dallas
Kansas City
New York
Philadelphia
San Francisco
www.bls.gov/ro4/
www.bls.gov/rol /
www.bls.gov/ro5/
www.bls.gov/ro6/
www.bls.gov/ro7/
www.bls.gov/ro2/
www.bls.gov/ro3/
www.bls .gov/ro9/
BLSinfoAtlanta@bls.gov
BLS infoBoston@bls.gov
BLSinfoChicago@bls.gov
BLSinfoDallas@bls.gov
BLSinfoKansasCity@bls.gov
BLSinfoNY@bls.gov
BLSinfoPhiladelphia@bls.gov
BLSinfoSF@bls.gov
Other Federal statistical agencies
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
www.fedstats.govI
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
@FedFRASER