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economy
■
Productivity under
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Benefit replacement rates
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Elaine L. Chao, Secretary
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Cover designed by Bruce Boyd
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Federal Reserve Bank of St. Louis
MONTHLY LABOR
REVIEW _ _ _ _ __ _ __
Volume 127, Number 11
November 2004
Annual measures of gross job gains and gross job losses
3
These statistics reveal the churning that underlies
net growth of employment among establishments
Joshua C. Pinkston and James R. Spletzer
Initial data from the Job Openings and Labor Turnover Survey
14
New data series show trends that are in line with other surveys,
and allow a more complete picture of the labor market
Kelly A. Clark
The U.S. ocean and coastal economy
24
The BLS Quarterly Census of Employment and Wages data
provide new industrial and geographic views of this economy
Charles S. Colgan
Industry productivity trends under NAICS
31
NA ICS-based productivity measures show strong
overall productivity growth during the 1990s and again after 2001
Matthew Russell, Paul Takac , and Lisa Usher
Federal statistics on healthcare benefits and cost trends
43
Federal Government statistical agencies provide a variety of information
on diverse aspects of the Nation's healthcare picture
John E. Buckley and Robert W. Van Giezen
Measuring defined benefit replacement rates with PenSync
57
Synthetic pension data created with regression and statistical matching procedures
evaluate the effectiveness of defined pensions in meeting the income needs of retirees
Jam es H. Moore, Jr.
Departments
Labor month in review
Precis
Publications received
Current labor statistics
Editor-in-Chief: William Park s • Executive Editor: Richard M. Devens •
Bake r, Kri sty S. Chri stianse n, Ric hard Hamilt on, Les li e Brown Joyner •
Catherine D. Bow man, Edith W. Peters
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Federal Reserve Bank of St. Louis
2
69
70
73
Managing Editor: Anna Huffman Hill • Editors: Brian I.
Boo k Rev iews: Ri chard Hamilton • Des ign a nd Layout:
•
t"tt~.;~
Labor In Month Review
; "i:/&t~· _
<
The November Review
Thomas Edison is credited with saying,
"Genius is one percent inspiration and
ninety-nine percent perspiration." However true that may be of genius, it is
entirely accurate in the field of economic
statistics. As Joshua C. Pinkston and
James R. Spletzer point out, there is
nothing easy about creating annual
measures of gross gains and losses in
employment from the quarterly statistics
that the Bureau of Labor Statistics collects; the only secret is to sweat the details. In the end, however, there is a clear
increase in economic understanding: "The
annual statistics show job gains and
losses over a year. The sum of quarterly
numbers looks at gains and losses during
a year." Each of these is the answer to a
different analytical question.
The Job Openings and Labor Turnover
Survey (JOLTS) was introduced to readers
of this Review in our December 2001 issue.
Kelly A. Clark, a co-author of that piece,
now shares some of the early findings of
that program. The basic trends in the
data are consistent with the results of
other surveys, but provide new insight
into the detailed working of the labor
market.
Charles S. Colgan uses data from the
Quarterly Census of Employment and
Wages to describe the "ocean economy"-as defined by sectors and industries
that use ocean resources as inputs-and
the "coastal economy"-as defined strictly by proximity to the oceans or Great
Lakes.
Matthew Russell, Paul Takac, and Lisa
Usher provide the latest chapter in the
adoption of the North American Industry
Classification System (NAICS). The
industry productivity data they work with
provide a detailed look at trends in output
per hour of labor.
John E. Buckley and Robert W. Van
Giezen survey the availability of Federal
Government statistics on healthcare
benefits and the cost of those benefits.
Their notes provide a very large num2
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
~
-
ber of very valuable links to more detailed information.
Social Security Administration
economist James H. Moore, Jr., contributes a report based on a synthetic
pension data set created by regression
and data matching techniques. One of
the calculations uses BLS data on
pension plans to estimate the income
replacement rate for retirees.
Occupations and poverty
The chance of being among the working
poor varies widely by occupation.
Workers in occupations requiring
higher education and characterized by
high earnings, such as managers and
professionals, were least likely to be
classified as working poor in 2002.
Only 2 percent of workers in these
occupations who had been in the labor
force more than half the year were
among the working poor.
On the other hand, persons employed in jobs that usually do not require
high levels of education and that are
characterized by low earnings were
more likely to be among the working
poor. For example, 10.3 percent of
service workers were classified as
working poor in 2002. Service
occupations, with 2.2 million working
poor, accounted for 29 .3 percent of all
those classified as the working poor.
These data are from the 2003 Annual
Social and Economic Supplement to the
Current Population Survey. For more
information, see A Profile of the
Working Poor, 2002, BLS Report 976.
Comparing factory
productivity and costs
Korea registered the largest gain in
manufacturing productivity in 2003 (9
percent). The increase in U.S.
manufacturing output per hour in 2003
was the second highest (6.8 percent).
Manufacturing productivity also
increased in all the compared
economies, except for Italy.
2004
As in 2002, U.S. productivity growth in
manufacturing in 2003 was substantially
above its average growth rate since 1979.
Seven of the other economies for which
comparisons are available also had 2003
productivity growth that exceeded their
annual average from 1979 through 2003.
Among the economies for which 2003
unit cost data are available, manufacturing
unit labor costs fell in U.S. dollar terms
only in Taiwan. In the United States, unit
labor costs in manufacturing rose 1.6
percent in 2003. Unit labor costs are
defined as the cost of labor input required
to produce one unit of output. They are
computed as nominal labor compensation
divided by real output.
There were double-digit increases in unit
labor costs (on a U.S. dollar basis) in 8 of the
13 economies studied. The widespread
increases in unit labor costs in U.S. dollar
terms are explained by the depreciation of
the dollar, particularly with respect to the
euro and other European currencies. The
U.S. dollar depreciated against the
currencies of all the economies, but the
depreciation was slight versus the Taiwan
dollar. For more information, see news
release, "International Comparisons of
Manufacturing Productivity and Unit Labor
Cost Trends, 2003," USDL 04-1945.
Women's earnings
Between 1979 and 2003, the earnings gap
between women and men narrowed for most
age groups. Overall, the women-to-men
earnings ratio was 80 percent in 2003, up
from 63 percent in 1979. The ratio of
women-to-men earnings among 16- to 24year-o lds was 93.3 percent in 2003,
compared with 78 .5 percent in 1979; that
for 25- to 34-year-olds was 87 percent in
2003, compared with 67.4percent in 1979.
Among 35- to 44-year-olds, women
earned 76.2 percent as much as men in 2003
and 58.3 percent in 1979, while among 45to 54-year-olds, women earned 73 percent
as much as men in 2003 and 56.9 percent
as much in 1979. For more information, see
Highlights of Women 's Earnings in 2003,
BLS Report
978.
□
Job Gains and Losses
,·»w-·~<':"-·
,;,W,@"i
Annual measures of gross
job gains and gross job losses
As a complement to the quarterly gross job flow statistics,
annual gross job gains and losses statistics reveal
the tremendous amount of churning that underlies
the net growth of employment
Joshua C. Pinkston
and
James R. Spletzer
Joshua C. Pinkston is a
research economist
and James R. Spletzer
is a senior research
economist in the
Office of Employment
and Unemployment
Statistics, Bureau of
Labor Statistics. E-mail:
Pinkston.Josh@bls.gov
and
Spletzer.Jim@bls.gov.
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Federal Reserve Bank of St. Louis
T
he new Business Employment Dynamics
data series from the Bureau of Labor
Statistics documents the quarterly gross
job gains and losses from 1992 to the present.
These data quantify the sizable number of jobs
that appear and disappear in the U.S. economy
each quarter, adding a new level of understanding
that traditional employment statistics cannot
provide. For example, these data show that the
2001 recession was characterized by a temporary
spike in gross job losses accompanied by a
decline in gross job gains that has yet to return
to pre-recessionary levels.'
This article builds on the quarterly Business
Employment Dynamics statistics by presenting
annual tabulations of gross job gains and losses.
These annual statistics provide information
about labor market dynamics in two ways. First,
in comparison to the quarterly statistics, the
annual statistics highlight the transitory nature
of short-run establishment level employment
changes. Many quarterly expansions and
contractions are temporary, and reverse themse Ives in other quarters during the year.
Furthermore, this article finds that a significant
number of establishment openings in the
quarterly statistics are continuous establishments that close and re-open during the year.
Second, the annual statistics provide a framework
for a longer run view of how establishments grow
and decline, and thus set the stage for understanding business survival. Particularly, this
article explains how establishment openings and
closings contribute to employment growth in
both the short run and in the longer run.
This article also highlights the importance of
understanding the difference between the annual
statistics presented in this article versus
"annualized" statistics created by summing four
quarterly statistics. Although this latter methodology is standard for creating and analyzing
net employment growth statistics over different
frequencies, the sum of four quarterly gross job
flow statistics is not the same as annual gross
job flow statistics. These two approaches
measure different concepts. The annual gross
job flow statistics examine the number of jobs
gained and the number of jobs lost over the year.
The sum of four quarterly gross job flow
statistics examine the number of jobs gained and
the number of jobs lost during the year. Whereas
the annual tabulations always have a clear
interpretation, this analysis shows that the sum
of four quarterly statistics (or the sum of 12
monthly statistics) can sometimes produce
results that are difficult to interpret.
The article begins by describing the
construction of annual statistics from the
Business Employment Dynamics quarterly
microdata. The algorithm for creating the annual
statistics is more complicated than a simple
comparison of two points in time that are I year
apart. The article then presents the annual gross
job gains and gross job loss statistics. The
Monthly Labor Review
November
2004
3
Job Gains and Losses
analysis focuses on a comparison of how the annual statistics
relate to the quarterly statistics, and the value added of the
annual statistics relative to the quarterly statistics. The article
concludes with a discussion of how annual gross job gains
and losses statistics provide a crosswalk between the new
BLS qua terly statistics and the annual statistics in much of
the existing gross job flows literature.
Sources, definitions, and the algorithm
The quarterly BLS Business Employment Dynamics data
series is constructed from microdata originating from the
Quarterly Census of Employment and Wages (QCEW), also
known as the ES-202 program. All employers subject to State
unemployment insurance laws are required to submit
quarterly contribution reports detailing their monthly
employment and quarterly wages to the State Employment
Security Agencies. After the microdata are edited and, if
necessary, corrected by the State Labor Market Information
staff, the States submit these data and other business
identification information to the Bureau of Labor Statistics as
part of the Federal -State cooperative QCEW program. The
data gathered in the QCEW program are a comprehensive and
accurate source of employment and wages, and provide a
virtual census (98 percent) of employees on nonfarm payrolls.
The quarterly gross job gains and gross job loss statistics
created in the BLS Business Employment Dynamics program
are tabulated by linking establishments across quarters, and
establishments are then classified as opening, expanding,
contracting, closing, or not changing their employment level.
The accuracy of the Business Employment Dynamics
statistics depends on the quality of the establishment level
microdata being reported to the States. Gross job gains are
the sum of all en1ployment increases at either opening or
expanding establishments; gross job losses are the sum of all
employment losses at either closing or contracting
establishments. The familiar net change in employment is
the difference between the gross jobs gained and the gross
jobs lost. 2
The quarterly Business Employment Dynamics microdata
provide the foundation for tabulations of annual gross job
gains and losses statistics. Creating the annual statistics is
more complicated than comparing two quarters of microdata
that are 1 year apart. The difficulties come from trying to
follow a specific establishment across several quarters,
esvcu~lly through periods of ownership changes, restructurings, or changes in how multi-establishment firms
report their unemployment insurance data to the States. The
annual statistics presented in this article are based on an
extension of the existing longitudinal linkage algorithm
developed by BLS for the quarterly gross job gains and losses
data series.
4
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
As part of the existing process of linking establishments
across consecutive quarters in the Business Employment
Dynamics program, BLS and the States identify what are
termed breakouts and consolidations. The term "breakout"
refers to a single establishment splitting into multiple
establishments, and the term "consolidation" refers to
multiple establishments merging into a single establishment.
Breakouts and consolidations may be actual economic events
representing business expansions and contractions, or
merely administrative reporting changes due to how an
employer with multiple establishments within a State reports
its data. Although BLS and the States continuously work
with employers to obtain data at the establishment level, some
employers with multiple establishments within a State report
their total employment and wages in a consolidated manner.
Occasionally, an employer reporting consolidated data will
disaggregate its data to the worksite level (or, much less
frequently, vice-versa).
Establishments involved in breakouts and consolidations
need to be treated with care when constructing gross job
gains and losses statistics. For example, an employer with
multiple establishments in the State that disaggregates its
data from a statewide level to a worksite reporting level would
initially appear in the microdata to be a closing of an existing
large establishment and the opening of several new small
establishments. The record linkage system used in the
Business Employment Dynamics program strives to identify
the relationships between the establishments that are
involved in all one-to-many breakouts and many-to-one
consolidations. These establishments can then be treated as
continuous, rather than as openings and closings, when
constructing the quarterly gross job gains and losses
statistics. 3
Breakouts and consolidations· cause additional difficulties
when users attempt to create annual gross job gains and
losses statistics. For example, if one wanted to accurately
track establishments from March of one year to March of the
following year, information on breakouts and consolidations
from all quarters within the year needs to be taken into account
in order to understand business survival and thus avoid
spuriously defining openings and closings.
The annual gross job gains and losses statistics reported
in this article are based upon an algorithm that takes into
account information on breakouts and consolidations from
all quarters within the year. Previous research shows that an
algorithm that uses all information within the year is preferable
to a more nai"ve approach which takes two quarters of
microdata that are I year apart and links establishments
without accounting for breakouts and consolidations that
occur within the year. Such a nai"ve approach, relative to the
algorithm used here, increases the annual gross job gains
and losses statistics by roughly 7 percent to 9 percent. 4
Quarterly and annual private-sector gross job gains and job losses, first quarter 1998 through first quarter 2002
!Not seasonallv adiustedl
Employment
Gross job gains
Period
Previous
quarter /year
Current
quarter /year
Change
Gross job losses
Expanding
establish-
Opening
establish-
Contracting
establish-
ments
ments
ments
Closing
establishments
Quarterly:
1998: I to 1998: 11 •• .•• .•.• •.••••• .••. •• .•.••.•
1998: II to 1998: Ill ..... ..... ... ....... ... .. ..
1998: Ill to 1998: IV ............... ........ ...
1998: IV to 1999: 1 •• ••... ••••••.••• •. .••.••••••
102,201,556
105,745,572
105,895,205
106,669,216
105,745,572
105,895,205
106,669,216
104,637,156
3,544,016
149,633
774,011
-2,032 ,060
7,823,083
6,045,188
6,872,921
5,881,407
2,443,361
1,696,143
1,600,934
2,305,245
5,128,625
6,049,428
6,108,728
7,621 ,358
1,593 ,803
1,542,270
1,591 ,116
2,597,354
Annual : .
1998: I to 1999: I ................... ..... .. .. ....
102,201 ,556
104,637,156
2,435 ,600
10,311 ,106
5,946,992
8,515 ,309
5,307 ,189
Quarterly :
1999: I to 1999: II ..... ... .... .. .. ........ .. .... .
1999: II to 1999: Ill ..... ..... ....... .... .... ...
1999: Ill to 1999: IV ...... .. .. .... ..... ...... ..
1999: IV to 2000: 1 •• ••••• •••••• ••••••••• •••••••
104,637,156
108,121 ,039
108,182,154
109,278,661
108,121 ,039
108,182,154
109,278,661
107,672,227
3,483,883
61 ,115
1,096,507
-1,606,434
8,075 ,511
6,316 ,593
7,207,652
6,097,257
2,285,719
1,705,902
1,823,796
2,111,495
5,311 ,276
6,277 ,917
6,298,406
7,531 ,814
1,566,071
1,683,463
1,636,535
2,283,372
Annual:
1999: I to 2000: I .......... .. .... ..... ... ...... ..
104,637,156
107,672,227
3,035,071
10,692,723
5,712,036
8,391,177
4,978 ,511
Quarterly :
2000: I to 2000 : II ... ... ..... .. .... ... ... ...... ..
2000: II to 2000: Ill ..... .......... ......... ....
2000: Ill to 2000: IV .......... .. ... ............
2000: IV to 2001 : I .. .... ... ... .. .... .... .... ...
107,672,227
111 ,115,514
110,783,450
111 ,182,910
111 ,115,514
110,783,450
111,182,910
108,561,077
3,443,287
-332 ,064
399,460
-2,621,833
8,269 ,019
6,284 ,783
6,985,872
5,924,318
2,037,883
1,631 ,545
1,641,856
1,955,772
5,384 ,637
6,582,852
6,622,454
8,018,068
1,478,978
1,665,540
1,605,814
2,483,855
Annual :
2000: I to 2001: I ..... ........... ............ ....
107,672,227
108,561,077
888,850
10,240,477
5,191 ,521
9,363,412
5,179,736
Quarterly:
2001: I to 2001 : II ...... ....... .... ........... .. ..
2001 : II to 2001 : Ill .... .......... ... ...... .......
2001 : Ill to 2001: IV .. .. ........ .................
2001: IV to 2002 : I ...... ............... ....... ...
108,561,077
110,734,261
109,000,401
108,173,134
110,734,261
109,000,401
108,173,134
105,810,039
2,173,184
-1,733,860
-827,267
-2,363,095
7,671,463
5,519,373
6,147,166
5,512 ,394
2,063,725
1,521,404
1,648,088
1,993,961
5,936,261
7,023,453
7,025,677
7,560,400
1,625,743
1,751 ,184
1,596 ,844
2,309,050
Annual :.
2001 : I to 2002 : I .... ........ ....... .. .... ..... ...
108,561,077
105,810,039
-2,751 ,038
8,752 ,075
5,201 ,011
11 ,148,760
5,555 ,364
SouRcE:
Authors' calculations using microdata from the
BLS
Business Employment Dynamics program.
This article uses data from the first quarter of 1998 through
the first quarter of 2002. The quarterly statistics that we present
replicate the official (seasonally unadjusted) statistics from
the BLS Bu s iness Employment Dynamics program. 5
Employment is defined as the number of workers covered by
unemployment insurance and earning wages during the pay
period that includes the 12th of the month. The gross job gains
and gross job loss statistics use reported employment data in
the third month of the quaner as the measure of the
establishment 's quarterly employment. Thus, employment
growth for the second quarter refers to employment growth
from March to June. To be consistent with much of the gross
job flows literature, many of the annual statistics that this
article presents measure employment growth from March of
one year to March of the following year.
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Federal Reserve Bank of St. Louis
Annual gross job gains and losses
Based on quarterly and annual tabulations of Bu si ness
Employment Dynamics statistics, tables I through 4 provide
the following statistics: table I presents the employment
levels in the current and previous time periods, the net
employment change, and the gross job gains and the gross job
losses. Table 2 shows these employment changes as rates
rather than levels. 6 The number and flows of establishments
underlying the employment statistics in table I are presented
in table 3, with corresponding rates prese nted in table 4. None
of the statistics in tables 1-4 are seasonally adjusted.
In March 2001 , there were 108,561 ,077 private sector jobs,
and I year later in March 2002, there wm· I05,8 I 0,039 private
sector jobs. (See the bottom row of table 1.) This annual
Monthly Labor Review
November
2004
5
Job Gains and Losses
decline in employment of 2,751,038 jobs is the sum of the four
seasonally unadjusted quarterly changes during the year: an
increase of 2,173,184 jobs between the first and second
quarters of 2001, and declines of 1,733,860, 827,267, and
2,363,095 jobs, respectively, during the next three quarters.
In percentage terms, this annual decline in employment was
2.57 percent. (See table 2.) This annual percentage decline is
also the sum of the four seasonally unadjusted quarterly
changes (1.98 percent, -1 .58 percent, --0.76 percent, and-2.21
percent).
This annual decline in employment is equivalent to stating
that fewer jobs were gained than were lost. The bottom row
of table 1 shows that for the year ending in March 2002,
employment in expanding establishments grew by 8,752,075
jobs, and employment in opening establishments grew by
5,201,011 jobs. The level of gross job gains was 13,953,086
11·••11=---
jobs during the year, a rate of 13.02 percent. Employment in
contracting establishments declined by 11 ,148,760 jobs, and
closing establishments accounted for the loss of 5,555,364
jobs. The level of gross job losses was 16,704,124 jobs during
the year, a rate of 15.58 percent. The difference between the
gross job gains and the gross job losses is the net
employment decline of 2,751,038 jobs, a rate of-2.57 percent.
An important component of the Business Employment
Dynamics data series is the establishment counts underlying
the gross job gains and losses. Looking at the annual
statistics for March 2001 to March 2002 in tables 3 and 4, one
can see that there were 1,633,498 expanding establishments
(26.2 percent of all establishments), and 790,237 establishments ( 12.7 percent) opening during the year. There were
1,735,071 contracting establishments (27.8 percent), and
785,786 establishments ( 12.6 percent) closing during the year.
Quarterly and annual private-sector gross job gains and job losses as a percentage of employment, first
quarter 1998 through first quarter 2002
[In percent]
Gross job gains
Period
Total
Expanding
establish-
Opening
establish-
ments
ments
Total
Contracting
establish-
Closing
establish-
ments
ments
Quarterly:
1998: I to 1998: II ..................... .
1998: II to 1998: 111 ••.••..• •. •..•••• •.•.
1998: Ill to 1998: IV .. .... ... ... .. .....
1998: IV to 1999: 1... ...•.••••. .. •.••.••
3.41
.14
.73
-1 .92
9.87
7.32
7.97
7.75
7.52
5.71
6.47
5.57
2.35
1.60
1.51
2.18
6.47
7.17
7.24
9.67
4.93
5.72
5.75
7.21
1.53
1.46
1.50
2.46
Annual:
1998: I to 1999 : I .. .. .... ...... ...... ....
2.36
15.72
9.97
5.75
13.37
8.23
5.13
Quarterly:
1999: I to 1999: 11 ..•..•..•.••.• .•..• •••.
1999: II to 1999: 111 •• ••••••• ••• •.• ••• .• •
1999: Ill to 1999 : IV .. .. .... ....... .. ..
1999: IV to 2000: 1.•.•••.•••• .•• .•. .•.•
3.27
.06
1.01
-1.48
9.74
7.42
8.31
7.57
7.59
5.84
6.63
5.62
2.15
1.58
1.68
1.95
6.46
7.36
7.30
9.05
4.99
5.80
5.79
6.94
1.47
1.56
1.51
2.10
Annual :
1999: I to 2000 : I .... ....... .. ...........
2.86
15.45
10.07
5.38
12.59
7.90
4.69
Quarterly:
2000: I to 2000: II ....... .... ... ..... .. ..
2000: II to 2000 : 111 ••.• •.•.••.•.....••••
2000: Ill to 2000 : IV ...................
2000: IV to 2001 : 1•••••••••••••••••••.•
3.15
- .30
.36
-2 .39
9.42
7.14
7.77
7.17
7.56
5.66
6.29
5.39
1.86
1.47
1.48
1.78
6.27
7.43
7.41
9.56
4.92
5.93
5.97
7.30
1.35
1.50
1.45
2.26
Annual:
2000: I to 2001 : I .. ..... ...... .... ... ... .
.82
14.27
9.47
4.80
13.45
8.66
4.79
Quarterly:
2001: I to 2001 : 11 .•• .•• .••.••••..••..•••
2001: II to 2001 : 111 •• •• •.••.••.•• .• ...• .
2001: Ill to 2001 : IV .......... .........
2001 : IV to 2002 : I .. ....................
1.98
-1 .58
- .76
-2 .21
8.88
6.41
7.18
7.02
7.00
5.02
5.66
5.15
1.88
1.38
1.52
1.86
6.90
7.99
7.94
9.22
5.41
6.39
6.47
7.07
1.48
1.59
1.47
2.16
Annual :
2001: I to 2002 : I ..................... ...
-2.57
13.02
8.17
4.85
15.58
10.40
5.18
SOURCE:
6
Net
change
Gross job losses
Authors' calculations using microdata from the
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
BLS
Business Employment Dynamics program .
Quarterly and annual establishments, by direction of employment change, first quarter 1998 through first
quarter 2002
[Not seasonally adjusted]
Establishments
Period
Establishments gaining jobs
Establishments losing jobs
Expanding
establish-
Opening
establish-
Contracting
establish-
Closing
establish-
ments
ments
ments
ments
Previous
quarter /year
Current
quarter /year
Change
5,954 ,688
6,102,056
6,112,675
6,139,037
6,100,295
6,111 ,290
6,141 ,350
6,047 ,343
145,607
9,234
28,675
-91,694
1,677,630
1,416,065
1,514,463
1,372,314
399 ,192
297,214
328,150
322,952
1,217 ,620
1,520,449
1,396,232
1,563,034
253,585
287 ,980
299,475
414 ,646
Annual : .
1998: I to 1999: I ..... .... ........... ... ..... ...
5,949 ,688
6,043 ,308
93 ,620
1,747 ,912
778,826
1,519 ,889
685,206
Quarterly:
1999: I to 1999: II ...... .. ... .... .. .. ... .. .. ... .
1999: II to 1999: 111 .............. ..... .... .....
1999: Ill to 1999 : IV ........ ...... .. .... .. ....
1999: IV to 2000: 1.. ...... ..... ...............
6,061,444
6,157,563
6,155,545
6,224 ,233
6,154,715
6,1 53,188
6,225 ,768
6,142 ,674
93,271
-4 ,375
70 ,223
-81 ,559
1,699,870
1,434,037
1,541 ,212
1,406,142
383,274
307,526
376,244
345,268
1,249,922
1,542 ,258
1,413,109
1,595,453
290,003
311 ,901
306 ,021
426 ,827
Annual :
1999: I to 2000 : I .............. ... .. ... ..... ... .
6,055,507
6,135,781
80 ,274
1,774,943
804,022
1,548,585
723,748
Quarterly:
2000: I to 2000 : II ...... ........... .............
2000: II to 2000: 111 ..... ..... .... ......... .....
2000 : Ill to 2000: IV ........ .......... .... ....
2000 : IV to 2001 : I ... .... .... .. ......... .. . . . .
6,159,683
6,275,908
6,273,940
6,325,421
6,273 ,531
6,271 ,181
6,326 ,260
6,220,660
113,848
-4 ,727
52 ,320
-104 ,761
1,721,043
1,442,389
1,511,533
1,386,268
391 ,847
314 ,945
365 ,672
333 ,506
1,292 ,080
1,580,817
1,477,681
1,611 ,652
277,999
319,672
313,352
438 ,267
Annual :
2000 : I to 2001 : ! .... ..... .... ..... ..... ... .....
6,154,016
6,213,658
59,642
1,723,162
809 ,301
1,645 ,873
749 ,659
Quarterly :
2001: I to 2001 : 11 .. ......... ... .... ... .. ..... ..
2001 : II to 2001 : 111 ........ ...... ..............
2001 : Ill to 2001 : IV ..... .. .. ..... ..... ... ....
2001: IV to 2002 : I ..... ...... .. .... . . .. . .... . .
6,236 ,791
6,330,657
6,294 ,785
6,345 ,811
6,327 ,460
6,292 ,660
6,344,623
6,243,771
90,669
-37,997
49 ,838
-102 ,040
1,668,308
1,357,255
1,426,118
1,329 ,571
377 ,140
297,385
361 ,787
328,795
1,320,988
1,628,835
1,506,839
1,603,277
286,471
335 ,382
311 ,949
430 ,835
Annual :
2001: I to 2002: I .... ..... ....... ............ ...
6,232 ,571
6,237 ,022
4,451
1,633,498
790,237
1,735,071
785,786
Quarterly:
1998: I to 1998: II ..... .... .... ...... ........ ...
1998: II to 1998: 111 ... ............ ... ...... .. .
1998: Ill to 1998: IV ..... .... .... ... .... .... ..
1998: IV to 1999: 1........... ... .. ..... .. .... ..
SouRCE:
I
Authors' calculations using microdata from the
BLS
Business Employment Dynamics program .
The statistics from tables I and 3 indicate that the average
expanding establishment added 5.4 jobs during the year
spanning March 200 I to March 2002 , and the average
contracting establishment lost 6.4 jobs during the year. A
s imilar calculation shows that the average opening
establishment starts with 6.6 employees in its first year of
positive employment, and the average closing establishment
is re sponsible for the loss of 7.1 employees in its final year
with employees.
Annual gross job gains and losses statistics add to the
labor market information currently available from BLS. A
trall ;lic nal measure of net employment change shows that
employment fell by 2,751,038 jobs during the year measured
from March 2001 to March 2002. The annual gross job gains
and losses statistics indicate that this net employment loss is
the result of 8,752,075 jobs added at 1,633,498 expanding
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Federal Reserve Bank of St. Louis
establishments, 5,201,011 jobs added at 790,237 opening
establishments, 11,148 ,760 jobs lost at 1,735,071 contracting
establishments, and 5,555,364 jobs lost at 785 ,786 closing
establishments. These annual statistics from the Business
Employment Dynamics data show the tremendous amount of
churning of jobs and establishments underlying the annual
net employment growth.
Annual statistics: uses and interpretations
To show how the annual statistics relate to the quarterly
statistics and the value added of the annual statistics relative
to the quarterly statistics, the following subsection directly
compares the annual and the quarterly statistics without
attempting to standardize the two to the same frequency of
measurement. The second subsection " annualizes" the
Monthly Labor Review
November
2004
7
Job Gains and Losses
-••lelr-..,,• Quarterly and annual establishments, by direction of employment change as a percentage of total
establishments, first quarter 1998 through first quarter 2002
[Not seasonally adjusted]
I
Establishments gaining jobs
Net
change
Period
Total
Expanding
establish-
Opening
establish-
ments
ments
Total
Contracting
establish-
Closing
establish-
ments
ments
Quarterly :
1998 : I to 1998: II ... .... .. ...... ..... ......
1998: II to 1998: Ill .............. ... ...... ..
1998: Ill to 1998: IV ... ..... .... ... ... ... ...
1998: IV to 1999: 1.......... ... ... ...... ... ..
2.42
.15
.47
-1 .50
34.46
28.06
30.07
'27.82
27.83
23.19
24.72
22.52
6.62
4.87
5.36
5.30
24.41
29.61
27.68
32.46
20.20
24.90
22.79
25.65
4.21
4.72
4.89
6.81
Annual:
1998: I to 1999 : I .. .............. ...... .. .... .
1.56
42.14
29.15
12.99
36.77
25.35
11.43
Quarterly :
1999: I to 1999: 11 .... ............ ....... .....
1999: II to 1999: 111 .. .. ....... ....... ..... ...
1999: Ill to 1999 : IV ........... .. .. .........
1999: IV to 2000: 1.. ..... ... .. ..............
1.53
- .07
1.13
-1 .32
34.10
28.29
30.97
28.32
27.83
23.30
24.90
22.74
6.27
5.00
6.08
5.58
25.21
30.12
27.77
32.70
20.46
25.06
22.83
25.80
4.75
5.07
4.94
6.90
Annual :
1999: I to 2000 : ! .... ........... ..............
1.32
42.31
29.12
13.19
37.28
25.40
11 .87
Quarterly :
2000: I to 2000: 11 .. .................... ... ...
2000: II to 2000: 111 ..... .. ...... ....... .. ... .
2000: Ill to 2000 : IV ..... ............. ... ...
2000: IV to 2001: I .. ... ............. .. ...... .
1.83
-.08
.83
-1 .67
33.99
28.01
29.80
27.42
27.68
22.99
23.99
22.10
6.30
5.02
5.80
5.32
25.26
30.29
28.43
32.68
20.78
25.20
23.45
25.69
4.47
5.10
4.97
6.99
Annual:
2000: I to 2001 : I .............................
.96
40.95
27.87
13.09
38.74
26.62
12.12
Quarterly :
2001 : I to 2001 : 11 .. .. ........................
2001 : II to 2001 : 111 ... .. ....... ............ ..
2001: Ill to 2001 : IV .. .... ... ... .... ...... ..
2001 : IV to 2002 : I .......... .... .. ... ..... .. .
1.44
- .60
.79
-1 .62
32.56
26.22
28.29
26.35
26.56
21.50
22 .57
21 .12
6.00
4.71
5.72
5.22
25.59
31.12
28.78
32.31
21 .03
25.81
23.84
25.47
4.56
5.31
4.94
6.84
Annual :
2001 : I to 2002 : I .. ... .... .... .......... ... ...
.07
38.87
26.20
12.67
40.43
27.83
12.60
SouRCE:
Authors' calculations using microdata from the
BLS
Business Employment Dynamics program .
quarterly statistics prior to comparison, and the third section
carefully examines the relationship between quarterly and
annual openings.
A simple comparison of annual statistics and quarterly
statistics. The annual gross job flow statistics are higher in
magnitude than the gross job flow statistics from any quarter
within the year. For example, in table 2, for the March 2001 to
March 2002 period, the annual gross job gains rate is 13.02
percent, and the annual gross job loss rate is 15.58 percent.
These annual statistics are higher than any of the quarterly
statistics within the year: the average quarterly gross job
gains rate for the four quarters between March 2001 and
March 2002 is 7 .37 percent, anc.l the average quarterly gross
job loss rate is 8.01 percent.
Additional analysis of the data in tables 1 and 2 reveals
that the larger annual statistic s correspond to a greater
importance of establishment openings and closings. That is,
8
Establishments losing jobs
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Federal Reserve Bank of St. Louis
November
2004
22.5 percent of quarterly gross job gains are due to
establishment openings, whereas 37.3 percent of annual
gross job gains are due to establishment openings. Similar
computations show that 20.9 percent of quarterly gross job
losses are due to establishment closings, whereas 33.2
percent of annual gross job losses are due to establishment
closings.
This greater importance of openings and closings in the
annual statistics, relative to the quarterly statistics, is due in
part to an increased number of establishment openings and
closings. Using data from March 2001 to March 2002, the
rate of establishment openings increases from 5.41 percent
on an average quarterly basis to 12.67 percent on an annual
basis, and the rate of establishment closings increases from
5.41 percent on an average quarterly basis to 12.60 percent
on an annual basis. (See table 4.) This striking difference
does not exist between the quarterly and annual rates of
expansions and contractions: the average quarterly expansion
rate is 22.94 percent, relative to an annual expansion rate of
26.20 percent, and the average quarterly and the annual
contraction rates are 24.04 percent and 27 .83 percent,
respectively.
In addition to an increased number of openings and
closings, one might expect the average size of establishment
openings and closings to increase as the time horizon is
lengthened over which employment growth is measured.
First, the composition of establishment openings is different
in the quarterly and the annual statistics, because many
openings that do not survive several quarters will not be in
the annual statistics. The existing literature finds that the
smallest establishments are the most likely to die shortly after
birth. 7 Second, if employment growth in surviving births is a
gradual process as these new establishments learn about
their business environment, then quarterly measures of
employment growth will understate (relative to annual
measures) the amount of gross job gains attributable to
openings. Similarly, if closing establishments decrease their
size gradually over time, then quarterly measures of gross
job losses will understate the jobs lost from these establishments. Calculations using March 2001-March 2002
statistics from tables 1 and 3 show an increasing average size
of openings and closings over a longer run horizon: The size
of the average opening increases from 5.3 jobs measured
quarterly to 6.6 jobs measured annually, and the average size
of a closing increases from 5.3 jobs measured quarterly to 7 .1
jobs measured annually.
Also, the average size of expansions and contractions is
larger in the annual statistics compared with the quarterly
statistics. The average expansion has 5.4 employees
measured annually versus 4.3 employees measured quarterly,
and the average contraction has 6.4 employees measured
annually versus 4.5 employees measured quarterly. One
explanation is that in the short run, some of the expansions
and contractions in the data are transitory fluctuations
caused by the hiring process taking some time. In the long
run, sustained expansions and contractions will distinguish
themselves from these short run transitory employment
fluctuations.
Comparing the annual statistics to the sum offour quarterly
statistics. The new quarterly Business Employment
Dynamics data series has been used by many analysts for
many applications. There has been a demand by the user
community for annual gross job gains and losses statistics,
and some users have "annualized" the quarterly statistics
themselves. 8 This section addresses whether it is appropriate
to use the sum of the four quarterly gross job flows statistics
as an annual gross job flows statistic.
As noted earlier, the sum of the four quarterly net
employment changes in table 1 is the annual net employment
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change. However, the sum of the four quarterly gross job
gains is much greater than the annual gross job gains, and
the sum of the four quarterly gross job losses is much greater
than the annual gross job losses. For example, the sum of
jobs created by expanding establishments in each quarter
from March 2001 to March 2002 is 24,850,396, whereas the
annual tabulation shows that only 8,752,075 jobs were added
by expanding establishments.
Caution should be used with regard to distinguishing
between annual statistics and the sum of four quarterly
statistics. Neither is inherently right or wrong; the two
different approaches are simply answers to different
questions. The annual statistics show job gains and losses
over a year. The sum of quarterly numbers look at the gains
and losses during a year.
The intuition for the difference between these two
concepts is straightforward. Many quarterly changes reverse
themselves over the course of a year. Many of these reversals
are due to lags in hiring for vacant positions (a gross job loss
in one quarter followed by a gross job gain in the subsequent
quarter), and many are due to seasonality (for example,
employment at amusement parks expands in the summer and
contracts in the winter). The data indicate that 53 percent of
the establishments that expanded in the quarter between
March and June of 2001 also expanded over the year from
March 2001 to March 2002. The data also indicate that 62
percent of the establishments that expanded over the year had
at least one quarter during the year in which they contracted.
Only 2 percent of the establishments that expanded over the
year expanded in all four quarters during the year.
Summing high frequency statistics, such as quarterly
statistics, to examine job gains and losses during a longer
period such as a year has two drawbacks. First, this method
will result in different answers depending on whether one
sums I 2 monthly statistics, 4 quarterly statistics, and so on.
To illustrate this, assume a user wants to know the gross jobs
gained during the 2-year period from March 2000 to March
2002. The sum of the two annual statistics from table 2
suggests that 29,385,084 jobs were gained during the 2-year
period, whereas the sum of the eight quarterly statistics
suggests that 66,808 ,622 jobs were gained during the 2-year
period. If one wanted to truly count every single job that was
gained or lost during a year, one would have to sum statistics
from time periods that are small enough such that no single
gain or loss has time to reverse itself.
A second drawback is that summing quarterly statistics
can produce strange results that are difficult to interpretthis is especially true for percentages, which may sum to
more than 100 percent. This can easily be seen using
statistics from table 4: between 26 percent and 32 percent of
establishments gained jobs in any quarter between
March 2001 and March 2002, but the sum of the four quarterly
Monthly Labor Review
November
2004
9
Job Gains and Losses
I•••"---- Quarterly and annual opening establishments, second quarter 2001 through first quarter 2002
Number of
establishments
Period
Conditional
percent
2001: II openings (n = 377,140) :
Remains open 2001: 111 •.•• .•• .• •.••.••. ••••• .••••• .••• ••• •..•.. •.••.•. .•..
Remains open 2001: IV .. .... .... ........................ ............ .... ..
Remains open 2002 : I ..... .......... .. ....... ... ....... ........ ....... .. .. .
Opening in annual table ............ ............... .... ...... ...... .
Continuous in annual table ................ ... .. ... .. .. ....... .. ..
318,561
278,575
232,157
232 ,157
0
84.47
73.87
61 .56
61.56
0.00
100.00
0.00
2001: Ill openings (n = 297 ,385):
Remains open 2001: IV ..... ..... .... ... ... .... .... ..... ... .. ..... .. ... ... .
Remains open 2002: I ..... ... .... .... ... ....... .... .... ....... ........ .. ...
Opening in annual table .. .. .. ......... ....... .. .... ......... ..... ..
Continuous in annual table ...... .. .... .. ............ ... ..... .. ...
248,040
219 ,007
170,821
48,186
83.41
73.64
57.44
16.20
78.00
22.00
2001: IV openings (n = 361,787) :
Remains open 2002 : I ... .... .................... .. .. ......... ............ ..
Opening in annual table ........ .. .. ...... ... .. ... ... ...... ...... ...
Continuous in annual table ........ .... ..... ..... ...... ... .. ... ...
247,679
175,646
72 ,033
68.46
48.55
19.91
70.92
29 .08
2002 : I openings (n = 328,795) :
Remains open 2002: I .. ........ ..... ... .... ....... ... ... ... ... ... ..... .. ..
Opening in annual table .. ............ .... ....... ........ .... ...... .
Continuous in annual table ......................... ............ ..
328,795
240,519
88,276
100.00
73.15
26.85
73.15
26.85
statistics cannot be interpreted as saying that 113.4 percent
of establishments gained jobs during the year.
A closer examination of quarterly and annual openings. A
comparison of quarterly openings with annual openings will
help illustrate why the sum of quarterly statistics differs from
the annual statistic. In table 3, there are 377,140 opening
establishments in the second quarter of 2001, 297,385 opening
establishments in the third quarter of 2001, 361 ,787 opening
establishments in the fourth quarter of 2001 , and 328,795
opening establishments in the first quarter of 2002. The sum
of these four quarterly statistics is 1,365,107, which is
substantially higher than the 790,237 opening establishments
reported in the annual tabulation. There are several reasons
for this difference.
The amount of time that opening establishments remain in
business is a major factor in understanding the relationship
between quarterly openings and annual openings. If an
establishment opens in the second quarter of 2001, but closes
before the first quarter of 2002, it would not be listed as an
opening establishment in the annual table. Statistics in table
5 examine the status of opening establishments over a
timeframe longer than one quarter. In the top panel of table 5,
there are 377,140 establishments that open in the second
quarter of 2001. One quarter later, 84.5 percent of these
establishments remain open, 73.9 percent are still open two
quarters later, and 61.6 percent are still open three quarters
later (in the first quarter of 2002). 9 The second panel of table
5, which tracks the status of establishments that open in the
third quarter of 2001 , indicates that 73.6 percent of these
quarterly openings are still open two quarters later.
l0
Percent of
openings
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
Another factor that affects the relationship between
quarterly openings and annual openings is the large number
of establishments that close and re-open within the year. To
understand this explanation, it is helpful to return to the
definition of opening and continuous establishments. By
definition, an annual opening in the March 2001-March 2002
tabulation either does not exist or has zero employment in the
first quarter of 2001, but has positive employment in the first
quarter of 2002. An annual continuous establishment, by
definition, has positive employment in both the first quarter
of 2001 and also in the first quarter of 2002. The continuous
establishments in the annual tabulations do not need to have
positive employment in all quarters between the first quarter
of 2001 and the first quarter of 2002. An annual continuous
establishment that has zero employment in some quarter
within the year would be classified as a closing in the quarter
it went from positive to zero employment, and then classified
as an opening in the quarter it went from zero to positive
employment. How often does this occur? Table 5 shows that
between 22 percent and 29 percent of establishments
classified as quarterly openings (in the third, fourth, and first
quarters) that remain open in the first quarter of 2002 are
classified as continuous establishments in the annual tables.
This finding illustrates that a significant number of establishment
openings in the quarterly statistics are continuous
establishments that close and re-open during the year.
There is one more interesting finding about opening
establishments that warrants mention. Table 5 shows that
232,157 establishments that opened in the second quarter of
2001 and remain open in the first quarter of 2002 are classified
as annual openings. The corresponding statistics for opening
establishments are 170,821 in the third quarter of 2001, 175,646
in the fourth quarter of 2001, and 240,519 in the first quarter of
2002. The addition of these four statistics is 819,143, which
exceeds the annual opening statistic of 790,237 by 28,906
establishments (or 3.7 percent). The explanation for this
difference is that 3.7 percent of establishments that are
classified as annual openings have two quarterly openings
within the year.
The time series of annual statistics
One of the most interesting conclusions that has come from
the new BLS Business Employment Dynamics data series is
that the 2001 recession is characterized by a decline in gross
job gains accompanied by an increase in gross job losses.
The most recent business cycle is also evident in the annual
job flow statistics. The annual net employment change in
table 2 is more than 2 percent in March 1999 and March 2000,
falls to 0.82 percent in March 2001, and is -2.57 percent in
March 2002. The business cycle is also evident in the annual
gross job gains and losses statistics. The annual rate of gross
job gains is essentially similar in 1999 and 2000, and then falls
from 15.45 percent in 2000 to 13.02 percent in 2002. The annual
rate of gross job losses is roughly steady if not declining
during 1999 through 2001, followed by a relatively large
increase in 2002. It is difficult to say much more about the
2001 recession, dated by the National Bureau of Economic
Research as occurring between March 2001 to November
200 I, because there are only four annual statistics in table 2.
However, it is possible to gain further information about
the business cycle by computing annual gross job gains and
losses for all quarters of the year. Table 6 presents statistics
that measure the annual rates of gross job gains and losses
from March to March, June to June, September to September,
and December to December. The 2001 recession is evident in
these statistics: the annual net employment change is more
than 2 percent for the first several quarters of 2000, and then
falls rapidly throughout 2001. This declining annual net
employment growth rate reflects two factors-a declining
annual gross job gains rate and a rising annual gross job loss
rate. (See chart 1.) This annual time series of gross job gains
and losses, computed quarterly, is consistent with the time
series pattern of the seasonally adjusted quarterly series from
the Business Employment Dynamics program.
The quarterly time series of annual tabulations in table 6 is
not seasonally adjusted, and doe-s not appear to show any
obvious seasonal effects. This is different than the quarterly
statistics in table 1 or table 2, where it is obvious that any time
series analysis of quarterly gross job gains and losses requires
seasonal adjustment of the data. Thus, the annual statistics
can serve as a crude alternative to seasonally adjusted
quarterly numbers, and could be especially useful for
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purposes where it may be infeasible to compute a long
enough time series for seasonal adjustment.
Comparisons with existing literature
The first influential studies of gross job gains and losses in
the U.S. economy were by Dunne, Roberts, and Samuelson,
and Davis, Haltiwanger, and Schuh. 10 Both of these studies
focused on data for the manufacturing sector from the
Census Bureau; later work by Anderson and Meyer, Foote,
and Spletzer used unemployment insurance data from various
States to examine how gross job flows in manufacturing may
not be representative of other industries. 11
From the heavily cited work of Davis, Haltiwanger, and
Schuh, one of the main conclusions is that the annual rate of
gross job gains in manufacturing during the 1973-88 period
is 9 .1 percent, and the annual rate of gross job losses in
manufacturing during the same period is 10.3 percent. These
rates are substantially lower than the annual rates presented
in table 2: for the entire U.S. economy during the 1999-2002
period, the average annual gross job gains rate is 15.1 percent,
and the average annual gross job loss rate is 14.3 percent.
Perhaps the most important explanation for this difference is
due to the difference in industry sectors; indeed, the quarterly
industry statistics recently released by the BLS Business
Employment Dynamics program show that the gross job flow
rates in manufacturing are lower than those in the economy
as a whole. 12
Annual gross job gains and losses statistics for the
manufacturing sector are computed from the Business
Employment Dynamics data. For the manufacturing sector,
the average annual rate of gross job gains over 4 years ( 19992002) is 9.4 percent and the average annual rate of gross job
losses is 12.6 percent. These rates are broadly similar to
those of Davis, Haltiwanger, and Schuh. The two crosswalks
described in this article-the crosswalk between the
manufacturing sector and the U.S. economy as a whole, and
the crosswalk between the quarterly and the annual
statistics-enables interested users to compare the quarterly
statistics from the BLS Business Employment Dynamics
program with the annual manufacturing statistics in the
existing literature.
presented annual gross job gains and gross job
loss statistics that were created using the quarterly microdata
from the Business Employment Dynamics program. The
annual gross job gains and losses statistics show the tremendous amount of churning that underlies the net growth
of employment. Indeed, every year in the U.S. economy, millions
of establishments remaining in operation are adding or
subtracting from their workforces, creating the turnover of
millions of jobs. At the same time, hundreds of thousands of
THIS ARTICLE
Monthly Labor Review
November
2004
ll
Job Gains and Losses
Annual private-sector gross job gains and job losses, March 1999 to March 2002
Employment
Gross job gains
Period
Total
Expanding
establishments
Opening
establishments
Total
Contracting
establishments
3,035,071
(2 .86)
2,994,475
(2 .73)
2,601,296
(2 .38)
1,904,249
(1 .73)
16,404,759
(15.45)
16,921,558
(15.44)
16,777,558
(15 .32)
16,226,533
(14 .72)
10,692,723
(10.07)
11 ,193,695
(10.21)
11 ,146,415
(10.18)
10,840,239
(9.83)
5,712,036
(5.38)
5,727,863
(5.23)
5,631 ,143
(5.14)
5,386,294
(4.89)
13,369 ,688
(12 .59)
13,927,083
(12 .71)
14,176,262
(12 .95)
14,322 ,284
(12 .99)
8,391 ,177
(7.90)
8,846,055
(8.07)
9,107,405
(8.32)
9,367,299
(8.50)
4 ,978,511
(4.69)
5,081 ,028
(4.64)
5,068 ,857
(4 .63)
4 ,954 ,985
(4 .50)
888,850
(.82)
110,734,261
-381 ,253
(-. 34)
109,000,401 -1,783 ,049
(-1 .62)
108,173,134 -3,009,776
(-2 .74)
15,431,998
(14.27)
15,441 ,137
(13.92)
14,708 ,760
(13.38)
14,286,714
(13.03)
10,240,477
(9.47)
10,135,482
(9 .14)
9,532 ,083
(8 .67)
9,146,066
(8.34)
5,191,521
(4 .80)
5,305 ,655
(4 .78)
5,176,677
(4.71)
5,140,648
(4 .69)
14,543,148
(13.45)
15,822 ,390
(14 .26)
16,491 ,809
(15.01)
17,296 ,490
(15.77)
9,363,412
(8.66)
10,276,408
(9 .26)
10,804,058
(9 .83)
11,594,516
(10.57)
5,179,736
(4.79)
5,545 ,982
(5 .00)
5,687 ,751
(5 .18)
5,701 ,974
(5.20)
105,810,039
13,953,086
(13.02)
8,752,075
(8 .17)
5,201 ,011
(4.85)
16,704,124
(15.58)
11 ,148,760
(10.40)
5,555,364
(5.18)
Previous
year
Current
year
.... .. .. .. ....
104,637,156
107,672,227
.... ... ... ..
108,121 ,039
111 ,115,514
September to September ..... ...
108,182,154
110,783,450
December to December ......... .
109,278,661
111 ,182,910
2000-2001 :
March to March ..... .. .... ......... .
107,672,227
108,561 ,077
June to June .... .... .... ·········· .. -·
111 ,115,514
September to September ...... ..
110,783,450
December to December ... ... ...
111 ,182,910
2001 - 2002:
March to March .. .. .. ..... ...... ....
108,561 ,077
1999- 2000:
March to March .
June to June
. . ...
.
. . .. . . . .... .
Gross job losses
Change
-2,751 ,038
(-2 .57)
Closing
establishments
NorE: Percentages are in parentheses.
Quarterly time series of annual private-sector gross job gains and losses, March 2000-02
~~rt
~~rt
16 ~ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ~ 16
12
12
8
4
4
o._.._______.______.______._______.______.______._~_____.______._,o
March
June
Sept.
Dec.
March
June
Sept.
Dec.
March
2000
2000
2000
2000
2001
2001
2001
2001
2002
NOTE: The 2001 recession, according to the National Bureau of Economic Research, occurred between March 2001
and November 2001 .
12
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
establishments open and close every year, causing the
simultaneous gain and loss of millions of jobs. This analysis
of the annual gross job flow statistics has highlighted their
value as a complement to the quarterly gross job flow
statistics released from the BLS Business Employment
Dynamics program.
D
NOTES
1
For a complete de sc ription and analysis of the new data series,
see James R. Spletzer, R. Jason Faberman, Akbar Sadeghi, David M.
Talan , and Richard L. Clayton , " Busi ness employment dynamics: new
data on gross job gains and losses," Monthly Labor Review, April 2004,
pp . 29-42. The Business Employment Dynamics Web site is
www.bls.gov/bdm .
2
Further details about definition s and the quarterly linkage
algorithm can be found in Spletzer and others, " Business employment
dynamics ," April 2004.
:i Establishments involved in ownership changes also need to be
treated with care when constructing gross job gains and gross job loss
stati stics. When an establi shment changes ownership , it is allowed to
change its State specific unemployment insurance number. But this
change will likely be identified by a State supplied predecessor or
s ucce sso r number or by the probabili sti c weighted match in the BLS
record linkage system, and as such, the unique establishment identifier
in the BLS longitudinal establishment database remains constant
through this period of ownership change.
4
A detailed description of the algorithm can be found in Joshua C.
Pinkston and James R. Spletzer, '·Annual Measures of Job Creation
and Job De struction Create d from Quarterly Microdata," American
Statistical As.r nciation 2002 Pro ceedings of the Se ction on Business
and Economic Statistics, pp. 3311-3316. This ASA paper reports
that the annual gross job gains rate for California increases from
18.7 perce nt to 20.0 percent, and the annual gross job loss rate for
California increases from I 5.4 percent to I 6.8 pe rcent, when not
using information on breakouts and consolidations within the year.
5
See Spletzer and others, " Business employment dynamics," April
2004, tabl e 5, page 40.
6
Percentages are calculated usi ng the average of the current and
previou s level s as the denominator. This ensures that increases and
decre ases are treated symmetrically. For example, conventional
calc ulati ons would describe an increase from 4 e mployees to 8 as a
I 00-percent increase, whereas a decrease from 8 to 4 would be a 50percen t decrease. Instead , when using average employment in the
denominator, both the in crease from 4 to 8 and the decrease from 8
to 4 are changes of 66.67 percent.
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7
See James R . Spletzer, " The Contribution of Establishment
Births and Deaths to Employment Growth ," Journal of Business
and Economic Statistics, January 2000, pp . 113- 26.
8
For instance, "i n 1999 alone, 33 million jobs were destroyed and
36 million created." See "A ll Jobs Count," Th e Washington Post,
Editorial , March 4, 2004, p. A22. These sta ti stics are the sum of the
four quarterly statistics in tabl e I.
9
We do not interpret th ese stati s tic s as survival probabilities,
primarily because the statistics in table 5 refer to th e opening and
closing of establishments, whereas th e literature o n establishment
survival refers to the birth and death of establishments. The statistics
in table 5 (84.5 percent, 73.9 percent , a nd 61 .6 percent) , are lower
than survival statistics in the litera ture. For examp le, th e quarterly
survival statistics in Spletzer, ·'The Contribution of Establishment
Birth s and Deaths to Employment Growth," January 2000, are 90.5
percent, 84.9 pe rcent, and 80 . 1 percent.
10
See Timothy Dunne, Mark J. Robert s, and Larry Samuelson,
" Plant Turnover and Gros s Employment Flows in the U.S.
Manufacturing Sector," Journal of Lahor Economics, vol. 7, no. I ,
1989, pp 48-71; and Steven J . Davis, John C. Haltiwanger, and Scott
Schuh, Job Creation and Destruction (Cambridge, MA, MIT Press, 1996).
11
See Patricia M. Anderson and Bruce D. Meyer, ""The Extent and
Con sequences of Job Turnover," Brookings Papers on Economic
Activity, 1994, pp. 177-236; Christopher L. Foote , ""Tre nd
Employment Growth and th e Bunching of Job Creation and
Destruction, " Quarterly Journal of Economics , vol. 11 3, No. 3, August
1998, pp . 809-34; and Spletze r, '·The Contribution of Establishment
Births and Deaths to Employment Growth ," January 2000.
12
Another possible explanation for the difference between the
statistics in this article and those of Davis and others, Job Crea tion
and Destruction, 1996, is different time periods. It is possible that
the late 1990s and early 2000s have higher gross job flow rates than
the 1970s and 1980s. However, figure 8 of R. Jason Faberman , ""Gross
Job Flows over the Past Two Business Cycles: Not all ·Recoveries ' are
Created Equal, " BLS Working Paper no. 372, June 2004, show s that
the gross job gains and gross job loss rate s for the manufactur in g
sector are arguably lower in the 1990s than in previous decades.
Monthly Labor Review
November
2004
13
Early Results from JOLTS
*·•~~\
"~"J~
The Job Openings and Labor Turnover
Survey: what initial data show
Early results from these new data series show
trends that are in line with other surveys,
both private industry and government,
and allow for a more complete picture of the labor market
Kelly A. Clark
Kelly A. Clark is an
economist in the
Division of Administrative Statistics and
Labor Turnover, Bureau
of Labor Statistics.
E-mail:
JOLTSinfo@bls.gov
14 Monthly Labor Review
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Federal Reserve Bank of St. Louis
D
ata on job openings and labor turnover
are useful in understanding the U.S.
labor market, the business cycle, and
the economy in general. The Bureau of Labor
Statistics (BLS) began publishing such estimates
in July 2002. These data include a measure of
unmet labor demand, which complements the
broadest measure of excess labor supply, the
unemployment rate, and yields a more complete picture of the labor market. Hires and
separations, measures of labor turnover, track
labor market movements over the course of the
business cycle and allow individual businesses
to compare their own turnover rates with the
national rates .
This article provides an overview of the estimates from the Job Openings and Labor Turnover Survey (JOLTS). 1 It briefly describes the
JOLTS program, highlights what job openings
and labor turnover data reveal about the labor
market and the economy, and compares and
contrasts the JOLTS series with other comparable
data series to understand and, in part, validate
movements in the JOLTS data. Ongoing and future uses for these valuable new data series are
also discussed.
The
JOLTS
program
BLS has collected both job openings and turnover information in several different survey s
during the past 50 years. However, these sur-
November 2004
veys were short-lived due to budget cuts, and
the scope was limited to certain industries or
States. The current JOLTS program began in
1999 as a comprehensive survey of job openings, hires, and separations at a time when new
data were needed to allow further analysis into
the U.S. labor market and movements in the
economy. 2
JOLTS collects monthly job openings, hires,
and separations data from a nationally representative sample of 16,000 private and public
business establishments. Job openings are
collected as of the last business day of the
month, serving as a snapshot of unmet labor
demand for the month. Hires and separations
are collected for the entire month and measure
the flow of labor during the month. Total
separations are the sum of three components:
quits (or voluntary separations); layoffs and
discharges (involuntary separations); and other
separations resulting from retirements, deaths,
and disability.
The job openings rate is designed to complement the unemployment rate. There are three
conditions for an opening to be reported in
JOLTS, just as there are three conditions for a
person to be considered unemployed. To be
considered a job opening, a job must be currently available, work for the job could start
within 30 days, and an employer must be actively recruiting to find someone to fill the job.
To be considered unemployed, a person must
be available for work, could start work immediately, and must
be actively searching for work.
JOLTS estimates were first released in July 2002, and
monthly estimates are available beginning with December
2000. In addition to the national totals, seasonally unadjusted
estimates are published for the private and public sectors, for
16 private industry divisions, and for 2 public industry divisions based on the North American Industry Classification
System (NAics). Estimates for four geographic regions also
are available. Seasonally adjusted estimates are available
for job openings, hires, total separations, and quits at the total nonfarm level as well as for the regions and selected industry sectors. 3 Neither layoffs and discharges nor other
separations showed a strong seasonal component, but these
data series, as well as the remaining unadjusted industry series, will be re-evaluated periodically to determine if and
when seasonal adjustment is possible.
The JOLTS data series were first published as developmental because the estimates from the new program were subject
to intense scrutiny and review, and BLS needed time to conduct a thorough methodological review before announcing
the series as official BLS labor market statistics. In addition,
the entire sample of establishments was not enrolled in the
survey until January 2002, and collection methods were refined in March 2002 to help respondents more accurately
report separations data.
In April 2004, the developmental status was lifted, and
seasonally adjusted data series were first released along with
monthly press releases, which provided some analysis of the
estimates. Also, the production process was altered to allow
preliminary, or first closing, estimates to be released; previously, final, or second closing, estimates had been released.
Even throughout the period when the series were classified
as developmental, the individual series showed movements
that were in line with other economic indicators and with the
cyclical movement of the economy. Although BLS advises
caution when using estimates prior to March 2002, those estimates are useful in evaluating the state of both the labor
market and economy in general during the recessionary period and the beginning of the recovery.
people who want a job already are employed. Unemployment tends to be low and openings tend to be high. However, when economic conditions worsen, employers are hesitant to post openings for "new" jobs, and the few openings
for existing jobs tend to be filled quickly. Unemployment is
usually higher due to reduced hiring and increased layoffs in
response to weak demand.
The Beveridge curve is the depiction of the relationship
between job openings and unemployment over time, shown
as an inverse relationship between the two rates, with movements along the curve distinguished from shifts of the curve
itself. (See illustrations below.)
Movement along the Beveridge curve
Vacancy
Rate
Contraction
UH,VL
Unemployment
Rate
A shift in the Beveridge curve
Vacancy
Rate
1
\
-\
\
\
\
Labor demand and the Beveridge curve
Statistics on job openings are a necessary complement to the
BLS unemployment data for a complete picture of the labor
market; job openings data represent unmet labor demand and
unemployment data represent excess labor supply. The parallel concept of these two data sources allows direct comparisons. In theory, job openings should move in the opposite direction of unemployment over the course of the business cycle. In good economic times, the labor market tends
to be tight, with employers searching for employees, but most
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Federal Reserve Bank of St. Louis
·,
\+-improved
matching
efficiency
\
reduced
matching
efficiency
·, .,
·, ·,
·, .
\
·,.,
·, .
' ·,
Unemployment
Rate
Movements along the curve are generally related to
changes in the business cycle and the cyclical fluctuations of
the demand for labor. Shifts of the curve are due to changes
Monthly Labor Review
November 2004
15
Early Results from JOLTS
in the efficiency with which workers match with open jobs.
These movements are based on changes in structural and frictional unemployment as the labor force changes and as industry and geographic trends influence the distribution of jobs.
As matching efficiency changes, the curve moves closer to or
further away from the origin. Even though the two movements
are not independent, it is possible to distinguish them when
graphing the Beveridge curve over long periods of time. 4
Although the JOLTS job openings series is rather short, a
preliminary look at the Beveridge curve shows the expected
inverse relationship between the job openings and unemployment rates. (See chart 1.) The correlation between the two
series, at-0.80, is negative and significant, as expected. The
chart shows that early 2001 was a period of low unemployment and high job openings. As the economy moved into
recession, unemployment increased and job openings decreased. In the post-recessionary period, unemployment
dropped slightly while job openings increased slightly. It
appears as though there have been only movements along the
curve (indicating changes in labor demand), rather than significant shifts in the curve (indicating changes in the efficiency with which open jobs match with workers), but a
longer time series will be able to better distinguish the movements and yield more insight into the labor market changes
during this period.
The short time series also does not allow much analysis of
the job openings rate prior to the start of the 2001 recession.
Research has predicted job openings lead at business cycle
peaks and lag at troughs. When sensing an economic downturn, employers generally first reduce job openings and hires
before separating current employees, and as conditions improve, it is less costly to recall workers from layoffs than to
begin recruiting and training new employees. The National
Bureau of Economic Research ( NBER) dated the most recent
recession as having started in March 2001 , and with the job
openings series beginning in December 2000, it is impossible to determine the number of months that the job openings rate dropped before the official start of the recession.
However, NBER declared the recession over in November
2001 , and it appears that job openings did not rebound
strongly in 2002 or 2003 , indicating lagging at the business
cycle trough. Chart 1 shows that the Beveridge curve may
be looping back along itself in 2004, showing that job openings have begun to increase as unemployment has decreased.
Job openings and unemployment levels
When examining the unemployment and job openings esti-
The Beveridge Curve, seasonally adjusted
Job
openings
rate
Job
openings
rate
3 .4
3 .4
3.2
3.2
3.0
3.0
2.8
2.8
2.6
2.6
2.4
2.4
2.2
2.2
2.0
2.0
1.8
3.6
1.8
4.0
4.4
4.8
5.2
Unemployment rate
16 Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
5.6
6.0
6.4
mates, it is easy to see that the two series are at different
levels, and another way to analyze the data series is to compare the two levels over time. Long before the United States
had a representative survey such as JOLTS to collect job openings data, Katharine Abraham suggested that the number of
persons unemployed is much larger than the number of job
openings. 5 Her research showed the number of unemployed
persons was indeed greater than the number of job openings
at any given time, but the ratio did shift over time. In the
mid-1960s, the ratio of unemployed persons to one job opening was approximately 2.5, which shifted to 4.0 in the early
1970s and then increased to 5.0 in the late 1970s. These
ratios can be used in determining the "tightness" of the labor
market. The ratio using the JOLTS job openings data ranges
from below 2.0 unemployed persons for every job opening
throughout the first half of 2001 , when the labor market was
perceived as being relatively tight, to 3.3 in August 2003,
when the labor market was seen as lagging the general economic recovery.
Because of these types of direct comparisons, there already has been talk of a "jobs deficit," or the difference between the number of unemployed persons and the number of
job openings. 6 It is important to remember that even with
carefully constructed parallel definitions, the reference periods are both snapshots, but different: the week of the 12th
for unemployment, compared with the last business day of
the month for job openings. Job openings that first become
open and are filled at any time before the end of the month
are not included in the job openings estimates. In addition,
the JOLTS definition of a job opening requires that a job be
unfilled to be counted. Experience suggests that some companies post openings and fill jobs while the departing employee is still working, in order to train the new employee,
and these openings would not be included in the JO LTS estimate. Another requirement for a job opening to be counted
is that work could begin within 30 days. For industries such
as education that tend to fill jobs well in advance of when
work will actually begin (posting jobs and hiring in the spring
for work to begin when school opens in the fall) , these openings will not be reflected in the JOLTS estimate. Furthermore,
the survey that measures unemployment, the Current Population Survey ( CPS) , has a different scope than the JO LTS program. The CPS is a household survey that includes agricultural workers, unpaid family workers, domestic workers in
private households, and the self-employed, all of whom are
not covered by establishment surveys such as JOLTS. It is
therefore better to compare the ratio of unemployed to job
openings over time rather than focusing on how the levels
compare at any one point in time.
In addition, Abraham was careful to note that it is not necessarily optimal for there to be a one-for-one relationship
between unemployment and job openings. 7 There are social
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costs involved with unemployment (for instance, a 10-percent unemployment rate would not be considered optimal,
even with a 10-percent job openings rate), and even if there
were a one-for-one relationship at a point in time, the people
looking for work may not meet the qualifications needed to
fill the job openings, or the job openings may not be in the
same location as the people looking for work. These frictions in the labor market (the source of frictional unemployment) keep job openings from being filled instantaneously.
Job openings and the Help-Wanted Index
From the beginning, the JOLTS program has tracked each data
series against other available series to help analyze the validity ofboth long-term trends and month-to-month movements.
The only other existing national measure of excess labor demand is the Conference Board's Help-Wanted Advertising
Index (Help-Wanted Index). 8 With some manipulation, the
Help-Wanted Index has been used in Beveridge curve analysis in the past. As a measure of the volume of help-wanted
advertising in major newspapers from across the country, this
index has been a good indicator when compared with unemployment. The job openings rate and the Help-Wanted Index, have trends that are roughly similar. (See chart 2.) However, the decrease from December 2000 to November 2001
was much sharper for the Help-Wanted Index, which experienced a drop of 42 percent, compared with a drop of 30 percent in the job openings rate. The differences in scope and
definition between the Help-Wanted Index and the job openings rate may account for some of this difference. A change
in the way employers advertise open positions also may help
to explain; for example, if a large number of employers
stopped posting advertisements in the newspaper in favor of
advertising on one of the many Internet sites, the decline in
the Help-Wanted Index would not represent an economic
movement. In addition, JO LTS estimates from December 2000
through 2001 had larger measures of error than the 2002 and
later estimates.
Employers who place help-wanted advertisements in
newspapers may not be representative of the national
economy, as ads tend to vary by skill level, education level,
and job type. Also, the growth of the Internet's popularity
for job postings may have affected the number of newspaper
advertisements in the long run. The Conference Board has
investigated ways to take account of advertising on the
Internet, but has not made any adjustments to the HelpWanted Index.
The various job search sites on the Internet are new options for employers seeking workers, but no single site is
comprehensive enough to be used as an indicator of labor
demand. Issues of coverage, scope, the existence of multiple
positions per ad, and fees for postings are obstacles in using
Monthly Labor Review
November 2004
17
Early Results from JOLTS
11i1111Ai1a
The Help-Wanted Index, unemployme nt rate, and job openings rate, seasonally adjusted
HelpWanted
Index
Unemployment and
job openings rates
7
90
6
80
5
70
4
60
3
50
2
40
Dec.
2000
NOTE :
Mar.
Jun. Sept.
2001
Dec.
Mar.
Jun. Sept.
2002
Dec.
Mar.
Jun. Sept.
2003
Dec.
Mar. Jun.
2004
30
Shaded area denotes recession .
these sites as indicators.
The Help-Wanted Index is not adjusted to account for
multiple positions per ad, and there are no limitations on the
types of ads placed in newspapers, some of which may be
placed to gather resumes for future hiring. Neither JOLTS nor
the Help-Wanted Index differentiates between full- or parttime openings, and neither includes occupational information or a measure of "good" jobs versus "bad" jobs or for
low-wage versus high-wage positions. As the JOLTS program
expands, questions related to these issues may be added to
the survey.
Labor turnover and the business cycle
Thus far, the job openings data series has confirmed much of
what previous research has suggested. However, some observers have been surprised by what the JOLTS hires and separations data series show, especially the amount of churning
in the labor market each month. Net employment changes
tend to be small from month to month, but there are millions
of hires and millions of separations occurring each month at
U.S. businesses. During the past decade, the annual employment change has averaged approximately plus or minus 2.2
million, but nearly 50 million hires and 50 million separa18 Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
tions occur during any 12-month period in the past 3 years.
These numbers dwarf the annual net employment change and
help show the dynamism of the labor market. Information
about labor market flows can therefore shed more light on
how the economy works.
Hires and separations estimates can be used along with
other economic indicators in examining movements in the
business cycle. Hires are procyclical, increasing when the
economy strengthens and decreasing when the economy
weakens. In examining employment and the hires rate, there
is a significant correlation between the two series. This indicates that employers tend to control their employment level
by altering their hiring patterns, as there are significant costs
associated with separations. 9 When economic times are good,
employers hire to replace employees who have separated and
may hire for newly created jobs. During recessions, employers may hold back on hiring to replace separated workers
until business conditions improve, rather than increase separations overall. There is a close trend movement between
the unadjusted series of employment and the hires rate and
the related movement of the quits rate, the largest part of
total separations. (See chart 3.) In fact, the correlations between hires and employment and quits and employment are
positive and significant. 10 As quits tend to behave
Employment, hires, and quits, not seasonally adjusted
Employment
(in thousands)
Hires and quits
(in thousands)
134,000
8,000
133,000
7,000
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Employment
I
6,000
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.I'\
V
•• ._ •• •
132,000
\
I
I
/
/
131 ,000
l•
• • •
:,, -:. :
':._:
I •••
:
:
. ,, .
..... ·••::
••
130,000
/
~
129,000
128,000
127,000
126,000
2,000
125,000
1,000
Dec. Mar.
2000
NOTE:
Jun. Sept. Dec.
2001
Mar.
Jun. Sept. Dec.
2002
Mar.
Jun. Sept. Dec.
2003
Mar. Jun .
2004
124,000
Shaded area denotes recession .
procyclically, increasing when the economy is strong (and
thus as employment increases), the correlation with employment is positive.
The movement of the separations rate is dominated by
quits. In fact, quits have ranged from 51.3 percent of total
separations in June 2003 to more than 60 percent in early
2001 and have averaged 54.7 percent over the course of the
published data series. This is an important fact in examining
how separations data move with the business cycle. Intuitively, separations would seem to be countercyclical; as economic conditions deteriorate, employers lay off workers.
However, because of the dominance of quits among the three
components of total separations, separations have behaved
procyclically. Total separations have decreased during the
current recessionary period, largely because of the decrease
in quits over that period and despite the uptick in layoffs and
discharges. (See chart 4.)
Layoffs and discharges did increase during the recession,
especially from June to October 2001, but perhaps not as
much as media reports would indicate. Often, companies
report a target number of"layoffs," but some companies may
actually decrease their workforce through attrition and by
decreased hiring during worsening economic conditions.
Other companies may lay off workers in their factories over-
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seas before cutting jobs at U.S . plants. In other cases, planned
layoffs never materialize.
The other separations rate, which includes retirements,
deaths, separations due to disability, and transfers to other
locations of an establishment, has remained relatively stable
over the course of the published series, fluctuating between
0.2 percent and 0.3 percent. A large proportion of other separations is thought to be retirements, and thus the demographic
shift in the composition of the labor force may affect the other
separations rate in coming years. As the baby-boom generation moves into retirement years, the result may be an increase in the other separations rate over time.
Turnover estimates and other economic indicators
As stated earlier, quits tend to decrease during recessions
because workers' outlook toward finding another job worsens with deteriorating economic conditions. 11 As economic
conditions worsened throughout 2001 and 2002, consumer
confidence plunged, and fewer people quit their jobs than at
the same time the prior year. (See chart 5.) The seasonally
adjusted quits series shows a decrease throughout the published series, and the consumer confidence index exhibits the
same downward trend as the quits rate over the course of the
Monthly Labor Review
November 2004
19
Early Results from JOLTS
,
Breakouts of total separations, seasonally adjusted
Percent
Dec.
Mar.
Jun.
2000
Sept. Dec.
Mar.
2001
Jun.
Sept. Dec.
Mar.
2002
Jun.
Sept. Dec.
2003
1.0
Mar.
Jun.
2004
Shaded area denotes recession. The "all other separations" series is derived by subtracting quits from total separations .
The layoffs and discharges and other separations series are not seasonally adjusted .
NOTE :
Consumer
Confidence
Index
Quits rate
2.2
150
135
2.0
120
1.8
105
1.6
90
75
1.4
60
1.2
45
1.0
Dec.
Mar.
Jun .
2000
NOTE :
Sept. Dec.
Mar.
2001
Shaded area denotes recession .
20 Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
Jun.
Sept. Dec .
2002
Mar.
Jun.
Sept. Dec.
2003
Mar.
Jun.
2004
30
series. The consumer confidence series shows something of
a rebound in late 2003 and early 2004, perhaps signaling that
quits may be expected to increase even further in late 2004.
The correlation of quits and consumer confidence is 0.80,
which is positive and significant.
One of the only other data series providing a national turnover rate has been the Bureau ofNationalAffairs (BNA) quarterly Job Absence and Turnover report. 12 This long-running
series provides results from approximately 300 U.S. member
companies surveyed. The JOLTS total separations data trend
with the BNA turnover series, but at a higher level partly because BNA does not include layoffs, job eliminations, or departures of temporary staff, whereas JOLTS includes all types
of separation during the reference month. (See chart 6.)
Although the BNA report provides a long time series for
turnover estimates, the JOLTS program provides a timely and
nationally representative indicator of turnover for both hires
and separations. In addition, with a much larger sample size
and a more inclusive definition of turnover, the JOLTS statistics are more reliable and useful. With the larger sample size,
JOLTS is able to publish more industry detail. However, the
BNA report publishes turnover rates by establishment size
class, which JOLTS may pursue in the future because turnover
rates appear to vary by establishment size.
In mid-2003, BLS once again added to the national statisti-
cal framework with data series showing what underlies net
employment changes, the Business Employment Dynamics
(srn). 13 Quarterly statistics on gross job gains and gross job
losses also prove an interesting comparison to hires and separations flows. (See chart 7.) These series track net employment changes at the establishment level. A preliminary analysis has shown JOLTS total private hires and separations,
summed for each quarter, have outpaced the gross job gains
and gross job losses, which is as expected. The gross job
gains and gross job losses are computed by comparing the
employment level of the third month of each quarter. JOLTS
measures each individual hire and separation that occurs during every month, and thus the data series are, by definition,
higher than the gross job gains and losses series. For example, if an establishment's employment level was 10 in the
third month of the first quarter and 10 in the third month of
the second quarter, there would be no employment change
and thus no effect on the gross job gains or losses. However,
there may have been three hires and three separations in between those two points, which JOLTS data would reflect.
Along with JOLTS, the Business Employment Dynamics statistics on gross job gains and gross job losses are additional
tools to use in labor market analysis. The JOLTS data series
will continue to be tracked against all of these data series
over time. As with job openings, the JOLTS series of hires and
......... Bureau of National Affairs (BNA) turnover and total separations rates, seasonally adjusted
Percent
Percent
4.0
4.0
3.5
3.5
3.0
3.0
2.5
2.5
2.0
2.0
1.5
1.5
1.0
1.0
.5
.5
0
0
Dec.
2000
NOTE :
Mar.
Jun.
Sept. Dec.
2001
Mar.
Jun .
Sept. Dec.
2002
Mar.
Jun.
Sept. Dec.
Mar.
2003
Jun.
2004
Shaded area denotes recession.
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Federal Reserve Bank of St. Louis
Monthly Labor Review
November 2004
21
Early Results from JOLTS
Quarterly Business Employment Dynamics gross job gains and losses and hires and total
separations, seasonally adjusted
In thousands
In thousands
14,000
14,000
13,000
13,000
Total /Separ
12,000
12,000
11 ,000
11,000
Hire
10,000
10,000
9,000
9,000
8,000
8,000
7 ,000
7,000
6,000
II
Ill
IV
2001
NOTE :
II
Ill
2002
IV
II
Ill
2003
IV
II
6,000
2004
Shaded area denotes recession .
separations are more comprehensive and statistically reliable
measures than other series currently available. However,
because the data are collected from businesses, it is not possible to track employment flows of individuals. For example,
if a person quits, there is no way of telling if they quit to
move into another job, become unemployed, or leave the labor force. Surveys that track labor force flows over time,
such as the BLS National Longitudinal Survey or the gross
flows statistics from the BLS Current Population Survey, are
more appropriate for those types of analysis. Combining
these indicators with JO LTS statistics allows a more complete
picture of the labor market for study and analysis.
Future uses of JOLTS estimates
Although the JO LTS program was designed to provide national
economic indicators, there are several things the estimates
do not provide. There is a demand for job openings by occupation and establishment size class, duration of vacancies,
and openings at the State and metropolitan area level. Some
industry or occupational associations have estimates of job
openings, and several States are conducting a job vacancy
survey, but there is no single comprehensive and statistically
reliable source for this type of information. The JOLT S pro22 Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
gram is currently investigating the feasibility of developing
estimates by establishment size class and estimates for the
total metropolitan and nonmetropolitan areas.
Another future use for JOLTS estimates concerns analysis
of wages. Using data serving as a proxy for job openings,
researchers have found that job openings may be a better
indicator of wage inflation than is unemployment. 14 This
certainly should be an area for research once the JO LTS job
openings series develops further. Econometric analysis involving wages (with data from the BLS Current Employment
Statistics program), unemployment, and job openings, including other factors , will be required to investigate the
strength and validity of the relationships.
The job matching function has been of interest to researchers for several years, and wages also play a role in this analysis. The matching function relates the flow of new matches
(hires) to the number of jobseekers (unemployed persons)
and job openings. The results of job matching are easily
observable from month-to-month changes in the job openings and unemployment data, but how jobseekers and employers with open jobs actually find each other is quite complicated. Factors such as wages, as well as external factors
such as demographics, educational structu tT, and geographic
concentration of industries all influence how open jobs and
jobseekers are matched. 15 As proxies of job openings had
been used in previous studies, analysis using the JOLTS job
openings data will help further this area of research.
It is apparent that there is a long list of research topics that
job openings and turnover data can be used t? investigate.
Alone or in combination with other national economic indi-
cators, the new JOLTS data series already have yielded valuable information about the U.S. labor market and economy
in general. The estimates have shown similar trends as other
national economic series, and they will continue to be tracked
over time as a validation exercise and as a research and analysis tool.
D
Notes
1
Job openings and labor turnover data, along with a brief analysis, are
released monthly in a press release, on the Internet at: http://www.bls.gov/
jlt/. Selected data also appear in the Current Labor Statistics department
of this publication each month.
2
For additional information about the development of the program,
see Kelly Clark and Rosemary Hyson, "New tools for labor market analysis : the Job Openings and Labor Turnover Survey," Monthly Labor Review, December 2001, pp. 32- 37 .
3
Natural resources and mining, information, financial activities, and
other services did not show strong seasonal patterns when seasonal adjustment diagnostics were first evaluated .
4
See Katharine G. Abraham, "Help-Wanted Advertising, Job Vacancies, and Unemployment," Brookings Papers on Economic Activity, no.
l, June 1987, pp. 207-48; and Hoyt Bleakley and Jeffrey C. Fuhrer, "Shifts
in the Beveridge Curve, Job Matching, and Labor Market Dynamics,"
New England Economic Review, September/October 1997, pp . 3- 19.
5
See Katharine G. Abraham, "Structural/Frictional vs . Deficient Demand Unemployment: Some New Evidence," American Economic Review,
1983 , vol. 73(4), pp. 708-24.
6
See Economic Snapshots, The Economic Policy Institute, Oct. 2, 2002 .
7
See Abraham, "Structural/Frictional," p. 708- 24.
8
For additional information about the Help-Wanted Advertising Index, see The Conference Board's website at www.conference-board.org
9
See Daniel S. Hamermesh, Wolter H.J. Hassink, and Jan C. van Ours,
"Job Turnover and Labor Turnover: A Taxonomy of Employment Dynam-
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Federal Reserve Bank of St. Louis
ics," Anna/es D 'Economie et de Statistique, no. 41 /42, 1996, pp . 21 - 40,
for their work concerning Dutch establishments; and John M. Abowd,
Patrick Corbel, and Francis Kramarz, "The Entry and Exit of Workers and
the Growth of Employment: An Analysis of French Establishments," Th e
Review of Economics and Statistics, 81(2), May 1999, pp. 170- 87 , for
their work concerning French establishments .
10
The correlation coefficient for hires and employment is 0.51 and for
quits and employment is 0.44 ; both are significant at the 95 percent confidence level.
11
See Hoyt Bleakley, Ann E. Ferri s, and Jeffrey C. Fuhrer, "New Data
on Worker Flows During Business Cycles," New England Economic Review, July/August 1999, pp. 49- 76 and Patricia M. Anderson and Bruce
D. Meyer, "The Extent and Consequences of Job Turnover," Brookings
Papers: Microeconomics, 1994, pp. 177- 248.
12
For additional information about the Job Absence and Turnover
Report, please see the Bureau of National Affairs' website at www.bna.com
13
For additional information about the business employment dynamics,
see James R. Spletzer, R. Jason Faberman, Akbar Sadeghi, David M. Ta Ian,
and Richard L. Clayton, "Business employment dynamics : new data on gross
job gains and losses," Monthly Labor Review, April 2004, pp. 29-42 .
14
See Katharine G. Abraham and James L. Medoff, "Unemployment,
Unsatisfied Demand for Labor, and Compensation Growth in the United
States, 1956-1980," National Bureau of Economic Research Working Paper
Series, no. 781, October 1981 .
15
See Barbara Petrongolo and Christopher A. Pissarides, "Looking
into the Black Box: A Survey of the Matching Function," Journal of Economic Literature, June 200 l , pp . 390-431 .
Monthly Labor Review
November 2004
23
Employment and wages for the
U.S. ocean and coastal econom y
Quarterly Census of Employment and Wages data
provide new industrial and geographic views
of the U.S. coastal and ocean economy
over the 1990-2001 period
Charles S. Colgan
Charles S. Colgan is a
chief economist with
the National Ocean
Economics Project
and a professor of
Public Policy and
Management in the
Edri 1und S. Muskie
School of Public
Service at the
University of Southern
Maine.
E-mail :
csc@usm.maine.edu
24 Monthly Labor Review
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Federal Reserve Bank of St. Louis
A
lthough national trends in employment
have shown a marked shift away from
manufacturing and natural re source
extraction over the past 40 years, interest in the
economic use of major natural re source s
remains a matter of substantial concern. This
has long been the case with agriculture, where
the farm/ nonfarm di stinction is a staple of
employment statistics. It is increasingly true
of other resource s, including those of the
oceans and Great Lakes. A substantial debate
about how to manage those resources is about
to be ~ngaged , driven in large part by two
recent major reports, one from a private
foundation and the other from a commission
chartered by Congress . 1
The analysis of major natural-resource-oriented economic sectors is relatively straight-forward in most cases. Agriculture is well documented; it and minerals both have their own
divisions within the Standard Industrial Classification ( S IC) system and the North American
Industry Classification System (N A ICs ) . Forest
products are well defined in SIC 24, 25 , and 26,
and in several NAICS codes. Moreover, each of
these resource industries is usually clearly defined geographically, with well-recognized agricultural , forest products, and mining regions.
The analysis of the ocean economy, however,
has none of these advantages.
The ocean economy consists of activities
measured in a number of industries, though
none, with the exception of ship and boat
No,1:..mber 2004
building , is a measured major industry or sector level. In the S IC codes, all are at the threeor four-digit level , and in the NAICS codes, most
are at the six-digit level. The span of industries includes primary production, manufacturing, transportation, retail, and services. Moreover, while the ocean economy is concentrated
in the 30 coas tal States (including the Great
Lakes states), it is found throughout the United
States. Seafood stores are found in Nebraska,
and North Sails builds the sails for the America's
Cup class boats at a sail loft in Nevada. Even
within the coastal States, the ocean economy
can be found in the largest cities and smallest
towns , making it geographically specific, but
across a wide range of regional economies.
This article summarizes the results of a preliminary analy sis of the coastal and ocean
economy of the United States over the 19902001 period. The analysis was conducted as
part of the National Ocean Economics Project
(No EP), which is funded by the National Oceanic and Atmospheric Administration (NOA A) to
develop nationally consistent estimates of both
the market-based and nonmarket-based economic values associated with the coasts and
oceans. Employment and wage estimates are
shown for the United States and the coastal
States using the Quarterly Census of Employment and Wages (QcEw) employment series
compiled from the BLS Longitudinal Database.
A comparison of the ocean economy measured
by SIC and NA IC S classifications is provided.
Conclusions and suggestions for further research are presented regarding the use of QCEW data for the measurement
of sectors involving complex multi-industry and geographic
attributes.
Defining the ocean and coastal economy
In this article, the term "oceans" includes the Atlantic and
Pacific Oceans, the Gulf of Mexico, the Great Lakes, and all
States bordering these bodies of water. Federal ocean and
coastal policies and programs are defined to include the Great
Lakes region, so the creation of ocean-related economic data
requires that the Great Lakes be included.
There have been several earlier attempts to define an
ocean economy, primarily by developing estimates for an
ocean-related portion of the gros~ domestic product (GDP). 2
The earliest of these efforts occurred in the 1970s, when the
U.S. Department of Commerce's Bureau of Economic Analysis identified the key dimensions for defining the ocean
economy: industry and geography. Existing data must be
organized using these two criteria while staying within the
rules of confidentiality.
A major issue with the level of industrial aggregation in
published statistics is that confidentiality protections limit
the availability of data for many of the three- and four-digit
industries required for analysis of the ocean economy. In
order to deal with these issues, establishment-level data must
be grouped into new industrial and sectoral definitions, which
can also be more descriptive of the ocean. (See exhibit 1.)
Data for the ocean economy need to be referenced to both
SIC and NAICS. (See exhibit 2.) Employment and wage data
for the ocean economy are measured on a SIC basis for 1990
and 2000. For 2001, data are measured on both a SIC and
NAICS basis for comparison purposes.
Regardless of their location, some industries, such as ship
building and seafood processing, are clearly connected to the
oceans: others, including all of those in the tourism and recreation sector, are ocean related rmly if they are located near
the shores of the oceans or Great Lakes. Fixing the geographic location of establishments in these industries is thus
particularly important. Previous studies have relied primarily on location in shore-bordering counties to define an establishment as ocean related, but counties present some obvious difficulties from the perspective of defining an ocean
economy. Counties come in very different sizes, from the
relatively compact counties of States like Alabama and Mississippi to the sprawling areas of Los Angeles County or the
boroughs of Alaska. Many county boundaries were fixed
two or more centuries ago for administrative and political
purposes, which may bear little relationship to modern concepts of ecosystem-based regions.
Thus, the problem is to find a level of geography that is
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Federal Reserve Bank of St. Louis
considerably closer to the shoreline than county boundaries,
but is also available on establishment records for identification purposes. Ideally, this could be done by choosing an
appropriate distance boundary (for example, 5 kilometers)
from the shoreline, and then selecting all establishments with
street addresses within that distance. The selection of appropriate addresses is a straightforward task using modern Geographic Information Systems (G1s) software; however, establishment data in the QCEW series are not yet coded properly to
permit this type of analysis.
An alternative is to use the zip code of the establishment
as the defining geography. Again using GIS analysis, zip codes
can also be identified by their intersection with appropriate
shoreline locations, and they appear on almost all establishment records. 3 Additionally, they meet the requirement of
being considerably more compact than counties, particularly
in large urban settings. Zip codes are increasingly used in
the presentation of a variety of socio-economic data. For
example, the Census Bureau publishes both population and
housing data and employment data in zip code geographies.
With the use of zip codes, the ocean economy can be defined
by reference to industries whose production processes and
products directly involve the use of ocean resources, or to
industries that indirectly use ocean inputs by virtue of their
physical location in a shore-adjacent zip code.
There are disadvantages to using zip codes. They are fixed
by the U.S. Postal Service (USPS) for their administrative convenience, and thus can have some rather odd shapes depending on the particular needs of the usPs. Unlike county boundaries, which are highly stable over time, zip code boundaries
change from time to time, with new zip codes added as popu-
Monthly Labor Review
November 2004
25
U.S. Ocean and Coastal Economy
Ocean economy sectors and Industries by sic and NA1cs codes
NAICS
NAICS
code
Construction
Marine related
construction
Livipg resources
Fish hatcheries and
aquaculture
Fishing
Seafood processing
~
i \ft
Minerals
Limestone, sand, and
gravel f<
Oil and gas exploration
and production
industry (1997
NAICS)
237120 Oil and gas pipeline and related structures
237990 Other heavy and civil engineering construction
1629
112511
112512
114111
114112
311711
311712
Finfish farming and fish hatcheries
Shellfish farming
Finfish fishing
Shellfish fishing
Seafood canning
Fresh and frozen seafood processing
0273
0921
0912
0913
2077
2091
2092
Animal aquaculture
Fish hatcheries and preserves
Finfish fishing
Shellfish fishing
Animal and m~rine fats and oils~,,
Cann~d and cured fish and se~f6ods
212321
212322
211111
213111
213112
541360
Construction sand and gravel mining
Industrial sand mining
Crude petroleum and natural gas extraction
Drilling oil and gas wells
Support activites for oil and gas operations
Geophysical exploration and mapping services
1422
1442
1446
1311
1321
1381
1382
1389
Crushed and broken limestone,
Construction sand and grave
Industrial sand
Crude petroleum and natural gas
Natural gas liquids
Drilling oil and gas wells
.. ,
OJI and gas fieitrexploration s~ ices .
Oil and gas.field services, not elsewhe~ cl
3732
3731
Boat building and repair
S~ip building an,drepair
Ship and boat building
Boat building and repair 336612 Boat building and repair
Ship builq{ng and repair , 33661 I Sh,ip building and repair
:i<\fu
)f'
Touri~m and recreation
Boat dealers
Eating and drinking
places
Hotels and lodging
places
Marinas
Recreational vehicles,
parks, and campsites
Scenic water tours
Sporting goods
Amuseme.nt and
recreation services
Zoos ancl) iquaria
Transportation
Deep sea freight
Marine passenger
transportation
Marine transportation
!>tl VJCc'S'
Search and navigation
equipmf~t
Warehousing
SIC
code
"
1~,;;
441222
722 l I O
722211
722212
722213
721110
721191
713930
721211
Boat dealers
Full service restaurants
Limited service eating places
Cafeterias
Snack and nonalcoholic beverage bars
Hotels (except casino hotels) and motels
Bed and breakfast inns
Marinas
RV parks and recreational camps
487210 Scenic and sightseeing transportation, water
339920 Sporting and athletic goods manufacturing
487990
611620
532292
713990
Scenic and sightseeing transportation, other
Sports and recreation instruction
Recreation goods rental
Amusement and recreation services, not
elsewhere classified
· 712130 Zoos and botanical gardens
712190 Nature parks and other similar institutions
483111 Deep sea freight transportation
483113 Coastal and Great Lakes freight
transportation
483112 Deep sea passenger transportation
483114 Coastal and Great Lakes passenger
transportation
488310 Port and harbor operations
•
488320 Marine cargo handling
488330 Navigational services to shipping
488390 Other support activities for water transportation
334511 Search, detection, navigation, guidance,
aeronautical and nautical system, and
instrument manufacturing
493110 General warehousing and storage
493120 Refrigerated warehousing and storage
493130 Farm product warehousing and storage
26 Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
5551
5812
Boat dealers
Eating places
7011
Hotels and motels
4493
7033
Marinas
Recreational vehicles, parks,
3949
7999
Sporting and athleti.c goods
classified
Amusement and•tecreation s
8422
Zoos and aquaria;
4412
4424
4449
4481
4482
4489
4491
4492
4499
Deep sea foreign transportation.of frei
Deep sea domes!ic. transport~ti<;,?g, of fre
Water transportation of freightt not ds
Deep sea transportation of passengers e
Ferries
Water transportation of passengers, not
M~dne cargo hapdling
Tdwing and tugboat serviceJ
Water transportation services, not else
3812
Search, detection, navigation, guidance, aeronautical
and nautical system, and iqs~rument man
4225
4222
4221
General warehousing and storage
Refrigerated warehousing and storage
Farm product warehousing and storage
.t:
kt :,~r,··:0~
lation and economic growth occurs.
This implies the need for continual
monitoring of the zip code administration process to assure use of appropriately dated codes.
Employment and wages
Private ocean economy (sic basis), 1990 and 2000
Ocean economy sector
1990
Total ..... .... ....... ... .... .. .... ..................
Construction ............. ....... ..... .. .......... .
Living resources ... ... ..... .. ... .. ...... ..... ...
Minerals ... ......... .. ....... ..... .... .. ......... ... .
Ship and boat building ...... ...... .. .... ....
Tourism and recreation .... .. ... .......... ..
Transportation .. .... ........ ...... ..... .... ..... .
Establishments
Employment
Wages
(millions of
current dollar;;)
91 ,203
2,144
5,098
1,829
3,192
71 ,958
6,982
1,924,014
30,198
71,819
45 ,099
230,097
1,182,809
363,992
$38,064
937
1,540
1,860
6,564
13,447
13,716
In total in 1990, the ocean sector employed 1.9 million people in wage and
salary employment and grew to 2.3
2000
Total ........ ........ ........ .... ..... ..............
116,736
2,279,006
$55,704
million in 2000. Two sectors are exConstruction ... ..... .. ... .. ...... .. ... ..... .......
2,064
31 ,835
1,364
cluded from the analysis at this time
Living resources .. ... ... ..... .......... ...... .. .
62 ,184
4,580
1,838
Minerals ....... ........ ........ ...... .. ... .... .. .. ...
1,984
40 ,097
2,432
- government and scientific research
Ship and boat building ............... .. .. .. .
3,684
176,098
6,952
- because of data limitations.4 (See
Tourism and recreation ....... ..... ..... ....
95 ,850
1,672 ,156
27 ,292
Transportation ...... .. .... ........ .... ... .... .. ..
8 ,572
296,634
15,826
tablt: 1.) This growth in employment
of 355,000 over the period , or 18.5
Nominal wages
Establishments
Employment
(millions)
percent, was significant, and actually
slightly exceeded the national growth
Change 1990-2000
rate of 18.2 percent for wage and salTotal ... ...... .... .... .. ... .... ..... ... .... .. .. .. ...
25 ,533
354,993
$17,640
Construction .. ... ......... ... ... ... ..... .. ...... ..
-80
1,638
427
ary jobs. Total wages and salaries meaLiving resources .. ....... ... .. ...... ......... ...
-518
-9 ,636
298
sured in current dollars grew by 46.3
Minerals .. .... ............ ... ... ............. ....... .
155
-5,002
572
Ship and boat building ................ .. ... .
492
-53 ,999
388
percent, substantially lagging behind
Tourism and recreation ...... ...... .. .......
23,892
489 ,346
13,845
the national growth of 76.2 percent.
Transportation .. ... ........ .................... ..
1,590
-67 ,357
2,110
The average wages in the ocean
Percent change 1990-2000
sector rose from $19,784 to $24,442
Total .... ....... .... ... .... .......... ... ...... ..... .
28.00
18.50
46.30
Construction .......... ...... .... .... .. .... .... ....
-3.70
5.40
45.60
per year in nominal dollars. (See table
Living resources .. ....... ....... .... ........... .
-10.20
-13.40
19.30
2.) This growth rate of 23.5 percent
Minerals .... ..... .... ..... .... .... ..... ........ ... ...
-11 .10
8.50
30.80
Ship and boat building ... ....... ..... .... ...
-23.50
15.40
5.90
also lagged significantly behind the
Tourism and recreation .... .... .. .... ... ... .
33.20
41.40
103.00
U.S. nominal growth rate in average
Transportation ...... .. .. .. ............. .. ....... .
-18.50
22.80
15.40
wages of 48.6 percent. While three of
SouRcEs: Bureau of Labor Statistics, Bureau of Economi c An alysi s, and National Ocean Economi cs
the five ocean economy sectors pay
Project.
average wages above the national average wage, the overall average wage
in the ocean economy lagged the U.S. average wage by more
• Productivity increases in the marine transportation and
than $3,500 in 1990 and by more than $10,000 in 2000.
oil and gas exploration and production industries, in
One major trend explains 'the observed changes in the
which capital investments resulted in a significantly
ocean economy and its rel ationship to the U.S. economy :
reduced demand for labor.
the dominance in both size and growth of the tourism and
• Declines in U.S. fi sheries from overfishing pressures.5
recreation sector. The touri sm and recreation sector was
the only ocean economy sector to show any significant
These large job losses were more than offset, however, by
an increase of 43 8,000 jobs in tourism and recreation, an inemployment growth over th e 1990-2000 period. As ide
crease of more than 40 percent during the decade. The leadfrom a small increase in jobs in the marine construction
indu stry, which is heavil y influenced by cyclical factor s
ing States in employment growth in touri sm and recreation
were along the Gulf of Mexico, including Louisiana, Missisand the choice of endpoints, the ocean economy lost
sippi , and Alabama, with more than 150 percent growth in
136,000 jobs in the nontouri sm and recreation sectors.
each State. 6 It should be noted that this estimate of the growth
There are a number of reasons for the se job losses, but
three predominate:
of ocean touri sm and recreation employment is an underestimate of actual growth because it excludes self-employment.
• Post-cold-war shifts away from the military, which
However, ocean touri sm and recreation employment
greatly affected ship building and search and navigagrowth does not pay the same level of wages as the other
sectors. Average annual wages are less than half of the U .S.
tion equipment manufacturing.
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Monthly Labor Review
November 2004
27
U.S. Ocean and Coastal Economy
-••1•11=--- Average annual wages, 1990 and 2000
Percent
change,
1990-2000
Ocean economy
sector
1990
2000
Total ...............................
Construction ......................
Living resources ................
Min8rals ... ... ... .... ...... ... .... ...
Ship and boat building ......
Tourism and recmation .. ...
Transportation ...... .. ...........
$19,784
31,029
21,443
41,243
28,527
11 ,369
37 ,682
$24,442
42,846
29,557
60 ,653
39,478
16,321
53 ,352
23.5
38.1
37.8
47.1
38.4
43.6
41.6
Average U.S. wages ...... .. ..
23,322
34 ,647
48.6
average wage, and are only two-thirds of the average ocean
economy annual wage. The dominance of tourism and recreation employment in the ocean economy employment picture accounts for the lower overall wages in the ocean
economy compared with the United States as a whole. Of
the other ocean economy sectors, only the living resources
sector pays below the U.S. average wage.
The average annual wage figures shown here do not represent an accurate measure of actual compensation because
of the highly seasonal nature of work in the ocean tourism
and recreation industry. All States except Florida show peak
employment in tourism and recreation in July and August
(Florida peaks in March), and on average in 2000, employment was 10 percent in the summer higher than the annual
average. In some States, such as Maine, the differential was
as high as 35 percent. This high level of seasonal employment naturally results in low annual average salaries. Even
taking seasonality into account, the wages and salaries in the
tourism and recreation sector are below average and account
for the combination of rapid overall employment growth, but
much slower overall wage growth.
When measuring the ocean sectors and industries for 2001
under the sic and NAICS definitions, the ocean economy is
smaller by about 400,000 jobs under NAICS. (See table 3.)
The principal differences arise in ship and boat building and
oil and gas exploration and production, primarily due to the
separation of establishments between production-related and
service-related functions.
The ocean economy under NAICS is somewhat smaller for
several reasons. First, there is increased precision in the indu strial definitions of the ocean economy, as illustrated in
two areas: hotels and general warehousing. Under NAICS,
hotels attached to casinos are now included in their own classification. Although there is significant employment in casinos located near the shore (the largest such area is Atlantic
City, NJ), it was decided to exclude these hotels from the
ocean sector. Under general warehousing, warehouses in the
near shore area are included in the transportation sector as
these are usually tied to the movement of freight by water.
Thi ~ classification under sic also included mini-warehouses
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and self-storage facilities that were largely unconnected with
marine freight; under NAICS, these facilities can be excluded
from the analysis.
NAICS also class ifies establishments based on the principal
functions of the establishments rather than the firm or parent
organization. Thus, in the manufacturing sector, for example,
establishments involved in production are classified in manufacturing , and establishments in administration are in services; this reduces the size of manufacturing sectors, and increases the size of service sectors. The manufacturing sectors, such as ship and boat building, are measured under the
NAICs-based ocean economy, but administrative establishments in the NAICS professional and business services sector
are not included in the ocean economy.
The coastal economy
There is a distinction to be drawn between the ocean and the
coastal economy. The former is defined by its use of ocean
resources as direct or indirect inputs; the latter is defined
purely by geography as the sum of economic activity taking
place within the coastal region. However, the term "coastal"
is ambiguous. It certainly encompasses the shoreline itself,
but how far inland the "coast" extends depends on the purposes for which a definition is being offered. The term
"coast" is used variously to describe the actual land-water
boundary, the area adjacent to the land-water boundary, the
areas surrounding estuaries, the land to the head of tide on
some rivers, the land " within a day's drive" of the shore, or
all the land within the watersheds of rivers. By the latter
definition, almost the entire land area of the United States,
excluding only the Great Basin, could be considered coastal.
Defining the coast necessitates a compromise among political, administrative, and natural boundaries. The approach
taken defines the coast as having three tiers:
• Near shore region -This is defined by zip codes adjacent to the shores of the oceans, Great Lakes, and major
bays. The selection of these zip codes is discussed in
greater detail in the section below on the ocean economy.
• Coastal zone counties - Coastal zone counties are any
county that includes in whole or part the area under the
jurisdiction of the Coastal Zone Management Act
(CZ MA) of 1972, as defined for that purpose by each
State participating in the program. Four States include
the entire State in the coastal zone (Rhode Island, Delaware, Florida, and Hawaii). Nine States (Washington,
Alaska, Texas, Louisiana, Georgia, South Carolina,
North Carolina, Virginia, and Maryland) define their
coastal zones using county or county-equivalent boundaries. Other States use various combinations of political (such as town boundaries) and geographic features
(adjacency to tidal waters) to define their coastal zones
■ r•1•ir=---
C omparison of ocean economy sectors and industries measured by sic and
Se ctor and industry
Establishments
NAICS,
2000
Employment
Wages (millions)
SIC
NAICS
118,451
102,305
2,208,861
1,866,355
$59,165.5
$43,165.9
Construction
Total .......... ...... ..... .... ...... ............. .......... ..... .. .. .. ... ... .... .
Marine related construction .. ..... .. ... .
1,919
1,919
1,702
1,702
30,992
30,992
24,304
24 ,304
1,421.9
1,421 .9
1,149.6
1,149.6
Living resources
Total ..... .. .... ..... ................... ... ........ .. .. .. .... ... ........ .......
Fish hatcheries and aquaculture .... ..... .......... ......... ... .. ..
Fishing ..... ..... .. ........ ..... ................ ..... .... ..... ........ ... ..... .... .
Seafood processing ............. ..... ... ....... ................ .... ..... ..
4,177
601
2,304
1,272
4,009
658
2,290
1,061
60,492
4,756
6,175
49,562
53,573
5,044
5,779
42 ,751
1,754 .5
117.4
240.8
1,396.2
1,455.1
123.1
221.2
1,110.7
Minerals
Total .. ........ ..................... ..... ... .................... ....... .. ..... .. .
Limestone, sand, and gravel ..... .. ..... .. ..... .. ... .... ... ..... .. ... .
Oil and gas exploration and production ... .. .... ... .. .. ....... ..
6,404
280
6,124
1,217
276
941
111 ,839
4,883
106,957
24,493
4,744
19,749
10,450.0
218.4
10,231 .6
1,612.4
212.4
1,399 .9
Ship an d boat building
Total .. ... .... .... ...... ......... ................. ..... ......................... .
Boat building and repair ....... ....... .... ........ ..... ..... ............ .
Ship building and repair ...... ......... ... .................. ........ .... .
3,759
2,954
805
1,942
1,303
639
168,146
51 ,886
116,260
154,504
43 ,284
111 ,220
6,987.8
1,592 .0
5,395.8
6,522.3
1,329.5
5,192.7
93, 189
6,578
2,032
70,825
10,599
1,947
643
402
163
87 ,818
4,747
2,029
65 ,990
10,520
1,944
642
417
162
1,367
1,602,614
114,175
15,395
1,084,479
353,472
13,944
4,762
8,472
7,914
1,415,635
44 ,399
15,390
1,012 ,925
299,624
13,869
4,747
8,363
8,194
8,124
26 ,831 .1
2,648.4
498.9
14,824 .7
7,853.6
386.8
84.7
350.4
183.6
.0
22,284 .0
874.8
498.4
13,421 .9
6,240.7
385.4
83.9
342 .0
262 .1
174.8
9,003
935
997
3,638
174
3,259
5,617
625
212
3,205
165
1,410
234,778
33,756
25,715
95,005
34,564
45,738
193,847
20,313
13,155
91 ,217
34,453
34,709
11 ,720.3
2,055.0
886.5
4,470.4
2,869 .8
1,438.6
10,142.6
1,348.3
559 .5
4,235.8
2,861 .0
1,137.9
Total ocean economy ... ..... .
Tourism and recreation
Total ....... ...................... .... ... .
AI11u::;ernent and recreation services .. ... ............ ..... ... .. ..
Boat dealers .......... ......... .. ..... .... ........ ......... .............. .... ..
.... .. ...... ... .... ...
Eating and drinking places .....
Hotels and lodging places ...... .. .. ... .... .... ... ...... ..... .... ... ... .
Marinas ....... ... ... ...... .... ... ... ...... ... .. ...... .... ............. ........ ...
Recreational vehicles, parks, and campsites ........ ..... ...
Sporting goods .... .. ............ .. ... ....... .... .. .......... ... ......... .... .
Zoos and aquaria .. ............ .... ... .... .... ........ ..... .......... ..... ..
Scenic tours . .... .... ... ..... ..... ...... ..... .. .... .... .... ..... ...... ... ... .
Tra nsportation
Total
........ ..... ... ... ... .... ..... .... ... ... ......... ....
Deep sea freight ........ ..... ..... ........ .. .. .... ....... ... .. ... ...... ..... .
Marine passenger transportation ... .... .. .... .... .... ..... ..... ... .
Marine transportation services .. ....... ..... .. ..... ........ ....... .
Search and navigation equipment ..... .... .. ..... .... .... .. ... ....
Warehousing ..... ... ... ... .. ....... ...... ....... ..... ... .. ... ...... ...... .....
SIC
NAICS
SIC
NAICS
NoTE : Data exclude Massachusetts, which does not permit access to their establishment level data. Dash indicates data not available.
for purposes of the CZ M A . All counties that, in such
circumstances , include territory defined as the coastal
zone are included in this category. Coastal zone counties were identified using G IS . Data showing the boundaries of each State's coastal zone were obtained from
NOA A 's Office of Coastal Resource Management and
overlaid on Census Bureau county boundary data to detrrmine the intersection. In the case of Illinois, which
does not participate in the CZ M A program, Cook County
was included to provide for nationally consistent totals.
• Coastal watershed counties - These are defined by
the U.S . Geological Survey (usGs) as the coastal zone
counties plus counties that include the headwaters of
coastal rivers. This definition excludes major continental river systems such as the Mississippi-MissouriOhio system. 7
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When analyzing employment growth over the 1990-2000
period in these three tiers of the coastal economy plus the
coastal States, population growth is included for comparison
because it is traditionally the principle variable employed
when discussing socio-economic change within the coastal
region. 8 (See table 4.)
Table 4 shows that employment growth was faster than
population growth in the country as a whole , but the differential was larger in the coastal areas, however defined. The
difference was largest in the near shore area, where employment growth was more than three times faster than population growth. In fact, while the near shore areas showed the
slowest population growth, they showed the fastest employment growth.
This is an important finding, because most of the discussion about socio-economic change in coastal areas focuses
Monthly Labor Review
November 2004
29
U.S. Ocean and Coastal Economy
exclusively on population growth. The addition of employment growth to the picture of economic growth in the coast
shifts attention away from the effects of population growth
alone to the effects of economic growth as a whole.
Another important characteristic of the coastal economy,
as distinct from the ocean economy, is that it is a high value
economy. (See table 5.) Not surprisingly, the near shore
area is the densest in terms of employment and establishments. However, it also pays the highest wages per acre, in
fact more than twice the U.S. average wage per acre, and 80
percent higher than the total wages per acre in the coastal
States. This makes the near shore region one of the most
valuable economic regions per acre in the United States.
QCEW DATA provides two different views of the
national economy that have not been available before. One
is industrial, based on the ocean economy and its resources.
While estimates of the ocean economy have been available
previously, the use of the QCEW data provides both a more
complete picture of the ocean economy by extending the
measurement to employment and wages, and also allows
State and even sub-State views of employment and wages in
this sector. The data reveal a natural resource economy in
the midst of substantial changes, which amplify larger trends
in the economy. The measurement of the ocean economy
under both sic and NAics also demonstrates the increased precision available under NAICS, as well as some of the drawbacks of all economic taxonomies.
The other new view is geographic , showing both the rapid
growth and the economic importance, which has not been
visible heretofore , of the near shore area. This use of the
THIS USE OF
-
• • • 11 ~ · •
Population and employment change in coastal
regions, 1990-2000
[Percent]
Regions
Population
Wage and salary
employment
United States .... ......... .. ... ..... ...
Coastal States .........................
Coastal watershed counties ....
Coastal zone counties .. ..... ..... .
Near shore .. ... .. ... .............. ..... .
13.2
12.3
11.2
11.5
10.9
20.8
31 .3
23.7
22.8
35.1
■ l•••
-~- Economic activity per acre in coastal regions, 2000
Regions
Establishments
Employment
Wages
(millions)
Total United States .. .
-
14.4
$0.53
Total coastal States ..
Coastal watershed
counties .... ............
Coastal zone
counties .......... ......
Near shore .. ... ..........
1.25
19.4
.70
1.70
26.9
1.03
1.69
2.51
26.0
34.3
.99
1.26
NorE: Acreage data are from the Census Bureau and reflect acres of
land , excluding water bodies and wetlands . Dash indicates data not
available.
QCEWdata demonstrates clearly what will undoubtedly be a
growing trend in the use of labor statistics over the next decade: the integration of economic data into new geographic
datasets required as Geographic Information Systems technologies become more widespread. This will present those
involved with the collection and distribution of economic
data with new challenges to provide meaningful data while
still meeting the strict standards of confidentiality required
of all federal statistics programs.
D
Notes
1
See America's Living Oceans: Charting a Course for Sea Change
(Washington, Pew Oceans Commission, May 2003) and An Ocea n Blueprint for the 21 s' Century: Report of the U.S. Commission on Ocean Policy
(U.S. Commission on Ocean Policy, September 2004), on the Internet at
www.oceancommission.gov
2 See Gross Product
Originating from Ocean-Related Activities (Bureau of Economic Analysis, 1974); G. Pontecorvo and others, " Contribu tion of the Ocean Sector to the U.S . Economy," Science 208, May 30,
1980, pp. 1000- 06; and Gross Product Originating from Ocea n Related
Activities: 1972 (Bureau of Economic Analysis, 1972).
3
Three addresses appear on each QCEW record: a physical address, a
mailing address (often a post office box), and an unemployment insurance
address, which is used when another party (for example, a corporate headquarters or payroll service) files the required employment reports. While
a physical addres s is required, it is not always present on the record filed
by employers. In such cases, the mailing address is used, and if that is
absent, the unemployment insurance address.
4
The problem with both sectors is that ocean-related activities are
embedded within larger organizations and the specific ocean-related components cannot easily be separated from those organizations. At the federal level, it is relatively easy to identify the Navy, Coast Guard , or NOAA,
but other agencies are much more difficult. Both the Environmental Protection Agency and Army Corps of Engineers have substantial programs
30 Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
that are ocean and coastal related, and the standard budget reporting does
not permit these to be easily identified . The problem is greatly magnified
at the State and local government levels. Most scientific research on the
ocean takes place within universities, which do not necessarily separate
ocean from nonocean research in their reporting. Development of specific employment and related data for this sector will require a significant
investment in research in individual programs.
5
The QCEW data series does not contain data for employment in the
fisheries harvesting sector, because firms in this sector are excluded
by law from the unemployment insurance system. Such firms operate
on a " lay," or share of catch payment system, rather than traditional
wages.
6
Mississippi's high rate of growth owed much to the establishment
of a number of casinos in the coastal region over the 1990s. As noted in
the discussion on the distinction between the sic and NA1cs codes, the sic
definition of hotels included casinos, while the NAICS definition permits
casino hotels to be separated from other hotels. The high rate of growth in
Mississippi ocean tourism and recreation is thus somewhat ambiguous.
7
There are 412 coastal zone counties and 669 counties. Lists of these
counties are on the Internet at www.oceaneconomics.org
8
Data exclude Massachusetts, which does not permit access to their
establishment level data.
·
,>~l~h~~
Industry Productivity under NAI■
~>
Industry productivity trends under the North
American Industry Classification system
The NAICS classification system presents a more consistent framework
and a conceptual improvement for productivity measurement;
while performance varied by industry, NA/CS-based productivity
measures show strong overall productivity growth during the 1990s
and again after 2001--especial ly in manufacturing, trade,
and in the newly defined information sector
Matthew Russell,
PaulTakac,
and
Lisa Usher
Matt Russell
and Paul Takac
are economists,
and Lisa Usher is
Chief of the Division
of lndustryProductivity
Studies, in the Office
of Productivity
and Technology,
Bureau of Labor
Statistics.
E-mail :
Russell. Matthew
@bis.gov
Takac.Paul@bls .gov
Usher.Lisa@bls.gov
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T
he Bureau of Labor Statistics has recently
completed converting its industry labor
productivity measures to the North American Industry Classification System (NAICS). 1 The
conversion mirrors efforts of the entire U.S. statistical system to more closely reflect the Nation's
changing economy by better identifying service
industries and new and emerging industries. This
article describes the conversion effects on the
industry productivity data, focusing on industry
structure and data availability, and the resulting
trends in industry labor productivity and related
measures.
NAICS replaces the existing Standard Industrial Classification (SIC) system that had been in
use since the l 930s. 2 While the SIC system was
revised periodically over the years to reflect
changes in the economy 's industrial composition,
its structure remained the same as first established
in the 1930s. The focus remained on the goodsproducing industries, particularly those in the
manufacturing sector, which was prominent when
the SIC was first introduced. The most recent
major revision to the SIC occurred in 1987, and
rapid changes since then in both the U.S. and
world economies necessitated additional changes
by the mid 1990s. The adoption of the North
American Free Trade Agreement in 1994 highlighted the need for cooperation between the
United States, Canada, and Mexico. The NAICS
classification system was developed as a cooperative effort by the statistical agencies of these
countries during the mid 1990s. The goal was to
provide an improved industry classification sys-
tern that would offer common industry definitions
based on a unified economic concept for the three
countries-and which would give special attention to service industries and to new, emerging,
and advanced-technology industries.
Industry productivity measures
The Bureau of Labor Statistics has been measuring productivity for more than 100 years. A study
of 60 manufacturing industries was published in
1898, and various other studies were conducted
over the following years. Today's industry productivity program began in 1941, after Congress
authorized the Bureau to undertake continuing
studies oflabor productivity. In 1959, BLS began
producing labor productivity measures for the
total private economy and major sectors on an
annual basis; quarterly measures of these series
were introduced in 1968. 3
Labor productivity indexes measure the
changes in the amount of goods or services produced relative to the labor hours used in producing that output. The indexes are calculated by
dividing an index of output for an industry by an
index of hours for that industry. Labor productivity measures reflect the joint effects of many
influences-including changes in technology;
capital investment; the use of purchased energy,
materials, and services; the organization of production; capacity utilization; managerial skill; and
the characteristics and effort of the workforce.
The conversion of the industry productivity
measures to conform to the NAICS cla~sification
Monthly Labor Review
November
2004
31
Industry Productivity under NAICS
system is one in a serie s of recent improvements to the
Bureau's industry productivity measurement efforts that began in the 1990s. In 1998 , industry coverage was expanded to
include labor productivity measures for all three- and fourdigit SIC manufacturing industries. Compensation and unit
labor cost measures for three-digit SIC industries were devel oped and published in 1999. In 2000, multifactor productivity
measures were published for all three:.digit SIC manufacturing
industries. Industry labor productivity and cost measures
were extended to cover all three- and four-digit SIC retail trade
industries in 2001 , and in 2002 for all three-digit SIC wholesale
trade industries. During thi s time, the adoption of superlative,
chain-weighted indexes for calculating output was accompanied by other changes aimed at streamlining and standardizing the industry labor productivity series.4
The transition to NAICS caused a di scontinuation of the
historical SIC-based data used for measuring industry productivity. In order to maintain consistent, continuous series for
measuring industry productivity trends, the historical SICbased industry measures were converted to a NAICS bas is
back to 1987. Converting industry productivity and cost measures to NAICS involved the separate conversion of output,
employment, hours, and compensation for each industry. 5
Some NAICS industries are the same as their SIC counterparts,
so that no special adjustments to data had to be made to
convert the output measures. 6 For some other industries, the
addition or removal of one or more products was all that was
needed to convert the output measures to a NA ICS bas is. For
other industries, however, constructing NAICS output series
required greater data adjustments. In most cases where a
NAICS industry was not a direct match to a corresponding SIC
industry, the NAICS output series were derived by applying a
constant conversion or " bridge" ratio to the entire historical
series (see Appendix for details). These historical NAICS estimates thus are based on the assumption of fixed historical
relationships between the SIC and NAICS series. Such an assumption may not be appropriate, particularly for new, emerging industries. 7 Revisions to current estimates based on ongoing research may be incorporated in future updates as more
and better information becomes available.
NAICS
reclassification
NAICS represents a completely new system for classifying industries.8 NAICS uses a six-digit code that is hierarchical like
the SIC code, but is unrelated to the SIC code. In the six-digit
NAICS code, the first two digits identify the sector; the third
digit designates the subsector; the fourth designates the industry group; the fifth designates the international industry;
and the sixth digit designates the national industry. (When
the U.S. industry is the same as the five-digit NAICS industry,
32
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November
2004
the industry has a zero as the sixth digit.) The six-digit codes
provide greater flexibility than the SIC, allowing for international comparability of industries at the five-digit level while
still permitting individual countries to identify unique sixdigit national industries.
There are fundamental differences between the NAICS and
SIC systems, and some of the differences have important implications for the measurement of industry productivity. For
example, NAICS represents a systematic restructuring of the
industry economic classification system. NAICS creates a
consistent system that classifies establishments based on
similarities in their production processes. This approach considers the way an establishment uses its production technology to produce its final output. The SIC was less unified in its
approach: SIC industry classifications were sometimes based
on supply -s ide factors such as the nature of the production
processes, while at other times were based on demand-side or
market-based factors such as the nature or uses of the final
products. Because productivity measures attempt to capture
changes in the efficiency with which industries use their inputs to create final goods or services, the NAICS system of
grouping together establishments with similar production processes represents an important improvement over the SIC classification system.
NAICS also differs from the SIC in its treatment of auxiliaries. NAICS classifies auxiliary units involved in management
or support activities such as transportation, warehousing,
accounting, payroll , or general management services into specialized industries rather than including them in the manufacturing, trade, or service industries they support, as in the SIC.
This change also has an impact on the industry productivity
measures. Under NAICS, the hours of workers employed in a
headquarters office or a warehouse facility of a manufacturing firm , for example, are no longer counted as hours of the
manufacturing industry. This reduces the overall number of
workers in the manufacturing industry and increases the concentration of workers directly involved in the manufacturing
process. As a result, the trend in labor hours (and therefore
the trend in labor productivity) may be different for the manufacturing industry under NAICS, even if the output of the industry is classified the same as the SIC industry. As employment and hours of auxiliary establishments are reclassified
into management and support industries under NAICS, the
levels of employment and hours will be lower in the industries
where they used to be classified. However, the effect on the
trends in industry hours depends on how the growth in employment and hours of these auxiliary workers compares to
that of the workers in the industries where they were previously class ified.
In addition to this different industry structure, the NAICS
system differs from the SIC system in its increased industry
detail, as well as its greater focus on service industries and
emerging and high-tech industries. This shift in focus toward
the service sector, which reflects the declining importance of
manufacturing and the growing importance of services in the
national economy, also has implications for productivity measurement. While NAICS adds industry detail, the increased
detail does not translate into an immediate increase in industries for which productivity measures are available-for several reasons. Much of the industry detail that was added under NAICS is in service industries where productivity measurement is currently not feasible. For many of these industries,
reliable data for measuring output or labor input have not been
collected. For some industries, lack of data is further complicated by conceptual issues regarding the proper measurement
of output. 9 For other industries, data have recently begun to
be collected but are available for only a few years. Furthermore, in some sectors such as manufacturing, where data availability for detailed industries was excellent under the SIC, the
conversion to NAICS has reduced the number of industries for
which reliable source data are available. Data have been discontinued for some detailed industries under NAICS, or are
available only for combinations of industries. This decline in
the availability of historical industry data limits the number of
NAICS industries for which labor productivity measures can be
calculated. Within manufacturing, for example, data limitations
reduced the number of detailed industries to 132 five-digit
NAICS industries and 148 additional six-digit NAICS industries--down from 458 four-digit SIC industries. 10 Manufacturing also was affected by a reduction in detail at the four-digit
NAICS "industry group" level. Although the Bureau continues to publish labor productivity measures for all manufacturing industry groups, the number of these groups dropped from
140 three-digit SIC groups to 86 four-digit groups (the comparable level of detail) under NAICS.
finance and insurance, real estate and rental and leasing, professional and technical services, accommodation and food
services, and other services. As shown in table 1, employment
coverage of the industry productivity measures varies for
these other sectors.
The conversion to NAICS resulted in the emergence of several newly defined industries and sectors and the reorganization of some industries between sectors. For example, a new
information sector was created under NAICS , bringing together
industries involved in producing and distributing information
and cultural products-industries that, under SIC , had been
spread across the manufacturing, communications and utilities, and services sectors. The manufacturing sector lost several publishing industries that were reclassified into the information sector, and also lost the logging industry, which was
transferred into the agriculture, forestry, fishing, and hunting
sector under NAICS. The conversion to NAICS also resulted in
the creation of a new accommodation and food services sector, as eating and drinking establishments were reclassified
out of retail trade and grouped with hotels and other lodging
places. In addition, under NAICS the criteria for defining wholesale and retail trade industries changed: whereas the SIC system focused on the class of customer, NAICS considers the
method of selling. As a result, establishments were reclassified from wholesale to retail trade and vice-versa. These various changes are reflected in the NAICS industry productivity
measures.
Because of the structural changes in industry classification that accompanied the conversion to NAICS , measures of
NAICS industry employment, hours, output, compensation and
Employment coverage of BLS industry labor
productivity measures by sector, 2001
NAICS sector
Sector title
Employment
coverage
(percent)
Private nonfarm business sector .... .
56
The industry productivity database
The industry productivity database includes productivity and
related measures for more than 480 unique industries at the
six-, five-, four-, three-, and in a few cases, two-digit NAICS
level. Labor productivity and related measures are currently
available from 1987 to 2001 , 2002, or 2003, depending on the
industry. 11 These labor productivity measures account for
nearly 58 percent of the four-digit NAICS industries in the nonfarm business sector of the economy and cover about 56 percent of employment. 12 Industry productivity measures cover
I 00 percent of employment in the mining, manufacturing,
wholesale trade and retail trade sectors, and nearly 100 percent in the accommodation and food services sector. 13 Productivity measures are also available for selected industries in utilities, transportation and warehousing, information,
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Federal Reserve Bank of St. Louis
21
23
31 - 33
Goods-producing .... .. ... ........... ... ... ....
Mining ............ ... ......... .... ... .. ....... .... .
Construction ... ...... ................ ... ...... .
Manufacturing .. ................. ....... .. .. .. .
Service-producing ... .. ............ ... ........
Utilities ................ .... ..... .... ... ............
Wholesale Trade ....... ........ .... ....... ...
Retail Trade ....... ... ... ............. ... ..... .. .
44-45
48-49
Transportation and warehousing ....
Information .. ..................... .............. .
51
Finance, insurance, and
52-53
real estate ... ......... ............ ........ ... .
Accommodation and food
72
services ........ ....... ...... .. ....... ...... ... .
54-56,61-62, 71,81 Other services . ........ .... ........ .. .... ... ..
22
42
71
100
0
100
51
92
100
100
46
71
21
100
10
NorE : Data for the nonfarm business sector exclude general
government, owner-occupied housing , and nonprofit organizations .
Monthly Labor Review
November
2004
33
Industry Productivity under NAICS
productivity are not always comparable to their SIC counterparts. Differences are apparent even at the major sector level
(two-digit NAICS). Table 2 shows employment in selected major industry groups for which BLS has complete or near complete coverage of industry productivity measures. Both the
manufacturing and the wholesale trade sectors as defined
under NAICS are smaller than under the SIC. In both of those
sectors, employment in establishments and industries that
moved out of the sector exceeded that which moved in. Thi s
reduction is partly due to the reclassification of auxiliary establishments. For example, a large number of manufacturing
employees were reorganized into new auxiliary NAICS industries outside the manufacturing sector. In addition, employment levels changed as entire industries were reclassified into
different sectors. The reclassification of several publishing
industries into the new information sector under NAICS caused
a noticeable reduction in manufacturing employment. Excluding the reclass ification of auxiliary establishments, about 80
percent of the workers that were moved out of manufacturing
in 2000 were reclassified into the new information sector. A
noticeable net redistribution of employment also occurred between the wholesale and retail trade sectors, as the employment in establishments reclassified from wholesale trade to
retail trade under NAICS exceeded th at from retail trade to
wholesale trade.
With the conversion to NAICS , productivity measures were
developed for several new industries and industry groups.
In manufacturing, for example , output per hour and rel ated
series are available for a new NAICS industry group, computer
and electronic products manufacturing (N AICS 334). Thi s
group brings together establishments that produce such
high-tech products as computers, semiconductors, and communication equipment, as well as measuring, analyzing, and
IJ•1•11=--
controlling instruments. Under the SIC, these firms had been
primarily distributed among three different two-digit SIC
groups. Labor productivity measures are also newly available for semiconductor machinery manufacturing (NAICS
333295) and printed circuit assembly manufacturing (334418).
In wholesale trade, labor productivity measures have been
developed for a new industry group, wholesale electronic
markets and agents and brokers (NAICS 425), as well as for the
two industries that compose that group: business to business electronic markets (NAICS 42511) and wholesale trade
agents and brokers (NAICS 42512). In retail trade, labor productivity measures are available for a redefined industry
group, health and personal care stores (NAICS 446), which
includes a new NAICS industry: cosmetics, beauty supply,
and perfume stores (NAICS 44612). Labor productivity measures are also newly available for electronic shopping and
mail order houses (NAICS 4541 ). Within the information sector, productivity measures are available for a variety of publishing, broadcasting, and telecommunications industries.
Under NAICS , the cable television industry has been divided
into separate industries-cable programming (NAICS 5152)
and cable distribution (NAICS 5175)-and labor productivity
measures are available for both industries. Productivity measures are al so available for a redefined industry group, publishing industries (NAICS 511 ), that includes the software publishing industry as well as industries involved in the more
traditional publishing of books, periodicals, and databases.
Productivity trends in major sectors
Productivity often exhibits predictable patterns over the
course of the business cycle , rising during expansions and
declining during rece ssions. This occurs as businesses
Employment in selected major industries in 2000, NAICS and SIC
2000
NAICS sector
employment
(000s)
Percent of
private nonfarm
business
2000
SIC sector
employment
(000s)
Percent of
private nonfarm
business
Private nonfarm business
59200.8
100.0
Private nonfarm business
60954.8
100.0
Manufacturing (NA1cs 31-33)
17262.9
29.2
Manufacturing (sic 20-39)
18394.4
30.2
5933.2
10.0
Wholesale trade (sic 50-51)
7024 .0
11 .5
15279.8
25.8
Retail trade (SIC 52-59)
Retail trade excluding eating
and drinking places (sic 52-57, 59)
Wholesale trade {NA1cs 42)
Retail trade (NAICS 44-45)
Retail trade excluding eating and
drinking places
Accommodation and food services
{NAICS 72)
16.9
10026.5
i
34
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
15193.1
24 .9
Eating and drinking places (sic 58)
8113 .7
13.3
Hotels (SIC 701)
1845.3
3.0
adjust their use of inputs to changes in the demand for
their goods and services. At the beginning of an expansion, for example, employment increases often lag behind
output increases, while at the beginning of a recession
reductions in output cause employers to cut back on employment and hours, but also with a lag. To minimize the
cyclical effects on productivity trends, it is appropriate to
analyze productivity changes over the course of a full
cycle. The decade of the 1990s represents such a period.
Economic activity in the United States peaked in July 1990
and again in March 2001. This article reviews the NAICS
industry productivity performance between 1990 and 2000.
Analyzing productivity trends between these years, when
the economy was at similar peak stages of the business
cycle, reduce s the effect of cyclical factors such as differences in capacity utilization on productivity change. The
effect on industry productivity of the recession that began
in 2001 is discussed later in the article.
Chart 1 shows labor productivity change in major industry
sectors for which BLS has complete coverage or covers a high
percentage of the industry. Led by the information sector,
labor productivity growth was strong over the 1990-2000 period in most of these sectors, compared with the private non-
farm business sector as a whole, where labor productivity
grew at an annual average of 2.0 percent. Manufacturing,
wholesale trade, and retail trade also showed strong growth,
while productivity grew slowly in the accommodation and
food services industries. Productivity growth typically slows
in recession years, and in the recession year of 2001 output
per hour growth slowed considerably in all of these sectors,
and actually declined in mining and accommodation and food
services. Productivity growth is typically unusually strong
as an economic recovery begins. For most of the sectors
considered here , productivity not only sped up after 2001,
but exceeded the growth over the 1990-2000 period. The
exception was the accommodation and food services sectoralthough output per hour in that sector rose 0.5 percent in
2002, the growth that year was less than the average 0.7 percent growth from I 990 to 2000.
Chart 2 divides the 1990-2000 period in half and depicts
the productivity growth rate for private nonfarm business
and other major sectors in each of the subperiods. The
chart shows that, of the sectors that have full or near-full
employment coverage, almost all experienced a productivity speedup from 1995 to 2000. Retail trade in particular
showed a large increase in the productivity growth rate in
Output per hour, 1990-2002
Percent
Average annual rates of change, select NAICS sectors
Percent
8
8
•
1990-2000
0
0
2000-01
2001-02
6
6
4
4
2
2
0
0
-2
-2
Mining
1
2
Manufacturing
Wholesale trade
Retail trade
lnformation
1
Indu stry o utput per hour measures for In fo rmation cover only 71 percent of empl oy ment in th at sector.
Accomodation and food serv ices measures cover 99.5 pe rcent of sector employment.
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Federal Reserve Bank of St. Louis
Monthly Labor Review
Accomodation and
food services2
November
2004
35
Industry Productivity under NAICS
Average annual rate of change in labor productivity, 1990-95 and 1995-2000
Percent
Percent
6
6
5
5
4
4
3
3
2
2
0
0
Private nonfarm
business
1
2
Mining
Manufacturing
Wholesale trade
lnformation 1
Accomodation and
food services 2
Industry output per hour measures for Information cover only 71 percent of employment in that sector.
Accomodation and food services measures cover 99.5 percent of sector employment.
the second half of the decade. Mining was the only sector
that experienced a falloff of productivity growth in the latter half of the 1990s. The average annual rate of change in
mining productivity fell from 3.4 percent in 1990---95 to 1.5
percent in 1995- 2000.
The changes in industry composition under NAICS result in some differences in sector productivity trends when
compared with the comparable SIC sectors. Table 3 shows
labor productivity change over the 1990-2000 period for
several sectors as defined under both classification systems. Productivity growth rates were the same or very
close for private non-farm business and for manufacturing,
but differed somewhat for wholesale and retail trade. In
both the wholesale and retail trade sectors, output per hour
grew more rapidly under the NAICS system than under the
SIC system. The reclassification of some auxiliary establishments out of the sectors, including those involved in
warehousing, may be one reason for the increase in productivity growth for both retail and wholesale trade under
NAICS. The eating and drinking places sector, so classified
under the SIC system, was moved out of retail trade and
combined with the accommodation industries under NAICS
to form the accommodation and food services sector-and
thus productivity trends are not comparable between those
NAICS and SIC categories.
36
Retail trade
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
Industry productivity and cost trends
1990-2000. Labor productivity increased from 1990 to 2000
in most of the detailed industries published by BLS. 14 Output
per hour rose in 156 of the 169 industries analyzed in this
article, representing 92 percent of the industries and employment covered . Output increased in 89 percent of the industries, while hours increased in 63 percent of the industries.
The wide-ranging, but genera11y positive, industry productivity performance during the period is reflected in chart 3. The
chart shows the distribution of average annual productivity
growth rates for the 1990---2000 period for all the published
industries (all four-digit NAICS industries together with additional published industries for which component four-digit
series have not been computed). The chart reflects a strong
central tendency despite a wide range of productivity performance. Roughly two-thirds of the industries experienced average annual rates of change in labor productivity that ranged
from 0.0 percent per year to an increase of 3.9 percent per year.
Although labor productivity trends for individual industries were largely positive during the 1990s, there was some
variation by industry and by sector. Of the NAICS industries
for which measures are available, productivity performance
ranged from an average annual decline of I .8 percent per year
in drinking places, alcoholic beverages (NAICS 7224) to an
■ 1•1• 1 B NAICS vs.
SIC
labor productivity trends in selected major sectors, 1990-2000
[Average annual rates of change]
Output per hour
1990-2000
NAICS sector
Output per hour
1990-2000
SIC sector
Private nonfarm business ...... ...... ....... .... ... ..... ... .... .
2.0
Private nonfarm business ..... ..... .. ......... .. .......... ..
2.0
Manufacturing (N A1cs 31-33) ...... .. .. .......... .... .. ...... ..
3.7
Manufacturing (sic 20-39) .... .. ...................... .. .. ..
3.8
Wholesale Trade {NAICS 42) ........ .. .. .. .... .... ............ . ..
3.9
Wholesale trade (sic 50-51) .... .......... .. .... .. ...... .. .
3.4
2.4
3.2
Retail trade ....... ...................... ....... .. ............ ...... ..
Retail trade excluding eating and .................. ...
drinking places (sic 52-57 , 59) .......... .... .... .. ... .
2.9
Eating and drinking places (sic 58) .... .. ...... .... .. ..
.3
1.7
Retail Trade ......................... .... .... .. ...........................
Retail trade excluding eating and drinking
places (NAICS 44-45) ...... .. ...... .. .. .. ...................... .
Accommodation and food services (NA1cs 72) .... ....
.7
Hotels (SIC 701) ... ...... .... ............................ .. ...... ..
Information (NA1cs 51) .... ... ... .. .... ............. ........... ... ..
4.9
Information ..... .... .................... ..... .. ..... ... ............. .
NorE: Dash indicates data not available.
average annual increase of 31 .7 percent per year in computer
and peripheral equipment manufacturing (NAICS 3341 ).
As seen in chart 3, the majority of industries experienced
labor productivity growth that averaged between 1 and 5 percent per year. Table 4 lists the eight industries with the highest
productivity growth rates over the 1990--2000 period. Each of
the industries in that table experienced growth in output per
hour of more than 12 percent per year, on average. Only three
of the eight industries are manufacturing industries, but two
of those experienced the faste st labor productivity growth of
all the measured industries. Output per hour grew 31.7 percent
per year, on average, in computer and peripheral equipment
manufacturing and 27 .0 percent per year in semiconductor and
other electronic component manufacturing (NAICS 3344). The
list of industries with the most rapid productivity growth re-
fleets the importance of the high-tech sector on the U.S.
economy during the 1990s, and includes industries that were
major users or distributors of high-tech equipment as well as
the industries producing those goods. After computer and
semiconductor manufacturing, productivity grew most rapidly
for professional and commercial equipment wholesalers (this
industry includes establishments engaged in the distribution
of such products as computers and other equipment); electronics and appliance stores; electronic shopping and mailorder houses; software publishers; communications equipment manufacturing; and electric goods wholesalers. After
these eight industries, the next 14 fastest growing industries
experienced average annual rates of change in labor productivity ranging from 5.0 percent per year for both audio and
video equipment manufacturing (NAICS 3343) and line-haul
ll·••1r~•- Industries with the highest productivity growth rates between 1990 and 2000
NAICS
code
Title
3341
3344
4234
443
4541
5112
3342
4236
I
I
Computer and peripheral equipment manufacturing ....... .... .... ....... ..
Semiconductor and other electronic component
manufacturing ..... ... .... .... ... ..... ..... ....... ..... .... ... .... ... ....... ... ... ............
Professional and commercial equipment and supplies
merchant wholesalers ... .. ........... ...... .. .. .. .. ....... ....................... .... ...
Electronics and appliance stores .. ............ ... ...... .. .. ..... ........ ..... ..... ..
Electronic shopping and mail-order houses .......................... ...... ....
Software publishers ..... .... .. .. .... .. .. ... .. .......................... .. .. ..................
Communications equipment manufacturing ......................... .. .... .. .. ..
Electrical and electronic goods merchant wholesalers .... ...............
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2001
Employment
(0OO's)
Average annual percent
change, 1990-2000
Output/Hour
Output
Hour
ULC
286
31 .7
29 .0
-2 .1
-21 .5
645
27.0
29.3
1.9
-18.3
710
593
263
269
234
414
16.2
14.5
13.9
13.8
13.4
12.4
18.3
17.5
21.0
25.9
14.0
14.3
1.8
2.6
6.3
10.7
0.6
1.6
-9 .2
-8.0
-6.7
-3.6
-6.8
-6.1
Monthly Labor Review
November
2004
37
Industry Productivity under NAICS
Distribution of average annual rates of change for output per hour, 1990-2000
Number of
industries
Number of
industries
40
40
35
35
30
30
25
25
20
20
15
15
10
10
5
5
0
-3 .0 or
less
-2 .0 to
-2.9
-1.0 to
- 1.9
-0 .1 to
- 1.9
0 .0 to
0 .9
1.0 to
1.9
2.0 to
2.9
3 .0 to
3.9
4 .0 to
4 .9
0
5 .0 to
5.9
6.0 to
6.9
7.0 to
7 .9
8 or
more
Average annual rate of change
Distribution of average annual rates of change for output per hour, 2000-01
Number of
industries
Number of
industries
40
40
35
35
30
30
25
25
20
20
0
-3 .0 or
less
- 2.0 to
-2 .9
- 1.0 to
- 1.9
-0 .1 to
- 1.9
0 .0 to
0 .9
1.0 to
1 .9
2 .0 to
2 .9
3 .0 to
3.9
4 .0 to
4.9
Average annual rate of change
38
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
5.0 to
5 .9
6 .0 to
6 .9
7.0 to
7 .9
8 or
more
11•1• =--11
Largest industries by 2001 employment size
Average annual percent change, 1990-2000
employment
(0OO's)
code
7221
7222
4451
7211
4521
4411
52211
8111
4529
56172
4441
446
4481
447
491
48412
2001
Title
NAICS
Full-service restaurants .... ... ....... .... ..... ......... ........ .. ..
Limited-service eating places ············ ·· ··············· ······
Grocery stores ... ........... .. ... .. .... ... ...... ......... ..... .. .. ......
Traveler accommodation .. ... .. ..... ... .. .... .. ....... ...... .. ......
Department stores ... ........... ... ............ .......... .. ... ..... ....
Automobile dealers .............................. ............ .. ...... ..
Commercial banking ·· ·············· ······· ··········· ······ ···· ·· ····
Automotive repair and maintenance ·· ···· ················· ··
Other general merchandise stores .. ..... ........ ............
Janitorial services .. ............... .......... ............ .. ...... ... ...
Building material and supplies dealers ......................
Health and personal care stores ... .... ........ ....... ........
Clothing stores .. ...... .. .... ................... .. ... .. ..... ... .. ... ... ..
Gasoline stations .. ......... ...................................... .....
Postal service .... ........ ..... ... ...... ......... ....... ... ....... ... .. ..
General freight trucking, long-distance .... ..... ...... ....
railroads (NAICS 482111) to 9.0 percent per year for other general merchandise stores (NAICS 4529).
The overall upward trend in productivity during the 1990s
was reflected in the productivity performance of the largest
industries. Table 5 presents the average annual rate of change
in output per hour and related indexes for industries with more
than 800,000 employees in 2001, in order of employment size.
Together, these 16 industries account for nearly 42 percent of
the employment covered by the industry labor productivity
measures. Output increased in each of these large industries
between 1990 and 2000, and productivity increased in all but
one. Productivity declined in grocery stores (NAICS 4451) despite rising output, as labor hours increased more rapidly.
Unit labor costs represent the cost of producing one unit of
output. The measure is calculated by dividing an index of
labor compensation by an index of real output, or by dividing
an index of compensation per hour by an index of output per
hour (labor productivity). The latter ratio reveals an inverse
relationship between labor productivity and unit labor costs:
when labor productivity increases, it offsets increases in
hourly compensation so that unit labor costs rise less rapidly
than compensation. If labor productivity declines or rises more
slowly than hourly compensation, unit labor costs will increase, but if output per hour increases faster than hourly
compensation, unit labor costs will fall. From 1990 to 2000,
labor compensation increased in about 95 percent of the industries examined in this article. 15 However, unit labor costs
increased in only about 70 percent of the industries, as labor
productivity increased more rapidly than hourly compensation in a number of industries. Unit labor costs declined in
each of the eight industries with the fastest growing produc-
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4020
3616
2618
1832
1769
1273
1258
1134
1091
1072
1027
1014
1000
946
873
849
Output/hour
0.2
.2
-.2
2.6
2.4
1.5
2.6
1.6
9.0
3.4
3.4
1.8
5.8
2.3
.9
1.8
Output
Hours
2.4
2.4
.2
4.2
4.3
3.2
1.8
3.2
9.8
4.5
5.8
3.6
5.4
1.7
1.9
4.8
2.2
2.2
.4
1.5
1.9
1.7
-.8
1.6
.7
1.0
2.3
1.8
-.4
-.6
1.0
3.0
ULC
3.4
3.4
3.0
1.4
.7
2.9
3.7
1.8
-5.6
-0.1
.2
2.3
-1 .5
.9
2.1
.3
t1v1ty rates. In contrast, all of the industries with declining
productivity over the period recorded increases in unit labor
costs.
The recession of 2001 and beyond. The performance of industry output, hours, and labor productivity after 2000 contrasts with the positive performance of the previous decade.
Output per hour grew in only about 57 percent of the industries in 2001, compared to more than 92 percent of industries
with productivity growth from 1990 to 2000. Output declined
in 2001 in nearly 70 percent of the industries examined here,
while hours declined in 77 percent. In 2001, a greater proportion of the industries experiencing productivity growth did so
by reducing hours rather than by increasing output. Whereas
output grew in more than 90 percent of the industries that
increased their productivity from 1990 to 2000, output increased in only 44 percent of the industries where productivity rose in 2001. Instead, declining hours were the major impetus for productivity growth in 2001, with more than 81 percent
of industries reducing hours in that year, compared with only
about 40 percent of industries where productivity grew from
1990 to 2000.
The reaction of labor productivity to the downturn in the
economy that began in 2001 is also apparent in comparing the
distribution of industry productivity growth rates in 200 l (see
chart 4) to the distribution of average annual productivity
growth rates for 1990-2000 (see chart 3). During the 1990s,
nearly 60 percent of the industries examined here experienced
labor productivity growth of 2 percent per year or more, and
none showed productivity declines of -2.0 percent or more.
Chart 4, which reflects the cyclical effects of the beginning of
Monthly Labor Review
November
2004
39
Industry Productivity under NAICS
the recession, shows a decidedly less positive productivity
picture. Productivity grew 2.0 percent or better in only about
36 percent of industries in 2001, while productivity declined
the conversion has reduced the number of industries and industry groups for which productivity measures are calculated
in certain sectors, such as manufacturing. In addition, the
by -2.0 percent or more in 24 percent of industries in that year.
While industry productivity data are not yet available
through 2002 for detailed manufacturing industries, labor pro-
assumption of a fixed relationship between NAICS and SIC industries that underlies the conversion for many industries may
not be appropriate, particularly for new and emerging industries. Nonetheless, a comparison of productivity trends for
several major sectors where BLS maintains extensive coverage
of productivity measures shows similar productivity trends
throughout the 1990s as compared to comparable SIC sectors.
ductivity for the manufacturing sector as a whole grew rapidly
in 2002. Data for other industries suggest that productivity
improvements were widespread. Output per hour increased
for almost 79 percent of the mining , trade, and service-providing industries for which output per hour measures are available. The improvement in labor productivity was accompanied by increases in industry output as well as continuing
reductions in hours. Although output rose in more than 55
percent of the industries measured in 2002, hours declined in
nearly 70 percent of the industries.
Conclusion
THE CONVERSION TO NAICS HAS IMPACTED the industry produc-
tivity measures in a number of ways. The NAICS classification
system is a more consistent framework and a conceptual improvement for productivity measurement. At the same time,
Like the SIC-based data, the NAICS productivity measures also
continue to show a productivity speedup in the latter half of
the 1990s, compared to the first half. Recognizing current data
limitations, improvements to current estimates based on ongoing research will be incorporated in future updates as more
and better information becomes available, and efforts to expand industry productivity coverage to new industries will
continue. Meanwhile, NAICS provides an improved road map
for classifying industries. By more accurately reflecting the
current structure of the economy and underlying similarities in
production processes, NAICS enhances our understanding of
current productivity developments.
□
Notes
1
Productivity and cost measures for 180 mainly four-digit NAICS industries
were first released on September 18, 2003. Since that time the Bureau has
revised and updated the measures for many industries and added measures for
more than 300 additional industries at the six-, five-, three-, and two-digit
NAICS level.
2
Executive Office of the President ( 1998), North American Industry
Classification System. Uni ted States. 2002, Washington, DC, U.S. Office
of Management and Budget. Copies of the manual can be obtained from
the National Technical Information Service (NTIS) on the Internet at
www.ntis.gov/products/bestsellers/naics.asp. For more information about the NAICS structure, see the Bureau of the Census on the
Internet at http://www.census.gov/epcd/www/naics.html.
-' Joseph P. Goldberg and William T. Moye, The First Hundred Years
of the Bureau of Labor Statistics, Bulletin 2235 (Bureau of Labor Statistics, September 1985), pp. 169, 203, and 249.
-1 For example, output measures based on the deflated value of output were
adopted for most industries (made possible by the expansion in coverage of
producer price indexes during the 1980s and 1990s). Previously, a large
number of industries were based on physical quantity of output. The expansion
of the Bureau 's industry productivity series was also accompanied by a decision
to use BLS employment and hours data from the Current Employment Statistics survey for measuring labor input for all manufacturing industries, rather
than using Census data for some industries as had been done in the past.
5
Industry employment and hours data from the BLS Current Employment
Statistics (CES) survey were converted to a NAICS basis by the Bureau 's Office
of Employment and Unemployment Statistics with the release of May 2003
data in June 2003. CES industry employment and hours data were converted to
NAICS back to 1990 for most industries, and to earlier years for some industries. The Office of Productivity and Technology extrapolated these estimates back to 1987 for many industries.
40
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November
2004
6
Slightly less than half of the six-digit NAICS industries included in
the industry productivity database are industries that are direct matches
to comparable SIC industries. More than half of the mining and wholesale trade industries, and almost half of the manufacturing industries,
were direct matches to the SIC industries. For other sectors, less than
half the industries covered were direct matches.
7
Recent work by researchers at the Bureau of the Census and the Federal
Reserve Board has attempted to assign historical records of individual manufacturing establishments from each of the quinquennial Censuses of Manufactures for 1963 through 1992 to NAICS industries. These recoded data are used
to calculate new conversion ratios that reflect the changing relationship between SIC and NAICS shipments in those years. Kimberly Bayard and Shawn
Klimek, "Creating a Historical Bridge for Manufacturing between the Standard
Industrial Classification System and the North American Industry Classification System." Paper presented at the Annual Meeting of the American Statistical Association, San Francisco, August 2003.
8
Executive Office of the President ( 1998), North American Industry
Classification System .. .
9
Mark Sherwood, "Problems in Measuring Service Industry Output,"
Monthly Labor Re view, March I 994, pp . 11 -19
10
Some industry detail has been collapsed or discontinued under NAICS
in the BLS Current Employment Statistics data. In addition, some sixdigit manufacturing industry detail will be collapsed in the 2003 Annual
Survey of Manufactures data from the Bureau of the Census.
11
Productivity measures are available through 2001 for detailed manufacturing industries, although measures for total manufacturing and for
durable and non-durabl e manufacturing are available for later years. Productivity series are available through 2003 for wholesale trade, retail
trade, and food service and drinking places industries. For all other industries covered, productivity measures are available through 2002.
12
Percentages represent the proportion of paid employees in the
sector that are in the industries covered by the productivity indexes, as
measured in the BLS Current Employment Statistics establishment survey.
The percentage of proprietors and unpaid family workers covered by the
productivity measures is not explicitly included in the ratios of employment coverage, but assumed to be the same as for paid employees.
i J Industries with labor productivity measures in the accommodation and
food services sector represent 99.5 percent of employment in the sector.
14
This article focuses on published industries at the mainly three- and fourdigit NAICS level. Indexes and rates of change in output per hour, output per
worker, output, hours, all workers, labor compensation, and unit labor costs for
APPEN01x:
these industries are available from the BLS Productivity and Costs Web site on
the Internet at http://www.bis.gov/lpc/home.htm. Comparable productivity and cost measures for NAICS five- and six-digit industries, as well as underlying data on the number of employees, total industry hours, and the value of
net production for published and unpublished industries are available upon
request by sending E-mail to dipsweb@bls.gov, or by calling the Division of
Industry Productivity Studies (202-691-5618). SIC-based industry data also
are available on the BLS Web site or by request. Historical productivity and
related series for three- and four-digit SIC industries through 2000 will continue
to be maintained, but will no longer be updated.
15
Five of the eight industries with declines in labor compensation
were in textile manufacturing.
Methods and data underlying the series
Labor productivity is calculated as output per employee hour or output per
hour of all persons working in the industry. The indexes of output per hour
are computed by dividing an index of output by an index of aggregate hours.
Industry output is measured as ..sectoral output," the total value of goods
and services leaving the industry. Depending on the industry, hours can refer
to hours of employees or hours of all workers. '"All workers" include selfemployed and unpaid family workers as well as employees. For industries
where there are few self-employed and unpaid family workers, such as
manufacturing industries, output per employee hour is measured. NAICSbased output and labor input series are created at the most detailed industry
level possible; measures for more aggregate industries are aggregated from the
detailed industry series.
Tornqvist in.dexes. Wherever possible, a Tomqvist formula is used to
aggregate the various products or services produced in an industry in
order to derive an output measure for the industry. The Tomqvist
formula aggregates the growth rates of the various products or services
between two periods with weights based on the products· shares in
industry value of production. The weight for each product equals its
average value share in the two periods. The Tomqvist formula yields
the ratio of output in a given year to that in the previous year. Ratios for
successive years are chained together to form an output index.
The quantities of products used in the output index are measured
either with deflated values of production or with actual physical quantities. For most industries in manufacturing, communications, wholesale and retail trade, and services, output indexes are derived from
detailed data on the value of industry output or sales, adjusted for price
change (that is, the deflated value of production). Tomqvist aggregations of these deflated values are then calculated to derive output indexes. For industries in utilities, and for many mining and transportation industries, physical quantity output indexes are derived as
Tornqvist aggregations of quantitie~ of component products. The
Tornqvist aggregation method is used in calculating the output index for
most industries; one notable exception is commercial banking, in which
the annual changes in different outputs are combined using employment
weights that are changed every 5 years.
Annual output in.dexes based on deflated values of production. Annual deflated value measures of real output are estimated by dividing
current dollar value of production or revenues by appropriate price
indexes. For most manufacturing industries, current dollar industry
production (calculated as shipments adjusted for inventory change,
intra-industry transfers, and resales) is distributed to product classes
based on shares of wherever-made product class shipments. These
values are deflated by appropriate price deflators (mostly BLS pro-
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ducer price indexes). The resulting estimates of constant dollar production by product class are Tornqvist aggregated to create industry
output indexes for each six-digit NAICS industry.
Similarly, current dollar retail trade industry sales are distributed
to individual merchandise lines based on their relative value shares,
and then deflated with appropriate price deflators (mainly BLS consumer price indexes). The resulting constant dollar values by merchandise line are aggregated into a single industry output measure
according to the Tornqvist formula.
For wholesale trade industries also, current dollar sales are deflated
with appropriate price indexes. For each wholesale trade industry,
total sales by merchant wholesalers and by manufacturers sales an.d
branch offices (MSBOs) are deflated with aggregate price indexes constructed by weighting together different producer price indexes (and in
the case of merchant wholesalers, also some import price indexes).
Once deflated, the annual sales of the two types of wholesalers are
aggregated according to the Tomqvist formula. A similar procedure is
used to develop and separately deflate sales of business-to-business
electronic markets and wholesale trade agents and btokers, and then to
aggregate the constant dollar values into an index for the electronic
markets and agents and brokers industry group.
For some industries in information and services, detailed categories
of revenues are available and are deflated with BLS producer price indexes and then aggregated to the industry level using the Tomqvist
index formula. For other information and services industries, and for
some mining and transportation industries, where less detail is available, data on the value of total industry revenues for each year are
divided by industry-level producer price indexes or consumer price
indexes to derive measures of changes in the industry's real output.
Annual output in.dexes based on physical quantities ofproduction. For
utilities and for many mining and transportation industries, industry
output reflects estimates of the physical quantity of production. Physical quantity indexes are, in all possible cases, Tomqvist aggregations of
the quantities of component products, using the finest level of detail
available. Examples of such products include tons of coal, BTUs of
electricity, or revenue passenger miles and freight ton miles.
In.dexes of labor input. The indexes of industry labor input used as the
denominator in the productivity formula are developed mainly from
basic data compiled by BLS. Data on employment and average weekly
hours are used to construct measures of total hours for different categories of workers. Data from the Current Employment Statistics Survey
(a monthly establishment survey in which 390,000 representative establishments report employment, hours, and earnings data to BLS and
Monthly Labor Review
November
2004
41
Industry Productivity under NAICS
supportive State agencies) are supplemented with data from the Current Population Survey (a monthly survey of approximately 60,000
households conducted by the Bureau of the Census for BLS).
Industry hours represent all employee hours or all worker hours.
For manufacturing and mining industries, estimated hours of production workers and nonproduction workers are combined. For the trade,
transportation, and service industries where self-employed are important, estimates of the hours of partners, proprietors, and unpaid family
workers are added to estimated hours of supervisory and
nonsupervisory workers. Employee hours for different types of workers are treated as homogenous and are directly aggregated. The indexes
of hours are developed by dividing the aggregate hours for each year by
the base-period aggregate.
Unit labor costs. Unit labor cost indexes reflect the cost of labor input
required to produce one unit of output. Unit labor costs are calculated
as the ratio of current dollar labor compensation to real or constant
dollar output. The indexes of unit labor costs for each industry are
computed by dividing an index of current dollar compensation by an
index of constant dollar output.
Compensation is a measure of the employer 's cost for securing the
services of labor. It is defined as payroll plus supplemental payments.
Payroll includes salaries, wages, commissions, dismissal pay, bonuses,
vacation and sick leave pay, and compensation in kind. Supplemental
payments are divided into legally required expenditures and payments
for voluntary programs. The legally required expenditures include employers' contributions to Social Security, unemployment insurance
taxes, and workers' compensation. Payments for voluntary programs
include all programs not specifically required by legislation, such as the
employer portion of private health insurance and pension plans.
for n 1anufacturing industries, annual compensation data are derived from
the Annual Survey of Manufactures and the Census of Manufactures produced by the U.S. Bureau of the Census. For industries outside of manufacturing, annual wage and salary data from the BLS Quarterly Census of Employment and Wages (QCEW) program are used. Because these data exclude
supplemental payments, they are adjusted with ratios of compensation to
payroll from the quinquennial census data, or (for utilities) from the Bureau
of Economic Analysis (BEA), U.S. Department of Commerce. For a few
industries, compensation data are obtained from other sources: for railroad
transportation, for example, labor compensation comes from the Surface
Transportation Board; for air transportation, labor compensation comes
from the Office of Airline Information of the U.S. Department of Transportation, and for the Postal Service, labor compensation comes from the U.S.
Postal Service.
Conversion to
NAICS
The conversion of industry productivity measures to the NAICS system
required the separate conversion of output and labor input measures.
The timing of this conversion was guided by the availability of historical BLS NAICS-based employment and hours estimates, as well as the
necessary data for converting historical output series to NAICS. Both
output and labor input measures were converted to NAICS at the most
detailed industry level possible.
Output. Industry output indexes are prepared from basic data published by
various public and private agencies, using the greatest level of detail available.
Data from the Bureau of the Census, U.S. Department of Commerce, are
used extensively in developing output series for manufacturing, trade, information, and service-providing industries, as well as in developing compensation and unit labor cost series for manufacturing industries. The 1997
Economic Census conducted by the Census Bureau was the first major U.S.
42
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November
2004
statistical program to implement NAICS, and data from the 1997 Census
were used extensively in the NAICS conversion of the industry output measures. The 1997 Economic Census questionnaires were designed to permit
the classification of establishments according to both NAICS and SIC. As a
result, the Census Bureau tabulated and published 1997 industry data on
both a NAICS and SIC basis for some variables. These dual-coded data were
used to calculate conversion ratios relating NAICS industry values to SIC
industry values. The conversion ratios were used primarily in converting
output for manufacturing and trade industries, and for converting compensation for manufacturing industries. Conversion ratios were applied to SICbased historical industry sales--0r in the case of manufacturing industries,
to values of shipments, inventories and labor compensation-to obtain
estimates for NAICS-based industries for 1987 to 1996. For retail trade and
merchant wholesalers, the Census Bureau provided data on a NAICS basis
back to 1992, so additional estimates for NAICS-based industries were only
necessary for 1987-91. Data were then aggregated according to NAICS
industry definitions. The NAICS industry data estimated in this way were
used in constructing the deflated value indexes for each industry.
For manufacturing industries, product shipment categories are used
to distribute industry production prior to aggregation with the Tomqvist
formula. Where NAICS product classes were not direct matches with SIC
product classes, historical sic-based product class shipments were converted to NAICS using conversion ratios developed by BLS. These conversion ratios were estimated using an SIC-to-NAICS product concordance developed by the Census Bureau, together with recent-year SIC
and NAICS product shipments values.
Price indexes. For the majority of industries, output indexes are developed
from data on the value of industry output adjusted for price change. This is
done by dividing the annual value of the detailed product or service by an
appropriate price index, often a BLS producer price index. For many industries, the NAICS-based revenue or shipment values are equivalent on an SIC
and NAICS basis. In these cases, the SIC-based producer price series was used.
Where NAICS industry or product data prior to 1997 were estimated, NAICSbased price series had to be estimated. In these cases, NAICS-based deflators
were constructed as Tomqvist-weighted indexes of the component SICbased PPis. For service or trade industries where consumer price indexes
(CPis) are used to deflate revenues, the product CPis are not coded by
industry and therefore did not need to be converted.
Labor hours. The
BLS Current Employment Statistics (CES) survey is the
primary source of data used in estimating labor hours for each industry. The
CES survey provides NAICS industry employment and average weekly hours
data for production and nonsupervisory workers, and employment data for
all employees, back to 1990 for all industries maintained by that program.
NAICS data for years prior to 1990 were available for some industries. Where
NAICS industry employment and hours data were not available prior to 1990,
the series were estimated back to 1987 by the industry productivity staff
using methods and conversion ratios similar to those used by the CES program. Industry labor productivity measures were calculated only for industries for which the CES program maintains employment and hours series.
Compensation. Compensation data used in calculating unit labor costs for
manufacturing industries come from the Annual Survey of Manufactures
and the Census of Manufactures of the U.S. Bureau of the Census. NAICS
estimates for manufacturing industries for years prior to 1997 were calculated using conversion ratios similar to those described in the Output section
above. Compensation data for non-manufacturing industries are based on
wage data from the Bureau of Labor Statistics, together with fringe ratios
from the Bureau of the Census. Compensation data for nonmanufacturing
industries were converted using methods similar to those used in converting
BLS employment and hours data.
·'"¾ti,
Healthcare Benefits
·:: i:•
,.,f.;;;:'i
Federal statistics on healthcare benefits
and cost trends: an overview
Federal Government statistical agencies
provide a variety of healthcare information
on diverse aspects of the Nation's
healthcare picture
John E. Buckley
and
Robert W.
VanGiezen
John Buckley and
Robert Van Giezen
are economists in the
Division of
Compensation Data
Analysis and
Planning , Bureau of
Labor Statistics.
E-mail :
Buckley .John@bls .gov
VanGiezen. Robert@
bis.gov
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
here are various Federal statistical surveys
that attempt to shed light on a major national topic-healthcare availability and
costs. Federal agencies-such as the Bureau of
Labor Statistics, the Bureau of the Census, the
Bureau of Economic Analysis, the National Center for Health Statistics, and the Centers for Medicare and Medicaid Services--rnllect, analyze, and
publish data that address different aspects of the
healthcare picture. Some statistical programs
such as those conducted by the Bureau of Labor
Statistics have as their primary mission the dissemination of statistics. Other agencies, such as
the Centers for Medicare and Medicaid Services,
publish data in conjunction with their primary
mission to provide services and enforce regulations. This article summarizes major Federal
healthcare statistical surveys and identifies selected benefit provisions, including incidence of
coverage and employer and employee costs. Two
types of surveys are examined separately-surveys of establishments (employers) and household surveys. In addition, Federal accounting
structures that provide a measurement of aggregate medical costs are reviewed.
T
Establishment surveys
The two major establishment-type surveys are
the Bureau of Labor Statistics' National Compensation Survey (NCS) and the Medical Expenditure
Panel Survey Insurance Component (MEPS-IC)
conducted by the Agency for Healthcare Research and Quality. Both establishment surveys
are conducted annually. Data for the NCS are col-
lected by personal visit initially and updated by
mail and telephone; the MEPS's data are collected
primarily by mail. Both survey types obtain some
detailed provisions from benefit plan documents
rather than directly from respondents. Tables 1
through 4 present examples of selected published
data from the NCS 1 and the MEPS-IC. 2
While both establishment surveys collect
health insurance data, the focus of each is considerably different. (Note that the NCS reference
to "medical care" is comparable to the MEPS'
"health care" term.) The NCS is designed to get
broad estimates of several types of employee
compensation , including wages and salaries,
overtime pay, sick leave, vacation benefits, health
and retirement benefits, and so forth. The following is a sample of the medical insurance details
available from the NCS:
•
•
•
•
•
•
•
•
•
•
•
Incidence of coverage of selected
medical services
Amount of plan deductibles
Coinsurance rates
Out-of-pocket expense provisions
Mental health and substance abuse
treatment provisions
Types of prescription drug coverage
Brand name drug provisions
Type of medical plan and financial
intermediary
Cost containment provisions
Dollar plan maximums
Employee share of total premiums and
average monthly contributions (see table 2)
Monthly Labor Review
November
2004
43
Healthcare Benefits
The MEPS is designed specifically for in-depth analysis of
healthcare benefits. 3 It provides, for example, cost of premiums and employees' contributions by private-sector (nongovernment) data, by industry groupings, and by such characteristics as ownership type and age of firm. (See table 4.)
The following is a sample of some other health insurance details available from the MEPS :
•
•
•
•
Private-sector data by firm size and selected
characteristics
Private-sector data by firm size and State
Public-sector data by government type, government
size, and census division
National totals for enrollees and cost of health
insurance coverage for the private and public
sectors
•
•
Private-sector data by proportion of employees who
are full time or low wage and State
Private-sector data by average wage quartiles and
State.
Within each of these categories , tables are subsequently
grouped by:
•
•
•
•
•
Establishment-level tables
Employee-level tables
Premiums, employee contributions, and enrollment
tables for single coverage plans
Premiums, employee contributions, and enrollment
tables for family coverage plans
Premiums, employee contributions, and enrollment
tables for employee-plus-one coverage plans.
Percent of workers participating in healthcare benefits, by selected characteristics, private industry,
Notional Compensation Survey, Morch 2004
Characteristic
Medical care
All employees .......... .. ...... .. ....... ........ ..... ........... ......
Dental care
Vision care
53
37
22
59
43
40
25
25
Worker characteristics
White-collar occupations ... .. ......... .. ... ... ................ ..
Blue-collar occupations .......................... ... ... .. ..... ..
Service occupations .... ....... ..... .... ...... ... ... ............. .
60
24
66
Full-time employees ... .... .. .... .......... ..... ........ .... .... .. ..
Part-time employees ....... ............ .. .. ..... ................. .
16
11
46
11
8
27
6
Union .. .. ... .... ................ .... .. .... .... ... ........ ... .... .... ...... .
Nonunion ........... .. ........ ... ... ... .... .. ...... ... ..... .... .... ... ... .
81
50
68
33
50
19
Average wage :
Less than $15 per hour .... .. ... ... ..... ..... .. ...... ... ...... .
$15 per hour or higher ... .... .. .... ..... ....... .... ..... ... .. ..
40
26
71
53
15
33
Goods-producing .. ... .... ...... .............. ....... ..... .. ..... ... .
Service-producing .. .. ..... ... .... ...... ... .. .... ....... ... ....... ..
69
48
49
33
30
20
1-99 workers ... .. ..................... ... .. ... .... ... ........... ...... .
100 workers or more ................ .. ... .... ... .. ..... .......... .
43
64
24
14
52
32
Metropolitan areas ..... ...... .. .... .. .... ........... ....... ... .... ..
Nonmetropolitan areas ...... ....... .. .. .... ....... ... .. .... ... ...
54
48
38
31
23
New England ..... ... ... .. ..... .. ... ... .. ...... ............... ......... .
Middle Atlantic ..... .. ........... ...... .. ........ .. ..... .. .......... ..
East North Central .... ... ... .. ... .... .... .............. ... ... ..... ..
West North Central .... .... ... ... ........... ... ... .... ... ...... .. ...
South Atlantic ..... ............ ... .... .. .. ............ ..... ... .... ... .
East South Central ................. .......... ... .................. .
West South Central .. .. .. ........ ..... ..... ... .... .. ........ .......
Mountain ... .... ... ................. ........................ ....... ...... .
Pacific .. .... .. .. ........ ... ............. .. ................. .. .... ...... ..
49
53
54
51
52
52
54
51
55
38
38
39
32
35
17
Establishment characteristics
Geographic areas
SouRcE:
Bureau of Labor Statistics, National Compensation Survey.
44 Monthly Labor Review
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November
2004
36
33
38
41
18
24
22
17
19
25
20
23
30
Household surveys
There are three major Federal household surveys that collect
data on healthcare benefits:
•
•
•
The Current Population Survey (CPS)
The Survey of Income and Program Participation (SIPP)
The Medical Expenditure Panel Survey Household
Component (MEPS-HC).
The Current Population Survey is a monthly household survey jointly conducted by the Bureau of Labor Statistics and
11•1• 11 : : . - -
the Bureau of the Census. Data are collected by personal and
telephone interviews. The CPS 4 is the primary source of information on the labor force characteristics of the U.S. population. Supplemental questions are often added to the regular
CPS questionnaire to produce estimates on a variety of topics,
including health and employee benefits. Table 5 presents selected demographic inform'1,tion related to health insurance
coverage.
The Survey of Income and Program Participation 5 is conducted by the Bureau of the Census and provides information
on the source and amount of income, labor force information,
Percent of medical insurance participants required to contribute and percentage and amount of premiums
paid by employees, by selected characteristics, private industry, National Compensation Survey, March 2004
Family coverage
Single coverage
Characteristic
Employee
contribution
required
(percent)
Employee
share of
premium
(percent)
Average
monthly
contribution
Employee
contribution
required
(percent)
Employee
share of
premium
(percent
Average
monthly
contribution
76
18
$67 .57
89
31
$264.59
White-collar occupations ... ......... ... ...................
Blue-collar occupations .... .. ... ...... .. ... .... ..... ..... ..
Service occupations ..... ......... .. ... ... .. .... .. .... ..... ..
78
70
81
19
16
21
69.07
63.15
72.40
91
84
91
32
28
35
271 .60
242.81
294 .58
Full-time employees ............. ................. .. .. ....... ..
Part-time employees .... ... .... ... .. .. ...................... .
76
71
18
21
67.05
78.61
89
83
31
33
263.65
284 .66
Union ..... .... ... ... .... ......... ... ..... ... ....... ....... .. .... .... ...
Nonunion ..... .... ... ... ... .. .... .... .. ...... .... ....... ............
57
79
11
20
56.53
68.98
67
93
17
33
195.12
273 .51
Average wage :
Less than $15 per hour ... .. ... ....... .. ... ...... ...... .
$15 per hour or higher ..... .... .. .... ...... .. .. ..... ... ...
79
73
20
17
70.27
65.22
92
86
34
28
275.81
255.05
74
16
19
59 .89
70.63
85
90
26
33
221 .25
281.44
83
18
18
74.02
63.33
87
90
36
27
307.78
231.23
Metropolitan areas .. .. .... ... . . . .. .... . ... .. ... ...... .... ... .
Non metropolitan areas ............. .. .. .... ........... ..... .
76
76
18
18
67.56
67.62
89
90
30
32
262.99
274.02
New England .................. .. ... ............ ...... ... ........ ..
Middl e Atlantic ..... ... ... ..... ....... ......... ..... ... .... .. .....
East North Central ····· ··········· ······ ···· ········· ·· ······ ·
West North Central .............................. .... .........
South Atlantic .... ......................... .... ...... .. ..... .....
East South Central .. .... ............. ... ..... ........ ..... ....
West South Central .. .......... .. ... ... .... .... ..... ... .......
Mountain .. ... ................... ............ ....... .............. ...
Pacific .... .. .. .... ...... ...... ..... ....... .... ..... ..... .. ....... ... .
84
73
76
20
17
18
18
21
19
19
18
16
69.37
67.43
67.73
66.60
72.02
64.16
66.49
64.04
65.19
91
84
84
86
95
94
97
89
26
27
27
30
35
33
36
32
31
224.98
246.61
252 .62
258.23
293.72
247 .83
288.84
269 .86
260.51
All employees .......... ...... .... ... ......... .... .... .. ..........
Worker characteristics
Establishment characteristics
Goods-producing ...... .. .... ...... ....... ... .... ..... .... .... ..
Service-producing .... .... ....... .... ... .... ... ....... .. ..... ..
1-99 workers ...... .... .... ... ...... ... ... ..... .. ....... ... ...... .
100 workers or more .. .... . .. . .. .. . .. .. . .. . . ..
77
67
Geographic areas
77
79
79
81
79
65
1
The average is presented for all covered workers in plans stating a flat
monthly cost and excludes workers without the plan provision.
NOTE: Average contributions in this table are limited to participants who
are required to contribute to medical insurance costs . The employee share
of premium category includes workers who do not have to make a contribution
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Federal Reserve Bank of St. Louis
85
as well as employees who do have to contribute. The employee contributions
referred to in table 4 include employees who do not contribute to medical
insurance premiums as well as those who do. Dashes indicate data did not
meet publication criteria .
SouRcE :
Bureau of Labor Statistics, National Compensation Survey.
Monthly Labor Review
November 2004
45
Healthcare Benefits
program participation and eligibility data, and general demographic characteristics to measure the effectiveness of existing Federal, State, and local programs. Data are collected by
personal interviews with telephone follow-ups. Data are used
to estimate future costs and coverage for government programs, such as food stamps, and to provide improved statistics on the distribution of income in the country. The survey
design is a continuous series of national panels, with a sample
of household interviews lasting about 2 l /2 to 4 years. Table
6 presents selected published data from the SIPP.
The MEPS Household Component Survey (HC), 6 a nationally
representative survey of the U.S. civilian noninstitution alized
population, collects medical expenditure data at both the person
and household levels. The MEPS- HC collects detailed data on
demographic characteristics, health conditions, health status,
use of medical care services, charges and payments, access to
care, satisfaction with care, health insurance coverage, income,
and employment. In addition to collecting data at the person
and household levels, expenditure data for the sampled households are then collected from the doctors, hospitals, and phar-
Percent of private-sector employees that are enrolled in health insurance plans at establishments that
offer
health insurance by selected firm size and selected characteristi cs, Medical Expenditure Panel Survey
(Insurance Component), United States, 2001
Characteristic
All firm sizes
1,000 or more
employees
Less than 50
employees
SO or more
employees
United States ....................... .. .............. .. .. .. .
62.2
64.4
60.5
62.6
Industry group:
Agriculture , fishing , forestry ........... ... ........
Mining and manufacturing ................ .......... .
Construction .... ... .. .. ..... ... ..... .......................
Utilities and transportation ..... .... .. ............. .
Wholesale trade ........... ... .............. ... ... .... ... .
Financial services and real estate ....... ..... .
Retail trade .. .. .. ......... ... .. ....... .... ..,. .. .. .......... .
Professional services ................ ................ .
Other services ... .................. ... .... .... ......... ...
59.5
80.4
64.9
72.7
75.4
72.8
47.6
65.8
41 .9
64.7
84.0
71.3
73.9
80.0
73.5
42.4
67.2
45.1
52.6
71.2
66.6
63.2
69.7
71 .3
58.8
66.0
41 .9
64.4
81 .8
63.2
73.6
77.3
73.0
44.7
65.8
42.0
Ownership:
For profit, incorporated ..... ................ .. ...... ..
For profit, unincorporated ............ .... .. ... ... ...
Nonprofit .... ....... .. .. .............. .... ....... ... ...... .. ...
Unknown .. ..... .. ....................... ...... ...... ....... .. .
63.3
57.1
58.5
62.7
64.8
61.0
63.1
62.9
62 .2
56.5
53.9
95.8
63.6
57.4
59.5
62.3
Age of firm :
Less than 5 years ..... .... ...... ....... ... ............ .
5-9 years ............................. .... ... ... ............ .
10-19years ................ ................. ........ .. .... .
20 or more years .. .. .... ........ .. ....... ..... ..........
Unknown ........... ... .. .. .... ..... ......... .. ...... ...... ...
53.9
52 .0
56.0
63.2
67.0
68.2
39.4
53.1
50.4
46.3
53.1
67 .1
57 .0
58.2
59 .8
62.2
56.3
67.1
Multi/single status:
2 or more locations ....... ..................... .. .... ...
1 location only .. ................... ..... ... .... ...... .. .. ..
63.6
59.2
64.5
58.9
58.2
60.7
63.8
57.2
Full-time employees:
Less than 25 percent ................ ............... ..
25-49 percent .................. ..... ............ ......... .
50-74 percent ..... .... .. ... .... ......................... ..
75 percent or more .. .... .... .... ...... ................ .
Unknown .. ..................... ... .... ...... .... .. ........ .. .
18.3
31.4
50.0
70.7
64.2
21.1
34.4
55.4
72.3
64.8
16.9
29.3
46.4
69 .2
71.9
18.8
31.8
50.9
71.1
64.0
Union presence:
No union employees ...... ............... ..... ... .. ... .
Has union employees ........... .. ................... .
t_ lriknown .. .... ... ... ...... ....................... .... ....... .
60.7
67 .7
64.2
62.4
68.7
64.8
60.1
66.0
71 .9
60.9
67.8
64.0
Percent of low-wage 1 employees :
50% or more low wage ..... ..... .. ..... ........ ...... .
Less than 50% low wage .... .. .. ................... .
Unknown ................. ... .. ........... .. ................. .
36.4
68.6
66.1
35.7
69.3
66.5
37.4
67.8
54.2
36.1
68.9
66.4
1
Defined as earning $9.50 per hour or less.
Agency for Healthcare Research and Quality.
SouRcE:
46
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November
2004
64 .3
63.5
macies used by these households. The primary collection
method uses Computer Aided Telephone Interviewing (CATI)
techniques. Table 7 presents selected data on the health insurance status of the population under age 65.
Establishment vs. household surveys
Why are there separate establishment and household surveys
covering what appears to be the same health topics? Each
■ 1•1en::.:.••
survey type provides information that is unavailable or not
readily available from the other. Establishment surveys provide more accurate data on the costs and details of health
plans than do household surveys; however, the latter are better vehicles for obtaining demographic data such as age, sex,
race, and marital status. 7 A question also is raised on the
rationale for conducting multiple establishment and multiple
household surveys. The answer again is that each survey is
Average annual single and family premiums, average employee contribution and percent of total per enrolled
employee at private-sector establishments that offer health insurance, by selected characteristics, Medical
Expenditure Panel Survey (Insurance Component), United States, 2001
Family coverage
Single coverage
Characteristic
Total cost
Employee
contribution
Employee
percent 1
Total cost
Employee
contribution
Employee
percent 1
United States ..... .. ....... ....... ...... .... ..... .... .. ... .. ..
$2 ,889
$498
17.3
$7 ,509
$1,741
23.2
Industry group:
Agriculture , fishing, forestry ... .............. ..........
Mining and manufacturing ······ ············ ··-··········
Construction ---- ---- ---- --- --·· ··· ····· ······· ······· ······· ··· ·
Utilities and transportation ..... ..... .. ... .......... .....
Wholesale trade ..............................................
Financial services and real estate .. .. ............ ..
Retail trade ...... ..................... ............... ....... .. ...
Professional services ....... .. .... .. ... ... ... .. ..... ..... ..
Other services ........ .. ... ......... ........ .. ................
2,709
2,738
2,632
2,817
2,735
2,944
2,774
2,992
3,062
449
423
442
393
427
539
643
439
607
16.6
15.5
16.8
14.0
15.6
18.3
23.2
14.7
19.8
6,859
7,308
7,154
7,362
7,331
7,878
7,171
7,746
7,735
1,106
1,311
1,839
1,271
1,650
1,913
2,234
1,921
2,088
16.1
17.9
25.7
17.3
22.5
24.3
31 .1
24.8
27.0
Ownership:
For profit, incorporated ...................... .. .........
For profit, unincorporated ...... .... .... .. ..... ....... .
Nonprofit ... ....... .. .......... ........ ... ............... ...... ..
Unknown .. ............................. ...... ... .... .... ........
2,821
3,032
3,182
2,839
512
472
443
499
18.1
15.6
13.9
17.6
7,463
7,775
7,759
7,416
1,701
2,359
1,757
1,671
22.8
30.3
22 .6
22 .5
Age of firm:
Less than 5 years .......... .. ...... ........ ..... .. ......... .
5-9 years .... ... ..... .. .... ...... .. .... .... .... ...................
10-19 years .... ... ...... .. ..... ........... ...... ....... .. ... ....
20 or more years ............. ..... .... .. ........ .... ... ......
Unknown .. .. ...... ........... ... .............. ... ..... .... .... ... .
3,013
2,819
2,838
2,956
2,747
509
499
495
493
16.9
19.3
17.6
16.7
17.9
7,684
7,408
7,570
7,544
7,415
2,126
2,340
1,996
1,714
1,586
27.7
31.6
26.4
22 .7
21.4
Multi/single status:
2 or more locations .... ... ........... ..... ...... .. ........
1 loi::ation only .. .... .... .............................. ... ....
2,857
2,947
521
459
18.2
15.6
7,476
7,601
1,644
2,013
22 .0
26.5
Full-time employees:
Less than 25 percent ... ............ ............. .... ....
25-49 percent .......... ....... .. ............... .............
50-74 percent .... .... .. .. .... .... .... .. .. ... .. ... ... ..... ...
75 percent or more ............. .... .... .... ...... .. .......
2,670
2,744
3,019
2,882
601
631
551
481
22.5
23.0
18.3
16.7
7,046
7,065
7,524
7,533
1,829
1,676
1,963
1,716
26.0
23.7
26.1
22 .8
Union presence ..... ... .... ... ... ... ... ........ .. .. .. .... .......
No union employees .... ...................... .. ........ ..
Has union employees ........ .... ... ... ...... .. ... .......
Unknown ........ ..... ..... .. ... .. ..... .... .... ... ....... ........
2,860
2,938
3,149
511
408
569
17.9
13.9
18.1
7,648
7,070
7,730
1,966
1,186
1,598
25.7
16.8
20.7
Percent of low-wage2 employees
50% or more low wage ........ .. .... .......... ...... .. ..
Less than 50% low wage .................... ... .... ...
Unknown .. ..... .. .... ......................... ... .... ..... ......
2,813
2,923
2,860
610
465
512
21 .7
15.9
17.9
7,113
7,626
7,426
2,227
1,802
1,571
31 .3
23.6
21.2
544
' Percents may vary slightly due to rounding.
2
Defined as earning $9.50 per hour or less.
SOURCE : Agency for Healthcare Research and Quality.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
2004
47
Healthcare Benefits
designed and funded for specific purposes, even though broad
subjects, such as healthcare, may be the concern of different
agencies. For example, as noted, the MEPS household survey
focu ses on such details as the health status of individuals,
their access to and use of healthcare services, and their income and employment status. The SIPP household survey,
while producing selected healthcare statistics, collects data
used to estimate future costs for government programs such
as the food stamps program .
Trends in healthcare costs
There are several Federal Government agencies that provide
estim ates on trends in health care costs. BLS publishes information from the NCS and the Consumer Price Index (CPI). The
Bureau of Economic Analysis from the Department of Commerce, and the Centers for Medicare and Medicaid Services
from the Department of Health and Human Services, also provide information on healthcare trends.
Bureau of Labor Statistics. The NCS provides trends in employer costs through the Employment Cost Index (ECI) and
the Employer Costs for Employee Compensation (ECEC). The
ECI measures the rate of change in employee compensation,
which includes employer costs for benefits, including health
insurance. 8 The ECEC measures the average cost per employee hour worked that employers pay for employee compensation, including health insurance benefits. The ECI and
ECEC provide data for the civilian economy, which includes
Percent of people with health insurance coverage for the entire year and type of coverage by selected characteristics,
Current Population Survey, 2002
Covered by private or government health insurance
Private health insurance
Characteristic
Total
Total ........ ....... .................
Government health insurance
Not
covered
Total
Total
Employment
Direct
purchase
Total
Medicaid
Medicare
Military
care
100
84.8
69.6
61 .3
9.3
25.7
11.6
13.4
3.5
15.2
100
100
83.3
86.1
69.6
69.6
62.2
60.4
8.6
10.0
23.6
27.8
10.5
12.7
11 .9
14.9
3.8
3.2
16.7
13.9
100
100
85.8
85.8
72 .3
72.4
63.2
63.3
10.1
10.2
24.8
24.7
9.8
9.6
14.2
14.4
3.5
3.5
14.2
14.2
100
89.3
77.4
67.3
11.4
24.6
7.7
15.8
3.8
10.7
100
100
80.1
79.8
54.2
54.0
50.4
50.3
4.3
4.4
33.8
33.7
23.4
23.1
10.3
10.5
3.6
3.5
19.9
20.2
100
100
100
82.0
81 .6
67.6
69.1
68.7
46 .0
60.6
60.0
42.4
9.5
9.8
3.7
18.7
18.4
26.1
10.6
10.4
20.2
8.1
8.5
6.4
2.8
2.3
1.8
18.0
18.4
32.4
Under 18 years .... ...... .
18 to 24 years ...... ... ...
25 to 34 years ..... .......
35 to 44 years ....... .....
45 to 64 years ....... .....
65 years and older ......
100
100
100
100
100
100
88.4
70.4
75.1
82 .3
86.5
99.2
67.5
60.4
67 .5
75.4
77 .7
60.4
63.0
48.9
63.2
70.7
71 .2
33.8
5.3
5.7
5.3
6.4
9.1
29.6
26.8
13.6
10.1
9.6
13.6
95.8
23.9
10.6
7.1
6.2
5.9
9.6
.7
.7
1.2
2.0
5.6
95.3
2.9
2.8
2.3
2.5
4.2
6.6
11 .6
29.6
24.9
17.7
13.5
.8
Nativity
Native ...... ... .................
Foreign born ..... ...........
Naturalized citizen ....
Not a citiz en .. ... ...... ..
100
100
100
100
87.2
66.6
82.5
56.7
71.9
52.2
64.8
44.4
63.3
46.0
56.3
39 .6
9.6
7.1
9.8
5.4
26.5
19.9
27.6
15.1
11 .8
10.5
9.8
10.9
13.7
11.3
20.7
5.5
3.8
1.5
2.5
.9
12.8
33.4
17.5
43.3
Region
Northeast ......... .... .. ... ..
Midwest .. .... ........ ...... ...
South ..........................
West ... ...... ... .
100
100
100
100
87 .0
88.3
82.5
82 .9
71.7
76.4
65.9
66.9
64.1
67.4
58.0
58.3
8.4
10.0
9.1
9.6
26.0
23.3
27.4
25.4
12.2
9.7
11.8
12.8
14.7
13.3
14.1
11.6
1.8
2.1
4.9
4.1
13.0
11 .7
17.5
17.1
Sex
Male ................. ........ .. .....
Female .... ...................... ..
Race and ethnicity
White alone or in
combination .... ..... .........
White alon e ................
White alone, not
Hispanic .... .... ... ... .....
Black alone or in
comb ination ................ ..
Black alone .. ..... ...... ....
Asian alone or in
combination .. ..... .. .. .. .... .
Asian alone ..... .... ........
Hispanic (of any race) ... .
Age
SOURCE: U.S. Census Bureau.
48
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November
2004
the total private nonfarm economy and State and local governments, excluding households and the Federal Government.
In December 2003, data were obtained from about 8,300 establishments in the private sector and 800 sample establishments
in State and local government. Although both use the same
data source, the ECI uses fixed employment weights based on
the Bureau's Occupational Employment Statistics survey to
derive industry and occupation series indexes. Since March
1995, 1990 employment counts have been used. The ECEC, on
the other hand , produces cost levels and is calculated by
using current, rather than fixed , employment weights.
The ECI is designed to measure how compensation paid by
employers would have changed over time if the industry/occupation composition of employment had not changed from
the base period, while the ECEC is designed to measure the
current cost for employee compensation. While the EC EC provides information about average compensation in the economy
at a point in time, the ECI should be used to examine changes
in benefit costs over time. However, by comparing the ECEC at
11e1•ir~..
different points in time, a measure of the change in average
compensation in the labor market can be observed. For health
insurance costs, for example, the change could indicate a shift
in firms providing health insurance benefits, a change in the
composition of premium costs between employer and employee, or a change in employee participation. 9 The share of
total compensation accounted for by health insurance in private industry rose from 6.0 percent in March 1991 to 6.6 percent in March 2004. Table 8 provides estimates on annual
benefit and health insurance cost trends from the ECI and ECEC
from March 1991 to March 2004.
The Consumer Price Index (CPI) is a measure of the average
change in the prices paid by urban consumers for a market
basket of goods and services purchased for day to day living. 10 The current CPI uses a market basket developed from
detailed expenditure information collected from the Consumer
Expenditure Survey. The 1998 CPI revision used information
provided by families and individuals on what they actually
bought over the years 1993 through 1995. Altogether, more
Health insurance coverage types by age, sex, and employment status, Survey of Income and Program Participation,
1997
[Numbers in thousands)
65 and older
45-64
15-44
15 years and older
Characteristic
Percent
Number
Percent
55 ,211
100.0
32,064
100.0
72.5
51 .7
1.5
15.2
6.1
39,485
23,619
1,479
7,323
126
71.5
59.8
3.7
18.5
.3
4,202
965
562
404
31
13.1
23.0
13.4
9.6
.7
4,602
2,387
15,411
5.3
2.7
17.6
2,601
468
3,868
6.6
1.2
9.8
1,524
649
67
36.3
15.4
1.6
2.7
12.9
12.7
7.7
4,445
485
485
425
3.7
10.9
10.9
9.6
970
199
209
( ')
1.8
20.5
21.6
( ')
112
24
7
( ')
.4
21.8
6.4
( ')
339
790
2,564
6.1
14.3
46.4
233
672
2,145
5.2
15.1
48.3
54
89
419
5.5
9.2
43.2
52
28
46.4
25.4
71,241
11 ,246
14,164
6,799
34.2
15.8
19.9
9.5
28,736
1,902
6,137
6,567
23.8
6.6
21.4
22 .9
14,756
2,938
4,780
114
26.7
19.9
32.4
.8
27,749
6,405
3,248
118
86.5
23.1
11 .7
.4
14,482
15,672
8,878
20.3
22.0
12.5
2,228
5,4 19
6,483
7.8
18.9
22.6
1,654
3,078
2,193
11 .2
20.9
14.9
10,600
7,176
202
38.2
25.9
.7
Number
Percent
Number
Percent
Total ... ................. .......... .. ..... .....
208 ,059
100.0
120,784
100.0
Employed ... .. ....... ..... ..................
Current employer .... .... .... .... .. .
Previous employer ............. ....
Spouse's employer ..... ...........
Other relative's employer ......
Privately purchased or
military-related ...... ... .... ..... ...
Public health insurance ..... ....
No health insurance ...... ...... ...
131,290
69 ,845
3,336
21 ,033
5,500
63.1
53.2
2.5
16.0
4.2
87 ,603
45,261
1,295
13,306
5,342
8,727
3,503
19,345
6.6
2.7
14.7
Unemployed ..... ..... .... ... ...... .. ......
Previous employer ..... ..... .... ...
Spouse's employer ......... .. .....
Other relative's employer ......
Privately purchased or
military-related ... .........
Public health insurance .... .....
No health insurance .......... .. ...
5,527
708
702
425
Not in labor force .......... .. ... .... ...
Previous employer .... ... ...... ....
Spouse's employer ................
Other relative's employer .. ....
Privately purchased or
military-related .. ....... ....... .... .
Public health insurance ..
No health insurance .. ....... ... ...
Number
' Represents zero or rounds to zero.
SOURCE:
U.S. Census Bureau.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
2004
49
Healthcare Benefits
than 30,000 individuals and families provided expenditure information for use in determining the importance, or weight, of
more than 2,000 categories in the CPI index structure. Using
Consumer Expenditure Survey data from 1999 through 2000,
the CPI began an ongoing 2-year weight revision with the publication of the 2002 indexes.
The CPI reflects spending patterns for two population
groups: All urban consumers (CPI -U) and Urban Wage Earn-
l!!!IJI
ers and Clerical Workers (CPI-W). The CPI-U represents about
87 percent of the total U.S. population. It is based on the
expenditures of almost all residents of metropolitan areas . It
excludes the spending patterns of persons in non-metropo litan areas, farm families, persons in the Armed Forces, and those
in institutions such as prison inmates. The CPI-W's population represents about 32 percent of the total U .S . population
and is a subset of the CPI-U 's population.
Health insurance coverage of the civilian noninstitutionalized population under age 65, Medical Expenditure
Panel Survey (Household Component), United States, first half of 2002
Population characteristic
Percent distribution
Population in thousands
Private
Public only
Uninsured
247 ,52 3
67.9
13.5
18.5
133,479
48 ,923
78.6
49.6
3.5
22.8
17.9
22 .7
Male .. ..... .. .... ............. ... .. .... ... .. .. ...... .. ., .. .. ... ...
Female ........ .. ... .... ...... .... .. .. .... ..... .. .... ... ....... ..
122,942
124,581
68. 3
67.5
12.0
15.1
19.7
17.4
Race/ethnicity
Hispanic ... ... .. ......... ..... .. .. .................. ... ...... ..
Black· ···· ···· ··· ················· ··· ··· ······· ··· ···· ······ ·· ···
White .. .. .... .. ...... .......... ... ... .. .... ... ... .. .......... ....
Other .. ......... ...... .... ...... ... .. ....... .. .. ..... ... .... .....
35,454
31 ,777
166,748
13,544
43.4
52.7
76.0
68.0
20.5
27.0
9.3
15.6
36.1
20.4
14.6
16.3
Hispanic male ...... ... .... ... .. .. ... ... ... ........ ....... ...
Black male ...... .. ...... ...... ......... ... ............... ... ..
White male ....... .... ... .. ... ... ... ..... ...... .. .... ...... ... .
Other male ............ .... .. .. ........ .. ... ... .. ...... ..... .. .
18,251
14,866
83,148
6,677
44.2
53.1
76.2
68.5
17.4
24.4
8.4
14.2
38.4
22.5
15.3
17.3
Hispanic female .......... .. ...... .... .... .... ...... .... .. .
Black female ........ .. ................ .... ...... ...... .... ..
White female ...... .. ....................................... .
Other female ........................................ .... ... .
17,203
16,911
83 ,600
6,867
42.5
52 .2
75.7
67.6
23.7
29.2
10.3
17.0
33.8
18.5
14.0
15.4
Marital status 2
Married ... ... ................... .. ..... ... ... ..... .... .... .... ...
Widowed ...... .. ... ... ......... .. .... .... ..... .... ... ...... ....
Divorced ...................... .... .... .... ... ........ ........ ..
Separated .... .. ...... .. ... .. .......... .. ....... ... .... .... .. ..
Never married .... . ...................................... .. .
98,352
3,282
20 ,493
3,946
56 ,852
80.3
56.0
64. 2
45.7
59 .1
4.9
20.2
12.5
20.7
12.4
14.9
23.8
23.3
33.6
28.6
Perceived health status
Excellent .. .. .. ... ....................................... .. .. ..
Very good .... .. ................ ...... ... .... .... .. ........... .
Good ...................... ..... ... .. ...... .. ..... ... ...... .... ..
Fair .......... .. .... .... .... .... ...... .. .. .. .. ... ........... .. .. ...
Poor ...... .... ... ... .... ... ... ..... ...... ........... ... ........ ...
84,060
81,487
59 ,080
17,076
5,594
71.6
72.3
63.3
54.1
40.4
12.3
10.2
14.9
23.3
37.4
16.1
17.5
21.8
22.7
22.3
Census region
Northeast .. ... ... ...... .. ... .. ............ .. .... .... ......... .
Midwest .......... ... ..... .. .............. ...... .. ............ ..
South ...................... ....... ......... ........ ............ ..
West ..... ... .. .. ....... ... .... ...... ... .. ...... .. ........ ....... .
46 ,026
56 ,152
87,689
57,656
73.3
75.0
63.2
64.1
13.1
10.2
14.8
15.3
13.6
14.8
22.1
20.7
Total 1 •••••• • •• ••••• •••••••• • • •• •
Employment status
Employed .. ....... .. .. ........ .. ..... ..... ....... .. ......... ..
Not employed ...... ...... ............ .. .................... .
Sex
1
Includes persons with unknown employment, unknown marital and
unknown perceived health status .
2
For individuals ages 16 and older.
NOTE: The estimates in this table cover the civilian noninstitutionalized
50
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November
2004
population under age 65. Percents may not add to 100 because of rounding .
SouRcE : Center fo r Fin a ncing , Access and Cost Trends , Agency for
Health-care Research and Quality : Medical Expenditure Panel Survey,
Household Component, 2002.
Trends in private industry employer health insurance costs, Notional Compensation Survey, Morch 1991 to
Morch 2004
■ 1•1•u-. .: •
private industry costs
(cents per hour worked)
ECEC
ECI private industry
(annual percent change)
private industry costs
(annual percent change)
ECEC
Date
All
benefits
Health
insurance
All
Health
insurance
benefits
All
benefits
Health
insurance
March
1991 ...... ...... .. ..... .... .. .. ...... .. .......... ... ........ .. ... .
1992 .... ... ...... .... ... ............. ... ... ..... .. ..... ... ....... .
1993 ... ......... ... ... ................ ....... ........... ......... .
1994 ·· ············· ··· ···· ···· ······· ·· ···· ···· ···
1995 ······· ····· ·· ········ ····· ··· ·· ········ ······················
1996 ....................................... ... ......... .. .... ... .
1997 .. ... ............ .. ....... .... .... .......... ................. .
1998 ······ ············· ··············· ·· ····· ····· ·· ··············
1999 ....... ................ ... .... .............. ........ ... ... ....
2000 .............................................. ... .. .... .... .. .
2001 ·· ···· ··· ······ ··· ······ ··· ···· ·· ········ ··· ·········· ······ ··
2002 ······ ···· ···· ··· ··· ·· ··· ···· ····· ·· ··· ·· ········· ·· ········· ·
2003 ......... .. ... .... ....... ... .... ....... .... ... ....... ... ..... .
2004 ········ ··················· ····· ·· ····· ······· ····· ········ ·· ·
$4.27
4.55
4.80
4.94
4.85
4.91
4.94
5.02
5.13
5.36
5.63
5.90
6.22
6.65
$0.92
1.02
1.10
1.14
1.06
1.04
.99
1.00
1.03
1.09
1.16
1.31
1.45
1.53
SouRcE:
Dash indicates percent change is not applicable.
NOTE:
n.1.1r~••
11 .5
10.3
8.1
5.7
1.6
5.8
6.3
5.6
4.4
2.9
1.6
2.0
2.3
2.2
5-5
5.0
4.8
6.1
7.0
10.9
7.9
3.6
-6.3
-1 .7
-4.4
1.8
2.6
5.9
8.0
12.6
11.2
5.5
6.6
5.5
2.9
-1.8
1.2
.6
1.6
2.2
4.5
5.0
4.8
5.4
6.9
- .3
0.2
2.2
3.7
7.6
8.1
10.5
9.8
9.3
Bureau of Labor Statistics, National Compensation Survey.
Trends in healthcare prices, Consumer Price Index, Morch 1991 to Morch 2004
CPI All items
( 1982-84= 100)
Medical care
( 1982-84= 100)
CPI
Medical care
services
( 1982-84= 100)
CPI
Medical care
commodities
( 1982-84= 100)
CPI
Date
Index
Percent
change
Index
Percent
change
Index
Percent
change
Index
Percent
change
March
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
·· ···· ····· ···· ···· ········· ··· ·· ·· ······
··· ··· ··· ···· ·· ·· ···· ······ ··············
·········· ········· ··· ··· ···· ··· ·· ··· ····
.... .. .. .. ... ···· ··· ··· ···· ··········· ··
...... .... .... ....... .. ................. .
·· ····· ····· ·············· ······· ······ ··
........... ....... ........ ..... ......... .
··· ···· ············· ··· ····· ···· ···· ·····
........................ .... .. ...... .... .
.. .... ..... ....... ...... ...... ...... .... .
.. ....... ............................... .
...... ......... ... ... ..... .... ...... .... .
··········· ···· ··· ···· ············· ·· ····
.. ...... ... ..... .......... ......... ... ...
NOTE:
135.0
139.3
143.6
147.2
151.4
155.7
160.0
162.2
165.0
171.2
176.2
178.8
184.2
187.4
3.2
3.1
2.5
2.9
2.9
2.8
1.4
1.7
3.8
2.9
1.5
3.0
1.7
173.7
187.3
198.6
208.3
218.4
226.6
233.4
239.8
248.3
258.1
270.0
282 .0
294.2
307.5
Dash indicates percent change is not applicable.
Medical care is one of the major item groups within the Consumer Price Index. This major group consists of medical care
commodities and medical care services. Medical care services,
the major component of medical care, includes physician, dental,
eye care, and other medical professional services, inpatient and
outpatient hospital care, and nursing home services. Medical
care commodities include prescription and non-prescription
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Federal Reserve Bank of St. Louis
7.8
6.0
4.9
4.9
3.8
3.0
2.8
3.6
4.0
4.6
4.5
4.3
4.5
173.8
187.4
199.7
210.4
221.8
230.7
237.7
244.8
253.1
263.2
275.9
288.9
302.6
318.4
7. 8
6.6
5.4
5.4
4.0
3.0
3.0
3.4
4.0
4.8
4.7
4.8
5.2
173.2
186.7
193.9
199.1
203.7
208.9
214.7
218.5
227.7
236.3
244.9
254.1
261.4
267.3
7.8
3.9
2.7
2.3
2.6
2.8
1.8
4.2
3. 8
3.6
3.8
2.9
2.3
Sou RcE: Bureau of Labor Statistics , Consumer Price Index , All Urban
Consumers, U.S. city average, not seasonally adjusted .
drugs and medical equipment and supp lies. Weights for CPI
medical care reflect household expenditures for health insurance
premiums, as we ll as out-of-pocket medical expenses not covered by health insurance. The CPI does not include employerpaid insurance premiums or government-paid healthcare such as
Medicare Part A. 11 Table 9 provides estimates on annual price
trends from the CPI from March 1991 to March 2004.
Month ly Labor Review
November
2004
51
Healthcare Benefits
Bureau of Economic Analysis. The Bureau of Economic
Analysis (BEA) is an agency of the Department of Commerce,
which along with the Bureau of the Census, are part of the
Economics and Statistics Administration. The cornerstone of
BEA 's estimates is the National Economic Accounts, which
feature the estimates of gross domestic product and related
measures. 12
The National Economic Accounts are aggregations of accounts belonging to four sectors of the economy: business,
personal, government, and foreign. For each sector, three accounts are created-a production account that records the
production attributable to that sector; an appropriation account that records the sources of that sector's income; and a
savings-investment account that records the sector's net increase in assets or liabilities. Taken together, these sector
accounts constitute a double-entry system in which an outlay
recorded in one account is also recorded as a receipt in another account.
The National Income and Product Accounts (NIPA) are a
combination of the sector accounts designed to display the
value and composition of national output and the di stribution
of incomes generated by its production. The NIPA consists of
seven accounts: (I) the domestic income and product account;
(2) the private enterprise income account; (3) personal income
and outlay account; (4) the government receipts and expenditures account; (5) the foreign transactions current account; (6)
the domestic capital account; and (7) the foreign transactions
capital account.'3
In producing NIPA estimates, BEA relies primarily on data
based on information gathered by regulatory or tax agencies
for other purposes as well as data from other statistical agencies, such as BLS and the Bureau of the Census. Comprehensive data on health insurance are difficult to obtain because
employer-provided health insurance has no single administrative source of data. Final estimates are based on a combination of regulatory information, survey data, and trade sources.
MEPS is the primary date source for the employer cost of the
employee health insurance component and for the medical care
and hospitalization insurance component of personal consumption expenditures. Estimates from the Employer Cost for
Employee Compensation published by BLS are used to estimate the annual growth rate of employer expenditures. Wage
data from the BLS annual tabulations of wages and salaries of
employees covered by State unemployment insurance reports
are also used.
Within the personal income and outlays account is the Personal Consumption Expenditures for medical care. Included
within this account are costs (in current dollars) for physicians, dentists, and other professional services; costs for hospital visits and nursing homes; and health insurance and workers' compensation costs. Changes in current dollar expenditures can be decomposed into quantity and price components.
Quantities or "real" measures and prices are expressed as index numbers with the reference year 2000, currently equal to
100. Annual changes in quantities and prices are calculated
using a Fisher formula that incorporates weights from two adjacent years. 14 The NIPA produces a "chained weighted" measure that updates the weights for every period. For example,
the growth rate between 1992 and I 993 is computed using
prices that prevailed in 1992 and 1993, while the growth rate
Trends in healthcare costs, Bureau of Economic Analysis, National Economic Accounts, March 1991 to
March 2003
Personal consumption
expenditures for medical care
(millions of dollars)
Index for personal consumption
expenditures for medical care
(2000=100)
Date
Millions of dollars
Percent change
Index
Percent change
- - - - -- - -~ - - -- -- - - - t - - - - - -- -- March
1991
1992
1993
1994
1995
1996
1907
1998
1999
2000
2001
2002
2003
.. ..... .. ..... ... ..... .
................ .. ... .................... ..
.. .... ....... ... .. ... ... ...... ............. .
...... .... ......... .. .. ... ....... .. ....... ..
···· ·· .... .. ........ .. ...... .............. ..
......... ... .... ..... ... ... ......... .... ...
. ··· ··· ····.. ····· ···-- ·····
········· ····· ···· ········ ·· ······ ··· ····· ··
.. ... .... .............................. ... ..
.... .. .. ... ... ............ ... .... .... ...... .
... .. .. ..... ......... ... .. ..... ..... ...... .
..... ... ........... .. .. .... .. .... .... .. .... .
..... .... .. ..... .... .. ..... .. .. .............
$590,667
656,587
703 ,754
741,349
789 ,806
821,476
859 ,878
911,398
944,276
1,003 ,564
1,084 ,582
1,175,209
1,272,391
11.2
7.2
5.4
6.5
4.0
4.7
6.0
3.6
6.3
8.1
8.4
8.3
NOTE: Dash indicates percent change is not applicable.
SouRcE: Bureau of Economi c Analysis , National Economic Accounts,
52
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Federal Reserve Bank of St. Louis
November
2004
72 .655
76 .633
80.483
83.911
87.485
89 .624
92.031
94.247
96.491
98 .934
102.819
105.410
108.369
5.5
5.0
4.3
4.3
2.5
2.7
2.4
2.4
2.5
3.9
2.9
2.8
National Income and Product Accounts tables, Table 2.4.4U Chain-Type Price
Indexes for Personal Consumption Expenditures, Medical care; Table 2.4.5U
Personal Consumption Expenditures by Type of Product, Medical Care.
between 1997 and 1998 is computed using prices that prevailed in 1997 and 1998. Chain-type estimates provide the best
available method for comparing the level of a given series at
two points in time. Table IO provides estimates on trends in
healthcare costs from the National Economic Accounts from
March I 991 to March 2003.
Centers for Medicare and Medicaid Services. The Centers
for Medicare and Medicaid Services (CMS) is an agency of the
Department of Health and Human Services. The CMS publishes the National Health Accounts (NHA), an annual series
of statistics presenting total national health expenditures. 15
The NHA consists of categories defining the sources of
healthcare dollars and the establishments from which services
are purchased with these funds. Funding sources are broadly
classified into private health insurance , out-of-pocket spending, and specific government programs such as Medicare and
Medicaid. A small portion of expenditures is estimated for
other private revenues, such as philanthropic giving and revenues received for nonhealth activities. Behind each NHA
source of funding is a sponsor, designated as business, households, governments, and other private funds, who provides
the financial support with which healthcare bills are paid. The
difference between the source of funds and the sponsor can
be illustrated using private health insurance. Although private health insurers pay claims on the behalf of individuals,
the premiums are paid or sponsored by employers (business,
government, and households). Although private health insurance is considered a private source of funding, in the NHA, the
payments are categorized into business, household, and government sponsor categories. The NHA is compatible with the
National Income and Product Accounts published by BEA.
The NHA includes the National Health Expenditures, historical and projected, and the State Health Expenditures. The
National Health Expenditure survey measures spending for
healthcare in the United States by type of service delivered
(hospital care, physician services, nursing homecare, and so
forth) and the source of funding for those services (private
health insurance, Medicare, Medicaid, out-of-pocket spending, and so forth). Total health expenditures are broadly classified into private health insurance, out-of-pocket spending,
and specific government programs such as Medicare and
Medicaid. A small portion of expenditures is estimated for
other private revenues such as philanthropic giving and revenues received by some healthcare providers from nonhealth
activities such as the operation of cafeterias and gift shops.
Private health expenditures include out-of-pocket expenses,
private insurance, and "other private revenues" described
above. Private health insurance expenditures are the cost of
premiums earned by private health providers. See the box
below for the definitions used by the National Health Expenditure Survey.
The primary source for estimating private and State and
local government contributions to employer-sponsored health
insurance plans is the MEPS-IC survey sponsored by the
Agency for Healthcare Research and Quality. Employer-paid
premiums were estimated forward using the annual growth in
private health premiums derived from the Employer Costs for
Employee Compensation component of the NCS. The U.S. Office of Personnel Management supplied estimates of the premium amounts paid by Federal employers on behalf of their
employees and retirees. Tables 11 and 12 provide estimates on
expenditures and trends in healthcare costs from the National
Health Expenditures Survey from 1993 to 2002.
Per capita health expenditures and growth in private health costs and private health insurance, National Health
Expenditures Survey, 1993-2002
Average annual percent growth from
previous year
Per capita health expenditures
Year
1993 .... ..... .... .... ........ .... .. .. ... .... .... .... ..... ..... .. ...
... ... ........ ..
1994 ·· ···· ···· ······ ···· ···· ···· ·
1995 ·· ·· ······ ···· ······· ···· ··· ·· ·· ·· ··· ···· ···· ···· ····· ······· ···
1996 ......... ... .. .. .............. .. ..................... ......... ..
1997 ·· ······ ·· ··· ···· ············ ········ ········ ····· ········ ······
1998 ....... .. ............ .... .. ..... .... .. .. .... .. ........ .. .... ....
1999 ······· ·· ··· ·· ····· ·· ··· ··· ················ ··· ·· ·· ··· ··· ······ ··
2000 ···· ··· ······ ···· ······ ······· ··· ···· ··· ······· ·· ······· ·· ··· ···
2001 ... ... .. ................. .. .... .. .... ...... ..... ..... ... .... ....
2002 ................. .. ..... .. .... .. .... ... .. .. ......... ...... ..... .
Per
capita
amount
Private
health
expencitures
Private
health
insurance
expenditures
Per
capita
growth
$3,381
3,534
3.698
3,847
4,007
4,179
4,402
4,670
5,021
5,440
$1 ,895
1,922
1,993
2,061
2,161
2,285
2,411
2,550
2,716
2,941
$989
8.5
5.5
5.7
5.0
5.1
5.3
6.3
7.1
8.5
9.3
1,078
1,119
1,171
1,243
1,319
1,422
1,545
1,679
Private
health
expenditure
growth
6.4
2.4
4.7
4.4
5.8
6.7
6.5
6.7
7.5
9.3
Private
health
insurance
growth
3.8
4.7
6.2
6.1
7.8
8.7
8.6
NorE : Dash indicates data not available.
SouRcE: Centers for Medicare and Medicaid Services, National Health Expenditures Survey.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
2004
53
Healthcare Benefits
Definitions used in the National Health Expenditure Survey
Out of pocket expenditures
Direct spending by consumers for all healthcare goods
and services. Included is the amount paid for services not covered by insurance and the amount of
coinsurance and deductibles required by private
health insurance and by public programs such as
Medicare and Medicaid. Enrollee premiums for private health insurance and Medicare are not included,
as are coinsurance and deductible amounts paid by
supplementary Medicare policies.
Private health insurance
Individually purchased and employer-sponsored insurance premiums paid for by a variety of plans, including traditional healthcare plan (Blue Cross and
Blue Shield) premiums, managed care, and self-insured plans. Managed care plans include Health
Maintenance Organiastions (HM0s), Preferred Provider Organizations (PP0s), and Point of Service
Plans (Poss). Self-insured plans are offered by employers who directly assume the major cost of health
insurance for their employees. Some self-insured
plans bear the entire risk, while others insure against
large claims by purchasing stop-loss insurance plans.
Stop-loss coverage limits the amount an employer will
have to pay for each person (individual limit) or for
the total expense of the company (group limit).
Other private funds
Revenues received for which no direct patient care
services are rendered. The most widely recognized
source of other private funds is philanthropy. Philanthropic support may be direct from individuals, obtained through fund-raising organizations such as the
United Way, or obtained from foundations or corporations. For some institutions, other private funds
54
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Federal Reserve Bank of St. Louis
November
2004
include income from the operation of gift shops, cafeterias, parking lots, as well as investment income.
Medicare
Payments from the Federal health insurance program
for people aged 65 and older and those with certain
disabilities. Medicare coverage provides for acute
hospital care, physician services, brief stays in skilled
nursing facilities, and short-term skilled homecare
related to a medical problem. Coverage is restricted
to medical care, and does not include prescription
drugs or custodial care at home or in nursing homes.
Medicaid
Payments from a Federal-State program that covers
health services for low-income individuals and families. Coverage and eligibility requirements vary by
State. Medicaid is the largest source of funding for
medical and health-related services for people with
limited income and the primary payer of nursing
homecare.
Other public funds
All other healthcare expenditures channeled through
any program established by public law. For example,
expenditures under workers' compensation programs and direct healthcare costs for the Dt7partment of Defense, Department of Veteran Affairs, and
Indian Health Service. Also included are State and
local hospitals, home health agencies, and school
health subsidies. Premiums paid by e9Iollees for
Medicare Supplementary Medical Insurance are included as a public expenditure; however, Medicare
coinsurance and deductibles are included under outof-pocket payments because they are paid directly
by the beneficiary to the provider of the service.
Amount and percent distribution of personal healthcare expenditures by source of funds, National Health
Expenditures Survey, selected calendar years 1993-2002
Year
Expenditure category
1993
1995
1997
$775.8
146.9
259.9
38.4
330.5
144.4
115.7
70.4
$865.7
146.5
288.8
44 .2
386.2
178.6
135.3
72.3
$959.2
162.1
319.2
51.4
426.6
203.6
151.7
71 .3
100.0
18.9
33.5
5.0
42.6
18.6
14.9
9.1
100.0
16.9
33.4
5.1
44.6
20.6
15.6
8.4
100.0
16.9
33.3
5.4
44.5
21.2
15.8
7.4
1999
2000
2001
2002
Amount (billions of dollars)
Total ... .. ... .. ... ... ...... .. ... ... ...... .......... ... ..... ....... ......
Out-of-pocket payments .. ... .. ... ... ....... .... ... .......
Private health insurance ...... ........... ... .... ... ......
Other private funds ···· ·· ·············· ·· ··· ·· ·· ············
Public funds .. .............. ......... ....... ..... .. ... .......... .
Medicare ... .. ... ..... .... ... ......... ... ... .... ...... ... ..... ..
Medicaid ··· ·· ··· ·· ···· ······· ··· ····· ······ ··· ······ ·· ····· ····
Other public funds ..... .. ... ... ... ...... ........ .. .. .....
$1,065.0
184.5
366.4
56 .2
457.9
206.2
173.7
78.0
$1 ,135.3
192.6
398.7
54.2
489 .8
217.5
188.3
84.0
$1 ,231.4
200.5
437.2
53.7
540.0
239.2
207.5
93.3
$1 ,304 .2
212.5
479 .3
56.2
592.2
259 .1
232.4
100.7
100.0
17.0
35.1
4.8
43.1
19.2
16.6
7.4
100.0
16.3
35.5
4.4
43.8
19.4
16.9
7.6
100.0
15.9
35.8
4.2
44.2
19.3
17.3
7.5
Percentage distribution
Total ...... ........... ....... .. ....... .. ....... ..... ....... ... ........ ..
Out-of-pocket payments .. .......... .... .... .. ...... .. ....
Private health insurance ..... .... .... ...... .. ... ...... ...
Other private funds ........ .. .... ....... ...... ... ..... .... ..
Public funds ............ ... ..... ... ... .... .. .. ... .... .. .. .... ....
Medicare ............................. ... ... .... ... ..... ..... .. .
Medicaid ······· ··· ··· ··· ··· ·· ···· ···· ········· ···· ··· ······ ····
Other public funds ........ ....... .. .. ..... ... .... ........
100.0
17.3
34.4
5.3
43.0
19.4
16.3
7.3
SouRcE : Centers for Medicare & Medicaid Services, Offi ce of the Actuary,
National Health Statistics Group ; U.S . Cen sus Bureau .
Summary
of the United States is highly decentralized, with a myriad of Federal agencies involved in the collection and analysis of health statistics. The missions of agencies differ, with some having a major focus of investigation,
regulation, or enforcement, while others such as BLS being
THE STATISTICAL SYSTEM
exclusively a statistical agency. These different purposes result in outputs varyi ng in scope of coverage, methodology,
and timing. The purpose of this article was to give an overview of the major Federal statistics on healthcare, not to provide an exhaustive list of all surveys and detailed differences
in methodology. For more information, visit the Internet si tes
listed in the Notes section.
D
Notes
1
More information on the National Compensati on Survey is avai lable on the Inte rnet at http://www.bls.gov/ncs/ (v isited Sept. 24, 2004).
2
More information on the Medical Expenditure Panel Survey (Ins u rance Compone nt) is available o n th e Inte rnet at http://
www.meps.ahcpr.gov/MEPSDATA/ic/2001/technote2001.pdf (visited Sept. 24, 2004).
' For more details on MEPS a nd NCS compar iso ns, see William
Wiatrow ski , Holly Harv ey, and Kath ari ne R . Levit, ·' EmploymentRelate d Hea lth In surance: Federal Age nci es' Roles in Meeting Data
Needs ," Hea lth Care Financing Re view , Spring 2002 , Volume 23, Number 3, pp. 115- 130. The article is available on the Internet at http:/
/www.c ms.hh s.gov/review/02s pring/02S pringpg 115. pd f (visited
Sept. 24, 2004).
4
More information on the Current Popu lati on Survey is ava ilable on
th e Internet at http://www.census.gov/prod/2003pub s/p60-223.pdf
(visited Se pt. 24, 2004).
5
More information on the Survey of Income and Program Participation is available o n th e Int e rn e t at http://www.c ensus.gov/prod/
2003pubs/p70-81.pdf (visited Se pt. 24, 2004).
https://fraser.stlouisfed.org
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6
More information on th e Medica l E x pe nd iture P ane l Survey
( Hou se hold Componen t) is ava il able o n th e Internet a t http://
www.meps.ahrq.gov/papers/rfl8_02-0006/rfl8.pdf (visited Sept. 24,
2004).
7
For a full di sc ussio n on co mparin g estab li shment and household
surveys, see Diane E. Herz, Joseph R. Meisenheimer II , and Harri e t G.
Wein ste in , ·' Hea lth and re ti rement benefits : data from tw o BLS surveys," Monthly Labor Review, March 2000, pp . 3- 20. Th e a rti c le is
avai lab le o n the Int e rn e t at http ://www.bls.gov/opub/mlr/2000/03/
artlfull.pdf (vis ited Sep t. 24, 2004).
8
More information on the methodology of the National Compensation Survey and hi stori ca l data for the Employment Cost Index and
Employer Costs for Emplo yee Com pe n sa ti o ns is availabl e o n th e
Internet at http://www.bl s.gov.ncs/ec t.hom e.ht m (v isited Sept. 24,
2004).
9
More information on usi ng and comparing estimates from the EC I
and ECEC is available from several articl es . See Michael K. Lettau, Mark
A . Loewenste in , a nd Aaron T. Cushner, " Explaining the Diffe re ntial
Monthly Labor Review
November
2004
55
Healthcare Benefits
Growth Rates of the EC! and the ECEC, --compensation and Working
Conditions, Summer 1997, pp . 15- 23; Albert E. Schwenk, .. Measuring
Trend s in the Structure and Levels of Employee Costs for Employee
Compensation," Co mpensa tion and WorkinR Conditions, Summer 1997,
pp . 3- 14; and Martha A.C. Walker and Bruce J. Bergman, .. Analyzing
Year-to-Year Changes in Employer Costs for Employee Co mpen sation ," Compensation and Working Conditions, Spring I 998 , pp. 17- 27.
10
More information on the methodology and hi storical data for the
Consumer Pric e Index is available on th e Internet at http://
www.bls.gov/cpi/home.htm (visited Sept. 24, 2004).
11
More information on measuring pri ce change for medical care in
the CP I is available on the Internet at http://www.bls.gov/cpi/
cpifact4.htm (visited Sept. 24, 2004).
12
More information o n the met hodology and hi storica l data for the
Nati o nai Economic Accounts is available o n the Inte rn et at http ://
56
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2004
www.bea.doc.gov/bea/mp.htm and http://www.bea.doc.gov/bea/
dnl.htm (visited Apr. I, 2004).
13
The number of accounts in NIPA increased to seven with the 2003
benchmark revision. For more information , see Nicole Mayerhauser,
Shelly Smith, and David F. Sullivan, .. Preview of the 2003 Comprehensive Revi sion of the National Income and Product Accounts," Survey of
Current Business, August 2003, pp. 7-31.
14
For more information on Fisher formulas and the use of "chained
weighted" index in the NIPA, see the news release, "Initial Results of the
2003 Comprehensive Re vi s io n of the National Income and Product
Accounts," Survey of Current Business, December 2003, Volume 83,
Number 12.
15
More information on the methodology and historical data for the
National Health Accounts is available on the Internet at http://
www.cms.hhs.gov/statistics/nhe/default.asp
and
http://
www.cms.hhs.gov/statistics/nhe/historicaV (visited Sept. 24, 2004).
''I?'
Defined Benefit Plan Rates
t
Measuring defined benefit plan
replacement rates with PenSync
A synthetic pension data set created with regression and statistical
matching procedures utilizes IRS data to evaluate the effectiveness
of a defined benefit pension plan in meeting the income needs
of retirees,· the findings suggest that variations in replacement rates
stem from differences in benefit formulas, earnings,
years in the plan, and employment characteristics
James H. Moore, Jr.
ill future generations of retirees have
adequate retirement income to maintain
their preretirement standard ofliving? In
an effort to better understand retirement income
security, the Social Security Administration (SSA)
developed a microsimulation model, called
Modeling Income in the Near Term (MINT), 1 to
project the retirement income of persons born
between 1926 and 1965. There are three main
sources of retirement income: Social Security,
employer pension benefits (from both defined
benefit and defined contribution pension plans),
and personal savings. This article focuses on a
method for projecting income from defined benefit
pension plans.
Version 1 of MINT used replacement rates calculated by the Bureau of Labor Statistics (BLS, the
W:
Bureau) to estimate retirement benefits from the
James H. Moore, Jr.,
is an economist in
the Office of
Research,
Evaluation, and
Statistics , Division of
Policy Evaluation,
Social Security
Administration,
Washington , oc.
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private sector, as well as from State and local government defined benefit plans. Because the Bureau
no longer publishes replacement rates, 2 and because there are no other sources from which to
obtain such rates, SSA has developed an experimental replacement rate calculation requiring BLS
data on pension plans. A file containing both the
statistically re-created BLS data and data from the
Survey oflncome and Program Participation (SIPP)
is linked to earnings histories. Work was done under
a memorandum of understanding between the
Bureau and the SSA such that BLS data would be
analyzed at the Bureau and only results of statistical
equations could be taken offsite.
Under the MINT, two key components-pension plan characteristics and preretirement earnings-are used to calculate replacement rates. The
statistical equations developed at the Bureau are
used to estimate pension plan characteristics as a
function of.job characteristics, which are statistically matched to SIPP individuals. SSA administrative data on earnings are used to develop two
measures of earnings and to calculate defined
benefit amounts. These amounts, together with
preretirement earnings, are then used to calculate
replacement rates. The resulting dataset is called
PenSy nc.
Estimating future pension income is especially
problematic in light of the major changes that have
occurred in the world of pensions. For example,
over the last two decades, the demographics of
individuals covered by a pension, as well as the
type of pension plan providing the coverage, have
changed drastically. As recently as the mid- l 990s,
the majority of full-time employees in medium-sized
and large private establishments who were covered
by a pension plan were covered by a defined benefit
plan.3 Currently, the majority of all employees (full
time and part time) in private industry are covered
by a defined contribution plan. 4 Not only has the
type of pension plan changed, but so has the
design of the plan. 5 A new type of pension plan has
evolved as well: the cash balance plan, which has
gained popularity over the past few years .6 According to data recently released by the Bureau,
participation in cash balance plans increased
Monthly Labor Review
November 2004
57
Defined Benefit Plan Rates
nearly fourfold between 1997 and 2000, from 6 percent to 23
percent.
Currently, no data set collects enough information to analyze
these changes in pension plan coverage and design. Through a
statistical match, the methodology in this article brings together
( 1) detailed information on pension plans and plan providers, (2)
survey data on plan participants, and (3) administrative data on
earnings histories, in order to improve the estimation of pension
income for future retirees.
The article begins with a presentation of the methodology,
including a brief description of the key components of a defined
benefit plan and the models used to replicate the employerbased survey (EBS) data. Next, the data are described, after which
the statistical matching procedure and the assumptions are
discussed. Finally, results are given and a conclusion proffered.
deferred wages from 1981 through 2001. These data are provided
to the Internal Revenue Service on Form w-2 from employers;
the form reports on all persons with wages, including nonfilers
and other noncovered employees. The Summary Earnings
Record contains Social Security-covered earnings derived from
payroll tax records for the years 1951 through 1999 (up to the
taxable wage ceiling). After a review of both data sets, it was
determined that the Detailed Earnings Record had significant
advantages over the Summary Earnings Record. One major
advantage to using the Detailed Earnings Record is that it has
earnings data for each job in each year, whereas the Summary
Earnings Record's earnings data is a sum of all earnings from all
jobs in each year. By using the Detailed Earnings Record, it is
possible to separate earnings out by job, which in tum makes it
possible to isolate one defined benefit plan with the earnings
from one job, instead ofhaving a sum ofearnings from multiple jobs.
Data
One of the major sources of data used in this study was the 1995
EBS. Because the 1993 SIPP data and the 1995 EBS data were
collected the same year, comparability of the two data sets is
facilitated. The EBS provides representative data on the
incidence and detailed provisions of the Nation's defined benefit
pension plans in all nonagricultural private-sector establishments employing 100 or more full- and part-time workers in all 50
States and the District of Columbia. The sample used in the
study contains 4,925 observations. Because defined benefit plan
provisions are difficult for the average person to interpret, the
appendix to this article briefly describes some of the major
provisions found in such a plan, including the benefit forrrulas and
some of their key components, as well as eligibility requirements. 7
Using representative samples of the Nation's households,
the SIPP collects data on sources and amounts of income, various
characteristics of the labor force, participation in government
programs, eligibility data, and general demographic characteristics. The study presented in this article focused on the data
collected in the Retirement Expectations Pension Plan Coverage
Topical Module and the Work History Topical Module. To make
the SIPP more comparable to the EBS, the SIPP sample was
restricted to nonagricultural private-sector wage and salary
workers who worked at an establishment with 100 or more
employees and who were covered by a defined benefit plan. The
self-employed are not included in the sample, and individuals
must have had at least 5 years of employment in their current job.
The sample consists of individuals who were born between 1930
and 1955 and who thus ranged in age from 40 to 65 in 1995. All
told, the sample has 2,508 observations for analysis.
Two sources of administrative earnings data were used for
the construction of the earnings measures: the Detailed Earnings
Record and the Summary Earnings Record, both maintained by
the Social Security Administration. The Detailed Earnings Record
contains information on wages, tips, other compensation, and
58
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Methodology
Chart 1 shows the flow of the systematic procedures applied to
create PenSync and to calculate replacement rates. The first
step is to determine the structure of the data and to select the
proper econometric technique that best fits the data. Ordinary
least-squares (OLS) regression is used to fit continuous explanatory and dependent variables. However, because the
dependent variable that represents the type of formula is
categorical, the traditional OLS multiple regression analysis is
not appropriate. A discrete dependent-variable model fits the
data substantially better than least-square methodology. 8
Therefore, the study used a multinomial logit (MNL) model to fit
the categorical dependent variable.
The next step involves estimating the MNL and the OLS
models to obtain estimates of the coefficients. The resulting
estimates are used to produce predicted values by a process of
multiplying the estimated coefficients by the observed EBS data.
The end product is a database called PenPred.
The next step in the process is to statistically match the
predicted pension plan characteristics (PenPred) to the SIPP by
job characteristics. This procedure assigns a defined benefit
pension plan with detailed characteristics to the analytical
sample of workers in the SIPP who reported being covered by
such a plan. The resulting dataset is called PenSync. The final
two steps involve constructing an algorithm to calculate benefit
amounts and then calculating the replacement rate for each
individual in the sample.
Model specification
MNL model specification. The employer's choice of pension
formula is modeled with McFadden's random utility framework. 9
Nine alternatives are identified: two flat-dollar formulas; four
types of terminal-earnings formulas; two types based on a
The aeatioo a PenSync aid replacement rates
Estimate the rvt,.JL
and a.s equations
Create
PenSync
Statistically matd"l
PenPred to Sipp
Create PenPred
Calculate
replaa:3ment
rate
Calculate benefit
amount
percentage of the worker's career average earnings; and a cash
balance plan.10 In choosing which type of formula to provide,
employers may consider a variety ofjob characteristics, such as
their employees' occupations and work schedules. The decision
may also be affected by the characteristics of the employers
themselves, such as the type of industry in which the establishment operates, the number of employees in the firm, and the
presence or absence of a union. (See table 1 for the descriptive
statistics of job characteristics variables used to model the
employer's choice ofbenefit formula.) For any employer i, the
utility of choice j to that employer is expressed as
(1)
Utility-maximizing behavior implies that employer i will
choose a particular alternative j only if UiJ > Uik for all k not
equal to j. The error term c: is assumed to be a random variable
and includes idiosyncrasies and measurement errors. Employer i chooses the alternative that produces the greatest
utility. The decision is random.
The probability of any given alternative j being chosen by
an employer can be expressed as
*
(2)
P = P( UiJ > U), for all k j.
By substitution of equation ( 1),
P = P( Vii+ c:iJ >Vik+ c:ik' for all k
*)).
where
Rearranging terms yields
UiJ is the overall utility of choice j for employer i,
V(E, 1-V) represents utility determined by the observed data,
E is a vector of employer characteristics,
Wis a vector of characteristics of employees within the firm,
£ is a vector of unobserved components, and
j denotes pension formula alternatives.
If the distribution of the random c:'s is known, the distribution
of each difference£ lj..- c:.kl , for all},}* k, can be derived. Then,
from equation (3), the probability that the employer will
choose alternative j can be calculated.
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Monthly Labor Review
November 2004
59
Defined Benefit Plan Rates
percentage of earnings, averaged over the last few years of
employment;
6. percentage of terminal earnings, together with a fixed
percentage of earnings, averaged over a specified period of
consecutive y ears of employment;
7. percentage of terminal earnings, together with a fixed
percentage of earnings, averaged over the employee's career;
8. percentage of terminal earnings, together with varying
percentages of earnings, averaged over the employee's
career;
9. cash balance plan.
Descriptive statistics for job characteristics
variables
Category
Number
Percent
56
49
1,330
804
154
444
1,106
982
1.14
.99
27.01
16.32
3.13
9 .02
22.46
19.94
1,564
1,652
1,709
31 .76
33 .54
34.70
3,547
1,378
72.02
27.98
308
4,617
6.25
93.75
Less than 250 .............. .
25G-499 ......................... .
500-999 .. ...... ........ .... .
1,000 or more ..... ...... ...... ........... ..
922
754
886
2,363
18.72
15.31
17.99
47.98
Number of observations .. ...... .... .
4,925
100.00
Industry
Mining ....... ..... ...................... .
Construction ... .... .......... ... .... ..
Manufacturing ... .. ..... .. .............. ..
Transportation . .... ........... .. ... .... .. .
Wholesale ... ..... ... .... ...... ... ... ..... .. . .
Retail ........ .. .. ............ ... ......... ..... ..
Finance .. .. .
Service ...... ..
Occupational groups
Professional ........... .......... ... ..... ..
Blue collar ... .. .. ... .............. .... .. ..
Clerical .
Union status
Not a union member .... .. ... ........ ..
Union member ...... ...... ............... .
Work Schedule
Part time .......... .
Full time .. ........... .. ..
Employment
SouRcE:
Author's calculation using EBS data.
Letting X..lj = (E.,l W.)l and assuming that Vis a linear function
of components of X operationalizes equation 2 as
(4)
where ~J is a vector of coefficients indicating the effect of the
various X/s on employer i's utility derived from option j.
Note that ~J is subscripted by the choice index}. This means
that, in the analysis, a given X y.. is allowed to "interact" with
each option. For example, union status may have one effect
on the utility of choosing a flat-dollar formula and another on
the utility of choosing a cash balance plan.
As mentioned earlier, an MNL approach is used to determine the probability that an employer will choose one of nine
mutually exclusive benefit formulas:
1. flat dollar amount times years of service, together with a
fixed dollar amount times years of service;
2. flat dollar amount times years of service, together with a
varying dollar amount times years of service;
3. percentage of terminal earnings, together with a fixed
percentage of earnings, averaged over the last few years of
employment;
4. percentage of terminal earnings, together with a varying
percentage of earnings, averaged over a specified period of
consecutive years of employment;
5. percentage of terminal earnings, together with a varying
60
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(Yet a 10th formula is a pension equity plan, based on terminal
earnings and to which interest rates do not apply. However,
the incidence of such plans is too scarce to estimate with any
precision.)
The MNL model is frequently used to analyze situations in
which there are a number of alternatives. However, it is widely
known that a potentially important drawback of the model is
the property called "independence from irrelevant alternatives" (IIA); that is, the model can be applied only to situations in which the alternatives from which one chooses are
totally independent.
To test for the existence of IIA, a model is constructed
such that the alternatives include choosing one type of
benefit formula over a different type ofbenefit formula. If the
employer views the alternatives as differing only along irrelevant dimensions, then, when the model is reestimated, it will
not show a significant difference in explanatory power from
that of the original model. The model used in this article
passed the IIA assumption.
That the model passed the IIA assumption is not entirely
surprising, given that there are many incentives embedded in
the different types of pension formulas offered by employers. Some types of pension formula are geared toward
retaining employees, while others encourage retirement.
Therefore, depending upon the incentive sought by the
employer, his or her decision to offer a particular type ofpension
formula is IIA. Again, the purpose of the IIA test is to ensure that
the alternatives presented to employers are indeed viewed as
independent. I I Consequently, in this context, for a given
employer i with characteristic xi, the probability of choosing
a given benefit formula can be estimated with the MNL model
eV
'I
K
L
evijk
k=I
where
BF iJ = the probability that employer i chose formula},
(5)
v lJ.. =
I~ mX IJm = the deterministic component of the utility of
score a new data set of predicted observations. 12 Table 2 gives
an overview of the accuracy of the MNL model. The model
predicted the correct formula 71 percent of the time, on average,
and many of the incorrect predictions were among similar types
offormulas. For example, the model predicted a flat-dollar formula
with a fixed dollar amount with a 95 .77-percent accuracy rate,
while predicting a flat-dollar formula with a varying dollar
amount 20.45 percent of the time. However, when the model
incorrectly predicted a flat-dollar formula with a varying dollar
amount, it predicted that that formula would be a flat-dollar
formula with a fixed dollar amount 50 percent of the time. Both
types of formula are similar in their design, and any attempts that
were made to increase the accuracy of the prediction flawed the
model with multicollinearity and overspecification. The results
from the OLS models are found in table 3.
To summarize the procedure, the first step involved estimating
equations 5 and 6 to generate a set of coefficient estimates,
which are used to replicate the EBS data. The resulting estimates
of the coefficients are used to produce predicted values by
multiplying each estimated coefficient by the corresponding
observed EBS data. This multiplication process is repeated for
each variable in the equations specified. The end product is a
database containing the predicted values for each observation
required to compute a pension benefit amount, along with the
related explanatory variables. The database is called PenPred.
To assess the quality of PenPred, the resulting means and
standard deviations are compared with those of the EBS. (See
table 4.)
formula) to employer i,
mth explanatory variable for formula) and employer
i, in which m = 1.. .M , and
~ m = coefficient to be estimated.
XiJm = the
The MNL model includes information on characteristics of
the employer, of his or her employees, and of the pension
plan the employer is offering. (For a description of the values
of the dependent variable, see exhibit 1.) In addition to
predicting the type of formula, the model estimates the
quantitative values common to each type, using OLS.
OLS model specification. The quantitative variables for
employer i and formula) can be written as
(6)
where QViJ is a set of quantitative pension provision variables
used in the pension benefit calculation and i denotes the ith
employer. In this model, the coefficients are estimated by a linear
least-squares multiple regression, ~ oi is a constant, Xis a vector
of job characteristics of the employer and his or her employees
and pension plan characteristics, and f\ is an error term. (See
exhibit 2 for a listing and definition of the quantitative pension
variables.)
Creating the synthetic pension file
Statistical matching. Statistical matching is a process of
linking data from multiple data sets on the basis of similar
characteristics rather than unique identifying information. In a
As shown in chart 1, the first two steps in creating PenSync
involve fitting the MNL and OLS models to the EBS data set to
tdolJaramowit
fvi
t withi, v~rying dollar amount timesye3ts o se .
centage of te1111IDa eammgs, together with a fixed percentage o(earwn8s,, averaged over the last ew yeacy
loyment
'
· ' _· · ·
·
ercentage of termi# earnings, together with a v:~g ~centage of earnings., averaged over a specified period
f consecutive years of employment :
. ,
.
.
ercentage of terminal eamin: s· etlier with a varying percentage of earnings, averaged over the last few years
~loyment •
,,y , · - ~
' · g
g
,erag _d ver a ~p~cifie~ peri~~ 0£
to
cutiv
·¾.•
centage
.
eer
centage of terminal · ' ·
to ether
·
· arying percen
•
\
'
.
veraged over the eJlll>loyee 's
es
· s, avera ed over ~
loyee 's
er
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Federal Reserve Bank of St. Louis
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November 2004
61
Defined Benefit Plan Rates
Definitions of quantitative variables
OOL
OOL
OOL
OOL
OOL
OOLl
OOL2
OOL3
YRSl
YRS2
First dollar-amount breakpoint used to calculate a flat-dollar formula
Second dollar-amount breakpoint used to calculate a flat-dollar formula
Third dollar-amount breakpoint used to calculate a flat-dollar formula
First years-of-service breakpoint used to calculate a flat-dollar formula
Second years-of-service breakpoint used to calculate a flat-dollar formula
NORM AAS Sum of normal retirement age and years of service
NORM AGE Normal retirement age
NORM SRV Normal retirement service requirement
NRPAY
Percentage of earnings contributed to a cash balance plan
NR INT
Interest rate
EBASEYRl
First breakpoint for number of years to be included in the calculation of benefits
EBASEYR2
Second breakpoint for number of years to be included in the calculation of benefits
POE OOLl
First dollar-amount breakpoint used to calculate a percentage-of-earnings formula
POE OOU
Second dollar-amount breakpoint used to calculate a percentage-of-earnings formula
First percentage-of-earnings breakpoint used to calculate a percentage-of-earnings formula
POE PCTl
Second percentage-of-earnings breakpoint used to calculate a percentage-of-earnings formula
POE PCI2
Third percentage-of-earnings breakpoint used to calculate a percentage-of-earnings formula
POE PCT3
Fourth percentage-of-earnings breakpoint used to calculate a percentage-of-earnings formula
POE PCT4
POE PCT5
Fifth percentage-of-earnings breakpoint used to calculate a percentage-of-earnings formula
POE YRSl
First breakpoint for number of years of service to be included in the calculation of benefits
POE YRS2
Second breakpoint for number of years of service to be included in the calculation of benef ·
statistical match, each observation in one microdata set (a base
database) is assigned one or more observations from another
microdata set (a secondary database). The assignment is made
on the basis of similar characteristics because the files lacked
the same unique identifier.
A substantial amount of research has been carried out
concerning the validity of using statistically matched data for
analysis. A number of the early researchers in the field carefully
documented some of the shortcomings of statistical matching. 13
In particular, Benjamin Okner pointed out some of the common
problems with statistical matching, including comparability of
the data, the handling of missing data, specific techniques for
matching, and the definition and evaluation of the goodness of
a match. The next subsection briefly discusses some steps taken
to address Okner 's concerns.
Data comparability.
In an effort to make the PenPred data
and the SIPP data compatible, the following harmonization
criteria, well discussed in the literature, were used: 14
1. Harmonization of units. It is necessary that records
from the different sources refer to the same unit. The unit of
analysis for this study is workers.
2. Harmonization of target population. If the data sets
refer to different target populations, it is important to select
just those records which refer to the population of interest.
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Both data sets comprise a sample of workers employed in
private nonagricultural industries and occupations and who
participate in a defined benefit plan.
3. Harmonization of variables. The common variables
should be defined in the same way. Both data sets use
Standard Industry Codes and Census Occupation Codes to
categorize the industry and occupation, respectively.
Missing data.
There are three common approaches to handling missing data: impute the missing data, model the probability
of "missingness," or ignore the missing data. After testing to
make sure that there were no significant differences on the key
variables between records with missing data and records without
missing data, the more conservative approach to handling
missing data was adopted. Hence, missing values are replaced
with means for each variable. 15
Selection of th e matching variables . Consider first
PenPred, henceforward called the universe U, consisting of a
set of N records. For each record, there are values for R
variables. U is represented by an N-by-R matrix, in which
each of the N rows contains the values of the R variables for
one record. The R variables represent the industry code, the
occupation code, and the union status, all of which are
considered key variables for matching based on analysis
performed on the EBS data. The SIPP consists of a set of M
1111
Accuracy of multinomial logit model
Predicted formula value
Frequency
and
percent
Observed
formula
value
Predicted
total ....
Frequency .
Percent ...
Frequency .
Percent .... .
Frequency.
Percent .. .. ..
Frequency .
Percent ..
Frequency .
Percent .......
Frequency ..
Percent .......
Frequency .
Percent ....
Frequency .
Percent ..
SOURCE:
1
2
3
4
5
6
7
8
Flat dollar
Career average
terminal earnings
1
2
3
4
873
816
95.77
22
50.00
0
.00
1
.07
0
.00
0
.00
0
.00
0
.00
20
6
.70
9
20.45
0
.00
1
.07
1
.29
3
.19
0
.00
0
.00
147
0
.00
0
.00
112
72.26
2
.14
29
8.36
4
.25
0
.00
0
.00
1,683
14
1.64
13
29.55
0
.00
1,182
84.73
1
.29
473
29.66
0
.00
0
.00
5
358
0
.00
0
.00
43
27.74
0
.00
315
90.78
0
.00
0
.00
0
.00
7
6
21
2
.23
0
.00
0
.00
1
.07
0
.00
6
.38
11
64.71
0
.00
1,446
1
.12
0
.00
0
.00
207
14.84
1
.29
1,099
68.90
6
35.29
132
61 .40
8
95
1
.12
0
.00
0
.00
1
.07
0
.00
10
.63
0
.00
83
38.60
Cash
balance
Observed
total
9
282
12
1.41
0
.00
0
.00
0
.00
0
.00
0
.00
0
.00
0
.00
4,925
852
...
44
...
155
1,395
347
1,595
17
...
215
Author's calculation using EBS and PenSync data .
records. For each record, there are values for the S variables
that are represented by an M-by-S matrix, in which each of
the M rows contains the values of the S variables for one
record. The S variables represent the industry code, the
occupational code, and the union status.
As mentioned earlier, to enable two or more data sources
to be statistically matched, a set of variables common to all
data sets must be found. These common characteristics are
referred to asXvariables, whereX= (xl' ... ,x)- In this equation,
values of all pension provisions; and Z = (z 1••• z,), where zi is a
vector of socioeconomic and work history variables.
Specification of the distance function. The statistical
matching procedure is carried out by minimizing a distance
function, defined as the absolute difference of the numerical
values of the occupations and the union statuses in two
cases: the distance between the ith worker in the U and the
}th worker in the SIPP is defined by
k
Du=
x 1 = the worker's two-digit standard industry
16
classification;
x 2 = the worker's three-digit standard occupation
classification; 17 and
x 3 = the worker's union status.The ith record in U is
denoted
(7)
and, as indicated, contains j observed variables. Similarly,
the ith record in the SIPP,
(8)
contains h observed variables. The remaining variables in
each of the files are referred to as Yon the PenPred file and Z
on the SIPP file. Y= 6\-·.Yq), where yi is a vector of predicted
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L (1in-J jn)+(oin-Ojn)+(uin-Ujn),
(9 )
n=I
where
n= l, ... ,k,
D lJ.. = the distance between the ith U record and the 1th SIPP
record,
I m - IJn = the distance between the values of the nth pair of
industry variables in the ith record,
0 m - 0 Jn = the distance between the values of the nth pair of
occupation code variables in the ith record, and
U.m - U.Jn = the distance between the values of the nth pair of
union status variables in the ith record.
Certain X variables may be treated as cohort variables. A
cohort variable establishes subclasses of the records in each
Monthly Labor Review
November 2004
63
Defined Benefit Plan Rates
■ 1• 1 •1r=---
Regression results for selected quantitative variables ordinary least squares model
Variable
DOL_DOL1 ...... . .... ............ ..
Constant
5.0851
(.80890)
4.5894
1
(.0735)
5.26057
1
(.076)
-2.6099
1
(.480)
.2800
2
(.0911)
-3143
(.2185)
-4.3253
(3.9373)
46.606
1
(2.01)
10.629
1
(1 .94)
1
CB PERCENT
... ... .. .... ... ... ..
CB INTEREST ... ..... ...... ... .. ..
POE 1 ... ............. .. ................
POE2 .. .. ...... ........................
YEARS 1 .................. ........ ...
YEARS2 ............ ..........
NORM_AGE ... .... ...... .......... .
NORM_SRV ... .. ........ ... ....... .
1
2
Size
-0 .0005
(.00001)
.0001
1
(.00001)
- .0001
(.00001)
.0002
2
(.00005)
.00002
(.000009)
.0001
1
(.000002)
- .0006
(.0004)
.001564
1
(.0002)
- .00152
1
(.0001)
1
Industry
Work
schedule
-2.862
(.3666)
.164
1
(.0322)
.0044
(.0333)
- .3918
(.2103)
.1202
(.0399)
.3194
2
(.0957)
4.3718
(1 .7254)
5.454
1
(.88)
-6.373
1
(.523)
-2.0372
1
(.4234)
- .0600
(.0372)
.043
(.0385)
1.8657
1
(.2429)
- .054
2
(.0461)
.0678
(.1106)
8.346
1
(1 .993)
-3.20707
2
(1 .01)
3.71762
1
(.604)
1
1.2767
(.2336)
- .0032
(.0205)
.0502
(.0212)
.6683
1
(.1340)
- .0807
(.0254)
- .062
(.0610)
-1.8145
(1 .1)
-2
2
(.56)
1.3416
1
(.333)
1
Union
status
Dollar
formula
Career
average
0.3024
(.2616)
- .0346
(.023)
.016
(.0238)
.8312
1
(.1501)
- .2721
1
(.0285)
.0314
(.0683)
3.6991
(1 .2312)
-2.98348
1
(2.98)
2.67692
31 .8015
1
(.5091)
-4.8377
1
(.0447)
-5.2488
1
(.0462)
- .3176
(.2921)
- .1862
2
(.0554)
- .3266
(.133)
-6.4945
(2.3964)
-2 .8452
(1 .22)
6.3605
1
(.723)
0.7117
(.4262)
-4.8791
1
(.0375)
-5.2148
1
(.0387)
12.9813
1
(.2445)
5662
1
(.0464)
3.3456
1
(.1113)
26.0477
1
(2.0059)
7.651
1
(1 .02)
1.856
(.61)
1(.7)
Jll
.74
1
.79
.79
.67
.18
.41
.12
.09
.10
Significant at 1-percent statistical level.
Significant at 5-percent statistical level.
of the two files, with matching permitted only between a pair
of cases in the same subclass. In this study, xi' "industry," is
the cohort variable. For example, a worker in the mining
industry in the SIPP file can be matched only to another worker
in the mining industry in the U file.
Three assumptions are relevant to the statistical matching procedures:
Assumptions.
I. No unobserved heterogeneity exists between the predicted data and the observed data. Stated differently, the
probabilities associated with being covered by a given
pension formula and having a particular set of job characteristics are analogous across the three data sets. Mathematically, this identifying assumption is captured in the
formula
n(x,yl X, Dat¾Ls) - n(x,y,I X, DatasIPP)
- n(x,y, I X, Dat~enSync ) = 0
(10)
where
= type of pension plan,
y = type of formula,
andX is a vector of individual job characteristics (for example,
industry, occupation, and union status).
Sensitivity analysis was conducted to check the validity
of this assumption. Basic descriptive analysis revealed that
the mean values of the observed data are similar to the mean
values of the predicted data. Cross tabulations also revealed
similarities between the three data sets.
x
2. Workers will remain on their current job until they reach
64
Occupation
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Federal Reserve Bank of St. Louis
November 2004
the normal retirement age.
This assumption is rendered
mathematically as
where
i = start year of current job, ... , retirement year.
Many defined benefit plans allow workers to retire prior to
the normal retirement date, but the worker's benefit is reduced
by an actuarial reduction factor. The current version of
PenSync does not have the capability to model early retirement; therefore, it is assumed that workers will remain on
their current job until they satisfy the normal retirement
provision specified in their defined benefit plan. Note that
the assertion that workers will remain on their current job
obviously presupposes that those workers will continue to
work in the same industry and occupation. To test the feasibility of remaining on the current job, the SIPP and the data
from the Detailed Earnings Record were used to measure
tenure on the current job and the frequency of job change.
The SIPP data reveal that the average tenure on the current
defined benefit pension job was 18 years, and the Detailed
Earnings Record data indicate that, between the starting year
(reported in the work history topical module of the SIPP) of
the current job and 2003, 63 percent of the workers in the
sample remained with their same employer. To test these
assumptions further, the SIPP data are used to check how
often a worker reports changing industry or occupation.
When the full panel of the SIPP is analyzed, it is found that 92
percent and 90 percent of the workers report remaining in the
same industry and occupation, respectively. (Recent growth
11•1•u~•-
Mean and standard deviation for predicted and observed quantitative variables
Standard deviation
Mean
Variables
DOL_DOL1 ...... ..... .... .......
DOL_DOL2 ......... ...... .... ..
DOL_DOL3 .... ........... ... ....
DOL_YRS1 .... ..... ..... ........
DOL_YRS2 .. ..... ... ......... ..
NORM_MS ..... ................
NORM_AGE ···· ····· ··· ···· ····
NORM_SRV . ... . ... ... .. .. .. . ..
NR_PAY . ... ..... .... ... ...... ... .
NR INT ... ......... ... ............
EBASEYR1
EBASEYR2 ...
POE- DOL1 .. .. .. . .. . .. . ... ... .. .
POE- DOL2 . ... .. . . ..... .. . . .. .
POE- PCT1 . ... ... .. .. ... ... ..
POE- PCT2
POE- PCT3
POE PCT4
POE_PCT5 ... ...... ........... ..
POE_YRS1 ..
POE- YRS2
~URGE :
Predicted
Observed
6.40
.04
.66
.15
.05
5.32
57.38
7.89
.31
.31
2.97
21 .24
243.58
.00
6.33
.09
.46
.11
.11
5.30
57.33
7.91
.30
.32
2.79
20.76
234.11
.00
10.24
.67
.18
.02
.04
5.22
.43
10.19
.76
.00
.00
.00
5.40
.50
Author's calculation using
EBS
Difference
0.06
- .05
.19
.04
-.06
.02
.04
- .02
.01
-.01
.18
.48
9.47
.00
- .04
.09
- .18
-.02
- .04
.18
.06
Observed
Difference
11 .81
.20
1.10
.36
.22
2.03
5.29
3.23
1.21
1.21
1.70
11 .67
146.37
.00
5.64
.43
.00
.00
.00
2.91
.50
13.83
1.44
5.20
1.14
1.81
20.10
17.77
10.59
1.34
1.41
2.40
35.52
1,877.95
.00
7.03
.85
.43
.14
.21
11 .30
2.28
-2.02
-1 .25
-4.10
- .78
-1 .59
-18 .07
-12 .49
-7.36
- .13
- .20
- .71
-23 .85
-1 ,731 .58
.00
-1 .39
- .42
- .43
- .14
- .21
-8.39
-1 .78
and PenSync data.
in cash balance plans may have affected the length of time
people stay in their jobs, but the timeframe of the data is
years before that growth.)
3. The SIPP-reported pension job for employer 1 is the job
with the highest earnings in the w-2 file in each year. Again,
mathematically, this assumption can be stated as
n(x,yl ~' Dat~ER) - n(x,yl ~, DatasIPP) = 0,
( 12)
where X = earnings in a given year and t = 1951 ... 2002. This
assumption assumes that the pension module job 1 in the
SIPP 18 is the same as the job reporting the highest wage on
the Detailed Employment Record. SIPP respondents are asked
the question about calendar-year wages and salaries twice
per panel and are encouraged to refer to their respective w-2
forms or other documents to ensure their accuracy.
To test the validity of the third assumption, the earnings
total reported in the SIPP for the pension job is compared with
the highest-wage job on the Detailed Employment Record for
the same year. The SIPP earnings are similar to the highest
earnings on the Detailed Employment Record, varying by
plus or minus $2,000 annually. Respondents in the SIPP also
can report earnings and pension coverage from two employers; therefore, to render it yet more likely that the probability that the pension job reported for employer 1 is indeed
the highest-wage job on the Detailed Employment Record,
the second job reported in the SIPP is analyzed. The analysis
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Federal Reserve Bank of St. Louis
Predicted
reveals that less than 3 percent of the unweighted individuals
who reported having a defined benefit type of pension
reported having the same type of pension on their second
job.
The matching algorithm. The match procedure is unconstrained, which has the advantage of permitting the closest
possible match for a U record, but at the cost of increasing the
sample variance of estimators involving the Y and Z variables.
To avoid violating the confidentially provision in the memorandum of understanding, particular attention is given to
tabulations based on small cell sizes. To avoid the possibility of
unauthorized disclosure, cells with three or fewer cases were
dropped from the sample.
The matching algorithm also employs a decision rule: if the
pair agrees on all three characteristics (that is, industry, occupation, and union status), designate the pair as a level- I match; or
else if the pair agrees on the two characteristics industry and
occupation, designate the pair as a level-2 match; or else if
the pair agrees on the two characteristics industry and major
occupational group, designate the pair as a level-3 match; or
else if the pair agrees on industry characteristics only,
designate the pair as a level-4 match; or else designate the
pair as a nonmatch. As shown in the following tabulation, the
final data file for analysis consists of 2,508 observations
containing detailed socioeconomic variables, along with indepth employer-provided pension data:
Monthly Labor Review
November 2004
65
Defined Benefit Plan Rates
Level
Number of matches Match rate (percent)
Total .. ... ... ... .
1 .. ... ... ............. .....
2 ...... ....................
3 ..... .......... ... .. ..... .
4 .......... ................
2,508
1,876
192
430
10
100
75
8
17
.004
This database is called PenSync.
Benefit algorithm. The final procedure used to create the
synthetic pension file involves constructing an algorithm to
calculate benefit amounts and replacement rates for each
individual in PenSync. The algorithm starts by determining
the type of formula assigned to an individual (for example,
career average earnings, terminal earnings, cash balance, or a
flat-dollar formula) . For individuals covered by a formula based
on a percentage of their earnings times years of service, a
subroutine is initiated to determine whether the earnings are
career average earnings or terminal earnings. For individuals
covered by a career average arrangement, the benefit amount is
•n------
11• 1
determined by multiplying a proportion of the average earnings
from the Detailed Earnings Record by the worker's total number
of credited years of service. 19 For individuals whose benefit
amounts are based upon a terminal earnings arrangement, the
algorithm multiplies a proportion of the average earnings from
the Detailed Earnings Record during a specified period, typically
near the individual's retirement age.
For individuals who are covered by a cash balance plan, the
benefit amounts are represented as an account balance equal to
a percentage of the individual's earnings during each year of
participation in the plan, credited with interest based on some
index. At retirement, a participant in a cash balance plan typically
receives his or her accumulated vested account as a lwnp sum.
For purposes of the analysis carried out in this article, once the
worker reaches the normal retirement age specified by the plan,
the accumulated vested account is transformed into an annuity.
Some benefits are associated, not with earnings, but rather, with
a dollar amount per year of service. For those individuals, the
benefit amount is determined by multiplying a fixed dollar amount
by years of service in the plan.
Pension income and replacement rote for workers who qualify for normal retirement prior to 2003
Category
All workers ......
Average earnings (dollars)
Replacement rate (percent)
Percent of
workers
High 3 of last 5
100
$37,958
$32,649
$1 ,012
32
19
54
10
17
35,858
38,921
32,233
40,600
30,068
34,381
28,192
32,614
818
1,144
781
960
21
38
21
32
36
39
18
43
49,779
25,148
32,308
42,579
22,607
27,606
1,415
579
815
42
24
26
33
25
27
40
60
37,828
38,044
32,999
32,417
913
1,079
26
36
27
31
16
15
10
12
26
22
28,015
31 ,144
33,406
29,837
45,759
47,428
23,711
27,315
29,080
26,122
38,206
41 ,674
256
502
845
955
1,178
1,840
9
18
28
30
33
61
11
20
31
34
33
41
66
35
39,594
34,852
33,930
30,219
917
1,202
25
46
27
32
High 5 of last 1O
Monthly benefit
High 3 of last 5
High 5 of last 10
29
Type of formula
Dollar formula .......
Terminal earnings
Career average ......
Cash balance ..... ... ...... .. .... ... ...
24
30
20
Occupation
Professional/technical ... .. ... ....
Administrative/clerical
Production/service ......... ...... ...
Industry
Goods producing .
Non-goods producing
Years in the plan
0-10 .... ................ ................... ..
11-15 ·· ····· ·· ·· ···· ······· ···· ···· ···· ···· ·
16-20 ..... ....... ... .... .. ..... .... ........
21-25 ..... .. .. ......... ...... ... ............
26-30 ... ..... .......... ......... ............
More than 30 ........ ..... ..............
Union status
Non-union member
Union member ........
NoTE: High 3 of last 5 is the average of the 3 highest years of earnings 5 years prior to the normal retirement date specified in the pension
plan . High 5 of last 10 is the average of the 5 highest years of earnings 10
years prior to the normal retirement date specified in the pension plan . All
earnings and benefit amounts are measured in 2003 dollars. Eligibility for
retirement depends on a worker's age or number of years of credited
service , or both. The mean normal retirement age in PenSync is 60, with
66
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Federal Reserve Bank of St. Louis
November 2004
an average of 25 years of service . The normal reti rement date is the year
in which the worker satisfies his or her pension plan provision which
specifies that the worker is eligible to receive an unreduced retirement
benefit. The year 2003 is used to verify whether an individual has satisfied
the normal retirement requirement. The mean normal retirement year in
PenSync is 1998.
SouRcE: Author's calculation using PenSync.
The final step in the algorithm produces a set of pension
benefits and replacement rate ratios for the two measures of
earnings: the last 10 years of earnings (Ll0YR) and the last 5
years of earnings (L5YR). Ll0YR is the average of the 5 highest
years of earnings 10 years prior to the normal retirement date
specified in the pension plan; L5YR is the average of the 3
highest years of earnings 5 years prior to the pension plan's
normal retirement date. The latter is the year in which the worker
satisfies provisions specified in the plan in order to receive an
unreduced retirement benefit. The year 2003 is used to verify
whether an individual has satisfied the pension plan's normal
retirement requirement. All earnings and benefit amounts are
measured in 2003 dollars.
percentage-point differential. Replacement rates were
considerably lower for those in administrative/clerical or
production/service jobs, compared with those in professional/
technical jobs, and were lower for those in goods-producing
industries than those in non-goods-producing industries. Union
members are estimated to have higher replacement rates than
non-union members, and more years ofparticipation in a pension
plan is associated with much higher replacement rates. Workers
who remain in the same pension plan for more than 30 years
have more than 60 percent of their earnings in the 5 years prior to
retirement replaced by their plans, compared with only a 9percent replacement rate for those with less than 10 years of
participation.
Results
PREDICTING RETIRMENT INCOME FROM A PENSION PLAN is a
For workers who are eligible for normal retirement benefits prior
to 2003, the defined benefit plan is estimated to replace about 30
percent of the last year of positive earnings. The average earnings are estimated to be about $35,000, and the average monthly
pension benefit is $1,012. (See table 5.) Pension replacement
rates are estimated to vary by the type of benefit formula,
employment characteristics, and years of participation in the
pension plan. Replacement rates were lowest for those in flatdollar or career average formulas and highest for those in terminal
earnings formulas or cash balance formulas, with a 16- to 17-
difficult task. The absence of good data is a major contributor to
the difficulty involved. Furthermore, the lack of comprehensive
data sources on pensions places limitations on pension research
and policy decisions. The methodologies applied in this article
have been in existence for decades, yet they remain more of an
art than a science. However, many challenges are inherent in the
employment of the procedure itself: the specification of an
appropriate model, data harmonization, and, probably most
important, the quality of the data. Nevertheless, the methodology set forth herein is a reasonable approach, given conD
straints from two different restricted data sets.
Notes
1
M INT was developed to estimate the distributional effects of
proposed Social Security policy alternatives on current and future
benefic iaries ' retirement income . The model projects retirement
income from Social Security, pensions , personal investments or
savings, and partial retirement earnings . For a complete description
of the MI NT project, see the final reports prepared by the RA N D
Corporation (Constantijn Panis and Lee Lillard, "Near Term Model
Development ," draft final report , SSA contract no . 600-96-27335
(Santa Monica, CA, RAND, 1999); Constantijn Panis , Michael Hurd,
David Loughran , Julie Zissimopoulos, Steven Haider, and Patricia
St . Clair, " The Effect of Changing Social Security Administration 's
Early Entitlement Age and the Normal Retirement Age," draft report,
SSA contract no . 600-96-27335 (Santa Monica, CA , RAN D, 2002)) ;
The Urban Institute (Eric Toder and others, " Modeling Income in the
Near Term- Projections of Retirement Income through 2020 for the
1931 - 1960 Birth Cohorts," final report, SSA contract no . 600- 9627332 (Washington, DC, The Urban Institute, 1999)); and the Social
Security Administration (Barbara A. Butrica, Howard M. lams, James
Moore, and Mikki Waid, Methods in Modeling Income in the Near Term
(MINT), ORES working study no. 91 (Social Security Administration, May
2001 )).
2
The last years the Bureau published replacement rates for full-time
employees were 1993 for those in medium and large private establi shments and 1994 for State and local government employees .
See Employee Benefits in Medium and Large Private Establishments,
1993 , Bulletin 2456 (Bureau of Labor Statistics, November 1994),
especially table I , p. 8.
3
4
See National Compensation Survey: Employee Benefits in Private
Industry in th e United States, 2000, Bulletin 2555 (Bureau of Labor
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Statistics, January 2003), especially table I, p. 4.
5
See Olivia Mitchell, " Developments in Pensions," NBE R Reporter
(Washington, DC, National Bureau of Economic Research, 1998); and
Leslie E . Papke, "Are 401 (k) Plans Replacing Other EmployerProvided Pensions? Evidence from Panel Data," Journal of Human
Resources, vol. 34, no . 2, spring 1999, pp . 346- 68 .
6
Kenneth R. Elliott and James H. Moore, "Cash Balance Pension
Plans : The New Wave," Compensation and Working Conditions, vol.
5, no . 2, summer 2000, pp . 3- 12 .
7
To learn more about defined benefit plans and their features , see
Gerald E. Cole, "An Explanation of Pension Plans," Employee Benefits
Journal, June 1999, pp . 3- 13 .
8
A. Agresti , Categorical Data Analy sis (New York, J. Wiley &
Sons, 1990).
9
D. McFadden, "Conditional Logit Analysis of Qualitative Choice
Behavior," in P. Zarembka, ed ., Frontiers in Econometrics (New York,
Academic Press, 1974), pp . 105- 42 .
10
See the appendix for a brief description of these alternatives .
Interested readers should refer to W. H. Green, Econometric Analysis
(New York, Macmillan , 1990); K. Train, Qualitative Choice Analysis:
11
Theory, Econometrics, and an Application to Automobile Demand
(Cambridge, MA , MIT Press, 1986); and Moshe Ben-Akiva and Steven
Lerman, Discrete Choice Analysis: Th eory and Application to Travel
Demand (Cambridge, MA , MI T Press, 1985 ; 4th printing, 1991).
Monthly Labor Review
November 2004
67
Defined Benefit Plan Rates
12
For a description of the SAS Proc Score procedure, visit the website
http://ftp.sa s.co m/techs up/down load/sta t/scoren ew. html. See
also SAS Technical Support Documents 650e, Multinomial Logit,
Discrete Choice Modeling : An Introduction to Designing Choice
Experiments, and Collecting, Processing, and Analyzing Choice Data
with SAS (Cary, NC, SAS Institute, Inc., 2001 ).
13
See Benjamin A. Okner, "Constructing a New Data Base from
Existing Microdata Sets: The 1966 Merge File," Annals of Economic
and Social Measurement, July 1972, pp . 325- 52, and "Data Matching
and Merging: An Overview," Annals of Economic and Social
Measurement, April 1974, pp . 347- 52; Horst E. Alter, "Creation of
a Synthetic Data Set by Linking Records of the Canadian Survey of
Consumer Finances with the Family Expenditure Survey 1970,"
Annals of Economic and Social Measurement , vol. 3, no . 2, 1974,
pp. 373- 94; D. B. Radner, R. Allen, M. E. Gonzalez, T. B. Jabine, and
H. J. Muller, Report on Exact and Statistical Matching Techniques,
statistical policy working paper (U.S. Dept. of Commerce, 1980); and
J . T. Barry, "An Investigation of Statistical Matching," Journal of
Applied Statistics, vol. 15, 1988, pp . 275-83 .
14
The statistical matching criteria for integrating data were taken
from Marcello D'Orazio, Marco Di Zio, and Mauro Scanu, "Statistical
Matching: a tool for integrating data in National Statistical Institutes"
(Rome, Italian National Statistical Institute, 2001 ); on the Internet
APPENDIX:
athttp://webfarm.jrc.cec.eu.int/ETK-NTTS/Papers/final_papers/
43.pdf.
15
See R. J . A. Little and D. B. Rubin, Statistical Analysis with
Missing Data (New York, J. Wiley and Sons, 1978); J. 0 . Kim and
J. Curry, "The treatment of missing data in multivariate analysis,"
Sociological Methods and Research, vol. 6, 1977, pp . 215- 40; and
P. L. Roth, "Missing data: A conceptual view for applied psychologists,"
Personnel Psychology, vol. 47, 1994, pp. 537- 60.
16
All workers are classified into one of more than 82 industries
according to their Standard Industrial Classification.
17
All workers are classified into one of more than 820 occupations
according to their Standard Occupational Classification .
18
The
19
For all individuals, regardless of type of formula, the number of
credited years of service is determined by subtracting the normal
retirement year specified in the pension plan from the year the worker
reported starting his or her current job. For years of earnings that are
outs.ide the scope of the Detailed Earnings Record, the Summary
Earnings Record is used to supplement the missing data.
of the employee's income during each year of participation in the
plan, and it is also credited with interest. The interest rate is often
based on an index, such as the rate ofreturn on 30-year Treasury
bonds.
Some benefits are associated, not with income, but rather, with
a dollar amount per year of service. In 2000, 14 percent of all
workers in the private sector who were covered by a defined benefit
plan had this type of plan. A formula incorporating a flat dollar
amount per year of service provides a benefit amount based on a
fixed dollar amount multiplied by years of service in the plan. To
illustrate, if a plan specifies a benefit of $40 a month for each year
of service, an employee with 30 years of participation in the plan
would receive a monthly benefit of $1,200.
Before an employee is entitled to benefits from the plan, he or
she must become vested, which means having a designated number
of years of service with an employer. A 5-year cliff-vesting requirement is the most prevalent provision. Therefore, the study presented in this article assumes that, upon satisfying the 5-year vesting
requirement, an individual is entitled to receive a nonforfeitable
accrued benefit upon separation or retirement.
Benefits under a defined benefit plan are usually paid when
the employee retires. All defined benefit plans are required to
specify an age, years of service, or some combination of the two
whence an employee can receive unreduced benefits. The normal
retirement age in most plans is 65 years. However, many defined
benefit plans allow retirement after a stated age that is earlier than
the declared normal retirement age, but the employee's benefit is
reduced by an actuarial reduction factor. This provision is called
early retirement.
Note to the appendix
68
These data can be found at http://www.bls.gov/ncs/ebs/sp/ebrp0001.pdf.
Monthly Labor Review
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Federal Reserve Bank of St. Louis
asks respondents about two jobs.
Brief description of defined benefit provisions
A defined benefit plan provides employees with guaranteed
retirement benefits based on a predetermined formula. There are
three basic types of defined benefit formulas found in the employerbased survey (EBS) data: ( 1) a percentage of earnings per year of
service, (2) a cash balance arrangement, and (3) a flat amount per
year of service.
According to the EBS data, the majority of workers who participate in a defined benefit plan are covered by a formula based on a
percentage of their earnings per year of service. 1 In this type of
arrangement, the employee benefit is based on a proportion of
earnings per year of service{or each year that an employee participates in the plan. The years of service credited may be based upon
either a career average or final earnings. Under a career average arrangement, the plan benefits accrue in accordance with the average
of the earnings paid over the entire period of the employee's participation in the plan. Under a final-pay arrangement, by contrast, the
plan benefits are based on an average of the employee's earnings
during a short period, typically near the employee's retirement age.
For example, the earnings may be averaged over the last 3 or 5 years
of employment or over the 3 or 5 consecutive years in the 10-year
period immediately prior to retirement, during which the employee's
earnings are typically the highest.
A cash balance plan is another type of defined benefit plan-one
whereby the benefit formula takes into account the employee's
income and the number of years of service credited. Although a cash
balance plan is structured to bear a resemblance to a defined contribution plan, the benefits are represented as an account balance
instead of as an annuity. The account balance is equal to a percentage
1
SIPP
November 2004
Concrete productivity
statistics
Persistent and substantial variations in
productivity among individual factories
have been observed, even in industries
that are narrowly defined. Attempts to
explain this variation have tended to
focus on technological or "supplyside" reasons such as management
approaches.
In "Market Structure and Productivity:
A Concrete Example" (NBER Working
Paper 10501 ), Chad Syverson of the
University of Chicago focuses on the
other side of the exchange process-the
demand side. Syverson states that, "The
more difficult it is for consumers to switch
between competing suppliers, the greater
the productivity dispersion that can be
sustained."
To investigate this notion, Syverson
considers a concrete example-literally.
He analyzes data from the Census of
Manufactures for a single four-digit
Standard Industrial Classification (SIC)
industry: ready-mixed concrete, SIC 3273.
An advantage of these data is that a
physical measure of the product is available (cubic yards), in addition to the dollar
value of shipments. Syverson focuses on
one aspect of substitutability in this
study, pertaining to transport costs. The
ready-mixed concrete industry has substantial transport costs, which implies that
there are separate geographic markets for
the product.
He uses the concrete data to test the
premise that, " in markets where it is easy
for industry consumers to switch suppliers, productivity distributions should
exhibit higher minima, less dispersion, and
higher central tendency than those in lowsubstitutability markets." His findings
support this premise: they show that
markets that have high demand densities
for this product have higher minimum and
mean productivity levels, and such mar-
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Federal Reserve Bank of St. Louis
kets have less dispersion in productivity
levels among producers.
Up the ladder
Top business people have always
enjoyed at least some celebrity. Even the
robber barons, such as Rockefeller and
Carnegie, had popular biographies written
about them attributing their success to
hard work, according to the introduction
to Peter Capelli and Monika Hamori 's
recent NBER Working Paper, "The Path to
the Top: Changes in the Attributes and
Careers of Corporate Executives, 1980 to
2001." In addition to the celebrity accorded some of today's top business leaders,
they hold important positions in the
world. Understanding the nature of
success in the business world, say Capelli
and Hamori, "says a great deal about
access to positions of influence, about
social mobility generally, and specifically
about career development practices."
The brief survey of literature that
introduces the concepts of executive
career studies is good reading. According
to the works cited by Capelli and Hamori,
there have been three broad eras of
executive recruitment since the beginning
of the 20th century. The first was an era
marked by a mix of entrepreneurial merit
in some cases and inherited wealth or
position in the early years of the century.
A second, broadly occupying the middle
years of the century, was marked by the
rise of what William A. Whyte labeled the
"organization man." The final era started
in the I 980s and is characterized by what
Michael B. Arthur and Denise M.
Rousseau call "the boundary less career."
The nature of successful, high-performance careers that may not reflect
secure, long-term commitments between
an organization and its members is the
subject of Capelli and Hamori 's new
research. They found significant difference between the attributes and career
paths of the top IO executives in the
Fortune I 00 companies in I 980 and those
in evidence among a similar panel in 200 I.
In terms of basic attributes, today's
executives are younger, more likely to
have a college degree, and somewhat more
likely to be women. The latter, as the
authors say, was "not a difficult achievement given that the number was zero in
1980."
In terms of career path, today 's top executives are less likely to have been lifetime
employees of their companies, took less
time to get to the top rungs of the corporate ladder, and had seen bigger promotions, as evidenced both by a direct
measure of promotion size and the fact
they had held fewer positions during their
successful careers.
These findings were robust to
several factors including restriction to
those executives for which Capelli and
Hamori could fill in a complete career
history and restriction of the sample to
firms that were in the Fortune 100 in
both 1980 and 200 I. One partition of
the data that did yield some interesting
differences was between firms in
manufacturing and service industries.
In I 980, there were very few
differences between executives in
manufacturing and top managers in
service firms. In 2001, according to the
data, "Executives in the service sector
are younger, more likely to be women
and to be Ivy League graduates. Most
important, they are much less likely to
have started their career in the same
company ... and they spent four and a
half fewer years in their current organization. They also got to the top about two
and a half years sooner than their peers in
manufacturing. The manufacturing/
service distinction apparently was
irrelevant in understanding differences
in executive experience in 1980 but has
become highly relevant in 2001." □
Monthly Labor Review
November 2004
69
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Education
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~'JW"
Current Labor Statistics
Notes on labor statistics ..............................
Comparative indicators
:}
"'::-:t-W~iw.1#J'*~
74
1. Labor market indicators .................................................... 87
2. Annual and quarterly percent changes in
compensation, prices, and productivity ....................... 88
3. Alternative measures of wages and
compensation changes................................................... 88
Labor force data
4. Employment status of the population,
seasonally adjusted ........................................................ 89
5. Selected employment indicators,
seasonally adjusted ....................................................... 90
6. Selected unemployment indicators,
seasonally adjusted ....................................................... 91
7. Duration of unemployment,
seasonally adjusted ....................................................... 91
8. Unemployed persons by reason for unemployment,
seasonally adjusted ....................................................... 92
9. Unemployment rates by sex and age,
seasonally adjusted ....................................................... 92
10. Unemployment rates by States,
seasonally adjusted ....................................................... 93
11. Employment of workers by States,
seasonally adjusted ....................................................... 93
12. Employment of workers by industry,
seasonally adjusted ....................................................... 94
13. Average weekly hours by industry,
seasonally adjusted ....................................................... 97
14. Average hourly earnings by industry,
seasonally adjusted........................................................ 98
15. Average hourly earnings by industry ................................ 99
16. Average weekly earnings by industry ............................... 100
17. Diffusion indexes of employment change,
seasonally adjusted ...................................................... .
101
18. Job openings levels and rates, by industry and regions,
seasonally adjusted......................................................... I 02
19. Hires levels and rates by industry and region,
seasonally adjusted.......................................................... 102
20. Separations levels and rates by industry and region,
seasonally adjusted.... ...................................................... 103
21. Quits levels and rates by industry and region,
seasonally adjusted.......................................................... I 03
22. Quarterly Census of Employment and Wages,
IO largest counties ...... ...... .. .. ..... .... ..... ....... ... ..... .... .... .... 104
23. Quarterly Census of Employment and Wages, by State 106
24. Annual data: Quarterly Census of Employment
and Wages, by ownership ............................................. 107
25. Annual data: Quarterly Census of Employment and Wages,
establishment size and employment, by supersector ... I 08
26. Annual data: Quarterly Census of Employment and
Wages, by metropolitan area ......................................... 109
27. Annual data: Employment status of the population ........ 114
28. Annual data: Employment levels by industry .................. 114
29. Annual data: Average hours and earnings level,
by industry ..................................................................... 115
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Labor compensation and collective
bargaining data
30. Employment Cost Index, compensation ............................. 116
31. Employment Cost Index, wages and salaries....................
32. Employment Cost Index, benefits, private industry ........
33. Employment Cost Index, private nonfarm workers,
by bargaining status, region, and area size ....................
34. Participants in benefit plans, medium and large firms ......
35. Participants in benefits plans, small firms
and government................. .... ........................................
36. Work stoppages involving 1,000 workers or more ...........
118
119
120
121
122
123
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 .... ...... .... .... .. ...... .... .... .. ...... ... ... .....
124
127
128
129
130
131
132
133
134
135
135
Productivity data
48. Indexes of productivity, hourly compensation,
and unit costs, data seasonally adjusted .......................
49. Annual indexes of multi factor productivity ......................
50. Annual indexes of productivity, hourly compensation,
unit costs, and prices ....................................................
51. Annual indexes of output per hour for select
industries.......................................................................
136
137
138
139
International comparisons data
52. Unemployment rates in nine countries, ............................ 142
53. Annual data: Employment status of the civilian
working-age population, IO countries............... ...... ....... 143
54. Annual indexes of productivity and related measures,
12 countries................................................................... 144
Injury and Illness data
55. Annual data: Occupational injury and illness
incidence rates ................................................................. 145
56. Fatal occupational injuries by event or exposure............. 147
Monthly Labor Review
November 2004
73
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 2004 issue of
the RtJview. Seasonally adjusted establishment survey data shown in tables I, 12- 14,
and 17 were revised in the March 2004 Review. A brief explanation of the seasonal
adjustment methodology appears in " Notes
on the data."
Revisions in the productivity data in
table 54 are usually introduced in the September issue. Seasonally adjusted indexes
and percent changes from month-to-month
and quarter-to-quarter are published for numerous Consumer and Producer Price Index series. However, seasonally adjusted indexes are not published for the U.S. average All-Items CPI. Only seasonally adjusted
percent changes are available for this series.
Adjustments for price changes. Some
data-such as the "real" earnings shown in
table 14--are adjusted to eliminate the effect of changes in price. These adjustments
are made by dividing current-dollar values
by the Consumer Price Index or the appropriate component of the index, then multiplying by 100. For example, given a current
hourly wage rate of $3 and a current price
74
:\.1onthly Labor Review
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index number of 150, where 1982 = 100,
the hourly rate expressed in 1982 dollars is
$2 ($3/150 x 100 = $2). The $2 (or any other
resulting values) are described as " real ,"
·'constant," or " 1982" dollars.
Sources of information
Data that supplement the tables in this section are published by the Bureau in a variety of sources. Definitions of each series and
notes on the data are contained in later sections of these Notes describing each set of
data. For detailed descriptions of each data
series, see BLS Handbook of Methods, Bulletin 2490. Users also may wish to consult
Major Programs of the Bureau of Labor Statistics, Report 919. News releases provide
the latest statistical information published
by the Bureau; the major recurring releases
are published according to the schedule appearing on the back cover of this issue.
More information about labor force, employment, and unemployment data and the
household and establishment surveys underlying the data are available in the Bureau 's
monthly publication, Employment and
Earnings. Historical unadjusted and seasonally adjusted data from the household survey are available on the Internet:
http://www.bls.gov/cps/
Historically comparable unadjusted and seasonally adjusted data from the establishment
survey also are available on the Internet:
http ://www.bls.gov/ces/
Additional information on labor force data
for areas below the national level are provided in the BLS annual report, Geographic
Profile of Employment and Unemployment.
For a comprehensive discussion of the
Employment Cost Index, see Employment
Cost Indexes and Levels, 1975-95, BLS Bulletin 2466. The most recent data from the
Employee Benefits Survey appear in the following Bureau of Labor Statistics bulletins:
Employee Benefits in Medium and Large
Firms; Employee Benefits in Small Private
Establishments; and Employee Benefits in
State and Local Governments.
More detailed data on consumer and producer prices are published in the monthly
periodicals, The CPI Detailed Report and
Producer Price Indexes. For an overview of
the 1998 revision of the CPI, see the December 1996 issue of the Monthly Labor Review. Additional data on international prices
appear in monthly news releases.
Listings of industries for which productivity indexes are available may be found
on the Internet:
http://www.bls.gov/lpc/
For additional information on interna-
November 2004
tional comparisons data, see International
Comparisons of Unemployment, Bulletin
1979.
Detailed data on the occupational injury
and illness series are published in Occupational Injuries and Illnesses in the United
States , by Industry, a BLS annual bulletin.
Finally, the Monthly Labor Review carries analytical articles on annual and longer
term developments in labor force, employment, and unemployment; employee compensation and collective bargaining; prices;
productivity; international comparisons;
and injury and illness data.
Symbols
n.e.c. = not elsewhere classified.
n.e.s.
not elsewhere specified.
p = preliminary. To increase the timeliness of some series, preliminary
figures are issued based on representative but incomplete returns.
r = revised. Generally, this revision
reflects the availability of later
data, but also may reflect other
adjustments.
Comparative Indicators
(Tables 1-3)
Comparative indicators tables provide an
overview and comparison of major BLS statistical series. Consequently, although many
of the included series are available monthly,
all measures in these comparative tables are
presented quarterly and annually.
Labor market indicators include employment measures from two major surveys
and information on rates of change in compensation provided by the Employment
Cost Index (EC)) 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 o{ wage and
compensation rates of change, which reflect the overall trend in labor costs, are summarized in table 3. Differences in concepts
and scope, related to the specific purposes
of the series, contribute to the variation in
changes among the individual measures.
Notes on the data
Definitions of each series and notes on the
data are contained in later sections of these
notes describing each set of data.
Employment and
Unemployment Data
(Tables I ; 4-29)
Household survey data
not work during the survey week, but were
available for work except for temporary illness and had looked for jobs within the preceding 4 weeks. Persons who did not look
for work because they were on layoff are also
counted among the unemployed. The unemployment rate represents the number unemployed as a percent of the civilian labor force.
The civilian labor force consists of all
employed or unemployed persons in the civilian noninstitutional population. Persons
not in the labor force are those not classified as employed or unemployed. This group
includes discouraged workers, defined as
persons who want and are available for a
job and who have looked for work sometime in the past 12 months (or since the end
of their last job if they held one within the
past 12 months), but are not currently looking, because they believe there are no jobs
available or there are none for which they
would qualify. The civilian noninstitutional population comprises all persons 16
years of age and older who are not inmates
of penal or mental institutions, sanitariums,
or homes for the aged, infirm, or needy. The
civilian labor force participation rate is
the proportion of the civilian noninstitutional population that is in the labor
force. The employment-population ratio is
employment as a percent of the civilian
noninstitutional population.
Notes on the data
Description of the series
Employment data in this section are obtained from the Current Population Survey,
a program of personal interviews conducted
monthly by the Bureau of the Census for the
Bureau of Labor Statistics. The sample consists of about 60,000 households selected to
represent the U.S. population 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: http://www.bls.gov/
ARIMA for seasonal adjustment of the
labor force data and the effects that it had
on the data.
At the beginning of each calendar year,
historical seasonally adjusted data usually
are revised , and projected seasonal adjustment factors are calculated for use during
the January-June period. The historical seasonally adjusted data usually are revised for
only the most recent 5 years. In July, new
seasonal adjustment factors, which incorporate the experience through June, are produced for the July-December period , but no
revisions are made in the historical data.
FOR ADDITIONAL INFORMATION on national household survey data , contact the
Division of Labor Force Statistics: (202)
691-6378.
X-12
Establishment survey data
Description of the series
Employment, hours, and earnings data in
this section are compiled from payroll
records reported monthly on a voluntary basis to the Bureau of Labor Statistics and its
cooperating State agencies by about
160,000 bus inesses 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
cps/rvcps03.pdt).
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-11 ARIMA
program which had been used since January
1980. See "Revision of Seasonally Adjusted
Labor Force Series in 2003," in the February 2003 issue of Employment and
Earnings (available on the BLS Web site
at http:www.bls.gov/cps/cpsrs.pdt) for a
discussion of the introduction of the use of
An establishment is an economic unit
which produces goods or services (such as
a factory or store) at a single location and is
engaged in one type of economic activity.
Employed persons are all persons who
received pay (including holiday and sick
pay) for any part of the payroll period including the 12th day of the month. Persons
holding more than one job (about 5 percent
of all persons in the labor force) are counted
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Monthly Labor Review
November 2004
75
Current Labor Statistics
in each establishment which reports them.
Production workers in the goods-produc i ng 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 ::.urvcy period, including premium pay
for overtime or late-shift work but excluding irregular bonuses and other special
payments. Real earnings are earnings adjusted to reflect the effects of changes in
consumer prices. The deflator for this series is derived from the Consumer Price Index for Urban Wage Earners and Clerical
Workers (CPI -W) .
Hours represent the average weekly
hours of production or nonsupervisory
workers for which pay was received, and are
different from standard or scheduled hours.
Overtime hours represent the portion of average weekly hours which was in excess of
regular hours and for which overtime premiums were paid.
The Diffusion Index represents the percent of industries in which employment was
rising over the indicated period , plus onehalf of the industries with unchanged employment; 50 percent indicates an equal balance between industries with increasing and
decreasing employment. In line with Bureau
practice, data for the 1-, 3-, and 6-month
spans are seasonally adjusted, while those
for the 11-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 .. benchm arks" ). The March
2003 benchmark was introduced in February 2004 with the release of data for January 2004, published in the March 2004 is-
76
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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
November 2004
third month of their appearance. Thus, December data are published as preliminary in
January and February and as final in March.
For the same reasons , quarterly establishment data (table I) are preliminary for the
first 2 months of publication and final in the
third month. Fourth-quarter data are published as preliminary in January and February and as final in March.
FOR ADDITIONAL INFORMATION on establishment survey data, contact the Division
of Current Employment Statistics: (202)
691-6555.
Unemployment data by
State
Description of the series
Data presented in this section are obtained
from the Local Area Unemployment Statistics (LAUS) program , which is conducted in
cooperation with State employment security
agencies.
Monthly estimates of the labor force,
employment, and unemployment for States
and sub-State areas are a key indicator of
local economic conditions, and form the basis for determining the eligibility of an area
for benefits under Federal economic assistance programs such as the Job Training
Partnership Act. Seasonally adjusted unemployment rates are presented in table I 0.
Insofar as possible , the concepts and definitions underlying these data are those
used in the national estimates obtained
from the CPS.
Notes on the data
Data refer to State of residence. Monthly
data for all States and the District of Columbia are derived using standardized procedures established by BLS. Once a year,
estimates are revised to new population controls, usually with publication of January
estimates, and benchmarked to annual average CPS levels.
FOR ADDITIONAL INFORMATION on data in
this series, call (202) 691-6392 (table I 0)
or (202) 691-6559 (table 11 ).
Quarterly Census of
Employment and Wages
Description of the series
Employment, wage, and establishment data
in thi s section are derived from the quarterly tax reports submitted to State employment security agencies by private and
State and local government employers sub-
ject to State unemployment insurance (u1)
laws and from Federal, agencies subject
to the Unemployment Compensation for
Federal Employees (ucFE) program. Each
quarter, State agencies edit and process the
data and send the information to the Bureau of Labor Statistics.
The Quarterly Census of Employment
and Wages (QCEW) data, also referred as ES202 data, are the most complete enumeration
of employment and wage information by indu~try 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 ofFmployment and Wages monthly employment data
represent the number of covered workers
who worked during, or received pay for, the
pay period that included the 12th day of the
month. Covered private industry employment includes most corporate officials, executives, supervisory personnel, professionals, clerical workers, wage earners, piece
workers, and part-time workers. It excludes
proprietors, the unincorporated self-employed, unpaid family members, and certain
farm and domestic workers. Certain types
of nonprofit employers, such as religious organizations , are given a choice of coverage
or exclusion in a number of States. Workers
in these organizations are, therefore , reported to a limited degree.
Persons on paid sick leave, paid holiday,
pairl vr1.ration, and the like, are included. Persons on the payroll of more than one firm
during the period are counted by each u,subject employer if they meet the employment definition noted earlier. The employment count excludes workers who earned no
wages during the entire applicable pay period because of work stoppages, temporary
layoffs, illness, or unpaid vacations.
Federal employment data are based on
reports of monthly employment and quarterly wages submitted each quarter to State
agencies for all Federal installations with
employees covered by the Unemployment
Compensation for Federal Employees ( ucFE)
program, except for certain national security agencies, which are omitted for security
reasons. Employment for all Federal agencies for any given month is based on the
number of persons who worked during or
received pay for the pay period that included
the 12th of the month.
An establishment is an economic unit,
such as a farm, mine, factory, or store, that
produces goods or provides services. It is
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Data reported for the first quarter are
typically at a single physical location and
engaged in one, or predominantly one, type tabulated into size categories ranging from
of economic activity for which a single in- worksites of very small size to those with
dustrial classification may be applied. Oc- 1,000 employees or more. The size category
casionally, a single physical location encom- is determined by the establishment's March
passes two or more distinct and significant employment level. It is important to note that
activities. Each activity should be reported each establishment of a multi-establishment
as a separate establishment if separate firm is tabulated separately into the approrecords are kept and the various activi- priate size category. The total employment
ties are classified under different NAICS level of the reporting multi-establishment
industries.
firm is not used in the size tabulation.
Most employers have only one establishCovered employers in most States report
ment; thus, the establishment is the predomi- total wages paid during the calendar quarnant reporting unit or statistical entity for ter, regardless of when the services were perreporting employment and wages data. Most formed. A few State laws, however, specify
employers, including State and local govern- that wages be reported for, or based on the
ments who operate more than one establish- period during which services are performed
ment in a State, file a Multiple Worksite Re- rather than the period during which comport each quarter, in addition to their quar- pensation is paid. Under most State laws or
terly u, report. The Multiple Worksite Re- regulations , wages include bonuses , stock
port is used to collect separate employment options, the cash value of meals and lodgand wage data for each of the employer's ing, tips and other gratuities, and, in some
establishments, which are not detailed on the States, employer contributions to certain deu, report. Some very small multi-establish- ferred compensation plans such as 40 I (k)
ment employers do not file a Multiple plans.
Worksite Report. When the total employCovered employer contributions for oldment in an employer's secondary establish- age, survivors, and disability insurance
ments (all establishments other than the larg- (OASDI), health insurance, unemployment inest) is IO or fewer, the employer generally surance, workers' compensation, and private
will file a consolidated report for all estab- pension and welfare funds are not reported
lishments. Also, some employers either can- as wages. Employee contributions for the
not or will not report at the establishment same purposes, however, as well as money
level and thus aggregate establishments into withheld for income taxes, union dues , and
one consolidated unit, or possibly several so forth, are reported even though they are
units, though not at the establishment level. deducted from the worker's gross pay.
For the Federal Government, the reportWages of covered Federal workers reping unit is the installation: a single loca- resent the gross amount of all payrolls for
tion at which a department, agency, or other all pay periods ending within the quarter.
government body has civilian employees. This includes cash allowances , the cash
Federal agencies follow slightly different cri- equivalent of any type of remuneration, sevteria than do private employers when break- erance pay, withholding taxes, and retireing down their reports by installation. They ment deductions. Federal employee remuare permitted to combine as a single state- neration generally covers the same types of
wide unit: I) all installations with IO or fewer services as for workers in private industry.
workers, and 2) all installations that have a
Average annual wage per employee for
combined total in the State of fewer than 50 any given industry are computed by dividworkers. Also, when there are fewer than 25 ing total annual wages by annual average emworkers in all secondary installations in a ployment. A further divi sion by 52 yields
State, the secondary installations may be average weekly wages per employee. Annual
combined and reported with the major in- pay data only approximate annual earnings
stallation. Last, if a Federal agency has fewer because an individual may not be employed
than five employees in a State, the agency by the same employer all year or may work
headquarters office (regional office, district for more than one employer at a time.
Average weekly or annual wage is afoffice) serving each State may consolidate
the employment and wages data for that State fected by the ratio of full-time to part-time
with the data reported to the State in which workers as well as the number of individuthe headquarters is located. As a result of als in high-paying and low-paying occupathese reporting rules, the number of report- tions. When average pay levels between
ing units is always larger than the number States and industries are compared, these
of employers (or government agencies) but factors should be taken into con sideration.
smaller than the number of actual establish- For example, industries characterized by
high proportions of part-time workers will
ments (or installations).
Monthly Labor Review
November 2004
77
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.
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 jurisNotes on the data
dictions and, in Alaska, those areas designated by the Census Bureau where counties
Beginning with the release of data for 200 I, have not been created.
County data also are
publications presenting data from the Cov- presented for the New England States for
ered Employment and Wages program have comparative purposes, even though townswitched to the 2002 version of the North ships are the more common designation used
American Industry Classification System in New England (and New Jersey).
(NAICS) as the basis for the assignment and
The Office of Management and Budget
tabulation of economic data by industry. (0MB) defines metropolitan areas for use in
NAICS is the product of a cooperative effort Federal statistical activities and updates
on the part of the statistical agencies of the these definitions as needed. Data in this table
United States, Canada, and Mexico. Due to use metropolitan area criteria established by
difference in NAICS and Standard Industrial 0MB in definitions issued June 30, 1999
Classification (SIC) structures, industry data (0MB Bulletin No. 99-04). These definitions
for 200 1 is not comparable to the SIC-based reflect information obtained from the 1990
data for earlier years.
Decennial Census and the 1998 U.S. CenEffective January 200 I, the program be- sus Bureau population estimate. A complete
gan assigning Indian Tribal Councils and relist of metropolitan area definitions is availlated establishments to local government able from the National Technical Informaownership. This BLS action was in response tion Service (NTIS), Document Sales, 5205
to a change in Federal law dealing with the Port Royal Road , Springfield, Va. 22161,
way Indian Tribes are treated under the Fed- telephone 1-800-553-6847.
eral Unemployment Tax Act. This law re0MB defines metropolitan areas in terms
quires federally recognized Indian Tribes to of entire counties , except in the six New
be treated similarly to State and local gov- England States where they are defined in
ernments. In the past, the Covered Employ- terms of cities and towns. New England data
ment and Wage (CEW) program coded Indian in this table , however, are based on a county
Tribal Councils and related establishments concept defined by 0MB as New England
in the private sector. As a result of the new County Metropolitan Areas (NECMA) belaw, CEW data reflects significant shifts in cause county-level data are the most detailed
employment and wages between the private available from the Quarterly Census of Emsector and local government from 2000 to ployment and Wages . The NECMA is a county200 I. Data also reflect industry changes. based alternative to the city- and town-based
Those accounts previously assigned to civic metropolitan areas in New England. The
and social organizations were assigned to NECMA for a Metropolitan Statistical Area
tribal governments. There were no required (MSA) incl11de: (I) the county containing the
industry changes for related establishments first-named city in that MSA title (this county
owned by these Tribal Councils. These tribal may include the first-named cities of other
business establishments continued to be MSA, and (2) each additional county having
coded according to the economic activity of at least half its population in the MSA in
that entity.
which first-named cities are in the county
To insure the highest poss ible quality
identified in step I. The NECMA is officially
of data, State employment security agen- defined areas that are meant to be used by
cies verify with employers and update , if statistical programs that cannot use the regunecessary, the industry, location , and own- lar metropolitan area definition s in New
ership classification of all establishments England.
on a 3-year cycle. Changes in establishFOR ADDITIONAL INFORMATION on the
ment classification codes resulting from the covered employment and wage data, contact
verification process are introduced with the the Divi sion of Administrative Statistics and
data reported for the first quarter of the year.
Labor Turnover at (202) 691-6567.
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November 2004
Job Openings and Labor
Turnover Survey
Description of the series
Data for the Job Openings and Labor Turnover Survey (JOLTS) are collected and compiled from a sample of 16,000 business establishments. Each month, data are collected
for total employment, job openings, hires,
quits , layoffs and discharges, and other separations. The JOLTS program covers all private
nonfarm establishments such as factories,
offices, and stores, as well as Federal, State,
and local government entities in the 50 States
and the District of Columbia. The JOLTS
sample design is a random sample drawn from
a universe of more than eight million establishments compiled as part of the operations
of the Quarterly Census of Employment and
Wages, or QCEW, program. This program includes all employers subject to State unemployment insurance (UI) laws and Federal
agencies subject to Unemployment Compensation for Federal Employees (UCFE) .
The sampling frame is stratified by ownership, region, industry sector, and size class.
Large firms fall into the sample with virtual
certainty. JOLTS total employment estimates are
controlled to the employment estimates of the
Current Employment Statistics (CES) survey.
A ratio of CES to JOLTS employment is used to
adjust the levels for all other JOLTS data elements. Rates then are computed from the adjusted levels.
The monthly JOLTS data series begin with
December 2000. Not seasonally adjusted data
on job openings, hires, total separations, quits,
layoffs and discharges, and other separations
levels and rates are available for the total nonfarm sector, 16 private industry divisions and
2 government divisions based on the North
American Industry Classification System
(NAICS), and four geographic regions. Seasonally adjusted data on job openings, hires, total
separations, and quits levels and rates are available for the total nonfarm sector, selected industry sectors, and four geographic regions.
Definitions
Establishments submit job openings information for the last business day of the reference month. A job opening requires that (I)
a specific position exists and there is work
available for that position ; and (2) work
could start within 30 days regardless of
whether a suitable candidate is found; and
(3) the employer is actively recruiting from
outside the establishment to fill the position.
Included are full-time, part-time, permanent,
short-term, and seasonal openings. Active
recruiting means that the establishment is
taking steps to fill a position by advertising
in newspapers or on the Internet, posting
help-wanted signs, accepting applications,
or using other simi lar methods.
Jobs to be filled only by internal transfers,
promotions, demotions, or recall from layoffs are excluded. Also excluded are jobs with
start dates more than 30 days in the future ,
jobs for which employees have been hired
but have not yet reported for work, and jobs
to be filled by employees of temporary help
agencies, employee leasing companies, outside contractors, or consultants. The Job
openings rate is computed by dividing the
number of job openings by the sum of employment and job openings, and multiplying
that quotient by 100.
Hires are the total number of additions to
the payroll occurring at any time during the
refe1ence 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 100.
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 100.
The quits, layoffs and discharges , and other
separations rates are computed similarly,
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dividing the number by employment and
multiplying by 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 (EC I) is a
quarterly measure of the rate of change in
compensation per hour worked and includes
wages, salaries, and employer costs of employee benefits. It uses a fixed market
basket of labor-similar in concept to the
Consumer Price Index's fixed market basket of goods and services-to measure
change over time in employer costs of employing labor.
Statistical series on total compensation
Monthly Labor Review
November 2004
79
Current Labor Statistics
costs, on wages and salaries, and on benefit costs are available for private nonfarm
workers excluding proprietors, the self-employed, and household workers. The total
compensation costs and wages and salaries
series are also available for State and local
government workers and for the civilian
nonfarm economy, which consists of private industry and State and local government workers combined. Federal workers
are excluded.
The Employment Cost Index probability
sample consists of about 4,400 private nonfarm establishments providing about 23,000
occupational observations and 1,000 State
and local government establishments providing 6,000 occupational observations selected to represent total employment in each
sector. On average, each reporting unit provides wage and compensation information
on five well-specified occupations. Data are
collected each quarter for the pay period including the 12th day of March, June, September, and December.
Beginning with June 1986 data, fixed
employment weights from the 1980 Census
of Population are used each quarter to
calculate the civilian and private indexes
and the index for State and local governments. (Prior to June 1986, the employment
weights are from the 1970 Census of Population.) These fixed weights, also used to
derive all of the industry and occupation
series indexes, ensure that changes in these
indexes reflect only changes in compensation, not employment shifts among industries or occupations with different levels of
wages and compensation. For the bargaining status, region, and metropolitan/non metropolitan 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
80
Monthly Labor Review
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benefits (such as Social Security, workers'
compensation, and unemployment insurance).
Excluded from wages and salaries and
employee benefits are such items as payment-in-kind, free room and board , and tips.
Notes on the data
The Employment Cost Index for changes in
wages and salaries in the private nonfarm
economy was published beginning in 1975.
Changes in total compensation cost-wages
and salaries and benefits combined-were
published beginning in 1980. The series of
changes in wages and salaries and for total
compensation in the State and local government sector and in the civilian nonfarm
economy (excluding Federal employees)
were published beginning in 1981. Historical indexes (June 1981 = I 00) are available
on the Internet:
http://www.bls.gov/ect/
FOR ADDITIONAL INFORMATION on the
Employment Cost Index, contact the Office
of Compensation Levels and Trends: (202)
691-6199.
Employee Benefits Survey
Description of the series
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 coritribution 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.
November 2004
Definitions
Employer-provided benefits are benefits
that are financed either wholly or partly by
the employer. They may be sponsored by a
union or other third party, as long as there is
some employer financing. However, some
benefits that are fully paid for by the employee also are included. For example, longterm care insurance and postretirement life
insurance paid entirely by the employee are
included because the guarantee of insurability and availability at group premium rates
are considered a benefit.
Participants are workers who are covered by a benefit, whether or not they use
that benefit. If the benefit plan is financed
wholly by employers and requires employees to complete a minimum length of service for eligibility, the workers are considered participants whether or not they have
met the requirement. If workers are required to contribute towards the cost of a
plan , they are considered participants only
if they elect the plan and agree to make the
required contributions.
Defined benefit pension plans use predetermined formulas to calculate a retirement benefit (1f 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-sponsor ed 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, I 00, or 250 workers,
depending on the industry (most service
industries were excluded). The survey conducted in 1987 covered only State and local governments with 50 or more employ-
ees. The surveys conducted in 1988 and
1989 included medium and large establishments with I 00 workers or more in private
industries. All surveys conducted over the
1979-89 period excluded establishments
in Alaska and Hawaii, as well as part-time
employees.
Beginning in 1990, surveys of State and
local governments and small private establishments were conducted in evl~P-numbered years, and surveys bf medium and
large establishments were conducted in oddnumbered years. The small establishment
survey includes all private nonfarm estab1ishments with fewer than I 00 workers,
while the State and local government survey includes all governments, regardless of
the number of workers. All three surveys include full- and part-time workers , and
workers in all 50 States and the District of
Columbia.
FOR ADDITIONAL INFORMATION on the
Employee Benefits Survey, contact the Office of Compensation Levels and Trends on
the Internet:
http://www.bls.gov/ebs/
Notes on the data
This series is not comparable with the one
terminated in 1981 that covered strikes involving six workers or more.
FOR ADDITIONAL INFORMATION on work
stoppages data, contact the Office of Compensation and Working Conditions: (202)
691-6282, or the Internet:
http:/www.bls.gov/cba/
Price Data
(Tables 2; 37-47)
Price data are gathered by the Bureau
of Labor Statistics from retail and primary markets in the United States. Price indexes are given in relation to a base periodDecember 2003 = I 00 for many Producer
Price Indexes (unless otherwise noted), 198284 = I00 for many Consumer Price Indexes
( unless otherwise noted), and 1990 = I00 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 C PI -U covers professional, managerial , and technical
workers, the self-employed , short-term
workers, the unemployed , retirees, and others not in the labor force.
The CPI is based on prices of food , clothing, shelter, fuel, drugs, transportation fares,
doctors ' and dentists ' fees, and other goods
and services that people buy for day-to-day
living. The quantity and quality of these
items are kept essentially unchanged be-
Data on work stoppages measure the number and duration of major strikes or lockouts (involving 1,000 workers or more) occurring during the month (or year), the number of workers involved , and the amount of
work time lost because of stoppage. These
data are presented in table 36.
Data are largely from a variety of published sources and cover only establishments directly involved in a stoppage. They
do not measure the indirect or secondary
effect of stoppages on other establishments
whose employees are idle owing to material
shortages or lack of service.
Definitions
Number of stoppages: The number of
strikes and lockouts involving 1,000 workers or more and lasting a full shift or longer.
Workers involved: The number of
workers directly involved in the stoppage.
Number of days idle: The aggregate
number of workdays lost by workers involved in the stoppages.
Days ofidleness as a percent of estimated
working time: Aggregate workdays lost as a
percent of the aggregate number of standard
workdays in the period multiplied by total employment in the period.
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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 PPI organizes products by similarity of end use or
material composition. The industry and
product structure of PP! organizes data in
accordance with the 2002 North American Industry Classification System and product
codes developed by the U.S. Census Bureau.
Monthly Labor Review
November 2004
81
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 ge nerally collected monthly, primarily by mail
questionnaire. Most prices are obtained directly from producing companies on a voluntary and confidential basis. Prices generally are reported for the Tuesday of the week
containing the 13th day of the month.
Since January I 992, price changes for
the various commodities have been averaged
together with implicit quantity weights representing their importance in the total net
selling value of all commodities as of 1987.
The detailed data are aggregated to obtain
indexes for stage-of-proce ss ing groupings,
commodity groupings, durability-of-p roduct groupings, and a number of special composite groups. All Producer Price Index data
are subject to revision 4 months after original publication.
FOR ADDITIONAL INFORM ATION , contact
the Division of Indu strial 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. res idents to
foreign buyers. (.. Re side nts" is defined as
in the national income accounts; it in cludes corporations, busine sses, and individuals , but doe s not require the organi zations 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 material s,
agricultural products , semifinished manufactures, and fini shed manufactures, including both capital and consumer goods. Price
data for these items are collected primarily
by mail questionnaire. In nearly al l cases,
the data are collected directly from the exporter or importer, although in a few cases,
prices are obtained from other sources.
To the extent possible, the data gathered
refer to prices at the U.S. border for exports
and at either the foreign border or the U.S.
border for imports. For nearly all products, the prices refer to transactions com-
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Federal Reserve Bank of St. Louis
pleted during the first week of the month.
Survey re spondents 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 so ld.
In addition to general indexes of prices for
U.S. expoi:ts 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 publi shes indexes for selected categories of internationally traded serv ices,
calculated on an international basi s and on
a balance-of-pay ments 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 neces sary to recognize when a
product's specifications or terms of transaction have been modified. For this reason, the
Bureau 's questionnaire requests detailed desc ription s 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 tem1s, 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 produ'-·tivity measures rel ate real out-
November 2004
put to real input. As such, they encompass a
family of meas ures 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.
Correspondin g 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 busi ness 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
soc ial insurance and private benefit pl ans,
plus an estimate of these payments for the
se lf-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 pay roll workers, self-employed
persons, and unpaid family workers.
Labor inputs are hours of all persons adjusted for the effects of changes in the education and experience of the labor force.
Capital services are the flow of services
from the capital stock used in production. It
is developed from measures of the net stock
of physical assets-equipment, structures,
land , and inventories-we ighted by rental
prices for each type of asset.
force; capital investment; level of output;
changes in the utilization of capacity, energy, material , and research and development; the organization of production; managerial skill; and characteristics and efforts
of the work force.
FOR ADDITIONAL INFORMATION on this
productivity series, contact the Division of
Productivity Research: (202) 691-5606.
ducing that output. Combined inputs include capital, labor, and intermediate purchases. The measure of capital input represents the flow of services from the capital
stock used in production. It is developed
from measures of the net stock of phy sical
assets-equipment, structures, land, and inventories. The measure of intermediate
purchases is a combination of purchased
materials, services, fuels , and electricity.
Industry productivity
measures
Notes on the data
Combined units of labor and capital
inputs are derived by combining changes in
labor and capital input with weights which
represent each component 's share of total
cost. Combined units of labor, capital, energy,
materials, and purchased business services are
similarly derived by combining changes in
each input with weights that represent each
input's share of total costs. The indexes for
each input and for combined units are based
on changing weights which are averages of the
shares in the current and preceding year (the
Tornquist index-number formula).
Notes on the data
Business sector output is an annuallyweighted index constructed by excluding
from real gross domestic product (GDP) the
following outputs: general government, nonprofit institutions, paid employees of private
households, and the rental value of owneroccupied dwellings. 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
Burea11 of Economic Analysis. Annual estimates of manufacturing sectoral output are
produced by the Bureau of Labor Statistic s.
Quarterly manufacturing output indexes
from the Federal Reserve Board are adjusted
to these annual output meas ures by the BLS.
Compensation data are developed from data
of the Bureau of Economic Analysis and the
Bureau of Labor Statistics. Hours J~ta are
developed from data of the Bureau of Labor
Statistics.
The productivity and associated cost
measures in tables 48-51 describe the relationship between output in real terms and
the labor and capital inputs involved in its
production. They show the changes from period to period in the amount of goods and
services produced per unit of input.
Although these measures relate output to
hours and capital services, they do not measure the contributions of labor, capital, or
any other specific factor of production.
Rather, they reflect the joint effect of many
influences, including changes in technology; shifts in the composition of the labor
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Federal Reserve Bank of St. Louis
Description of the series
The BLS industry productivity indexes measure the relationship between output and
inputs for selected industries and industry
groups, and thus reflect trends in industry
efficiency over time. Industry measures include labor productivity, multifactor productivity, compensation, and unit labor
costs.
The industry measures differ in methodology and data sources from the productivity measures for the major sectors because the industry measures are developed
independently of the National Income and
Product Accounts framework used for the
major sector measures.
The industry measures are compiled from
data produced by the Bureau of Labor Statistics and the Census Bureau, with additional data supplied by other government
agencies, trade associations, a nd other
sources.
FOR ADDITIONAL INFORMATION on thi s series , contact the Division of Industry Productivity Studies: (202) 691-5618.
International Comparisons
(Tables 52-54)
Labor force and
unemployment
Description of the series
Definitions
Output per hour is derived by dividing an
index of industry output by an index of labor input. For most industries , output indexes are derived from data on the value of
industry output adjusted for price change.
For the remaining industries, output indexes
are derived from data on the physical quantity of production.
The labor input series is based on the
hours of all workers or, in the case of some
transportation industries, on the number of
employees. For most industries, the series
consists of the hours of all employees. For
some trade and services industries, the series also includes the hours of partners, proprietors, and unpaid family workers.
Unit labor costs repre se nt the labor
compensation costs per unit of output produced, and are derived by dividing an index
of labor compensation by an index of output. Labor compensation includes payroll
as well as supplemental payments, including both legally required expenditures and
payments for voluntary programs.
Multifactor productivity is derived by
dividing an index of industry output by an
index of combined inputs consumed in pro-
Tables 52 and 53 present comparative measures of the labor force, employment, and
unemployment approximating U.S. concepts for the United States, Canada, Australia, Japan, and six European countries. The
labor force statistics published by other industrial countries are not, in most cases, comparable to U.S. concepts. Therefore, the Bureau
adjusts the figures for selected countries, for
all known major definitional differences, to the
extent that data to prepare adjustments are
available. Although precise comparability may
not be achieved, these adjusted figures provide a better bas is 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 La,bor Review, June 2000, pp. 3-20
(available on the BLS Web site at http://
www.bls.gov/opu b/ml r/2000/06/
artl full.pd[).
Definitions
For the principal U.S. definitions of the labor force , employment, and unemployment,
see the Notes section on Employment and
Monthly Labor Review
November 2004
83
Current Labor Statistics
Unemployme nt D ata: Hou sehold survey
data.
Notes on the data
The foreign country data are adjusted as
closely as possible to U.S. concepts, with the
exception oflower age limits and the treatment
of layoffs. These adjustments include, but are
not limited to: including older person~ in the
labor force by imposing no upper age limit,
adding unemployed students to the
unemployed, excluding the military and family
workers working fewer than 15 hours from the
employed, and excluding persons engaged in
passive job search from the unemployed.
Data for the United States relate to the
population 16 years of age and older. The U.S.
concept of the working age population has
no upper age limit. The adjusted to U.S.
concepts statistics have been adapted, insofar
as possible, to the age at which compulsory
schooling ends in each country, and the
Swedish statistics have been adjusted to
include persons older than the Swedish upper
age limit of 64 years. The adjusted statistics
presented here relate to the population 16
years of age and older in France, Sweden,
and the United Kingdom; 15 years of age and
older in Australia, Japan, Germany, Italy, and
the Netherlands. An exception to this rule is
that the Canadian statistics are adjusted to
cover the population 16 years of age and
older, whereas the age at which compulsory
schooling ends remains at 15 years. In the labor
force participation rates and employmentpopulation ratios, the denominator is the
civilian noninstitution alized working age
popul ation , 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
class ified as employed due to their strong job
attachment. No adj ustment is made for the
countries that classify those on layoff as
empln~.'ed. 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 pl ac ing or answering adve rti se ments,
contacting employers directly,or registering
with an employment agency (simply reading
ads is not enough to qualify as ac tive search).
Canada and the European countries classify
84
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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, I 997, 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 unemployme nt ra te by 0.1
percentage point. Other breaks noted relate
to changes in population controls that had
virtually no effect on unemployment rates.
For a description of all the changes in the
U.S. labor force survey over time and their
impact, see Historical Comparability in the
··Household Data" section of the BLS publication Employment and Earnings (available
on the BLS Web site at http://www.bls.gov/
cps/eetech _ methods.pdf).
For Australia, the 200 I break reflects the
introduction in April 200 I of a redesigned
labor force survey that allowed for a closer
applicat ion of Intern atio nal 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 active ly
looked for work because they were waiting to
begin a new job from "not in the labor force"
to " unemployed" could only be incorporated
for April 200 I forward. This reclassification
diverges from the U.S. definition where
persons waiting to start a new job but not
ac tively 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 li ving in private househo lds only.
For further qualifications and historical
data, see Comparative Civilian Labor Force
Statistics, Ten Countries, on the BLS Web site
at http://www.bis.gov/fls/flsl fore.pd f
November 2004
FOR ADDITIONAL INFORMATION on this
se rie s, contact the Division of Foreign
Labor Statistics: (202) 691-5654 or
flshelp@bls.gov
Manufacturing productivity
and labor costs
Description of the series
Table 54 presents comparative indexes of
manufacturing labor productivity (output per
hour), output, total hours, compensation per
hour, and unit labor costs for the United States,
Canada, Japan, and nine European countries.
These measures are trend comparisons- that
is, series that measure changes over timerather than level comparisons. There are greater
technical proble~ns in comparing the levels of
manufacturing output among countries.
BLS constructs the comparative indexes
from three basic aggregate measures--ou tput , total labor hours , and total compensation. The hours and compensation measures
refer to all employed persons (wage and salary earners plus self-employed persons and
unpaid family workers) in the United States,
Canada, Japan, France, Germany, Norway,
and Sweden , and to all employees (wage and
salary earners) in the other countries.
Definitions
Output, in general, refers to value added in
manufacturing from the national accounts
of each country. However, the output series for Japan prior to 1970 is an index of
industrial production, and the national accounts measures for the United Kingdom
are essentially identical to their indexes of
industrial production.
The 1977- 97 output data for the United
States are the gross product originating
(value added) measures prepared by the
Bureau of Economic Analysis of the U.S.
Department of Commerce. Comparable
manufacturing output data currently are not
available prior to 1977.
U.S. gross product originating is a chaintype annual-weight ed series. (For more information on the U.S. measure, see Robert
E. Yuskavage, ·'Improved Estimates of Gross
Product by Industry, 1959-94," Survey of
Current Business, August 1996, pp. 13355.) The Japanese value added series is based
upon one set of fixed price weights for the
years 1970 through 1997. Output series for
the other foreign economies also employ
fixed price weights, but the weights are updated periodically (for example, every 5 or I 0
years).
To preserve the comparability of the U.S.
measures with those for other economies,
BLS uses gross product originating in manufacturing for the United States for these comparative measures. The gross product originating series differs from the manufacturing output series that BLS publishes in its
news releases on quarterly measures of U.S.
productivity and costs (and that underlies the
measures that appear in tables 48 and 50 in
this section). The quarterly measures are on
a "sectoral output" basis, rather than a valueadded basis. Sectoral output is gross output
less intrasector transactions.
Total labor hours refers to hours worked
in all countries. The measures are developed from statistics of manufacturing employment and average hours. The series used
for France (from 1970 forward), Norway,
and Sweden are official series published with
the national accounts. Where official total
hours series are not available, the measures
are developed by BLS using employment figures published with the national accounts,
or other comprehensive employment series,
and estimates of annual hours worked. For
Germany, BLS uses estimates of Jverage
hours worked developed by a research institute connected to the Ministry of Labor
for use with the national accounts employment figures. For the other countries, BLS
constructs its own estimates of average
hours.
An hours series is not available for Denmark after 1993; therefore, the BLS measure of labor input for Denm ark ends in
1993.
Total compensation (labor cost) includes all payments in cash or in-kind made
directly to employees plus employer expenditures for legally required insurance programs and contractual and private benefit
plans. The measures are from the national
accounts of each country, except those for
Belgium , which are developed by BLS using
stati~tic--; on employment, average hours, and
hourly compensation. For Canada, France,
and Sweden, compensation is increased to
account for other significant taxes on payroll or employment. For the United Kin gdom , compensation is reduced between 1967
and 1991 to account for employment-related
subsid ies. Self-employed workers are included in the all-employed-persons m..,qsures
by assuming that their hourly compensation
is equal to the average for wage and salary
employees.
Notes on the data
In general, the measures relate to total manufacturing as defined by the International
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Federal Reserve Bank of St. Louis
Standard Industrial Classification. However,
the measures for France (for all years) and
Italy (beginning in 1970) refer to mining and
manufacturing less energy-related products,
and the measures for Denmark include mining and exclude manufacturing handicrafts
from 1960 to 1966.
The measures for recent years may be
based on current indicators of manufacturing output (such as industrial production indexes), employment, average hours, and
hourly compensation until national accounts
and other statistics used for the long-term
measures become available.
FOR ADDITIONAL INFORMATION on this series, contact the Division of Foreign Labor
Statistics : (202) 691-5654.
Occupational Injury
and Illness Data
(Tables 55-56)
Survey of Occupational
Injuries and Illnesses
Description of the series
The Survey of Occupational Injuries and Illnesses collects data from employers about
their workers' job-related nonfatal injuries and
illnesses. The information that employers provide is based on records that they maintain under the Occupational Safety and Health Act of
1970. Self-employed individuals, farms with
fewer than 11 employees, employers regulated
by other Federal safety and health laws, and
Federal, State, and local government agencies
are excluded from the survey.
The survey is a Federal-State cooperative program with an independent sample
selected for each participating State. A stratified random sample with a Neyman allocation is selected to represent all private industries in the State. The survey is stratified
by Standard Industrial Classi fication and
size of employment.
Definitions
Under the Occupational Safety and Health
Act, employers maintain records of nonfatal work-related injuries and illnesses that
involve one or more of the following: loss
of consciousness, restriction of work or motion, transfer to another job, or medical
treatment other than first aid.
Occupational injury is any injury such
as a cut, fracture, sprain, or amputation that
results from a work-related event or a
single, instantaneous exposure in the work
environment.
Occupational illness is an abnormal condition or disorder, other than one resulting
from an occupational injury, caused by exposure to factors associated with employment. It includes acute and chronic illnesses
or disease which may be 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 I 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 recognized and repo1ted. These long-term latent ill-
Monthly Labor Review
November 2004
85
Current Labor Statistics
nesses are believed to be understated in the
survey 's illness measure. In contrast, the overwhelming majority of the reported new illnesses are those which are easier to directly
relate to workplace activity (for example, contact dermatitis and carpal tunnel syndrome).
Most of the estimates are in the form of
incidence rates, defined as the number of injuries and illnesses per I 00 equivalent fulltime workers. For this purpose, 200,000 employee hours represent I 00 employee years
(2,000 hours per employee). Full detail on
the avai lable measures is presented in the annual bulletin , Occupational Injuries and Illnesses: Counts, Rates, and Characteristics.
Comparable data for more than 40 States
and territories are available from the BLS Office of Safety, Health and Working Conditions. Many of these States publish data on
State and local government employees in addition to private industry data.
Mining and railroad data are furnished to
BLS by the Mine Safety and Health Administration and the Federal Railroad Administration. Data from these organizations are included in both the national and State data published annually.
With the 1992 survey, BLS began publishing details on serious, nonfatal incidents resulting in days away from work. Included are
some major characteristics of the injured and
ill workers, such as occupation, age, gender,
race, and length of service, as well as the circumstances of their injuries and illnesses (nature of the disabling condition, part of body
affected, event and exposure, and the source
directly producing the condition). In general,
these data are available nationwide for detailed
86
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Federal Reserve Bank of St. Louis
industries and for individual States at more
aggregated industry levels.
FOR ADDITIONAL INFORMATION on occupational injuries and illnesses, contact the Office of Occupational Safety, Health and
Working Conditions at (202) 691-6180, or
access the Internet at:
http://www.bis.gov/iif/
Census of Fatal
Occupational Injuries
The Census of Fatal Occupational Injuries
compiles a complete roster of fatal job-related injuries , including detailed data about
the fatally injured workers and the fatal
events. The program collects 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.
November 2004
Definition
A fatal work injury is any intentional or unintentional wound or damage to the body resulting in death from acute exposure to energy,
such as heat or electricity, or kinetic energy
from a crash, or from the absence of such essentials as heat or oxygen caused by a specific
event or incident or series of events within a
single workday or shift. Fatalities that occur
during a person 's commute to or from work
are excluded from the census, as well as workrelated illnesses , which can be difficult
to identify due to long latency periods.
Notes on the data
Twenty-eight data elements are collected,
coded , and tabulated in the fatality program,
including information about the fatally injured worker, the fatal incident, and the machinery or equipment involved. Summary
worker demographic data and event characteristic s are included in a national news release that is available about 8 months after
the end of the reference year. The Census of
Fatal Occupational Injuries was initiated in
1992 as a joint Federal-State effort. Most
States issue summary information at the
time of the national news release.
FOR ADDITIONAL INFORMATION on the
Census of Fatal Occupational Injuries contact the BLS Office of Safety, Health, and
Working Conditions at (202) 691-6 I 75, or
the Internet at:
http://www.bls.gov/iif/
1. Labor market indicators
2002
Selected indicators
2002
2003
Ill
2004
2003
Ill
II
IV
Ill
II
IV
EfT1>1oyment data
Elll)loyment status of the civilian noninstitutional
population (household survey):
1
Labor force participation rate ......... ............................................. .
66.6
66.2
66.6
66.5
66.3
66.4
66.2
66.1
66.0
65.9
66.0
Employment-population ratio .............................................. ........ .
62.7
62.3
62.8
62.5
62.4
62.3
62.1
62.3
62.2
62.2
62.4
Unemployment rate .... ....... ........ ... .. ....... ... ...... ............... .. ..
5.8
6.0
5.8
5.9
5.8
6.1
6.1
5.9
5.6
5.6
5.5
Men .... ... ... .......... .. ... ... ... ... .... ........ ....... .... ... ..... ... ....... .. .
5.9
6.3
5.9
6.1
6.1
6.5
6.4
6.1
5.7
5.7
5.6
16 to 24 years ..... ...................... ............................................. ..
12.8
13.4
13.1
12.5
12.6
14.0
13.8
13.1
12.5
12.9
12.5
4.4
25 years and older ....................................... ...... ..................... .
4.7
5.0
4.7
4.9
5.0
5.2
5.1
4.9
4.5
4.5
Women .. ... ... .. . .. . ..... . ... .. ... ..... .... .... .. ......... ............... .... ..
5.6
5.7
5.6
5.7
5.5
5.7
5.8
5.6
5.6
5.4
5.4
16 to 24 years ..... ................................... ... .... ......... ... ............ ...
11 .1
11 .4
10.9
11 .4
11.2
11.8
11 .5
10.9
11 .1
10.9
11.0
25 years and older..................... ................ ....... ...................... .
4.6
4.6
4.6
4.6
4.5
4.6
4.7
4.6
4.5
4.4
4.3
1
Elll)loyment, nonfarm (payroll data), in thousands:
Total nonfarm . ...... ..... ..... ... ............................................ ..... ..... .
130,341
129,931
130,287
130,248
130,047
129,878
130,002
130,367
131 ,125
131 ,521
Total private .. ............. ... .... .. .... ...................................... ... .
108,828
108,356
108,736
108,654
108,428
108,309
108,260
108,453
108,827
109,577
109,897
Goods-producing .... ... ... .... .. ............ ............ ... ... ...... ... ..... .. .... .. .
22,557
21,817
22,466
22,252
22,025
21,848
21,718
21 ,676
21 ,719
21 ,869
21 ,927
Manufacturing ..... ..... .......... ......... ........ ...... ..... ........... .
22,557
21,817
15,197
14,979
14,775
14,570
14,410
14,340
14,326
14,385
14,403
107,789
108,114
107,821
107,995
108,022
108,030
108,102
108,326
108,648
109,256
109,595
Servi~oviding ..... .. ............ ..
129,820
Average hours:
Totll private ................................................ .. .......................... .
33.9
33.7
33.9
33.8
33.8
33.7
33.6
33.7
33.8
33.7
33.8
Manufacturing . ............ . .. ... .......... .. . .. ............................ .
40.5
40.4
40.4
40.4
40.4
40.2
40.2
40.6
41 .0
40.9
40.8
Overtime .... ........................ ..... .. ......... .. ....... ..... ... .... .. .
4.2
4.2
4.3
4.2
4.2
4.1
4.1
4.4
4.6
4.6
4.6
1.0
EfT1>1oyment Cost lndex2
Percent change in the ECI, compensation :
NI workers (exduding farm, household and Federal workers) ... ...
3.4
3.8
.9
.6
1.4
.8
1.1
.5
1.4
.9
Private industry workers ...... ......... .. .... .. ... ................ ............. ... .
3.2
4.0
.6
.4
1.7
.8
1.0
.4
1.5
.9
.8
Goods-producing ... .... ... .................. .................... . ......... ... ... .
3.7
4.0
.6
.9
1.8
.9
.7
.5
2.3
.9
.9
Servi~oviding3 .... ......... .. ............. ..
3.1
4.1
4.0
3.3
.6
2.2
.2
.9
1.5
.7
.8
.4
1.1
1.7
.5
.5
1.1
.7
1.0
.4
.8
1.7
Union .... ......... ....... .. ... ...... .... .. ... . .. . .. ... . ..... ... . ..... . ............ .
4.2
4.6
1.2
.9
1.6
1.2
1.0
.7
2.8
1.5
.8
Nonunion ........... ..... ... ... ... ... . .. .... .. . .. ...... ... ... ... ... ... ... ... .... ..
3.2
3.9
.5
.4
1.6
.8
1.0
.4
1.3
.8
.9
3
State and local government workers
Workers by bargaining status (private industry):
1
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
using the last month of each quarter.
controls. Nonfarm data reflect the conversion to the 2002 version of the North American
Industry Classification System (NAICS), replacing the Standard Industrial Classification (SIC)
3
system. NAICS-based data by industry are not comparable ...,,;th SIG-based data.
2
Goods-producing industries include mining, construction , and manufacturing. Service-
providing industries include all other private sector industries.
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Federal Reserve Bank of St. Louis
Monthly Labor Review
November 2004
87
Current Labor Statistics: Comparative Indicators
2. Annual and quarterly percent changes in c ompensation, pric es, and productivity
Selected measures
2002
2003
2002
Ill
2003
IV
II
2004
Ill
IV
II
Ill
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
3.4
3.2
3.8
4.0
0.9
.6
0.6
.4
1.4
1.7
0.8
.8
1.1
0.5
1.0
.4
2.9
2.7
2.9
3.0
.7
.4
.4
.3
1.0
1.1
.6
.7
.9
.8
2.3
2.3
.6
-.1
1.8
-.3
3.2
4.2
.4
4.6
25.2
3.2
4.2
.4
4.6
25.2
.2
.0
- .7
1.1
1.9
-.1
-.3
.6
.1
6.5
3.7
2.4
.6
6.5
28.0
4.3
4.4
4.4
4.5
4.4
5.4
4.8
4.5
4.1
1.2
1.6
3.4
3.9
3.7
3.2
1.4
1.5
0.9
.9
1.0
.8
.4
.6
.7
.6
.7
.9
.9
-.2
-.2
1.2
1.2
.2
-.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
7.6
6.7
9.1
8.5
9.0
9.4
2.4
3.1
5.0
3.9
3.7
.1
1.5
3.9
2.7
2.3
1.9
.3
1
Consumer Price Index (All Urban Consumers) : All Items ......
Producer Price Index:
Finished goods ..... .......................................... .....................
Finished consumer goods .... .... ....... .................... .............
Capital equipment.. ........................ ...... ... .. ...
Intermediate materials, su pplies , and components .
Crude materials .... ....... .......... ................ .....
Productivity data
3
Output per hour of all persons:
Business sector ......... .............................. ....................... ......
Nonfarm business sector ............ ···················
Nonfinancial cornorations
4
..
1
Annual changes are December-to-December changes. Quarterly changes are
calculated using the last month of each quarter. Compensation and price data are not
seasonally adjusted, and the price data are not compou nded.
2
Excludes Federal and private household workers.
3
Annual rates of change are computed by comparing annual averages.
Quarterly percent changes reflect annual rates of change in quarterly indexes .
The data are seasonally adjusted.
4
Output per hour of all employees.
NOTE: Dash indicates data not available.
3. Alternative measures of wage and compensation changes
Quarterly change
Components
2003
IV
Ill
Average hourly compensation :1
All persons, business sector
All persons, nonfarm business sector. ....
Four quarters ending-
2004
II
2003
Ill
Ill
2004
IV
II
Ill
5.6
6.1
4.0
4.4
2.8
2.0
4.3
4.9
3.8
3.6
4.6
4.6
5.3
5.4
4.6
4.5
4.2
4.4
3.7
3.7
1.1
1.0
1.0
1.0
1.7
.5
.4
.7
.4
.5
1.4
1.5
2.8
1.3
.7
.9
.9
1.5
.8
.4
1.0
.8
.8
.9
1.7
3.9
4.0
4.8
3.8
3.6
3.8
4.0
4.6
3.9
3.3
3.8
3.9
5.7
3.6
3.3
3.9
4.0
6.0
3.5
3.4
3.8
3.7
5.8
3.4
3.4
.3
.4
.6
.2
.4
.6
.7
.6
.7
.4
.6
.7
1.0
.6
.2
.9
.9
.8
.8
1.0
2.9
3.0
2.6
3.1
2.3
2.9
3.0
2.4
3.1
2.1
2.5
2.6
2.5
2.6
2.1
2.5
2.6
2.9
2.5
1.9
2.4
2.6
3.0
2.5
2.0
Employment Cost Index-compensation:
2
Civilian nonfarm ... . . .. ....... .. . . . . .. ........ . ......... . .. .. . .
Private nonfarm ................. .. ..... ... ... ..... .. ....................... .. ......... .
Union ....... .................. ... ........... ................. .
Nonunion .. .. .. .................... ... ..... ... .. ........... ... ........... .... .... ...... ..
State and local governments .................. ................. ... ...............
Employment Cost Index-wages and salaries :
2
Civilian nonfarm ..
. ...... . ............... . . . .. . .. . . . .. . .. . ..... .. . . . . .
Private nonfarm .................. ................ ................... ................. .. ..
Union .... ..... .... ..... ... .. ... ..... ......... .... .. ... .. ....... ..... ... ... ... ..
Nonunion ......... .... ................................................... .............. .
State and local governments ................................................... ..
·I
.8
.6 1
.9
1.0
1
Seasonally adjusted. "Quarterly average" is percent change from a quarter ago, at an annual rate.
2
Excludes Federal and household workers.
88
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
4. Employment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
2004
2003
Annual average
Employment status
2002
2003
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
217.570
144,863
66.6
136,485
221 ,168
146,510
66 .2
137,736
221,779
146,610
66 .1
137,644
222,039
146,892
66.2
138,095
222,279
147,187
66 .2
138,533
222.509
146,878
66.0
138,479
222,161
146,863
66.1
138,566
222,357
146,471
65.9
138,301
222,550
146,650
65.9
138,298
222,757
146,741
65 .9
138,576
222,967
146,974
65.9
138,772
223,196
147,279
66 .0
139,031
223,422
147,856
66 .2
139,660
223,677
147,704
66.0
139,681
223,941
147,483
65.9
139,480
62 .7
8,378
5.8
72,707
62.3
8,774
6.0
74,658
62 .1
8,966
6.1
75,168
62 .2
8,797
6.0
75,147
62. 3
8,653
5.9
75,093
62.2
8,398
5.7
75,631
62.4
8,297
5.6
75,298
62 .2
8,170
5.6
75,886
62. 1
8,352
5.7
75,900
62 .2
8, 164
5.6
76,016
62.2
8,203
5.6
75 ,993
62.3
8,248
5.6
75,916
62 .5
8,196
5.5
75,565
62.4
8,022
5.4
75,973
62.3
8,003
5.4
76,458
TOTAL
Civilian noninstitutional
1
population
Civilian labor fo rce ..
Participation rate ..
Employed ..
Employm ent-population ratio 2 .
Unemployed ..
Unemploym ent rate .. .
Not in the labor fo rce ·······
Men, 20 years and over
Civil ian noninstitutional
1
population
Civ ilian labor fo rce ..
Part icipation rate .. .
Employed ..
Em ployment-pop2
ulation ratio ...
Unemployed ..
Unemployment rate ..
Not in the labor force ..
96,439
98,272
98,568
98 ,696
98,814
98,927
98,866
98,966
99,065
99,170
99 ,2 79
99,396
99,512
99,642
99,776
73,630
76.3
69 ,734
74,623
75.9
70,4 15
74,905
76.0
70,596
74,942
75.9
70,726
75,188
76.1
70 ,964
75,044
75 .9
71 ,099
75,171
76.0
71 ,329
74,797
75.6
70,969
75,018
75.7
71,128
74,871
75 .5
71 ,118
75,048
75.6
71 ,162
75,372
75.8
71 ,570
75,577
75.9
71,847
75,639
75.9
71,870
75,443
75.6
71 ,677
72 .3
3,896
5.3
22,809
71.7
4,209
5.6
23,649
71.6
4,309
5.8
23,663
71 .7
4,216
5.6
23,754
71.8
4,224
5.6
23,620
71.9
3,945
5.3
23,882
72.1
3,842
5. 1
23,694
71.7
3,828
5.1
24,168
71.8
3,890
5.2
24,047
71 .7
3,753
5.0
24,299
71.7
3,886
5.2
24 ,23 1
72.0
3,802
5.0
24,023
72.2
3,730
4.9
23,935
72.1
3,768
5.0
24,003
72.0
3,766
5.0
24,332
105, 136
106,800
107,080
107,197
107,303
107 ,404
107, 131
107,216
107,299
107,389
107,483
107,586
107,687
107,801
107,920
63,648
60.5
60,420
64,7 16
60.6
61,402
64,608
60.3
6 1,191
64,899
60.5
61,524
64,917
60 .5
61,597
64,846
60.4
6 1,521
64,515
60.2
61,260
64,629
60.3
61,456
64,687
60.3
61,373
64,785
60.3
61,571
64,813
60.3
61,721
64,893
60.3
61,629
65,122
60.5
61,918
64,903
60.2
61,870
64,989
60.2
6 1,925
57.5
3,228
5. 1
41,488
57.5
3,314
5.1
42, 083
57 .1
3,417
5.3
42,472
57.4
3,375
5.2
42,299
57.4
3,320
5. 1
42,387
57.3
3,326
5.1
42,558
57.2
3,255
5.0
42,6 17
57 .3
3,172
4.9
42,587
57.2
3,314
5.1
42,6 13
57 .3
3,215
5.0
42,604
57.4
3,092
4.8
42 ,670
57.3
3,264
5. 0
42,693
57 .5
3,204
4.9
42,565
57.4
3,033
4.7
42,898
57 .4
3,064
4.7
42,93 1
15,994
16,096
16,13 1
16,145
16,162
16,178
16, 164
16, 175
16, 186
16,198
16,205
16,214
16,222
16,234
16,246
7,585
47.4
6,332
7,170
44 .5
5,919
7,097
44 .0
5,857
7,05 1
43.7
5,846
7,082
43.8
5,972
6,987
43.2
5,859
7,177
44.4
5,977
7,045
43.6
5,875
6,945
42 .9
5,797
7,085
43.7
5,888
7,113
43.9
5,888
7,014
43.3
5,832
7,157
44.1
5,896
7,162
44.1
5,941
7,051
43.4
5,877
39.6
1,253
16.5
8,409
36.8
1,251
17.5
8,926
36 .3
1,240
17.5
9,034
36.2
1,205
17.1
9,094
37.0
1,109
15.7
9,080
36.2
1,128
16.1
9,191
37.0
1,200
16.7
8,987
36.3
1, 170
16.6
9,130
35.8
1,148
16.5
9,240
36.3
1,197
16.9
9,113
36.3
1,225
17.2
9,092
36.0
1,181
16. 8
9,200
36.3
1,262
17.6
9,065
36.6
1,220
17.0
9,072
36 .2
1,173
16.6
9,195
Women, 20 years and over
Civilian noninstitutional
1
population
Civilian labo r fo rce . . . . .
Part icipation rate ..
Empl oyed ..
Employment-pop-
.. ....
ulation ratio 2
.....
Unemployed ..
Unemployment rate ..
Not in the labor force ..
Both sexes, 16 to 19 years
Civilian noninstitutional
1
population
Civilian labor force ..
Part ici pation rate ....
Empl oyed ...
Employm ent-pop2
ulation ratio
Unemployed ...
Unemploym ent rate ..
Not in the labor force ..
3
White
Civilian noninstitutional
1
popul ation
Civili an labo r force ..
Participation rate ..
Employed ..
Employment-pop2
ulation ratio ...
Unemployed ..
Unemployment rate ..
Not in the labor force ....
Black or African American
179,783
181 ,292
181,696
18 1,87 1
182,032
182,185
181 ,879
182,001
182,001
182,252
182,384
182 ,531
182,676
182,846
183,022
120, 150
66. 8
114,013
120,546
66.5
114,235
120,411
66 .3
114,015
120,736
66.4
114,535
121,041
66 .5
114,783
120,751
66 .3
114,678
120,723
66.4
11 4,765
120,540
66. 2
114,602
120,542
66.2
114,433
120, 675
66 .2
114,712
120,984
66.3
114,976
121, 180
66.4
115,152
121,428
66.5
115,623
121,300
66.3
115,547
121 ,016
66 .1
115,323
63.4
6,137
5. 1
59,633
63.0
6,3 11
5.2
60,746
62 .8
6,397
5.3
61,285
63.0
6, 200
5.1
61,135
63.1
6, 258
5.2
60,991
62.9
6,073
5.0
61,434
63.1
5,958
4.9
61 ,156
63.0
5,938
4.9
61,460
62 .8
6,109
5.1
61,579
62 .9
5,963
4.9
6 1,577
63.0
6,008
5.0
61,400
63.1
6,028
5.0
61,351
63 .3
5,805
4.8
61,248
63 .2
5,753
4.7
61,546
63.0
5,693
4.7
62,006
25,578
25 ,686
25,784
25,825
25,860
25,894
25, 867
25,900
25,932
25,967
26,002
26,040
26,078
26,120
26 ,163
16,565
64 .8
14,872
16,526
64.3
14,739
166,677
64.7
14,826
16,589
64. 2
14 ,696
16, 524
63.9
14,812
16,365
63.2
14,679
16,602
64.2
14,886
16,404
63.3
14,804
16,595
64.0
14,909
16,485
63.5
14,878
16,442
63.2
14,818
16,506
63.4
14,833
16,755
64.3
14,926
16,724
64.0
14,983
16,703
63.8
14,981
58.1
1,693
10.2
9,013
57.4
1,787
10.8
9,161
57.5
1,851
11.1
9, 107
56.9
1,893
11.4
9,236
57.3
1,712
10.4
9,336
56.7
1,686
10.3
9,529
57.5
1,736
10.5
9,2 65
57.2
1,600
9.8
9,495
57 .2
1,686
10.2
9,337
57.3
1,607
9.7
9,482
57.0
1,624
9.9
9,560
57.0
1,673
10.1
9,534
57 .2
1,829
10.9
9,323
57.4
1,741
10.4
9,396
57 .3
1,722
10.3
9,460
3
Civi lian noninstitution al
1
population
Civilian labor force ..
Part icipation rat e ..
Employed ..
Em ployment-pop2
ulation rati o
....
Unemployed ..
Unem ployment rate ..
Not in the labor force ..
See footnotes at end of table
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November 2004
89
Current Labor Statistics:
Labor Force Data
4. Continued-Emp loyment status of the population, by sex, age, race, and Hispanic origin, monthly data seasonally adjusted
[Numbers in thousands]
Annual average
Employment status
2003
2004
2002
2003
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
25,963
17,943
69.1
16,590
27,551
18,813
68.3
17,372
27,808
18,877
67.9
17,456
27,913
18,940
67.9
17,556
28,016
19,125
68.3
17,709
28,116
19,035
67.7
17,784
27,619
18,811
68.1
17,441
27,705
18,693
67.5
17,303
27,791
19,010
68.4
17,596
27,879
19,064
68.4
17,693
27,968
19,313
69.1
17,958
28,059
19,304
68.8
18,019
28,150
19,450
69.1
18,118
28,243
19,482
69.0
18,144
28,338
19,446
68.6
18,073
63.9
1,353
7.5
8,020
63. 1
1,441
7.7
8,738
62 .8
1,421
7.5
8,931
62 .9
1,383
7.3
8,974
63.2
1,416
7.4
8,891
63.3
1,250
6.6
9,082
63 .2
1,370
7.3
8,807
62.5
1,389
7.4
9,012
63.3
1,414
7.4
8,781
63.5
1,371
7.2
8,815
64.2
1,355
7.0
8,654
64.2
1,285
6.7
8,755
64.4
1,332
6.8
8,700
64.2
1,338
6.9
8.761
63.8
1,372
7.1
8,892
Hispanic or Latino
ethnicity
Civilian noninstitutional
1
oooulation . .
Civilian labor force ..............
Participation rate .. ... ... .
Fniployed ........... ........ ....
Employment-population ratio 2 . .
Unemployed ...................
Unemployment rate ....
Not in the labor force ..
1
The population figures are not seasonally adjusted .
Civilian employment as a percent of the civilian noninstitutional population .
3
Beginning in 2003, persons who selected this race group only; persons who selected
more 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
NOTE: Estimates for the above race groups (white and black or African American) do not sum
to totals because data are not pre sented for all races . In addition , persons whose ethnicity is
identified as Hispanic or Latino may be of any race and , therefore, are classified by ethnicity as
well as by race. Beginning in January 2003, data reflect revised population controls used in the
household survey.
5. Selected employment indicators, monthly data seasonally adjusted
[In thousands]
Selected categories
Characteristic
Employed , 16 years and over ..
Men .... ... ..... ... .. .
Women ...
Married men , spouse
present. .................
Married women, spouse
present .. .. .......
Annual average
2003
2004
2002
2003
Sept.
Oct.
Nov.
136,845
72,903
63.582
137 ,736
73,332
64,404
137,644
73,488
64 .155
138,095
73,643
64.452
138 ,533
73 ,915
64 ,6 18
Dec.
138,479
74,085
64 ,394
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
138,566
74 ,343
64 ,223
138,301
73 ,901
64 ,400
138,298
74,006
64 ,292
138,576
74,053
64 ,523
138,772
74 ,035
64 ,737
139,031
74,476
64 ,555
139,660
74 ,822
64,838
139,681
74 ,860
64 ,822
139,480
74,601
64 ,879
44,116
44,653
44,566
44 ,684
45 ,152
45,431
45,490
45,128
45,043
44 ,735
44 ,723
44,938
44,935
45 ,106
45,034
34 ,155
34 ,695
34,612
34,993
35,076
35 ,034
34 ,585
34.502
34 ,256
34 ,339
34 ,522
34,461
34,599
34.448
34 ,601
Persons at work part time'
All industries :
Part time for economic
reasons .. ... ...... .... ..... ....
Slack work or business
conditions ..
Could on ly find part-time
work ... ... ... .. ....
Part time for noneconomic
reasons ..
Nonagricultural industries:
Part time for economic
reasons ..
Slack work or business
conditions ... .
Could only find part-time
work ..
Part tim e for non economic
reasons ...
1
4 ,213
4 ,701
4,896
4,800
4 ,880
4 ,788
4 ,714
4,437
4,733
4,574
4,665
4,513
4,490
4,504
4,452
2,788
3 ,118
3 ,185
3 ,030
3 ,226
3,205
2,996
2,865
3,011
2,8 19
2. 853
2,803
2,660
2,812
2,808
1,124
1,279
1,334
1,356
1,350
1,295
1,380
1,347
1,427
1,439
1,467
1,404
1.500
1,461
1,312
18,843
19,014
19,021
18,935
19 ,110
18.561
18,905
18.900
19,006
19, 000
19,621
19,531
19,741
19.680
19,386
4 ,11 9
4 ,596
4 ,794
4.690
4 ,782
4 ,727
4.613
4.328
4,622
4,471
4,605
4,442
4.400
4,391
4 .379
2,726
3 ,052
3, 127
2 ,964
3 ,153
3 ,144
2 ,911
2,778
2,927
2,756
2,812
2,762
2,605
2,714
2,753
1,114
1,264
1,335
1,349
1,353
1,279
1.399
1,340
1.414
1,43 1
1,476
1,387
1,496
1,442
1.3 15
18,487
18,658
18,633
18,628
18,752
18,367
18,636
18,691
18,693
18,664
19,220
19,072
19,290
19,2 13
19,025
Excludes person s "with a job but not at work" during th e survey period for such reasons as vacation, illness, or industrial disputes.
NOTE: Beginning in January 2003 , data reflect revised population controls used in th e household survey .
90
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
6. Selected unemployment indicators, monthly data seasonally adjusted
[Unemployment rates]
Annual average
Selected categories
2002
2003
2003
Sept.
Oct.
2004
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Characteristic
Total, 16 years and older .................. ... .. ...
Both sexes. 16 to 19 years ... .. ...... .........
Men , 20 years and older .... .. ....... .... .....
Women, 20 years an d older ........... .. .....
5.8
16.5
5.3
5. 1
6.0
17.5
5.6
5.1
6.1
17.5
5.8
5.3
6.0
17.1
5.6
5.2
5.9
15.7
5.6
5 .1
5.7
16. 1
5.3
5.1
5.6
16.7
5.3
5.0
5.6
16.6
5.1
4.9
5.7
16.5
5.2
5.1
5.6
16.9
5 .0
5.0
5.6
17.2
5.2
4.8
5.6
16.8
5.0
5.0
5.5
17.6
4.9
4.9
5.4
17.0
5.0
4.7
5.4
16.6
5 .0
4.7
White, total' .. ....
Both sexes , 16 to 19 years. .. .. ....
Men, 16 to 19 years .......................
Women , 16 to 19 years ......... .. .... ..
Men, 20 years and older ............... ...
Women, 20 years and older .. ... ... .. .
5.1
14.5
15.9
13.1
4.7
4.4
5.2
15.2
17.1
13.3
5.0
5.1
14.3
15.9
12.6
4.9
4.4
5.2
14.3
16.8
11 .5
5.0
4.4
5.0
14.8
16.3
13.1
4.7
4.3
4.9
14.1
14.0
14.2
4.5
4 .4
5.3
15.1
17.6
12.6
5.0
4.5
4.4
4.9
15.2
15.5
14.9
4.5
4.2
5.1
14.8
16 .2
13.3
4.7
4.4
4.9
15.7
17.9
13.3
4.5
4.2
5.0
15.7
18.6
12.7
4.7
4.1
5.0
14.8
16.4
13.2
4.5
4.4
4.8
14.9
15.5
14.3
4.3
4.2
4.7
15.3
15.8
14.8
4.4
4.0
4.7
14.7
15.8
13.6
4.3
4.0
Black or African American . total ' ······ ··
Both sexes. 16 to 19 years ...............
Men . 16 to 19 years .. ... .. ... ............ .
Women , 16 to 19 years .. ........ ····· ·
Men, 20 years and older ... ................
Women . 20 years and older ............. .
10.2
29.8
31.3
28.3
9.5
8.8
10.8
33.0
36.0
30.3
10.3
9.2
11 .1
32.7
34.2
31.4
11 .0
9.2
11.4
37.3
40.9
33.2
10.5
9.8
10.4
28.9
32.5
25.7
10.1
9 .1
10.3
27.3
28.4
26.5
9.3
9.7
10.5
32.5
42.1
25 .8
9.6
9 .1
9.8
25.1
29.6
21 .9
9.4
8.8
10.2
29.4
36.6
22 .8
9.2
9.3
9.7
28.3
30.9
26.1
9.3
8.7
9.9
32.5
30.3
34.1
9.3
8.4
10.1
32.6
33.9
31.4
9.3
8.9
10.9
37.0
37.8
36.3
10.3
9.1
10.4
28 .9
33.9
24.1
10.4
8.7
10.3
28 .9
36.0
2 1.6
10.4
8.9
Hispanic or Lati no ethnicity ....... .. ..... ..
Married men. spouse present.. ......... ....
Married wome n, spouse present..
Full-time workers .. .... .................... ... ....
Part-time workers.. .. ....... .. ... .... .... ... .. ....
7.5
3.6
3.7
5.9
5.2
7.7
3.8
3.7
6.1
5.5
7.5
3.8
3.9
6.2
5.7
7.3
3.8
3.8
6.1
5.5
7.4
3.7
7.3
3.3
3. 7
5.7
5.4
7.4
3.4
3.6
5.6
5.2
7.4
3.2
3.7
5.8
5.4
7.2
3.1
3.7
5.6
5.3
7.0
3.1
3.3
5.7
5.2
6.7
3.2
3.7
5.6
5.5
6.8
3.2
3.5
5.6
5.2
6.9
3.1
3.5
5.5
5.2
7.1
3.0
3.2
5.6
5.0
Educational attainment2
Less than a high school diploma ....... ...... ...
8.4
8.8
8 .7
High school graduates. no college 3 ..........
Some college or associate degree ...........
5.3
4.5
5.5
4.8
5.4
4.8
2.9
3.1
3.2
Bachelor's degree and higher
4
........... . .. ..
6.1
5.1
6.6
3.3 ·
3.9 I
5.8
5.3
8.8
8.5
8.1
8.8
8.5
8.8
8.7
8.8
8.8
8.3
8 .1
8 .8
5.5
4.8
5.4
4.8
5.5
4.5
4.9
4.5
5.0
4.4
5.3
4.7
5.2
4.1
5.0
4.0
5.1
4.2
5.1
4.2
4.9
4.0
4.8
4.0
3.1
3.1
3.0
2.9
2.9
2.9
2.9
2.9
2.7
2.7
2.7
2.5
''I
' Beginning in 2003 , persons who selected this race group only ; persons who
Includes high school diploma or equivalent.
selected more than on e race group are not included . Prior to 2003, persons who
reported more than one race we re 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 revi sed population controls used in the
Data refer to persons 25 years and older.
household survey .
7. Duration of unemployment, monthly data seasonally adjusted
[Numbers in thousands]
Weeks of
unemployment
Annual average
2002
2003
2003
2004
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug .
Sept.
2,61 2
2,394
3,365
1,467
1,898
2,468
2,4 12
3,274
1,403
1,87 1
2,589
2,4 14
3,320
1,332
1,988
2,792
2,369
2,969
1,170
1.800
2,707
2, 376
3,077
1,288
1,789
2,688
2,405
3,065
1,306
1,759
2,805
2,476
2,878
1,21 1
1,667
2,604
2,52 1
2,903
1,239
1,664
2 ,790
2,255
2,954
1,253
1,747
20.3
10.3
20.1
10.3
19.7
9.5
20.0
10.0
19.9
10.8
18.6
8.9
19.0
9.4
19.6
9.5
Less than 5 weeks ......... ... ..... ...
5 to 14 weeks . .. . . . . . .. . .. . . .. . . ···· ···· .. ..
15 weeks and over .. . .... ... ... .. .. ..... .
15 to 26 weeks ..
27 weeks and over .. ...... .. .. ... ...
2,893
2,580
2,904
1,369
1,535
2,785
2,61 2
3,378
1,442
1,936
2,749
2,736
3,51 1
1,438
2,073
2,733
2,585
3,478
1,460
2,018
2,622
2,556
3,484
1,448
2,036
2,627
2,450
3,403
1,513
1,890
Mean duration, in weeks . ... . . ... .. . . • . .
Median duration. in weeks ..
16.6
9.1
19.2
10.1
19.6
10.1
19.4
10.3
20.0
10.4
19.6
10.4
, •• I
10.7
i-.J0 H :: Beginn ing in January 2003, data reflect revised popul ati on co ntrols used in th e household survey.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly La b or Revi ew
November 2004
91
Current Labor Statistics: Labor Force Data
8. Unemployed persons by reason for unemployment, monthly data seasonally adjusted
[Nu mbers in thousands]
Annual average
Reason for
unemployment
2002
1
Job losers ... ... . ········· · ·· · ·· ···
On temporary layoff. .
... ..
Not on temporary layoff ..
Job leavers ... .. ... . .... .. ..... .. ... ..........
Reentrants .. ··· •····•··•· ·•· ·• . ......
New entrants ......... ..................... . .
2003
2003
Sept.
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
4,877
1,097
3,780
789
2,518
653
4,719
1,055
3,664
931
2,440
619
4,618
1,060
3,558
783
2,366
694
4,382
1,028
3,353
804
2,509
68 1
4,323
1,064
3,258
827
2,424
676
4,607
1,040
3,567
836
2,424
627
4,399
994
3,405
822
2,3 14
645
4,211
926
3,286
846
2,438
713
4,099
1,01 1
3,088
902
2,435
636
4,1 81
1,065
3,116
895
2,330
680
3,936
982
2 ,955
884
2,447
694
3,984
917
3,068
827
2,424
692
4,607
1,124
3,483
866
2,368
536
4,838
1,121
3,717
818
2,477
641
4,947
1,110
3,837
836
2,436
684
55.0
55.1
55.6
55.2
54 .2
54.6
52.3
52.4
54.2
53.8
51. 3
50.8
51 .7
49.4
50 .3
13.4
41 .6
10.3
28.3
6.4
12.8
42.4
9.3
28.2
7.3
12.5
43 .1
9.4
27.4
7.7
12.4
42.8
8.9
28.5
7.4
12.1
42 .1
10.7
28.0
7.1
12.5
42.0
9.3
28.0
8 .2
12.3
40 .0
9.6
30.0
8.1
12.9
39.8
10.0
29.4
8.2
12.2
42.0
9 .8
28.5
7.4
12.1
41.6
10. 1
28.3
7.9
11 .3
40.0
10.3
29 .7
8.7
12.5
38.3
11 .2
30.2
7.9
13.2
38.5
11 .1
28.8
8.4
12.3
37. 1
11 .1
30 .7
8 .7
11 .6
38.7
10 .4
30 .6
8.7
3.2
3.3
3.4
3.3
3.2
3.1
3.0
3.0
3.1
3.0
2.9
2.8
2.8
.6
1.7
.4
.6
1.7
.5
.5
1.7
.4
.6
1.7
.4
.5
1.6
.5
.5
1.7
.5
.6
1.7
.5
.6
1.7
.4
.6
1.6
.4
.6
1.7
.5
.6
1.7
.4
.6
1.6
.5
2.7
. .6
1.7
.5
2.7
.6
1.6
.4
Percent of unemployed
1
Job losers . •• ......... ..... ....... .. ....
On te mporary layoff. ...... .... .......
Not on temporary layoff. .. .. .... ... ...
Job leavers ........ . ... . ..... .. . ... .... . .. .. .•. .
Reentrants ....... ...... ... ........ . ..... .
New entrants .... ....
Percent of civilian
labor force
1
Job losers ... . .. .. . . .... . . . . .. . . ... . . . .
Job leavers ..... .. .. .... .. .... ········· . ......
Reen tr ants .. ... ....... . ... . . .. ........... .. .
New entrants. ···· ········· ········· ········· ·
.6
1.6
.5
' Includes persons who completed temporary jobs.
NOTE: Beginning in January 2003 , data reflect revised population co ntrol s used in the household survey.
9. Unemployment rates by sex and age , monthly data seasonally adjusted
[Civilian wo rkers]
Annual average
Sex and age
2002
2003
2003
Sept.
Oct.
2004
Nov.
Dec.
Jan.
Mar.
Apr.
May
June
July
Aug.
Sept.
Total. 16 years and older.. .. ...
16 to 24 years ....... .... ... ...
16 to 19 years . . . . . . . . . . . . .. ... ... .
16 to 17 years. ........... ....
18 to 19 years .. . ..• . . •..• •• • .. ...
20 to 24 years .. . .......... ... . ·····
25 years and older .. .. .... ... .. ....
25 to 54 years . . . . . . . . . . . . . . . .. ..
55 years and older ·· ······ ... .
5.8
12.0
16.5
18.8
15.1
9.7
4.6
4. 8
3.8
6.0
12.4
17.5
19.1
16.4
10.0
4.8
5.0
4.1
6.1
12.8
17.5
19.3
16.2
10.6
4.9
5.1
4.0
6.0
12.3
17.1
20.2
15.2
10.1
4.9
5.1
3.8
5.9
12.1
15.7
17.5
14.7
10.4
4.8
5. 0
3.9
5.7
11 .7
16.1
18.3
14.7
9.6
4.7
4.9
3.9
5.6
12.0
16.7
18.2
15.7
9.8
4.5
4.7
3.7
5.6
11.8
16.6
17.6
15.7
9.5
4.5
4. 7
3.8
5.7
11 .8
16.5
19.4
14.5
9.6
4.6
4.9
3.8
5.6
11.6
16.9
20.2
14.7
9.2
4.5
4.6
3.8
5.6
12.1
17.2
21.6
14.7
9.7
4.4
4.5
3.9
5.6
12.0
16.8
20.6
14.3
9.8
4.5
4.5
3.9
5.5
12.0
17.6
20.2
16.1
9.3
4.4
4.6
3.7
5.4
11 .6
17.0
20.8
14.9
9.0
4.3
4.5
3.7
5.4
11.8
16 .6
19.6
14.9
9.5
4.3
4.4
3.7
Men, 16 year s and older ...... . ... .
16 to 24 years ... ........................
16 to 19 years .. .. ...... .. ... . .. ..
16 to 17 years . ···············
18 to 19 years ......... ....... ..
20 to 24 years .. ..... ....... ...... ..
25 years and older ........... ... ...
25 to 54 years ...... ............
55 years and older ...............
5.9
12.8
18.1
21.1
16.4
10.2
4.7
4.8
4.1
6.3
13.4
19.3
20. 7
18.4
10.6
5.0
5.2
4.4
6.4
14.1
19.6
22.1
18.2
11 .7
5.0
5.2
4.2
6.2
13.2
18.7
20.4
17.9
10.8
5.0
5.2
4.0
6.2
13.4
18.3
18.3
18.1
11.2
5.0
5.2
4.1
5.8
12.6
17.4
18.4
16.9
10.4
4.7
4.9
4.0
5.7
12.7
17.5
19.3
16.2
10.5
4.5
4.7
3.6
5.7
12.2
17.2
19.4
15.7
10.0
4.5
4.7
3.7
5.8
12.6
18.3
22.3
15.8
10.1
4.6
4.8
3.8
5.7
12.8
19.1
23 .4
16.5
10.0
4.4
4.5
3. 9
5.8
13.0
19.1
23 .3
16.6
10.3
4.6
4.7
4.1
5.6
12.8
18.1
22.8
15.8
10.4
4.4
4.4
4.3
5.5
12.2
17.7
21.2
15.7
9.7
4.4
4.5
3.8
5.6
12.4
18.0
21 .9
16.0
9.9
4.4
4.5
4.0
5.6
12.9
18.1
20.6
16.8
10.6
4.3
4.4
3.9
Women, 16 years and older ..
16 to 24 years ... . ... .... ...... . ·······
16 to 19 years ..............••..
16 to 17 years ······· .. . ·.. ... .
18 tO 19 years .... .... . . ...
20 to 24 years .. ·· ····· .... .....
25 years and older . . .. . .. . . . . . . .. . . • . .
25 to 54 years ..... . .•. . . .......•..
5.6
11.1
14.9
16.6
13.8
9.1
4.6
4.8
5.7
11.4
15.6
17.5
14.2
9.3
4.6
4.8
5.8
11.4
15.2
16.5
14.1
9 .5
4.7
4.9
5.7
11.3
15.4
20 .1
12.5
9.3
4.7
4.9
5.5
10.7
13.0
16.6
11 .1
9.6
4.6
4.8
5.6
10.7
14.7
18.2
12.2
8.8
4.6
5.0
5.6
11 .3
15.9
17.1
15.2
8.9
4.6
4.8
5.5
11.2
16.0
15.9
15.6
8.9
4.4
4.5
5.6
10.8
14.7
16.9
13.0
8.9
4.6
4.9
5.4
10.3
14.5
17.3
12.6
8.3
4.6
4.7
5.3
11.1
15.3
20.1
12.7
9. 0
4.2
4.4
5.6
11.2
15.6
18.7
12.6
9.0
4.5
4.7
5.6
11.7
17.5
19.4
16.5
8.8
4.5
4.7
5.3
10.7
16.1
19.7
13.6
8.0
4.3
4.4
5.2
10.6
15.2
18.6
12.9
8.3
4.3
4.4
3.6
3.7
3.8
3.4
3.5
3.5
4.1
3.9
3.5
3. 3
3.3
3.8
3.8
3.9
3.5
..
55 years and older' . ... .. ..
' Data are not seasonally adjusted.
NOTE: Beginning in January 2003, data reflect revised population co ntrols used in the household survey.
92
Feb.
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
10. Unemolovment raes by Stae, seasondlv cxtiusted
State
Aug.
July
Aug.
2003
2004P
2004P
State
Aug.
July
Aug.
2003
2004P
2004P
Alabama ............. ................... ...... ......... .
Alaska ..... ..................................................
Arizona ............................ ... .... .... ... ... .... .
Arkansas .................................................. .
California ............................... ..... .......... .
5.8
8.1
5.7
6.4
6.8
5.7
7.2
4.3
5.6
6.2
6.0
7.6
4.4
5.4
5.9
Missouri
Montana ... .............................. ............. ......
Nebraska ................ ...... .. ....... ......... .... ...
Nevada ............................. ....................... .
New Hampshire .......................... ........... .
5.8
4.8
4.1
5.4
4.3
5.5
4.3
3.4
4.4
3.9
5.5
4.8
3.6
4.0
3.7
Colorado ........................... .... .. ..................
Connecticut... ................... .. ............. ..... . .
Delaware ........ .. .................. .................... .. .
Distri ct of Columbia........ ..................... ... . .
Florida .. .. ........ ............ ............ ... .... ............
6.1
5.6
4.6
7.1
5.2
5.1
4.6
3.9
7.8
4.5
5.1
4.6
3.6
7.5
4.6
New Jersey .. .............................................
New Mexico .............. ................. .... ....... .
New York .......... ... ........ ... ................ ........ ..
North Carolina .............................. ......... .
North Dakota ..... .. ...................... ................
5.9
6.6
6.4
6.5
4.0
5.0
5.3
5.9
5.1
3.1
4.8
5.4
5.6
5.0
3.3
Georgia .............................. ..... ....... ... ... .
Hawaii .................. ..... ................ .. ............. .
Idaho ................................ ... ............... .
Illinois .. .................................. ..... .. ..... .. .... .
Indiana ................ ................ .... ... ... ....... .
4.6
4.5
5.5
6.8
5.3
4.1
3.0
4.9
6.1
5.1
4.2
2.9
5.0
6.1
5.1
Ohio .... .................. .. ....... .......... ... ........ .
Oklahoma ................................................ .
Oregon .................................. ........ .
Pennsylvania ......... ... ............. ................. ..
Rhode Island ............. .................. ........ .. .
6.1
5.8
8.4
5.5
5.2
6.0
4.5
6.8
5.3
5.8
6.3
4.1
7.4
5.6
5.5
Iowa .......... ...... .............. .... .. ... .... ........ ..
Kansas ............ ..... .. .... ...................... ....... .
Kentucky ........ ............. ..... .... ...... ..... .... ..
Louisiana ............ ...... ................... ....... .. ... .
Maine ....... .................... .. ... .. ... ............. .
4.6
5.4
6.2
6.8
5.2
4.4
4.7
5.3
6.1
4.2
4.5
4.8
5.1
5.0
4.5
South Carolina ...... ... .................... .... ..... . .
South.Dakota ............. ................ .. .. .. ...... .. .
Tennessee .. ......... ..................... ....... ... .. .
Texas .......... ........................ .......... ... .. ...... .
Utah ... .. .......................... .............. ...... . .
6.8
3.7
6.0
6.8
5.5
6.0
3.4
4.5
5.7
4.8
6.4
3.2
4.9
5.7
4.7
Maryland .... ................. ............. ...... ...... .
Massachusetts ..... ....... ... ... .. .............. ....... .
Michigan .............................. ...... ..... ... ... .
Minnesota ....... ......................................... .
Mississippi ............. .............. ................ . .
4.5
5.9
7.5
5.0
6.1
4.1
5.3
6.8
4.4
5.9
4.3
5.4
6.7
4.8
5.9
Vermont... .. ............................... ...... ..... .
Virginia ... .................................... ..... ......... .
Washington ........................... .... ... ... .. .. .. .
West Virginia ............. .............. ..................
Wisconsin .. ......... .. .................. .. . .
Wyoming ....................... ..... .. ....... .. .. ..........
4.6
4.1
7.7
6.2
5.7
4.3
3.3
3.5
6.0
5.2
4.7
3.6
3.3
3.6
6.2
5.5
4.8
3.7
P
= preliminary
11. Employment of workers on nonfarm payrolls by State, seasonally adjusted
State
Aug.
July
Aug.
2003
2004P
2004P
State
Aug.
July
Aug
2003
2004P
2004P
Alabama......... .............
Alaska......... ...................................
Arizona... ..... ... ... .. ............. ....... ....
Arkansas........................................
California.................. ........ ... .........
2.156.597
333.223
2.698.556
12.620.518
1.747.380
2.167,420
344.300
2.762 ,685
1.318,180
17.684.902
2.171 ,032
345.845
2,765,225
1.321 ,281
17.646 .871
Missouri
Montana ... ....................... .
Nebraska .............. ........ ... ..... .. ... . .
Nevada ....................... .. ... ..............
New Hampshire .... ... .. ......... ....... . ..
3.019.768
478.342
977,500
1.144.514
723.142
3.056.674
481 .813
989.063
1.187.711
731 .739
3.048.875
483,962
990.212
1.185.851
730 .469
Colorado........................... .............
Connecticut... ......................... .. ... .
Delaware.................. ........... ..........
District of Columbia............... ........
Florida.... ...... .. ..... ... ..... ........... ... .. ..
2.485.666
1.803.513
417.705
301 ,841
8.192,302
2,517,202
1.793.946
426.819
297.456
8.382.532
2.521 .641
1.788,315
424,091
301,032
8,400,607
New Jersey ......... ...................... .... .
New Mexico ... ........... ..... ......... ..... .
New York .. ........... .. .. ..................... .
North Carolina ........... .. ............... .. .
North Dakota ...... ... .. ................... .. ..
4.383.949
900.291
9.296.355
4.249.180
347.368
4,422,455
905.651
9.329.716
4,191 ,547
349.109
4.425.145
910.889
9.308.448
4,183.628
350,563
Georgia ....... ... ...... .. .......... .... ... ... .
Hawaii ................ ...................... .. ... .
Idaho.......... ... ......... ....... ..... ... .
Illinois ............................................ .
Indiana ... ....................... ... .... ... ... .
4,433.298
621.967
6 93488
6.336.573
3,195.342
4,423,456
630.939
706.094
6,385,051
3.170,913
4,439,453
630.197
710,466
6,388,300
3.147.244
Ohio .................... .... .. ...... .. .. .... ... .
Oklahoma .... ..................... ... ........ ..
Oregon ..................... ....... ..... ...... .
Pennsylvania ....... ......................... .
Rhode Island .. ... .... .. ........... .. .... ... .
5,923.188
1.695.930
1.861 .355
6.153.061
574.263
5.872.882
1.709.172
1,855 ,215
6,263.438
572.605
5.875,960
1.698,816
1.850 .802
6.275.025
568.893
Iowa ............................. ...... ..... ... .
Kansas .................... ................... ... .
Kentucky .................... ......... .... .... .
Louisiana ............ ................ ... .. ......
Maine .. .......... ........ .......... ... ........ .
1.598.880
1,436.277
1.960.213
2.030.838
695,582
1,626,036
1,466.312
1.990,046
2.048,042
697.483
1,632,557
1,471.017
1,982.539
2,032 .997
701.541
South Carolina ..... ...................... .. .
South Dakota ........ .. ...... ................ .
Tennessee ............................ ... .. . .
Texas .... ............. ................. ...........
Utah ........ .... .... ............. .... ....... ... .
2.007,596
425.511
2.906,469
10.935,944
1.188,573
2.066,923
425.051
2.920.251
10.953,035
1.208.191
2.068.869
424.034
2.931 .130
10.963.157
1.211,405
Maryland .. .. ..... ..... .. ... ............ ... . .
Massachusetts ..... ... ....... ............ ... .
Michigan .......... ........................... .
Minnesota ........ ........................ ... ...
Mississippi. ............................ ...... .
2,906.522
3,407.669
5,037,317
2.926,194
1.316.565
2.951.793
3,415.216
5,046,983
2,953,076
1,328,078
2,948.541
3,412.958
5,052,968
2,969.386
1,325.882
Vermont... ...................... ... ... .... ... .
Virginia ......................... ... .............. .
Washington .......... ........... ........ .... .
West Virginia ..................... ... .........
Wisconsin .......... .. ... ............. ... .. ... .
Wyoming ............ .. .......... .. ............. .
350.899
3.778.538
3.142.922
787.602
3,091 .687
279.960
354.165
3,847.041
3.195.787
801,062
3.108.959
279.569
354.281
3,846,077
3.211 .058
803,717
3.115,623
279.926
P
= preliminary.
NOTE: some data in this table may differ from data published elsewhere because of the continual updating of the data base.
Monthly Labor Review
November 2004
93
Current Labor Statistics: Labor Force Data
12. Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Industry
Annual average
TOTAL NONFARM ...............
TOTAL PRIVATE......................
GOODS-PRODUCING ................
Natural resources and
mh,i,,g ... ... .. . .. ............ ......... ...
Logging ······ · · · ··· ·· ..... . . ...... ••..
Mining .. ··························
Oil and gas extraction ..
Jan.
Feb.
Mar.
Apr.
May
Sept.P
131 ,343
131,541
131,680
109.771
21,906
109.912
21 ,939
110.007
21 ,935
587
64.5
522.7
132.0
592
64 .5
527.5
132.2
591
64 .6
526.6
132.7
592
65.0
527 .1
132.9
207.8
207.9
211 .2
209 .2
208 .8
72.9
183.1
73.5
182.8
75.0
184.1
74 .6
184.7
74.4
185.4
Dec.
129,944
130,027
130,035
130,194
130,277
130,630
130,954
131 ,162
131,258
108.384
21 ,674
108.483
21,686
108.491
21,668
108.667
21 ,696
108,738
21,684
109,077
21 ,778
109.382
21 ,822
109.618
21,894
109.730
21 ,891
568
67.4
500.8
123.6
569
67.9
501.5
124.1
571
67.6
503.4
123.9
570
65.9
504.3
124.6
570
65.1
505.1
126.9
572
64.2
508.1
128.9
581
65.9
514.9
130.0
585
66.7
518.5
131 .0
589
65.6
523.2
132 .3
202.7
201 .6
202.1
202.4
202 .0
202.8
69.2
175.6
69.6
175.3
69.5
177.1
69.8
177.7
200.0
69.6
178.2
200.6
70.4
176.8
70.2
178.6
70.6
182.1
205.2
71.8
182.3
Sept.
130,341
129,931
129,856
108,828
22 ,557
108.356
21 ,817
108,3 17
21,697
583
70.4
512.2
121 .9
571
68.5
502.3
122.9
210.6
74.4
179.8
Oct.
July
Aug.P
June
Nov.
2003
Minina. exceot oil and aas' ..
Coal minina .................. .. ..
Support activities for mining ..
2004
2003
2002
Construction ... ...........................
6,716
6,722
6,754
6.754
6,771
6,774
6,8 12
6,791
6,853
6,872
6,909
6,9 11
6,916
6,930
6.945
Construction of buildinas ... ....
Heavy and civil enaineerina ....
Soeciality trade cont ractors ..
Manufacturing ............................
1,574.8
930.6
4.210.4
15,259
1,575.9
910.7
4.235 .5
14,525
1,577.7
915.2
4,260 .9
14,375
1,579.4
910.8
4,263.7
14,351
1.583.9
918.8
4.268.6
14,344
1,585.1
920.7
4,268.4
14,324
1,593.3
928.0
4,290.2
14,314
1,590.9
924.0
4 ,276.5
14,32 1
1.607.6
926.8
4,318.9
14,344
1,609.8
924.7
4,337.3
14,365
1,622.9
924.3
4,362 .2
14,396
1,625.9
920.9
4,364.6
14,393
1,629.7
920.2
4,365.6
14,398
1,635.5
921 .9
4,378.9
14.4 12
1,645.3
921.0
4,378 .6
14,398
Production workers ...
Durable goods.........................
10,766
9,483
10,200
8,970
10.077
8,867
10.058
8,854
10,048
8,874
10,044
8,868
10,035
8,869
10.038
8,882
10,058
8,889
10,085
8,924
10,123
8,946
10.128
8,955
10.141
8,955
10.162
8,986
10.142
8,978
Production workers ...
Wood oroducts .. .... .... ...
Nonmetallic mineral oroducts
Primary metals .. .
Fabricated metal oroducts ...
Machinerv ..
Comouter and electronic
6,529
554.9
516.0
509.4
1,548.5
1.229.5
6.157
536.1
492.6
476.7
1.478.4
1.153.5
6,077
531.8
488
466.3
1.461.1
1,139.4
6.066
533.4
486 .6
463.4
1.461 .3
1,137.0
6,089
536.3
489.7
464.1
1.468.1
1,142.5
6,079
536.6
487.5
464.6
1.471 .2
1.140.4
6,081
536.3
492.7
432.2
1.471.8
1,138.7
6.088
538.4
490.5
462 .2
1.476.6
1,141 .2
6.101
539.7
493.2
462.0
1.478.5
1,145.1
6,126
540.0
497.8
462.5
1.486.7
1.152.0
6,152
543.0
501.4
464.0
1.494.5
1,153.3
6.164
543.8
501.7
465.4
1.497.6
1,156.7
6.167
544.1
502.6
467.0
1,50 1.3
1,160.4
6.195
545.9
501 .6
465.4
1,504.7
1.163 .3
6.181
544.8
502 .0
464.2
1.505.6
1,160.8
oroducts' . . ·· ··· ········· ·····
Comoute r and oerioheral
equipment ..... ..... ...... .. ......
Communications equipment ..
Semiconductors and
electronic components ... .. ...
Electronic instruments .........
Electrical equipment and
appliances ...............
Transportation equipment ..
Furniture and related
products .....
Miscellaneous manufacturing
1,507.2
1,360.9
1,339.2
1,332.8
1,334.4
1,332.2
1,333.2
1,333.9
1,338.0
1,339.7
1,345.8
1,346.2
1,351 .9
1,353.0
1,351 .2
219.1
154.4
217.8
153. 0
219.4
154.8
219.0
154.8
218.6
155.0
218.1
155.1
218.8
155.9
217.7
157.1
217.2
158.2
217 .9
158.5
217.2
157.8
250.0
185.8
225.7
157 .0
221.9
154.1
219.3
1 53.9
524.5
450.0
461.8
429.3
453.3
425.5
449.4
425.1
451 .2
425.2
45 1.3
425.3
450.2
423.7
451.4
423.3
452.1
426.8
453.4
427 .5
455.8
430.1
458.0
429.8
460.7
432.4
460 .2
433 .0
460.0
433.3
496.5
1,828.9
459.9
1,775.4
452 .1
1,765.6
450.8
1,765.5
450.9
1,766.5
451 .2
1,762 .7
449.8
1,760.6
448.6
1,766.5
446.8
1,769.1
446.5
1,768.8
447.3
1,764.4
448.6
1,765.1
449.2
1,745.9
449.6
1,774.4
449.3
1,773.1
604.1
688.3
573.5
662 .8
568.0
655.9
568.2
655.2
568.9
652.7
569.3
65 1.9
57 1.3
652.0
571.2
653.0
573.4
653.0
576.5
653.0
577.6
654.4
575.0
654.6
576.7
655.5
574.6
653.6
574. 1
653.0
Nondurable goods ...................
P;oduction workers .. .
5,775
4,239
5,555
4,043
5,508
4,000
5.497
3,992
5.470
3,959
5,456
3,965
5,445
3,954
5, 439
3,950
5.445
3,957
5,441
3,959
5,450
3,971
5.438
3,964
5,443
3,974
5, 426
3,967
5.420
3,961
Food manufacturing ...
Beve rages and tobacco
products ..
Textile mills . ...... ....
Textile product mill s .. ...... ...
Apparel. .
Leather and allied products ....
Paper and paper products ...
Printing and related suppo rt
. .......
activities ..
Petroleum and coal products .. .
Chemicals ...
1,525.7
1.518.7
1,526.0
1,528.2
1,508.3
1,506.3
1,500.7
1,502.4
1,504.5
1,502.7
1,507.0
1,502.8
1,508.0
1.499.6
1,497.5
207.4
290.9
194.6
359.7
50.2
546.6
200.6
260.3
179.8
312 .7
45.2
519.0
200.2
250.2
173.7
299.8
44 .2
513.8
201 .0
247.0
172.6
299.7
43.7
513.3
198.3
245.1
175.2
297.7
44.1
51 1.7
198.3
241 .0
174.3
297.7
44.3
510.3
197.7
239.2
176.9
296.1
44.6
509.8
195.9
237.3
176.6
297.1
44 .8
508.0
197.2
237.1
179.7
294.3
44.8
508.8
197.8
235.8
180.1
292.7
44.6
507.0
197.5
236.1
181 .4
290.8
45.1
508.1
197.6
235.0
179.7
286.8
44.7
506.7
198.4
235.6
179.3
284.8
45.3
509.0
197 .2
234.4
179.4
284.2
44 .8
509.8
198.7
233.8
180.0
282. 1
45.2
508.5
706.6
118.1
927 .5
680.0
114.6
7.9
676.2
112.9
902 .7
673.3
112.6
899.1
673.1
112.0
897.6
670.1
112.4
895.9
667.6
114.3
893.7
665.0
11 2.9
894.7
664.4
113.1
894.9
663.6
112.6
896.4
665.9
113.1
895.0
667.0
113.8
895.2
663.8
113.6
894.2
662 .2
11 4.1
891 .9
659.5
114.1
891 .5
848.0
815.9
808.4
806.3
806.5
805.8
804.8
803.9
806.3
807 .5
810.2
808.6
811.2
808.8
809.0
SERVICE-PROVIDING ..................
107,784
108,11 4
108,159
108,270
108,341
108,367
108.498
108,593
108,852
109,132
109,268
109,367
109,437
109,602
109.745
PAIVA TE SERVICEPROVIDING ...... ....... ...... ... ...
86,271
86,538 '
86,620
86,710
86,797
86,823
86,971
87,054
87,299
87,560
87,724
87,839
87,865
87 ,973
88,072
25.497
5,652 .3
3,007 .9
2,015.0
25,275
5,605.0
2,949.2
2,002 .1
25,252
5,585.1
2,932 .1
1,995.9
25,272
5,581 .6
2,932.0
1,992.4
25,261
5,592.7
2,943.9
1,989.2
25,2 11
5,598.4
2,945.8
1,991 .8
25,312
5,6 11 .4
2,954.9
1,993.7
25,331
5,612.2
2,953.8
1,994.5
25,415
5,623.5
2,963 .4
1,995.3
25.448
5,632.5
2, 967.5
1,996.3
25,477
5,636.7
2,969.7
1,997.2
25,497
5,639.5
2,975.6
1,994.3
25,499
5,649.6
2,986.0
1,994.3
25,516
5,652 .8
2,989.6
1,992 .1
25,530
5,662.9
2,992 .9
1,992 .5
654.3
6657.1
660.8
662.8
663.9
664.8
668.7
669.8
669.6
671 .5
670.7
674.0
15,047.6 15.054 .9 15,038.1
15,048.8
15.043. 1
1,908.1
1,259.2
1,904.9
1,256.8
1,904.9
1,253.3
546.4
548.7
548.5
510.7
511 .6
5 12.7
Plastics and rubber products ..
Trade , transportation,
and utilities............. ........... .......
Wholesale trade .............. .........
Durable goods ..
Nondu rable goods . . . . . . . . . . . . . .
Electronic markets and
agents and brokers ..
629.4
Retail trade ............................. .. 15,025. 1
Motor vehicles and parts
dealers' ...................
Automobile dealers ....... ...
Furniture and home
furnishings stores .........
Electronics and appliance
stores ... ... ... ..................
657.2
659.6
14.911 .5 14,926.8 14,948.1
14,921.7
14,963.0 15.013.0 15,0371
1,879.4
1,252 .8
1,883.5
1,255.1
1,889.8
1,259.7
1,889.7
1,259.6
1,892.9
1,258.9
1,893.7
1,259.5
1,895.4
1,26 1.3
1,900.9
1,262.9
1,906.9
1,263.9
1,910.9
1,264.7
1,911.4
1,263.6
538.7
542.9
539.7
540.2
544.8
547.2
546.4
544.5
544.8
544.5
545.7
525.3
511 .9
506.7
506.5
512.8
511.9
509.3
508.2
511 .7
514.1
512 .6
See notes at end of table .
Monthly Labor Review
94
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
14 ,876.0 14,944.8
November 2004
1,908.5
1,262.3
''''.I
511 .5
12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Industry
Building material and garden
supply stores ...
Food and beverage stores .. ... .
Health and personal care
stores .. .. .. ........ .... .. . .... .
Gasoline stations ...
.. ..
Clothing and clothing
accessories stores .
Sporting goods, hobby,
book, and music stores ..
General merchandise stores1.
Department stores .. ...
Miscellaneous store retailers ..
Nonstore retailers ..
Transportati on and
warehousing ..........................
Air transportation ...... . .. . ..
Rail transportation ..
Water transportation ..
Truck transportation ..
Transit and ground passenger
transportation ..
Pipeline transportation ..
Scenic and sightseeing
transportation ...
Support activities for
transportation ..
Couriers and messengers ..
Warehousing and storage
Utilities ............... ............... ......
Information .......... .. ... ...... .... ...
Publishing industries, except
Internet. .
Motion picture and sound
recording industries ..
Broadcasting, except Internet..
Internet publishing and
broadcasting . ... .... . ······ ·· ··
Telecommunications .... ........
ISPs, search portals, and
data processing ..
Other information services ..
Financial activities ..
Finance and insurance .. .
Monetary authorities--ce ntral bank ..
2003
Annual average
2004
2002
2003
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
1,176.5
2,88 1.6
1,191.1
2,840.9
1,203.4
2,829.4
1,204.0
2,838.7
1,210.0
2,821.4
1,209.5
2,813.9
1,221.4
2,826.3
1,231.4
2,831 .3
1,243.5
2,838.9
1,247 .3
2,839.9
1,248.7
2,845.3
1,245.8
2,839.7
1,246.9
2,834.5
1,251.7
2,832 .9
1,256 .2
2,834 .0
938.8
895.9
943.1
879.9
943.1
877.9
948.3
873.8
951.6
875.2
952.6
871.1
954.1
875 .. 1
954.9
871 .8
958.2
873.0
957 .9
872.4
957 .1
871.6
957.2
870.3
956.7
869.9
956.4
870.3
956.6
873.5
1,312.5
1,296.7
1,295.6
1,302.6
1,297.1
1,301.0
1,304.3
1,311.3
1,321.8
1,328.0
1,335.5
1,346.5
1,349.0
1,355.2
1,350.3
661 .3
2,812 .0
1,684.0
959.5
443.7
645.0
2,815.2
1,618.8
934.1
427.5
642 .8
2,839.9
1,623.7
931 .7
426 .8
642 .0
2,842.9
1,623.5
933.5
425.9
641.6
2,826.4
1,612.6
930.9
417 .3
633.2
2,793.4
1,601 .3
924.4
424.1
635.9
2,822.7
1,603.4
929.6
424.3
636.8
2,822.5
1,602.7
924.6
424 .8
636.5
2,824.4
1,604.9
926.9
427 .4
635.8
2,831 .0
16.7
927.9
429 .8
636.1
2,830.5
1,610.9
925.7
427 .4
635.7
2837.4
1,614.9
928.4
427.6
635.5
2825.3
1,609.9
926.2
428.9
638.4
2832.8
1,607.9
927.1
427 .8
638.1
2814.6
1,600.5
924.6
429 .1
4,223.6
563.5
217.8
52.6
1,339.3
4,176.7
527 .3
215.4
52.5
1,328.0
4.160.8
511 .8
215.6
51.5
1,328.7
4,162 .9
506.1
2 15.2
52.5
1,329.3
4,168.0
511 .5
215.5
50.9
1,335.7
4.157.0
512.9
2 15.5
50.0
1,338.7
4,175.9
510.2
215.4
50.6
1,343.6
4,175.8
511 .6
215.7
48.8
1,344.1
4,197.0
512 .9
216.0
49.2
1,346.4
4,196 .5
513.3
216.3
50.6
1,352.2
4,209.9
5 14.7
2 16.4
51.1
1,353.9
4220.9
513.8
217.3
51.7
1,353.9
4228.3
512.4
217.8
51 .7
1,361 .9
4232 .5
511 .8
217.4
50.3
1,363.7
4240.3
512 .3
217.9
50.3
1,368.7
380.8
41 .7
380.3
40.0
380.7
39.3
389.2
39.0
385.7
38.7
385.0
38.8
382.3
38.3
380.1
38.2
380.5
38.1
372 .3
38.1
381.5
38.3
374.6
38.4
374.2
38.5
374.5
38.5
374 .7
38.6
25.6
28.0
28.9
29.0
28.7
29.4
28.7
29 .7
31 .4
31.1
30.6
32.6
32.6
32.7
32 .9
524.7
560.9
516.7
596.2
3,395
516.3
566.6
522.3
580.8
3,198
515.4
566.5
522 .4
578 .9
3,175
514 .3
565.0
522 .6
579.2
511.6
559.0
516.1
579.3
3,175
514.1
566.9
525.8
580.2
3,163
515.5
567.7
524.4
580.0
3,169
518.5
572.1
531.9
58 1.2
3,169
519 .1
570.9
532 .6
582 .1
3,173
5 19.5
572.8
531. 1
582.3
3,177
520.8
578.2
534.0
581.7
3,182
523.7
579.2
536.3
582.6
3,173
525.1
580.4
538.1
3,166
512.4
564 .7
524.2
578 .9
3, 172
525 .3
581.1
538.5
583 .3
3,158
964.1
926.4
919.3
918.0
918.4
917.4
914.0
915.1
915.3
916.3
916.2
916.6
914.7
914.3
914.3
387.9
334 .1
376.1
327.0
375.4
327 .6
373.4
326.0
382.7
327 .0
385.2
329.5
379.7
329.7
382 .7
331 .8
381 .2
333.0
385.7
333.3
390.8
335.4
394.9
335.5
391.0
336.4
388.0
336.6
388.7
336.9
33.7
1,186.5
30.0
1,082.6
30 .1
1,069.4
29.9
1,065.2
30.4
1.062.2
30.4
1,061 .2
30.8
1,061 .3
31 .9
1,058.2
31 .9
1,055.0
32 .5
1,051 .9
32.5
1,047 .3
33.6
1,044.8
33.6
1,042.3
34.2
1,037.5
34.6
1,027.9
441 .0
47 .3
407.5
48.1
405.4
48 .0
404 .8
48.3
402.6
48.2
402.6
48.2
400.1
47.8
401.1
48.0
403.7
48.6
404 .0
49.6
405.1
49.6
406.5
50 .0
404.9
49.8
404.3
50.0
404 .7
49.7
7,847
5.817.3
7,974
5,920.5
8,004
5,945.6
7.990
5,930.2
7,985
5,922.7
7,981
5,916 .5
7,981
5,9 17.1
7,989
5,924.7
8,003
5,933.0
8,015
5.947 .7
8,029
5,946.0
8.049
5,960.4
8,044
5,951 .9
8,077
5,962.4
8,094
5,973 .6
23.4
22.7
22.6
22.5
22.5
22 .5
22.4
22.4
22.3
22 .3
2 1.8
21 .9
21.8
21 .8
21.8
2,686.0
2,785.6
2,808 .1
2,801 .0
2,790 .3
2,783.3
2,785.3
2,787.2
2,793.8
2,802.1
2,800.8
2,809.9
2,804.1
2,807.3
2,815.4
1,733.0
1.278.1
1,752. 1
1.281.1
1,757.9
1.283 .6
1,760.1
1,284.4
1,758.1
1,280.5
1,757.1
1,278.9
1,758.7
1.280.4
1,762.6
1.283.5
1,762.8
1.284.1
1,765 .0
1,285.0
1,765.2
1,284.2
1,768.8
1.285.9
1,766.9
1.284.0
1,768.3
1.283.0
1,772.4
1.287.3
789.4
764.4
761 .7
762.0
769.1
771 .9
773.8
778.2
780.8
781.0
782.8
787.2
787.8
791 .6
793.0
2,233.2
2,266.1
2,271 .9
2,264.7
2,261.2
2,258.1
2,255.8
2,257.4
2,257.1
2,259.5
2,262.7
2,263.8
2,260.2
2,263.9
2,265.8
85.4
81.7
81 .3
80.0
79.6
80.7
79.8
79.5
79 .0
78.8
77 .9
77.6
78.0
77.8
77.6
2,029.8
1,352.9
649.1
2,053.6
1,384.4
640.8
2, 057 .9
1,388.8
639 .8
2,060.2
1,390.6
639.9
2,062.7
1,394.5
639.0
2, 064.0
1,395.7
638.3
2,063.6
1,397.7
636.0
2,064.5
1,400.2
634.2
2,069.5
1,405.8
634.1
2,07 1.6
1,409.2
633.2
2,083.1
1,418.7
635 .4
2, 088.1
1,418.8
640.5
2,092.0
1,422.1
641.4
2,090.6
1.424.1
638.0
2,193.1
1,431 .1
643.7
27.6
28.4
29 .3
29.7
29.2
30.0
29.9
30.1
29.6
29.2
29.0
28.8
28.5
28.5
28 .3
15,976
15,999
16,051
16,070
16,114
16,159
16,172
16,196
16,237
16,363
16,432
16.457
16.490
16,518
16.562
6,675.6
1,115.3
6,623.5
1,136.8
6,606.3
1.136.6
6,624.1
1,140.4
6,647.9
1,142.9
6,669.3
1,140.5
6,657 .9
1,138.7
6,658.1
1,139.2
6,679.8
1, 138.4
6,701 .4
1, 141 .9
6,708.1
1,143.3
6,732.6
1.146.3
6,739.9
1,1 48.2
6,762.0
1,146.2
6,788.5
1,149.3
837 .3
815.6
802 .5
801 .5
810.6
826.6
815.2
813.3
812.8
818.5
806.3
811.6
811.9
815.3
817.7
1,246.1
1,228.0
1,230.1
1,230.9
1,233.9
1,235.2
1,230.9
1,240.0
1,246.4
1,254.1
1.258.3
1,261.9
1,264.4
1,269.3
1,274.4
Aug.P
Sept.P
582 .0
3,166
Credit intermediation and
related activrties' ..
Deoositorv credit
intermediation ' ..
Commercial bankina ...... ....
Secu rities, commodity
contracts, investments ..
Insurance carriers and
related activities .... ......
Funds, trusts, and other
financial vehicles ..
Real estate and rental
and leasing ..
Real estate ..
Rental and leasing services ..
Lessors of nonfinancial
intangible assets .. ... .. .........
Professional and busi ness
services .. .... .... ........ ........ .....
Professional and technical
services '
····· ····" ···· ...
Legal services ..
Accounting and bookkeeping
services ..
Architectural and engineering
services ..
See notes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November 2004
95
Current Labor Statistics:
Labor Forc e Data
12. Continued-Employment of workers on nonfarm payrolls by industry, monthly data seasonally adjusted
[In thousands]
Industry
Annual average
2003
2004
2002
2003
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
1.152.8
1,108.3
1,103.3
1,107.0
1,105.7
1.105.7
1.104.6
1,099.8
1,103.5
1,103.5
1,110.1
1,117.7
1,120.5
1,129.7
1,136.4
792 .2
794.3
795.9
Computer systems design
and related services ..
Management and technical
consulting services ..
Management of compani es
and enterprises ..
Administrative and waste
Aug.P
Sept.P
734 .4
747 .3
749.3
755.6
760.6
764.0
765.4
767.9
774.0
780.9
785.9
791.4
1,705.4
1,675.5
1,671 .7
1,669.1
1,671 .6
1,670.2
1,675.1
1,675.6
1,676.6
1,679.7
1,683.3
1,684.5
1,685.9
1,682.5
1,677.2
7,595.2
7,698.3
7,773. 1
7, 776.3
7,794.5
7,819.2
7,838.5
7,862.4
7,880.1
7,982.3
8,040.1
8,040.0
8,064.3
8,073.0
8,096. 1
Adm inistrative and suooort
services' ..
7,276.8
73,764.0
7,451 .6
7,456.0
7,473.7
7,496.3
7,517.5
7,539.6
7,556.8
7,657.0
7,715.6
7,713.0
7,738.1
7,746.6
7,770.2
Employment se rvices 1 .•
3,246.5
3,336.2
3,389.1
3,402. 0
3,427 .6
3,461 .3
3,473.8
3,493.8
3,492 .3
3,553.7
3,591 .5
3,573.4
3,606.8
3,607.8
3,641 .1
2.193.7
756.6
2.243.2
747.4
2.287.2
753.2
2.291.7
753.2
2.319.4
746.7
2.355.3
745.1
2.344.3
739.0
2.370.4
739.8
2.380.3
746.0
2.423.8
748.6
2.451 .7
751 .2
2.449.4
754.0
2.460.2
749.9
2.474.7
751 .5
2.508.2
745.7
1.606.1
1.631 .7
1.645.2
1.639.6
1.639.4
1.635.9
1.637.1
1.639.5
1.646.2
1.674.5
1.686.0
1.694.1
1.691.5
1.691 .6
1.690.4
318.3
321 .9
321 .5
320.3
320.8
322.9
321
322 .8
323.3
325.3
324.5
327
326.2
326.4
325.9
16,199
2,642.8
16,577
2,688.5
16,672
2,689.1
16,678
2,707 .7
16,705
2,723.1
16,731
2,728.0
16,746
2,729.3
16,764
2,727.4
16,813
2,736.0
16,854
2,740.8
16,871
2,731 .1
16,897
2,727.4
16,901
2,73 1.2
16,965
2,746.4
16,984
1,756.4
13,555.7
13,888.0
13,933.3
13,970.0
13,981 .5
14,003.2
14,017.1
14,036.8
14,077.1 14,113.1 14,140.1 14,169.8
14,169.3
14,218.3
14,227.9
4,633.2
1,967.8
413.0
679.8
4,776.0
2,003.8
423.1
727 .1
4,792.8
2, 008.2
422.9
732.8
4,812 .8
2,018.5
423.3
737 .7
4,818.7
2,023.3
426.4
735.7
4,831 .0
2,030.0
425.0
739.9
4,840.3
2,032.3
427.8
740.2
4,855.3
2,034.4
431 .1
741 .5
4,868.0
2,043.5
430.3
743.8
4,920.8
2,057.5
437.6
756.8
4,935.1
2,062 .1
438.0
760.1
4,939.3
2,068.5
437. 0
760.7
4,1 59. 6
4,252 .5
4, 264.4
4,268.9
4,278.1
4,283.9
4,287.8
4,284.1
4,298.0
4,305.1
4,315.4
4,318.3
4,322.0
4,330.5
4,332 .0
2,743.3
2,784 .3
2,789.3
2,794.2
2,792.8
2,793.0
2,792.1
2,791.1
2,798.4
2,802.8
2,806.3
2,809.0
2,812.0
2,814.0
2,819.5
Nursina care facilities ..
1.573.2
2,0 19.7
Social assistance 1 •. ••• •• ······ ··
Child day care services .. ......
744.1
Leisure and hospitality ....... ....
11 ,986
Arts, entertainment,
and recreation . . . . . . . . . . . . . . . .
1,782.6
Performing arts and
spectator sports ..
. . .. ..
363.7
Museums, historical sites,
zoos, and parks ..
114.0
Amu sements, gambling, and
recreation . .......... .. ........
1,305.0
Accommodations and
food services ..
10,203.2
Accom modat ions .... .... .. · ·· ··
1,778.6
Food services and drinking
places ..
8,424.6
Other services .... ............... .....
5,372
Repair and maintenance ....... . 1,246.9
Personal and laundry services
1,257.2
Membership associations and
organizations ..
2,867 .8
Government. ...............................
21,513
Federal. ...
2,767
Federal, except U.S. Postal
Service ... ...
1,923.8
U.S. Postal Service ........
842.4
State ····· ····· ···· ·· ············ ····
5,029
Education . ............ .........
2,242.8
Other State government..
2,786.3
Local. .
13,718
Education ... .... ... .. .. .. ... ... .... ... 7, 654.4
Other local government ...... ...
6,063.2
1.582. 8
2,075.2
760 .5
12, 128
1. 583.1
2,086.8
1.585.2
2,094 .1
1.584.1
2,091 .9
1.58 1.7
2,095.3
1.580.3
2,096.9
1.578.7
2, 106.3
1.582.1
2,112.7
1.584 .0
2,121 .6
1.585.3
2,12 1.6
1.586.5
2,132 .9
1.586.7
2,114.5
1.586.3
2,138. 7
1.586.2
2,137. 1
765.8
12, 126
771.6
12,147
766.3
12,1 78
770
12,192
766.3
12,2 18
772.2
12,229
773.7
12,271
777 .6
12,303
777.1
12,331
786
12,339
752.1
12,344
792.7
12,341
782.8
12,351
1,801.0
1,794.4
1,796.9
1,799.4
1,795.2
1,801 .4
1,796.7
1,798.7
1,791.1
1,793.1
1,792.0
1,791 .9
1,785.6
1,792.7
370.2
372.0
369.6
371 .7
368.8
369.4
366.5
364.6
361.4
358.8
359.3
357.1
356.0
363.2
114.1
113.4
114.2
113.3
113.1
113.4
113.7
114.2
114.6
115.6
116.1
116.6
116.7
11 6. 3
1,316.6
1,309.0
1,3 13.1
1,314.4
1,313.3
1,318.6
1,316.5
1,319.9
1,315 .1
1,3 18.7
1,316.6
1,318.2
1,312.9
1,313.2
10.33 1.7 10,350.4
1,739.1
1,733.7
10,378.9
10,396.3
10,416.5
10,432.3
10,742.0
10,551 .7
10,555.6
10,558.1
1,763.0
1,752.1
1,754.4
1,753.4
10,511.8 105,837.9
1,758.5
1,758.5
10,546.7
1,751 .7
1,764.7
1,764.4
1,767.9
1,765.3
services ..
Temoorarv helo services ....
Business suooort services ....
Services to buildinas
and dwellinas ........ ........ ..
Waste man agement and
remediation services ..
Educational and health
...........
services . .. ... . .
Educational services . ... ... .... ..
Health care and social
assistance ..
Ambulatorv health care
services' ... .... .... .......
Offices of physician s ..
Outpatient care centers .. ....
Home health care services ..
Hospitals ..
4,883.6
2,046.1
432 .2
748.4
4,896.8
2,049.6
435.1
751.7
4,909.6
2,053.9
436.0
754.2
Nursina and residential
r.:1rA f;:1r.ilitiA!=:. 1
1
10,324.4
1,765.2
8.559.2
5,393
1,236.2
1,258.2
8,592.6
5,390
1,240.4
1,252.7
8,616.7
5,387
1,237.6
1,254.6
8,627 .2
5,382
1,234.4
1,254.1
8,633.3
5,37 4
1,228.5
1,250.2
8,664.4
5,379
1,233.5
1,251 .2
8,677.9
5,376
1,230.5
1,247.6
8,718.6
5,39 1
1,239.4
1,255.9
8,753 .3
5,404
1,238 .2
1,260.5
8.779.4
5,407
1,237.7
1,265.5
8,782 .0
5,418
1,235.1
1,268.4
8,787.7
5,414
1,236.3
1,262.1
8,787.7
5,414
1,235.2
1,259.9
8,792 .8
5,410
1,236.8
1,254.1
2,898.0
2,896.5
2,895.2
2,893.9
2,895.7
2,894.5
2,898.3
2,895.2
2,904.8
2,903.7
2,914.9
2,915.9
2,919.1
5,919.2
21,575
2,756
21,539
2,747
21, 560
2,736
21,544
2,723
21 ,544
2,720
21 ,527
2,715
21,539
2,716
21 ,553
2,710
21,572
2,727
21,544
2,712
21 ,528
2,716
21,572
2,710
21 ,629
2,712
21,673
2,710
1,947.0
809.1
5,017
2,266.4
2,750.7
13,802
7,699.1
6,104.0
1,942.1
804 .8
5, 019
2,278.8
2,740.4
13,773
7,673.9
6,099.3
1,932.9
803.3
5, 031
2,290.4
2,740.4
13,793
7,687.0
6,105.9
1,924.9
798.1
5,023
2,282.5
2,740.0
13, 798
7,684.5
6,113.1
1,928.9
791.4
5,027
2,285.7
2,740.9
13.797
7,687.1
6,109.7
1,921 .5
793.1
5, 007
2,268.0
2,738.9
13,805
7,692.2
6,112.7
1,923.8
791 .7
5,018
2,279.6
2,738.4
13,805
7,694 .3
6,110.8
1,921 .1
789. 1
5,023
2,283.2
2,739.7
13,820
7,704.7
6,114.8
1,939.5
787 .3
5,019
2,278.3
2,740.6
13,826
7,710.9
6,1 15.4
1,925.7
786.5
5,004
2,261 .4
2,742.8
13,828
7,710.2
6,117.9
1,930.5
785.4
5,004
2,257.8
2,746.1
13,808
7,695.1
6,113.3
1,922.5
787.2
5,019
2,271 .1
2,747.8
13,843
7,715.7
6,11 6. 8
1,926.3
785.3
5,035
2,285.2
2,749.4
13,882
7,758.4
6,123.2
1,926.3
784.0
5,052
2,302.3
1,749.2
13,9 11
7,778.2
6,132.7
Includes other industries not shown separately.
p - preliminary.
96
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Classification System (NAICS). replacing the Standard Industrial Classification (SIC) system .
NAICS-based data by industry are not comparable wit h sic-based data. See "Notes on the
NOTE: Data reflect the conversion to th e 2002 version of th e Nort h American indu stry
November 2004
dat a" for a description of th e most recent benchm ark revision.
13. Average weekly hours of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry, monthly
data seasonally adjusted
Industry
2003
Annual average
2002
2003
Sept.
2004
Jan.
Feb.
Mar.
33.8
33.8
Nov.
Dec.
33.7
33.8
33.6
39.9
40.2
40.3
40.2
40.0
40.3
40.0
40.1
40.1
40.1
44.5
44.1
44.2
44.3
44.2
43.9
44.1
44.4
44.6
Oct.
Apr.
May
June
July
33.8
33.6
33.8
Aug.P Sept.P
TOTAL PRIVATE ... .... ... ........ ... ..... . ..
33.9
33.7
GOODS-PRODUCING ...... ...... ............ ..
39.9
39.8
39.8
39.9
40.1
Natural resources and mining ...... ......
43.2
43.6
43.6
43.7
43.9
43.6
Construction ....................................
38.4
38.4
38.4
38.4
38.5
38.1
38.5
38.5
38.6
38.2
38.3
38.1
38.4
38.1
38.3
Manufacturing ....................................
Overtime hours ..................... ........ ....
40.5
4.2
40.4
4.2
40.4
4.2
40.5
4.3
40.8
4.5
40 .6
4.5
41 .0
4.5
41.0
4.6
40.9
4.6
40.7
4.5
41 .1
4.6
40.8
4.6
40.9
4.6
40.9
4.6
40.8
4.6
Durable goods ........ ... .. .... ... ..... .........
Overtime hours .......... ... ....................
Wood products ............ .... ..... ........... ...
Nonmetallic mineral products ..... ...... ..
Primary metals ......... .. ... ... ..... .. ........ .. ..
Fabricated metal products ..................
Machinery .. ......... ....................... ..
Computer and electronic products .....
Electrical equipment and appliances ..
Transportation equipment... ... .............
Furniture and related products .... .... ..
Miscellaneous manufacturing .............
40.8
4.2
39.9
42.0
42 .4
40.6
40 .5
39.7
40.1
42.5
39.2
38.6
40.8
4.3
40.4
42 .2
42 .3
40.7
40.8
40.4
40.6
41.9
38.9
38.4
40.8
4.3
40.4
41 .9
42.2
40.7
41 .0
40 .6
40.6
42.0
39.1
38.3
40.9
4.4
40.6
42.1
42.3
40.8
40.9
40.7
40.9
41.9
39.1
38.3
41 .3
4.7
41 .2
42.4
42.7
40.9
41 .1
40.7
40.8
42.7
39.9
38.9
41 .2
4.7
41.0
42.3
42.7
40.8
41 .1
40.4
40.7
42.7
39.7
38.5
41 .5
4.7
40.9
42.5
43.1
41.2
41.8
40.8
41 .1
42.8
39.7
39.0
41.5
4.8
41 .1
42.5
43.0
41 .2
41.8
41.2
40.7
42.9
39.4
38.7
41 .4
4.8
41 .0
42.9
43.2
41.1
41 .7
40.7
40 .8
42.8
39.6
38.7
41 .2
4.7
41 .0
42.3
43.1
41 .0
41 .6
40.5
40 .8
42.4
39.5
38.3
41.6
4.8
41 .4
42.0
43.4
41 .3
42.3
40.8
41 .6
42.8
40.0
38.9
41 .2
4.7
40.5
41 .8
43.5
41.0
42.0
40.5
40.8
42.3
39.7
38.4
41 .3
4.7
40.7
42.1
43.3
41 .2
42.0
40.9
40.8
42 .4
39.4
38.5
41.3
4.7
40.9
42.3
43.3
41.2
42.1
40.5
41 .0
42.5
39.5
38.5
41 .3
4.7
40.4
42.4
43.1
41.2
42.3
40.4
40.7
42.4
39.3
38.3
Nondurable goods ................................
Overtime hours ....... ..........................
Food manufacturing ............................
Beverage and tobacco products .........
Textile mills ............................. .....
Textile product mills .. ........ ....... ... ...
Apparel .. ......................... ....................
Leather and allied products .. ..............
Paper and paper products ....... ..... ...
Printing and related support
activities .............. ..............................
Petroleum and coal products .... .. ... .. .
Chemicals .... ....... ....... ..... ........ .... .
Plastics and rubber products .. .. ... .. ...
40.1
4.2
39.6
39.4
40.6
39.2
36.7
37.5
41 .8
39.8
4.1
39.3
39.1
39.1
39.6
35.6
39.3
42.1
39.8
4.1
39.3
39.1
39.0
40.7
35. 1
38.4
41 .2
39.9
4.1
39.3
38.8
39.1
40.4
35.8
38.9
41 .5
40.1
4.3
39.2
39.9
40.0
40.0
36.2
39.3
41 .. 9
39.9
4.2
39.1
39.1
39.7
39.8
35.8
40.3
41.8
40.2
4.3
39.5
39.6
40.0
39.4
35.7
39.8
41 .9
40.3
4.3
39.4
40.3
40.0
39.9
36.2
39.5
42.0
40.1
4.3
39.3
39.4
40 .2
38.8
36.3
39.4
41.8
40 .0
4.3
39.1
39.6
39.5
38.3
35.9
39.1
41 .9
40.3
4.4
39.6
39.2
40.3
38.8
36.1
38.4
42.6
40.1
4.4
39.4
38.7
40.3
38.9
35.9
38.0
42.0
40.1
4.4
39.3
39.2
40.5
38.5
36.1
37.2
42.4
40.2
4.4
39.3
39.5
40.5
38.7
36.1
37.8
42.5
40 .1
4.4
39.5
39.1
40.2
38.9
36.1
37.8
42.2
38.4
43.0
42.3
40 .6
38.2
44.5
42.4
40 .4
38.2
44.2
42.2
40.5
38.5
44.9
42 .0
40 .6
38.4
45.6
42.7
40 .7
38.2
44.2
42.5
40 .4
38.6
43.8
42.9
40.8
38.6
44.1
43.2
40.9
38.4
43.7
43.0
40.9
38.4
43.9
43.0
40.7
38.6
45.0
42.9
40.9
38.5
45.0
42.6
40.8
38.6
45.0
42.8
40.5
38.5
46.3
42.7
40.5
38.2
45.9
42.7
40.2
32.5
32.4
32.3
32.3
32.4
32.2
32.4
32.4
32.4
32.3
32.4
32.3
32.4
32.4
32.5
33.6
38.0
30.9
36.8
40.9
36.5
33.5
37.8
30.9
36.9
41 .1
36.2
33.5
37.8
30.9
36.9
40.4
36.1
33.6
38.0
30.9
37.1
41 .0
36.1
33.6
38.0
30.9
37.0
41.4
33.5
37.8
30.8
36.7
40.8
36.2
33.7
38.0
30.9
37.2
41 .0
36.3
35.6
35.5
35.4
35.5
33.6
37.9
31.0
36.9
40.8
36.2
35.7
35.5
33.6
38.0
30.8
36.9
41 .2
36.3
35.5
33.5
38.0
30.7
36.9
41.2
36.3
35.6
33.5
37.8
30.7
37.3
41.3
36.4
35.8
33.3
37.6
30.5
36.9
41 .1
36.5
35.5
33.4
37.8
30.6
37.1
41.0
36.4
35.6
33.5
37.6
30.7
37.2
40.9
36.4
35.5
33.6
37.8
30.8
37.3
41 .4
36.3
35.5
34.2
32.4
25.8
34.1
32.3
25.6
33.9
34.0
32 .3
25.6
34.1
32.4
33.8
32 .4
25.6
34.2
32.4
25.8
34.1
32.4
25.7
34.1
32.4
25.7
34.2
32.5
25.7
32.5
25.7
34.2
32.6
25.6
34.2
25.7
34.1
32.4
25.7
33.9
32.3
25.5
32.5
25.5
34.5
32.5
25.5
32.0
31 .4
31 .2
31 .3
31.2
31 .0
31 .1
31.1
31.2
31 .1
31 .2
31.0
31.1
31.1
31 .1
PRIVATE SERVICEPROVIDING ......... ......... ........ ........
Trade, transportation, and
utilities............. ... ... ... ... .....................
Vvnolesale trade .......... ..... .. ....... ........
Retail trade .................. .... .... .... .... ..
Transportation and warehousing ...... ..
Utilities .... ....... .... .. .. .... .... .... ....... ....
Information .......................................
Financial activities ............ ... ......... ....
Professional and business
services... ........ ....... ... .... .. ............ ..
Education and health services ...... ......
Leisure and hospitality ........... .. ..... ... .
Other services............ ..........................
1
33.6
Data relate to production workers in natural resources and mining and manu-
36.3
35.5
35.3
33.8
33.7
33.7
33.8
NOTE: Data reflect the conversion to the 2002 version of the North American
facturing , construction workers in construction. and nonsupervisory workers in the
Industry Classification System (NAICS), replacing the Standard industrial Classification
service-providing industries.
(SIC) system. NAICS-based data by industry are not comparable with SIC-based data.
See "Notes on the data" for a description of the most recent benchmark revision .
p • preliminary.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November 2004
97
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
2003
Annual average
Industry
2004
2002
2003
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
TOTAL PRIVATE
Current dollars . ....... ..... ............
Constant (1982) dollars . ..... ... . ....
$14.95
8.24
$15.35
$15.41
$15.43
$15.46
$15.45
$15.49
$15.52
$15.71
$15.76
8.28
8.23
8.30
8.27
8.27
8.25
$15.63
8.21
$15.66
8.25
$15.55
8.24
$15.59
8.27
8.20
8.23
8.26
8.25
GOODS-PRODUCING ........... ..................
16.33
16.80
16.91
16.90
16.94
16.97
17.00
17.06
17.08
17.13
17.13
17.16
17.19
17.24
17.50
Natural resources and mining .............
Construction ................................. ....... ..
17.19
17.58
17.66
17.72
18.10
18.24
18.15
18.12
18.05
19.18
19.17
19.20
19.20
19.19
19.22
19.25
19.28
Manufacturing ..................... ..................
15.29
15.74
19.06
15.83
17.95
19.11
18.08
19.05
15.84
17.91
19.04
18.10
18.95
17.79
19.06
18.01
18.52
Excluding overtime ................ ... .. ....
Durable goods .. .... ....... ... .. .. ... ... .. ...
14.54
14.96
16.02
16.46
15.06
16.57
15.03
16.54
Nondurable goods .... . .. . .. . ... .. .... .....
14.15
14.63
14.70
14.56
14.96
15.01
Aug.P Sept.P
$15.78
15.89
15.93
15.94
15.99
16.01
16.08
16.08
16.13
16.16
16.23
16.30
15.06
15.09
15.11
15.14
15.16
15.24
15.23
15.27
15.37
15.43
16.64
16.63
16.68
16.69
16.75
16.75
16.78
16.90
16.99
14.72
16.58
14.79
15.30
16.81
14.81
14.85
14.89
14.93
15.00
15.02
15.08
15.12
15.15
15.19
15.03
15.06
15.05
15.08
15.10
15.13
15.17
15.23
15.26
15.31
15.36
15.38
PRIVATE SERVICEPROVIDING ............................... .........
Trade,transportatlon, and
utilities ........... . ..... . ...... ... .... ........ .
14.02
14.34
14.38
14.41
14.44
14.41
14.45
14.49
14.50
14.57
14.61
14.65
14.70
14.73
14.75
Wholesale trade ......... .. ........ ...............
Retail trade .............. ........... ..... ............
16.98
11 .67
17.36
11.90
17.44
17.47
17.47
17.54
11 .99
17.67
12.10
17.76
11 .98
17.63
12.06
17.70
11 .97
17.60
12.01
17.71
11 .95
17.53
11 .95
17.54
11 .94
17.46
11 .95
12.12
12.16
12.16
Transportation and warehousing .. ....
Utilities .. ... .. ... ... . ..... ... ..... ..... ... .... .
Information ................. ......... ..................
15.76
16.25
16.31
16.32
16.35
16.46
16.52
16.53
16.71
16.75
16.82
16.89
16.99
16.95
23.96
24.76
24.96
25.44
25.57
20.99
21 .15
25.38
21 .25
25.46
21 .21
25.35
21 .24
25.67
21.01
25.36
21 .10
25.32
20.20
25.17
21 .21
16.33
25.13
21.29
21.42
21 .30
21.45
25.54
21 .53
25.73
21.61
16.17
17.13
17.27
17.29
17.30
17.30
17.35
17.32
17.41
17.46
17.49
17.50
17.55
17.58
17.62
16.81
17.20
17.19
17.25
17.29
17.25
17.24
17.25
17.27
17.29
17.36
17.42
17.44
17.56
17.52
15.21
15.64
15.70
15.73
15.77
15.81
15.87
15.90
15.96
15.99
16.06
16.12
16.18
16.19
19.22
8.58
8.76
13.84
8.78
13.81
8.78
8.82
13.81
8.84
8.85
13.84
8.86
8.87
8.86
13.84
8.87
13.90
8.95
13.87
8.85
13.88
8.91
13.84
8.86
13.85
13.92
13.96
Financial activities ................................
Professional and business
services...............................................
Education and health
services............. .......................... ........
Leisure and hospitality ........................
Other services.......................................
13.72
13.80
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.
p - preliminary.
98
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
13.80
NOTE: Data reflect the conversion to the 2002 version of the North American industry
Classification System (NAICS), replacing the Standard Industrial Classification (SIC) system. NAICS
based data by industry are not comparable with SIC-based data. See "Notes on the data" for a
description of the most recent benchmark revision .
15. Average hourly earnings of production or nonsupervisory workers' on private nonfarm payrolls, by industry
2002
TOTAL PRIVATE ...... .... ...... ...... ... .. . $14.95
Seasonally adjusted ..... ......... ... ..
15.18
2003
2004
2003
Annual average
Industry
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.P Sept.P
15.47
$15.44
15.41
$15.42
15.41
$15 .52
15.43
$15.48
15.45
$15.56
15.49
$15.60
15.52
$15.55
15.55
$15.59
15.59
$15.63
15.63
$15.57
15.66
$15.59
15.71
$15.67
15.76
$15.80
15.78
17.41
$15.35
GOODS-PRODUCING .............................
16.33
16.8
17.01
16.95
16.98
17.03
16.94
16.95
17.00
17.09
17.10
17.14
17.18
17.28
Natural resources and mining ... ........
17.19
17.58
17.69
17.69
17.15
17.97
18.00
18.05
18.17
18.14
18.06
18.18
18.07
18.01
18.03
Construction .. .... .................................
18.52
18.95
19.19
19.13
19.08
19.19
19.01
19.07
19.07
19.15
19.15
19.12
19.25
19.33
19.42
Manufacturing .. ....... ... ......... .... ..... ...
15.29
15.74
15.87
15.81
15.92
16.05
15.98
15.99
16.01
16.07
16.05
16.09
16.04
16.17
16.37
Durable goods ...... ......... ..... ...... ......
Wood products .... ..... ... .............. ... ....
Nonmetallic mineral products ... .. .. ..
Primary metals ......... ........................
Fabricated metal products ... ............
Machinery .. ... ..... ...... ...... .. ..........
Computer and electronic products ...
Electrical equipment and appliances
16.46
12.71
15.77
18.13
15.01
16.30
16.68
14.35
21.25
12.98
13.30
16.62
12.83
15.84
18.30
15.09
16.40
16.77
14.49
21 .56
13.10
13.42
16.55
12.82
15.95
18.25
15.03
16.35
16.77
14.37
21.35
13.01
13.47
16.64
12.95
15.99
18.32
15.06
16.49
16.78
14.54
21 .48
13.08
13.53
16.78
12.93
15.98
18.39
15.23
16.62
16.85
14.68
21 .74
13.08
13.60
16.66
12.90
16.03
18.39
15.20
16.53
16.81
14.50
21 .38
12.95
13.68
16.68
12.91
16.00
18.36
15.18
16.50
16.92
14.58
21 .37
12.92
13.75
16.69
12.93
16.02
18.33
15.25
16.49
16.93
14.68
16.72
13.00
16.19
18.52
15.21
16.53
17.01
14.80
Transportation equipment ..... ..... ......
Furniture and related products .........
Miscellaneous manufacturing ..........
16.02
12.33
15.40
17.68
14.68
15.92
16.20
13.98
20 .64
12.61
12.91
21.34
12.96
13.78
21 .36
13.09
13.70
16.71
13.03
16.18
18.48
15.20
16.53
17.11
14.83
21.29
13.04
13.76
16.75
12.98
16.24
18.51
15.23
16.56
17.21
14.88
21 .36
13.10
13.81
16.61
13.03
16.38
18.66
15.26
16.68
17.29
14.88
20.77
13.11
13.89
16.85
13.01
16.29
18.58
15.27
16.72
17.37
14.98
21.54
13.27
13.87
17.08
13.13
16.51
18.91
15.43
16.83
17.45
15.03
21 .98
13.37
13.97
Nondurable goods .. .......... ....... .... .. .
Food manufacturing ..... ......... ........ ...
Beverages and tobacco products ....
14.15
12.55
17.73
14.63
12.80
17.96
14.73
12.90
17.73
14.67
12.77
18.05
14.80
12.91
18.64
14.88
12.95
18.58
14.89
12.91
18.88
14.88
12.87
18.76
14.90
12.89
19.13
15.01
12.96
19.60
14.98
12.94
19.55
15.03
13.00
19.39
15.14
13.05
19.29
15.09
12.99
19.10
15.24
13.08
19.16
Textile mills ......... .............. ............. ..
Textile product mills .. .......................
Apparel ...... ..... .. .... ....... .... ... ... ....... ..
Leather and allied products ....... ... .
Paper and paper products ..... ... .. ...
Printing and related support activitiei
Petroleum and coal products ..... ....
Chemicals ... ....... ...... ... .......... .....
Plastics and rubber products .. ..........
11 .73
10.96
9.10
11.00
16.85
14.93
23.04
17.97
13.55
12.00
11.24
9.56
11.67
17.32
15.37
23.64
18.52
14.18
12.07
11.47
9.77
11 .63
17.41
15.46
23.45
18.66
14.30
12.02
11 .37
9.69
11 .83
17.44
15.41
23.63
18.66
14.19
12.08
11 .35
9.71
11.87
17.58
15.48
24.00
18.77
14.27
12.21
11 .44
9.80
11.90
17.60
15.56
24.06
18.79
14.47
12.11
11 .45
9.74
11.94
17.63
15.53
24.13
18.83
14.43
12.13
11.40
9.58
11 .76
17.55
15.57
24.32
18.85
14.45
12.09
11 .37
9.60
11 .64
17.59
15.61
24.82
18.87
14.45
12.23
11.33
9.71
11 .65
17.84
15.54
24.48
19.02
14.58
12.08
11 .30
9.55
11.49
17.88
15.51
24.41
19.05
14.55
12.15
11.29
9.60
11.59
17.86
15.54
24.24
19.20
14.59
12.07
11 .48
9.74
11.68
17.91
15.71
24.35
19.36
14.69
12.08
11 .46
9.73
11 .68
17.84
15.86
24.07
19.29
14.66
12.24
11.53
9.78
11 .55
18.20
15.97
24.52
19.51
14.75
PRIVATE SERVICEPROVIDING .... .... .. ........... .... .. .........
14.56
14.96
15.00
15.01
15.13
15.07
15.19
15.24
15.16
15.20
15.24
15.14
15.17
15.24
15.37
Trade, transportation, and
utilities .. .... ... .. ............ ........................
Wholesale trade ..... .. ... .. ............... .
Retail trade ...... ..... ...... ....... ... .. .....
Transportation and warehousing ... ...
Utilities .. ...... .... ... .. ... ............. ... .....
14.02
16.98
11 .67
15.76
23.96
14.34
17.36
11.90
16.25
24.76
14.42
17.41
11 .99
16.31
25.15
14.38
17.42
11.91
16.31
25.23
14.44
17.56
11.92
16.40
25.50
14.31
17.46
11 .87
16.33
25.26
14.50
17.56
11 .98
16.46
25.38
14.58
17.60
12.04
16.58
25.29
14.53
17.47
12.03
16.51
25.36
14.64
17.60
12.08
16.73
25.69
14.64
17.67
12.08
16.72
25.53
14.61
17.58
12.09
16.80
25.33
14.62
17.66
12.07
16.86
25.43
14.66
17.69
12.09
16.98
25.33
14.79
17.74
12.23
16.94
25.89
20.20
21.01
21.35
21 .25
21 .28
21.10
21 .21
21.28
21 .17
21 .24
21.41
21.18
21.30
21.44
21.73
16.17
17.13
17.27
17.25
17.42
17.26
17.35
17.47
17.37
17.45
17.62
17.38
17.44
17.58
17.60
16.81
17.20
17.11
17.13
17.41
17.29
17.38
17.47
17.28
17.26
17.45
17.28
17.31
17.46
17.43
16.24
Financial activities .............................
Professional and business
services ........ .. ... .. ..... .............. ... ...
Education and health
services ......... .......... ... ....... ......... .
15.21
15.64
15.71
15.73
15.79
15.86
15.94
15.95
15.94
15.99
16.00
16.06
16.18
16.16
Leisure and hospitality ...... .... ........ ..
8.58
8.76
8.78
8.78
8.83
8.94
8.89
8.92
8.89
8.84
8.85
8.78
8.78
8.80
8.94
Other services ... .... ............................
13.72
13.84
13.82
13.78
13.85
13.88
13.89
13.90
13.85
13.87
13.90
13.82
13.78
13.84
13.98
1
Data relate to production workers in natural resources and mining and
NOTE: Data reflect the conversion to the 2002 version of the North American Industry
manufacturing, construction workers in construction, and nonsupervisory workers in
Classification System (NAICS) , replacing the Standard Industrial Classification (SIC)
the service-providing industries.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
system . NAICS-based data by industry are not comparable with SIC-based data. See
"Notes on the data" lor a description ol the most recent benchmark revision.
Monthly Labor Review
November 2004
99
Current Labor Statistics:
Labor Force Data
16. Average weekly earnings of production or nonsupervisory workers 1 on private nonfarm payrolls, by industry
Annual average
Industry
2002
TOTAL PRIVATE .. ... ... ... ......... $506.07
Seasonally adjusted .. ... .....
2004
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.P
Sept.P
$517.36
$519.65
519.99
$527.68
522.55
$520.13
519.12
$518.15
523.56
$527.28
524 .58
$520.93
525.59
$522.27
525.38
$531 .42
528.29
$524 .71
526.18
$528.50
531 .00
$535.91
531.11
$530.88
533.36
682.90
674.21
674.61
681 .70
678.47
690.84
689.03
687.20
698.11
691.18
802.31
754 .60
806.85
755.80
796.93
730.19
-
-
$520.33
517.78
651.61
669.23
685.50
681 .39
684.29
741 .97
711.82
766.83
727.11
780.13
778.36
784.55
781.70
784 .80
786.98
797.66
Construction ............. .... .........
752.25
744.16
730.76
714 .34
712.88
711 .31
732.29
794 .53
721.96
798.25
741.11
809.01
738.03
Manufacturing .................. ......
618.75
636.07
647.50
643.47
655.90
662 .87
650.39
652.39
653.21
652 .44
659.66
659 .69
646 .41
661 .35
664 .62
652.97
671.53
684 .74
680.21
692.22
703.08
688.06
688.88
690.97
687.19
695.14
695.13
674 .37
695.91
698.91
492.00
646.91
749.32
596.38
645.55
513.92
665.11
767.63
610.33
664 .79
526.03
676.37
777.75
617.18
672.40
525.62
679.47
771 .98
616.23
667.08
537.43
681 .17
785.93
621 .98
682 .69
531 .42
669.56
799.97
635.09
696.38
517.29
663.64
796.29
626.24
689.30
521 .56
664.00
787.64
623.90
691 .35
524.96
680.85
790.02
625.25
690.93
530 .40
684 .84
800.06
620.27
987.65
544 .65
684 .41
803.88
627.76
700.87
533.48
690.20
808.89
627.48
698.83
531 .62
694.51
791.18
621 .08
692 .22
538.61
700.47
798.94
627.60
697 .22
521 .26
708.28
809.35
628.00
698.45
642. 87
674 .68
684 .22
684.22
693.01
695.91
680.81
695.41
690.74
683.80
694 .67
698.73
696.79
700.01
701 .49
560.24
877.87
582.68
890.32
588.29
918.46
592.04
905.24
601 .96
925.79
616.56
950.04
594.50
915.06
591 .95
916.77
596.01
917.62
599.40
905.66
613.96
915.47
611 .57
912.07
599.66
841.19
611 .21
911 .14
604.21
929.75
494.01
505.23
518.76
508.69
523.20
528.43
510.23
505.17
510.62
517.06
517.69
521 .38
515.22
529.47
518.76
GOODS-PRODUCING .... .... ........
Natural resources
and mining ... ...............
.......
Wood products ........... ...... .. .....
Durable goods ... ..... .......
Nonmetallic mineral products....
Primary metals ..
Fabricated metal products..... ...
Machinery ..... ... ....................
(.;omputer and electronic
products ..... ·· ············ ..........
Electrical equipment and
appliances ...... ... .. .. .. ..
Transportation equipment ......
Furniture and related
products ... .. . ..... ......... ... .... .
Miscellaneous
manufacturing ····· ···· ··· .......
499.13
510.69
515.33
515.90
530.38
533.12
532.15
533.50
534.66
524.71
535.26
530.30
527.82
534.00
529.46
.......... ··········
566.84
582.65
593.62
588.27
600.88
602 .64
594 .11
595.20
596.00
595.90
602.20
604.21
602.57
606.62
611.12
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 .. . . ......... ... ..• ... .. .
496.91
502.61
517.29
505.69
515.11
514 .12
504 .78
499. 36
498.84
497.66
511 .13
512.20
512.87
514 .40
521 .89
698.39
476.52
429.01
333.66
412.99
705.62
702.75
469.47
445.08
340.22
458.26
719.21
707.43
475.56
467.98
341 .95
445 .43
726.00
707.56
469.98
458.21
348.84
462.55
727.25
751 .19
485.62
456.27
356.36
465.30
743.63
722.76
490 .84
464 .46
352.80
485.52
751 .52
728.77
485.61
447.70
343.82
471 .63
738.70
737.27
486.41
450.30
345.84
464 .52
731 .84
744 .16
490.85
441 .16
350.40
464 .44
731 .74
780.08
484 .31
435.07
347.76
460 .18
745.71
774 .18
486.82
436.18
346.67
441 .22
756.32
760.09
490.86
444 .83
348.48
442.74
748.33
760.03
481 .59
435.09
348.69
422.82
750.43
762.09
489.24
443.50
353.20
441 .50
754 .63
764 .48
487.15
445.06
346.21
429.66
773.50
573.05
587.42
599.85
597.91
603.72
602.17
593.25
597.89
600.99
593.63
594 .03
593.63
600.12
610.61
611 .65
990.88
759.53
1,052.97
784 .56
1,045.87
793.05
1,068.08
785.59
1,099.20
808.99
1,061 .05
806.09
1,068.96
804 .04
1,074.94
816.21
1,079.67
811 .41
1,062.43
814.06
1,091 .13
815.34
1,095.65
819.84
1,120.10
816.99
1,097.59
823.68
1,125.47
831 .13
549.85
572.23
583.44
578.95
586.50
596.16
585.86
588.12
589.56
594.86
595.10
599.65
583.19
589.33
590.00
472. 88
484 .00
483.00
484.82
493.24
485. 25
484 .56
496.82
486.64
487.92
496.82
489.02
493.03
501 .40
496.45
Nondurable goods
PRIVATE SERVICEPROVIDING .... ..... .......................
Trade, transportation,
and utilities .. ... .......... ............ 471 .27
Wholesale trade ..... ..... ........... 644.38
Retail trade · · ······ · · · · · · · · · ···
360.81
Transportation and
warehousing ... .. ... ...... . . . . . . . . . 579.75
Utilities .... ···· ······· · ·· ······ ·
979.09
.
1
2003
2003
481 .10
485.95
483.17
486.63
480.82
477.05
488.43
482.40
486.05
493 .. 37
489.44
494 .46
498.44
496.94
657.12
367.28
658.10
371 .69
661 .96
366.83
676.06
365.94
659.99
367. 97
656.74
361.80
670.56
368.42
658.62
365.7 1
665.28
367.23
674.99
372.06
661 .01
372.37
665.78
376.58
672.22
378.42
667.02
377.91
597.79
1,016.94
606.73
1,026.12
603.47
1,039.48
615.00
1,068.45
602.58
1,028.08
597.50
1,032.97
613.46
1,039.42
604.27
1,039.76
610.65
1,053.29
627.00
1,054.39
621 .60
1,046.13
627.19
1,032.46
641 .84
1,030 .93
628.47
1,074.44
Information ... ... ... .... ... .... ...... ...
738.17
761 .13
770.74
769.25
783.10
761 .71
763.56
776.72
760.00
764.64
777.18
775.19
773.19
788.99
788.80
Financial activities ... ... ......... ...
575.51
608.87
607.90
608.93
628.86
607.55
612.10
630.67
611.42
615.99
637.84
613.51
617.38
634 .64
619.52
Professional and
business services .. ......... .......
597.16
582.67
583.97
602.72
587.52
588.57
603.77
587.52
590.27
604 .12
592.62
526.18
574 .66
586.68
578.32
580.71
Education and
health services ... ...................
492. 74
505.76
505.86
506.51
516.33
512.28
514.86
519.97
513.27
516.48
521 .60
520.34
527.47
530.05
Leisure and hospitality ... .. .. .... .
221 .26
224. 35
222.13
223.89
226.05
225.29
221 .36
230.14
225.80
224.81
229.22
227.40
230.91
234.08
226.18
Other services ............ ...... ..... ,
439.76
434.49
431 .18
431.31
434.89
430.28
429.20
433.68
428.73
428.58
435.07
428.42
429.94
434.58
431 .98
Data relate to production workers in natural resources and mining and manufacturing ,
Industry Classification System (NAICS), replacing the Standard Industrial Classifification (SIC)
construction workers in construction, and nonsupervisory workers in the service-
system. NAICS-based data by industry are not comparable with sic-based data. See "Notes on
providing industries.
the data" for a description of the most recent benchmark revision .
NOTE:
Dash indicates data not available. p = preliminary.
Data reflect the conversion to the 2002 version of the North American
100 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
17. Diffusion indexes of employment change, seasonally adjusted
[In percent]
Timespan and year
Jan.
Feb.
Mar.
Apr.
May
June July
Aug. Sept.
Oct.
Nov.
Dec.
Private nonfarm payrolls, 278 industries
Over 1-month span:
2000 .............................. .. ... ........ .. .
2001 ......... ... ... .. ........................ .. ...
2002 ............................................. .
2003 ......... ... ............... .. ... ..............
2004 ....... .. ..... .. ........... .. ................ .
Over 3-month span:
2000 ....... .. .............................. ...... .
2001 .................. ..... .. ...... .............. .
2002 ............................................. .
2003 ..................... ........... .. ............
2004 ........ .......................... .. ..........
61.9
52.2
40.1
41 .2
52.3
62.9
47.8
35.1
35.1
56.1
63.3
50.4
41 .0
38.1
68.7
59.5
34.4
41 .5
41 .4
67.6
46.9
41 .4
41 .7
42.8
63.8
61 .7
39.2
47.8
40.1
60.6
63.1
37.1
44.1
40 .5
55.2
52.5
38.8
44.1
39.7
56.3
51 .5
38.3
42 .8
49.3
59.2
53.4
32.4
39.0
46.0
56.8
36.7
38.7
51.1
53.8
34.9
34.5
49.1
69.2
52.7
34.0
66.2
50.4
37.4
67.8
50.4
35.1
68.3
43.5
36.2
60.1
38.8
36.7
36.5
54.0
32.6
55.2
36.3
62.8
35.1
70.0
40.5
74.5
58.1
34.9
39.4
42.6
68.7
56.3
36.2
39.9
37.4
64.6
61.5
37.9
40.8
35.4
57.2
56.5
34.7
38.7
40.1
61.0
53.2
35.3
37.1
45.5
52.9
30.8
34.4
50.5
56.8
32.0
34.7
51 .1
66.7
38.1
37.2
36.2
68.3
60.8
35.4
39.0
36.5
71.6
59.0
32.2
34.7
40.5
67.3
55.0
33.1
36.5
39.4
59.7
31.5
35.3
42.6
54.0
31 .1
33.3
41 .7
70 .0
43.3
32.0
34. 7
64.0
70.3
43.9
31.3
33.1
63.8
70.3
39.9
30.0
37.6
65.3
65.6
37.8
29.5
37.4
63.8
37.1
32.9
33.1
62 .1
34.9
34.7
35.4
Over 6-month span:
2000 ........................................... .. .
2001 ........ ..... .. .... .. .............. .......... .
2002 .......... .. .... .. ......................... .. .
2003 ......................... .......... ... ........
2004 .................. ........ ....................
67.3
51 .8
29.5
69.1
50.0
30.0
75.2
51 .8
31 .1
72.5
47.3
31 .1
67.4
43.5
31.7
33.6
48.9
31 .1
54.1
31 .7
59.6
31 .7
64.7
33.5
67.8
67.8
41 .5
37.1
37.8
71 .2
Over 12-month span:
2000 ......... .. ..... .. ......... .. ................ .
2001 ......... ...... ..... .. ....................... .
2002 ......... ....... .. ....... ..... .. ... ... ....... .
2003. ........ .... .. .............. .. .... ... ...... ..
2004. ........ .......................... ....... .. ..
70.9
69.2
59.5
59.5
33.6
31 .7
34.5
31 .5
37.8 1 43.21
73.2
53.4
30.2
32.9
47.3
71.0
49.3
30.4
33.5
50.7
69.8
48.6
30.2
36.2
54.9
71 .0
45.0
29.1
34.4
60.3
Manufacturing payrolls, 84 industries
Over 1-month span:
2000...................................... ........
2001.. ...... ...... .............. .. ... ... .... .. ....
2002.......................................... ....
2003........................................ .. ... .
2004..................... .... ... .. ....... ...... .. .
48.2
22 .6
21 .4
26.2
42.9
58.3
22.0
18.5
15.5
55.4
50.0
21.4
23.8
22.6
60.1
50 .0
16.1
35.1
13.7
66.1
41 .1
15.5
29.8
26.2
64.9
57.1
23.2
32.7
25.0
54.2
60.7
13.7
40.5
28.0
57.1
28.6
14.3
28.0
26.2
48.2
25.0
19.0
31.0
27.4
42.3
35.1
17.9
11 .9
28.6
39.9
14.9
15.5
51 .2
41 .1
10.1
17.9
45.8
Over 3-month span:
2000 ......................................... .....
2001 ......... .. ...... ................. .......... ..
2002......... .. ... .................... ............
2003...................................... .. ... ...
2004.... .. .... .. ...... .. ........ ....... ...........
53.6
35.7
9.5
13.7
48.8
53.6
21 .4
10.1
13.1
51 .8
56.0
16.1
11 .3
16.7
59.5
54.8
14.3
17.9
10.1
66.1
44.0
13.1
17.3
13.1
71.4
44.0
13.7
19.0
14.9
65.5
51 .2
11 .9
28.0
16.1
65.5
47.6
8.9
22 .0
16.1
51 .8
32.7
8.3
23.8
16.1
53.0
25.0
13.1
15.5
24.4
23.2
8.9
6.5
27.4
38.7
10.1
4.8
41 .7
Over 6-month span:
2000.......... ... ................. .. ............ ..
2001 ......... .....................................
2002........... ...................................
2003. ...... ...... ... ... .... ... ... .. .. .. . ........ ..
2004............. .................................
44.0
22.0
6.5
11.3
28.6
52 .4
23.8
8.9
9.5
36.9
55.4
22.0
7.7
6.0
46.4
57.7
20.8
8.3
7.1
56.5
47.6
14.3
7.7
8.9
61.3
51.8
13.7
14.3
13.1
64.9
56.0
14.3
14.9
8.9
66.7
45.2
10.1
10.7
13.1
66.1
39.3
10.7
12.5
13.1
58.9
34.5
5.4
10.1
16.7
32.1
7.1
8.9
19.0
27.4
4.8
8.9
19.6
Over 12-month span:
2000.......................... ...... ...... .... .. ..
2001 ..............................................
2002 ........................ .... .. .. .... ..........
2003........ .. ....... ... ........... .. ... ... .. .....
2004.......................................... .. ..
41.7
29.8
7.1
10.7
9.5
39.3
32.1
6.0
6.0
19.0
47.0
20.8
6.0
6.5
16.7
50.0
19.0
6.5
5.4
26.2
46.4
13.1
7.1
8.3
29.8
52.4
12.5
3.6
51 .8
10.7
4.8
9.5
50.0
49.4
11 .9
6.0
9.5
50.6
46.4
11.9
4.8
10.7
53.6
40.5
10.1
7.1
11.9
35.1
8.3
4.8
9.5
33.3
6.0
8.3
11.3
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.
9.5
40.5
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
November 2004
l Ol
Current Labor Statistics:
Labor Force Data
18. Job openings levels and rates by industry and region, seasonally adjusted
1
Levels (in thousands)
Industry and region
Rates
2004
2004
Mar.
Totai2 ............... .. ......... ... ... .... ... ........ .. ...
Apr.
May
July
June
Aug.
Sept.P
Mar.
Apr.
May
June
July
Aug.
Sept.P
3,079
3,135
3,105
3,022
3,237
32
3,235
2.3
2.3
2.3
2.3
2.4
2.4
2.4
Total private 2 ••...•••...•••••••• •• • ••• • ••••• . .....
2,740
2,778
2,746
2,640
2,894
2,859
2,889
2.5
2.5
2.4
2.3
2.6
2.5
2.6
Construction ............. .. ......... ..... ... .
113
105
108
94
88
121
126
1.6
1.5
1.5
1.3
1.3
1.7
1.8
Manufacturing ... ..... ........ .. ....... .......
232
251
244
247
240
234
246
1.6
1.7
1.7
1.7
1.6
1.6
1.7
Industry
Trade, transportation, and utilities ... ...
524
531
521
503
567
551
561
2.0
2.0
2.0
1.9
2.2
2.1
2.2
Professional and business services ....
502
518
530
494
583
594
564
3.0
3.1
3.1
2.9
3.4
3.5
3.3
......
559
576
542
496
537
536
546
3.2
3.3
3.1
2.9
3.1
3.1
3.1
Leisure and hospitality .. ........ ...........
370
376
391
421
435
410
411
2.9
3.0
3.1
3.3
3.4
3.2
3.2
Government ................. . ....... .. ........ ....
353
354
360
380
343
337
339
1.6
1.6
1.6
1..7
1.6
1.5
1.5
Education and health services .. .
Reglon 3
Northeast. ...... ...... ...... ..... ..... .... .
South ........... .. .......... ... ..... .... .. . .....
569
560
526
546
545
540
547
2.2
2.2
2.0
2.1
2.1
2.1
2.1
1,176
1,191
1,164
1,164
1,280
1,259
1,210
2.5
2.5
2.5
2.4
2.7
2.6
2.5
Midwest ..... ... ... .. ....... ... ........ .. .. ....
663
692
688
631
635
613
696
2.1
2.2
2.2
2.0
2.0
1.9
2.2
West. ........ ... .... .... ...... ................ ...
655
694
765
677
738
771
778
2.2
2.4
2.6
2.3
2.5
2.6
2.6
1
Detail will not necessarily add to totals because of the independent seasonal
adjustment of the various series.
2
Includes natural resources and mining, information , financial activities, and other
services, not shown separately.
3
Northeast: Connecticut, Maine, Massachusetts, New Hampshire, New Jersey,
New York, Pennsylvania, Rhode Island, Vermont; South : Alabama, Arkansas,
Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland,
Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia,
West Virginia; Midwest: Illinois, Indiana, Iowa, Kansas, Michigan , Minnesota
Missouri , Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah
Washington, Wyoming.
NOTE: The job openings level is the number of job openings on the last business day 01
the month ; the job openings rate is the number of job openings on the last business day 01
the month as a percent of total employment plus job openings.
= preliminary.
P
19. · Hires levels and rates by industry and region, seasonally adjusted
1
Levels (in thousands)
Industry and region
Rates
2004
2004
Mar.
Total 2 • ..•.•• .. •. •..•• . •..•.. . •. ..• . • . ....•... • ..........
Apr.
May
June
July
Aug.
Sept.P
Mar.
Apr.
May
June
July
Aug
Sept.P
4,603
4,398
4,206
4,433
4,229
4,375
4,297
3.5
3.4
3.2
3.4
3.2
3.3
Total private 2 ... .. .... .. .. ........ .. .... ....... ..
4,256
4,090
3,938
4,110
3,930
4,058
3,948
3.9
3.7
3.6
3.7
3.6
3.7
3.6
Construction ....... ......... .... ... ...... ... .
437
421
406
436
368
401
388
6.4
6.1
5.9
6.3
5.3
5.8
5.6
2.6
3.3
Industry
Manufacturing ... ... .................. ........
361
354
336
370
352
356
379
2.5
2.5
2.3
2.6
2.4
2.5
Trade, transportation, and utilities ... ....
1,009
1,032
938
945
957
984
879
4.0
4.1
3.7
3.7
3.8
3.9
3.4
Professional and business services ....
713
609
631
692
621
690
674
4.4
3.7
3.8
4.2
3.8
4.2
4.1
Education and health services ... ..... ...
444
460
451
428
418
470
403
2.6
2.7
2.7
2.5
2.5
2.8
62.4
Leisure and hospitality .......... ...... ....
810
766
739
749
760
760
834
6.6
6.2
6.0
6.1
6.2
6.1
6.7
Government ................ .. ·· ····"·'· ·· ···· ···
343
300
272
328
310
322
339
1.6
1.4
1.3
1.5
1.4
1.5
1.6
Region
1
3
Northeast. ................. ..... ..... ..... ....
744
810
708
703
720
763
758
3.0
3.2
2.8
2.8
2.9
3.0
3.0
South ....... .. ................ . ................
1,781
1,582
1,606
1,709
1,640
1,643
1,659
3.9
3.4
3.5
3.7
3.5
3.5
3.6
Midwest. ................... ... . . . . . . . . . . . . . . . .
1,040
991
956
1,009
935
945
939
3.4
3.2
3.1
3.2
3.0
3.0
3.0
West ..... .. .... ........ ....... ... .. ...... ... .... .
1,029
1,093
951
1,023
685
1,018
960
3.6
3.8
3.3
3.6
3.0
3.5
3.3
Midwest:
Illinois,
Detail will not necessari ly add to totals because of the independent seasonal
Indiana,
Iowa,
Kansas,
Michigan, Minnesota; Missouri,
adjustment of the various series.
Nebraska, North Dakota, Ohio, South Dakota, Wisconsin ; West: Alaska, Arizona,
2
California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah,
Includes natural resources and mining, information, financial activities, and other
services, not shown separately.
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
District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi,
is the number of hires during the entire month as a percent of total employment.
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;
102
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
P=
oreliminarv.
20. Total separations levels and rates by industry and region, seasonally adjusted
1
Rates
Levels (in thousands)
2004
2004
Industry and region
Mar.
Totai2 ... .. .... ... .... ......... .. ... ... ... .. ... .. ........
Apr.
May
June
July
Aug.
Sept.P
4,165
Mar.
Apr
June
May
3.2
3.1
3.1
July
3.1
Aug.
3.1
Sept.P
3.1
3.2
4,134
4,088
4,040
4,069
4,074
4,134
Total private 2 •• • • • • • • • • • •• •• • • •••• , • • • • •••• • , • • •• • •
3,868
3,843
3,761
3,789
3,793
3,894
3,876
3.5
3.5
3.4
3.5
3.5
3.5
3.5
Construction .. ... .... ......... ... ... . ..... .. ..
392
391
367
382
364
391
367
5.7
5.7
5.3
5.5
5.3
5.6
5.3
Industry
Manufacturing .. ...... ............... ... ......
377
353
377
343
367
379
379
2.6
2.5
2.6
2.4
2.5
2.6
2.6
Trade, transportation, and utilities .......
978
1,013
917
927
972
951
906
3.8
4.0
3.6
3.6
3.8
3.7
3.6
Professional and business services .. ...
597
606
556
607
613
575
588
3.7
3.7
3.4
3.7
3.7
3.5
3.6
Education and health services ... ........
382
386
379
362
363
380
386
2.3
2.3
2.2
2.1
2.1
2.2
2.3
Leisure and hospitality .......... ... ....... ..
715
679
696
734
694
760
769
5.8
5.5
5.6
5.9
5.6
6.2
6.2
Government. .. ........ .... ........ ... ...... .......
284
245
268
270
273
246
290
1.3
1.1
1.2
1.3
1.3
1.1
1.3
Reglon
3
Northeast. ... .. ...... .. .. ... ... , ... ........ ....
666
716
648
704
674
717
724
2.7
2.9
2.6
2.8
2.7
2.8
2.9
South .... ...... .. .... .. .. ... ... .. ... .. ..... .. ...
1,612
1,524
1,504
1,533
1,545
1,527
1,504
3.5
3.3
3.2
3.3
3.3
3.3
3.2
938
877
833
853
935
831
934
3.0
2.8
2.7
2.7
3.0
2.7
3.0
1,003
959
1,008
979
945
1,087
991
3.5
3.4
3.5
3.4
3.3
3.8
3.5
Midwest. ... .. .. ........ .................... .... .
West. ....... .. ... ....... ........ ... ... .. ........
1
Detail will not necessarily add to totals because of the independent seasonal adjustment
of the various series.
Midwest: Illinois, Indiana, Iowa,
Kansas, Michigan, Minnesota, Missouri, Nebraska,
North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona, California,
Includes natural resources and mining , information, financial activities, and other
services, not shown separately.
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,
District of Columbia, Florida, Georgia,
Kentucky, Louisiana,
Maryland,
Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia;
NOTE: The total separations level is the number of total separations during the entire
month; the total separations rate is the number of total separations during the entire
mon th as a percent of total employment.
P
= preliminary.
21. Quits levels and rates by industry and region, seasonally adjusted
1
Levels (in thousands)
Industry and region
Mar.
Total 2 ..• . .. ....• . . . . ... . ...• . ... . ... .. . .. ... . • •. • •• ... . . .
Rates
2004
2004
Apr.
May
June
July
Aug.
Sept.P
, Mar.
Apr.
May
2,271
2,278
2,173
2,284
2,265
2,252
2,258
1.7
1.7
Total private 2 .. . .. .. ... • ...•. • ..... • .. . . • • .. . • .. • ..
2,144
2,151
2,026
2,162
2,141
2,140
2,130
2.0
Construction ....... ... ........... ... ... ..... ..
154
149
144
156
101
147
132
2.3
Manufacturing ....... ... ..... ....... ... .... ...
176
189
171
171
174
165
186
Trade, transportation, and utilities ...... .
530
563
525
536
559
552
539
June
July
Aug.
Sept.P
1.7
1.7
2.0
1.9
1.9
1.5
2.1
1.9
1.2
1.2
1.1
1.3
2.1
2.1
2.2
2.2
2.1
1.9
1.7
1.7
1.7
2.0
1.9
2.0
2.2
2.1
2.3
1.2
1.3
1.2
2.1
2.2
Industry
Professional and business services ....
309
323
259
322
322
308
309
1.9
2.0
1.6
2.0
2.0
1.9
Education and health services .... ... ... .
252
245
223
225
271
239
244
244.0
1.5
1.3
1.3
1.6
1.4
1.4
Leisure and hospitality .. .. ..... .. ....... ....
465
429
455
480
442
476
457
3.8
3.5
3.7
3.9
3.6
3.9
3.7
Government. ········ ·· ·· ·· ........ ..... .. .. .... .
129
129
129
123
126
116
129
.6
.6
.6
.6
.6
.5
.6
Northeast ......... ..... .. .. ..... ... .... .... ....
314
390
318
334
338
339
323
1.3
1.6
1.3
1.3
1.3
1.3
1.3
South ... ........... ...... .. .. , ... . , .......... ...
957
888
857
910
901
897
916
2.1
1.9
1.8
2.0
1.9
1.9
2.0
Midwest. ............ ... .... .... .. ...... ... ......
474
479
479
485
505
447
464
1.5
1.5
1.5
1.6
1.6
1.4
1.5
West. .. ....... .... .... .... .. .... ...... ........ ..
565
524
521
573
519
566
552
2.0
1.8
1.8
2.0
1.8
2.0
1.9
Reglon
1
3
Detail will not necessarily add to totals because of the independent seasonal adjustment
Midwest:
Illinois, Indiana,
Iowa,
Kansas, Michigan, Minnesota, Missouri,
Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; West: Alaska, Arizona,
of the various series.
Includes natural resources and mining, information, financial activities, and other
services , not shown separately.
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 quits level is the number of quits during the entire month; the quits rate
District of Columbia, Florida, Georgia, Kentucky,
is the number of quits during the entire month as a percent of total employment.
Louisiana, Maryland , Mississippi,
North Carolina, Oklahoma, South Carolina, Tennessee, Texas. Virginia, West Virginia;
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
P
= preliminary.
Monthly Labor Review
November 2004
103
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)
Average weekly wage 1
Employment
December
2003
(thousands)
Percent change,
December
2002-032
Fourth
quarter
2003
Percent change,
fourth quarter
2002-03 2
United States 3 ..... ... ......... ........ ............ ... ................... ........ ..•.• ......
Private industry ............... .. .. .......... .. ... ..... ............................ ... ..
Natural resources and mining ............................... .... ....... ....
Construction ....................................... ............... ... ... ... ...... .. ..
Manufacturing ...................................... ...... .. ...... ......... ... ..... .
Trade, transportation, and utilities ... ........... .. .... ....................
Information .. ... ............................ .... .... ... .............. .... ...... .... .. .
Financial activities ....................... .................. ...................... .
Professional and business services .... ..... .. ... .............. ........ .
Education and health services ...... ...... ............. ................. .. .
Leisure and hospitality ............ .... ....................... ............. .... .
Other services .................................. .. .. ... ... ................ ......... .
Government ............... .. .................................... .. ... ... ............... .
8,314.1
8,048.7
123.7
804.9
376.8
1,853.6
145.2
767.0
1,329.4
732.2
669.9
1,080.6
265.3
129,341 .5
108,215.1
1,557.8
6,689.5
14,307.8
25,957.3
3,165.9
7,874.7
16,113.2
15,974.0
12,042.8
4,274.1
21 ,126.3
0.0
.0
.1
1.2
-4.2
-.3
-4.0
1.2
.6
2.1
1.7
-.1
-.2
$767
769
703
837
943
665
1,139
1,138
945
731
335
494
757
3.6
3.9
4.9
2.3
6.7
3.4
3.9
5.9
3.8
3.8
3.4
3.1
2.4
Los Angeles, CA ............. .......... .... .. ... .................................... ..... .
Private industry ... .. ....... .. ........................................ ....... ... ....... .
Natural resources and mining ..... ......................... .. .... .. ...... ..
Construction ... ....................... ....... ............ ...... .... ................. .
Manufacturing ........................................... .............. .. .......... .
l rade , 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
6.4
5.5
7.9
16.1
2.6
7.6
6.1
6 .1
.1
Harris, TX .... ... ..... .. ......... .... ....... ... ......................................... .... ...
Private industry . ... .. .. .. ...
............ .. .. ... ....... .. .. ......... ..... .... ... .
Natural resources and mining .......... ......... ............ .... ......... ..
Construction ....................... .............. .. .. ... ..... .. .................. ... .
Manufacturing .......... ......... ............ .... .. ........... ............. .........
Trade, transportation, and utilities ..... .. ................ .............. ...
Information ...... ....................... .................... ...... ... ... .. ........... .
Financial activities ...... ......................................... ............... ..
Professional and business services .................. ........... ...... .
Education and health services .................... ......... ............... .
Leisure and hospitality ......... ... ....... ........................ .......... ... .
Other services ............... ..... .............................................. .. ..
Government .......... ...................................... ...................... .. .. ...
89.4
89.0
1.2
6.3
4.7
21.1
1.4
9.7
17.0
8.8
6.5
10.3
.4
1,841.5
1,595.2
62.5
135.5
164.0
403.2
33.8
113.1
279.0
188.3
155.2
56.3
246.3
-.9
-1.2
8.7
-5.0
-4.9
-2.1
-3.9
1.7
-1 .7
1.5
.7
-3.1
1.1
906
929
2 ,185
919
1,106
821
1,098
1,181
1,073
812
335
539
759
2.1
2.1
-.9
2.6
2.3
1.0
.4
4.9
3.2
1.8
-.9
.4
3.1
Maricopa, AZ ............................. .. .. ............ ............. ......................
Private industry .... .................. ............................ ... .................. .
Natural resources and mining .............. ................... .. ... ... .. .. .
Construction ...... ........................................... .. .................. .. . .
Manufacturing ........... ............ ... ................ .. .. .... .. .. .............. ..
Trade, transportation, and utilities ......... ..........................
Information ... ... .. ......
. ........... .............. ............ .... ....... ..
Financial activities ............................. .. ................................ .
Professional and business services ......... ....... ....... .. ..... ...... .
Education and health services ........ ............... ...... .. ............ ..
Leisure and hospitality .......................... .... .. ... .. ............ ...... ..
Other services ........... .......................... .. ....... ................. ... ... .
Government ........................................ .............. .. ........ ... ..
80.9
80.5
.5
8.4
3.3
18.6
1.6
9.5
18.1
7.6
5.6
5.7
.5
1,621 .2
1,401 .8
9.8
131.7
128.0
336.4
36.6
133.3
261.5
160.5
155.8
44.7
219.4
(4)
2.2
-2.6
5.9
-2.5
1.5
-4.1
1.5
4.2
5.6
.8
-2.6
1.6
757
755
545
779
1,050
712
872
933
776
842
364
500
766
4.0
3.9
4.4
2.1
See footnotes at end of table.
104 Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
3.4
8.2
3.2
.5
3.7
3.5
5.0
2.8
2.2
3.7
22. Continued-Quarterly Census of Employment and Wages: 10 largest counties, fourth quarter 2003.
County by NAICS supersector
Establishments,
fourth quarter
2003
(thousands)
December
2003
(thousands)
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
Orange, CA ...... ............ ............ ............................. .. ... ... .. .............
Private industry .. ................ .. ...... .. .. .......... .................. ... .. ...... .. .
Natural resources and mining ....................... .. .................... .
Construction ............ .. ..... .. ........ ............ ........ ........................
Manufacturing ............... ... ........... .. .. ........ .............................
Trade, transportation, and utilities .. .. .................... ............... .
Information .. ......... ............................ .. ................... ...............
Financial activities .. .................. .. ..... ...... .. ........... ... ........ ... ....
Professional and business services ......... ........................... .
Education and health services ......... ......... .. .............. .. .........
Leisure and hospitality ............................................... ..........
Other services ..... .......... ...................... ............... ...... .. ..........
Government .................................. ........................ ........ .. .........
88.8
87.4
.3
6.4
6.1
17.3
1.5
9.7
17.4
9.1
6.6
12.9
1.4
1,436.6
1,305.5
6.1
85.5
179.9
278.8
6.8
73.0
144.9
326.1
64.0
140.0
237.7
131 .4
127.5
40.5
156.2
3.0
6.0
765
742
421
788
695
689
990
1,062
948
748
432
450
886
3.5
3.6
4.0
2.7
5.8
4.2
1.7
-1 .1
5.2
2.3
9.9
3.0
2.8
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
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
358
518
859
-.5
-.7
-1 .8
.3
-9.8
-1.7
-3.2
.7
-.2
1.4
2.1
80.2
79.9
.5
4.9
2.8
23.2
1.7
8.2
15.9
7.8
5.3
7.5
.3
Totais for the United States do not include data for Puerto Rico or the
849
5.3
5.2
.2
5.9
11 .4
2.7
5.3
6.2
2.8
3.7
.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 .. .. ......................... ............... ...... .......................... .
3
874
875
579
969
1,036
802
1,152
1,354
942
935
944
1,109
921
1,176
804
1,829
1,114
1,160
746
390
463
882
6.2
2.7
14.8
1.5
6.1
11 .7
5.9
5.4
26.4
.6
Percent changes were computed from quarterly employment and pay data
adjusted for noneconomic county reclassifications. See Notes on Current Labor
Statistics.
1.3
2.1
.2
.1
-11 .3
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
Average weekly wages were calculated using unrounded data.
1,272
1,215
1,152
887
432
587
800
22.7
5.5
6.8
5.2
8.7
2.9
4.2
2.7
4.3
2.8
-.1
815
809
491
869
1,129
655
1,582
1,058
989
778
346
449
843
81.6
81 .0
2
898
-5.1
1.2
.0
2.4
.0
-3.4
-1 .8
4.3
4.8
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
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 ..... ...... ... .................. .. ............. .. ... ............ .. ........ ...
.4
$952
970
2,680
909
1,075
-3.3
8.3
Percent change,
fourth quarter
2002-032
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
85.3
83.9
.9
6.4
3.6
14.2
1.4
8.8
-1.4
-1.4
-20.5
-2.2
-3.1
Fourth
quarter
2003
127.8
261.0
126.6
159.9
46.0
131. 1
33.8
14.9
7.6
6.5
19.5
1.3
Percent change,
December
2002-03 2
4.4
-3.0
.6
-4.4
9.9
1.0
6.1
2.5
6.3
-5.7
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 ........ ........................ ............... ..... .. .............. .. .. ... ..
1
Average weekly wage 1
Employment
-.4
-8.2
1.1
.8
2.4
.7
1.5
2.9
1.2
1.0
-1 .8
.5
3.8
2.6
2.5
1.0
.7
11 .5
.9
-2.0
.4
2.8
5.7
2.4
2.7
2.9
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
November 2004
105
Current Labor Statistics:
Labor Force Data
23. Quarterly Census of Employment and Wages: by State, fourth quarter 2003.
State
Establishments,
fourth quarter
2003
(thousands)
December
2003
(thousands)
Percent change,
December
2002-03
Fourth
quarter
2003
Percent change,
fourth quarter
2002-03
United States 2 ....... .. ..... ...... ...... .. .. .... .
8,314.1
129,341 .5
0.0
$767
3.6
Alabama ............ .. ............ .. ............. .. .
Alaska ........... .. .................. ............. .. .
Arizona ...... .. .... .. ................ ............... .
Arkansas ............. ... ... .. ......... .. ...... .... .
California .. .........................................
Colorado ........ .. ........... .. .......... ........ ..
Connecticut ........................ ... ........... .
Delaware .................. .. ........ ... ... ... ... .. .
District of Columbia .......................... .
Florida ..................... ...... .... .. ..... ........ .
111.8
20.0
126.9
75.2
1,190.8
160.0
109.1
27.1
30.0
504.1
1,838.1
282.7
2,352.1
1,133.6
14,922.3
2,134.6
1,648.9
408.4
654.8
7,424.5
-.1
2.2
.5
.0
-1.1
-.7
.5
-.4
.8
657
746
710
587
869
784
992
825
1,238
685
4.0
1.1
3.8
4.1
3.8
2.0
3.8
5.0
3.9
3.8
Georgia ................ ...... .. .. ....... .. ......... .
Hawaii ... ....... ... .. .......... ..................... .
Idaho ........................... ..... ................ .
Illinois ..................... ... ... .....................
Indiana .. .... .. .. .. ... .......... ............ ........ .
Iowa ..... .. ...... .... .... ............... ..... .. ...... .
Kansas ............ .... ... ..................... ... .. .
Kentucky ..... .. ........................... ..... ... .
Louisiana ............. ... ............. ..... ........ .
Maine .................. .................... ......... .
245.6
37.4
48.5
325.7
152.1
90.6
82.2
105.7
114.0
47.4
3,845.6
583.0
577.5
5,738.7
2,852.2
1,418.5
1,298.3
1,740.6
1,870.9
595.8
.2
1.3
.6
-1.2
-.3
.0
-.9
.3
.5
.7
734
678
579
827
675
626
631
645
628
631
2.8
3.7
1.8
3.2
3.5
4.7
2.8
3.5
2.4
4.6
Maryland ..................... ..................... .
Massachusetts .. ........ .......... ........ ..... .
Michigan ............................. .......... ... . .
Minnesota ... ..................................... .
Mississippi .......... ...................... ........ .
Missouri ............................. ..... .......... .
Montana ........... ... .... ........... .............. .
Nebraska ...... ........................... ......... .
Nevada .............. ......................... ...... .
New Hampshire ............................... .
150.4
206.6
251 .3
159.0
65.6
165.4
42.0
55.3
60.3
47.0
2,466.4
3,154.6
4,365.8
2,591 .9
1,108.1
2,633.6
396.6
884.4
1,111.2
614.9
.7
-1.9
-1 .1
-.5
.4
-.7
1.1
.6
4.4
.6
831
954
806
777
559
676
549
613
721
788
3.6
5.2
3.9
3.2
3.7
2.4
4.0
3.2
5.1
4.0
New Jersey ......................... ............ ..
New Mexico .................................. ... .
New York ................... ............ .... .. ... . .
North Carolina .................................. .
North Dakota ........... .... .. ............... ... . .
Ohio ............................. .. .. ... .... .. ........
Oklahoma ............................. ............ .
Oregon ..............................................
Pennsylvania ......... ........... ................ .
Rhode Island ... .. .... ... ..... ........ ............
268.1
50.4
550.3
227.8
24.0
294.2
91 .6
118.8
326.9
34.7
3,912.8
757.1
8,379.2
3,759.6
317.6
5,322.4
1,423.4
1,579.8
5,524.5
480.5
.1
1.4
-.4
-.1
.9
-.7
-1.3
.2
-.2
1.2
945
612
959
679
563
713
597
694
750
738
3.4
4.1
5.2
4.5
4.3
3.8
4.2
3.3
4.7
5.1
South Carolina .. ......... .................. .... .
South Dakota .. .............. ... ... ............. .
Tennessee ............................. ....... ... .
Texas .... ....... .. ...... .. ... .... ..... .............. .
Utah ......... ........... ...... .. ...... .. .. .. ......... .
Vermont .......... .. ...... .. .... ........... ........ .
Virginia ............... ..... ...... .. ................. .
Washington ................... ................... .
108.4
28.1
128.4
505.3
73.9
24.1
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
202.6
1.1
~:~~~~~~'.~.::::::::::::::::: : ::::::::::::::::·:
222.7
47.2
157.6
Wyoming ........... .. .. ..... ...................... .
22.0
241.6
1.7
616
4.1
Puerto Rico ..... .. ....... ........ ................ .
Virgin Islands ........... ............... ..... ... ..
50.2
3.2
1,074.1
42.5
3.5
-.2
450
629
4.7
2.4
1
Average weekly wages were calculated using unrounded data.
2 Totals for the United States do not include data for Puerto Rico
or the Virgin Islands.
106
Average weekly wage 1
Employment
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
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
3,21 5,921,236
3,414 ,514,808
3,674,031,718
3,967,072,423
4,235,579,204
4,587,708,584
4,695,225,123
4,714,374,741
$26,361
26 ,939
27,846
28,946
30,353
31,945
33,340
35,323
36,219
36,764
$507
518
536
557
584
614
641
679
697
707
$26,055
26,633
27,567
28,658
30,058
31,676
33,094
35,077
35,943
36,428
$501
512
530
551
578
609
636
675
691
701
$25,934
26,496
27,441
28,582
30 ,064
31 ,762
33,244
35,337
36,157
36,539
$499
510
528
550
578
611
639
680
695
703
$28,643
29,518
30,497
31,397
32,521
33,605
34 ,681
36,296
37,814
39,212
$551
568
586
604
625
646
667
698
727
754
$26,095
26,717
27,552
28,320
29,134
30,251
31,234
32,387
33,521
34 ,605
$502
514
530
545
560
582
601
623
645
665
$36,940
38,038
38,523
40,414
42,732
43,688
44,287
46,228
48,940
52,050
$7 10
731
741
777
822
840
852
889
941
1,001
UI covered
1993 ... ..... ............................ ..............
1994 ... ........... .......... ... ... ................... .
1995 ....... .. ........................................ .
1996 ........................................... .. .... .
1997 ........... .... .. ...................... .. .. ...... .
1998 .... .. ... .. .................... .. .. .............. .
1999 .... .. ................................... ... .... ..
2000 ·············· ···· ·· .. ··· ····'"·'" ·'" ·'"·'" ·'" .. ..
2001 ....................... .. ... ... .. .... ............ .
2002 ····· ···· ··········· ·· ···· ························
6,632,221
6,778,300
6,990,594
7,1 37,644
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
7,1 21,182
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 ,49 1
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 total s 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
November 2004
107
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
108
Total
Fewer than
5 workers 1
5 to9
workers
10 to 19
workers
20 to 49
workers
50 to 99
workers
100 to 249
workers
250 to 499
workers
500to 999
workers
1,000 or
more
workers
Total ali lndustries 2
Establishments, first quarter ..................
Employment, March ...............................
7,933,974
105,583,548
4,768,812
7,095,128
1,331 ,834
8,810,097
872,241
11,763,253
597,662
18,025,655
203,030
13,970,194
115,598
17,299,058
28,856
9,864,934
10,454
7,090,739
5,487
11,664,490
Natural resources and mining
Establishments, first quarter ..................
Employment, March ·······························
124,527
1,526,176
72 ,088
110,155
23,248
153,629
14,773
198,895
9,226
275,811
2,893
198,122
1,593
241 ,559
501
171,063
161
108,563
44
68,379
Construction
Establishments, first quarter ..................
Employment, March ...............................
795,029
6,285,841
523,747
746 ,296
129,201
846 ,521
76,215
1,021 ,722
46,096
1,371 ,071
12,837
872,274
5,604
823,846
1,006
338,107
262
172,944
61
93,060
Manufacturing
Establishments, first quarter
··················
Employment, March ...............................
381,159
14,606,928
148,469
252,443
65,027
436,028
57,354
788,581
54,261
1,685,563
25,927
1,815,385
19,813
3,043,444
6,506
2,245,183
2,565
1,732,368
1,237
2,607,933
Trade, transportation, and utilities
Establishments, first quarter ..................
Employment, March ·······························
1,851 ,662
24,683,356
992,180
1,646,304
378,157
2,514,548
239,637
3,204,840
149,960
4,527 ,709
51 ,507
3,564,316
31,351
4,661 ,898
6,681
2,277,121
1,619
1,070,141
570
1,216,479
Information
Establishments, first quarter ..................
Employment, March ...............................
147,062
3,208,667
84,906
112,409
20,744
138,076
16,130
220,618
13,539
416,670
5,920
410,513
3,773
576,674
1,223
418,113
575
399,366
252
516,228
Financial activities
Establishments, first quarter ..................
Employment, March ·······························
753,064
7,753,717
480,485
788,607
135,759
892,451
76,733
1,017,662
39,003
1,162,498
11,743
801,140
6,195
934,618
1,794
620,183
883
601 ,549
469
935,009
Professional and business services
Establishments, first quarter ..................
Employment, March
·······························
1,307,697
15,648,435
887,875
1,230,208
180,458
1,184,745
111,532
1,501,470
73,599
2,232,506
28,471
1,969,466
17,856
2,707,203
5,153
1,762,251
1,919
1,307,870
834
1,752,716
Education and health services
Establishments, first quarter ··············· ···
Employment, March ...............................
720,207
15,680,834
338,139
629,968
164,622
1,092,329
103,683
1,392,099
65,173
1,955,861
24,086
1,679,708
17,122
2,558,300
3,929
1,337,188
1,761
1,220,921
1,692
3,814,460
Leisure and hospitality
Establisnments, 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
1
Includes establishments that reported no workers in March 2003.
2
Includes data for unclassified establishments, not shown separately.
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
NOTE : Details may not add to totals due to rounding . Data are only produced for
first quarter. Data are preliminary.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
26. Annual data: Quarterly Census of Employment and Wages, by
metropolitan area, 2001-02
Average annual wage2
Metropolitan area1
Percent
change,
2001-02
2001
2002
Metropolitan areasJ .. ...... .. .. .. .... ............ .... .... ..... ........ ... ....... .
$37,908
$38,423
1.4
Abilene, TX .... .. ............. ...... ... ........... ....... ...............................
Akron, OH ........ .......... .... ... .. ..... ...... ......... .. ... ...... ......... .. .... ... .. .
Albany, GA ..... ... ................... .... .. .. ......... ... .... ... ... .............. ..... .
Albany-Schenectady-Troy, NY ... ..... .. ..... ... .. .... ............. ...... ... .
Albuquerque, NM ................. .. ................................... ............. .
Alexandria, LA ... ...... ..... ... .. .. .. ....... ..... .... .. ..... ........ ....... ... ........
Allentown-Bethlehem-Easton, PA .... ................. ............ .... .....
Altoona, PA .... ... ... ..... ........... .. ....... ........... ... ..... ..... ........ ..... .....
Amarillo, TX .............. ........ ..... ....... .... .......... ........ ... ..... ... ...... ...
Anchorage , AK ... ....... ........ ....... .... ..... ..... ... ........ .... ........ ... ..... .
25,141
32,930
28,877
35,355
31,667
26,296
33,569
26,869
27,422
37,998
25,517
34,037
29,913
35,994
32,475
27,300
34,789
27,360
28,274
39,112
1.5
3.4
3.6
1.8
2.6
3.8
3.6
1.8
3.1
2.9
Ann Arbor, Ml ..... .... ...... ...... ....... ....... ...... .. ... .... .......... ........... ..
Anniston, AL ...... .. .... ........ .... .... ................. .................... ... .......
Appleton-Oshkosh-Neenah, WI ........ ... .. ...... .. .. ... ..... ... .. ...... ... .
Asheville, NC .... .... ................. ... .... .. .......... .. ........ ... .... ..... ....... .
Athens, GA ... .......... ........ .... ... ....... .. .... .... .... .... ..... ...... ........... ..
Atlanta, GA ... ... ........................... .. ... ....... ......... ..... ........ .... ..... .
Atlantic-Cape May. NJ ............... .. .. ..... ..... .. ........................ .... .
Auburn-Opelika, AL .. .. ..... ... ... .................................. .. ... ... ... ....
Augusta-Aiken, GA-SC ... .... ......... .... .... ..... ...... .. .. ..... .. ... .. ........
Austin-San Marcos, TX ... ..... .. ... .. .. ........ .... .. ... ... .. .... ........ ..... ...
37,582
26,486
32,652
28,511
28,966
40,559
31 ,268
25,753
30,626
40,831
39,220
27,547
33,020
28,771
29,942
41 ,123
32 ,201
26,405
31,743
39,540
4.4
4.0
1.1
3.4
1.4
3.0
2.5
3.6
-3.2
Bakersfield, CA ..... ... .. ........... .. .... ..... ... ............... ... ..... ........... ..
Baltimore, MD .. .. ...... .. ... .......... ..... .......... .. .... .... ...... .... ... ... ... ... .
Bangor, ME ....... ... ....... ... .... ... ..... ..... ... .. ... .... .... ....................... .
Barnstable-Yarmouth, MA .. .. .... ....... .... ... ........... .................... .
Baton Rouge, LA ... ....... .... .. ... .. ........ ..... ... ........... ... .... ... ..........
Beaumont-Port Arthur, TX ................ .... ... ... .. ...... ...... .... ....... ...
Bellingham, WA ... ...................... ... .... ...... .. ... .. ......... ..... ... ...... ..
Benton Harbor, Ml ... ..... ......... .. .. ............ ... ..... ... ............. .........
Bergen-Passaic, NJ ..... ... .... .... ..... ............... ... ..... ...... .... ..... .....
Billings, MT .................. ............ ........ ...... ...... .. ... .......... .... ... .... .
30,106
37,495
27,850
31,025
30,321
31,798
27,724
31 ,140
44,701
27,889
31 ,192
38,718
28,446
32,028
31 ,366
32,577
28,284
32,627
45,185
28,553
3.6
3.3
2.1
3.2
3.4
2.4
2.0
4.8
1.1
2.4
Biloxi-Gulfport-Pascagoula, MS .... .. ................. ......... ... .......... .
Binghamton, NY ............... ... ... ...... ...... .... ..... ....... .. ......... ........ .
Birmingham, AL ... ..... ... ..... .. .. ......... ...... .......... .. ... ... ... ... .......... .
Bismarck, ND ... .. ........ ... ....... ... ..... .... ... ............. ... .... ..... ......... ..
Bloomington, IN ......... .. ..... ... .......... ........ ..... .... ... .... ... ... .......... .
Bloomington-Normal, IL .. ... ............. ..... ......... ...... ..... ... ...... .. .. ..
Boise City, ID ........ ....... ....... ... .......... .. ........... .... .... .. ............... .
Boston-Worcester-Lawrence-Lowell-Brockton, MA-NH .........
Boulder-Longmont, CO ... ........................ .......... ..... ... ... ... ..... ...
Brazoria, TX .. ....... ... ... .. ........... .. ..... .................. ..... ..... ....... .. ... .
28,351
31 ,187
34,519
27,116
28,013
35.111
31,624
45,766
44,310
35.655
28,515
31 ,832
35,940
27,993
28,855
36.133
31 ,955
45,685
44,037
36,253
Bremerton, WA ............................ ......... ... .. ... .. ... .... ... .. .... ..... .. .
Brownsville-Harlingen-San Benito, TX ........ ... ......... .. .............
Bryan-College Station , TX .............. ..... ..... .. ......................... .. .
Buffal~Niagara Falls, NY .... ..... ... .............. ........ .. ....... ........... .
Burlington, VT .. ... ..... ......... ...... ............. ...... ............ ... ............. .
Canton-Massillon, OH .. ... ............ ........ .... .................... .... ...... .
Casper, WY .. .... ... ...... .. ..... .. ........... .. ........ .... ......... ..... ..... .... ... .
Cedar Rapids, IA .......... .... ... .. ..... .. .. .... .. .. ..... ... ............... ... ... .. .
Champaign-Urbana, IL ... .... .. .... ............. ....... .......... .... ........ ... .
Charleston-North Charleston. SC ...... .. .... ... ..... ... .... ... .. .. ..... ....
31,525
22,142
25,755
32,054
34,363
29,020
28,264
34,649
30,488
28,887
33,775
22,892
26,051
32,777
35.169
29,689
28,886
34,730
31 ,995
29,993
7.1
3.4
1.1
2.3
2.3
2.3
Charleston, WV ..................... ....... .............. ... .... ............... ..... .
Charlotte-Gastonia-Rock Hill. NC-SC ......... ........... ...... .... ...... .
Charlottesville, VA ........ ..... ...... .... ............................... ... ..... ... .
Chattanooga. TN-GA .... .......... ... .. ... ..... ..... .... ... ..................... ..
Cheyenne, WY ...... .. .. ... ... ...... ................ ... .. .. .............. ........... .
Chicago, IL .......... ...... ... ............... ...... ..... ... ...... .. ... ....... .......... .
Chic~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
Colorado Springs, CO ............................ ... .... ..... ... ....... ... ... ... .
Columbia, MO .... ...... ...... ..... ........ ... ...... .. .... .. ... .......... .... .. .... ... .
Columbia, SC .... .... ... .. ........ ....... ....... .......... ..... ...... ..... ...... ..... .
Columbus, GA-AL ......... .. ... ... ................... ........ .... ................ ...
Columbus. OH ................. ... .. ..... .. ... .... ... ..... ... .............. .......... .
Corpus Christi, TX .. ... ... .... ........ .... ............. .... ..... ...... .... ......... .
Corvallis, OR .. ............. ................. .. ........... ...... ......... .. ........... .
Cumberland, MD-WV .. ... .. ... ......... ... ...... .. ..... .. ... ............ ........ .
Dallas, TX ....... ............ .. ... ... ... ... .. ... ....... .... ... .............. .. ....... .. . .
Danville. VA .... .... ... ...... .................... ......... .. ........ .. .... ... ... ....... .
34,391
28,490
29,904
28,412
35,028
29,361
35,525
25,504
42 ,706
25,465
34,681
29,135
30,721
29,207
36,144
30,168
36,766
26,704
43,000
26,116
.9
.6
2.1
4.1
3.2
3.0
2.9
1.0
-.2
-.6
1.7
2.2
.2
4.9
3.8
2.2
4.5
1.3
2.6
3.1
5.4
1.7
.8
2.3
2.7
2.8
3.2
2.7
3.5
4.7
.7
2.6
See foo!notes at end of table.
Monthly Labor Review
November 2004
109
Current Labor Statistics:
Labor Force Data
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area 1
2001
2002
Percent
change,
2001-02
Davenport-Moline-Rock Island, IA-IL ............. ... .......... ..... ...... .
Dayton-Springfield, OH ................ .. .... ... .... .... .. ................... .. ...
Daytona Beach , FL ...... ........... ..... ........ ... ....... .. .... ........ ... ... .....
Decatur, AL .... ... .... .. .. ... ..... ... .. .. ........................... .. ......... ... ..... .
Decatur, IL ... ................................. .......... .. ... ...........................
Denver, CO .. ..... ..... ................. .. ................ .. ... ................. ... ... ..
Des Moines, IA ... ............. ... ... .............................. ................. ..
Detroit, Ml ..... .. ... ........... ... ... ... ....... .... ....... .. ....... .. .... ... ..... .. ..... .
Dothan, AL ... .............. .. ... ... .... ..... ... .... ................... ........... .. .... .
Dover, DE .. ... ............ ...... .... ......... .. ........... ... ... ...... ............ ... ...
$31 ,275
33,619
25,953
30,891
33,354
42,351
34,303
42,704
28,026
27,754
$32,118
34,327
26,898
30,370
33,215
42 ,133
35,641
43,224
29,270
29,818
2.7
2.1
3.6
- 1.7
-.4
-. 5
3.9
1.2
4.4
7.4
Dubuque, IA ............................ ... ................. ........... .... ........ .....
Duluth-Superior, MN-WI ........... ... .. ........................... ..... ....... ..
Dutchess County, NY ... ... ......... ...... .... ........... .... ... ..... ........ .... .
Eau Claire, WI .................... ..... .... ............. ... ........... .... ... ... .... ..
El Paso, TX ...... ............. ......... ......... ..... ..... ..... ..... .. ........ ......... .
Elkhart-Goshen, IN ...... .... .... .... .. .. ...... .... ...... .. .. ....... .... ... ...... .. .
Elmira, NY ............. ............ ... .. ................ ........... ... ... ..... .. .. ... .. .
Enid, OK ................. ........................ ... ..... .... .............. ..... ... ......
Erie, PA ...... .... ............. .. ... .... .... ... ... ... ..... ............. ................ .. .
Eugene-Springfield , OR ...................... ..... .. ....... .. .. .. ..... .... .. .... .
28,402
29,415
38,748
27 ,680
25,847
30,797
28 ,669
24,836
29,293
28,983
29,208
30,581
38,221
28,760
26,604
32,427
29,151
25,507
29,780
29,427
2.8
4.0
-1.4
3.9
2.9
5.3
1.7
2.7
1.7
1.5
Evansville-Henderson, IN-KY .... ... ..... ... .. .... .. .. ... ........ .. .. ........ .
Fargo-Moorhead, ND-MN .... .. ...... ... .... ... ..... ...... ..... ... .. .. .... .. ... .
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 table.
110
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
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
Percent
change,
2001-02
2001
2002
Jamestown, NY ............. .... ........... .. ... ...... .. ... .................. .. ..... .
Janesville-Beloit, WI .. ........ .......... .... .... ...... ... .. .... ........ ... ... ... .. .
Jersey City, NJ ........ ........................................... .... .... ........... .
Johnson City-Kingsport-Bristol, TN-VA .... .. ............... .. ... ....... .
Johnstown , PA ........................... ................... ................. ..... ... .
Jonesboro, AR ................ .................. .. ... ..... ....... .... .... ...... ...... .
Joplin, MO ..... .... ... ................. ............................. ... .... ..... ....... .
Kalamazoo-Battle Creek, Ml ........... ...... ..................... .... .... .... .
Kankakee, IL ................................ .. ... .............. ... .. ..... ............. .
Kansas City, MO-KS ....................... ............... ....... ...... .......... ..
$25,913
31,482
47,638
28,543
25,569
25,337
26,011
32 ,905
29,104
35,794
$26,430
32,837
49,562
29,076
26,161
26,165
26,594
34,237
30,015
36,731
2.0
4.3
4.0
1.9
2.3
3.3
2.2
4.0
3.1
2.6
Kenosha, WI ... ................ .. ......... .. ... .... .... .... ............... .. .... ...... .
Killeen-Temple, TX .. ....................... ........................... .. .... ... ....
Knoxville, TN .............................. ... ..... ..... .......... .... ..... ..... ..... ..
Kokomo, IN .......... ........... ............. ... ... ...... ... ..... .. .. .... .... .. .. ... ... .
La Crosse, WI-MN ..... .. ........... .. .... .... .... ... ................. .... ..... .... .
Lafayette, LA ...................... ... .... ........ ................... ... ... .. .... ..... .
Lafayette, IN .. ............... ................ ... ... ............... .. ... ....... ........ .
Lake Charles, LA ... ....................... .. ...................... ............ ..... .
Lakeland-Winter Haven, FL ................ .... ..... .... .. .. .... ... ........... .
Lancaster, PA ............................... .. ..... .... .......... .. .... ... ... .... .. ...
31 ,562
26,193
30,422
39,599
27,774
29,693
31,484
29,782
28,890
31,493
32,473
27,299
31 ,338
40,778
28,719
30 ,104
31,700
30,346
29,505
32 ,197
2.9
4.2
3.0
3.0
3.4
1.4
.7
1.9
2.1
2.2
Lansing-East Lansing, Ml ..................... ..... .... ........................ .
Laredo, TX .. ... ....................... .... .... .... ......................................
Las Cruces, NM ............ ...... .. ... .... ............ .. .... ............. .. ......... .
Las Vegas, NV-AZ ................. ...... .................... ..... ... .............. .
Lawrence, KS .......... ..................... ..... ... .... .......... .... .... ... ....... ..
Lawton, OK ..... ... ...... .... .. .......... .... .............. .... ... .. ........ ... ... .... . .
Lewiston-Auburn, ME .. ..... .. .... ..... ... .......... ........ .. .. ..... ..... .... ... .
Lexington, KY ... ... .... .. .. ......... ... ........ ..... .... .. .. ... ... .... .... .. ......... .
Lima, OH ............. .. .............. ... .. ...... .... ... ... ... .... ...... .. .... .... ... .. . .
Lincoln, NE ............................ ...... .... ..... .... ..... .. .. ................ ... . .
34,724
24,128
24,310
32,239
25,923
24,812
27 ,092
31 ,593
29,644
29,352
35,785
24,739
25,256
33,280
26,621
25,392
28,435
32,776
30,379
30,614
3.1
2.5
3.9
3.2
2.7
2.3
5.0
3.7
2.5
4.3
Little Rock-North Little Rock, AR .... .... .. .... ........ .. ........... ... .... . .
Longview-Marshall, TX ................. .. .... .. ... ........ .... ............... ... .
Los Angeles-Long Beach, CA .. .. .............. ... ...... .............. .... .. .
Louisville, KY-IN .. ............ ... ... ..... .. ............... ........... ............... .
Lubbock, TX ... ..... ............... ...... ................... .... .......... ..... ....... .
Lynchburg, VA .. ... ............... ... ......... .... ..... .................... ......... ..
Macon, GA .......................... .... ........... ................. ................... .
Madison, WI .... ......................... ..... .. .... .... .... ...... ... ... ... ... ... .... .. .
Mansfield, OH ... ..... .... ... .... ..... ......... .......... ............ .. ... ... .... ..... .
McAllen-Edinburg-Mission, TX .............. .... ........ .................. ...
30,858
28,029
40,891
33,058
26,577
28,859
30 ,595
34,097
28,808
22,313
31 ,634
28,172
41,709
33,901
27,625
29,444
31 ,884
35,410
30 ,104
23,179
2.5
Medford-Ashland , OR .. ......... ...... .... ....... ... ...... .... .... ......... .. .... .
Melbourne-Titusville-Palm Bay, FL .... ..... .......... .. ..... ... .. ..... ... ..
Memphis, TN-AR-MS ................ ............ ........... .... ... ... ........... .
Merced, CA ........... ............................. .. .. .. ... .......... .... ............. .
Miami, FL ............................ ... .. ... .... ... ............... .. .. ... .. .......... .. .
Middlesex-Somerset-Hunterdon, NJ ........ .... ..... ......... .......... . .
Milwaukee-Waukesha, WI ... ..... .... .......... .............. ................ ..
Minneapolis-St. Paul, MN-WI .. ........ ... .......... .. .... ...... ... ..... .... ..
Missoula, MT ........................ ........ ... .. .. ...... ....... ...... ... .......... .
Mobile, AL ...... ..................... ......... ... ... .... .... .............. .... .. ... .... ..
27,224
32,798
34,603
25,479
34,524
49,950
35,617
40 ,868
26,181
28,129
28,098
33,913
35,922
26,771
35,694
50,457
36,523
41 ,722
27 ,249
28,742
3.2
3.4
3.8
5.1
3.4
1.0
2.5
2.1
4.1
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
37,7 10
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
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
47,7 15
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
.5
2.0
2.6
3.9
2.0
4.2
3.9
4.5
3.9
2.2
2.2
3.1
1.9
-2.0
.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
November 2004
111
Current Labor Statistics:
Labor Force Data
26. Continued-Annual data: Quarterly Census of Employment and
Wages, by metropolitan area, 2001-02
Average annual wage2
Metropolitan area,
2001
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
Phoenix-Mesa, AZ .... ... ............ ...... ................ ... ............. ........ .
Pine Bluff, AR ................................................... ... ..... ... .. .........
Pittsburgh, PA ..... .... ... ................................ ................. ... ...... .. .
Pittsfield, MA .... .. ... .... .... ................. .... ... .. ... .. ......... ..... .... .... .....
Pocatello, ID ............. .. ... .... ... ...... ............................................
Portland, ME .. .......................... .... .. .. ....................... ............... .
Portland-Vancouver, OR-WA ........................... ....... .. .... ...... .. .
Providence-Warwick-Pawtucket, RI ........... ..... .... ............. .. ... .
Provo-Orem, UT ........ ............ ......... ............................ ... ........ .
Pueblo, CO .... .... .................. ....... ...... ...... .. ........... .................. .
35,514
27,561
35,024
31,561
24,621
32,327
37,285
33,403
28,266
27,097
36,045
28,698
35,625
32,707
25,219
33,309
37,650
34,610
28,416
27,763
Punta Gorda, FL ...... .... .......................................................... .
Racine, WI ............... ....... ....... ...... .... ... .... ... ...... ....... ............... .
Raleigh-Durham-Chapel Hill, NC ............ ....... ...... .................. .
Rapid City, SD ..... .. ............................ ... .. ................ .... ........... .
Reading, PA ............. ............. ...... .......... ................................ .
Redding, CA .......................................... ......... .. ..................... .
Reno, NV .. .......... .. ....... ... .... .. ... .. ...... .................... .... ...... .. ..... ..
Richland-Kennewick-Pasco, WA .. .. ... .. .... .............................. .
Richmond-Petersburg, VA .... ............ ... ..... .. ....... .. .................. .
Riverside-San Bernardino, CA ................ .... .. ... .... ................. .
25,404
33,319
38,691
25,508
32,807
28,129
34,231
33,370
35,879
30,510
26,119
34,368
39,056
26,434
33,912
28,961
34,744
35,174
36,751
31,591
Roanoke, VA .... ... ... .. ..... ......... .. .............. ....... .. ... ... ..... .... ........
Rochester, MN .. ................................. .... .. .............................. .
Rochester, NY ... ........... ...................... .... ... ............................ .
Rockford, IL ........ ........... .......................... ..... ... .... ......... ... ...... .
Rocky Mount, NC ............... ...... .. ......................... ................ .. .
Sacramento, CA .. ............. ................... ... .. ... .................... ... ... .
Saginaw-Bay City-Midland, Ml .. ...... ... ............. .. ........ .... ........ .
St. Cloud, MN .. ...................................................................... .
St. Joseph, MO ........ .................... .. ... .... .... .. ............................
St. Louis, MO-IL .................... ..... .... ...... .. .......... ................ .......
30,330
37,753
34,327
32,104
28,770
38,016
35,429
28,263
27,734
35,928
31 ,775
39,036
34,827
32,827
28,893
39,354
35,444
29,535
28,507
36,712
Salem, OR ........................ ..................... .. .... .......... .. .............. .
Salinas, CA .... .. ............. ........ .......... .. ................ .......... ........... .
Salt Lake City-Ogden, UT ................... ..... ............. ..... ............ .
San Angelo, TX ..... .. ... ...... ... ............. ..... .... ...................... ...... .
San Antonio, TX ...................... ............... ................... ... ... ... .. ..
San Diego, CA .... ..... .................. ... .. .... ...... ............ ..... .. ... .... ... .
San Francisco, CA ...................... .... ................. ............ .. ........ .
San Jose, CA ............. .... ................ .........................................
San Luis Obispo-Atascadero-Paso Robles, CA .................... .
Santa Barbara-Santa Maria-Lompoc, CA ... ..... .. .................... .
28,336
31,735
31,965
26,147
30,650
38,418
59,654
65,931
29,092
33,626
29,210
32,463
32,600
26,321
31,336
39,305
56,602
63,056
29,981
34,382
Santa Cruz-Watsonville, CA .. .. ....... ...... .... .......................... ... .
Santa Fe, NM ............... ............................... ... ................ ....... .
Santa Rosa, CA ..... ................ .... .. ..... ..... ........................ ... ..... .
Sarasota-Bradenton, FL ...................... ......... .. ... ... ... ...... ..... ... .
Savannah, GA .... ........... ........ ..... .............. ... .... .... ... ....... ... .. ... .
Scranton-Wilkes-Barre-Hazleto n, PA .................................. .
Seattle-Bellevue-Everett, WA ............... ......... ........... ... ...... .... .
Sharon, PA ................................. ... .. .... ................... ...... ..... ... ..
Sheboygan, WI ..... ... ... .......................................................... . .
Sherman-Denison, TX ......... ........... ......... ......... .. ................ ....
35,022
30,671
36,145
27,958
30,176
28,642
45,299
26,707
30 ,840
30,397
35,721
32,269
36,494
28,950
30,796
29,336
46,093
27,872
32,148
30,085
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
See footnotes at end of table.
112
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
Percent
change,
2001-02
3.0
3.9
2.4
3.8
3.3
3.8
5.8
.5
2.9
2.2
1.5
4.1
1.7
3.6
2.4
3.0
1.0
3.6
.5
2.5
2.8
3.1
.9
3.6
3.4
3.0
1.5
5.4
2.4
3.5
4.8
3.4
1.5
2.3
.4
3.5
.0
4.5
2.8
2.2
3.1
2.3
2.0
.7
2.2
2.3
-5.1
-4.4
3.1
2.2
2.0
5.2
1.0
3.5
2.1
2.4
1.8
4.4
4.2
-1.0
3.3
2.9
3.5
3.4
2.5
3.5
2.4
3.6
3.2
2.3
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
Percent
change,
2001-02
2001
2002
Stockton-Lodi, CA .................................. .. ........... ........ ... .. .. .. .. .
Sumter, SC ... ...... .. ......... ... .... ... .............. ... .......... .... ............... .
Syracuse, NY ....................................................... ... ............... .
Tacoma, WA .... ... ..... .. .. .... .... .. ....... ... ... .... ... ....... ..... ................ .
Tallahassee, FL .. .............. ... ... ............... .. .......... .................... .
Tampa-St. Petersburg-Clearwater, FL .. .. ....................... ....... .
Terre Haute, IN ... ........... ... .................................. .......... ... .. .... .
Texarkana, TX-Texarkana, AR .. .. .. ..... .. .. .. .... .. .......... ............ ..
Toledo, OH ..................... .... .. .......... ... .......... ..... ..................... .
Topeka, KS .... .. ......... .. ... ................ .. ... ....................................
$30,818
24,450
32,254
31 ,261
29,708
31 ,678
27,334
26,492
32,299
30,513
$31 ,958
24,982
33,752
32,507
30,895
32,458
28,415
27,717
33,513
31,707
3.7
2.2
4.6
4.0
4.0
2.5
4.0
4.6
3.8
3.9
Trenton, NJ .. .............. ... .............. .......................... ........... ...... .
Tucson, AZ. ......................................... ....... ... ....... .................. .
Tulsa, OK ................. .......... ... ... ... .............. .. ..... .................. .. .. .
Tuscaloosa, AL ........... ... ......... .... .. ............ ...... .. ............... ...... .
Tyler, TX ......... ............. ............. .. .......... .......................... ....... .
Utica-Rome, NY ........ .. .. .......... ... .. ............... ........................... .
Vallejo-Fairfield-Napa, CA .. ........... ... ..................................... .
Ventura, CA ...... ..................................................................... .
Victoria, TX ....... ........ .. ........ .. ... .... .. ..... ............................... ... ..
Vineland-Millville-Bridgeton, NJ ... ......... ... ................. ............. .
46,831
30 ,690
31,904
29,972
30,551
27,777
33,903
37,783
29,068
32,571
47,969
31 ,673
32,241
30,745
31,050
28,500
34,543
38,195
29,168
33,625
2.4
3.2
1.1
2.6
1.6
2.6
1.9
1.1
Visalia-Tulare-Porterville, CA ....................... ................ ..........
Waco, TX ................ .............. ............... .. ......................... ...... ..
Washington, DC-MD-VA-WV ...................... ........ .................. ..
Waterloo-Cedar Falls, IA ........................................... ........... ..
Wausau, WI ............. .......... .................................. ......... ... ...... .
West Palm Beach-Boca Raton, FL .............. ... ..................... ...
Wheeling, WV-OH ... ......... ... ..................... ..................... ........ .
Wichita, KS ................ .......... ............ .... ....... ........................... .
Wichita Falls, TX .... .. ... ......... .. .... .... ......... ............. .......... ........ .
Williamsport, PA .................. .. .... ..... .. ......... ............... ..... ........ .
24,732
28,245
47,589
29,119
29,402
35,957
26,282
32,983
25,557
27,801
25,650
28,885
48,430
29,916
30,292
36,550
26,693
33,429
26,387
27,988
3.7
2.3
1.8
2.7
3.0
1.6
1.6
1.4
3.2
Wilmington-Newark, DE-MD ......... ...... .. .... .......... ....... .. ... ... .. .. .
Wilmington, NC ....... .. .... .. ......... .... .. .. ...... .. ..... ...... ... .... .. ... .... .. ..
Yakima, WA .. .......................... ...................... .................... ... ...
Yolo, CA .................... ... .............. .. ............ ... .......................... .
York, PA ........ .. ......... ............................................................ ..
Youngstown-Warren, OH ............. .. ... ................. .. ................. .
43,401
29,157
24,934
35,591
32,609
29,799
28,967
23,429
2.9
-.4
3.0
~~~~~~· .~~.:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
42,177
29,287
24,204
35,352
31 ,936
28,789
27,781
22,415
Aguadilla, PR .................. ... ...... ... ... ... .......... .... .. .......... .. ... .... ...
Arecibo, PR ...... ......................................................................
Caguas, PR ......... .. .................. .......................... .................... .
Mayaguez, PR .......... ............... ... ..... .. ................. ... .................
Ponce, PR .. .. .. .... .. ............ ............ ..................... .. .. .... ............ .
San Juan-Bayamon, PR ....................... ................................. .
18,061
16,600
18,655
17,101
17,397
20,948
19,283
18,063
19,706
17,500
18,187
21 ,930
6.8
8.8
5.6
2.3
4.5
4.7
.3
3.2
.7
.7
2.1
3.5
4.3
4.5
1 Includes data for Metropolitan Statistical Areas (MSA) and Primary Metropolitan Statistical Areas
(PMSA) as defined by 0MB 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
November 2004
113
Current Labor Statistics:
Labor Force Data
27. Annual data: Employment status of the population
[Numbers in thousands]
Employment status
Civilian noninstitutional population .......... .
Civilian labor force ............................... .
Labor force participation rate .. ............
Employed ....... ........ ..................... .. .
Employment-population ratio ...... ...
Unemployed ............. ....... ... ..... ... ....
Unemployment rate .................. .....
Not in the labor force ............... ..... .........
1
1993
19941
1995
1996
199i
1998 1
1999 1
2000 1
2001
2002
2003
194,838
129,200
196,814
131 ,056
66.6
123,060
62.5
7,996
198,584
132,304
66.6
124,900
62.9
7,404
200,591
133,943
203,133
136,297
207,753
139,368
212,577
142,583
67.1
129,558
63.8
6,739
67.1
133,488
64.3
5,880
67.1
136,891
64.4
5,692
221,168
146,510
66.2
137,736
6.1
65,758
5.6
66,280
5.4
66,647
66,836
4.5
67,547
4.2
68,385
4.0
69,994
215,092
143,734
66.8
136,933
63.7
6,801
4.7
217,570
144,863
66.8
126,708
63.2
7,236
205,220
137,673
67.1
131,463
64.1
6,210
66.3
120,259
61.7
8,940
6.9
65,638
4.9
71,359
66.6
136,485
62.7
8,378
5.8
72,707
62.3
8,774
6.0
74,658
Not strictly comparable with prior years.
28. Annual data: Employment levels by industry
[In thousands]
Industry
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
..
91 ,855
95,016
97,866
100,169
103,113
106,021
108,686
110,996
110,707
108,828
108,356
Total nonfarm employment.. ......... ........ ...
Goods-producing ......... .................... ....... .
Natural resources and mining ................
Construction .. ... .................. ..... ..... .. ..... .
Manufacturing ............. .................. ........
110,844
22 ,219
666
4,779
16,744
114,291
22,774
659
5,095
17,021
117,298
23,156
641
5,274
17,241
119,708
23,410
637
5,536
17,237
122,770
23,886
654
5,813
17,419
125,930
24,354
645
6,149
17,560
128,993
24,465
598
6,545
17,322
131,785
24,649
599
6,787
17,263
131,826
23,873
606
6,826
16,441
130,341
22,557
583
6,716
15,259
129,931
21 ,817
571
6,722
14,525
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 ..... ........ ... .... .. ... ...... ..
69,636
22,378
5,093.2
13,020.5
3,553.8
710.7
2,668
6,709
11,495
12,303
9,732
4,350
72 ,242
23,128
5,247.3
13,490.8
3,701.0
689.3
2,738
6,867
12,174
12,807
10,100
4,428
74,710
23,834
5,433.1
13,896.7
3,837.8
666.2
2,843
6,827
12,844
13,289
10,501
4,572
76,759
24,239
5,522 .0
14,142.5
3,935.3
639.6
2,940
6,969
13,462
13,683
10,777
4,690
79,227
24,700
5,663.9
14,388.9
4,026.5
620 .9
3,084
7,178
14,335
14,087
11,018
4,825
81,667
25,186
5,795.2
14,609.3
4,168.0
613.4
3,218
7,462
15,147
14,446
11,232
4,976
84,221
25,771
5,892 .5
14,970 .1
4,300.3
608.5
3,419
7,648
15,957
14,798
11,543
5,087
86,346
26,225
5,933.2
15,279.8
4,410.3
601 .3
3,631
7,687
16,666
15,109
11 ,862
5,168
86,834
25,983
5,772.7
15,238.6
4,372.0
599.4
3,629
7,807
16,476
15,645
12,036
5,258
86,271
25,497
5,652 .3
15,025.1
4,223.6
596.2
3,395
7,847
15,976
16,199
11 ,986
5,372
86,538
25,275
5,605.6
14,911 .5
4,176.7
580.8
3,198
7,974
15,997
16,577
12,125
5,393
Total private employment... .. ................ .....
Government. ........... .. .................... .......
18,989
19,275
19,432
19,539
19,664
19,909
20,307
20,790
21 ,118
21 ,513
NOTE : Data reflect the conversion to the 2002 version of the North American Industry Classification System (NAICS), replacing the Standard lndustrrial Classification (SIC)
system. NAICS-based data by industry are not comparable with SIC-based data. See "Notes on the data" for a description of the most recent benchmark revision.
114
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
21,575
29. Annual data: Average hours and earnings of production or nonsupervisory workers on nonfarm
payrolls, by industry
1996
1997
1998
1999
2000
2001
Industry
1993
1994
1995
2002
2003
Private sector:
Average weekly hours ........ .... .......... ... ..... ... .. .... ... .. .
Average hourly earnings (in dollars) ... ... .......... ........
Average weekly earnings (in dollars) ........ .......... ... ..
34.3
11.03
378.40
34.5
11 .32
390.73
34.3
11.64
399.53
34.3
12.03
412.74
34.5
12.49
431.25
34.5
13.00
448.04
34.3
13.47
462.49
34.3
14.00
480 .41
34.0
14.53
493.20
33.9
14.95
506.07
33.7
15.35
517.36
Goods-produclnQ:
Average weekly hours .................................... ........
Average hourly earnings (in dollars) ......................
Average weekly earnings (in dollars) ....... ........ ......
40.6
12.28
498.82
41.1
12.63
519.58
40.8
12.96
528.62
40.8
13.38
546.48
41.1
13.82
568.43
40 .8
14.23
580.99
40.8
14.71
599.99
40.7
15.27
621 .86
39.9
15.78
630.04
39.9
16.33
651 .61
39.8
16.80
669.23
44.9
14.12
634.77
45.3
14.41
653.14
45.3
14.78
670.32
46.0
15.10
695.07
46.2
15.57
720.11
44.9
16.20
727.28
44.2
16.33
721 .74
44.4
16.55
734.92
44.6
17.00
757.92
43.2
17.19
741 .97
43.6
17.58
766.83
38.4
14.04
539.81
38.8
14.38
558.53
38.8
14.73
571 .57
38.9
15.11
588.48
38.9
15.67
609.48
38.8
16.23
629.75
39.0
16.80
655.11
39.2
17.48
685.78
38.7
18.00
695.89
38.4
18.52
711 .82
38.4
18.95
727.11
41 .1
11 .70
480.80
41 .7
12.04
502.12
41 .3
12.34
509.26
41 .3
12.75
526.55
41 .7
13.14
548.22
41 .4
13.45
557.12
41 .4
13.85
573.17
41.3
14.32
590.65
40.3
14.76
595.19
40.5
15.29
618.75
40.4
15.74
636.07
32.5
10.60
345.03
32.7
10.87
354.97
32.6
11.19
364.14
32.6
11.57
376.72
32.8
12.05
394.77
32.8
12.59
412 .78
32.7
13.07
427.30
32.7
13.60
445.00
32.5
14.16
460 .32
32.5
14.56
472.88
32.4
14.96
484.00
34.1
10.55
359.33
34.3
10.80
370.38
34.1
11.10
378.79
34.1
11.46
390.64
34.3
11.90
407.57
34.2
12.39
423.30
33.9
12.82
434.31
33.8
13.31
449.88
33.5
13.70
459.53
33.6
14.02
471.27
33.6
14.34
481.10
38.5
12.57
484.46
38.8
12.93
501.17
38.6
13.34
515.14
38.6
13.80
533.29
38.8
14.41
559.39
38.6
15.07
582.21
38.6
15.62
602.77
38.8
16.28
631 .40
38.4
16.77
643.45
38.0
16.98
644.38
37.8
17.36
657.12
30.7
8.36
484.46
30.9
8.61
501.17
30.8
8.85
515.14
30.7
9.21
533.29
30.9
9.59
559.39
30.9
10.05
582.21
30.8
10.45
602.77
30.7
10.86
631.40
30 .7
11 .29
643.45
30.9
11.67
644.38
30.9
11 .90
657.12
38.9
12.71
494.36
39.5
12.84
507.27
38.9
13.18
513.37
39.1
13.45
525.60
39.4
13.78
542.55
38.7
14.12
546.86
37.6
14.55
547.97
37.4
15.05
562.31
36.7
15.33
562.70
36.8
15.76
579.75
36.8
16.25
597.79
42.1
17.95
756.35
42.3
18.66
789.98
42.3
19.19
811 .52
42.0
19.78
830.74
42.0
20.59
865.26
42.0
21 .48
902.94
42.0
22.03
924.59
42.0
22.75
955.66
41 .4
23.58
977.18
40.9
23.96
979.09
41 .1
24 .76
1,016.94
36.0
14.86
535.25
36.0
15.32
551.28
36.0
15.68
564.98
36.4
16.30
592.68
36.3
17.14
622.40
36.6
17.67
646.52
36.7
18.40
675.32
36.8
19.07
700.89
36.9
19.80
731 .11
36.5
20.20
738.17
36.2
21 .01
761.13
35.5
11.36
403.02
35.5
11.82
419.20
35.5
12.28
436.12
35.5
12.71
451 .49
35.7
13.22
472.37
36.0
13.93
500 .95
35.8
14.47
517.57
35.9
14.98
537.37
35.8
15.59
558.02
35.6
16.17
575.51
35.5
17.13
608.87
34.0
11.96
406.20
34.1
12.15
414.16
34.0
12.53
426.44
34.1
13.00
442.81
34.3
13.57
465.51
34.3
14.27
490.00
34.4
14.85
510.99
34.5
15.52
535.07
34.2
16.33
557.84
34.2
16.81
574.66
34.1
17.20
586.68
32.0
11 .21
359.08
32.0
11 .50
368.14
32.0
11 .80
377.73
31 .9
12.17
388.27
32.2
12.56
404.65
32.2
13.00
418.82
32 .1
13.44
431.35
32.2
13.95
449.29
32.3
14.64
473.39
32.4
15.21
492.74
32.3
15.64
505.76
25.9
6.32
163.45
26.0
6.46
168.00
25.9
6.62
171 .43
25.9
6.82
176.48
26.0
7.13
185.81
26.2
7.48
195.82
26.1
7.76
202.87
26.1
8.11
211 .79
25.8
8.35
215.19
25.8
8.58
221 .26
25.6
8.76
224.25
32.6
9.90
322.69
32.7
10.18
332.44
32.6
10.51
342.36
32.5
10.85
352.62
32.7
11 .29
368.63
32.6
11.79
384.25
32.5
12.26
398.77
32.5
12.73
413.41
32.3
13.27
428.64
32.0
13.72
439.76
31 .4
13.84
434.49
Natural resources and mlnlnA
Average weekly hours ..................... .. ........ ... .........
Average hourly earnings (in dollars) .....................
Average weekly earnings (in dollars) ....................
Construction:
Average weekly hours ...........................................
Average hourly earnings (in dollars) .......... ... ... .... .
Average weekly earnings (in dollars) .. .... ...... .. .. ....
Manufacturing:
Average weekly hours ....... .. ............................. .....
Average hourly earnings (in dollars) .....................
Average weekly earnings (in dollars) ....................
Private service-providing:
Average weekly hours ... ....... ................................
Average hourly earnings (in dollars) ............. .........
Average weekly earnings (in dollars) .....................
Trade, transportation, and utllltles:
Average weekly hours .................................. .. ........
Average hourly earnings (in dollars) ........... ....... ....
Average weekly earnings (in dollars) .. ...................
Wholesale trade:
Average weekly hours ........................................
Average hourly earnings (in dollars) ... .... ... ........
Average weekly earnings (in dollars) ... .. ... .... .....
Retall trade:
Average weekly hours ... .... .. ...............................
Average hourly earnings (in dollars) ... .... ......... ..
Average weekly earnings (in dollars) .................
Transportation and warehousing:
Average weekly hours ........................................
Average hourly earnings (in dollars) ..................
Average weekly earnings (in dollars) .. ... ..... ... ....
Utllltles:
Average weekly hours ............................ .. ..........
Average hourly earnings (in dollars) .... ... ... ........
Average weekly earnings (in dollars) ... .. ....... .....
Information:
Average weekly hours .. ... ............... ....................
Average hourly earnings (in dollars) .. ... ....... .. ....
Average weekly earnings (in dollars) ......... .. .... ..
Financial activities:
Average weekly hours ... ... ..................................
Average hourly earnings (in dollars) .. ................
Average weekly earnings (in dollars) .. ... ... .........
Professional and business services:
Average weekly hours ............... ................... .. .. ..
Average hourly earnings (in dollars) ..................
Average weekly earnings (in dollars) .................
Education and health services:
Average weekly hours ... .. ... .. .... ... .......................
Average hourly earnings (in dollars) ..................
Average weekly earnings (in dollars) .. ... ............
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) .................
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
November 2004
115
Current Labor Statistics: Compensation & Industrial Relations
30. Employment Cost Index, compensation, 1 by occupation and industry group
[June 1989 = 100]
2002
Series
Sept.
2003
Dec.
Mar.
June
2004
Sept.
Dec.
Mar.
June
Percent change
Sept.
3 months
12 months
ended
ended
Sept. 2004
Civilian workers
2
161 .3
162.2
164.5
165.8
167.6
168.4
170.7
172.2
173.9
1.0
3.8
163.5
161 .4
166.3
164.9
156.4
161.3
164.3
162.4
166.7
166.1
157.5
162.2
166.7
164.1
171 .1
168.3
159.8
164.1
167.9
165.0
172.0
170.0
161.4
165.0
169.9
167.0
174.0
171 .7
162.9
166.8
170.7
168.0
174.9
172.5
163.7
167.9
172.7
170.2
175.8
175.3
166.9
169.7
174.0
171.2
177.1
177.2
168.8
170.9
175.8
173.6
178.2
178.7
170.1
172.7
1.0
1.4
.6
.8
.8
1.1
3.5
4.0
2.4
4.1
4.4
3.5
158.7
159.1
162.2
163.2
163.1
165.7
161 .6
160.2
161 .7
169.2
160.5
162.8
163.9
164.5
167.6
162.8
161.7
162.4
163.1
164.0
165.0
165.3
166.4
169.9
163.6
163.4
164.5
164.6
165.4
166.2
166.3
167.6
170.8
164.2
164.3
165.8
165.8
166.5
168.2
168.5
169.3
173.1
166.9
167.3
167.8
166.8
167.1
169.1
169.5
170.7
174.8
167.6
168.1
168.6
170.4
171 .7
170.8
171.2
173.0
176.8
168.5
170.1
170.4
171 .9
173.2
172.3
172.3
174.4
178.2
168.9
171.4
171 .8
173.4
174.9
174.0
174.5
176.7
180.5
171.8
174.1
173.5
.9
1.0
1.0
1.3
1.3
1.3
1.7
1.6
1.0
4.6
5.0
3.4
3.6
4.4
4.3
2.9
4.1
3.4
Private industry workers ........... ... ..... .. ........ ................ .
Excluding sales occupations ........................................ ..
161 .6
161 .6
162.3
162.4
165.0
165.1
166.4
166.6
168.1
168.1
168.8
169.0
171.4
171.6
173.0
173.2
174.4
174.6
.8
.9
3.7
3.9
Workers, by occupational group:
White-collar workers ................. .......................... ............ ..
Excluding sales occupations .. ..................................... .
Professional specialty and technical occupations ........ ..
Executive, adminitrative, and managerial occupations ..
Sales occupations .... .... ........................... .................... .
Administrative support occupations, including clerical. ..
Blue-collar workers .. .................. ............................... ..... ..
Precision production , craft, and repair occupations ...... .
Machine operators, assemblers, and inspectors .......... ..
Transportation and material moving occupations ......... ..
Handlers, equipment cleaners, helpers, and laborers .. ..
164.6
165.3
163.6
167.0
161 .6
165.6
156.3
156.9
155.4
151.0
161 .4
165.2
165.9
164.4
167.2
161 .9
166.7
157.3
157.8
156.7
151 .8
162.9
168.1
169.1
166.5
172.1
163.5
169.0
159.7
160.0
159.9
153.2
164.9
169.4
170.4
167.7
173.1
165.1
170.9
161 .4
162.0
161 .1
155.1
166.8
171.2
172.1
169.4
175.0
167.2
172.3
162.8
163.1
162.6
156.7
168.6
172.0
173.0
170.5
175.9
167.1
173.2
163.6
164.2
163.2
156.9
169.5
174.2
175.3
173.4
176.8
169.2
176.1
166.9
167.1
168.7
158.5
171 .7
175.7
176.7
174.7
178.1
171 .2
178.1
168.8
169.1
170.5
160.6
173.2
177.3
178.3
176.8
179.2
173.1
179.4
170.1
170.2
172.2
161.8
174.3
.8
.9
.9
1.2
.6
1.1
.7
.8
.7
1.0
.7
3.6
3.6
4.4
2.4
3.5
4.1
4.5
4.4
5 .9
3.3
3.4
..
Workers, by occupational group:
White-collar workers ............. ............................................. .
Professional specialty and technical. .............................. .
Executive, adminitrative, and managerial. ............ ........ .
Administrative support, including clerical. ...... .... ..... ..... ..
Blue-collar workers .................................................. ........ ..
Service occupations ................... ........ ........ ....................... .
Workers, by industry division:
Goods-producing ... ... .................... ... ......... .... .. ............... .... .
Manufacturing .......................... .. ........... .......................... .
Service-producing ................... ..... ...... .... .
Services ............... ....... ............................. ... ........ .. .......... .
Health services ............................................. ................. .
Hospitals .. ........ ..... ....... ............................................... .
Educational services ........ ... ........... ..................... ...........
3
Public administration ...... ..... ..... .... ... ......•..•...
Nonmanufacturing ........ ........... .......... ........... ...
Service occupations .......... ............................................ .
159.0
159.8
161 .7
162.6
163.8
164.3
166.9
168.2
168.9
.6
3.1
159.7
160.5
162.6
164.1
165.7
166.6
169.3
171 .0
172.4
.4
4.0
Workers, by industry division:
Goods-producing ........... ............ .................. .. ... ............... .
Excluding sales occupations ..................... ... ... ... ...... ..
White-collar occupations ................................ ... ....... ... .
Excluding sales occupations ................................... ..
Blue-collar occupations ......... ...................................... .
Construction ............................ .. .................................... .
Manufacturing ........ ... .. ....... .. ...... ....... .. .... ...................... .
White-collar occupations ....................... ...................... .
Excluding sales occupations .. .... .. ........................... ..
Blue-collar occupations ................................... ....... ..... .
Durables ............................. ....... ....... ............................ .
Nondurables .................. .. .............. .. ...... ........................ .
158.6
157.9
162.9
161 .1
155.9
156.3
159.1
162.2
159.6
156.7
158.9
159.2
160.1
159.2
164.3
162.3
157.3
157.9
160.5
163.3
160.7
158.3
160.6
160.3
163.0
162.4
167.8
166.3
159.9
159.1
164.0
167.1
165.1
161.6
164.4
163.1
164.5
163.8
169.2
167.5
161 .5
161.1
165.4
168.7
166.4
162.8
165.5
164.9
165.7
165.0
170.1
168.5
162.9
162.3
166.5
169.5
167.4
164.1
166.6
166.0
166.5
165.9
170.5
169.2
163.9
163.3
167.1
169.6
167.8
165.1
167.3
166.6
170.3
169.8
173.5
172.2
168.1
164.6
171.7
173.2
171 .3
170.4
172.4
170.4
171.8
171.2
174.7
173.3
169.8
165.9
173.2
174.6
172.6
172.0
174.0
171 .7
173.3
172.5
176.4
174.5
171.3
167.0
174.9
176.4
174.1
173.7
175.8
173.1
.8
.9
.8
1.0
.7
.9
.7
1.0
.9
1.0
1.0
.8
4.6
4.5
3.3
3.6
5.2
2.9
5.0
4.1
4.0
5.9
5.5
4.3
Service-producing ...... ...... .. ......................... ... ... ... ... .. .. ... .. .
Excluding sales occupations ............. .. ............ .......... .
White-collar occupations .................... ........... ...... ........ .
Excluding sales occupations .................................... .
Blue-collar occupations .. ............................... .. .... ........ .
Service occupations ........... ..... ............... .. .................. .
Transportation and public utilities .................. .............. ..
Transportation ....... .... ..................................... ... ...... .... .
Public utilities ..................... .. ...... ............. ... ... ...
Communications ............. .... ....... .. ............................. .
Electric, gas, and sanitary services .......................... .
Wholesale and retail trade ............................. ............... .
Excluding sales occupations ................................... ..
Wholesale trade ....................... ........... .. ... .............. ..... .
Excluding sales occupations ............. ................ ... ... ..
Retail trade ... ......................... ................... ...... .. .......... .
General merchandise stores ............. ............... ........ ..
Food stores ............................ .................................. ..
162.7
163.5
164.7
166.5
156.6
158.5
160.8
155.4
168.2
169.0
167.2
159.6
160.3
165.9
166.1
156.0
156.1
156.3
163.1
164.0
165.1
167.0
156.9
159.3
161.7
156.1
169.2
170.1
168.1
159.7
160.4
166.7
167.2
155.8
155.1
156.3
165.6
166.6
167.9
169.9
158.7
161 .1
163.2
157.8
170.5
171 .3
169.5
161 .3
161 .8
169.5
168.4
156.6
156.4
157.5
167.0
168.0
169.2
171.3
160.8
162.0
165.4
158.9
174.2
175.5
172.6
162.5
162.7
171 .3
169.9
157.4
159.2
158.6
168.8
169.7
171 .2
173.1
162.2
163.2
166.5
159.4
176.4
178.4
173.8
164.3
165.0
172.0
171.2
159.9
161 .2
159.3
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
.8
.8
.9
.9
.6
.4
.6
.9
.3
.1
.5
.6
.6
1.1
.7
.3
1.6
.0
3.5
3.5
3.6
3.6
3.2
3.0
4.3
4.3
4.1
3.0
5.5
2.9
2.8
3.4
2.4
2.7
4.7
2.6
Production and nonsupervisory occupations
4
..
~ - - ~ - - ~ - - ~ - - - ~ - - ~ - - ~ - - - ~- - ~ - -~ ------+-------
See footnotes at end of table.
116
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November 2004
30. Continued-Employment Cost Index, compensation, 1 by occupation and industry group
[June 1989 = 100)
2002
Series
Sept.
2003
Dec.
Mar.
June
2004
Dec.
Sept.
Mar.
June
Percent change
Sept.
3 months
12 months
ended
ended
Sept. 2004
Finance, insurance, and real estate .. ... ..........................
168.0
168.5
176.7
178.3
180.2
180.9
182.5
183.6
184.8
0.7
2.6
Excluding sales occupations .. ......... .......... ... ... ......... .
BankinQ, savinQs and loan. and other credit aQencies.
Insurance .................. .................. ..................................
Services ... ....... .. ................. .... ..... .................... ... ...... .. ... ..
Business services .................... .... ...... ..........................
Health services .. .. ... ... .......... .. .. ... ...... .... ... ................ .....
Hospitals ........... ......................................... ................
Educational services ......
······· ··········· ············· ·
Colleges and universities ............. .. ... .. ... .... .. ... ...........
172.1
184.6
167.1
164.9
167.2
163.2
166.2
173.5
172.0
173.1
185.3
167.9
165.4
167.5
164.4
168.1
175.2
173.7
182.0
204.3
172.1
167.1
168.5
166.5
170.8
176.3
174.5
184.0
206.3
173.9
168.4
169.2
167.9
171.9
177.1
175.4
1,853.0
207.6
175.1
170.4
171.9
169.4
173.9
180.2
178.4
186.1
209.0
176.2
171.4
172.6
170.8
175.9
181.3
179.4
186.6
207.2
177.8
173.5
174.8
173.3
178.1
183.1
181 .2
188.7
208.9
180.5
175.1
176.9
174.8
179.7
184.2
182.5
190.9
210.5
182.1
176.9
178.5
177.0
181 .8
187.0
185.2
.1
.8
.9
1.0
.9
1.3
1.2
1.5
1.5
2.5
1.4
4.0
3.8
3.8
4.5
4.5
3.8
3.8
Nonmanufacturing ....................................... .. .............. ...
162.0
162.5
164.9
166.4
168.1
169.0
170.9
172.5
173.9
.8
3.5
.. ........... ............. .......
White-collar workers ... .....
Excl uding sales occupations ........................... .. .. .... .
Blue-collar occupations ....... .. ........................... .. ..... .....
Service occupations ..... ................. .. ......... ................. .
164.8
166.6
155.4
158.4
165.3
167.1
155.9
159.2
168.0
170.0
157.5
161 .1
169.3
171.4
159.7
162.0
171.2
173.2
161 .1
163.2
172.1
174.2
161.7
162.4
174.1
176.2
163.4
166.0
175.7
177.7
165.5
167.3
177.2
179.3
166.4
168.0
.9
.9
.5
.4
3.5
3.5
3.3
2.9
St;1te and local government workers ..... ..............................
160.1
161 .5
162.6
163.2
165.9
166.8
168.0
168.7
171.5
1.7
3.4
159.3
158.1
162.3
161.0
158.4
160.7
159.4
163.8
162.4
159.8
161.7
160.2
165.3
163.8
161 .3
162.2
160.8
165.7
164.4
161 .7
164.9
163.4
168.0
167.9
163.6
165.7
164.1
169.1
168.5
165.2
166.8
165.1
170.1
170.4
166.7
167.5
165.6
171.0
171.8
167.5
170.0
168.4
172.1
174.3
169.9
1.5
1.7
.6
1.5
1.4
3.1
3.1
2.4
3.8
3.9
Services .................. .. .. ... ... ........ ........ ... ........ ...... .... ... ...... ..
159.7
160.9
161 .8
162.3
162.8
165.5
166.2
160.3
160.7
158.8
165.8
161 .7
164.0
166.4
167.0
161 .1
161 .4
159.4
167.0
163.4
164.2
166.7
167.3
161.7
162.0
160.0
167.5
164.3
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
1.7
161.0
163.5
164.1
159.2
159.6
157.7
164.7
160.2
164.9
166.8
169.5
170.3
164.3
164.7
163.0
169.2
167.3
169.7
Services excludinQ schools5 .
Health services .................
···· ··· ····· ·······
Hospitals .. ............................... ................. ... ... .... ........
Educational services ..... .. .......... ... .. ...............................
Schools ........... .. ....... ..................................... ............
Elementary and secondary ................... .. ...... .. ... .....
Colleges and universities .. .... .. ... .. ............. ............ ..
173.0
175.7
176.3
168.8
169.2
168.0
172.4
174.1
1.7
1.6
1.8
1.7
1.7
2.1
.8
1.6
2.9
3.7
3.7
3.5
2.7
2.7
3.1
1.9
4.1
Workers, by occupational group:
White-collar workers ......... ....... .. ........................................ .
Professional specialty and technical. .......... .................. .. .
Executive, administrative, and managerial .. ......... ..........
Admini strative support, including clerical .. ... .
Blue-collar workers ........... .................................. .......... .....
Workers , by industry division:
Public administration
3
..
1
Cost (cents per hour worked) measured in the Employment Cost Index consists of
wages, salaries, and employer cost of employee benefits.
2
Consi sts of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
3
Consists of legislative, judicial, administrative, and regulatory activities.
4
This series has the same industry and occupational coverage as the Hourly
Earni ngs index, whi ch was discontinued in January 1989.
5
Includes, for example, library, social, and health services.
Monthly Labor Review
November
2004
117
Current Labor Statistics:
Compensation & Industrial Relations
31. Employment Cost Index, wages and salaries, by occupation and industry group
[June 1989 = 100]
2002
2003
2004
Percent change
Series
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
3 months
12 months
ended
ended
Sept. 2004
1
Clvlllan workers ..... ······· ······························· ··············•·· ····
157.2
157.8
159.3
160.3
161 .8
162.3
163.3
164.3
165.7
0.9
2.4
Workers, by occupational group:
White-collar workers ....................................................... ....
Professional specialty and technical. ...............................
l:.xecutive, adminitrative, and managerial.. .. .... ..............
Administrative support, including clerical... .............. ......
Blue-collar workers ... .. .. ...................... ................. ... ...........
Service occupations ................................ ............... ... .........
159.6
158.0
163.5
159.6
151 .9
·55.2
160.1
158.6
163.8
160.6
152.6
156.9
161.9
159.3
167.9
161 .8
153.8
158.0
162.9
160.1
169.0
163.1
154.8
158.7
164.5
161 .8
170.5
164.3
155.8
159.8
165.1
162.5
171.2
164.9
156.3
160.6
166.1
163.8
171.4
166.3
157.3
161.2
167.1
164.4
172.4
167.5
158.4
161 .9
168.7
166.5
173.4
168.8
159.7
162.8
1.0
1.3
.6
.8
.8
.6
2.6
2.9
1.7
2.7
2.5
1.9
Workers, by industry division:
Goods-producing ................... .................. ........ ...................
Manufacturing ................................................. .... .. .... .......
Service-producing ............ ........... .............................. .........
Services ............... ........................ .... ... ...... .. .................... .
Health services ................... ........ .... ... .... ...... ...................
Hospitals .............................................................. ........
Educational services ........................................ ..............
153.9
155.4
156.4
160.7
159.6
160.3
159.3
155.1
156.5
158.8
161.1
160.9
162.2
160.1
156.3
158.0
160.5
161.9
162.0
163,5
160.4
157.5
159.0
161.4
162.8
163.2
164.4
160.7
158.3
159.7
163.0
164.7
164.7
166.3
162.7
160.6
160.1
163.6
165.4
165.9
167.7
163.2
159.9
161.3
164.6
166.5
167.7
169.0
163.6
161 .0
162.4
165.5
167.4
168.6
169.9
163.8
162.3
163.8
167.0
167.3
170.8
171 .8
166.0
.8
.9
.9
1.1
1.3
1.1
1.3
2.5
2.6
2.5
2.8
3.7
3.3
2.0
Public administration ........ ... .. •... ..• .....
Nonmanufacturing ................. .............................................
154.8
157.5
155.8
158.0
157.2
159.6
158.0
160.5
159.4
162.1
160.0
162.7
161.1
163.7
161.4
164.6
162.6
166.0
.7
.9
2.0
2.4
Private Industry workers ... .. .. ... ... .. .... ......... ................. .
Excluding sales occupations .. ............ ......... ...................
157.0
157.0
157.5
157.9
159.3
159.4
160.4
160.5
161 .7
161 .7
162.3
162.4
163.4
163.5
164.5
164.5
165.9
165.8
.9
.8
2.6
2.5
Workers, by occupational group:
White-collar workers .......... .. ........ .. ....... ..... ........ ........ ... ....
Excluding sales occupations ............ ................. .. .. .......
Professional specialty and technical occupations ..........
Executive, adminitrative, and managerial occupations ..
Sales occupations ..... ...................................................
Administrative support occupations, including clerical. ..
Blue-collar workers ......... ............................... ..................
Precision production, craft, and repair occupations .......
Machine operators, assemblers, and inspectors ....... .....
Transportation and material moving occupations ........ ...
Handlers, equipment cleaners, helpers, and laborers ....
160.0
169.8
158.2
164.3
156.9
160.3
151 .7
151 .8
152.0
146.3
156.0
160.4
160.8
158.5
164.5
156.8
161.3
152.4
152.3
153.2
146.9
157.2
162.6
163.6
159.5
169.1
158.1
162.6
153.6
153.4
154.7
147.8
158.4
163.8
164.8
160.5
170.3
159.3
164.0
154.6
154.7
155.3
149.0
159.0
165.3
166.2
162.1
171 .8
161 .6
165.1
155.6
155.5
156.8
149.8
159.9
165.9
167.0
163.0
172.5
161 .1
165.7
156.1
156.2
156.9
149.8
160.6
167.1
168.1
164.7
172.7
162.6
167.2
157.2
157.1
158.6
150.4
161 .8
168.2
169.2
165.5
173.9
163.9
168.6
158.3
158.3
159.8
151.8
162.7
169.7
170.6
167.6
174.9
165.9
169.7
159.5
159.3
161.6
152.9
163.6
.9
.8
1.3
.6
1.2
.7
.8
.6
1.1
.7
.6
2.7
2.6
3.4
1.8
2.7
2.8
2.5
2
Service occupations ... ........ ............................................
Production and nonsupervisory occupations
3
.. ..............
Worll.,m;, 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 ...................................................... ...... ......
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 .............................................
Wholesale trade .. .................................. .... .. ... ... .. .. ... ... .
Excluding sales occupations ..................... .. ..............
Retail trade ....... ..... ... .. ...... ...........................................
General merchandise stores .. ..... ...................... .........
Food stores ................................................................
See footnotes at end of table.
118
153.9
154.4
155.5
156.1
157.1
157.8
158.4
159.3
159.8
.3
1.7
154.7
155.2
156.4
157.4
158.8
159.4
160.7
161.7
163.1
.9
2.7
153.9
153.0
157.9
155.4
151 .5
149.0
155.4
157.7
155.0
153.5
156.0
156.3
155.4
160.0
158.0
153.8
150.6
158.0
160.1
157.7
156.3
158.8
156.6
157.4
156.5
161.4
159.2
154.8
154.4
155.0
154.0
158.6
156.3
152.6
150.2
156.5
158.6
155.9
154.7
157.3
155.2
159.0
161 .6
158.9
156.9
159.7
157.8
158.3
157.4
161.9
159.9
155.9
153.6
159.7
162.0
159.5
157.9
160.6
158.3
158.7
158.0
162.1
160.4
156.4
154.0
160.1
162.1
160.0
158.5
160.9
158.7
159.9
159.2
163.2
161.5
157.7
155.1
161.3
163.3
161 .2
159.8
161.9
160.4
160.9
160.2
164.5
162.7
158.6
155.9
162.4
164.7
162.5
160.6
162.9
161.6
162.3
161 .2
166.0
163.6
159.8
157.1
163.8
166.1
163.5
162.1
164.5
162.8
.9
.6
.9
.6
.8
.8
.9
.9
.6
.9
1.0
.7
2.5
2.4
2.5
2.3
2.5
2.3
2.6
2.5
2.5
2.7
2.4
2.8
158.4
159.3
160.5
162.5
151 .8
153.5
153.4
149.6
158.2
159.6
156.5
155.5
160.4
162.6
152.9
150.1
150.1
158.6
159.6
160.7
162.8
152.0
154.1
154.1
150.1
159.3
160.7
157.4
155.5
161 .0
163.7
152.7
149.2
150.3
160.6
161 .7
163.0
165.3
153.2
155.1
154.8
150.5
160.4
161.9
158.6
156.7
163.4
163.9
153.1
149.8
151 .0
161 .7
162.8
164.1
166.5
154.3
155.6
155.6
150.6
162.1
163.4
160.4
157.5
164.7
165.2
153.8
152.0
151 .6
163.3
164.2
166.0
168.2
155.1
156.6
156.0
150.4
163.4
165.4
161.0
159.2
164.8
165.7
156.3
153.1
152.2
163.9
165.0
166.6
169.0
155.4
157.4
156.5
150.8
164.1
165.9
161 .8
159.5
165.3
166.3
156.5
153.6
152.8
165.0
166.0
167.8
170.2
156.2
158.0
157.6
151.7
165.3
167.0
163.3
160.3
166.2
167.8
157.3
154.1
153.8
166.1
167.1
168.9
171 .2
157.8
158.8
159.1
153.4
166.4
167.5
165.1
161.6
167.8
167.6
158.4
154.9
154.3
167.5
168.5
170.4
172.8
158.9
159.4
160.4
155.0
167.5
168.8
165.9
162.5
169.7
168.6
158.7
157.5
154.5
.8
.8
.9
.9
.7
.4
.8
1.0
.7
.8
.5
.6
1.1
.6
.2
1.7
.1
2.6
2.6
2.7
2.7
2.5
Monthly Labor Review · November 2004
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
2.4
3.1
2.1
2.3
152.4
1.8
2.8
3.1
2.5
2.1
3.0
2.1
3.0
1.8
1.5
2.9
1.5
31. Continued-Employme nt Cost Index, wages and salaries, by occupation and industry group
[June 1989
= 100]
2002
2003
2004
Percent change
Series
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
3 months
12 months
ended
ended
Sept. 2004
Finance, insurance, and real estate ...............................
Excluding sales occupations .... .. ............. ..... ........ .. ...
Banking, savings and loan, and other credit agencies.
Insurance ...... ..... ......... .. ... .. .......... ........... .. ........... ..... ....
Services ........ .... ... ......... ................................. ........... ......
Business services ... ... ......... ................ ...................... ...
Health services ................ ...... .... ..... ................... ...... .....
Hospitals ...... ... ... ....... ......... ....... ... ......... ..... ..... ....... ... .
Educational services .... .................... ............................
Colleges and universities .... ............. .......... ... ... ....... ...
162.4
166.1
182.7
159.6
161 .5
164.6
159.9
160.2
165.2
163.1
162.6
167.3
183.9
159.1
161 .7
164.8
160.7
162.1
166.5
164.3
171.1
176.7
206.4
161.6
162.8
165.6
161.9
163.6
167.1
164.4
172.4
178.5
208.7
163.0
164.0
166.4
163.2
164.6
167.5
165.1
174.1
179.2
209.1
163.9
165.9
169.1
164.6
166.5
170.3
167.6
174.5
210 .2
164.5
164.5
166.7
169.8
135.8
167.9
171 .0
168.4
175.2
179.2
206.7
165.1
168.1
171 .0
167.8
169.4
171 .9
169.5
175.3
180.5
207.6
167.2
169.3
172.7
168.8
170.5
172.6
170.0
176.5
181 .8
209.5
168.9
171 .1
174.3
170.9
172.4
175.5
172.9
Nonmanufacturing ... ... .... .. ... .... ........ ...... ..... ........ ...... .. ....
White-collar workers ....... .......... .. ...... .. ... .. .. .... ...............
Excluding sales occupations ....................... ... ... .......
Blue-collar occupations .... .. .. ...... ........... ............ .... .... ...
Service occupations .. .... .. .... ......... ............... .. ..... ........
157.2
160.2
162.1
149.8
153.4
157.5
160.5
162.5
150.2
154.0
159.4
162.8
164.9
151 .1
155.0
160.5
163.9
166.1
152.4
155.5
162.1
165.7
167.7
153.4
156.5
162.6
166.3
168.5
153.8
157.3
163.7
167.5
169.7
154.7
157.9
164.8
168.6
170.7
156.1
158.7
166.2
170.1
172.3
157.1
159.2
State and local government workers ................ .... ..... ..... .
160.1
161 .5
162.6
163.2
165.9
166.8
168.0
168.7
Workers, by occupational group:
White-collar workers ... .... ... ...... ..... .............. .. .. ........ .. .. ....... .
Professional specialty and technical. ..... .... ... .. .. ... .. . . ....
Executive, administrative, and managerial. .. .... .... .... ......
Administrative support, including clerical. .... .... .. ............
Blue-collar workers ........ .. ................ .. ..... .... ..... ..
157.4
157.5
159.0
155.1
154.5
158.4
158.4
160.1
156.0
155.1
158.9
158.8
160.9
156.9
156.2
159.2
159.1
161.0
157,2
156.5
161 .0
161 .0
162.5
159.1
157.6
161 .5
161.4
163.3
159.5
158.3
162.1
162.1
163.5
160.4
158.9
162.4
162.3
163.8
160.8
159.2
Workers, by industry division:
Services .... ... .. .. ....... ... ... ... ......... ....... ... ................. ......... ....
4
Services excluding schools ... .. ..... .. ........ ... .. ...... ......... .. .
Health services ..... ........................ ...... ........ ..................
Hospitals ... ... ... ............... .. .... ....... ... ....... ........ .. .... .... ...
Educational services ...... .. ........ ...... .. .... .. .... .............. ... .
Schools .. .... ............ ................ ... ......... ... ........ .. ... ........
Elementary and secondary ..... .. ...... ... ......... .. ..........
Colleges and universities .......... .. ... .... .... .................
Public administration2 ...
1.4
1.5
.2
3.1
3.1
3.1
3.8
3.5
3.1
3.2
.8
.9
.6
.3
2.5
2.7
2.7
2.4
1.7
171 .5
1.0
2.0
164.1
164.4
164.3
162.6
160.7
1.0
1.3
.3
1.1
.9
1.9
2.1
1.1
2.2
2.0
.9
158.4
159.2
159.5
159.8
161.6
162.1
162.6
162.7
164.8
1.3
2.0
159.1
160.5
160.6
158.1
158.3
157.4
160.7
160.3
162.2
162.5
158.9
159.0
158.1
161 .6
161.4
162.9
163.1
159.1
159.2
158.2
162.1
161 .8
163.5
163.8
159.3
159.5
158.5
162.1
163.2
165.1
165.5
161.2
161.4
160.6
163.5
164.5
166.7
166.7
161 .6
161 .8
160.9
164.0
165.1
167.4
167.4
162.0
162.1
161 .3
164.3
165.6
167.8
167.9
162.1
162.3
161 .5
164.4
167.5
169.6
169.9
164.2
164.3
163.8
165.4
1.1
1.1
1.2
1.3
1.2
1.4
.6
2.6
2.7
2.7
1.9
1.8
2.0
1.2
154.8
155.8
157.2
158.0
159.4
160.0
161 .1
161 .4
162.6
.7
2.0
1
Consists of private industry workers (excluding farm and household workers) and
State and local government (excluding Federal Government) workers.
2
0.7
.7
.9
1.0
1.1
.9
1.2
1.1
1.7
1.7
Consists of legislative, judicial, administrative, and regulatory activities.
3
This series has the same industry and occupational coverage as the Hourly
Earnings index, which was discontinued in January 1989.
4
Includes, for example, library, social, and health services.
32. Employment Cost Index, benefits, private industry workers by occupation and Industry group
[June 1989 = 100]
2002
2003
2004
Percent change
Series
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
3 months
12 months
ended
ended
Sept .2004
Private Industry workers ......................................................
173.1
174.6
179.6
182.0
184.3
185.8
192.2
195.3
196.9
0.8
6.8
Workers, by occupational group:
White-collar workers ... ....... ...... ... ....... .......... ............. ........ ..
Blue-collar workers .. ............... ... ...... ... .................. .......... ...
177.2
166.2
178.5
167.8
183.6
172.7
185.5
176.1
187.7
178.4
189.2
179.9
194.4
188.3
197.4
191 .8
199.1
193.3
.9
.8
6.1
8.4
Workers, by industry division:
Goods-producing .. .. .... .... ..... ........ .. .. ... .... ....................... .. ...
Service-producing ...... .... ... ... ... ................ ... ... .. .. ... .. ... ........ .
Manufacturing ............... ... ............ .. ................... ... ..... ..... .. ...
Nonmanufacturing .... ... ... ....... .. ........... .... .. ... ............ ...... .. ..
168.8
174.9
166.8
175.2
171.0
175.9
168.9
176.3
178.0
179.9
176.9
180.3
180.2
182.3
179.0
182.8
182.3
184.7
181 .1
185.1
183.8
186.2
182.3
186.7
193.7
190.6
194.4
190.9
196.2
194.1
196.9
194.3
198.1
195.5
199.2
195.7
1.0
.7
1.2
.7
8.7
5.8
10.0
5.7
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
2004
119
Current Labor Statistics:
Compensation & Industrial Relations
33. Employment Cost Index, priv<Jte nonfarm workers by bargaining status, region, and area size
[June 1989 = 100]
2002
2003
2004
Percent change
Series
Sept.
Dec.
Mar.
June
Sept.
Dec.
Mar.
June
Sept.
3 months
12 months
ended
ended
Sept. 2004
COMPENSATION
Workers, by bargaining status
1
Union .......................................................................................
Goods-producing ........................................................ .........
Service-producing ....... ......................... ............... ............... .
Manufacturing .......... .. ..... ......................................... ... ........ .
Nonmanufacturing .. .......................................... ..................
158.1
156.2
159.9
155.9
158.8
159.5
157.8
161 .1
157.9
159.9
162.1
161.4
162.6
162.3
161 .4
164.1
163.4
164.6
163.8
163.7
165.7
164.7
166.5
165.0
165.5
166.8
165.9
167.5
166.3
166.5
171 .4
172.3
170.2
175.0
168.8
173.9
174.6
172.9
177.0
171.6
175.3
176.0
174.4
178.4
173.0
0.8
.8
.9
.8
.8
5.8
6.9
4.7
8.1
4.5
Nonunion ............................... .. .................................. .. ........... .
Goods-producing ......... .. ............................................. .........
Service-producing ... .......................... .............. ................... .
Manufacturing ............ ...... ........................................ ........... .
Nonmanufacturing .. ............................................ ............... .
162.5
159.5
162.9
160.1
162.4
162.8
160.8
163.3
161 .3
162.9
165.4
163.6
165.9
164.5
165.4
166.8
164.9
167.2
165.8
166.7
168.4
166.1
169.0
166.9
168.5
169.1
166.7
169.8
167.3
139.3
171.3
169.7
171 .6
170.6
171.1
172.7
170.9
173.2
172.0
172.6
174.2
172.4
174.6
173.8
174.0
.9
.9
.8
1.0
.8
3.4
3.8
3.3
4.1
3.3
160.5
158.9
163.5
163.8
161.3
159.0
164.6
165.0
163.8
160.6
169.0
167.3
165.2
161 .6
170.4
169.5
166.9
163.2
171 .7
171.4
167.9
163.9
172.5
172.2
170.2
166.4
174.7
175.3
172.3
167.9
176.2
176.8
173.7
169.5
177.6
178.1
.8
1.0
.8
.7
4.1
3.9
3.4
3.9
161 .8
160.0
162.5
169.8
165.2
163.5
166.6
165.0
168.3
166.1
169.1
166.9
171 .5
170.2
173.1
172.1
174.6
173.3
.9
.7
3.7
4.3
Union ......................................... .. ..... ...... .. .......... ... ................. .
Goods-producing ............................................ .................... .
Service-producing ... .................... ... .. ................ ... ........ ... .....
Manufacturing ..................................... .. .................. .............
Nonmanufacturing .............................................. .. ..............
151 .3
150.0
152.9
151.6
151 .1
152.5
151.2
154.1
153.1
152.1
153.3
152.4
154.6
154.6
152.5
154.3
153.9
155.1
155.9
153.5
155.3
154.8
156.3
156.7
154.6
156.2
155.4
157.3
157.1
155.6
157.2
156.3
158.5
158.1
156.6
158.7
157.5
160.3
159.2
158.4
160.0
158.7
161 .7
160.5
159.6
.8
.8
.9
.8
.8
3.0
2.5
3.5
2.4
3.2
Nonunion ............ .. .................................................................. .
Goods-producing ..................... ..................... .. ........ ......... ....
Service-producing .............................................................. .
Manufacturing ................................ .............. ....................... .
Nonmanufacturing .. ...................................................... .. ... .
158.1
155.5
158.9
156.8
158.1
158.5
156.6
159.0
157.8
158.3
160.4
157.8
161 .2
159.3
160.4
161.5
158.9
162.3
160.2
161 .5
163.0
159.7
164.0
160.9
163.1
163.4
160.1
164.5
161 .3
163.7
164.6
161 .4
165.6
162.6
164.7
165.6
162.4
166.6
163.7
165.7
167.0
163.8
168.0
165.2
167.1
.8
.9
.8
.9
.8
2.5
2.6
2.4
2.7
2.5
155.1
154.7
159.2
159.3
155.7
154.6
160.2
160.1
157.3
155.3
164.1
161 .3
158.4
156.1
165.0
163.1
160.0
157.4
166.1
164.7
160.9
157.9
166.5
165.2
162.0
159.1
166.9
166.8
163.6
160.1
167.7
167.9
164.9
161.6
169.2
169.1
.8
.9
.9
.7
3.1
2.7
1.9
2.7
157.4
153.8
157.9
154.8
159.6
156.8
160.7
158.0
162.2
158.9
162.7
159.5
163.8
160.8
164.9
162.1
163.3
162.1
.8
.7
2.5
2.8
Workers, by region
1
Northeast. ............... .. .............................................. .. ............. .
South ............. ................................................... ..................... .
Midwest (formerly North Central) ... .. ........... .. .. ...................... .
West. ...................... .. ....................................... ............... ........
Workers, by area size 1
Metropolitan areas ............. ....... .. ........ ... .. ... ................ ... .........
Other areas .......... ..... ............ ................................................ .
WAGES AND SALARIES
Workers, by bargaining status
1
Workers, by region 1
Northeast.. .................................................. .. ........ .............. ....
South .. ..... .. ....... ............. .......... .. ......... ....................................
Midwest (formerly North Central) .... ...... ..... ..... ..... ..... .. .. .. ... ... .
West. ..... ............ ........ ..... ... ... ..... ...................... .............. ........ .
Workers, by area size1
Metropolitan areas .. ... ................................................. ... ........ .
Other areas ............................ ............................................... .
1
l ne 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.
120
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Federal Reserve Bank of St. Louis
November 2004
34. Percent of full-time employees participating in employer-provided benefit plans, and in selected features within plans,
medium and large private establishments, selected years, 1980-97
Item
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 ,est 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 ...... ..... ..... .. ... .... . ..
1980
1982
1984
1986
1988
1989
1991
1993
1997
1995
21 ,352
21 ,043
21,013
21,303
31 ,059
32,428
31 ,163
28,728
33,374
38,409
20,711
20,498
17,936
20,412
20 ,201
17,676
20,383
20,172
17,231
20,238
20,451
16,190
27,953
28,574
19,567
29,834
30,482
20,430
25,865
29,293
18,386
23,519
26,175
16,015
25,546
29,078
17,417
29,340
33,495
19,202
10
9
;fl
3.2
99
11
29
72
26
85
3.2
9.4
24
3.3
98
10
26
71
26
84
3.3
97
9.2
22
3.1
97
9
25
76
25
10
27
72
26
8
75
30
67
28
80
3.3
92
10.2
21
3.3
96
29
68
26
83
3.0
91
9.4
21
3.1
97
80
3.3
89
9.1
22
3.3
96
81
3.7
89
9.3
20
3.5
95
69
33
16
68
37
18
67
37
26
65
60
53
58
56
84
93
I
9i
26
99
99
10.1
20
10.0
24
3.8
9.8
23
3.6
~I
10.0
100
99
99
25
3.7
100
62
67
67
70
88
96
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
contribut ion required for :
Self coverage .. ... .......... .... ..... .. ..... ... .. ..
Average monthly contribution ..... ...... .. .. ....... ... .
Family coverage .... .... .... .. .... .. .. ........ ... .. .. .
Average monthly contnbut,on .... ..... .. .. ...... .
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 .. ... .... .. .... ....... .. ........... ... .. .
97
58
26
46
97
97
62
46
62
27
51
95
90
92
83
82
77
76
8
66
70
18
76
79
28
75
80
28
81
80
30
86
82
42
78
73
56
85
78
63
36
$11.93
58
$35.93
43
$12.80
63
$41.40
44
$60.07
47
$25.31
66
$72 .10
51
$26.60
69
$96.97
61
$31 .55
76
$107.42
67
$33.92
78
$118.33
69
$39.14
80
$130.07
$19.29
64
96
96
96
96
92
94
94
91
87
87
69
72
74
78
71
71
76
77
8
7
6
5
7
64
72
10
59
49
42
44
41
37
74
6
33
40
43
48
42
45
40
41
42
43
54
51
49
46
43
45
44
53
55
51
Participants in short-term disability plans '
Retirement plans
Participants in defined benefit pension plans
Percent of parti cipants with :
Normal retirement prior to age 65
Early retirement available ..... .... .... .. .
Ad hoc pension increase in last 5 years ..
Terminal earnings formula ..
Benefit coordinated with Social Security .
84
84
82
76
63
63
59
56
52
50
55
58
97
52
45
64
98
35
57
62
59
98
26
53
45
63
97
47
54
56
62
62
97
22
64
63
55
98
56
54
52
95
6
61
48
52
96
4
58
51
52
95
10
56
49
60
45
48
48
49
55
57
33
36
41
44
43
54
55
2
5
12
9
23
10
36
12
52
12
38
5
13
32
Participants in defined contribution plans ...... .
Participants in plans with tax-deferred savings
arrangements ... ...... .............. ... .. .
55
98
7
Other benefits
Employees eligible for:
Flexible benefits plans ..... .. ... ...... .... .
2
Reimbursement accounts .. ... .
Premium conversion olans .. .... .. ... .... .. ........ .. .... ..
' The definitions for paid sick leave and short-term disability (previously sickness and
5
7
lits at less than full pay.
accident insurance) were changed for the 1995 survey. Paid sick leave now includes only
2
plans that specify either a maximum number of days per year or unlimited days. Short-
specifically allow medical plan participants to pay required plan premiums with pretax
Prior to 1995, reimbursement accounts included premium conversion plans, which
terms disability now includes all insured, self-insured, and State-mandated plans available
dollars. Also, reimbursement accounts that were part of flexible benefit plans were
on a per-disability basis, as well as the unfunded per-disability plans previously reported as
tabul ated separat ely.
sick leave. Sickness and accident insurance, reported in years prior to this survey, included
only insured, self-insured, and State-mandated plans providing per-disability bene-
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NOTE: Dash indicates data not available.
Monthly Labor Review
November 2004
121
Current Labor Statistics: Compensation & Industrial Relations
35. Percent of full-time employees participating in employer-provided benefit plans, and in selected features
within plans, small private establishments and State and local governments, 1987, 1990, 1992, 1994, and 1996
Small private establishments
Item
1990
1992
1994
State and local governments
1996
1987
1990
1992
1994
Scope of survey (in 000's) ............... .. ...... .......... .
32,466
34,360
35,910
39,816
10,321
12,972
12,466
12,907
Number of employees (in 000's) :
With medical care .... .... ..... ...... ...... .
With life insurance ..... .. .... .... .... .. ...... ... ........ ... .
With defined benefit plan ....... .. .. ... .... .... ... ... ... . .
22,402
20,778
6,493
24,396
21,990
7,559
23,536
21 ,955
5,480
25,599
24,635
5,883
9,599
8,773
9,599
12,064
11,415
11 ,675
11,219
11 ,095
10,845
11 ,192
11,194
11,708
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 ....
17
37
48
27
47
2.9
84
37
49
26
50
3.0
82
50
3.1
82
51
3.0
80
58
29
56
3.7
81
11
36
56
29
63
3.7
74
10
34
53
29
65
3.7
75
62
3.7
73
Averaoe days per year' ... ..... .... .... .... .. ... ... .
Paid personal leave ... ............ ...... ............ ... .. .. . .
Average days per year .. .... .... .... ...... .. .. .... .... .. . .
Paid vacations ..
9.5
11
2.8
88
9.2
12
2.6
7.5
13
2.6
88
88
7.6
14
3.0
86
10.9
38
2.7
72
13.6
39
2.9
67
14.2
38
2.9
67
11 .5
38
3.0
66
47
53
50
50
97
95
95
94
17
18
8
7
57
30
51
33
59
44
Paid sick leave
2
• •• •• ••• • • • • • ••••• • •• • •• •.•• • ••••• . •• • •. . .•..
Unpaid leave ................... ......... ......... ..... .. ..... .
Unpaid paternity leave .
Unpaid family leave .. .... .. ........... .. ..... .............. .
Insurance plans
Participants in medical care plans ....... ..... .......... . .
Percent of participants with coverage for :
Home health care
......... ... ... .......... . .
Extended care faci lities
Physical exam ...... .. .. .... .... ........... .... .. .... .... ... .
Percent of participants with employee
cc,n tribution requi red for :
Self coverage ..... .................................. .. .... .. .
Average monthly contribution
Family coverage
............... ... .
Average monthly cont ribut ion
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 ............................
Participants in short-term disability plans
8
34
47
48
66
64
69
71
79
83
26
80
84
28
42
$25.13
67
47
$36.5 1
73
52
$40.97
76
$109.34
$150.54
64
64
78
76
1
1
19
19
93
93
93
90
87
76
78
36
82
79
36
87
84
47
84
81
55
52
$42.63
75
35
$15.74
71
38
$25.53
65
43
$28.97
72
47
$30.20
71
$159.63
$181 .53
$71 .89
$117 .59
$139.23
$149.70
61
62
85
88
89
87
77
67
67
74
1
1
25
79
2
20
13
55
45
46
64
2
46
23
20
22
31
27
28
30
26
26
14
21
22
21
15
93
90
87
91
47
92
89
53
44
92
90
33
100
18
92
89
10
100
10
92
87
13
99
49
9
9
45
24
2
1
29
Retirement plans
Participants in defined benefit pension plans .. ....... .
Percent of participants with:
Normal retirement prior to age 65
Early retirem ent available ... .. ............ . .. ... .... .
Ad hoc pension increase in last 5 years ..
Terminal earnings formula .. .. ... ..... ........ ......... .
Benefit coordinated with Social Security
Participants in defined contribution plans ... .
Participants in plans with tax-deferred savings
arrangements .. .. ........ .............. ..... .. .. ........... ....... .
20
22
54
95
58
49
50
95 1
4·
54
46
31
33
34
38
9
17
24
23
28
28
7
15
88
16
100
8
45
Other benefits
Employees eligible for :
Flexible benefits plans .... ............ .
Reimbursement accounts 3 •.•••. . ••
Premium conversion plans
1
2
3
4
5
5
5
5
8
14
19
12
5
31
50
64
7
' Methods used to calcu late the average number of paid holidays were revised
in 1994 to count partial days more precisely. Average holidays for 1994 are
Sickness and accident insurance, reported in years prior to this survey,
included only insured, self-insured, and State-mandated plan s 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
premium s with pretax dollars. Also, reimbursement accounts that were part of
per year or unlimited days. Short-term disability now includes all insured, selfinsured, and State-mandated plans available on a per-disability basi s, as well
flexible benefit plans were tabulated separately.
as the unfunded per-disability plans previously reported as sick leave.
NOTE: Dash indicates data not available.
122
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
36. Work stoppages involving 1,000 workers or more
Annual totals
Measure
2002
Number of stoppages:
Beginning in period ................ .. ...........
In effect during period ........................
19
20
Workers involved:
Beginning in period (in thousands) ....
In effect during period (in thousands) .
2003
2003
Sept.
2004P
Nov.
Oct.
Jan.
Dec.
Feb.
Mar.
Apr.
May
June
14
15
0
2
5
5
0
3
0
2
0
1
1
2
1
1
0
1
46
129.2
82.2
82.2
8.0
76.7
.0
70.5
2.2
2.2
103.0
61.3
6.5
66.5
.0
130.5
.0
3.2
.0
47
2.2
103.0
6,596
4,091 .2
51.3
1,168.5
1,219.0
1,473.4
1,203.9
1,146.5
44.0
26.4
204.0
(2)
.01
.04
.04
.05
.05
.05
.05
.00
.00
.01
2
2
3
4
July
Aug.
Sept.
0
1
2
2
2
3
27.6
.0
28.6
1.6
3.7
3.7
6.0
8.0
94.0
3.2
52.5
60.0
.00
.00
.00
.00
Days idle:
Number (in thousands) ............ ..........
Percent of estimated workina time
1
....
1
Agricultural and government employees are included in the total employed and total
working time; private household, forestry, and fishery employees are excluded. An
explanation of the measurement of idleness as a percentage of the total time worked
is found in "Total economy measures of strike idleness,"
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Federal Reserve Bank of St. Louis
Monthly Labor Review , October 1968, pp.54-56.
2
Less than 0.005.
NOTE: Dash indicates data not available. P = preliminary.
Monthly Labor Review
November 2004
123
Current Labor Statistics:
Price Data
37. Consumer Price Indexes for All Urban Consumers and for Urban Wage Earners and Clerical Workers: U.S. city average,
by expenditure category and commodity or service group
(1982-84 = 100, unless otherwise indicated]
Annual average
2003
Series
2002
2003
Sept.
Oct.
2004
Nov.
Dec.
Jan
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
CONSUMER PRICE INDEX
FOR ALL URBAN CONSUMERS
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 veqetables .. ..... ... ..
. ... ... ....
Nonalcoholic beverages and beverage
materials .......
················ ...... ..........
Other foods at home .. · ························•······
Sugar and sweets .. ····················· ........ .......
Fats and oils ... .... ... ....... ........ ... .......... ···········
Other foods .. ........... ... ..... ... .. ... ... ...... ..... .. ....
Other miscellaneous foods
12
·
1
Food away from home ..
····· ··• ···
12
Other food away from home · ······· ··· ·····
Alcoholic beverages .. .... ...... ............... .. ... ......
Housing ........
.... .... ................. ........ . .. ..... .
Shelter .. ..... ... ..... ...... .................... .. ..............
Rent of primary residence ...... .....................
Lodging away from home ........... .. ·······--·--·····
Owners' equivalent rent of primary residence 3 . .
1
Tenants' and household insurance •2
Fuels and utilities . .... ............... ........... ...... ..... ...... .
Fuels .... .... ..... .... ..................... ... ..... .... .•. .. ... ..
Fuel oil and other fuels .... .. .. ...... .... . ..........
Gas (piped) and electricity ..... ...... ............. .. ....
Household furnishings and operations ............. ....
Apparel ..
.. .. . ........ ... . ........
Men's and boys' apparel. ... ..... ....... ............ ........
Women's and girls' apparel. . .....................
1
Infants' and toddlers' apparel
Footwear ..
. .... . ..... ... ..... ... .•••• .• •. •
······"······ .....
Transportation ... ....... .. ..... .... .. .. .. .. ..... .. .... ... .
Private transportation ...
.........
New and used motor vehicles2 ····· • ...... .. .... .
New vehicles .. ····························"··· ................
1
i.Jsed cars and trucks
Motor fuel. ... . ............... ................... ........
Gasoline (all types) .. ..................... ....................
Motor vehicle parts and equipment..
Motor vehicle maintenance and repair ...... .........
Public transportation .. ........ ... . . . . .. . .. .. . .. ................
.
Medical care ..... .. .. .... .... ...... .. ...... ... ... .... ... ...... . ...... .
Medical care commodities ······· ·••··· ........ . ......... ..
Medical care services ........... ..... . .. .... ..... ............
Professional services ... ......... ... .. ....... .......... ...
Hospital and related services. ..... .. ...... ...
Rer.rn::ition 2
..
Vir!AO ::inrt ::11,rtio 1·2
Education and commun ication2
2
Education ... ..............................................
Educational books and supplies ... ..... ... .. ... ... .....
Tuition, other school fees , and child care ....... .
1
Communir.::itinn ·2 .
Information and information processinq 1 •2
12
Telephone services ·
Information and information processing
olhAr th::in IAIAohonA sArvir.As l .4
Personal computers and peripheral
12
equipment •
.
Other goods and services . . . . . . .. . . . . . .. . .. .. ... ... .....
Tobacco and smoking products ..... .... ··· ···· ..
1
Personal care .. .
............ ..... ......
1
Personal care products .
Personal care services
· · •· · •
........ .... ... ..
1
······· • · • ·
........
··•• ·
..
...
179.9
538.8
176.8
176.2
175.6
198.0
162.1
184.0
551 .1
180.5
180.0
179.4
202.8
169.3
185.2
554.7
181 .3
180.7
180.1
203.5
171.1
185.0
554.3
182.2
181 .7
181 .5
203.1
174.0
184.5
552 .7
182.9
182.4
182.4
202 .5
179.3
184.3
552 .1
184.7
180.0
184.1
202 .9
181.1
185.2
554.9
184.3
183.8
184.0
203.9
179.9
186.2
557.9
184.5
184.1
184.0
204.4
179.7
187.4
561 .5
184.9
184.4
184.3
204 .8
179.5
188.0
563.2
185.0
184.5
184.1
205.5
179.2
189.1
566.4
186.5
186.1
186.6
206.1
181.1
189.7
568.2
186.8
186.3
186.8
206 .8
182.3
189.4
567.5
187.2
186.8
187.1
207.2
183.7
189.5
567.6
187.3
186.8
186.7
207.2
183.7
189.9
568.7
187.2
186.7
186.1
206.4
183.4
168.1
220.9
167.9
225.9
170.3
224.4
171.8
226.3
171 .2
227.5
173.0
232.4
172.4
232 .4
172.1
229.7
171 .9
230.1
174.0
228.3
185.9
231 .7
188.8
226.7
187.7
224.5
184.9
224.0
181 .6
226.0
139.2
160.8
159.0
155.4
177.1
139.8
162.6
162.0
157.4
178.8
139.2
163.1
162.3
157.6
179.4
140.5
163.0
162.5
159.7
178.7
137.9
162.0
161 .7
157.3
177.9
139.3
163.0
161 .0
157.7
179.6
140.7
162.8
163.0
160.7
178.0
140.8
165.1
163.3
166.2
180.4
140.5
166.0
163.8
171.9
180.3
140.3
166.2
164.4
169.7
180.9
140.3
165.2
163.5
170.4
179.4
110.3
182.1
121 .3
187.2
111 .0
182.8
121 .8
187. 9
110.7
183.3
122.3
188.1
109.0
183.8
122.7
188.6
109.8
184.3
122.9
188.7
109.1
184.9
123.9
189.4
169.9
165.4
163.5
169.4
180.1
110.8
186.7
124.8
191 .7
139.8
165.8
162.8
171 .3
180.5
109.2
178.3
117.7
183.6
180.3
208.1
199.7
118.3
214.7
141 .4
163.7
163.9
162.3
178.9
-109.5
110.9
187.0
124.8
192.4
111 .5
188.4
125.4
192.5
110.5
188.9
125.9
193.4
184.8
213.1
205.5
119.3
219.9
185.8
213.8
206.6
118.5
220.7
185.7
214.7
206.9
120.9
221 .4
185.1
214.2
207.5
115.0
221.9
185.1
213.1
205.5
119.3
219.9
191 .2
220.3
211 .9
130.6
225.7
191 .0
220.2
212.4
127.0
226.1
108.7
143.6
127.2
115.5
134.4
128.3
124.0
121.7
115.8
114.8
154.5
138.2
139.5
145.0
126.1
120.9
118.0
113.1
11 5.9
159.6
143.4
130.5
151.5
125.2
122.0
117.3
115.5
116.0
155.0
138.2
131.4
145.6
125.1
124.8
120.8
118.8
114.3
152.9
135.7
134.8
142.6
124.9
123.1
121.4
115.7
126.4
121.4
152.9
148.8
122.1
119.6
157.6
153.6
124.1
120.3
159.4
155.4
125.2
121.8
157.1
153.0
99.2
140.0
152.0
116.6
116.0
106.9
190.2
207.4
285.6
256.4
292.9
253.9
367.8
106.2
102.6
107.9
96.5
137.9
142.9
135.8
135.1
107.8
195.6
209.3
297.1
262 .8
306.0
261.2
394.8
107.5
103.6
95.1
136.4
139.0
147.1
146.5
107.7
196.2
211 .2
299.2
264 .9
308.2
262.2
399.6
107.7
103.5
94.6
136.5
135.1
136.6
136.0
107.9
196.9
211 .3
299.9
264.7
309.1
263.0
400.7
107.6
103.5
109.8
110.9
126.0
317.6
362 .1
92.3
134.4
335.4
362 .1
89.7
138.7
338.2
400.0
88.6
90.8
99.7
87.8
98.3
86.7
97.4
18.3
16.1
22.2
293.2
461.5
174.7
154.7
188.4
185.5
124.0
189.9
111.7
185.8
124.1
190.8
139.7
165.0
162.6
166.2
180.4
110.5
186.2
124.7
191.8
186.3
215.2
208.3
117.2
222.6
187.0
216.0
208.8
120.0
222.9
187.9
217 .8
209 .2
128.1
223 .3
188.4
218.4
209.7
129.1
223.9
188.9
218.7
210.2
128.2
224.3
190.3
219.2
210.7
129.1
224 .7
109.4
187.8
125.1
192.2
190.9
220.0
211.2
132.2
225.1
114.8
154.5
138.7
139.1
145.0
124.7
119.0
118.0
110.9
114.8
156.3
139.2
149.9
145.5
125.3
115.8
115.5
105.7
115.0
156.9
139.5
155.1
145.5
125.7
118.6
117.1
110.3
115.1
155.2
137.6
152.5
143.5
125.7
123.5
119.8
117.6
115.7
155.6
138.0
149.6
144.2
125.6
124.3
120.3
118.7
116.1
158.1
140.4
150.4
146.8
125.4
123.4
120.3
116.9
116.2
165.5
148.5
150.7
155.8
125.6
120.1
117.7
112.3
116.1
166.6
149.5
151 .1
156.9
125.2
115.9
115.2
106.1
116.3
167.7
150.5
157.4
157.6
124.8
116.5
113.8
107.5
116.6
166.7
149.3
161 .6
156.0
125.0
121.2
116.2
123.0
121 .0
155.7
151.7
119.2
118.5
154.7
150.8
11 7. 7
115.9
157.0
153.2
119.3
117.0
158.8
154.9
12 1.9
120.1
160.5
156.6
120.5
121 .0
161 .8
157.9
118.1
120.3
165.2
161.5
116.2
118.4
165.7
161.9
114.5
115.1
164.0
160.0
115.0
117.3
162.9
159.1
119.5
121 .7
162.9
159.4
94.4
138.0
131 .0
127.8
127.2
107.8
198.0
205.6
302.1
265.0
311 .9
261.2
407.0
107.7
103.3
110.9
94.3
138.0
130.8
136.7
136.1
108.0
198.2
206.3
303.6
265.5
313.8
262.5
409.7
107.9
103.6
111.1
94.4
138.3
131.0
143.1
142.5
108.0
198.2
208.1
306.0
266.7
316.6
268.0
412.5
108.4
104.1
111 .2
94.2
137.9
131 .2
150.5
149.8
107 .8
198.5
209.9
307 .5
267.3
318 .4
269.7
413.8
108.8
104 .3
111 .1
94.1
137.6
131 .3
155.9
155.3
107.9
198.6
211 .5
308.3
268.5
319.2
270.6
413.6
109.0
104.7
94.0
137.4
131 .8
170.5
169.8
107.9
199.0
210.7
309.0
269.1
319.8
270.9
414.6
108.8
104.6
110.9
94.6
137.5
132.0
131 .2
130.6
107.9
197.2
207.9
300.8
264.0
310.6
263.0
405.6
107.8
103.8
110.8
110.9
110.6
93.6
137.2
130.6
173.3
172.7
108.2
199.7
212 .3
310.0
269.6
321.0
271.6
416.9
108.9
104.4
110.8
93.5
135.9
132.1
165.2
164,5
108.8
200.3
214.4
311 .0
269.9
322.3
272.3
419.1
108.7
104.4
110.9
93.4
134.9
133.8
162.0
161 .2
109.0
200.8
209.7
311 .6
270.0
323.1
273.3
418.8
108.5
104.1
111.7
93.9
134.9
136.5
161.2
160.5
109.3
201 .7
205.3
312.3
270.9
323.7
273.3
420.3
108.6
104.0
112.9
139.1
339.7
401 .1
88.4
139.0
336.0
401.2
88.2
139.4
342 .8
401 .7
88.2
140.1
345.4
140.6
348.9
404 .7
87.7
140.7
349.5
404.9
87.4
140.9
349.6
405.6
86.9
141.6
350.6
407 .6
86.8
142.1
403.6
88.1
140.4
348.6
404 .2
88.1
349.5
409.4
86.5
145.1
353.3
418.3
86.1
147.9
352.8
427.4
86.2
86.4
97.1
86.2
97.2
86.2
97.2
86.1
97.0
86.1
97 .1
85.7
96.7
85.4
96.5
84.8
95.9
84.7
95.8
84.5
95.6
84 .0
95.0
84.1
95 .3
15.6
15.6
15.4
15.3
15.3
15.2
15.2
15.0
14.9
14.9
14.8
14.7
14.7
17.6
298.7
469 .0
16.3
299.9
468 .7
16.5
300.2
469.5
16.3
300.0
469.1
16.2
300.2
470.4
16.2
301.4
473.0
16.0
302 .3
472.6
15.8
303 .1
473.6
15.9
303.6
47 3.3
15.7
303.8
473.5
15.5
304 .1
476.0
15.3
305.1
480.5
15.1
305.5
481 .6
15.0
306.3
482.9
178.0
153.5
193.2
179.0
153.4
195.4
179.1
153.6
195.6
179.0
153.2
194.2
179.0
153.4
194.3
179.7
153.8
194.6
180.4
154.5
195.2
180.9
154.5
195.8
181.3
154.5
196.1
181.4
154.6
196.6
181.4
153.8
196.9
181.7
153.4
197.5
181.9
152.8
198.9
182.3
153.5
199.1
!
See footnotes at end of table .
124
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
114.4
November 2004
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]
Ann ual average
Series
2002
2003
2003
Sept.
Oct.
2004
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
Miscellaneous personal services .............
274.4
Commodity and service group:
Commodities .. .... .. . . . . .. . .. .. . .. . .. .. ... . .... .. . .. ... .. ..
Food and beverages ..
Commodities less food and beverages ...
Non durables less food and beverages ... .. ... .. .
Apparel .
149.7
151 .2
152.0
151.4
150.9
150.4
151 .1
152.3
153.7
154.3
156.0
155.8
154.5
154.2
154 .9
176.8
134.2
145.1
124.0
180.5
134.5
149.7
120.9
181 .3
135.4
153.1
122.0
182.2
134.1
151.2
124 .8
182.9
132.9
149.0
123.1
184.1
131 .7
146.7
119.0
184 .3
132.6
148.4
115.8
184 .5
134.2
151.4
118.6
184.9
136.0
155.3
123.5
185.0
136.9
157.2
124.3
186.5
138.6
160.9
123.4
186.8
138.2
160.5
120.1
187.2
136.1
156.7
11 5. 9
187.3
135.6
156.1
116.5
187.2
136.7
157.8
121 .2
Nondurables less food , beverages.
and apparel ....... .. ..................
Durables ... .... .... .. .. .... .... ......... . .. . . ••....••.•.
162.2
121.4
171 .5
117.5
176.4
115.7
171 .6
115.2
169.1
115.1
167.7
115.0
172.3
115.1
175.6
115.3
179.1
115.1
181 .7
115.0
188.2
114.8
189.5
114.5
185.8
114.1
184.4
113.7
184 .4
114 .1
. ..
283.5
285.3
285.8
287.0
287.1
288.8
290.4
291 .6
292.7
293.1
293.6
294.4
295.2
295.9
. ... .... ... .. .. ... ...... .... ...
209.8
216.5
2 18.1
218.4
217. 9
217.9
219. 1
219.9
221.0
22 1.5
22 1.9
223.3
224 .1
224.5
224.5
Rent of shelter3 . .
· ··• · · · •· · · ··• · ·· "• ··
Transporatation services .... ... ... ...... ... .. ...
216.7
209.1
221 .9
216.3
222.6
223.5
223.0
222.9
224 .1
224 .9
226.8
227.4
227.7
228.3
229.2
229.4
229.3
. ... ......... .... ...
24 6.4
254.4
216.8
257.0
218.9
257.2
218.6
257.3
217.7
257.4
218.7
258.4
219.3
259. 2
219.7
259.5
220.0
259.7
220 .0
259.6
220.5
260 .2
221.6
260 .5
220.8
261 .9
220.1
263. 8
Services
. . .. . . .. . .. .. . . .. . .. .
Oth er services
. . . . . . . . . .. . . . . . . . . . .
Special indexes:
All items less food
. .... .. ... .. .. .. ....
180.5
184 .7
186.0
185.6
184.9
184 .4
185. 5
186.6
188.0
188.6
189.6
190.3
189.9
189.9
190.4
170.8
174 .6
176.0
175.5
174.9
174.7
175.6
176.7
177.6
178.2
179.6
180.2
179.6
179.5
180.1
174.3
136.0
178.1
136.5
183.5
140.3
183.2
138.2
183.2
137.7
183.6
138.8
162.4
189.0
174 .0
158.8
185.6
172.2
158.2
No!ldurables less food and apparel. . ··· ··· ···
Nondurables ..
.... .......... .......
147 .4
163.3
161 .1
182.9
140.6
162. 8
187.7
174 .1
184.3
159.9
184.4
172.8
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 ..
. . . . .. . .. .. . .
........ .. ..... .... . . ..... .
All items less medical care .... ..... .... ...........
Commodities less food ....... .... .... ...... . ....
Nondurables less food . . . . . . . . . . . . . . . . . . .. . . . . . . . .. .
All items less shelter ...
.
179.2
179.1
178.5
178.2
136.1
153.3
172.2
166.8
135.0
15 1.3
170.0
166.1
133.8
149.2
168.8
165.4
179.1
134.7
180.1
136.3
181 .3
138.0
181 .8
138.9
15 1.9
172.1
165.3
137.3
155.2
176.6
167.4
150.8
173.0
166.4
153.7
176.1
168.1
157.5
179.4
170.3
159.3
181 .7
171.4
2 17.5
226.4
229.2
228.7
228.2
228.4
229.7
230.6
230.7
23 1. 1
231 .7
234 .2
235.0
235.6
235.9
202.5
121 .7
187.7
190.5
208.7
136.5
190.6
193.2
210 .3
144.6
191 .0
193.6
210.5
136.9
191 .7
194.3
209.9
133.1
191 .6
193.9
209.9
131 .8
191 .5
193.6
211 .0
137.4
191 .9
194.0
211 .7
140.6
192.7
194.9
212.7
143.1
193.7
213.6
154.1
194 .3
196.5
215.0
159.7
194. 4
196.6
215.8
156.3
194 .5
196.6
216.2
155.3
216 .1
154 .3
194.7
196.8
195.2
197.4
143.7
117.1
217.5
140.9
136.7
223.8
140.2
146.9
140.4
137.0
225.8
139.9
132.1
225.6
139.0
129.0
225.5
138.5
138.2
226.6
139.3
144 .6
227.5
196.1
140.3
151 .3
228.9
213.2
145.9
194.1
196.5
140.5
156.3
229.4
140.2
170.1
229.6
139.4
172.8
230 .2
138.2
165.1
231 .0
138.1
162.5
231 .4
139.4
162.0
231 .6
175.9
523.9
179.8
181 .0
180.7
535.6
539.2
538.2
180.2
536.7
179.9
536.0
180.9
538.7
181 .9
54 1.7
182.9
544 .8
183.5
546.5
184.7
550.2
185.3
551 .9
184.9
550 .8
185.0
551.0
185.4
552.4
183. 6
183.1
184.0
184.4
184.5
183.8
183.5
183.9
183.3
186.0
185.6
185.8
186.4
183.5
183.2
185.9
186.1
186.8
186.3
186.3
186.9
186.4
186.1
186.8
186.2
185.5
224 .9
171 .9
CONSUMER PRICE INDEX FOR URBAN
WAGE EARNERS AND CLERICAL WORKERS
All items ...
.. .... ......
All items (1967 = 100) .... .... ... .... .. ....... .. .... .
.. ..... . .
176.1
179.9
180.7
181 .7
182.4
Food ... ... ........ ... ...... ... .. ...... ... .. ... .. ... ... .. ... ... .. .. ..
Food at home ..
...... .............. .• .... . .. .. ....
Cereals and bakery products ... ... .. .... .... .. .. ..
Meats, poultry, fish, and eggs ..... ..... .. ..........
176.5
175.1
179.4
178.5
180.2
179.4
181 .2
180.7
181 .9
181 .6
183.3
183. 8
183.3
183.2
198.0
162.0
202. 8
169.2
2035
170.9
203.2
173.8
202.4
179.2
202.4
181.0
203. 8
179.9
204.4
179.7
204.9
179.6
205.5
179.1
206.0
181.1
206.7
182.4
20 7.2
183.7
207.0
183.7
206.3
183.4
167.2
222.9
167.6
224 .3
170.2
223.4
171 .7
224 .9
171 .0
225.3
172.7
229. 7
172.2
229.7
171 .7
227 .5
171 .3
227.8
173.6
225. 5
186.1
228.9
189.0
224 .3
187.8
222 .3
184 .9
222.2
181.4
223.9
138.6
160.4
139.1
162.2
139.8
162.5
137.3
161 .6
138.6
162 .5
140.1
164.7
139.1
164.6
161.4
157.3
178.3
160.5
157.7
162.6
166.0
180.8
161 .9
166.1
180.8
180.8
162.9
172.0
180 .7
169.9
180.0
163.2
162 .2
179.4
139.3
165.5
162.2
171 .4
139.6
165.8
162.1
159.6
179 .0
139. 3
165.1
162.9
169.4
180.5
139.8
165.6
161 .6
157.4
179.2
140.0
162.3
162.4
160.7
178.4
140.8
163.3
158.8
155.3
177.6
138.5
162.8
162.1
157.6
180.0
139.7
164.8
163.1
170.3
179.7
109.7
110.8
111 .3
11 1.2
109.5
110.3
109.6
110.1
112.2
111 .0
111.2
111 .4
109.7
112.0
111 .0
178.2
118.1
183.3
182.0
121 .5
187.1
182.7
183.3
183.7
184.2
184 .8
185.3
185.6
186.1
186.6
186.8
187.6
188.2
188.8
122.0
187.7
122.5
188.1
122.9
188.8
123.1
188.9
123. 6
189.5
123.8
190.0
123.8
191.2
124.3
192.1
124 .6
192.0
124 .7
192.7
124.9
192.2
125.2
192.8
125.8
194.0
175.7
20 1.9
180.4
206.9
181 .6
207.6
181 .3
208. 3
180.9
208.2
181 .0
208.2
182.1
209.2
182. 6
209.8
183.2
2 11 .0
183. 6
2 11 .5
184 .1
211.8
185.6
212.2
186.2
2 13.0
186.6
2 13.4
186.5
213.4
211 .6
Food and beverages ... ... . ... ....... . .... ....... .
Dairy and related products
Fruits and vegetables ..
1
Nonalcoholic beverages and beverage
. .. ..... ............. .. .. .
materials ..
Other foods at home .. ................ . ... .. .........
Sugar and sweets .. .... .. .. .. ..... ..... .. ... . .......
Fats and oils ..
........ ..... ... .............
Other foods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ...
.
Other miscellaneous foods
Food away from home
12
· ..
.. ....
..
1
12
home ·
Other food away from
Alcoholic beverages .... ... .... .... .... .
Housing ............................. .. ....... ... .... ...
•
• • O•• •• OOO• •• •OO • •
..
. . . . . . ... .
Shelter ············
................. ....... .... ..... . ... .
Rent of primary residence .. .... . ... ... ··············
LodQinQ away from home
2
Own ers· equivalent rent of prim ary residence
12
3
Tenants' and household insurance ·
Fuels and utilities ..... .... ... ... .. .. .. .. ........ ... ... ...
Fuels ...
....... ................... .... .......
Fuel oil and other fuels ........ ... ..... ..... .. ... ...
Gas (piped) and electricity . . . . . . . . . . . . . . . . . . . . .
Household furnishinQ s and operation s ........ ...
Apparel .. ...... ..... ..... ... ... .... . ......... .....
Men's and boys' apparel ... . ... .. .. . . ... .. .... .. ....
Wom en's and girls' apparel. . ............. .. ..
.
.
1
Infants' and toddlers' aooarel .. ...... . ........
Footwear ..
...... ...... ...........
Transportation ..
. ....... ... .... ... .. ....
Private transportation . .. .. .. . . . .. . ....•..... . ..• •. . .
New and used motor vehicles2
163.8
181 .4
199.0
204 .7
205.8
206.1
206.6
207.0
207.4
208.0
208.4
208.9
209 .4
209.9
210.3
211 .0
118.4
119.8
119.8
121.7
116.2
113.4
118.5
121 .1
128.8
129.8
128.2
128.8
133.0
131 .6
127.7
195. 1
199.7
200.4
20 1.0
20 1.4
201 .7
202.1
202.3
202.7
203.1
203.6
203.9
204 .2
204 .7
205.1
108.7
142.9
126.1
114.7
153.9
137.0
115.8
159.1
142.3
116.0
154.3
137.0
116.0
155.1
137.0
116.4
157.4
139.3
116.5
165.0
147.4
116.3
166.1
148.4
116.5
167.2
149.3
116.8
166.2
148.2
121.9
129.4
150.6
121 0
130.7
144.6
120.9
149.6
144.7
121.0
115.1
156.2
138.3
154.5
144 .7
121.4
115.2
154.7
136.6
138.7
144 .1
114.4
153.0
135.4
136.2
142.5
120.4
114.9
155.6
138.0
115.0
133. 4
124.4
114.4
152.3
134.7
134.4
141 .9
120.7
152.0
142.9
12 1.4
148.9
143.5
12 1.3
149.6
146.1
12 1.1
150.2
156.2
120.7
123. 1
121.7
114.6
120.0
117.5
11 2. 1
121.0
116.5
114 .5
123.9
120.0
118.2
122.6
121 .1
115.3
118.7
117.8
110.5
115.7
115.6
105.5
118.3
117.4
109.8
122.9
120.0
117.4
123.8
120.6
118.4
122.8
120.3
116.7
149.8
155.1
121.3
119.6
117.8
11 2. 2
156.8
156.8
120.4
115.9
113.3
106.9
161 .1
155.3
120.6
120.6
115.6
114.0
128.6
121 .2
151.8
149.0
124.1
126.5
119.6
158.1
155.3
127.7
121 .1
155.4
152.5
125.0
122.2
117.0
116.4
156.8
119.6
159.9
159. 3
11 7.6
116.3
161.4
158.6
120.4
161 .6
157.1
117.0
164 .0
161 .3
114.4
162.2
154 .0
120.9
119.0
163.6
160.9
118.8
115.6
154.9
152.2
125.2
118.6
158.5
155.7
123.4
120.4
153.6
150.8
121.4
117.8
152.5
149.7
120.1
119.1
156.3
153.5
99.4
96.0
94 .4
93.5
93.1
92.8
92.7
92 .8
92.6
92 .6
92.5
92.1
92.1
92. 2
92 .3
2004
125
115.6
115.2
106.0
122.3
159.1
See footnotes at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
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
2002
... . .. . . ... . .. ... .•... ...... ...
New vehicles .... ··· ·· ·····
1
Used cars and trucks ..... .... .. .... . ...
Motor fu el . ..... .... ... .... ..... .... .. .. .. ......... , .. ........
Gasoline (all types) ..
.... . . •. ••... ... .
Motor vehicle part s and equipment..
Motor vehicle maintenance and repair ...
Public transportation .. ... .. ..... .. . .. ... . ·· ···
......
Medical care . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. .. ··· ··········· ......
Medical care commodities ... .... ... . ... . . . . . . . . . . . . .
Medical care services .... ..... ... ... .......... ... .... ... ..
Professional services ..... ..... ·· ·· ··"·•· .............
Hospital and related services .... ..... , .... ......... .
Rocrn;:itinn2
Vir1P.o ;:i nn
1
;:i11nio ·2
Educ;:ition and communication 2
2003
2003
Sept.
Oct.
2004
Nov.
Dec.
Jan .
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
141 .1
139.0
137.6
137.8
138.7
139.2
139.2
139.5
139.0
138.7
138.5
138.2
137.0
136.0
136.0
152.8
143.7
139.8
135.9
132.8
131.7
131.6
131 .7
132.0
132.1
132.6
131.4
133.0
134 .6
137.3
117.0
116.4
106.1
191 .7
202.6
136.1
135.5
147.5
147.0
136.9
136.4
131 .5
130.9
137.1
150.9
150.3
173.8
173.2
161 .7
161 .0
107.5
198.9
205.8
107.6
200.1
206.2
107.4
200.3
208.0
107.5
200.8
208.8
107.8
201.5
210.0
165.6
165.0
108.2
202. 1
212.1
162.4
161.7
107.5
198.6
208.7
156.5
155.8
107.5
200.4
209.4
171 .1
170.4
107.2
197.9
208.4
136.6
107.6
199.9
204 .6
143.6
143.0
107.3
197.3
206.0
128.1
127.6
107.3
199.8
203.6
108.4
202.7
208.0
108.7
202.7
203. 1
284.6
251 .1
296.3
257.4
298.3
259.4
299.1
259.2
300.1
258.5
302.8
259.8
305.4
260 .9
306.9
26 1.5
307 .7
262 .5
308.4
263.3
311.7
263.8
310.4
263.7
311 .0
259.4
263.8
264 .8
292.5
256.0
363.2
305.9
263.4
39 1.2
307.9
264.4
309.1
265.2
313.8
267.8
405.9
408.7
318.6
272.3
409.9
319.4
273.2
409.8
320 .0
273.5
397.5
311.9
266.5
403.4
316.8
270.6
395.8
310 .6
265.2
402.4
410.7
321.2
274 .1
413.0
322.4
274.8
415.2
323.2
275.8
414.9
323.9
275.9
416.4
104 .6
105.5
105.5
105.4
105.6
105.5
105.6
106.2
106.5
106.7
106.6
106.7
106.3
106.1
106.2
102.0
102.9
102.7
102.8
103.0
102.5
102.7
103.2
103.5
103.9
103.9
103.7
103.7
103.4
103.3
30 1.4
309.4
107.6
109.0
109.7
109.7
109.6
109.7
109 .8
110.0
109.8
109.6
109.2
109.4
109.4
109.9
110.8
125.9
318.5
133.8
336.5
137.8
339.6
138.1
340.6
138.0
337.5
138.0
343.8
139.1
346.1
139.4
349.5
139.6
349.9
139.7
350.4
139.9
350 .4
140.6
351 .5
14 1.0
350.4
143.6
354.7
146.3
354.8
354 .8
93.7
377.3
91 .2
389.2
90.2
390 .1
89.9
390.2
89.8
390.7
89.7
392.8
89 .6
393.3
89.6
393.8
89.3
394 .1
89.0
394 .6
88 .. 4
396.7
88.4
398.1
88.1
405 .8
87.6
414 .0
92 .7
89.9
89.1
88.5
88.4
88.3
88.2
88.2
87.9
87.5
87.0
86.9
86.7
86.2
86.3
Teleph one
Information an d information processinq
99.9
98.5
97 .6
97.3
97.4
97.4
97 .2
97 .3
96.9
96.7
96.1
96.1
95.8
95.2
95.5
olhf!r th;:in IAIAnhonA sArvir.As i ,4
Personal compu ters and periph eral
19.0
16.7
16.1
16.2
15.9
15.8
15.8
15.8
15.7
15.5
15.4
15.4
15.3
15.3
15.2
2
Education
Education al books and supplies ....
..........
Tuition, ot her schoo l fees, and child care .. ...
C-:omm, inir.;:ition 1·2
Information and information processinq 1 ·2 . ..
12
services ·
12
equi pment ·
Other goods and services ... ........... ................... .
Tobacco and smoking products ... .... ······· ·······
Personal care
1
...
. . . . . . . . .. . . .. . . .
. ..
.... ...
1
Personal care products ···· ·•• ··· ·"
• · ··• • ··
1
Personal care services
Miscellaneous personal services .. ..... ...... ... ..
Commodity and service group:
Commodities
.... . ... ...... . ······ •" ·
87.8
21 .8
17.3
16.0
16.2
16.0
15.9
15.8
15.7
15.5
15.6
15.4
15.2
15.0
14.9
14.8
302.0
307.0
307 .9
308.2
307 .7
308.1
309.3
310 .0
310.8
311 .3
311 .5
311 .8
313.2
313.5
314 .4
463.2
470.5
469.9
470 .7
470.2
471.5
473.8
473.2
474 .2
474.1
474 .4
476.9
48 1.6
482. 6
483.9
174. 1
177.0
177.9
178.0
177.7
177.8
177.4
179.1
179.7
180.1
180.2
180.0
180.3
180.5
180.9
155.5
154.2
154.0
154. 1
153.8
154. 2
154.3
155.0
155.0
155.1
155.1
154.3
153.9
153.1
154.0
189.1
193.9
196.1
196.3
194 .8
194.9
195.1
195.7
196.3
196.6
197. 1
197.5
198.1
199.5
199.7
274 .0
283.3
285.2
285.6
286 .7
286.6
288.4
290.2
29 1.6
292 .9
293.1
293.5
294 .7
295.4
296.2
151 .9
181.7
135.2
153.6
151 .3
182.4
133.8
15 1.4
150.7
183.6
152.7
184 .0
135.2
154.3
154.1
184.4
137.0
158.4
154.8
184 .5
132.5
149.0
15 1.5
183.8
133.5
15 1.0
138.0
160.5
156.7
186.0
140 .0
164 .7
156.6
186.4
139.6
164.4
155.2
186.8
137.5
160.4
154.9
186.9
137.1
159 .5
155.7
186.8
138.2
161 .2
123.9
122.6
118.7
115.7
118.3
122. 9
123.8
122. 8
119.6
115.6
115.9
120.6
172.9
114 .2
171.6
114.0
176. 5
114 .0
180.2
184.1
194.5
113.9
191 .8
190 .2
190.1
114.0
187.0
113.9
196.0
1142 .0
113.5
113.2
113. 1
113.7
150.4
151.8
176.1
135.5
147 .0
179.9
135.8
152.1
152.7
180.7
136.7
155.9
123.1
120.0
121 .0
165.3
121 .8
175.6
117.4
181 .2
175.7
115.5
114.7
205.9
212.6
214.3
214.4
214 .1
214 .2
215.3
216.0
216.7
217.1
217.6
219.0
219.7
220 .2
220.3
194 .5
207.7
241 .6
199.2
216.2
248.5
199.9
216.8
250.6
200.6
219.0
250.7
200.5
218.8
250 .7
200.6
218.0
250.9
201.4
219.1
251 .8
202 .0
219.7
252 .6
203.2
220.0
252.9
203.7
220.2
253.0
203.9
220 .3
252 .7
204.4
220.7
253.3
205. 1
221.6
253.5
205 .5
221 .0
254.4
205.5
220.5
256.0
All items less food . . . . . . . . . . . . .... .. . ... . ... .... ........
All items less shelter . . . . . . . . . . . . .. .. . ... ... ... ..... .....
All items less medical care ... ... .. ..... . .. .. ••. . ... •..
Commodities less food .... ... . . . . .. . . . .. ... .. ..... .. .
Nondurables less food .. .. ... .... .. ... ..... ...... .....
Nondurables less food and apparel .. .. . •• ..... •....
175.8
168.3
171.1
137.3
149.2
166.1
179.7
171 .9
174.8
137.7
154.2
181 .0
173.3
176.0
138.6
157.9
181.1
180.4
172.6
175.6
137.0
155.7
176.1
179.7
171.9
175.0
135.8
153.7
173.6
179.2
171 .6
174 .7
134.5
151.4
172.1
180.2
172.5
175.6
135.5
153.3
176.9
18 1.4
173.7
182.6
174.7
177.6
138.9
160.4
184.0
184.4
176.8
179.4
141 .8
166.4
193.5
185.0
177.5
180.0
141 .5
166.2
194 .8
184 .5
176.7
179.6
139.4
162.3
191 .0
184 .5
176.6
176.6
137.1
156.4
180.2
183.2
175.3
178.2
139.9
162 .4
186.6
185.1
177.3
180.0
140.2
163.2
189.7
................ ...
161.4
166.4
168.8
168.1
167.3
166.6
167.8
169.5
171.8
173.0
175.9
175.9
174.0
173.6
174.5
Services less rent of shelter3
Services less medical care services .. .... .. .... .. ..
Energy. .. ........
. .. .... ... ...... .. ... .. . .. ... . ...
193.1
201.3
203.7
203.2
202 .7
202 .9
204 .1
204.9
204 .9
205.2
205.8
208.2
208.9
209.3
209.5
198.9
120 .9
205.2
135.9
206.8
144 .2
206.9
136.3
206.5
132.4
206.6
131 .1
207.6
136.9
208.2
140.2
208.8
143.0
209.2
146.0
209.7
154.5
211 .1
159.9
211.8
156.2
212 .2
155.1
2 12.3
154.2
183.6
186.4
188.1
140.2
187.0
188.6
140.3
187.0
188.4
186.9
188.0
141 .1
187.2
188.3
138.2
187.9
189.1
139.0
188.7
190.1
140.0
189.0
190.4
140.1
189.3
190.4
139.9
189.3
190.3
189.3
139.0
190 .3
138.0
189.5
190.5
138.0
190.2
191.4
139.5
147.2
22 1.3
137.2
222.1
136.8
222. 1
138.3
223.1
144.7
223.9
151.5
224.9
156.7
225.3
170.7
225.5
173.3
226.0
165.5
226.7
162.8
227. 1
162.3
22 7.4
. . . . . .. . . . .. . .. .. .
Food and beverages ...
........ ..... .... ....... ... ..... .
Commodities less food and beverages . . . . . . . . . . .
Nondurables less food and beve rages ........ ...
Apparel ...... . .................
Nondurables less food, beverages,
.
an d apparel ..... .. .. .. .. ...... .
Durables ..
..............
.. .. .• ..... ..
. ... .. .... ... .... . .. .... ..... ..
. . . . .. . . . . . . . . . . . .
Services .. ..... ... .. ... ............ .... . ............
··· ··· ··••O,•
Rent of shelter3
Transporatation services ···· ····"··· .. ... .............
Other services .... .... .. ..... .. ......... ...... .. ........ .... ..
Special indexes:
.
.
.
Nondurables .. ······ ····· ······· ···· ····· ··
175.9
All items less energy .. .... .... ........ .... .. ... ...... .
All items less food and energy ..... ....... .. .. ......
Commodities less food and energy ......... ..
185.6
144.4
186. 1
187.9
141.1
Energy commodities ··· ······· ...... ...........
Services less energy ....... •.•.. •• .... •. .
17.3
213.9
136.8
220.2
1
Not season ally adjusted.
2
Indexes on a December 1997 ; 100 base.
3
Indexes on a December 1982 ; 100 base.
126
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
139.7
132.1
222. 1
4
179.6
139.0
161.5
189 .6
Indexes on a December 1988 ; 100 base.
Dash indicates data not avai lable.
NOTE: Index applied to a mon th as a whole , not to any specifi c date.
November 2004
38. Consumer Price Index: U.S. city average and available local area data: all items
[1982-84
= 100, unless otherwise indicated]
Pricing
All Urban Consumers
sched-
2004
ule
U.S. city average ............ .... ................ ...... .......... ..
M
1
Apr.
188.0
May
189.1
June
189.7
Urban Wage Earners
2004
July
189.4
Aug.
Sept.
189.5
189.9
Apr.
183.5
May
June
184.7
185.3
July
184.9
Aug.
Sept.
185.0
185.4
197.7
Region and area slze2
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
M
199.4
199.9
201.1
201 .0
201 .0
201 .2
195.7
196.4
197.5
197.3
197.2
M
201.4
202.0
203.3
203.0
203.1
203.2
196.3
197.1
198.3
198.0
198.1
198.4
M
118.1
118.3
118.7
119.2
118.9
119.2
118.1
118.4
118.8
119.1
118.7
119.2
M
181.5
182.9
183.3
183.2
183.3
183.6
176.3
177.8
178.2
178
178.2
178.6
M
183.7
185.0
185.3
185.4
185.6
189.5
177.9
179.4
179.4
179.5
179.8
180.2
115.9
Size B/C-50,000 to 1,500,000 .. ....... .. ...... .. ....... .....
Size D-Nonmetropolitan (less than 50,000) .. .. .. .. .... .. ...
M
115.6
116.4
116.8
116.3
116.5
116.8
114.6
115.5
116.0
115.5
115.7
M
173.9
176.0
176.9
177.1
176.3
176.4
171 .2
173.2
174.1
173.7
173.4
173.7
South urban ... .. ... .. ............ ............................ .. .... .. ...... .. .....
M
180.9
182.0
182.9
182.6
182.6
185.8
180.9
178.9
179.7
179.3
179.4
179.7
Size A-More than 1,500,000 ..........................................
M
182.5
183.4
184.3
183.7
183.7
184.0
179.7
180.8
181 .9
181 .2
181 .2
181.4
Size B/C-50,000 to 1,500,000 3 ...... ... ... ... .... .
Size D-Nonmetropolitan (less than 50,000) ........ .........
West urban ................................. .......................................
M
115.6
116.4
117.0
116.9
116.9
116.9
114.0
114.8
115.3
115.2
115.3
115.4
M
178.7
179.4
180.5
180.1
180.0
181.2
177.8
179
180
179.4
179.5
180.7
M
192.3
193.4
193.3
192.9
193.0
193.8
187.3
188.6
188.6
188.0
188.0
188.8
Size A-More than 1,500,000 ................................ ..........
M
194.6
195.9
195.9
195.4
195.5
196.4
188.2
189.6
189.7
188.9
188.9
189.9
Size B/C-50,000 to 1,500,000 3 . .... . ........ .... . ........
M
117.8
118.2
117.9
117.9
118.1
118.4
117.2
117.8
117.6
117.4
117.6
117.8
M
M
M
172.0
116.3
179.3
172.9
117.0
180.9
173.4
117.3
181.8
173.1
117.3
181 .3
173.2
117.3
181.0
173.6
117.4
181 .8
170.0
115.3
177.2
171.2
116.0
178.8
171 .7
116.4
179.7
171 .3
116.2
179.0
171.4
116.2
178.8
171 .8
11 6.5
179.7
M
190.0
194.5
180.6
185.2
182.2
186.8
182.5
187.4
182.4
186.8
183.2
186.5
183.1
187.8
Size classes:
As ......
· ········· •··· ··-··· • ·· •· ·•··•··• . . . . . . . . .. .
3
B/C .... ... .. .......... . .... ... ............. ... ................. .............. ... .....
0 ........ .. ....... .... ... .. .......................................................
Selected local areas•
Chicago-Gary-Kenosha, IL-IN-WI. ...... .... ........... ... ... ...
Los Angeles-Riverside-Orange County, CA ...... .. .. .... .. ....
M
187.2
191.9
188.7
193.3
189.1
193.7
189.2
193.4
190.2
193.1
New York, NY-Northern NJ-Long Island, NY-NJ-CT-PA ..
M
204.0
204.4
206.0
205.5
205.7
205.9
198.5
199.1
200.4
200 .1
200.3
200.6
Boston-Brockton-Nashua, MA-NH-ME-CT ..... ...... .... ... .
Cleveland-Akron, OH ......... ...... .... .... ..... .... ... .. ... ... ... ...
1
181.3
-
209.8
-
207 .9
172.6
-
172.8
Dallas-Ft Worth, TX ..... .. ........................................... .
1
-
118.9
-
179.1
-
179.7
-
179.5
179.4
120.8
-
118.4
-
119.7
-
208.8
183.8
-
207 .9
179.1
-
208.9
1
-
184.1
-
180.0
-
184.0
-
182.5
186.8
179.3
-
180.4
-
181 .5
166.8
-
167.6
182.6
-
-
181 .7
Washinqton-Baltimore, DC-MO-VA-WV .. ..... .. .............
Atlanta, GA ......... ..... ... .. ................. ... .... ..... ... .. ..... .... .
1
-
118.9
-
120.2
2
182.3
185.7
Detroit-Ann Arbor-Flint, Ml. .. ........ ........ ... .... .. ... ...... .... .
2
184.7
-
-
Houston-Galveston-Brazoria, TX .......... .. ... ... .... ...... .....
2
169.7
-
169.3
-
169.1
-
Miami-Ft. Lauderdale, FL .. ... .. ............................ .. .......
2
185.2
185.6
-
2
194.8
198.0
-
185.1
Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD .....
San Francisco-Oakland-San Jose, CA ...................... ....
Seattle-Tacoma-Bremerton, WA .... ... ............... . .. .. ..... ...
-
199.1
-
194.0
2
198.3
-
199.0
198.7
194.3
-
195.3
-
194.7
2
-
7
1
Foods, fuels , and several other items priced every month in all areas; most other
goods and services priced as indicated:
185.8
194.6
189.1
183.4
197.3
195.4
190.4
MO-IL; San Diego, CA; Tampa- St. Petersburg-Clearwater, FL.
1-January, March, May, July, September, and November.
2-February, April, June, August, October, and December.
7
2
Regions defined as the four Census regions.
Indexes on a December 1996 = 100 base.
4 The
"North Central" region has been renamed the "Midwest" region by the
Census Bureau . It is composed of the same geographic entities.
Indexes on a December 1986 = 100 base.
s In addition, the following metropolitan areas are published semiannually and
120.4
167.4
182.9
198.0
195.0
189.6
Report: Anchorage, AK; Cincinnatti , OH-KY-IN; Kansas City, MO-KS; Milwaukee-Racine,
WI; Minneapolis-St. Paul , MN- WI; Pittsburgh, PA; Port-land- Salem , OR- WA; St Louis,
M-Every month.
3
174.8
180.0
Indexes on a November 1996 = 100 base.
NOTE: Local area CPI indexes are byproducts of the national CPI program. Each local
index has a smaller sample size and is, therefore, subject to substantially more sampling
and other measurement error. As a result, local area indexes show greater volatility than
the national index, although their long-term trends are similar. Therefore, the Bureau of
Labor Statistics strongly urges users to consider adopting the national average CPI for use
in their escalator clauses. Index applies to a month as a whole, not to any specific date.
appear in tables 34 and 39 of the January and July issues of the CPI Detailed
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Dash indicates data not available.
Monthly Labor Review
November
2004
127
Current Labor Statistics:
Price Data
39. Annual dote: Consumer Price Index, U.S. city overage, oll items ond mojor groups
[1982-84 = 100)
1993
Series
Consumer Price Index for All Urban Consumers:
All items:
Index ............... .... ... ... ........... .. .... ............ ..... .. ...... ... .
Percent change ... ..... .. ..... ..... .. ........ .. ......... ...... ... .
Food and beverages:
Index ... ................................ ... ................................ .
Percent change .... .... ... ................... ..... ..... ........ .. .
Housing :
Index .......... .......... ..... ................ ....... ................... .
Percent change ........... ... ... ..... ........ ..... ... .. .... ...... .
Apparel:
Index ............. .. ......... ..... ............ ............................. .
Percent change ......... .. ........ .. ............. ........ ........ .
Transportation:
Index ......... .. ......... .... ......... .... ..... .... ... ................ ... ..
Percent change ............ ................ .. .. ......... ... ...... .
Medical care:
Index ... .. .. ..................... .... .......... .... .................... .... .
Percent change ........... ... .. ..... .. ...... .. ...... .. .. ....... .. .
Other goods and services:
Index .... .. ........... ... ... ............ ...... ........ ..................... .
Percent change ............................. ............. ........ .
Consumer Price Index for Urban Wage Earners
and Clerical Workers:
All items:
Index ........ ... ... ... ... ... ... ..... ............ ... . .... ... ...... .. ......
Percent change .... ................ .. ...... .. .. .... ... .... .. .... .
Monthly Labor Review
128
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
144.5
3.0
148.2
2.6
152.4
2.8
156.9
3.0
160.5
2.3
163.0
1.6
166.6
2.2
172.2
3.4
177.1
2.8
179.9
1.6
184.0
2.3
141 .6
2.1
144.9
2.3
148.9
2.8
153.7
3.2
157.7
2.6
161 .1
2.2
164.6
2.2
168.4
2.3
173.6
3.1
176.8
1.8
180.5
2.1
141 .2
2.7
144.8
2.5
148.5
2.6
152.8
2.9
156.8
2.6
160.4
2.3
163.9
2.2
169.6
3.5
176.4
4.0
180.3
2.2
184.8
2.5
133.7
1.4
133.4
-.2
132.0
-1.0
131 .7
-.2
132.9
.9
133.0
.1
131 .3
- 1.3
129.6
- 1.3
127.3
- 1.8
124.0
- 2.6
120.9
-2.5
130.4
3.1
134.3
3.0
139.1
3.6
143.0
2.8
144.3
0.9
141 .6
-1 .9
144.4
2.0
153.3
6.2
154.3
0.7
152.9
-.9
157.6
3.1
201.4
5.9
211 .0
4.8
220 .5
4.5
228.2
3.5
234.6
2.8
242.1
3.2
250.6
3.5
260.8
4.1
272.8
4.6
285.6
4.7
297.1
4.0
192.9
5.2
198.5
2.9
206.9
4.2
215.4
4.1
224.8
4.4
237.7
5.7
258.3
8.7
27 1.1
5.0
282.6
4.2
293.2
3.8
298.7
1.9
142. 1
2.8
145.6
2.5
149.8
2.9
154.1
2.9
157.6
2.3
159.7
1.3
163.2
2.2
168.9
3.5
173.5
2.7
175.9
1.4
179.8
2.2
November
2004
40. Producer Price Indexes, by stage of processing
(1982 = 100]
Annual average
Grouping
2002
2003
2003
Sept.
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
JulyP
Aug.P
Sept.P
Finished goods...... ... ... ........................
Fini shed consumer goods. ....... .... · • ......
Finished consumer foods .. ....... .. ........
138.9
139.4
140.1
143.3
145 .3
145.9
144.0
146.4
148.0
145.5
147.7
151 .0
144.5
146.5
150.1
144.5
146.7
150.3
145.4
147.8
148.1
145.3
147.8
148.4
146.3
149.0
150.7
147.3
150.4
152.7
148.9
152.5
155.5
148.7
152.0
155.0
148.7
152 .0
152.1
148.6
151 .9
152.2
148.7
152.0
152.2
Finshed consumer goods
excluding foods .. . ·········· ···· ·· ········· ·······
Nondurable goods less food .. .............
Durable goods ... .................................
Capital equ ipment... .. ······ ·· ........ ...... .. ...
138.8
139.8
133.0
139.1
144.7
148.4
133.1
139.5
145.5
150.4
131 .1
138.9
146.2
149.4
135.6
140.8
144.8
147.6
135.0
140.5
145.0
148.2
134.3
140.2
147.4
151 .7
134.3
140.5
147.3
151 .6
134.2
140.2
148.0
152.4
134.7
140.5
149.1
154.3
134.4
140.6
150.9
156.7
134.8
140.8
150.5
156.0
134.9
141 .1
151 .7
157.9
134.6
141 .2
151.4
158.0
133.7
14 1.1
151 .5
158.1
133.8
14 1.3
Intermediate materials,
supplies, and components..... ... ...........
127.8
133.7
134.1
134.1
134.1
134.5
136.2
137.3
138.3
140.2
142.0
142.8
143.8
144.9
145.3
Materials and components
for manufacturing ... ..... ..... .................. .....
Materials for food manufacturing ..
Materials for nondurable manufacturing ..
Materials for durable manufacturing ..
Components for manufacturing. ... .........
126.1
123.2
129.2
124.7
126.1
129.7
134.4
137.2
127.9
125.9
129.8
137.4
136.4
128.6
125.8
130.5
141 .8
137.5
129.5
125.8
130.7
141 .6
137.2
130.5
125.8
130.9
140.7
137.9
131 .2
125.8
131 .9
138.4
140.2
132.9
125.9
133.2
139.3
141 .0
137.3
126.2
134.3
141 .7
141 .4
140.7
126.5
136.2
146.6
143.5
144.3
127.1
137.4
152.2
144.5
146.9
127.3
137.7
152.0
145.9
145.8
127.6
138.6
147.9
147.2
149.4
127.8
139.6
145.4
149.5
151 .0
128.1
140.8
144.2
152.1
153.3
128.0
Materials an d components
for constru ction ............ ... .. .... ........
Processed fuels and lubricants ..... ............
Containers ... ....... ... ........... ....... .... .. ....
Supplies .. ..... .................... ......... . . . . . •.. •... •
151 .3
96.3
152.1
138.9
153.6
112.6
153.7
141.5
155.0
113.7
153.5
141 .7
155.2
111.5
153.2
141 .9
155.6
110.3
153.4
142.6
155.6
111 .7
153.5
142.8
156.2
116.8
153.9
143.2
159.0
116.8
153.7
143.8
161 .9
116.5
154.1
144.8
164.7
118.4
154.9
146.4
166.9
122.3
156.7
147.2
166.9
124.9
158.9
147.3
167.8
126.5
159.5
148.1
170.0
128.5
161.4
147.5
171.1
127.1
162.5
147.7
Crude materials for further
processing .......................... ... .... ... .......
Foodstuff s and feedstuffs . . . . . . . . . . . . . ..... . .....
Crude nonfood materials ·············· ··· ·········
108.1
99.5
111 .4
135.3
113.5
148.2
134.7
119.0
142.8
138.3
128.1
141.1
137.0
125.7
141.4
141 .1
124.7
149.5
147.8
117.1
167.3
150.1
122.2
167.3
152.9
131 .7
164.8
155.7
135.4
166.6
161 .8
141 .1
172.9
163.0
137.4
178.0
162.0
131 .0
181.3
160.7
124.7
183.9
153.8
12 1.7
174.1
Special groupings:
Finished goods, excluding foods .... .... ... ...
Finished energy goods . .. . .. ......... ......... ...
Finished goods less energy..
Finished consum er goods less energy ......
Finished goods less food and energy .... ...
138.3
88.8
14 7.3
150.8
150.2
142.4
102.0
149.0
153.1
150.5
142.7
105.2
149.0
153.3
149.7
143.8
103.2
151.4
156.1
152.0
142.8
100.4
151 .0
155.5
151 .7
142.8
101 .0
150.9
155.5
151 .4
144.5
106.0
150.6
154.9
151 .8
144.3
105.7
150.5
155.0
151 .7
144.9
107.0
151.3
156.1
152.0
145.7
109.5
151 .9
156.9
152.1
147.0
113.6
152.7
158.0
152.2
146.8
112.5
152.7
157.9
152.3
147.6
115.1
152. 1
156.8
152.4
147.4
115.1
151.9
156.6
152.2
147.5
114.9
152.1
156.8
152.5
Finished consumer goods less food
and energy ..... ..... ..... ......... .....
157.6
157.9
157.0
159.5
159.2
159.0
159.4
159.4
159.7
159.8
159.9
160.0
160.0
159. 7
160.0
Consumer nondurable goods less food
and energy ..
177. 5
177.9
177.8
178.6
178.5
178.9
179.7
179.8
179.8
180.5
180.2
180.2
180.5
180.8
181.3
Intermediate materials less foods
and feeds. .. ..... .. .... .... .................. ...........
Intermediate foods and feeds .. ...... ...
Intermediate energy goods ...... ..................
Intermediate goods less energy. ..............
128.5
115.5
95.9
134.5
134.2
125.9
111 .9
137.7
134.5
128.4
112.8
138.0
134.4
131 .9
110.7
138.5
134.2
134.8
109.5
138.8
134.7
134.1
110.9
139.0
136.5
132.2
115.8
139.8
137.6
133.7
115.8
141 .1
138.4
137.0
115.6
142.4
140.2
143.2
117.3
144.4
141 .9
147.7
1~1.1
145.7
142.8
144.9
123.7
146.0
144.0
143.2
125.4
146.8
145.4
136.0
127.1
147.7
146.0
133.8
126.0
148.5
Intermediate materials less foods
and energy . ........ . ··········· .... ...............
135.8
138.5
138.7
139.0
139.2
139.5
140.4
141 .7
142.9
144.6
145.7
146.2
147.1
148.5
149.5
Crude energy materials ... ........... .. .. . ........
Crude material s less energy ..
Crude nonfood materials less energy ..
102.0
108.7
135.7
147.2
123.4
152.5
138.2
128.2
155.5
134.3
135.9
159.5
132. 5
135.5
164.8
141 .8
136.2
170.1
163.5
133.2
179.3
158.9
139.8
189.9
153.0
148.0
195.2
158.8
148.7
187.6
172.1
150.1
177.9
180.0
147.0
176.3
178.3
146.5
191 .6
178.1
144.5
200.9
166.3
140.9
195.4
November 2004
129
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
Current Labor Statistics:
Price Data
41. Producer Price Indexes for the net output of major industry groups
[December 2003 = 100, unless otherwise indicated]
NAICS
2004
2003
Industry
Dec.
Jan.
Feb.
Apr.
May
June
July"
Aug.P
Sept.P
-
Total mining Industries (December 1984:100) ................................. .....
129.0
144.6
140.3
136.6
140.9
149.5
155.5
155.2
157.2
148.8
211
212
213
Oil and gas extraction(December 1985=100} ... ..... ... ..... ... .... ................
Mining, except oil and gas ...
······· ···· ···· ···· ··· ·· ·· ···
Mining support activities ...... ... ... ..
155.1
100.0
100.0
181.1
103.3
101 .2
172.5
105.2
100.8
165.4
105.9
100.8
171.7
108.5
101.0
188.1
107.3
101 .3
198.0
108.1
102.2
196.9
108.5
103.5
198.7
110.2
105.5
182.8
111.6
107.5
137.7
141 .1
100.0
100.0
100.0
138.9
139.3
101.4
100.4
99.9
139.3
140.4
101 .2
100.3
99.7
140.3
142.4
100.7
100.2
99.8
141.8
146.1
101.5
100.7
99.9
143.3
149.1
100.2
101.1
100.0
142.9
148.6
101.2
101 .3
99.8
143.4
146.7
100.9
101.6
99.6
143.7
144.4
101.4
101.6
99.6
144.1
143.3
101 .0
101.2
99.9
316
321
322
323
Total manufacturina industries /December 1984:1001........................
Food manufacturing (December 1984= 100) .. ..
Beverage and tobacco manufacturing ········· · ···· ······················ ·· ·· ·······
Textile mills ... ............................... ......... .......... ..... .............. .... ....... .. ... .....
Apparel manufacturing .. .... ·· ··· ·················· ··· ·· ···· ··· ····· ·· ···· ·· ·· ···· ···
Leather and allied product manufacturing (December 1984=100) ...
Wood products manufacturing ... .. ..... .......... ....... .. ... .. .. ....... .. .. .....
Paper manufacturing .......... .... ..... .......... ........ ... ....... ........... ..... ... ............
............ ...................
Printing and related support activities.
143.4
100.0
100.0
100.0
143.3
99.3
99.3
100.2
143.6
102.7
99.4
100.2
143.8
105.9
99.5
100.4
143.5
108.1
100.1
100.8
143.4
110.2
101 .1
100.8
143.5
108.3
102.3
101.0
143.6
106.7
103.4
101.3
143.7
109.9
104.2
101 .5
143.5
110.8
104.9
102.0
324
325
326
331
332
333
334
335
336
337
339
Petroleum and coal products manufacturing (December 1984=100) .. ..
Chemical manufacturing (December 1984= 100)
Plastics and rubber products manufacturing (December 1984=100) ..
Primary metal manufacturing (December 1984= 100) ......
Fabricated metal product manufacturing (December 1984=100) .... .... .
Machinery manufacturing ... ... ... ... ....
··· ·· ···· ·· ······ ············
Com outer and electronic oroducts manufacturina ... ....
Electrical equipment, appliance, and components manufacturing .....
Transportation equipment manufacturing .....
Furniture and related product manufacturing(December 1984=100) ... .
............... ... ......... . .... ..
Miscel laneous manufacturing ...
117.5
165.3
128.8
12 1.4
133.7
100.0
100.0
100.0
100.0
147.6
100.0
131.5
167.0
128.9
124.0
134.6
100.3
99.8
100.2
100.2
147.4
100.5
130.7
167.9
129.4
128.5
135.7
100.6
99.5
100.7
100.1
148.7
100.9
134.3
168.8
129.6
132.3
137.5
100.9
99.3
101.8
100.4
149.0
100.8
141.9
169.7
130.0
138.4
139.4
101.3
99.5
102.7
100.2
149.7
101 .0
152.0
170.3
130.4
142.2
140.8
101.6
99.3
103.3
100.4
151.4
100.9
144.1
171 .6
130.8
142.3
141.9
101.8
99.1
103.5
100.6
151 .7
101 .2
152.0
172.0
131.4
147.6
142.6
102.1
99.0
103.7
100.4
152.1
101.3
155.6
173.2
131 .8
149.1
143.7
102.2
98.9
103.8
99 .9
152.7
101.0
158.9
175.6
132.5
150.9
144.2
102.5
98.9
104.1
99 .9
152.7
101.6
311
312
313
315
441
442
443
446
447
454
Retail trade
Motor vehicle and parts dealers .. .. .......... .. ...... ........ ....... .. .. ..
Furniture and home furnishings stores ..
........ ....... .... ..
Electronics and appliance stores ...... .
Health and personal care stores ....
Gasoline stations (June 2001=100)
............... ... ....
.... ... ... ..... .... ... ..... .. ..... ... ........
Nonstore retailers ...
100.0
100.0
100.0
100.0
47.9
100.0
101 .6
99.5
101.4
99.6
45.5
102.9
101.7
100.8
99.7
99.9
46.6
105.4
103.2
101.8
99.9
96.9
55.4
113.2
103.8
102.0
101 .2
97.4
56.6
108.6
103.7
101.4
101.2
97.5
53.2
107.0
103.7
102.8
98.8
98.7
59.3
108.7
104.0
102.5
99.9
99.5
46.0
106.1
103.4
103.0
98.8
101 .5
47.0
103.6
103.5
103.6
101 .6
107.3
45.8
107.5
481
483
491
Transoortatlon and warehousino
Air transportation (December 1992= 100) .. .. ... ... .... ..... ..... .. .... ...
Water transportation
Postal service (June 1989= 100)
162.7
100.0
155.0
163.3
99.0
155.0
163.6
98.9
155.0
162.0
99.4
155.0
162.3
100.1
155.0
162.2
100.3
155.0
162.8
100.3
155.0
163.4
100.4
155.0
165.1
100.5
155.0
160.6
103.0
155.0
221
Utilities
Utilities .. ...... . .. ..... . . ..
100.0
101.7
102.5
101 .2
101 .8
103.1
106.9
107.1
107.5
105.1
Health care and social assistance
..... . .. . .. .. . .. ... .
Office of physicians (December 1996=100) .
Medical and diagnostic laboratories .
Home health care services (December 1996=100) ... ... .... ... ....... .......
Hospitals (December 1992=100} ..
Nursing care facilities ...
•••• •• •••••••••••••• • • •• • • • • • • oo •• • •• • • •• • oo••• •
Residential mental retardation facilities ...... .. ..... ... ..... .... .... .... ....... ..
112.8
100.0
119.0
137.6
100.0
100.0
114.1
100.3
119.5
139.5
101 .2
100.1
114.3
99.8
119.6
140.1
101.4
99.9
114.3
99.8
119.6
140.3
101.6
99.9
114.4
99.8
119.7
140.7
101.9
99.9
114.4
100.0
119.7
140.8
102.0
100.5
114.3
100.0
119.7
140.9
102.0
100.5
114.5
100.0
119.9
142.3
102.1
99.9
114.5
100.0
119.8
142.1
102.9
100.6
114.5
100.1
119.7
142.4
103.1
100.6
100.0
100.0
100.0
100.0
100.0
100.9
97.8
100.4
99.9
101 .8
101 .3
99.1
100.0
98.9
102.0
101 .3
100.3
100.2
98.4
101 .7
101.4
101.6
100.1
98.5
102.3
101.3
103.1
99 .9
98.9
102.4
101.4
102.7
99.9
99.0
102.7
101.8
100.5
99.7
99.0
102.5
101.2
100.1
100.0
99.0
102.3
101 .0
101.9
99 .5
98.8
103.2
100.0
100.0
100.0
109.1
126.5
100.0
99.1
100.0
100.1
107.9
131.4
100.8
99.4
100.2
100.6
109.8
131 .7
100.7
99.6
100.7
101.1
107.4
131 .7
100.8
101 .0
100.8
101 .3
106.0
131.8
101 .1
102.6
100.8
101.9
104.5
131 .8
101 .2
102.1
101 .0
98.5
105.6
131.8
101.1
103.2
101.1
101 .5
109.7
132.0
101.3
105.2
101 .1
102.7
111 .0
131.9
101.6
104.7
101.1
100.7
108.2
132.3
101.8
125.3
100.0
112.1
100.0
100.0
100.0
120.5
125.7
99.6
112.1
99.0
100.3
100.8
122.2
125.9
99.6
112.5
98.7
100.3
101.3
123.6
126.5
99.8
113.2
98.7
100.4
100.8
124.9
126.6
99.9
113.1
98.7
100.5
101.3
124.8
126.5
99 .9
113.4
98.7
100.6
101 .5
124.4
126.6
99.9
113.8
97.4
101 .0
101 .5
125.6
126.9
100.3
114.0
96 .1
100.8
101 .3
128.6
126.9
100.7
114.8
95.4
101 .6
101 .3
128.6
127.2
100.4
114.8
94.8
100.9
101.3
125.4
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.. .... .... ....
Telecommunication, ...... .... . .. ... ... ..... . .. . .. .. .. .... ...
Data processing and related services ... . .. . ......... . . .. . ... .. .. . . .... .
Securitv. commoditv contracts. and like activitv ..... .. .. .. ... .... ..
Lessors or nonresidental buildings (except miniwarehouse) .......
Offices of real estate agents and brokers .. .... .. . . . ......
Real estate support activities .... ... ...... .... ..... ... .. .. .... ........ .... ... .
Automotive equipment rental and leasing (June 2001=100) ..... .... ... ...
. . . . . . . . . . . . . . . ...
Legal services (December 1996=100}
Offices of certified public accountants ....... .... .. .... ... ... .... ...... ..... ......
Architectural, engineering, and related services
(December 1996=100) ... .. . . ... .. ... .. . .. . .. .
Advertising agencies ..
····················· ······ ···· ······ ··· ··· ···· ···· ·······
Employment services (December 1996= 100) ... ... ..... .... ... ..... ... .... ....
Travel agencies .. .
····· ···· ······· ·· ··············· ·· ········ ······· ·· ·
Janitorial services
.... ... .... .......... ... ..... .. ...... ...... ......... ....
Waste collection .. .
.. . . . . . . . . . . . . . . . . . . . . .. . . .. . . . .
Accommodation (December 1996=100) .... .... ..... ... .. ..... .. .. .
NOTE: Data reflect the conversion to the 2002 version of th e North American Industry Classification System
(NAICS) , replacing the Standard Industrial Classification (SIC) system.
130
Mar.
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November 2004
42. Annual data: Producer Price Indexes, by stage of processing
(1982 = 100]
Index
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Finished goods
Total. ....... .... ..... .. ... .......... ... ........ .............. .... .. .. ... .......... .
Foods ..... ......... .. .......... .. .... .... ... .. .. . ..... ... .. ... ....... . .
Energy .... ..... .... ... . .. .... ... .. ........ ......... ...... . ... . ....... .
Other ............ ............ .......... .. ... ... .. . .... . ....... ........... .
124.7
125.7
78.0
135.8
125.5
126.8
77.0
137.1
127.9
129.0
78.1
140.0
131.3
133.6
83.2
142.0
131.8
134.5
83.4
142.4
130.7
134.3
75.1
143.7
133.0
135.1
78.8
146.1
138.0
137.2
94.1
148.0
140.7
141.3
96.8
150.0
138.9
140.1
88.8
150.2
143.3
146.0
102.0
150.5
Intermediate materials, supplies, and
components
Total. ... ......... .............. ...... .. .......... ... ... ... ... ..
Foods ............... .. ... .... .. .. .... ..... . .. .. .. .. .. .. ...... .... ... . .
Energy .... ...... .. .. .......... ...... .......... .. .. . .. .... ....... . ...... . .
Other. .. ..... ......... ..... .... . ............ ... .. .... ... ........ ... .. .. .. .
116.2
115.6
84.6
123.8
118.5
118.5
83.0
127.1
124.9
119.5
84.1
135.2
125.7
125.3
89.8
134.0
125.6
123.2
89.0
134.2
123.0
123.2
80.8
133.5
123.2
120.8
84.3
133.1
129.2
119.2
101 .7
136.6
129.7
124.3
104.1
136.4
127.8
123.3
95.9
135.8
133.7
134.4
111 .9
138.5
102.4
108.4
76.7
94.1
101 .8
106.5
72.1
97.0
102.7
105.8
69.4
105.8
113.8
121.5
85.0
105.7
111 .1
112.2
87.3
103.5
96.8
103.9
68.6
84.5
98.2
98.7
78.5
91 .1
120.6
100.2
122.1
118.0
121 .3
106.2
122.8
101 .8
108.1
99.5
102.0
101 .0
135.3
113.5
147.5
116.8
Crude materials for turther processing
Total. ..... .............. ..... .. ...... ... .. ... ......... ........... ................. .
Foods ......... ... ....... .. ....... . .
Energy ... .... ....... .. .... ..... ....... .... ... ...... .. ...... .... ...... .
Other ...... ... .... ... ... .......... .... ..... ... ... ........ ...... ........ .. .
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November
2004
131
Current Labor Statistics:
Price Data
43. U.S. export price indexes by Standard International Trade Classification
(2000 = 100]
SITC
2003
Industry
Rev. 3
2004
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
0 Food and live animals ... .. .. .. .. ... .. .. ..... ............ .. .. ...... .
01
Meat and meat preparations ..... ...... ..... ....... .. . . . . . . . . . . . . . . .. .
Cereals and cereal preparations ..... ...... ............. .........
04
Vegetables, fruit , and nuts, prepared fresh or dry ..
05
112.1
117.2
124.2
101 .4
112.2
123.5
119.4
103.2
115.2
125.6
125.6
102.8
116.5
123.0
130.8
103.2
117.0
122.8
131 .6
103.1
119.9
125.0
135.2
108.4
122.7
127.1
139.6
110.1
126.1
127.6
147.7
109.5
126.7
127.7
146.0
113.3
123.9
127.3
141 .2
111 .1
119.8
123.0
128.0
110.0
116.5
126.1
120.6
113.2
117.8
124.6
122.0
120.2
2 Crude materials, inedible, except fuels ............... ............
Oilseeds and oleaginous fruit s ............................... .. .. ....
22
Cork and wood .. ....... ...................... ,....... ,.... ....... ... ...... ..
24
Pulp and waste paper .. ... ...... ........ ·····························
25
···
Textile fi bers and their waste ... .... .......... .... ......•.. . .......
26
Metalliferous ores and metal scrap ..... ...... ... ... .... .
28
106.2
121.1
91.6
88.8
109.6
119.9
111.2
136.7
92.0
90 .8
121.4
121.1
116.3
150.9
92 .5
91 .9
128.5
129.6
116.9
152.5
93.7
91.7
121 .2
136.6
120.2
157.2
94 .5
91.7
123.7
148.9
122.3
160.9
95.6
92.5
122.2
156.8
129.0
181 .6
96.5
94.2
121 .9
171.4
132.8
197.1
97.6
98.8
115.9
176.2
132.5
199.0
98.2
100.4
114.9
170.6
125.7
168.5
98.3
100.8
108.7
167.5
132.1
184.5
98.9
100.1
102.9
190.2
117.9
117.4
98.8
99.5
101.1
183.0
119.1
125.1
99.1
98.7
102.1
177.2
3 Mineral fuels, lubricants, and related products .............
1?
Coal, coke , and briquettes ... ..........................................
Petroleum, petroleum products , an d related materials ..
33
108.7
111 .6
104.2
108.2
111.6
104.1
106.3
111.6
101.2
11 0.7
112.9
106.2
120.5
119.3
-
137.5
141.2
-
-
-
139.3
-
-
-
116.8
114.7
-
120.1
-
119.8
135.0
129.7
134.5
136.2
138.0
5 Chemicals and related products, n.e.s. .......... .... ..... ... ....
Medicinal and pharmaceutical products ...... .... ..............
54
Essential oils; polishing and cleaning preparations .... .....
55
Plastics in primary forms ........ ......... ..... ...................... .. ..
57
Plastics in nonprimary forms . . . . . . . . . . . .. . . . . . . . . . . . . . . .....
58
Chemical materials and products, n.e.s. ..... .... .... ... ··· ···
59
100.3
105.4
98.2
95.4
98.2
101.9
100.7
105.9
98.9
95.5
98.3
102.4
100.9
106.5
99.4
95.8
97.1
102.5
101.4
105.8
100.1
96.5
97 .2
102.6
102.9
105.4
104.3
98.3
96.8
105.0
104.0
105.3
104.2
100.9
97.2
105.2
104.9
105.5
104.3
102.1
97.4
104.8
105.5
105.7
104.1
102.2
96.9
104.8
105.6
105.7
104.4
102.9
96.7
104.8
105.8
105.8
104.3
103.2
96.5
104.9
107.0
107.9
104.1
104.8
97.2
104.6
108.7
108.1
105.0
107.5
97.2
106.3
109.6
108.0
105.5
109.6
97.5
105.5
6 Manufactured goods classified chiefly by materials .....
62
Rubber manufactures. n.e.s................................ ....... ...
64
Paoer , oaoerboard . and articles of oaoer. oulo.
and oaoerboard ... .. .
.. ........ ...... .. . ......... ..... ... .
66
Nonmetallic mineral manufactures. n.e .s.... .............. .... .
68
Nonferrous metals .. ... .. .... ........ .. ...... ..... . · ·· ·· ··· ···· ·· ····· ·
7 Machinery and transport equipment... .. .. .................. .. ... .
71
Power generatin g machinery and equipment.... .. ... .......
Machi nery specialized for particular industries .. .. .... ·····
72
74
General industrial machines and parts , n.e.s.,
and machine parts ..... . .. ..................... ..... . ........ ...........
Computer equipment and office machines .. . . . . . . . . . . . . . . . ..
75
76
Telecommunications and sound recording and
77
78
reproducing apparatus and equipment..
Electrical machinery and equipment... ...... .... .. .. ............
Road vehicles ............. ..... ..... ...... ... .. .... ... ... ..................
123.0
123.2
135.1
131.8
Sept.
100.2
100.3
100.7
100.8
101.7
103.0
104.1
105.6
106.6
107.0
108.5
109.6
110.5
109.2
109.2
109.5
109.9
11 0.4
110.9
11 0.4
110.9
110.8
111 .2
111 .8
112.0
111.2
98.3
99.5
81 .6
97.4
99.5
81 .9
97 .9
99.7
83.4
97.6
99.8
84.5
97.9
99 .7
85.9
97 .8
99 .6
90.9
97.9
99 .7
94 .1
98.7
99 .7
98.1
99.0
99.5
97.6
99.2
99 .9
95.4
101.2
99 .9
95.4
101 .9
100. 2
96 .7
102.7
100.5
98.5
97 .9
107.5
103.1
97.7
107.9
103.1
97 .7
108.5
103.3
97 .8
108.7
103.4
97.9
109.3
103.9
98.1
109.4
104.0
98.2
109.4
104.2
98.4
108.7
105.1
98.4
108.7
105.4
98.2
108.7
105.4
98.2
108.9
105.7
98.2
109.0
105.9
98.3
109.0
106.1
102.6
87.8
102.6
87.9
102.8
88.0
102.8
88.6
103.3
87.7
103.5
88.2
104.0
88.4
104.5
88.8
104.8
88.6
104.9
87.2
105.2
86 .6
105.3
86.4
105.3
86.2
93.3
89.4
101 .4
92.8
88.6
101 .5
92.2
88.2
101 .6
92 .0
88.1
101 .5
92.6
88.0
101 .7
92 .5
88.3
101 .9
92.4
88.6
101.9
92 .2
88.5
102.3
92.0
88.6
102 .3
91 .8
88.2
102.4
91.5
88.3
102.5
90 .7
88.2
102.5
90.7
88.2
102.8
102.2
102.1
102.3
102.3
102.2
102.3
102.3
102.2
102.1
102.0
101 .7
101 .9
101 .8
87 Professional, scientific, and controlling
instruments and apparatus .................... .. .... ...... .... .
132
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Federal Reserve Bank of St. Louis
November
2004
44. U.S. import price Indexes by Standard International Trade Classification
= 100)
[2000
SITC
2003
Industry
Rev. 3
Sept.
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
0 Food and live an imals ..... .......... ............. ... .... .... .......
01
Meat and meat preparations ..... ............ ... .........•.. ..... ..
Fish and crustaceans, mollusks, and other
03
100.0
112.8
100.3
115.2
100.0
117.2
101.0
120.4
102.2
117.7
104.7
118.0
105.4
120.4
106.4
121 .7
106.1
124.4
106.9
128.9
107.4
133.7
107.4
134.2
109.2
135.1
aquatic invertebrates ··· ··· · ··· ·· ···· ········ ··· · ······ ············
Vegetables , fruit, and nuts, prepared fresh or dry .... ......
Coffee, tea, cocoa, spices, and manufactures
thereof ... ............. ... ... ....... . ... ..... .. ... ... ... .... .... ... ....
82 .2
105.0
79.8
106.4
79.3
108.9
79.2
109.4
78.2
112.3
80.0
115.7
83.3
111 .3
85.1
109.5
84.1
106.1
84.1
105.9
86.1
102.1
86.9
100.6
86.1
109.2
98.6
95 .5
93.1
96 .0
100.1
101.9
101.7
103.6
102.4
107.0
102.7
103.3
105.6
1 Beverages and tobacco ........... .......... ... ........... .. ..... .
11
Beverages . .. . ... .. .... ... ...... ...... ...... ..... ....... ..... ... .....
104.0
103.9
104.3
104.2
104.4
104.2
104.4
104.3
104.7
104.9
105.0
105.2
105.3
105.5
105.3
105.5
105.4
105.7
105.3
105.6
105.9
106.4
106.1
106.6
106.2
106.7
2 Crude materi als, 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
106.1
113.0
90.4
103.7
95.7
104.2
106.2
90.8
104.3
95.1
104.5
103.2
91 .9
108.7
94.8
107.9
108.0
92.8
115.3
99 .6
109.5
108.9
93.3
124.2
98.9
114.1
115.7
91.9
134.6
99.5
120.0
123.3
95.4
148.0
99.7
122.9
127.8
100.8
148.2
99.3
127.3
139.0
103.4
143.5
102.1
125.8
136.1
106.5
140.4
98.0
125.7
132.1
108.0
145.3
101 .2
134.1
149.0
107.7
160.8
97.6
135.1
151 .1
105.5
162.4
98.7
3 Mineral fuels, lubricants, and related products .............
Petroleum, petrole um products, and related materials .. .
33
Gas, natural and manufactured ........ ..... .. ············· .........
34
101 .5
99 .4
114.4
101 .3
100.1
106.2
103.3
102.3
106.6
108.2
106.9
113.9
117.3
114.0
138.0
117.7
114.5
137.1
120.8
120.0
122.9
121 .1
120.3
123.3
131 .6
131 .5
129.5
131 .5
130.0
140.0
133.9
133.0
134.8
144.1
144.6
136.3
146.1
148.7
122.0
5 Chemicals and related products, n.e.s. ................... ......
Inorganic chemicals .. . ····························· ......................
52
Dying, tanning, and coloring materials ..................
53
54
Medicinal and pharmaceutical products .. .. ....................
Essential oils; polishing and cleaning preparations ....... .
55
Plastics in primary forms ...... ... ............ ............ ....
57
Plastic s in nonprimary forms ........ ... . ·· ·· ····· ··· ·· ·· ·· ···
58
Chemical materials and products, n.e.s. ........................
59
99.2
105.4
97.7
101 .9
91 .6
102.7
101 .4
91 .8
100.2
108.8
98.1
102.3
91.2
105.6
101.7
92.3
100.8
11 1.9
99.0
103.4
91 .6
105.6
101 .7
93.1
101 .1
114.0
99.6
103.4
91.6
105.5
101 .8
93.3
103.0
119.3
99.9
107.2
92.7
104.4
102.1
94.3
103.4
120.6
99.7
107.7
93.3
105.2
102.4
94.9
103.8
120.5
99.5
108.1
93.7
106.9
102.9
95.8
103.5
115.9
100.6
107.7
93.5
105.5
102.9
95 .4
103.5
117.5
100 .8
107.3
93.4
105.8
102.9
95.1
103.8
119.8
100.3
107.1
93.5
104.6
102.3
95.2
104.6
122.2
98.3
107.3
93.5
107.8
103.0
94 .7
105.1
124.0
98.4
107.0
96 .4
108.4
103.3
94 .1
105.7
124.4
98.4
106.5
93.4
109.2
103.6
94.5
6 Manufactured goods classified chiefly by materi als .....
Rubber manufactures, n.e.s. ................. .. ........ ... ... ......• •
62
Paper, paperboard, and articles of paper, pulp,
64
95.7
98.5
96.5
98.5
97.4
98.6
97.8
98.8
98.9
99.0
101.4
99.2
103.6
99.7
105.6
99 .9
106.9
100.0
106.1
100.5
106.1
100.5
107.5
100.8
108.7
100.8
and paperboard ....... .... . . . . ... .. . .. . . ....... . .. . .. .... .......
Nonmetallic mineral manufactures, n.e.s. ............... ......
Nonferrous metals .... ....... .... .......... ....................... ,......... .
Manufactures of metals, n.e .s. .. . .. . . . ... .. ... . .... ........ ... ..
94.5
97.8
80 .7
98.5
94.7
97.9
82.0
98.7
94.2
98. 1
85.1
99.1
93.7
98.1
87.7
99.5
94.1
98.5
92.3
99.7
94.5
98.9
97.0
100.3
95.0
99.0
102.6
101.1
94.8
99 .3
105.8
102.3
95.5
99.4
106.1
102.4
95.5
99.4
101 .6
102.4
96.4
99.3
102.3
102.7
96.8
100.2
105.2
103.3
97.9
100.3
105.7
103.9
7 Machinery and transport equ ipment ............. ..................
Machinery specialized for particular industries .........
72
General industrial machines and parts, n.e .s.,
74
and machine parts .. · ··· ··· · ······· · ·· ······ ····· ............. . ..... ...
Computer equipment and office machines ..... ...... ..........
75
Telecommunications and sound recording and
76
95.5
102.2
95.3
102.4
95.4
103.3
95.3
103.6
95.4
104.9
95.5
106.4
95.5
106.7
95.2
106.5
95.2
106.7
95.1
106.6
95.0
107.2
95.0
107.6
95.0
107.5
100.2
80.5
100.4
78.6
100.9
78.5
101 .2
78.2
101 .8
78.0
102.5
78.0
103.3
77.7
103.5
76 .5
103.6
76.4
103.5
75.5
104.0
74 .9
104.2
74.3
104.4
74.0
88.6
96.0
100.6
87.7
95.9
101.3
87.5
96.0
101.4
86.7
95.3
101 .6
86.4
95.4
101 .9
85.4
95.7
102.0
85.1
95.6
102.0
84 .9
94 .9
102.2
84.9
94.8
102.3
84.7
94.7
102.4
84 .3
94.6
102.6
84 .0
94 .7
102.8
83.8
94.6
103.0
05
07
66
68
69
78
reproducing apparatus and equipment..
Electrical mac hinery and equipment ..... ··········· ... ·······
....... .... .•... .... .. . . ....
Road vehicles ...... .... ...
85
Footwear ······· ··· ······· ·· ··· ····· ··· ····· ····· ····
................ ..... .
99.9
100.0
100.1
100.1
100.5
100.5
100.6
100.6
100.6
100.4
100.4
100.1
100.5
88
Photographic apparatus, equipment, and su pplies,
and optical qoods n .e.s . ....... .... ..... ... ..... .. ... ... .... ... ..... .
99 .2
99.3
99.8
99 .9
99.9
100.3
100.0
99.4
99 .3
99.0
98.2
98.2
98.2
November 2004
133
77
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Federal Reserve Bank of St. Louis
Monthly Labor Review
Current Labor Statistics:
Price Data
45. U.S. export price indexes by end-use category
(2000 = 100]
2003
Category
Sept.
2004
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
ALL COMMODITIES ..................................................
99.8
100.0
100.5
100.8
101.5
102.2
103.0
103.7
104.1
103.4
103.9
103.4
103.8
Foods, feeds, and beverages ... .. .... ... ... .. ..... ... .. .....
Agricultural foods , feeds, and beverages ......... .... .. .
Nonagricultural (fish, beverages) food products .....
115.3
116.3
106.5
117.2
118.4
105.6
121 .4
122.8
107.5
122.4
123.8
108.5
123.1
124.6
109.5
125.6
127.2
110.7
130.5
132.4
112.1
134.8
137.0
113.4
135.6
138.0
112.7
129.1
131 .1
110.7
128.0
129.9
110.1
116.5
117.0
111 .6
118.8
119.2
114.4
Industrial supplies and materials .... ................... .... .
100.2
101 .0
101 .7
102.5
105.1
106.4
108.1
109.1
110.2
109.9
112.0
113.1
113.8
Agricultural industrial supplies and materials .... .. ....
107.3
113.3
119.0
117.5
118.6
116.6
117.2
114.8
113.7
110.7
109.0
108.4
109.4
Fuels and lubricants ................................ .......... ... .
Nonagricultural supplies and materials,
excluding fuel and building materials ......... .........
Selected building materials ..... .. .... ..... .. ..... ............. .
97.6
97.5
96.4
99.0
106.1
106.5
108.9
109.6
117.5
114.9
118.6
120.4
120.8
100.5
98.4
101 .1
98.8
101 .7
99.1
102.5
99.5
104.7
98.7
106.4
100.9
108.1
102.3
109.4
103.4
109.9
103.9
110.0
103.4
112.4
102.8
113.5
103.3
114.3
104.0
Capital goods ... ................... ............................ .. .
Electric and electrical generating equipment.. ........
Nonelectrical machinery ........... ..............................
97.5
101 .7
94.3
97.3
101 .7
93.9
97.3
101 .7
93.9
97.5
101 .7
94.1
97.5
102.0
93.9
97.8
101 .9
94.3
98.0
102.0
94.5
98.1
101 .7
94.6
98.1
101 .7
94.6
97.8
102.0
94.1
97.8
102.2
94.0
97.8
102.3
94.0
97.9
102.3
94.0
Automotive vehicles, parts, and engines ........ ... .... ..
101 .8
101 .9
101 .9
101 .8
101.9
102.0
101 .9
102.2
102.3
102.3
102.4
102 .6
102.6
Consumer goods, excluding automotive ... ..... .... ......
Nondurables, manufactured .......................... .........
Durables, manufactured ......... .. ....... ....................
99.4
98.5
100.1
99.8
99.0
100.3
100.0
99.4
100.3
99.9
99.2
100.3
100.2
99.9
100.1
100.1
99.9
100.0
100.2
99.9
100.1
100.4
100.1
100.5
100.5
100.1
100.6
100.4
100.0
100.7
100.9
100.8
100.8
101 .1
101.0
101 .0
101 .0
101.0
100.9
Agricultural commodities ..... ..... ....... ........ .............
Nonagricultural commodities ...................... ... .... ... .
114.7
98.6
117.5
98.7
122.2
98.8
122.7
99.1
123.5
99.8
125.3
100.4
129.7
100.9
133.0
101 .4
133.7
101 .7
127.4
101.5
126.1
102.2
115.5
102.6
117.5
102.8
134
Monthly Labor Review
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Federal Reserve Bank of St. Louis
November
2004
Sept.
46. U.S. import price indexes by end-use category
[2000 = 100]
2003
Category
Sept.
Oct.
ALL COMMODITIES ..................................................
96.2
96.3
Foods, feeds, and beverages ....... ...... ..... ..... ..... ... .
Agricultural foods, feeds , and beverages .... ... ...... .. .
Nonagricultural (fish , beverages) food products .. ...
101 .8
108.3
87.6
Industrial supplies and materials .. ..
. . ..
2004
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
May
June
July
Aug.
Sept.
96.8
97.5
99.0
99.4
100.2
100.4
101 .9
101.7
102.1
103.5
104.0
101.9
109.0
86.3
102.4
109.7
86 .0
103.2
110.9
86.0
103.7
112.0
85.1
105.3
113.4
87.2
105.9
113.0
90.1
107.2
114.2
91.7
106.8
114.0
90.6
106.9
114.3
90.3
107.5
114.5
91.8
107.3
114.0
92 .3
108.7
116.4
91 .5
1
... .. ..
98.9
99.5
100.7
103.6
108.5
110.0
112.7
113.9
119.7
119.3
120.6
126.4
128.1
Fuels and lubricants ........... .......... ........ ... ... .. ..... .. ..
Petroleum and petroleum products .... ...............
99.4
97.2
100.1
98 .8
102.0
100.9
107.2
106.0
116.5
113.7
117.0
114.3
120.2
120.1
120.6
119.9
131 .0
131 .2
130.9
129.7
133.2
132.7
143.2
144.2
145.4
148.3
Paper and paper base stocks ......... ... .......... .... .. .. ...
Materials associated with nondurable
supplies and materials .... ... ..... ........................... ..
Selected building materials .................................. .. .
Unfinished metals associated with durable goods ..
Nonmetals associated with durable goods ............. .
94.0
94.0
93.9
93.9
94.1
94.2
95.6
96.8
98.2
99.0
100.0
100.4
101.2
102.5
110.3
93.4
97.5
103.4
109.5
94.4
97.7
104.2
108.1
96.4
98.1
104.4
108.0
99.2
98.2
104.7
106.8
104.5
98.5
104.8
113.7
109.5
99.2
105.4
118.4
114.9
99.3
105.1
120.2
121 .7
99 .3
105.4
123.6
126.2
99.1
106.0
120.5
124.4
98.7
106.5
117.6
126.1
98.5
107.7
124.0
129.2
98.5
107.9
125.6
132.3
98.8
Capital goods ... ............ ... .... .............. ... .............
Electric and electrical generating equipment.. ... .... .
Nonelectrical machinery ..... .... ............... ............... ..
93.5
95.8
92.1
93.0
96.2
91.4
93.3
96.5
91.6
92.9
96.8
91.1
93.1
97.4
91.2
93.1
97 .9
91.2
93.1
97 .8
91 .2
92.6
97 .2
90 .6
92.6
97.1
90 .5
92.2
97 .0
90.1
92.2
97 .5
90 .0
92.1
97.5
89.9
92.0
97 .4
89.8
Automotive vehicles, parts, and engines ....... ..........
100.5
101 .2
101.2
101.4
101 .6
101.7
101.8
102.0
102.0
102.2
102.3
102.5
102.6
Consumer goods, excluding automotive .. ... .... .........
Nondurables, manufactured ..... ... ........ ...... .............
Durables . manufactured ... ... .. ........ .. .. . ..... .... ... ..
Nonmanufactured consumer goods .... .... ... ..... ..
97.9
99.7
96.2
95.7
97.9
99.8
96.1
95.8
98.1
100.0
96.2
95.8
98.1
100.1
96.2
96.2
98 .6
101 .1
96.3
95.9
98.7
101 .2
96.3
96.2
98.7
101 .3
96.3
96.4
98.6
101 .1
96.3
96.4
98.5
101 .0
96.0
97.3
98 .5
100.9
96.1
96.8
98.5
101 .0
95.9
97.4
98.4
100.9
95.9
97.9
98.4
100.8
95.9
97 .9
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
.. . .. ... .. .
47. U.S. international price Indexes for selected categories of services
[2000
= 100, unless indicated otherwise]
Dec.
Sept.
Mar.
Sept.
116.2
96.1
116.6
99.0
118.7
100.7
-
-
116.1
116.2
100.0
100.0
117.7
105.1
99.3
119.1
106.1
114.2
121.1
110.1
114.2
120.3
108.8
97. 2
109.4
95.4
Inbound air passenger fares (Dec. 2003 = 100) ....... ...
Outbound air passenger fares (Dec. 2003 = 100)) ..
Ocean liner freight (inbound) ......... ........ .... ............. ...
-
-
94 .0
June
112.9
94.9
105.9
95.4
93.3
Mar.
112.5
95.5
100.3
97.3
-
Dec.
Sept.
June
Air freight (inbound) ........... .... .......... ................. ......... .
Air freight (outbound) ........... .. ... .... .. ... .... ..... ..... ... ...
93.5
2004
2003
2002
Category
-
NOTE: Dash indicates data not available.
Monthly Labor Review
November 2004
135
Current Labor Statistics:
Productivity Data
48. Indexes of productivity, hourly compensation, and unit costs, quarterly data seasonally adjusted
(1992
= 100]
2001
Item
2002
Ill
IV
I
Business
Output per hour of all persons ... ... .......... ............ ... .. ......
Compensation per hour ...... .... .. .... .. .. ..... ...... ..... ... ..
Real compensation per hour. ... ...... ....... ... ...... .. .. .. .. .
Unit labor costs ..................... .............. .... ..... .... ..... ....
Unit nonlabor payments .... ...... ..... .................... .... .. ..
Implicit price deflater .. .. ........... .... ..... ....... ...... .... ... .
118.8
140.4
113.2
118.2
110.2
115.2
120.9
141 .5
114.2
117.0
113.1
115.6
Nonfarm business
Output per hour of all persons .... .. .... ......... .. ... .. ... ..........
Compensation per hour .. .... .......... ........ ..... .... ....... .
Real compensation per hour .... ........ ........ ... .. .... .... ..
Unit labor costs .... .. .. .. ..... .... ................ ............ .. ... .....
Unit nonlabor payments .. ....... ...... .. .......... .. ...... ........
Implicit price deflater ....... .... ...... ..... .... .. ... ... .... .. .....
118.5
139.6
112.5
117.8
111 .9
115.6
Nonflnanclal corporations
Output per hour of all employees ..... .. .. ...... .......... ......
Compensation per hour ...... ..... .. ... ......... ..... ... .. ... ...
Real compensation per hour .. ... ....... .. ... .... ....... ...... .
Total unit costs ................................. ... ... ... ..... ..........
Unit labor costs .......... ........... .... ... ... ... .. ... ................. ....
Unit nonlabor costs .... .. .... ........ .. ........... .. .....................
Unit profits .. ................. ........ ... ... .. ............. .. .... ..... ... ........
Unit nonlabor payments .. ... ...... ...... ....... .......... .. ... ....
Implicit price deflater ..... ..... ... .. .... .... ... ... ...... ..........
Manufacturing
Output per hour of all persons ..... ....... ................ ........ ...
Compensation per hour ...... ...... ... .... .. .. ...... .... .. .... ..
Real compensation per hour ...... ... .... ..... .... .. .... ..... ..
Unit labor costs .............. ......... ............ ... .. ........ ..... ....
136
Monthly Labor Review
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Federal Reserve Bank of St. Louis
2003
2004
II
Ill
IV
I
II
Ill
IV
I
II
Ill
122.7
143.2
115.2
116.7
113.4
115.5
123.2
144.4
115.2
117.2
113.6
115.9
124.7
145.0
115.0
116.3
115.7
116.1
125.0
145.5
114.8
116.3
116.8
116.5
126.2
147.4
115.3
116.8
117.7
117.1
128.6
149.6
116.8
116.4
119.0
117.3
131.2
151.7
117.7
115.6
120.8
117.5
132.0
153.2
118.7
116.0
120.7
117.8
133.3
154.2
118.4
115.7
122.9
118.4
134.2
155.9
118.3
116.1
124.8
119.4
135.0
157.3
118.9
116.6
124.8
119.6
120.4
140.7
113.5
116.8
114.7
116.0
122.4
142.6
114.7
116.4
115.1
116.0
122.8
143.8
114.7
117.1
115.4
116.5
124.1
144.3
114.4
116.2
117.7
116.8
124.6
144.7
114.3
116.1
118.9
117.2
125.8
146.6
114.7
116.6
119.6
117.7
127.8
148.7
116.1
116.3
120.4
117.8
130.6
150.9
117.1
115.5
122.3
118.0
131.7
152.5
118.2
115.9
121 .9
118.1
132.8
153.3
117.7
115.4
124.3
118.7
134.1
155.2
117.8
115.7
126.1
119.6
134.7
156.5
118.3
116.2
126.6
120.0
123.0
137.9
111.1
112.8
112.1
114.7
79.4
105.2
109.8
123.9
139.3
112.5
113.4
112.4
116.2
75.8
105.4
110.1
126.3
139.9
112.6
111 .6
1,110.8
114.0
89.1
107.4
109.6
127.9
141 .3
112.7
111 .2
110.5
112.9
94.7
108.1
109.7
129.2
142.1
112.7
110.7
110.0
112.7
95.7
108.2
109.4
130.2
142.9
112.8
110.4
109.7
112.3
101 .8
109.5
109.6
131 .3
144.1
112.7
110.7
109.8
113.2
99.2
109.4
109.7
134.1
146.3
114.2
109.7
109.1
111 .4
111 .0
111 .3
109.8
137.2
148.5
115.3
109.0
108.2
111 .1
118.7
113.1
109.9
138.9
150.0
116.2
108.7
108.0
110.5
123.2
113.9
110.0
138.9
150.9
115.9
108.8
108.6
109.5
128.1
114.5
110.6
139.9
152.6
115.8
109.4
109.1
110.0
134.5
116.6
111.6
-
-
136.9
137.3
110.6
100.3
140.4
139.4
112.5
99.3
143.8
144.1
115.9
100.2
145.7
147.0
117.2
100.8
147.8
148.6
117.8
100.5
148.8
149.9
118.3
100.7
151 .0
155.7
121.8
103.1
152.1
158.5
123.8
104.2
155.9
161 .6
125.4
103.6
157.2
163.9
127.0
104.2
158.3
162.2
124.5
102.5
161 .5
163.7
124.3
101.4
163.2
165.5
125.0
101 .4
November
2004
-
-
49. Annual indexes of multifactor productivity and related measures, selected years
(1996
= 100)
Item
1980
1990
1991
1992
1993
1994
1995
1997
1998
1999
2000
2001
Private business
Productivity:
Output per hour of ail persons ....... ... ...... ... ........ .......
Output per unit of capital services ...... ................ .. ..
Muitifactor productivity ...... .. .. .......... .... .... ... .. .... ....
Output. ... .. ... ..... ... ...... .... ...... ....... ... ... ... .... ............ ....
Inputs:
Labor input. .. .......... .. ........ ............ ....... .... ... ... ... .... ..... ..
Capital services .......... ......... .... .. .. ... .. .... ...... ..... .. ...
Combined units of labor and capital input.. ... ...... .....
Capital per hour of ail persons ........................ ........ ....
75.8
103.3
88.8
59.4
90.2
99.7
95.5
83.6
91 .3
96.5
94.5
82 .6
94.8
98.0
96.7
85.7
95.4
98.7
97.1
88.5
96.6
100.4
98.2
92 .8
97.3
99.8
98.4
95.8
102.2
100.3
101 .2
105.2
105.0
99.3
102.5
110.5
107.7
98.2
103.4
115.7
111 .0
96.6
105.0
120.4
11 2.4
92.8
103.9
120.2
71.9
57 .6
67.0
73.4
89.4
83.8
87 .5
90 .4
88.3
85.7
87.4
94.6
89.3
87.5
88.7
96.8
91 .8
89.7
91 .1
96.6
95.6
92 .5
94.6
96.2
98.0
96.0
97.3
97.5
103.5
104.9
104.0
101.9
106.1
111 .3
107.9
105.8
109.0
117.9
110.9
109.7
110.1
124.5
114.7
114.8
109.5
129.6
115.7
121 .1
77.3
107.6
91 .0
59.6
90 .3
100.4
95.8
83.5
91 .4
97.0
94.8
82.5
94.8
98.2
96.7
85.5
95.3
99.0
97.2
88.4
96.5
100.4
98.2
92.6
97.5
100.0
98.6
95.8
102.0
100.0
101 .0
105.1
104.7
99.0
102.2
110.5
107.1
97 .6
102.9
115.7
110.3
95.9
104.4
120.2
111 .6
92.0
103.3
120.1
70.7
55.4
65.5
71 .8
89.2
83.2
87.2
89.9
87.9
85.1
87.0
94.3
89.0
87.0
88.4
96.5
91.8
89.4
91 .0
96.3
95.4
92.2
94.3
96.1
97 .8
95.8
97 .2
97 .6
103.6
105.1
104.1
101 .9
106.4
111 .7
108.1
105.8
109.5
118.5
112.4
109.7
110.6
125.4
115.2
115.0
110.1
130.5
116.3
121.3
62.0
97.2
81 .2
64.3
82.2
97.5
93.3
83.2
84.1
93.6
92.4
81 .5
88.6
95.9
94.0
85.5
90.2
96.9
95.1
88.3
93.0
99.7
97.3
92.9
96.5
100.6
99.2
96.9
103.8
101.4
103.1
105.6
108.9
101 .7
105.7
110.5
114.0
101 .7
108.7
114.7
118.3
101.0
111 .3
11 7.4
119.7
95.1
110.3
112.1
103.7
66.1
86.1
63.9
65.8
79.2
101 .1
85.3
93.1
77.5
84.7
89.1
96.9
87.1
93.2
78.5
84.6
88.3
96.5
89.1
93.1
83.5
92 .0
90 .9
97.8
91 .1
96.6
86.5
92.9
92.8
99.9
93.2
99.9
90 .3
96.0
95.5
100.4
96.4
102.3
93.1
100.4
97.7
101.7
104.1
97.5
101 .9
103.9
102.4
101 .5
108.7
100.6
107.5
103.1
104.6
100.7
112.8
102.9
107.9
105.4
105.5
99.2
116.2
104.3
106.9
106.5
105.5
99.6
117.9
98.9
105.5
97.7
101 .6
November
2004
137
Private nontarm business
Productivity:
Output per hour of ail 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 ail persons .......... ......... .......... .. ..
Manufacturing
Productivity:
Output per hour of ail 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 .... ... .. ... ...... .. ... ...
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
Current Labor Statistics: Productivity Data
50. Annual indexes of productivity, hourly compensation , unit costs, and prices, selected years
[1992
= 100]
Item
1960
1970
1980
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
Business
Output per hour of all persons ........ ........ ......... .. .... ........
Compensation per hour .... .. .. ... ........ ..... .................
Real compensation per hour ..... ...... ........... ......... ....
Unit labor costs ..
Unit non labor paym ents ...... .. ...... .. .. ................. ........
Implicit price deflater .............. ...... .. ...... ... .... .... .. ....
48.7
13.8
60.5
28.4
24.9
27 .1
66.0
23.5
78.4
35.6
31.5
34.1
79.0
54 .0
88.9
68.4
61 .3
65.8
94 .4
90 .5
96 .1
95.9
93.9
95.1
101.7
106.0
98.9
104.3
108.2
105.7
104.5
109.5
99 .5
104.8
111 .9
107.4
106.5
113.0
100.5
106.1
113.9
109.0
109.3
119.7
105.0
109.5
109.9
109.7
112.4
125.4
107.8
111 .6
109.2
110.7
115.7
134.2
111 .6
116.0
107.2
112.7
118.3
139.7
113.0
118.1
109.5
114.9
124.0
147.8
113.7
115.2
117.0
115.8
129.6
147.9
115.1
114.1
123.0
117.4
Nontarm business
Output per hour of all persons ............... ... ..... ................
Compensation per hour ..... ... ...... .... ........ .... ... ........
Real compensation per hour ... ... ..... ....................... .
Unit labor costs ................................ .........................
Unit non labor payments .. ..... ................ .... ......... .... .. .
Implicit price deflater .... .. .
51 .6
14.4
63.0
27 .9
24.3
26.6
67.7
23.6
78.8
34.9
31.1
33.5
80.3
54 .2
89 .2
67.5
60.4
64.9
94.4
90 .3
95.9
95.6
93.6
94 .9
102.1
106.0
98.9
103.8
109.2
105.8
104.7
109.4
99.4
104.5
112.1
107.3
106.4
112.8
100.3
106.0
114.6
109.1
109.2
119.4
104.7
109.3
110.9
109.9
112.2
124.9
107.3
111 .3
110.8
111 .1
115.3
133.7
111 .2
116.0
108.8
113.3
117.8
138.9
112.4
118.0
111.1
115.4
123.6
142.1
113.2
115.0
119.0
116.4
129.1
147.0
114.4
113.9
124.8
117.9
Nontinancial corporations
Output per hour of all employees ................
Compen sati on per hour ....
Real co mpensation per hour .. ... ......... ............ ..... .. ..
Total unit costs ....................... .. .......... ........ ... ...... .... ..
Unit labor costs ... ............... .......... .... .... .. .................... ..
Unit nonlabor costs ..... .............. .. ... ..................... .... ... ..
Unit profits ... ........... .. ....... ... ..... ... .. ... .. ..... ... .. ... .. ... ...........
Unit non labor payments .. ............. .... .
Implicit price deflater ... ... .......... ......... ... .. ...... ..... ....
56.6
16.1
70.3
26.9
28.4
23.0
49 .5
30.1
28.9
70.4
25.6
85.3
35.1
36.3
31 .7
43.7
34.9
35.9
81 .0
57 .0
93.8
68.8
70.4
64.5
66.5
65.1
68.6
95.5
91.0
96.7
95.4
95.3
97.1
96.7
97.0
95.9
103.4
105.4
98 .3
101.8
102.0
101.3
136.9
110.8
104.9
107.1
108.4
98.5
100.9
101.2
99 .9
149.9
113.3
105.3
109.8
111 .7
99 .3
101 .2
101 .7
99 .8
154.4
114.4
105.9
112.8
117.9
103.4
103.2
104.5
99.9
137.5
109.9
106.3
116.4
123.3
105.9
104.6
106.0
101 .0
129.8
108.7
106.9
120.6
131.7
109.5
108.0
109.2
104.8
109.3
106.1
108.1
122.7
137.0
110.8
111 .2
111 .6
110.2
91.4
105.2
109.5
128.9
140.1
111 .5
109.4
108.6
111 .5
111.4
111 .5
109.6
136.3
145.9
113.5
107.4
107.0
108.4
134.2
115.3
109.8
Manufacturing
Output per hour of all persons.
..... ... .... ....... .
Compensation per hour .... ... .... .... .. ..... ................ ...
Real compensation per hour .. .... ....... .. .
····· ······· ··· ···
Unit labor costs .... .... ........... ....... ...... .... ....... .... ...... ....
Unit nonlabor payments ......... ........ ...... .. .... ... ..... ......
Implicit price deflater ...... .. .......... .. ... .. ...... ..... .. .......
41.8
14.9
65.0
35.6
26 .8
30.2
54.2
23.7
79.2
43.8
29.3
35.0
70.1
55.6
91.4
79 .3
80.2
79.9
92 .9
90.1
95.7
97.0
101 .1
99.5
110.1
107.7
100.5
97 .8
107.6
103.9
113.9
109.9
99.8
96 .5
110.4
105.2
117.9
112.0
99.7
95.0
110.5
104.6
123.5
118.8
104.2
96.2
104.1
101 .1
128.2
123.8
106.3
96 .6
105.0
101.8
134.2
135.0
112.3
100.6
107.0
104.6
137.1
138.3
111 .8
100.8
105.8
103.9
147.1
143. 8
114.5
97 .8
154.6
151.9
118.2
98.2
Dash indicates data not availabl e.
138
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November
2004
51. Annual indexes of output per hour for selected NAICS industries, 1990-2002
(1997=100)
1990
NAICS
Industry
21
211
212
2121
2122
2123
Mining .. ... . .... .... ...... ·· ····· ···· ·· ······· ····· ··· ··
Oil and gas extraction ........ . .. .. · · ·· · · ·· · ·· · · ·· · ·· · ···
Mining, except oil and gas .. ... ... ..... ... ·· ······ ··· ·· ·
... .... ·· ··· •····· · · · •·
Coal mining ..
Metal ore mining ........ .... ... ... .. .. ..... . .. .. ... .. . .. ..
Nonmetallic mineral mining and quarrying .. ····· ···
2211
2212
Power generation and supply ..... .. ... .... ... .. ... .....
Natural gas distribution ................... .. ........ .... ..
3111
3112
3113
3114
3115
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Mining
86.0
78.4
79.3
68.1
79.9
92.3
86 .8
78 .8
80 .0
69 .3
82 .7
89 .5
95.2
81 .9
86.8
75.3
91.7
96.1
96.2
85.1
89.9
79.9
102.2
93.6
99 .6
90.3
93.0
83.9
104 .1
96.9
101 .8
95.5
94 .0
88.2
98.5
97.3
101 .7
98.9
96.0
94 .9
95.3
97.1
100.0
100.0
100.0
100.0
100.0
100.0
103.4
101.6
104.6
106.5
109.5
101.3
111 .1
107.9
105.9
110.3
11 2.7
101.2
109.5
115.2
106.8
115.8
124.4
96 .2
107.7
117.4
109.0
114.4
131.8
99 .3
11 2. 3
119.3
111 .7
11 2 .2
143.9
103.8
71.2
71.4
73 .8
72.7
74.2
75.8
78.7
79.8
83.0
82.1
88.6
89.0
95.5
96.1
100.0
100.0
103.8
99.1
104.1
103.1
107.0
11 3.1
106.4
110.0
102.4
114.9
Animal food ... ... .... ....... . ..... . ....... ... ... .. ...... ....
Grain and oilseed milling ... ..... . .... . .. .... . .. . . .
Sugar and confecti onery products . .. ... .. ... ... ... ..
Fruit and vegetable preserving and specialty .......
Dairy products .............. .. ....... . .. ... . ... ..... . .. ... ...
90.1
89.0
91.0
86.4
90.8
89 .3
91 .2
93 .8
89 .7
92 .1
90.2
91 .1
90.5
90.7
95.4
90.2
93.8
92.5
93.8
93.9
87.3
94 .7
94.0
94.9
95.4
94 .0
99.1
94.3
97.1
98.7
87 .5
91 .3
98.2
98.2
98 .0
100.0
100.0
100.0
100.0
100.0
109.4
107.5
104.0
106.8
99 .1
109.5
114.2
107. 1
108.4
94 .5
109.7
112.5
111 .9
109.8
96.0
127.2
117.3
109.9
11 7.0
96 .2
-
3116
3117
3118
3119
3121
Animal slaughtering and processing ... ... .. .. ........
Seafood product preparation and packaging .. . .... .
Bakeries and tortilla manufacturing ............ ... .. .. .
Other food products ... ..... .... .. . .. . ··· ····· ····· ·· ... .
Beverages ... .. ... ....... ... .... ... ... .. ... ... . .. .. .. . . . . .. .
94.5
117.5
92.6
91 .9
86.5
96 .8
112.0
92 .3
93 .5
90.1
101 .5
115.3
95.6
95.9
93.8
100.9
113.9
96.0
102.8
93.2
97.4
114.1
96.7
100.3
97.7
98.5
108.4
99.7
101.3
99.6
94.3
11 6.2
97.7
103.0
101 .1
100.0
100.0
100.0
100.0
100.0
99.9
117.0
103.8
106.9
98 .5
100.3
130.2
105.4
108.8
92.4
101 .9
137.6
105.3
110.2
90 .6
102.7
147.3
106.3
103.2
9 1.7
-
3122
3131
3132
3133
3141
Tobacco and tobacco products .. ... .... ... .. .. . ... ..
Fiber, yarn , and thread mills .. ... . .. .... .. .. ... ......... .
Fabric mills ....... .... . .... ... ... ... .... .... ..... ... .... .. ...
Textile and fabric finishing mills ...... . . , ..... .. .. .. ..
Textile furnishings mills ... ..... ........... .. .. .. ... .....
81.4
73.9
75.0
81.7
88.2
77.3
74.7
77.7
80.4
88.6
79.6
80.1
81 .5
83.7
93.0
73.7
84.6
85.0
86.0
93.7
89.8
87.2
91 .9
87.8
90.1
97.5
92.0
95.8
84.5
92.5
99.4
98.7
98.0
85.0
93.3
100.0
100 .0
100.0
100.0
100.0
98 .1
102.2
103.9
100.6
99.9
92. 1
104.6
109.8
101 .7
101.2
98.0
102.6
110.2
104.0
106.8
100.0
110.5
109.1
109.7
106.9
3149
315 1
3152
3159
3161
Oth er textile product millsv
Apparel knitting mills .. .. .... ... . .. .. .... ....... .... ... .... .
Cut and sew apparel. ... .... .. . ... .. .... . .. .. . . . .. . . . . .. .
Accessories and other apparel. .... .... .. .... .... .... ...
Leather and hide tanning and fini shing ........ . ......
91 .1
85.6
70.1
100.9
60.8
90.0
88.7
72 .0
97 .3
56. 6
92 .0
93.2
73.1
98.7
76.7
90.3
102.5
76.6
99.0
83.1
94.5
104.3
80.5
104.6
75.9
95.9
109.5
85.5
112.4
78.6
96.3
121.9
90.5
11 2.6
91 .5
100.0
100.0
100.0
100.0
100.0
97 .0
96.6
104.0
110.8
98.0
110.4
102.0
118.8
103.3
10 1.6
110.4
110.2
127.7
104 .9
110.0
105.0
108.4
131 .7
114.8
109.7
-
3162
3169
3211
3212
3219
Footwear ...... ... ................ .... ......... .. .. .... ... .....
Other leather produ cts ...... .. .... .. .. ....... .. ....... ...
Sawmills and wood preservation .... ... . . . . . . . . . . . . . . .
Plywood and engineered wood products ... . .. .....
Other wood products. ..... .. .. ... . ... . ... . .. .. ........ .
77.1
102.5
79.2
102.3
105.4
74.7
100.2
81 .6
107.4
104 .7
83.1
97.0
86.1
114.7
104.0
81.7
94 .3
82.6
108.9
103.0
90.4
80.0
85.1
105.8
99.3
95.6
73.2
91.0
101 .8
100.4
103.4
79.7
96.2
101 .2
100.8
100.0
100.0
100.0
100.0
100. 0
100.9
109.2
100.8
105.6
101.5
116.8
100.4
105.4
99.9
105.4
124.1
107.6
106.5
100.5
104.0
142.7
114.1
109.0
105.0
104 .6
-
3221
3222
3231
3241
3251
Pulp, paper, and paperboard mills ... ....... ... ..... ...
Converted paper produ cts ... . ... ... . . . .. .. .. . . .. . . .. .
Printing and related support activities .. ... . . . . . .... .
Petroleum and coal products .... .... . ......... ..........
Basic chemicals ... ........ ... ... .... ... .... ..... ..... .... ...
88.5
90.5
96.6
76.7
91 .4
88 .1
93 .5
95 .4
75.8
90 .1
92.3
93.7
101 .3
78.9
89.4
92.9
96.3
100.1
84.5
89.9
97.6
97.6
98.3
85.6
95.1
102.0
97.2
98.8
90.1
92 .3
97.6
98 .3
99.6
94 .8
90.0
100.0
100.0
100.0
100.0
100.0
103 .1
102.7
100.5
102 .1
102.5
111 .4
101 .5
103.5
107.8
11 4.7
115.7
101 .9
104 .9
113.2
118.4
117.5
101 .0
105.6
112.2
111 .0
-
3252
3253
3254
3255
3256
Resin , rubber, and artificial fibers .... ............ ......
Agricultural chemicals ... .. .. ... .. ... .. ... ... ..... .. .. .....
Pharmaceuticals and medicines ......... ... ... .........
Paints, coatings, and adhesives ... . . .... .. .....
Soap, cleaning compounds, and toiletries. .... .. . .
75.8
84.6
91 .4
85.1
83.2
74 .7
81 .0
92.6
85.9
84 .2
80. 6
81 .3
88.2
87.6
83.4
83.8
85.6
88.1
90 .9
86.9
93.5
87.4
92.4
94.1
88.6
95.9
90.7
96.3
92.7
93.9
93.3
92 .1
99.9
98.3
95.6
100.0
100.0
100.0
100.0
100.0
105.5
98.8
92 .9
99.1
96 .6
108.8
87.6
94 .6
98.8
91.1
108.1
91.4
93.4
98.5
99 .2
103.8
91 .1
97.4
102.1
102.7
3259
3261
3262
3271
3272
Other chemical products and preparations .. .. .
Plastics products .. . .. .... ..... .... .... .. .. . .. . . . ...... ..
Rubber products .... .......... ....... .. .. .. ... ...... ....... .
Clay products and refractories. · ····· · ··· ··"··· " ·
Glass and glass products ..... .. .... . .. .... ...... ... .. ..
76.6
84.7
83.0
89.2
80.0
78. 0
86 .3
83.8
87 .5
79. 1
84 .7
90 .3
84 .9
91 .5
84.3
90 .6
91 .9
90.4
91 .9
86.1
92.6
94.4
90.3
96.6
87.5
94.4
94.5
92.8
97.4
88.8
94. 2
97.0
94.4
102.6
96.5
100.0
100.0
100.0
100 .0
100.0
99 .4
103.5
100.5
101 .3
102.7
109.2
109.3
101.4
103.5
108.6
120.0
111.2
103.9
103.6
109.7
111 .3
113.3
104.2
97.6
105.2
-
3273
3274
3279
3311
331 2
Cement and concrete products ..... .... ....... ... .....
Lime and gypsum products ...... ..... .... .... ... .. ... ...
Other nonmetallic mineral products .. . ......... . .....
Iron and steel mills and ferroalloy production ..
Steel products from purchased steel. . .. .. . .. ... .. .
94.8
84.1
79.8
69.6
83.8
93 .7
82 .7
81 .4
67.2
86.4
94.8
88.5
90.2
74 .1
89.9
96.5
90.1
89.3
81.7
95 .9
95.0
87 .8
90.5
87 .2
100.0
98.2
88.8
91 .7
89.7
100.5
100 .6
92.4
96.5
94 .1
100.5
100.0
100.0
100.0
100.0
100.0
103.5
113.1
98.8
101 .7
100. 3
104.1
102.7
95.5
106.5
94 .2
100.4
97.0
95.6
108.5
96.4
97 .1
100.1
96.8
106.7
97 .1
-
3313
3314
3315
3321
3322
Alumina and aluminum production .... . ..... ... ··· ···
Other nonferrous metal produ ction ... ........ ....
Foundries ....... ...... .... . ... ... ... .... .. ....... .. ........ .
.. ... ... .....
91 .9
95.6
85.3
88.6
85.1
93.3
95.8
84 .5
86.5
85 .4
96.8
98.8
85.8
91 .7
87.2
96.0
101 .8
89.8
94.6
91.7
100.3
105.1
91 .4
93 .7
94.4
96.8
102.9
93.1
94 .2
97 .8
95.9
105.7
96.2
97.6
104.4
100.0
100.0
100.0
100.0
100.0
101 .1
111 .2
101.6
103.7
100.0
104.3
108.9
104.9
110.9
107.8
97.8
103.1
104.0
121.3
105.8
96.9
100.5
109.3
121 .8
110.2
-
3323
3324
3325
3326
3327
Architectural and structural metals ... . ... ... .. ..... ...
Boilers, tanks, and shipping containers ... ..... .. ... ..
Hardware .. .... ... .... . ..... .. . ... . .. ..... . ... .. ... ... .. . . .
Spring and wire products .. ... . .. ..... ...... ..... .........
Machine shops and threaded products ... ... .........
87.8
90.4
84.4
85.2
78.8
89 .1
92.6
83.8
88.4
79.8
92.5
95.3
86.9
90 .9
87 .2
93.4
94.8
89 .6
95.3
86.9
95. 1
100.5
95.7
91.5
91 .6
93.9
97.8
97.3
99.5
98.7
94.2
100.7
102.6
102.8
100.0
100.0
100.0
100.0
100.0
100.0
101.1
101 .3
101 .0
111 .6
99 .3
101 .8
98.9
106.5
112.9
103.9
101 .0
97.7
115.8
114.6
107.2
100.7
98.2
11 4. 6
110.6
107.2
-
Utilities
Manufacturing
Forging and stamping ........ ... ... ...... ... .. .. ...... ... .
Cutlery and hand tools .. .. .
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
.
..... ....
. ..
Monthly Labor Review
November
2004
-
-
-
139
Current Labor Statistics: Productivity Data
51. Continue<>-Annu al indexes of output per hour for selected NAICS industries, 1990-2002
[1997: 100]
140
NAICS
Industry
1992
1993
1994
1995
1996
1997
1998
1999
2000
3328
3329
3331
3332
3333
Coating, engraving , and heat treating metals ....
Other fabricated metal produ cts .. . .... .......
Agriculture. construction . and mining machinery
Industrial machinery ..
Commercial and service industry machinery ... ... ..
81 .6
86 .7
82.8
80.6
91.4
78.1
85 .9
77 .2
81 .1
88.6
86.9
90.6
79.6
79.5
96.5
91.9
92.1
84.1
84.9
101 .7
96.5
95.0
91.0
90.0
101.2
102.8
97 .1
95.6
97 .9
103.0
102.9
98.9
95.9
98.8
106.3
100.0
100.0
100.0
100.0
100.0
101 .7
102.3
104.2
94 .4
107.5
101 .5
100.2
95 .0
105.2
111 .2
105.9
100.8
101 .0
129.7
101.4
105.1
98.2
99.5
104.6
3334
3335
3336
3339
HVAC and co mmercia l refrigerati on equipment
Metalworking machinery ..
... ... . . . . . . . . . . . . . . . .. . . . .
Turbin e and power tran smission equipment.. .. .. .
Other general purpo se machinery .... .. . ......
88 .8
85 .3
85.1
85.9
88 .2
82 .3
84 .6
85 .2
90.8
89.3
81 .2
85.1
93.8
89.3
84.8
89.8
97 .3
94 .0
93.3
91 .5
96 .6
99.1
92 .1
94 .6
97.8
98.1
97.9
95.1
100.0
100.0
100.0
100.0
106.6
99.1
106.4
103.2
110.4
100.5
113.3
105.6
108.3
106.4
117.1
113.0
110.8
102.0
130.2
109.4
3341
Computer and peripheral equipment ... .. ....... . ..
14 .3
15.8
20.6
27.9
35.9
51 .3
72.6
100.0
138.6
190.3
225.4
237.0
3342
3343
3344
3345
3346
Communications equipm ent . . ... . .. . . . .. . . . .. . .. . .
Audio and video equipm ent.. .. ..
Semiconductors and electronic components
···· ···
Electroni c instruments .. . .. .. .. ....... .. . . . ....... .......
Magneti c media manufacturing and reproduction
47.3
75 .5
21 .4
76.0
86 .6
49 .3
82 .8
24.5
80.5
91 .2
59.3
92 .1
29.6
83.1
93.0
62.1
98.8
34.1
85.8
96.8
70.1
108.5
43.1
88.8
106.1
74.6
140.0
63 .4
96.8
106./
84 .3
104.7
81 .8
97.7
103.8
100.0
100.0
100.0
100.0
100.0
102.7
103.1
125.2
101.3
10!:J.4
134.0
116.2
174.5
105.1
106.8
165.5
123.3
233.3
114.3
104.0
155.2
126.3
231.6
116.1
98.6
3351
3352
3353
3359
336 1
Electri c lighting equipment ..... . ... ... . ... .. . . . . .. .
Household applian ces. •• · • ...... .. . .. .. .. ..
Electrical equipment. ... ... ... .. . .....
Other electri cal equipment and co mponents . . .. . . .
Motor vehicles ..
87.3
76 .4
73.6
75.3
86.0
88 .5
76.4
72 .7
74 .2
82.4
93.6
82.4
78.9
81 .6
91.2
90.8
88.9
85.8
86.8
89.8
94 .5
95.0
89.0
89.4
90.3
92 .2
92 .7
98 .1
92 .0
88.6
95.6
93.1
100.2
96.0
91 .0
100.0
100.0
100.0
100.0
100.0
103.8
105.1
99 .8
105.5
113.3
102.5
104.3
98 .9
114.8
123.3
101 .9
117 .5
100.6
120.5
110.4
105.4
122.6
101 .0
113.5
108.7
3362
3363
3364
3365
3366
Motor vehicle bodies and trailers ·····
·· ··· ···· ··· ....
Motor vehicle parts .... . . . . . .. . .. .. . ..... . . ... ..... ... ... .
Aerospace produ cts and parts . . . . . . . . . . . . . . .. ....
Railroad rolling stock .. . . . . . . . . . . . . . . . . . . . ....... . ...... ..
tih1p and boat bu1ld1ng .. .. . ... ..
. . ... ........ ..
75 .8
75 .7
87.7
77.2
99 .6
71 .8
74.5
92.1
80 .0
92.6
88.3
82 .4
94 .1
81.1
98.5
96.3
88.5
98.2
82.3
101 .3
97 .7
91 .8
93.8
83.1
99.0
97 .3
92 .3
93.7
82.0
93.1
98.4
93.1
98.1
80.9
94.1
100.0
100.0
100.0
100.0
100.0
102.7
104.8
118.5
102.9
100.3
103.1
110.4
118.0
116.0
112.2
98 .4
99.4
112.7
101 .0
117.7
120.1
114.8
114.7
124.7
119.8
3369
3371
3372
3379
3391
3399
Oth er transportati on equipmen t .. .... . . ... .. . .. . . . . . .
Hou sehold and institutional furniture .. .......
Office furn iture and fi xtures. . .. . .. . . .... .... .... .. .. . ..
Oth er furniture-related products .. . . .. . ..
Medical equipment and supplies. .. .... .. .... .. . . . . ..
Oth er miscellaneous manufacturing . . . .. . . . . . . . . . . . . .
62 .6
87.6
80.8
88.1
81.2
90.1
62 .0
88.2
78.8
88 .6
83.1
90.6
88.4
92 .9
86.2
88.4
88.1
90.0
99.8
93.8
87.9
90.5
91 .1
92.3
93.4
94 .1
83.4
93 .6
90.8
93.0
93 .1
97.1
84.3
94 .5
95.0
96.0
99.8
99.5
85.6
96.7
100.0
99.6
100.0
100.0
100.0
100.0
100.0
100.0
110.8
102.7
100.1
107.2
108.9
101 .9
113.3
103.7
98 .5
102.5
109.6
105.2
130.9
102.5
100.2
100.1
114.2
112.9
146.9
106.1
97.1
105.3
119.0
110.9
42
423
4231
4232
4233
Wholesale trade
Whol esale trade ·· · ··· · ···· · ·· · · · · · · ·· · · · · · · ·· · ··· · ·· · · · .. .
Durable goods ....... . . .. .. . . .. .. ... .. . . . . . . . . . . . . . .. .. . ..
Motor vehicles and parts ·•····· ····· ·· ···· ··· ····
··· ....
furniture and furnishings .... .. .. ... .... .... .... ... .....
Lumber and constru cti on supplies . .. ... .. .. .. .. .. . ..
77 .8
65 .7
76 .6
82.4
115.0
79 .1
66 .1
73.3
8/.2
113.2
86.2
75.0
82.2
92 .0
119.6
89.5
80.5
88.0
95.8
113.9
91 .3
84 .5
94 .1
93.3
111.9
93 .3
88 .9
93.6
96 .8
103.6
96.2
94 .0
94.9
9/.0
103.0
100.0
100.0
100.0
100.0
100.0
104.4
105.6
104.7
97.5
102.9
110.9
115.3
119.8
100.8
104 .8
114.1
119.6
114.0
105.5
101.7
117.1
120.3
114.1
105.4
108.6
123.6
127.7
121 .7
101.8
119.2
4234
4235
4236
4237
4238
Commercial equipment . . . . . . . . . . . .. .... .. ...... ...... .
Metal s and minerals . . ... .. . ... . . . . . . . . . . . . . .. .
El ectri c goods . . . . . . . . . . . . . . . . . . . . . . . ... .. .... ... .... . ... .
Hardware and plumbing .. . ........ ..... ......... ... ....
Machinery and supplies .. .... . . . . . . . .. . . . . . . . ... . .. ..
33.8
101.6
46 .8
88.8
78 .9
37.3
102 .6
47 .6
86.5
74.2
48.2
109.1
51 .4
95.6
79.7
56.2
111 .7
59.1
94 .3
84.3
60.5
110.1
68.2
101.3
85.4
74 .7
101 .2
79 .3
98.0
89 .7
88.4
102.7
87.8
99.1
93.9
100.0
100.0
100.0
100.0
100.0
118.2
102.4
105.9
103.5
104.2
141 .1
96 .0
126.2
107.8
101 .4
148.9
99.2
151 .7
111 .1
104 .1
164.9
102.2
148.1
102.6
102.7
189.4
102.2
161 .2
107.9
100.2
4239
424
4241
4242
<i24'.j
Miscellaneous durable goods ..
··· ···· ····· ... .....
Nondurable goods .. . . . ... .... ... ...... ... .... .......
Paper and paper products . . . . . . . .. .. .. . .. . . .... .. . .
Urugg1sts· goods . . . . . . . . . . . . . . . . . . . . ... .. . .. . ......
Apparel and piece goods. .. .. ·· ·······
··· ··· ···········
89.5
98 .4
81 .0
81.8
103.9
96 .6
99 .8
85.5
86.6
103.3
112.1
103.2
96 .5
91 .8
100.1
113.2
103.0
97.2
89.3
97.7
106.1
101 .8
101.5
92 .8
103.8
99 .2
99.7
99 .0
9b.4
92 .2
101 .0
99.2
96.5
98.3
99.0
100.0
100.0
100.0
100.0
100.0
101 .8
102.8
100.4
99.6
104.1
112 .6
104.1
105.5
101./
103.5
116.7
103.5
105.5
96.8
102 .7
116.1
106.9
109.0
101.2
102.4
125.5
112.6
120.2
116.0
111 .5
4244
4245
4246
4247
4248
Groce ry and related products .... . .. . . .. . . . . ...
Farm product raw materials ... ..... .. ..... ........ .. . .. .
Ch emicals .. . ......... ......
.. .. .... .... ...........
Petrol eum ·· · ··· · ········· . . . .... . .... ...... ... ...
Alcoholic beverages ... .. .. .. .. . ..... .... . . . .. .... .. ..
96.4
80 .6
107.3
97 .3
109.4
98.2
85.9
106.6
107.0
111 .2
103.6
85.9
112.5
118.3
107.4
105.1
84.0
110.0
119.1
105.6
103.3
80.4
110.5
115.8
105.9
103.0
87.7
102.1
108.7
102.5
99.8
90.6
100.0
105.9
104.5
100.0
100.0
100.0
100.0
100.0
101 .9
100.4
99.3
115.0
109.7
103.6
114.2
98 .0
112 .0
110.1
105.2
119.0
95.8
112.5
111 .0
109.4
120.0
93.6
116.5
111 .6
111.8
135.4
96.9
126.0
117.3
4249
425
42511
42b12
Miscellaneous nondurable goods ..
Electronic markets and agents and brokers .. ... ...
Business to business electronic markets ... ... ... ..
Wholesale trade agents and brokers .... ..... ... ....
107.3
70.7
70.4
70.8
98.2
73.6
72 .6
74.0
93.9
81 .5
80.3
82.3
97.5
85.9
84 .8
86.8
94 .8
88.0
88.3
88.4
96.2
91 .1
90.5
91 .8
98.7
95.7
95.3
96.1
100.0
100.0
100.0
100.0
101 .7
104.6
103.5
104.8
99.6
114.4
121 .7
110.5
106.2
124.1
141 .3
11b.7
104.2
131.3
169.4
114.2
97.0
132.6
205.0
109.3
44-45
441
44 11
4412
4413
Retail trade
Retail trad e .. ...... ....... .. . ............ . . .. . ... . . . . . . . . . .
Motor vehicle and parts dealers. ·••· ··· ······
···· ....
Automobi le dealers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . .
Oth er motor vehicle dealers ...... ......... . .. ... . ..
Auto parts. accessories. and tire stores .. .. . . ..
83.2
89.7
92 .1
69.0
8b.0
83.3
88.3
90 .8
71 .7
84 .0
86.8
92 .6
94.8
78.3
89.1
89.4
94.0
96.0
84.1
90.6
92 .8
96.9
98 .0
90.2
9b.4
94 .7
97.0
97 .2
91 .0
97.9
97.7
98.8
98.9
97.7
98.3
100.0
100.0
100.0
100.0
100.0
104.3
102.7
102.7
105.9
105.7
110.3
106.4
106.4
113.0
110.0
114.2
107.2
106.6
108.6
112.0
117.4
110.0
109.1
112.6
109.3
122.7
109.7
106.0
116.4
115.8
442
4421
4422
443
444
Furniture and home furnishings stores ... . . .. ..
Furniture stores . .. . . .. ... ···· ············· .. .. ... ..
Home furnishings stores ... . . . . . . . . . . . . . .. ..... . . . .
Electronics and applian ce stores .... .. ........ ... .. ....
Building material and garden supply stores .. .. . .. .
80 .7
82 .1
78.5
46.0
81.8
81 .1
83.5
77.6
49.2
80.2
88.1
89.0
86.8
56.9
84.0
88.3
89.0
87.2
65.5
88.0
90.4
88 .9
92 .1
77.6
93 .7
94 .1
92.5
95 .9
89.2
93 .7
99.4
97.8
101 .3
95.0
97.5
100.0
100.0
100.0
100.0
100.0
101.7
102.1
101.3
122.9
106.7
109.6
108.2
111.4
152.2
112.3
115.7
114.8
116.8
177.7
113.1
118.5
121 .1
115.6
199.1
115.8
125.1
128.6
121.4
240.0
119.9
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
1990
November -2004
1991
2001
2002
94.4
51. Continued - Annual indexes of output per hour for selected NAICS industries, 1990-2002
[1997z100]
1990
1994
1997
1998
1999
97 .6
97 .1
100.3
100.8
95.5
100.0
100.0
100.0
100.0
100.0
107.6
101 .2
99.9
100.3
95 .0
11 3.7
103.5
103.7
104.3
99.6
11 3.8
108.2
105.1
104 .9
105.6
115.3
119.4
107.6
107.5
110.8
119.8
12 1.2
110.3
110.3
114.2
96.2
103.1
100.0
105.8
99.8
111.1
110.4
111 .8
93 .0
99 .7
91.2
95.7
99.4
97.9
100.0
100.0
100.0
104.1
105.6
105.4
106.9
110.6
112.8
111 .4
106.5
120.3
11 2.7
109.8
123.5
118.8
11 7.5
129.0
81 .9
90.1
97 .1
100.0
106.7
113.3
120.9
125.2
132.7
79 .2
77.1
84 .0
80.6
91 .6
88.3
85.0
87.2
83.9
94 .5
93.7
94 .1
93.0
92 .3
94 .5
102.4
97 .3
94 .7
92 .5
99.3
100.0
100.0
100.0
100.0
100.0
97 .8
107.0
108.7
112.9
101 .0
104.9
118.3
114.9
120.4
104.7
109.6
128.0
121.1
128.3
108.0
11 5.8
122.5
125.4
130.4
11 6.0
120.0
121.5
132.9
137.9
123.8
83.0
91 .6
69.7
74 .2
85.1
88.5
95 .0
77.8
79.1
91 .4
90.6
95.1
82 .6
87.0
85.4
92.2
94.7
87.6
89.5
83.5
96.9
98 .4
94.3
95 .0
96. 1
100.0
100.0
100.0
100.0
100.0
105.0
100.6
113.4
108.3
101 .2
113.1
104.5
129.8
109.8
117.3
119.9
106.3
145.9
111.3
11 6.0
124.2
104.0
162.1
108.4
108.6
130.5
104.7
177.5
115.6
120.7
66.3
83.1
69.2
55.0
46.7
95.4
67.6
71 .5
89.7
74 .7
63.4
50.6
95.1
82 .1
75.8
88.9
80.5
66.7
58.3
92 .8
79.7
87 .5
87 .3
89.7
73.8
62.9
94 .1
89.2
90 .9
90.2
90.5
80.9
71.9
89.3
94.7
91 .8
97.4
98.0
91 .6
84.4
96 .9
102.2
100.0
100.0
100.0
100.0
100.0
100.0
100.0
113.0
113.5
105.0
11 1.3
118.2
114.1
96.2
118.0
109.8
101.6
125.4
14 1.5
118.1
96.3
124.1
115.7
99.6
142.8
1!)9.8
127.1
104.3
125.1
115.0
93 .2
146.9
177 .5
110.4
98.7
140.3
121.4
92.8
169.6
209.8
113.3
110.2
77.5
69.8
88.5
96.1
78.2
75.3
92.4
95.8
81 .4
82 .3
97.5
96 .5
84 .7
85.7
95.6
99 .0
90.8
88 .6
98 .1
98 .5
95.3
92 .0
95.4
98 .3
98.8
98.4
95.7
96.7
100.0
100.0
100.0
100.0
97.6
102.1
99 .1
101.4
98.2
105.5
102.0
102.4
98.2
114.3
105.5
104.9
91 .9
121 .9
104.2
106.1
103.2
131 .9
109.4
107.0
Information
Newspaper, book, and directory publish ers . .......
Software publishers ..... . .. . .. .. .. . · ·· · ··· · · · · . .. .. ..
Motion picture and video exhibition ..... . ... ... .. .. ... .
Radio and television broadcasting .... . . . . . . . . . . . . . .. .
Cable and other subscription programming ... ... ..
Wired telecommunications carriers .. ...... . .. ... . ... ..
Wireless telecommunications carriers .. . ... ........ ..
Cabl e and other program distribution ..
97.4
28.6
109.4
96.1
98.8
64.8
76.3
99.1
96 .1
30.6
108.9
97.8
94.3
68.4
73.8
94.3
95.8
42 .7
104.1
102 .8
96 .0
74 .5
85 .6
95 .9
95 .3
51 .7
104.6
101.4
93.6
79.7
94 .8
93.5
93.0
64 .6
103.4
106.0
92.0
85.1
97.1
91 .9
93.5
73.0
99.9
106.1
94 .4
90 .6
98.3
94 .2
92 .7
88.0
100.0
104.1
93.7
97.5
103.0
93.5
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
104 .5
115.9
99.9
99.1
129.3
105.5
114.2
95 .7
108.5
113.0
102.0
99.4
133.2
112.7
134.3
94 .5
110.1
103.9
106.5
98.4
135.7
119.9
139.0
90.4
106.4
10 1.9
104.7
94.3
125.3
121 .0
172.7
87.6
108.1
106.7
104.4
100.4
13 1.4
130.6
192.0
93.5
52211
Finance and Insurance
Commercial banking .. ... .... ... ... . .. . ............ .. ... .
80.5
83.2
83 .3
90.3
92.9
96 .0
99 .3
100.0
98.0
101.5
104 .2
101.6
103.8
532111
53212
Real estate and rental and leasing
Passenger car rental. .... . ..... ...... . . . .... . . ...... .. . .
I ru ck, trailer and RV rental and leasing ....... ... ....
89.8
70.7
97.8
71 .7
104.4
69 .5
106.1
75 .8
107.9
82.0
101 .1
90.3
108.9
96.7
100.0
100.0
101 .2
93 .7
113.1
97.8
112.0
95.9
112.1
93.6
113.3
91 .4
541213
!)4181
Tax preparation services ... .. .... .... ... ...... ........ .. .
Advertising agencies .. . .. . . . .. .. .. . .. . .... . ....... .....
92.4
10!).0
84 .7
99./
99.5
111 .9
119.1
111 .3
119.9
106.8
96.2
101.4
92 .1
102. 1
100.0
100.0
105.1
9!J.8
99.2
110.1
91 .8
116.6
78.2
116./
92.1
123.9
7211
722
7221
7222
7223
7224
Accomodatlon and food services
Traveler acco mmodations ..
Food services and drinking places .... .. ..... .. .... .. ..
Full-service restaurants ... ..... .... ..... .... ..............
Limited-service eating places .. ... ... . . . . . .. . .. ..
Special food services ............... .... ......... .. ··· ··· ·
Drinking places, alcoholic beverages .... ... ..... .... .
82.9
102.9
99.1
103.3
107.2
125.7
85.4
102.3
98 .3
103.3
106.9
121.2
92 .9
101.7
97.5
102.7
106.4
121 .5
93.0
102.3
97.7
105.6
103.8
112.7
97.0
100.8
97 .8
103.6
101 .1
102.6
99.2
100.6
96.6
104.7
99.3
104.4
100.1
99.2
96 .3
102.2
97.6
102.4
100.0
100.0
100.0
100 .0
100.0
100.0
100.0
101.2
100.0
102.4
102.1
100.0
103.6
101.1
99.2
102.5
106.0
99.4
107.7
103.5
100.8
105.1
111.7
100.4
102 .0
103.7
100.8
106.6
108.4
98.2
104.1
104.9
102.0
107.1
108.1
107.2
8111
81211
81221
8123
81292
Other services (except public administration)
Automotive repair and maintenance ....... .. .... .. ... .
Hair . nail and skin care servi ce s . ..... ... ...... . . . ....
Funeral homes and funeral services .. ... .... .. ... ....
Drycleaning and laundry services ..... . ... .... ... ... ..
Photofinishing ... .. .... .. .. . . . . . ... .. .... ........ .. ... .. ..
92.8
81 .6
96.1
95.6
117.3
86.5
79.8
94 .3
93.2
115.6
90.0
85 .6
104.7
94 .9
116.2
91 .2
84.3
100.4
93.8
123.6
96.7
88 .7
103.6
95.9
124.9
102.9
92 .4
100.4
98.8
114 .7
98.9
97 .1
97.9
101 .6
103.2
100.0
100.0
100.0
100.0
100.0
105.0
102.7
103.8
105.0
99.4
106.9
103.6
100.4
109.5
106.9
108.6
103.0
94.5
113.7
107.6
109.3
109. 5
93.9
121.1
115.0
103.7
104.2
90.9
120.2
133.6
November
2004
141
1991
1992
1993
1995
NAICS
Industry
4441
4442
445
4451
4452
Building material and supplies dealers ......... .... .
Lawn and garden equipment and supplies stores
Food and beverage stores ....... .... .. .. ................
Grocery stores .. ... .... .. .. ···· ···· ·· ···· ·· ········ ·· ·······
Specialty food stores .. .. ......... ... .................. ...
83.2
74.5
107.1
106.5
122.9
80.7
77 .5
106.6
106.6
115.0
84 .7
80 .2
106.9
106.7
111.4
89 .1
81 .5
105.4
105.9
107.6
94 .8
86.9
104.3
104.9
104.5
94 .8
87.0
102.5
103.0
101 .1
4453
Beer, wine and liquor stores .... ........... .......... .. ..
100.1
100.2
101 .0
94.4
92 .9
446
447
448
Health and personal care stores .. . .......... .. ..... ....
Gasoline stations ....... ......... .. . ... .. .... ... ..... .... .
Clothing and clothing accessories stores .......... ..
92.0
84.8
69.5
91 .6
85 .7
70.5
90.7
88 .5
75.3
91.9
92 .8
78 .9
91 .8
96 .8
83.3
4481
Clothing stores ............... ... ... .............. . ... .. .....
68.9
71.4
77.1
79 .2
4482
4483
451
4511
4512
Shoe stores ...... ........ .. ....... .... . .... . . .. ..... . . .. . ..
Jewelry, luggage , and leather goods stores .........
Sporting goods, hobby, book, and music stores ...
Sporting goods and musical instrument stores ....
Book, periodical, and music stores ... ... .. . .... ... ....
73.7
68.6
80.8
77.1
89.0
73 .1
64.5
85 .6
82 .8
91 .8
78 .2
65.0
83.8
79.8
92 .5
452
4521
4529
453
4531
General merchandise stores .. . ... .... .. .. . ... ..... . .....
Department stores .. .. .... . . . . . . .. . . . . . . . . ....... ........
Other general merchandise stores .... .. ... .... ..... ...
Miscellaneous store retailers ................... .. .... ...
Florists ... . .. .............. .. ... ..... .. .. ...... .... .... ..
75.3
84.0
61 .4
70.6
75.1
79 .0
88.3
64.8
68 .0
75.9
4532
4533
4539
454
4541
4542
4543
Office supplies, stationery and gift stores ... .... ....
Used merchandise stores ...... ..... .... ..... ........ ... .
Other miscellaneous store retailers .. . . . . . . . . . .. .
Non store retailers .. .... ...... ..... .. .. ... .. . ...... ... ......
Electronic shopping and mail-order houses . ... ... .
Vending machine operators . .. . . . ... .. . ..... . . . ...... . ..
Direct selling establishments .... ....... ............ .. ...
64.6
84.9
79.6
54.4
43.5
97.1
70.0
481
482111
48412
491
Transportation and warehousing
Air transportation ...... ..... .................. ..... ... .....
Line-haul railroads .. . .... ... .. .... .... .. ... ............ ..
General freight trucking , long-distance . ......... ....
U.S. Postal service .. . .. ........ ... ... .....................
5111
5112
51213
5151
5152
5171
5172
5175
1996
2000
2001
2002
Professional, scientific, and technical services
NOTE: Dash indicates data are not available.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
Current Labor Statistics: International Comparison
52. Unemployment rates, approximating U.S. concepts, in nine countries, quarterly data
seasonally adjusted
Annual average
Country
2002
2003
2002
I
II
2003
Ill
I
IV
II
2004
Ill
I
IV
United States ...... ..
Canada ....... ..... .. ..
Australia .. .... .. .......
Japan ............. .....
5.8
7.0
6.4
5.4
5.3
5.4
5.4
5.5
5.4
5.4
5.4
5.2
5.1
5.0
France ... ... . .. . ....
8.7
9.3
8.5
8.6
8.7
8.9
9.0
9.2
9.4
9.4
9.4
6.0
6.9
6.1
5.7
7. 1
6.7
5.8
6.9
6.4
5.7
7.0
6.3
5.9
6.9
6.2
5.8
6.7
6.2
6.1
6.9
6.2
6.1
7.2
6.1
5.9
6.8
5.8
5.6
6.7
5.7
Germany ...... ..... ...
8.6
9.3
8.3
8. 5
8.7
8.9
9.2
9.4
9.4
9.3
9.2
Italy ' .... ........... ... ..
9.1
8.8
9.2
9.2
9.1
9.0
9.0
8.8
8.7
8.6
8.6
5.1
5.2
5.8
5.0
5.2
5.1
5.0
5.2
5.1
5.2
5.2
5.1
5.2
5.1
5.6
5.0
5.8
5.0
6.2
4.9
6.6
4.8
2
Sweden ••• ••••• ••.• •••
United Kinadom .. ...
Quarterly rates are for the first month of the quarter.
"Notes on the data" for information on breaks in series. For further
qualifications and historical data, see Comparative Civilian Labor
Force Sta tistics, Ten Countries, 1959-2003 (Bureau of Labor
Preliminary data for 2003.
NOTE: Quarterly figures for France and Germany are calculated
by applying annual adjustment factors to current published data,
and therefore should be viewed as less precise indicators of
Statistics, June 23, 2004) , on the Internet at
unemployment under U.S. concepts than the annual figures . See
also on this site.
142
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November
2004
http://www.bls.gov/fls/home.htm.
Monthly and quarterly unemployment rates , updated monthly , are
53. Annual data: employment status of the working-age population, approximating U.S. concepts, 10 countries
[Numbers in thousands]
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
129,200
131 ,056
132,304
133,943
136,297
137,673
139,368
142 ,583
143,734
144,863
146,510
14,308
14,400
14,517
14,669
14,958
15,237
15,536
15,78'.J
16,027
16,475
16,819
Australia .. .... ....... .... ........ .
Japan .... ................... . ... ..... ... . .... ...... . ... ... ...... .
France ... . . .. ... .. ..... ........ ... ...... .............. ...... ... . .
Germany. .... .. .
. .. .. .... ... ... .. ..... ... .. .. ...... .. .. .
8,613
65,470
8,770
8,995
9,115
9,204
9,414
9,907
10,092
65,990
66,450
67,200
67 ,090
9,59:J
66 ,99:)
9,752
65,780
9,339
67,240
66 ,870
66,240
66 ,010
24,480
39,102
24,670
39,074
24,760
38,980
25 ,010
39 ,142
25,130
39,415
25 ,460
39,754
25 ,790
39,375
26,071)1
39,302
26 ,350
39,459
26 ,590
39 ,413
26,730
39 ,276
. ... .... ... .. ... .. . .... ...... ... ....... .. .. .... .. .... .
22,570
22,450
22,460
22,570
22,680
22 ,960
23,130
23,340
23,540
23,750
23,880
Netherlands ............ ...... .. .. .. ....... .... ........ .. .. .. .... .
Sweden ...... ..... .
United Kingdom ..
. . ..... . ............. .. .. .
7,010
4,444
7,210
4,460
28,157
7,300
4,459
7,540
4,418
7,620
4,402
7,850
4,430
8 ,150
4,489
8,340
4,530
8 ,300
4,544
8,330
4,567
28,260
28,417
28,479
28,769
28,930
29,053
29, 288
29,4 90
Emolovment status and countrv
1993
Civilian labor force
United States ..
Canada ... ..... ....... ... ... ..... . ... ..... ....... ........ .
Italy .
Participation rate
7,150 ,
28,165
4,418
28,149
1
United States .. . .. .. ..... ... .... ..... ... ............. ... .
66.3
66.6
66.6
66.8
67.1
67.1
67 .1
67 .1
66 .8
66.6
66 .2
Canada .. .. ........... ..... ..... ...... ..... .... .. ... ... .. ... .
Australia ........... ........ ......... . ........ . .. . .
Japan .... .
65.5
65.2
64.9
64.7
65.4
65.8
65.9
66 .0
63.9
64.5
64 .6
64.3
64.4
64.4
63 .3
63.1
62 .9
63.0
63.2
62 .8
64.0
62.4
66 .8
64 .4
67.3
63.5
65.0
64 .3
62.0
61.6
60.8
60.3
France .. .... ... ....... ....... . .... .. ........ ..... .. .. .
Germany .. .... ........ . ... ... .. ............ ... ........... .... .
Italy ........ ......... .... ..... .. .. .... .. ..... .... .... ..... ... .. .
Netherlands ... ... ... .. ........ ...... ........ ..... ...... ..... . .
Sweden .... ...... ... ............ .... ... ... . ...... .
55.4
55.5
55.4
55.6
57.0
57.0
57.1
57.1
56.3 1
56.8
56.8
57.4
55.9
57.7
56.6
57.8
55.5
57.3
56 .6
56. 6
56 .3
56 .1
47.9
47.3
47. 1
47.1
47.2
47.6
47.8
48.1
48.3
48.6
48.8
57.9
58.6
58.8
59.2
60.8
61 .1
62.6
64.5
65 .8
65 .0
64.6
64.5
63.7
64.1
64.0
63.3
62 .8
62.8
63.8
63 .7
64.0
64.0
United Kingdom ............... .... .... .
62.7
62.6
62.4
62.4
62.6
62 .5
62.9
62.9
62.7
62.9
62 .9
United States ............ .. ...... . .
Canada .. .
Australia ..... .... ..... .. ............. . .... .. ..... . ........... . ... .
120,259
123,060
124,900
126,708
129,558
131,463
133,488
136,891
136,933
136,485
137 ,736
12,770
13,027
13,271
13,380
13,705
14,068
14,456
14 ,827
14 ,997
15,325
15,660
7,699
7,942
8,256
8,364
8,444
8,618
8,762
8,989
9,091
9,27 1
9,481
Japan ...... ..... .. ............... ..... ... .. ............. ..... ... .
France ... ... ... ............. .. ... ... .... ... . .. .. ........ ... .
Germany . .. ... ... ... ... .. ....... ... ..... ... .... ..... ... .. ... .
63,810
63,860
63,890
21,960
64 ,200
64 ,900
64,450
63,920
63 ,790
63,470
62,650
62,510
64 .6
Employed
Italy .. ............ ............. .... .. ... . ... .. ..... . .... ... .... .
Netherlands .. ...... .. . .. ... ........ .. .. .. ............ ......... .
Sweden ........... ...... ... ............. .. .. ... .
United Kingdom
.... ........ ........ .. .
Employment-population ratio
21,710
35,989
21,750
35,780
22,040
35,637
22 ,170
35,508
22,600
36,061
23,050
36,042
23,690
36,236
24 ,140
36 ,350
24 ,280
36 ,018
24,250
35,615
20,270
35,756
19,940
19,820
19,920
19,990
20 ,2 10
20,460
20,840
21 ,270
21,580
21 ,790
6,570
6,660
6,730
6,860
7,160
7,320
7,910
8,130
8,070
8,010
4,028
3,992
25,429
4,056
25,718
4,019
25,964
3,973
26,433
4,034
26,696
7,600
4,117
4,229
27,350
4,303
27,570
4,310
4,303
27,768
28,0 11
25,242
27,048
2
United States .. ...... .... ..... ... ... ... .. ...... .
61 .7
62 .5
62.9
63.2
63.8
64.1
64.3
64.4
63 .7
62.7
62.3
Canada .. .. ... ....... .... .. ..... . ... ... ... ......... ..... . .. .. .
Australia .... .... .. .
Japan ..... .
France .. .... ..... . .. . .... .. .. .. ......... .... ........ .... ......... .
58.5
59.0
59.4
59.1
62.1
61 .9
57.8
59.2
59.3
60.4
59.3
61.3
56.8
61.7
59.7
59.0
60.3
60 .1
63.0
60 .7
61 .3
60.9
60.9
61.0
60.2
59 .6
59.4
62.4
60 .3
59.0
58 .4
57 .5
57 .1
49.1
49.0
49.1
49.0
49.0
49.7
50.3
51.4
52 .0
52. 0
51.7
Germany ...... ... ... ... ... ...... ... ...... ......... .. ... . ... ...... .
53.2
52.6
52.4
52.0
51.6
52 .3
52 .0
52.2
52.2
51.5
50.9
Italy ..... .. ..... ... .. ... . ... ..... ........... ... . .
Netherlands. ..
. ..... ...... .. ...... . .
43.0
54.2
42 .0
54.6
41.5
41 .6
42 .3
42.9
44.6
58.7
60 .6
62.6
43 .6
64.2
44.1
55.7
41.6
57.8
41.9
54.9
63.2
62.1
Sweden ................................. ...... .. ..... ... ..... .... .
United Kingdom ....... .. ...... .... ........ ..... . ....... .. .
58.5
57.6
58.3
56 .9
57.6
56.5
57.0
58.2
58.6
58.4
59.1
60 .1
56.2
57.7
57.4
60.5
59.5
60 .7
59.6
59 .8
United States
Canada .. .... .. . .
Australia
8,940
7,996
7,404
7,236
6,739
6,2 10
5,880
5,692
1,539
1,373
1,246
1,289
1,252
1,080
962
6,801
1,031
8,378
1,150
8,774
1,159
914
829
739
751
759
1,169
721
652
602
661
636
611
Japan .... .
France ........... ..... ...... .. .. ... ....... ... ... ... .
Germ""Y ..... ... ....... .... ... ... .. .. ........ . .
Italy ....... .... .... .. .. . ... .... .. ... .... ..... ...... ... ..... .
Netherlands ............. . ... ... .... .. . ..... .... .... . .. .. .
Sweden .... .. ... .. .... ...... .. ... ........ ...... . .
United Kingdom. .
. ...... .............. .
1,660
2,770
1,920
2,920
2,100
2,800
2,250
2 ,970
2,300
2,960
2,790
2,870
3,170
3 ,200
2,380
3,400
3,590
3 ,500
2,210
2 ,310
2,480
3,113
2,300
3,318
2,510
3,200
2,640
3,505
2,650
3,907
2,690
3,693
2,750
3,333
2,670
490
480
440
370
300
3,661
2 ,100
320
416
2,916
426
2,7 16
404
2,439
440
2,297
445
1,985
368
1,783
250
313
1,721
3,110
2,270
210
3,396
2 ,160
440
3,065
2,500
240
260
1,580
227
1,483
4.9
8.4
4.5
7.7
4.0
6.1
6.4
5.8
7. 0
6.0
6.9
59.4
60 .3
Unemployed
2,740
230
234
1,520
264
1,479
Unemployment rate
United States .... .
Canada .... ......... ... .. .... ... .
Australia ................ ................................. .... .. .
Japan .. . ..... . ....... ......... ... ... ... ......... .. ........... .
France .. ... .... ...... ...... ...... .... ........ .... ... ..... .... .. ... .
Germany
. ........ ..... ... ..... .
Italy ... .. ...... ....... ... ... .... .......... ... .... ... ........ ... .. ... .
Netherlands .. ... .... .... .... .... .... .... .. .. . ... ... .. ... .. .. .. .
Sweden .. . ..
United Kingdom ... .. .
4.2
4 .7
6.9
6.1
5.6
5.4
10.8
9.5
8.6
8.8
10.6
9.4
8.2
8.2
8.3
7.7
6.9
6.3
6.8
6.4
6.1
2.5
2.9
3.4
4 .1
11 .3
4.7
10.6
4.8
9.1
5.1
8.4
5.3
11 .8
3.4
11 .8
5.4
11.3
3.2
11.3
8.7
9.3
8.0
8.5
8.2
9.9
8.5
7.8
7.9
8.6
9 .3
10.2
11.2
11.8
11.5
10.7
9.6
9.1
8 .8
6.3
6.9
2.9
2.5
2.8
3.8
9.4
9.6
9.6
6.7
9.1
5.8
5. 0
5.1
5.8
8.7
5.5
5.1
5.2
5.0
10.4
11 .9
7.0
9.0
11 .7
11 .9
9.3
12.0
6.0
4.9
3.9
9.9
10.1
8.4
3.2
7.1
8.1
7.0
6.3
6.0
' Labor force as a percent of the working-age population .
For furth er qualifications and historical data, see Comparative Civilian Labor Force Statistics.
2
Ten Countries, 1959-2003 (Bureau of Labor Statistics, June 23, 2004 ), on the Intern et at:
http://www.bls.gov/fls/home.htm .
Employment as a percent of the working-age population.
NOTE: See "Notes on the data" for information on breaks in series.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
2004
143
Current Labor Statistics:
International Comparison
54. Annual indexes of manufacturing productivity and related measures, 12 countries
[1992 = 100)
1960
Item and country
1970
1980
1990
1991
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Output per hour
.
United States ................... .. . .. . . . .. . . . . . .. ....... .. .
Canada .. . .. . .. . .... ..... .. ....... ... .. .. ........ ... .. . . ......
Japan ..... ......... . ........ ................ ····· ·••·······•···
Belgium .... ..... .. .. .... . .... .. . ........ ....... . ........
Denmark .. .............. ...... ....... ....... . ....... ..
France .. . . . . . . . . . . . . . ...... .. ....... ....... ...... . ······ •··
Germany .............. ..... ....... ........ ... .... ... ..... .
Italy ····· ···· ·· ·· · ········•·······• ..... ........... .... ......
Netherlands .. . ········· ...... ........ .. ..... .. ..... ...
Norway
........ .. ............. .. ....... . ........
Sweden ... .... . .. . . . . . .. . . .. . . .. .... .. .. .... ... .. ...... . .. ....... .
United Kingdom . ............................. .. ... ....
.
-
-
37.8
13.8
18.0
28.1
19.9
29.2
24.6
18.8
37. 6
27.3
30.0
54 .9
37.5
32 .9
49.4
39.0
52. 0
46.2
38 .5
59. 1
52.2
43 .2
70.5
72. 9
63.2
65.4
86.2
61.6
77.2
78.6
69.1
77.9
73.1
54.4
96.9
93.4
94.4
96.8
99.1
93.9
99.0
96.6
98.7
98.1
94.6
89.2
97.9
95.3
99 .0
99.1
99.5
97.0
98.3
96.1
99.0
98 .2
95.5
93.8
102.1
105.8
10 1.7
102.5
99.3
101 .0
101.8
101.2
102.0
99.6
107.3
103.9
-
-
-
-
-
-
-
-
108.9
109.6
104.8
113.1
99.6
117.8
108.5
114.4
112.3
107.9
117.3
100.7
124.5
106.5
114.7
114.7
108.3
119.3
102.5
129.5
105.8
12 1.7
120.4
110.3
121.4
102.0
141.0
107.7
127. 9
122.0
110.8
124.1
99.9
149.5
109.2
133.0
121.4
110.6
127.0
103.6
162.7
114.4
143.2
127.0
113.6
132.7
106.6
175.5
121 .9
148.0
127.8
115.9
132.3
108.9
170.3
126.4
152.1
131.0
114.3
133.1
110.9
184.3
127.6
101 .6
106.0
97 .1
101 .0
102.8
99.1
99.1
99.4
99.0
101.4
110.1
105.4
98.3
99.0
102. 0
100.7
101 .5
99 .8
102.3
99.3
99.8
99.0
104.1
100.1
103.5
105.9
96.3
97 .0
95.6
95.7
92 .4
96.5
97 .7
101 .7
101 .9
101 .5
111 .1
114.1
94 .9
101.4
105.6
100.3
95 .1
102.4
104.5
104.6
117.0
106.2
118.4
119.6
98.9
104.2
111 .6
104.9
95.2
107.2
108.2
107.3
131 .9
107.8
121.3
119.6
103.0
105.9
106.7
104.6
92.5
105.4
108.9
110.3
136.4
108.7
127.9
127.7
106.5
112.7
115.2
109.7
95.7
108.8
111 .6
114.2
146.5
110.7
133.1
133.9
100.2
114.4
115.7
115.0
97.7
110.7
114.9
113.7
158.3
111.4
139.5
144.9
101 .9
114.4
117.7
118.7
95.8
110.3
117.6
113.6
172.5
112.2
146.1
159.2
109.2
119.9
122.1
124.3
100.1
113.7
122.8
112.8
188.3
114.9
137.3
153.6
105.5
120.4
127.5
128.0
99.9
114.6
121 .7
113.4
183.1
1134.0
139.8
158.0
103.4
121.6
127.8
128.1
99.6
113.8
119.7
112.6
189.3
109.4
103.6
103.0
91 .9
93.6
104 .0
106.4
89.1
92 .0
103.6
109.0
88.7
91 .0
105.4
112.4
88.0
89.8
105.2
115.9
82.7
90.2
104.4
118.7
80.4
91.2
102.8
123.1
80.3
91.7
96.3
120.9
77.7
90.8
89.7
121 .1
74.2
85 .8
107.3
110.8
103.3
108.4
113.8
112.4
111 .0
113.2
117.0
109.7
116.1
116.3
121 .3
113.5
12 1.0
125.5
126.5
115.5
121 .2
126.9
-
133.7
122.1
126.7
125.5
142.1
129.3
135.9
130.8
142.7
127.0
135.9
132.6
155.9
130.5
139.5
141.7
Output
United States ...
..... ...... ..... ......
Canada ...... ........ ... .. ... .. ....... ... .... ... ..... . .... ....
Japan ..
... . . .. ···• ••·· ... , .................. ..... .. ...... .
Belgium ... ....... ....... ....... ..........
.. .... ...
Denmark... •·• •···--· ........ ........ ... .... ........ . .......
France . . . . . . . . . . . . . . .......... ..... ......... ..... .. .. .......
Germany ........ . ........ ....... ..... .. .. .... .. . .... ... ..
Italy ........ ........... .. .. ....... ... ... ...... .. . . ..... .. ...... ....
Netherlands ........... .. ......... ...... ....... .. ........
Norway ... ..... .. ...... ....... ....... ... ..... .. ..... .. ... .....
Sweden ... ....................... ..... .. .. ... ... ..... ... . . . . . . . . . . . .
United Kingdom .. .....•............................. •.. .
......
.
-
-
33.4
10.7
30.7
44 .4
30.0
41.5
23.0
31 .9
57.7
45.9
67.5
58. 9
39 .2
57.6
73."9
57 .7
70.9
48.1
59 .8
91 .0
80 .7
90.2
75.8
83.6
60.4
78.2
94.4
81 .6
85. 3
84.4
76.9
104.9
90.7
87 .2
92 .1
88.3
77.8
170.7
157.8
140.3
142.3
93 .5
169.8
153.6
168.3
224.6
104.4
107.1
104.4
174 .7
149.5
147.8
136.3
104 .0
155.5
153.9
154.7
208 .8
107.5
114.6
95.6
11 9.7
109.6
132.5
110.5
107. 4
111 .2
134.7
124.0
160.5
104.8
113.5
102.9
104.3
103.7
105.6
100.1
102.9
100.3
103.4
116.4
11 8.1
100.4
103.9
103.1
101 .5
102.1
102.9
104.1
103.3
100.8
100.8
109.0
106.6
101.4
100.1
94.7
94 .7
96.2
94.7
90.8
95.4
95.8
102.1
94 .9
92 .7
14.9
10.0
4.3
5.4
3.8
4.3
8.1
1.8
6.2
4.7
4.1
2.9
23.7
17.1
16.4
13.7
11 .1
10.5
20.7
5.3
19.4
11 .8
10 .7
6.1
55.6
47 .5
58.5
52 .5
45.0
41.2
53.6
30.4
60.5
39.0
37 .3
32. 1
90.8
88.3
90.6
90.1
92 .7
90.9
89.4
87.6
89.8
92.3
87 .8
82.9
95.6
95.0
96.5
97 .3
96.0
96.4
91.5
94.2
94 .8
97.5
95.5
93.8
102.7
102.0
102.7
104.8
103.0
103.1
106.4
105.7
104.5
101 .5
97.4
105.1
-
-
-
-
-
-
-
-
-
106.5
111 .8
106.8
109.0
104.4
99.8
108.0
110.4
117.6
111 .3
112.1
109.2
106.8
109.5
112.2
123.3
119.0
114.4
113.6
115.2
111 .3
111 .8
125. 7
123.0
117.2
118.7
12 1.0
116.1
112 .7
127.6
122 .2
122.0
125.7
125.6
123.1
116.6
130.6
124.2
126.0
133.0
130.3
130.4
123.4
137.4
127.8
132.0
140.5
136.8
137.7
128.2
142.0
132.4
138.9
148.2
143.8
144.2
132.4
145.5
135.6
146.0
157.2
149.2
149.2
-
-
26.4
31 .3
30.1
13.6
21 .7
27.8
7.5
32 .9
12.6
15.0
9.8
31 .1
43 .8
41.7
22 .4
26.8
39 .8
11 .9
50.4
20.0
20 .6
14.1
78 .8
65.2
92.6
80.3
52.2
67.0
69.4
38. 7
87.6
50.0
51.0
59. 0
93.7
94.6
95.9
93 .0
93.5
96.8
90.3
90 .7
91.1
94.2
92.9
92.9
97.6
99.6
97.5
98.1
96.5
99.3
93.1
98.0
95.7
99.2
100.0
99.9
100.6
96.4
101 .0
102.3
103.7
102.0
104.5
104.5
102.4
101 .9
90.8
100.6
98.5
93 .6
101 .4
97 .9
96.2
97.8
102 .0
101 .9
96.4
104 .8
84.7
99.6
94.8
94.3
97.5
96.4
96.4
96.5
104.7
103.2
95.6
108.4
85.8
102.8
93.5
97.5
94.0
95 .5
103.2
97.8
107.5
109.8
95.9
110.8
89.0
105.2
91 .9
96.2
93.0
91 .8
99.4
91 .9
104.5
111.4
96.5
116.4
85.8
107.8
92.8
96.7
95.2
92. 2
102. 8
88.1
104.6
110.3
98.3
125.7
84.0
112.7
91.3
94.9
90.6
94.4
103.7
87.6
107.6
11 2.3
99.1
128.4
80.1
114.0
92 .3
92.5
83.6
92 .2
101 .8
86.2
108.1
112.5
99.5
131 .9
77.9
113.0
94.1
97.4
84.4
95 .9
101.3
86.6
111 .2
114.2
105.0
136.1
84.4
114.2
90.2
97.1
88.0
96.4
102.1
87.1
111 .1
118.7
109.7
141 .8
80.9
116.9
78.8
67.4
51 .8
88.3
55. 9
83.9
59 .6
55.7
77 .5
62. 9
70.2
77 .7
93.7
98.0
83.9
89.5
91.2
94.1
87.3
93.3
87 .9
93.6
91 .3
93.8
97.6
105. 1
91 .8
92.3
91 .0
93.1
87 .5
97.3
90.0
95.0
96.3
100.0
100.6
90.3
115.3
95.1
96.5
95.3
98.7
81 .8
96 .9
89.2
67.8
85.6
98 .5
82.8
125.8
94.2
91.4
93.4
98.2
77.9
93.2
92 .3
64 .0
86 .3
94 .8
83.0
131.6
105.2
104.0
102.5
114.2
78.0
104.8
106.4
70.0
91 .8
93.5
86.4
109.5
99.1
107.5
101 .2
111 .6
87.7
100.0
106.6
77.3
93.0
91 .9
84 .0
97.4
82.4
90.8
83.3
94.0
80.6
87 .0
102.1
65.4
99.9
92.8
78.8
92.2
81 .6
92.6
79.1
92 .9
78.2
87 .2
103.5
61 .5
105. 7
91.3
77.2
101 .0
80.2
89.5
75.3
91.5
76 .2
84.3
102 .2
56.4
104.4
92.3
75.3
98.4
67.8
76.0
64.2
79.7
66.1
73.3
93.0
49.5
96.9
94.1
76.0
88.0
68.4
73.4
62.6
79.5
65.1
75. 0
94.0
47.6
93.0
90.2
74.8
89.1
72 .6
78.2
66.4
83.9
71.4
82.8
110.3
48.5
99.4
Total hours
.
United States ....
. . . . . . . . . . . . . . . . . . . . . . . . . . . ..... .....
Canada ... .. ..... ..... .... ..... ..... ... ... .... ... .. ... . ........
Japan .. .. ...... .. .. . ... ... ....... ... ..... .. .... ... ... ...... . .......
Belgium .. ....... ........ ....... ..... .. ..... .. ...... ....
Denmark ... ............... ...... ..... . . . .. ......... .. ... ....
France ..... ..... .... .. ..... .. ..... ...... .. ....... ... .. ... ..
Germany ........ ........... ..... ... .......... .... .. .... .. ..
It aly .................. ••.. ...... ....... ........ ........ . ... .....
Netherlands ........... ....... ..... .... ......... ...........
Norway . . . . . . . . . . . . . . . . . . . . . . . . ..... .. ... ... .. . . .........
Sweden ..
............ ......... .... . ............
United Kingdom ....
.
-
-
-
-
-
-
-
92 .1
86.8
97.7
92.4
105.0
99.4
97 .9
91.7
84 .8
99.4
92.3
106.6
105. 9
101.2
91.2
80.6
97.3
91.2
107.6
105.3
102.8
90.2
79.5
98.6
91 .9
11 2.0
103.9
102.8
89.9
80.1
99.9
92 .6
113.7
105.9
101.9
89.2
78.9
99.8
92 .6
109.6
106.0
98 .1
86.8
78.8
100.1
92.5
105.9
107.3
94 .3
86.5
78.2
98.9
91 .9
104.1
107.5
89.8
84 .2
76.1
99.5
89.9
101 .6
102.7
85.7
105.6
103.7
104 .7
106.1
107.9
106.0
108.3
109.2
109.4
107.0
109.1
111 .1
111 .5
109.3
112.6
115.2
11 7.4
111 .7
115.4
117.0
122 .1
115.8
114.8
118.5
131 .1
119.6
113.7
120.6
134.3
123.8
114 .5
127.2
140.6
126.8
122.8
136.5
-
-
Compensation per hour
.
United Stat es . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Canada ......... ............ . ................. .... .. .. ..... ....
Japan . . . . . . . . . . .. .. .. .. ... .. .... ... .. .. ... .. .. .. . .. .. ......
Belgium ... ............ .......... .... .. ...... . . ..... ....
Denmark .......... ... ....... ........ ....... ........... .. .. .. .... .
France ············· ....... ........ .... .... ... .... .... .... ..
Ge rm any ...... .. ..... ..... ..... .... .. .. ......
....
It aly . .. .. .. . .. . .. . .. .. ....... ...... .. ..... .. ....... ... .. .. .....
Netherlands ... .............. ............ ............. ......
Norway ...........
... .. .. ....... .... .. ..... ..... .. .. .
Sweden ... ............. ........ ........ ....... ...... .. .. . . . . . . . .
United Kingdom ... .... .......... .... ...............
..
Unit labor costs : National currency basis
United States ...... .. ........ .... ..........................
Canada .. .. . . . .. . . . . . . . . . . . . . .... .. .. .. ... .. . .... .. .. . .. ......
Japan ..... .... ... .. .. ...... .... .... ....... .. ....... .........
Belgium ......... .... .... ....... ........... ... ... .... .. . .......... .
Denmark ............. ......... .. .. ...
.. .......... ..
France ..... .. ..... .. .... .. ... ..... .. .. ..... .. ....... .... .... .. ....
Germany ......... ............ ...... . ... .. ....... ... ...... .. ..... ..
Italy . . . . . . . . . . . . . . . . . . ....... .. ...... .......... . .....
Netherlands .... ..... ...... ....... ... .. ... ... ... ............
Norway. ............... ..... ... ...... . ... ... .. . . ...... . ... .....
Sweden .. ........... ............ .... .. ... ... ... ...... ...
United Kingdom ... ........
.
.
Unit labor costs : U.S. dollar basis
United States ..
Canada . . . . . . . . . .. . ··• .. ...... ...... . ...... . ...... .. .. ..
Japan ··········--·· ........... ..... ....... .. ... ... .. .. .... .. .......
Belgium .. .... ... ... ..... .... ... .. ... .. .. ....... .. ........
Denmark . . . . . . . . . . . . ........ ....... ........ ...... ....
France ...
... ..... ... .... ... ........ ... .. ..... .. ......
Germany .... ..... . ... .. ... ..... .. ......... ..... ... ....... . ......
Italy ..... ........... ... .. ..... .. .. ...... . ... ... ... .... .. .. .
Netherlands ...
.... ......... ....... ....... .. ... ...
Norway ...... ... ... .... ....... ................... .... .. .......
Sweden ........... .. ..... . ... ... . ........ ..... .... .........
United Kingdom ....
-
-
32. 9
11 .0
19.4
12. 0
23.4
10.4
14.3
15.3
11 .0
16.9
15.6
36.0
15.5
27.0
18.0
25.7
17. 1
22 .3
24.5
17.4
23.1
19. 1
NOTE: Data for Germany for years before 1991 are for the form er West Germany . Data for 1991 onward are for unified Germany. Dash indicates data not available.
144
Monthly Labor Review
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
November
2,004
1
55. Occupational injury and illness rates by industry, United States
Industry and type of case 2
Incidence rates per 100 full-time workers
1989
1
1990
1991
1992
1993
4
1994
4
1995
4
1996
4
1997
3
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
Total cases .
Lost workday cases
Lost workdays ..
8.5
4.8
137.2
8.3
5.0
119.5
7.4
4.5
129.6
7.3
4.1
204.7
6.8
3.9
6.3
3.9
6.2
3.9
5.4
3.2
5.9
3.7
4.9
2.9
4.4
2.7
4.7
3.0
4.0
2.4
Construction
Total cases ........ ............ ........ .
Lost workday cases
Lost workdays .......................... .
14.3
6.8
143.3
14.2
6.7
147.9
13.0
6.1
148.1
13.1
5.8
161 .9
12 2
5.5
11 .8
5.5
10.6
4.9
9.9
4 .5
9.5
4.4
8.8
4.0
8.6
4.2
8.3
4.1
7.9
4.0
General building contractors :
Total cases.
Lost workday cases
Lost workdays .... ...... . ...... .. ... .............. .. ......... .
13.9
6.5
137.3
13.4
6.4
137.6
12.0
5.5
132.0
12.2
5.4
142.7
11 .5
51
10.9
5.1
9.8
4.4
9.0
4.0
8.5
3.7
8.4
3.9
8. 0
3.7
7.8
3.9
6.9
3.5
Heavv construction . exceot buildina:
Total cases.
Lost workday cases
Lost workdays ..
13.8
6.5
147.1
13.8
6.3
144.6
12.8
6.0
160.1
12.1
5.4
165.8
11.1
5.1
10.2
5. 0
9.9
4.8
9.0
4.3
8.7
4.3
8.2
4.1
7. 8
3.8
7.6
3.7
7.8
4.0
Soecial trades contractors:
Total cases ..
Lost workday cases ..
Lost workdays ..
14.6
6.9
144.9
14.7
6.9
153.1
13.5
6.3
151 .3
13.8
6.1
168.3
12.8
5.8
12.5
5.8
11.1
5.0
10.4
4.8
10.0
4.7
9.1
4 .1
8.9
4.4
8.6
4.3
8.2
4.1
13. 1
~8
113.0
13.2
~8
120.7
12.7
~6
121 .5
12.5
~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
14.1
6.0
116.5
14.2
6.0
123.3
13.6
5.7
122.9
13. 4
Lost workday cases ...
Lost workdays ...
5.5
126.7
13. 1
5.4
13.5
5.7
12.8
5.6
11.6
5.1
11. 3 1
5.1
10.7
5.0
10.1
4.8
Lumber and wood products:
Total cases .. . .............. ..... ..... .. .
Lost workday cases ..
Lost workdays ...
18.4
9.4
177.5
18.1
8.8
172.5
16.8
8.3
172.0
16.3
7.6
165.8
15.9
7.6
15.7
7.7
14.9
7.0
14.2
6.8
13.5
6.5
13.2
6.8
13.0
6.7
12.1
6.1
10.6
5.5
16.1
7.2
16.9
~8
15.9
7.2
14.8
~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 oroducts:
Total cases .. .. .... ................. .......... .
Lost workd ay cases
Lost workdays .....
15.5
7.4
149.8
15.4
~3
160.5
14.8
6B
156.0
13.6
~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 oroducts:
Total cases .
Lost workday cases.
Lost workdays ...................... ...... ...... .... .... ..... . .
18.5
7.9
147.6
18.7
7.9
155.7
17.4
7.1
146.6
16.8
6.6
144.0
16.2
6.7
16.4
6.7
15.8
6.9
14.4
6.2
14.2
6.4
13.9
6.5
12. 6
6.0
11 .9
5.5
11 .1
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
9.1
3. 9
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
77.5
9.1
3.8
79.4
17.7
6.8
138.6
17.8
6.9
153.7
18.3
7.0
166.1
18.7
7. 1
186.6
18.5
7.1
19.6
7.8
18.6
7.9
16.3
7.0
15.4
6.6
14.6
6.6
13.7
6.4
13.7
6.3
12.6
6.0
Instruments and related oroducts:
Total cases ..
Lost workday cases
Lost workdays
5.6
2. 5
55.4
5.9
2.7
57.8
6.0
2.7
64.4
5.9
2.7
653
5.6
2.5
5.9
2. 7
5.3
2.4
5. 1
2.3
4.8
2.3
4.0
1.9
4.0
1.8
4 .5
2.2
4.0
2.0
Miscellaneous manufacturina industries:
Total cases
Lost workday cases .. .
Lost workdays ........................ .
11 .1
5.1
97.6
11 .3
5. 1
113.1
11 .3
5.1
104.0
10.7
5.0
108.2
10.0
4.6
9.9
4.5
9.1
4.3
9.5
4.4
8.9
4. 2
8.1
3.9
8.4
4.0
7.2
3.6
6.4
3.2
Agriculture, forestry, and fishings
Total cases ................... ...... .................................. .. ...... .
Lost workday cases .......... ............
Lost workdays ..
Mining
Manufacturing
Total cases.
Lost workday cases
Lost workdays ..
Durable goods:
Total cases
Furniture and fi xtu res:
Total cases ..... ............... .. ... .
Lost workday cases .. .
Lost workdays .. .
Industrial machinery and equ ipment:
Total cases ............................. .. ......... .
Lost workday cases .................. .. ........ ..
Lost workdays
Electronic and other electrical eauioment :
Total cases
Lost workday cases ....
Lost workdays
Transoortation eauioment:
Total cases .
Lost workday cases
Lost workdays
8.8
4 .3
5.3
See footnot es at end of table.
https://fraser.stlouisfed.org
Federal Reserve Bank of St. Louis
Monthly Labor Review
November
2004
145
Current Labor Statistics:
Injury and Illness
55. Continued-Occupational injury and illness rates by industry, 1 United States
Incidence rates per 100 workers3
Industry and type of case 2
1989
Nondurable goods:
Total cases
Lost workday cases
Lost workdays ..... .. .................. .... .. .. .. .
1
1990
1991
1992
1993 4 1994 4
1995 4
1996 4 1997 4
1998.
1999.
2000.
2001.
11 .6
5.5
107.8
11 .7
5.6
116.9
11 .5
5.5
11 9.7
11 .3
5.3
121 .8
10.7
5.0
10.5
5.1
9.9
4.9
9.2
4.6
8.8
4.4
8.2
4 .3
7.8
4.2
7.8
4.2
6.8
3.8
18.5
9.3
174 .7
20.0
9.9
202.6
19.5
9.9
207.2
18.8
9.5
211.9
17.6
8.9
17.1
9.2
16.3
8.7
15.0
8.0
14.5
8.0
13.6
7.5
12.7
7.3
12.4
7.3
10.9
6.3
Tobacco oroducts:
Total cases
Lost workday cases
Lost workdays .. .. .. ........ .. .. ..
8.7
3.4
64.2
7.7
3.2
62.3
6.4
2.8
52.0
6.0
2.4
42.9
5.8
2.3
5.3
2.4
5.6
2.6
6.7
2.8
5.9
2.7
6.4
3.4
5.5
2.2
6.2
3.1
6.7
4.2
Textile mill oroduct s:
Total cases
Lost workday cases
Lost workdays
10.3
4.2
81.4
9.6
4.0
85.1
10 .1
4.4
88.3
9.9
4.2
87.1
9.7
4 .1
8.7
4.0
8.2
4 .1
7.8
3.6
6.7
3.1
7.4
3.4
6.4
3.2
6.0
3.2
5.2
2.7
Aooarel and oth er textile oroducts:
Total cases .
Lost workday cases
Lost workdays .... .. ..................... .
8.6
3.8
80.5
8.8
3.9
92.1
9.2
4.2
99.9
9 .5
4.0
104.6
9.0
3.8
8.9
3.9
8.2
3.6
7.4
3.3
7.0
3.1
6.2
2.6
5.8
2.8
6.1
3.0
5.0
2 .4
12.7
5.8
132.9
12.1
5.5
124 .8
11 .2
5.0
122.7
11 .0
5.0
125.9
9.9
4 .6
9.6
4.5
8.5
4.2
7.9
3.8
7.3
3.7
7.1
3.7
7.0
3.7
6.5
3.4
6.0
3.2
Printina and oublishina :
Total cases. . ...... .. .. ...... .. ..
Lost workday cases
Lost workdays . ....... ..... ... .. ................ .. ..... ..... ..
6.9
3.3
63. 8
6.9
3.3
69.8
6.7
3.2
74 .5
7.3
3.2
74 .8
6.9
3.1
6.7
3.0
6.4
3.0
6.0
2.8
5.7
2.7
5.4
2.8
5.0
2.6
5.1
2.6
4 .6
2 .4
Chemicals and al lied oroducts:
Total cases ................................. .. ...... . ..... .. .... ..
Lost workday cases
.. ...... ....... ..... .... .
Lost workdays .... .. .......... .. .... ..... .. ..
7.0
3.2
63.4
6.5
3.1
61.6
6.4
3.1
62.4
6.0
2.8
64 .2
5.9
2.7
5.7
2.8
5.5
2.7
4 .8
2.4
4.8
2.3
4 .2
2.1
4.4
2 .3
4.2
2.2
4.0
2.1
Petroleum and coal oroducts:
Total cases ................................. .. .
Lost workday cases ..
Lost workdays .. .. .. .. .. .............. .......... ............... .
6.6
3.3
68. 1
6 .6
3. 1
77 .3
6.2
2.9
68.2
5.9
2.8
71.2
5.2
2.5
4.7
2.3
4.8
2.4
4.6
2.5
4.3
2.2
3.9
1.8
4.1
1.8
3.7
1.9
2.9
1.4
Rubber and misce llaneous olastics oroducts:
Total cases .. .. .. .. .. .......... ........ . .. .. .......... .. ...... .. .
Lost workday cases ..
Lost workdays .......... .. ................. .. ...
16.2
8.0
147.2
16.2
7.8
15 1.3
15.1
7.2
150.9
14.5
6.8
153.3
13.9
6.5
14.0
6.7
12.9
6.5
12.3
6.3
11 .9
5.8
11.2
5.8
10.1
5.5
10.7
5.8
8.7
4.8
Leather and leath er oroducts:
Total cases .... .. .. .. .. ................. .. . . ..
Lost workday cases
Lost workdays ...
13.6
6.5
130.4
12.1
5.9
152.3
12.5
5.9
140.8
12.1
5.4
128.5
12.1
5.5
12.0
5.3
11.4
4 .8
10.7
4.5
10.6
4.3
9.8
4 .5
10.3
5.0
9.0
4.3
8.7
4.4
Transportation and public utilities
Total cases .. ................... .. .................. .. .. ...... ............ ..
Lost workday cases....
.. .......... .. .. ....... ..
Lost workdays ............................ .
9.2
5.3
121.5
9.6
5.5
134.1
9.3 1
5.4
140.0
9.1
5.1
144.0
9.5
5.4
9.3
5.5
9.1
5.2
8.7
5.1
8.2
4.8
7.3
4.3
7.3
4.4
6.9
4.3
6.9
4 .3
8.0
3.6
63.5
7.9
3.5
65.6
7.6
3.4
72.0
8.4
3.5
80.1
8.1
3.4
7.9
3.4
7.5
3.2
6.8
2.9
6.7
3.0
6.5
2.8
6 .1
2.7
5.9
2.7
6.6
2 .5
Wh olesale trad e:
Total cases
Losl workday cases ..
Lost workdays
7.7
4.0
71 .9
7.4
3.7
7 1.5
7.2
3.7
79 .2
7.6
3.6
82.4
7.8
3.7
7.7
3.8
7.5
3.6
6.6
3.4
6.5
3.2
6.5
3.3
6.3
3.3
5.8
3.1
5.3
2.8
Retail trade:
Total cases ............... .... .......... .
Lost workday cases ..
Lost workdays .. .... .. .. ...................... .......... .. .... .. ...... .. ..
8.1
3.4
60.0
8.1
3.4
63.2
7.7
3.3
69.1
8.7
3.4
79.2
8.2
3.3
7.9
3.3
7.5
3.0
6.9
2 .8
6.8
2.9
6.5
2.7
6.1
2.5
5.9
2.5
5.7
2.4
2. 0
.9
17.6
2.4
2.9
1.2
32.9
2.9
1.2
2.7
2.6
1.0
2.4
.9
2.2
.7
.9
.5
1.8
.8
1.9
1.1
.8
1.8
.7
27.3
2.4
1.1
24 .1
5.5
2. 7
51.2
6.0
2.8
56. 4
6.2 1
2.8
60.0
7.1
3.0
68.6
6.7
2 .8
6.5
2.8
6.4
2.8
6.0
2.6
5.6
2.5
5.2
2.4
4.9
2 .2
4.9
2.2
4.6
2.2
Food and kindred products:
Total cases . .... .......................... ........ .. ...... . .. .
Lost workday cases
Lost workdays
Paoer and allied orodu cts:
Total cases
Lost workday cases
Lost workday s
Wholesale and retail trade
Total cases .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. .. . .. .....
Lost workday cases.
Lost workdays ..
Finance, insurance, and real estate
Total cases ....................................... .. .. .... ........ .......... ..
Lost workday cases
Lost workdays .. .
1.1
I
Services
Total cases ..
Lost workday cases .
Lost workdays ...
1
Data for 1989 and subsequent years are based on the Standard Industrial Classification Manual . 1987 Edition . For th is reason. th ey are not strictly comparable with data
for the years 1985-88, which we re based on the Standard Industrial Classification
Manual . 1972 Edition, 1977 Supplement.
2
Beg inning with the 1992 survey, the annual survey measures only nonfatal injuries and
N
= number of injuries and
illnesses or lost workdays ;
EH = total hours worked by all employees during the calendar year; and
200,000 = base for 100 full-tim e equivalent workers (working 40 hours per week. 50 weeks
per year).
4
Beginning with the 1993 survey, lost workday estimates will not be generated. As of 1992,
illn esses. while past surveys covered both fatal and non fat al inciden ts. 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 Injuri es
3
Excludes farms with fewer than 11 employees since 1976.
Th e incidence rates represent th e number of injuries and illn esses or lost workdays per
100
full-time
workers
and
were
calculated
146
Monthly Labor Review
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Federal Reserve Bank of St. Louis
as
(N/EH)
X
November 2004
200,000,
where:
NOTE: Dash indicates data not available.
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Federal Reserve Bank of St. Louis
56. Fatal occupational injuries by event or exposure, 1997-2002
Fatalities
Event or exposure
1
2002
1997-2001
average
Total. .......... .
Transportation incidents ........................................ ...................... .
Highway incident. . ..
...... ..... ... .... .. .. ........ ............... .... .........
Collision between vehicles, mobile equipment.. ..
Moving in same direction .... .. ..... ... .... .... .............. .. .... ....... .. .
Moving in opposite directions, oncoming .. .... .
.... .... .... ...... ..
Moving in intersection. ... ... ... ... .... ..
Vehicle struck stationary object or equipment.. ..... .. .... .. .. ... ..... .
Noncollision incident ........... ........ ........... ....... .. ........ ......... ... ..... .
Jackknifed or overturned-no collision .
. ...... ..... .. ... .... .. ..
Nonhighway (farm, industrial premises) incident... ...... .. ..... ..... ..... .
Overturned ... ............. ... .......... .. ....... .. .. .......... ............ ... ... .. ... ...
Aircraft. .... .. ....... ..... ... .... ..... ...... ..... ... ... .. .. ... . ... ... ..... , . .. .. .. .
Worker struck by a vehicle ............ ... ... ..... .... .... .. ... .... .... ...... .
Water vehicle .. .. ....... ..... ..... .... .. ... ..... ... ... ... ..... .. ... ... .. ... ... .... ......... .
Rail vehicle ... ........ . ... ... .. ... .. .. ....... . ....... .. ... .... ... ..... .. .
Number
Number
5,915
5,524
100
2,593
1.421
697
126
254
148
300
369
300
368
202
248
382
2,524
1,409
727
142
257
138
297
339
273
326
158
247
383
90
62
2,381
1,372
635
155
202
145
326
373
312
322
164
192
356
71
64
43
25
11
840
609
469
58
82
199
15
11
8
9::1
Assaults and violent acts ............................................................. .
Homicides .................. .
Shooting ..... .. .. .... . ....
. ... ... ... ... . ........ . .. ... ...... .
Stabbing ..
. ..... .. ..... .......... . . .
Other, including bombing .. ... ........... ... ... . .. .. .. ........... . .
Self-inflicted injuries ......
..... .. .... ..... ... ...... .. ..... .
5::1
78
221
908
643
509
58
76
230
Contact with objects and equipment. ........... ............................ .
Struck by object. .... .... .............. ... .... .......... ................... ... ............ .
Struck by falling object.. .... .......... .... .. ..
Struck by flying object. .. ...... .. .. . ... .. .... ..... .. .... ........ .
Caught in or compressed by equipment or objects ..
Caught in running equipment or machinery .... ....... ... .. .
Caught in or crushed in collapsing materials .. ..... .
995
562
352
58
290
156
126
962
553
343
60
266
144
122
873
506
303
38
231
110
116
Falls.... ................... .. .................................................................. .
Fall to lower level. .. ... .................. ..... .. .. .. .......... .............. .
Fall from ladder ........ ..... .. ... ... ... .. .... ............. .. ............ ... ........... .
Fall from roof. ... ... .. ... ....... .
Fall from scaffold , staging
Fall on same level. ..
737
654
111
155
91
61
810
700
123
159
91
84
714
634
126
143
87
63
Exposure to harmful substances or environments ................. .
Contact with electric current
...... ... .. ... ... ....
Contact with overhead power lines ... ... . .... .. ... ... ... ........... ........ .
Contact with temperature extremes .... .. .... .. ... . .... .... ...... .. .. .... ... ..
Exposure to caustic, noxious, or allergenic substances .... ... .. ..... .
Inhalation of substances ....... ....... ..... ... ....... ... .. ........... .. .. .
Oxygen deficiency .. .... ............. ...... ........ ............. ... ................. ... .
Drowning, submersion .... ........ ... ..... ... ... .... .. ..... .. ......
529
291
134
41
106
52
89
71
499
285
124
35
96
49
83
59
538
239 1
122
60
98
49
90
60
Fires and explosions ............... ............................... ..................
197
188
165
Other events or exposures
24 ,
21
....... .. ........ . ...• . .. . ......... . ...... .. ... .
' Based on the 1992 BLS Occupational Injury and Illness
Classification Structures.
2
709
3
3
Percent
6,036
3
4
3
6
7
6
6
3
3
6
10
5
2
1
2
1
2
3
13
Totals for 200 1 exclude fatalities from the September 11
terrorist attacks.
The BLS news release issued Sept. 25, 2002 , reported a
total of 5,900 fatal work injuries for calendar year 2001. Since
3
Includes the category "Bodily reaction and exertion. "
NOTE:
Totals
for
major categories
may include sub-
then , an additional 15 job-related fatalities were identified.
categories not shown separately. Percentages may not add
bringing the total job-related fatality count for 2001 to 5,9 15.
to totals because of rounding . Dash indicates less than 0.5
percent.
Monthly Labor Review
November 2004
147
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date
Period
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Series
Release
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Period
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Productivity an d costs
November 4
3rd quarter December 7
3rd quqrter
Employment situation
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December 3
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January 7
December
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Ja nuary 19
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2. 37 -39
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October
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J,rnu ary 19
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